Accounting for Variability and Uncertainty in Life Cycle Assessments: Oil Sands Case Studies

Sylvia Sleep

A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Department of Civil and Mineral Engineering University of Toronto

Accounting for Variability and Uncertainty in Life Cycle Assessment: Oil Sands Case Studies

Sylvia Sleep

Doctor of Philosophy

Department of Civil and Mineral Engineering University of Toronto

2019 Abstract

Along the life cycle of oil sands-derived products, variability in terms of resource heterogeneity, operating decisions, as well as extraction and processing technologies affect a project’s GHG intensity. Previous LCAs that have quantified emissions from bitumen production and processing have not captured all sources of variability along the life cycle, either by including only some projects or employing simplified refinery modeling or modeling refining only of some crude types. Three studies are completed to address this literature gap.

In the first study, a statistically-enhanced version of the GreenHouse gas emissions of current Oil

Sands Technologies model (GHOST-SE) is developed. Median lifetime GHG intensities for projects producing synthetic crude oil (SCO) range from 89-137 kg CO2eq/bbl SCO and for the project producing dilbit are 51 kg CO2eq/bbl dilbit. Projects show significant temporal variability. No project reaches steady-state in terms of GHG intensity. Next, GHOST-SE is integrated with a pipeline transportation model (COPTEM) and a refinery model (PRELIM) and variability in life cycle emissions intensities are quantified. Allocation to products affects the relative GHG intensities of different projects (e.g., Project 1 has lowest median life cycle GHG intensity per MJ gasoline but highest per MJ diesel). These results demonstrate that there is no

ii representative project or crude type, even across projects within the same pathway (e.g., Mining

SCO pathway).

In the final study, expert elicitation methods are employed to assess the potential role for emerging technologies to decrease upstream GHG intensity between 2014 and 2034. Experts surveyed do not expect emerging technologies to play a major role in reducing upstream oil sands energy consumption but are more likely to be applied to access marginal resources not economic with current production technologies.

Accurate characterizations of the emissions from the life cycle of oil-sands derived fuels has the potential to assist oil sands operators and policymakers to: set benchmarks, develop projections of future emissions, and identify opportunities for GHG intensity reductions along the life cycle of the fuel. The findings of this thesis can also inform operators and policymakers about the potential unintended consequences of policy decisions.

iii

Acknowledgments

First and foremost, I would like to thank my supervisor, Dr. Heather MacLean, for the guidance, encouragement, and insightful feedback she provided throughout this thesis. This would not have been possible without her continuous support. Many thanks also to our collaborator, Dr. Joule Bergerson, for devoting her time and expertise to this research.

I am grateful to our industry collaborator Ian Laurenzi for going above and beyond in providing guidance on the research. As well, to Glynis Carling, Dan Burt, and other oil sands industry partners whose input has strengthened this thesis.

I also thank Dr. James Wallace for serving on my supervisory committee and Dr. Daniel Posen for his role on my examination committee.

ExxonMobil Corporate Strategic Research, Carbon Management Canada, and Ontario Graduate Scholarship provided research funding, for which I am very grateful.

I also thank the many members of the Life Cycle Assessment of Oil Sands Technologies research group who have assisted with various aspects of this thesis: Nicolas Choquette-Levy, Ganesh Doluweera, John Guo, Jennifer McKellar, Kavan Motazedi, Andrea Orellana, and Diana Pacheco.

Finally, many thanks to my family and friends for supporting me in everything I do.

Table of Contents

Acknowledgments ...... iv

Table of Contents ...... v

List of Tables ...... xiii

List of Figures ...... xvi

List of Appendices ...... xix

Abbreviations and Acronyms ...... xx

Chapter 1 Introduction ...... 1

1.1 Environmental Impacts of the Petroleum Life Cycle ...... 1

1.2 Oil Sands Case Study ...... 3

1.3 Thesis Objectives ...... 6

1.4 Publications Contained in this Thesis ...... 8

1.5 Publication Related to this Thesis ...... 10

1.6 References ...... 10

Chapter 2 Background and Literature Review ...... 14

2.1 Oil Sands Background ...... 14

2.1.1 Properties of Oil Sands Bitumen ...... 15

2.1.2 Current Oil Sands Technologies ...... 17

2.1.3 Emerging Oil Sands Technologies ...... 22

2.1.4 Policy and Regulatory Context for Managing GHG Emissions from the Oil Sands ...... 26

2.2 Life Cycle Assessment – Development and Applications ...... 29

2.2.1 What is Life Cycle Assessment? ...... 29

2.2.2 Life Cycle Assessments of Transportation Fuels ...... 32

2.2.3 Applications of Life Cycle Assessment in Energy Policy ...... 33

2.2.4 Variability and Uncertainty Analysis in Life Cycle Assessment ...... 35

2.3 Life Cycle Assessments of Oil Sands Technologies ...... 37

2.3.1 Life Cycle Studies of Oil Sands Prior to 2010 ...... 38

2.3.2 Current Models that Assess Life Cycle Emissions of Oil Sands Technologies ... 39

2.3.3 Gaps in LCA-OST Literature ...... 42

2.4 References ...... 48

Chapter 3 Methods ...... 59

3.1 Life Cycle Assessment Methodology ...... 59

3.1.1 Limitations of Life Cycle Assessment Methods ...... 59

3.1.2 Developments in Life Cycle Assessment Methods ...... 61

3.1.3 Consequential Life Cycle Assessment ...... 62

3.2 Methods for Accounting for Variability and Uncertainty in Life Cycle Assessment ...... 65

3.2.1 Sensitivity Analysis ...... 65

3.2.2 Scenario Analysis ...... 66

3.2.3 Analytical Methods ...... 66

3.2.4 Statistical Methods ...... 66

3.3 Expert Elicitation ...... 68

3.4 References ...... 69

Chapter 4 Evaluation of Variability in Greenhouse Gas Intensity of Canadian Oil Sands Surface Mining and Upgrading Operations ...... 75

4.1 Abstract ...... 75

4.2 Introduction ...... 76

4.3 Methods ...... 77

4.3.1 Processes Involved with Mining and Upgrading Oil Sands Bitumen ...... 78

4.3.2 Mining and Upgrading Projects ...... 79

4.3.3 Data Collection ...... 80

4.3.4 Monte Carlo Simulations of GHG Emissions from Oil Sands Mining and Upgrading Projects ...... 83 vi

4.4 Results ...... 84

4.4.1 Variability of GHG intensity Across Mining and Upgrading Projects ...... 84

4.4.2 Temporal Variability of GHG Intensity ...... 86

4.4.3 Drivers of Variability in GHG Intensity ...... 91

4.4.4 Comparison of GHOST-SE GHG Emissions for Mining and Upgrading Projects to those Reported in the Literature ...... 95

4.5 Discussion ...... 96

4.6 References ...... 98

Chapter 5 Quantifying Variability in Well-to-Wheel Greenhouse Gas Emission Intensities of Transportation Fuels Derived from Canadian Oil Sands Mining Operations ...... 103

5.1 Abstract ...... 103

5.2 Introduction ...... 104

5.3 Material and Methods ...... 107

5.3.1 Quantifying Upstream GHG Emissions: GHOST-SE ...... 110

5.3.2 Pipeline Transportation Model: COPTEM ...... 111

5.3.3 Refining of Oil Sands Products: PRELIM ...... 111

5.3.4 Refined Products Transportation ...... 113

5.3.5 Vehicle Use Emissions ...... 113

5.3.6 Comparison of WTW GHG Intensities of Transportation Fuels Derived from Mined Bitumen to Literature ...... 113

5.4 Results ...... 114

5.4.1 Inter- and Intraproject Variability of WTW GHG Intensities of Transportation Fuels Derived from Mined Bitumen ...... 114

5.4.2 Contribution of Life Cycle Stages to WTW Variability in GHG Intensity of Fuels Produced by Mining and Upgrading Projects ...... 116

5.4.3 Variability of WTW GHG Intensities of Transportation Fuels Across Oil Sands Mining and In situ Pathways ...... 121

5.4.4 Comparison of Oil Sands Mining and In Situ Pathway GHG Intensities to Literature Values ...... 122

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5.5 Discussion ...... 125

5.6 References ...... 127

Chapter 6 Expert Assessments of Emerging Oil Sands Technologies ...... 133

6.1 Abstract ...... 133

6.2 Introduction ...... 134

6.3 Material and Methods ...... 138

6.3.1 Survey 2 Development ...... 138

6.3.2 Ranking technology and process changes (Type i) ...... 140

6.3.3 Identifying barriers to the adoption of emerging technologies and incremental process changes (Type ii) ...... 140

6.3.4 Project economics (Type iii) ...... 140

6.3.5 Quantitative responses on specific technologies (Type iv) ...... 141

6.3.6 Survey Deployment ...... 141

6.3.7 Analysis ...... 141

6.4 Results and Discussion ...... 142

6.4.1 Technology and Process Changes Ranking: In Situ Bitumen Production ...... 144

6.4.2 Barriers to the Adoption of Emerging Technologies ...... 146

6.4.3 Project Economics ...... 148

6.4.4 Quantitative Responses about Specific In Situ Technologies ...... 149

6.5 Conclusions ...... 156

6.6 References ...... 158

Chapter 7 Conclusions ...... 161

7.1 Key Findings ...... 161

7.2 Future Work ...... 165

7.2.1 Quantifying Non-GHG Environmental Impacts ...... 165

7.2.2 Improvements to Chapter 4: Improved Characterization of Mining Project Fugitive GHG Emissions and Diesel Consumption ...... 165 viii

7.2.3 Extensions to Chapter 4: Distinguishing between Resource Characteristics, Technology Choices, and Operating Decisions ...... 167

7.2.4 Improved Characterizations of Emissions from Refining ...... 168

7.2.5 Variability in Vehicle Use Emissions ...... 168

7.2.6 How Best to Develop the Oil Sands Resource? A Conceptual Framework for Comparing Bitumen Production and Processing Pathways ...... 169

7.2.7 Extensions to Chapter 6: Evaluating the GHG Emissions Intensity Reduction Potential of Emerging Oil Sands Technologies ...... 171

7.3 References ...... 172

Appendix A ...... 174

A.1 Methods: Process Diagram of Oil Sands Mining and Upgrading/Dilution Pathways .... 174

A.2 Methods: Additional Steps Taken to Develop Distributions for GHOST-SE ...... 174

A.2.1 In Situ Bitumen Upgraded at Integrated Mining and Upgrading Project ...... 174

A.2.2 Procedure for Filling Gaps in AER Data ...... 175

A.2.3 Summary of Mining Project Characteristics ...... 176

A.2.4 Emissions Associated with Grid Electricity Consumption ...... 176

A.2.5 Emissions Credit for Surplus Electricity Exported to Grid from Upstream Projects ...... 177

A.2.6 Diluent Emissions Factors...... 178

A.2.7 Linking Statistically-Dependent Input Parameters Using Lookup Tables ...... 178

A.3 Methods: Comparison of GHOST and GHOST-SE Input Parameters ...... 179

A.4 Results: Variability Across Projects ...... 180

A.5 Results: Outliers for Time Series GHG Emissions Intensity Distributions (Figure 4-2) 181

A.6 Results: Credit for Surplus Electricity Exported to Grid ...... 181

A.7 Results: Sensitivity to Input Parameters, Other Projects ...... 186

A.8 Results: Comparison of GHOST-SE Results with Literature GHG Intensity Estimates 190

A.9 References ...... 192

Appendix B ...... 195 ix

B.1 Methods: Global Warming Potentials ...... 195

B.2 Methods: Emissions Factors Employed in GHOST-SE Model ...... 195

B.2.1 Non-Electricity Emissions Factors ...... 195

B.2.2 Emissions Associated with Grid Electricity Consumption ...... 196

B.2.3 Emissions Credit for Surplus Electricity Exported to Grid from Upstream Projects ...... 196

B.3 Methods: Electricity Grid GHG intensity for Refining and Refined Products Transportation ...... 197

B.4 Methods: Modeling Upstream GHG Emissions: Defining GHOST-SE Input Parameters ...... 199

B.4.1 Summary of Oil Sands Mining Projects ...... 199

B.4.2 Mining and Upgrading Project Historic Energy Consumption Reported by the Alberta Energy Regulator ...... 200

B.4.3 Diesel Consumption for Mines from COSIA Mine Templates ...... 201

B.4.4 Fugitive GHG Emissions from the Alberta Environmental, Monitoring, Evaluation and Reporting Agency ...... 201

B.5 Methods: Crude Transportation Models ...... 202

B.5.1 COPTEM ...... 202

B.5.2 Enbridge 2015 ...... 203

B.5.3 Hooker 1981 ...... 204

B.5.4 Tarnoczi 2013 ...... 204

B.6 Methods: Modeling the Refining of Oil Sands Crudes with PRELIM ...... 205

B.6.1 Crude Assays for Oil Sands Mining Projects ...... 207

B.6.2 PRELIM Assay Blending Tool ...... 207

B.6.3 Uncertainty of PRELIM Emissions Estimates ...... 210

B.7 Methods: Refined Products Transportation ...... 210

B.8 Methods: Method Employed for Estimating GHG Emissions from Vehicle Use ...... 211

B.9 Results: Additional WTW Model Runs Employing Refinery-Level Allocation ...... 213

B.10 Results: Sensitivity of WTW GHG Intensity to Variations in Model Inputs ...... 215

B.11 Results: Contribution of Each Life Cycle Stage to WTW GHG Intensity, Additional Figures ...... 217

B.12 Results: Drivers of Refinery Emissions Across Oil Sands Projects and Refinery Configurations ...... 220

B.13 Results: Comparison of Refinery Emissions for Mined and In Situ Dilbit ...... 221

B.14 Results: Influence of Varying Fuel Properties on Vehicle Use Emissions ...... 222

B.15 Results: Comparison of Oil Sands Pathway WTW GHG Intensities to Literature Values 222

B.15.1 Modeling Differences Across Literature Sources – Upstream ...... 225

B.15.2 Modeling Differences Across Literature Sources – Refinery ...... 225

B.15.3 Modeling Differences Across Literature Sources – Vehicle Use ...... 225

B.16 References ...... 226

Appendix C ...... 231

C.1 Background on Oil Sands Technologies ...... 231

C.1.1 Current Recovery and Extraction Technologies ...... 231

C.1.2 Process Improvements to SAGD ...... 232

C.1.3 Hybrid Steam-Solvent Processes ...... 232

C.1.4 Electro-Thermal ...... 232

C.1.5 In Situ Combustion ...... 233

C.1.6 Surface Mining Process Improvements ...... 233

C.2 Procedure for Selecting Technologies to Include in Survey 2 ...... 234

C.3 Project Economics ...... 236

C.4 Participant Summary ...... 236

C.5 Electro-Thermal Responses ...... 237

C.6 Surface Mining and Upgrading Responses ...... 239

C.6.1 Technology and Process Changes Ranking: Surface Mining ...... 239

C.6.2 Barriers to the Adoption of Emerging Technologies ...... 240

C.7 Detailed Survey Responses ...... 242

C.7.1 Detailed Responses – Incremental Process Changes (In Situ) ...... 242

C.7.2 Detailed Responses – In Situ Technology Barriers ...... 243

C.7.3 Detailed Responses – Hybrid Steam-Solvents ...... 244

C.7.4 Detailed Responses – In Situ Combustion ...... 245

C.7.5 Detailed Responses – Electro-Thermal ...... 245

C.7.6 Detailed Responses – Incremental Process Changes (Surface Mining) ...... 246

C.7.7 Detailed Responses – Surface Mining Technologies ...... 247

C.8 References ...... 248

Appendix D ...... 250

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List of Tables

Table 2-1. Petroleum Classifications by API gravity ...... 15

Table 2-2. Comparison of Oil Sands Products to Conventional Crude ...... 16

Table 2-3. Summary of Oil Sands Surface Mining Projects ...... 19

Table 2-4. Characteristics of Operating Oil Sands Upgraders ...... 22

Table 2-5. Emerging In Situ Technologies ...... 25

Table 2-6. Carbon prices applied over time in Alberta ...... 28

Table 2-7. Selection of Statutes and Regulations from the Climate Leadership Plan ...... 28

Table 2-8. Summary of LCFS-Type Policies ...... 34

Table 2-9. Characteristics of LCA Models Characterizing Oil Sands Crude Production ...... 41

Table 2-10. Methods Employed by LCA Models for Characterizing GHG Emissions from Oil Sands Crude Production Pathways ...... 45

Table 3-1. Limitations of Life Cycle Assessment Methods by Stage ...... 61

Table 4-1. GHOST-SE input parameters for oil sands mining and upgrading projects...... 80

Table 5-1. Summary of Mining Project Characteristics...... 110

Table 5-2. API gravity, sulfur, and mean refinery GHG intensities for mining projects and in situ pathways across refinery configurations (FCC+GO-HC configuration)...... 119

Table 5-3. Comparison of GHOST-SE WTW GHG intensity distributions to literature values (g

CO2q/MJ gasoline)...... 123

Table 6-1. Experts’ ranking of the technology or process change that will have the biggest impact on energy consumption of a commercial oil sands in situ project by 2034 ...... 146

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Table 6-2. Experts’ ranking of the primary barriers to the adoption of emerging technologies and incremental process changes ...... 147

Table A-1. Percentage of missing data in AER dataset (AER 2007, 2015) ...... 175

Table A-2. Summary of mining project characteristics ...... 176

Table A-3. Annual Alberta electricity grid GHG emissions intensity ...... 177

Table A-4. GHG emissions factors for the supply of diluent to Alberta’s oil sands ...... 178

Table A-5. Comparison of GHOST-SE model input parameters to GHOST input ranges ...... 179

Table A-6. Literature GHG emissions intensity estimates ...... 191

Table A-7. Default emissions factors and fuel properties employed in transforming literature values ...... 192

Table B-1. Average emissions factors employed in GHOST-SE...... 195

Table B-2. Annual Alberta electricity grid GHG intensity ...... 196

Table B-3. GHG intensity by electricity generation technology ...... 197

Table B-4. Electricity generation by technology and NERC region ...... 198

Table B-5. Grid GHG intensity by region ...... 198

Table B-6. Summary of statistical distributions for input parameters to GHOST-SE...... 202

Table B-7. Comparison of crude transportation models...... 204

Table B-8. Project 1 annual SCO production volumes, 2005-2015...... 208

Table B-9. Summary of whole crude properties for Project 1 assay blends...... 209

Table B-10. Summary of whole crude properties for each mining project...... 209

Table B-11. Vehicle use GHG intensities reported by GREET...... 211

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Table B-12. Example properties of gasoline and diesel produced by each project...... 212

Table B-14. Comparison of GHOST-SE WTW GHG intensity distributions to literature values (g

CO2q/MJ diesel)...... 224

Table C-2. Experts’ expected internal rate of return required to invest in a project and expected operating cost that could be obtained in a project employing this technology...... 236

Table C-3. Summary of experts who completed the survey...... 237

Table C-4. Experts’ projections of expected performance achievable by 2034 at more than one commercial operating project using technology...... 238

Table C-5. Experts’ ranking of the technology or process change that will have the biggest impact on energy consumption of a commercial oil sands surface mining project by 2034...... 240

Table C-6. Experts’ ranking of the primary barriers to the adoption of emerging technologies and incremental process changes...... 240

Table C-7. Experts’ responses on the percentage of upgrading by current and emerging upgrading technologies in 2034...... 241

List of Figures

Figure 2-1. Well-to-tank diagram of oil sands transportation fuel production (Charpentier et al. 2009) ...... 33

Figure 4-1. Mining and upgrading/dilution GHG intensity distributions for oil sands mining projects over the life of the project and industry average comparison with literature...... 86

Figure 4-2. Time series of oil sands mining and upgrading/dilution GHG intensity distributions for all major oil sands mining projects...... 91

Figure 4-3. Tornado diagrams showing sensitivity of oil sands mining and upgrading/dilution GHG intensities to model inputs, Projects 1, 3, and 6...... 94

Figure 5-1. Submodels Employed in Quantifying WTW GHG Intensity of Transportation Fuels from Mined Bitumen...... 108

Figure 5-2. WTW GHG intensity of gasoline and diesel produced by oil sands mining projects for Base Case...... 115

Figure 5-3. WTW GHG intensity distribution for gasoline produced through oil sands mining pathways (Mining SCO and Mining Dilbit) disaggregated by life cycle stage...... 116

Figure 5-4. Comparison of WTW GHG emissions for gasoline and diesel production from oil sands mining and in situ pathways...... 122

Figure 6-1. Experts’ responses on the percentage of in situ bitumen production from current and emerging technologies in 2034 ...... 145

Figure 6-2. Experts’ projections of expected industry-average anticipated SOR achievable in 2034 once process changes are adopted...... 150

Figure 6-3. Experts’ projections of the expected performance of hybrid steam-solvent processes...... 152

Figure 6-4. Experts’ projections of the expected performance of electrothermal projects ...... 154

Figure A-1. Process diagram of oil sands mining and upgrading/dilution pathways ...... 174 xvi

Figure A-2. GHG emissions intensity distributions for oil sands mining and upgrading/dilution projects based on 2015 operating data...... 180

Figure A-7. Tornado diagrams showing sensitivity of oil sands mining and upgrading/dilution GHG intensities to model inputs, Projects 2, 4, and 5...... 187

Figure A-8. Tornado diagrams showing sensitivity of oil sands mining and upgrading/dilution GHG intensities to model inputs based on 2015 operating data, Projects 1, 3, and 6...... 188

Figure A-9. Tornado diagrams showing sensitivity of oil sands mining and upgrading/dilution GHG intensities to model inputs based on 2015 operating data, Projects 2, 4, and 5...... 190

Figure B-1. North American Electric Reliability Corporation (NERC) Regions...... 199

Figure B-2. PRELIM model structure (Source: Abella and Bergerson, 2012)...... 206

Figure B-3. Distillation yield for crudes produced by each mining project and in situ pathway...... 207

Figure B-4. Assessment of the precision and accuracy of PRELIM 1.2.1 based on the actual GHG emissions of 22 ExxonMobil refineries in 2012...... 210 xvii

Figure B-5. WTW GHG intensity of transportation fuels derived from oil sands mining projects using refinery-level allocation...... 214

Figure B-6. Sensitivity of WTW GHG intensity to variations in model inputs, Projects 1 (top), 2 (middle), and 3 (bottom)...... 215

Figure B-7. Sensitivity of WTW GHG intensity to variations in model inputs, Projects 4 (top), 5 (middle), and 6 (bottom)...... 216

Figure B-8. WTW GHG intensity distribution of mining pathways disaggregated by life cycle stage, Mining SCO and Mining Dilbit pathways, reported per MJ diesel...... 217

Figure B-9. WTW GHG intensity distribution of mining pathways disaggregated by life cycle stage, Projects 1-6, reported per MJ gasoline...... 218

Figure B-10. WTW GHG intensity distribution of mining pathways disaggregated by life cycle stage, Projects 1-6, reported per MJ diesel...... 219

Figure B-11. Product slate for each mining project for medium and deep conversion, FCC refineries...... 220

Figure B-12. Detailed PRELIM output: Breakdown of refinery GHG emissions by refinery configuration and mining project, disaggregated by process fuel consumption...... 221

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List of Appendices

Appendix A Supporting Information for Chapter 4 ...... 174

Appendix B Supporting Information for Chapter 5 ...... 195

Appendix C Supporting Information for Chapter 6 ...... 231

Appendix D Expert Elicitation Survey Questions ...... 250

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Abbreviations and Acronyms

AB Alberta CI Confidence interval

AEMERA Alberta Environmental CLCA Consequential life cycle assessment Monitoring, Evaluation, and CLP Climate Leadership Plan Reporting Agency CNRL Canadian Natural Resources Limited AER Alberta Energy Regulator CO Carbon monoxide ALCA Attributional life cycle assessment

CO2 Carbon dioxide ALCP Alberta Climate Leadership Plan

CO2eq Carbon dioxide equivalent API American Petroleum Institute Cogen Cogeneration AR5 Fifth Assessment Report of the IPCC COPTEM Crude Oil Pipeline Transportation ARB Air and Resources Board Emissions Model AUC Alberta Utilities Commission COSIA Canadian Oil Sands Innovation bbl Barrel Alliance bbl/d Barrels per day CSS Cyclic steam stimulation

BC British Columbia d Day bit Bitumen DEQ Department of Environmental Quality CAC Criteria air contaminant EC Environment Canada CCA Council of Canadian Academies EIA U.S. Energy Information CERI Canadian Energy Research Institute Administration CGE Computable General Equilibrium EPA Environmental Protection Agency

CH4 Methane

EPPA Emissions Prediction and Policy Gt Gigatonne Analysis GREET Greenhouse gases, Regulated ES-SAGD Expanding solvent steam-assisted Emissions, and Energy use in gravity drainage Transportation

ET-DSP Electro-thermal dynamic stripping GTAP Global Trade Analysis Project process H2 Hydrogen EU European Union HHV Higher heating value FCC Fluid Catalytic Cracking HT Hydrotreater FCC+GO-HC Fluid Catalytic Cracking with IEA International Energy Agency Gas-Oil Hydrocracking IET EU Institute for Energy and FCC-feed HT Fluid Catalytic Cracking- Transportation feed Hydrotreater ILCD International Reference Life Cycle FQD Fuel Quality Directive Data System g Gram iLUC indirect land use change g CO2eq/MJ Grams carbon dioxide- IPCC Intergovernmental Panel for Climate equivalent per megajoule Change GHG Greenhouse gas ISO International Standards Organization GHOST GreenHouse gas emissions of Oil kg Kilogram Sands Technologies

km Kilometre GHOST-SE GreenHouse gas emissions of Oil Sands Technologies – kWh Kilowatt-hour Statistically-Enhanced L Litre GJ Gigajoule LCA Life cycle assessment GO-HC Gas-Oil Hydrocracker

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LCA-OST Life Cycle Assessment of Oil PRELIM the Petroleum Refinery Life Cycle Sands Technologies Inventory Model

LCFS Low carbon fuel standard P10 Tenth percentile

LCI Life cycle inventory P50 Fiftieth percentile

LHV Lower heating value P90 Ninetieth percentile

LUC Land use change RFG Reformulated gasoline m³ Cubic metre RFS Renewable Fuel Standard

MJ Megajoule RFS2 Renewable Fuel Standard 2

Mt Megatonne SAGD Steam-assisted gravity drainage

MW Megawatt SAP Solvent-aided processes

MWh Megawatt-hour SETAC Society of Environmental Toxicology and Chemistry NETL National Energy Technology Laboratory SCO Synthetic crude oil

NFT Naphthenic froth treatment SGER Specified Gas Emitters Regulation

NG Natural gas SMR Steam methane reforming

NPV Net present value SOR Steam-to-oil ratio

N2O Nitrous oxide t Tonne

OPGEE Oil Production Greenhouse gas THAI Toe-to-Heel Air Injection Emissions Estimator TTW Tank-to-wheel PE Partial Equilibrium ULSD Ultra-low sulphur diesel PFT Paraffinic froth treatment UNEP United Nations Environment PG Process gas Programme xxii

U.S. United States WTR Well-to-refinery entrance gate vol % Percent by volume WTT Well-to-tank

WCS Western Canadian Select WTW Well-to-wheel wt % Percent by mass y Year

WTI West Texas Intermediate

xxiii 1

Chapter 1 Introduction Environmental Impacts of the Petroleum Life Cycle

The production and use of petroleum fuels in the transportation sector has drawn concern regarding environmental impacts, especially greenhouse gas (GHG) emissions, and energy security. The most recent Assessment Report of the International Panel on Climate Change (IPCC; IPCC 2013) estimated that in 2010, globally, the transportation sector emitted 7.0 gigatonnes carbon dioxide-equivalent (Gt CO2eq), accounting for 23% of energy-related CO2 emissions. This represents an 11% increase in emissions compared to their 2007 findings (6.3 Gt

CO2eq), despite increases in vehicle efficiency, adoption of electric vehicles, biofuel mandates and other policies aimed at curtailing GHG emissions from the industry due primarily to increasing vehicle kilometres (km) travelled in developed economies and increasing car ownership rates in emerging economies (IPCC 2013). The majority (94% in 2010; IPCC 2013) of energy demand for transportation is met by the supply of crude oil. In the near-term, according to the International Energy Agency (IEA), crude oil production is expected to increase, growing by 6.4 million bbl/day globally over 2018-2023, with approximately half of that demand met by U.S. light tight oil (IEA 2018). Despite low oil prices and pipeline capacity constraints that limit access to crude markets, growth projections indicate that production of bitumen, the extra heavy oil present in the Canadian oil sands, is expected to increase from 2017 production rates by 1.1 million barrels per day (bbl/day), to 3.6 million bbl/day in 2023 (AER 2018a).

Long-term forecasts predict that, with the availability of unconventional oil (either heavy oil such as bitumen or light tight oil), available oil resources will exceed demand for the remainder of the 21st century, with oil consumption between 2015-2100 exceeding cumulative historic production (~1,330 billion barrels; Brandt et al. 2018). While much of the focus on GHG mitigation has been on demand-side reductions and transitioning away from fossil fuels, Brandt et al. (2018) identified switching to fuels derived from crudes with lower life-cycle GHG intensities (i.e., emissions associated with crude oil production, processing, and combustion) as having a mitigation potential about half that of demand-side reductions. Meeting this objective

requires data and models able to accurately characterize the GHG emissions from the development of these resources to support robust decision-making by policymakers. Since 2006, low carbon fuel standards (LCFS) have been adopted in several regions (e.g., California, ARB 2017, British Columbia, BC Laws 2017), with the objective of reducing the average life cycle GHG emissions intensity of transportation fuels consumed in the region. For example, the California LCFS mandates a 10% reduction in the average GHG intensity of gasoline and diesel

(measured in grams of carbon dioxide-equivalent per megajoule of fuel, g CO2eq/MJ fuel) from 2010 levels by 2020 (ARB 2017). Regulated parties (typically fuel producers) under an LCFS can meet LCFS requirements by either reducing the average GHG intensity of the fuels they import or produce (e.g., by making reductions in GHG emission intensities over the life cycle of fuels they produce or blending with lower-carbon fuels) or by purchasing credits from other regulated parties (ARB 2017). A national Canadian Clean Fuel Standard is also under development, with requirements expecting to be enforced beginning in 2022 (ECCC 2017).

Adopted with the intention of reducing reliance on petroleum and decreasing the life cycle GHG intensity of transportation fuels, Renewable Fuel Standards require that transportation fuels sold in the region contain a minimum volume of renewable fuels. For example, under the U.S. Renewable Fuel Standard (RFS2), increasing volumes of renewable fuels must be blended into transportation fuels, reaching 36 billion gallons by 2022 (EPA 2010). Renewable fuels are divided into four categories (conventional biofuel, advanced biofuel, biomass-based diesel, and cellulosic biofuel). Specified volumes of fuel must be supplied from each category, and for each category a certain percentage improvement in GHG emissions intensity relative to a petroleum baseline must be demonstrated (ARB 2017). In RFS2, the National Energy Technology Laboratory (NETL) 2005 petroleum baseline (NETL 2009) was adopted as the baseline against which to measure the performance of renewable fuels.

Ensuring that policy objectives (i.e., net GHG emissions reductions) are being met requires an understanding of the GHG emissions intensities of each fuel production pathway considered within the scope of a policy. Life cycle assessment (LCA) results are being increasingly incorporated into climate policies such as LCFS and RFS. The LCA method can be used to assess the environmental burdens of a product or process from raw material extraction through

manufacturing to its use and disposal (cradle to grave; ISO 2006a,b). For transportation fuels, LCAs are typically undertaken on a well-to-wheel (WTW) basis, quantifying the impacts from feedstock extraction or production (“well”), fuel transportation, refining to products (e.g., gasoline, diesel), product transportation to vehicle refueling stations, and vehicle use (“wheel”), and reported on a MJ transportation fuel basis.

Models, as simplified representations of the real world, can support decision-making in the face of uncertainty. However, there are limitations to using models to represent complex, real-world systems. Limitations to LCA models have been identified in the literature that affect the ability of LCA results to accurately inform policy decisions (Björklund 2002; Finnveden et al. 2009; Plevin et al. 2013; Reap et al. 2008a,b). For example, while LCA results are often reported as point estimates (e.g., a single value to represent the GHG emissions intensity of fuel produced from a certain production pathway), the data and methods employed in conducting an LCA can lead to significant uncertainty and variability such that the point estimate reported may not accurately represent real world operations. The implication for LCFS-type policies is that uncertainty and variability in GHG emissions intensity estimates for fuel pathways may be greater than the reduction in life cycle GHG intensity mandated by the policy (Kocoloski et al. 2013; Mullins et al. 2011; Venkatesh et al. 2011). Additionally, how GHG emissions intensities of different fuel pathways will change as emerging technologies are deployed provides an additional source of uncertainty when setting GHG emissions intensity reduction targets, or assessing how a policy such as a RFS will translate into real-world GHG intensity reductions. Additional methodological issues arise when accounting for uncertainty with respect to emerging technology deployment or using historic data to make projections about potential future emissions.

Oil Sands Case Study

With 177 billion bbl in established reserves, the Canadian oil sands are the third largest established reserve of petroleum after Saudi Arabia and Venezuela (AER 2018). Located in northern Alberta, oil sands deposits are a blend consisting primarily of clay, sand, and bitumen, an extra heavy oil that cannot be recovered using conventional techniques. Bitumen can be recovered through surface mining or thermal in situ (e.g., steam-assisted gravity drainage,

SAGD, or cyclic steam stimulation, CSS) techniques. For shallower deposits oil sands material is mined and brought to a centralized extraction facility where bitumen is separated from sand using warm water and chemicals. Two primary extraction technologies exist for separating bitumen from the sand and water: naphthenic froth treatment (NFT) and paraffinic froth treatment (PFT), which involve the application of naphthetic or paraffinic solvents, respectively, along with hot water (Rao and Liu 2013). PFT is a newer process (the first commercial PFT project began producing bitumen in 2002). The addition of paraffinic solvents in froth treatment results in the precipitation of asphaltenes from the bitumen, leading to a higher quality bitumen with fewer solids and other contaminants. For in situ bitumen production, steam, solvents, or a combination of steam and solvents is injected into the reservoir to reduce the bitumen’s viscosity so that it can be recovered. Once bitumen is extracted, it can either be upgraded or diluted before being transported via pipeline to a refinery. Currently all NFT mining projects upgrade the bitumen they produce while two of four PFT projects upgrade mined bitumen, as the higher quality bitumen is more suitable for dilution to meet product specifications for pipeline transport than NFT dilbit (Romanova et al. 2006). Due to the characteristics of oil sands reserves (e.g., bitumen properties that requires specialized extraction techniques and more extensive refining), producing transportation fuels from bitumen is generally a more energy-intensive process, with higher life cycle GHG emissions, compared to producing transportation fuels from conventional petroleum resources (Bergerson et al. 2012; Cai et al. 2015). The focus of this thesis is quantifying GHG intensities, however production of oil sands-derived transportation fuels results in other environmental burdens (e.g., other air emissions from mining trucks, land use change; see for example Jordaan et al. 2009), which should be accounted for when comparing tradeoffs between different energy resources and energy systems.

In addition to LCFS policies that regulate the supply of transportation fuels across the transportation sector, climate change policies within Alberta have been adopted with the aim of limiting GHG emissions across the oil sands industry (e.g., Specified Gas Emitters Regulation, SGER, AB 2007; Alberta Climate Leadership Plan, AB 2015). Current and forthcoming regulations provide additional incentives to accurately characterize the GHG emissions from the life cycle of oil sands-derived transportation fuels, so that policymakers can incentivize production techniques that minimize GHG intensity across the life cycle.

To that end, several models have been developed that quantify the GHG intensity of various stages along the life cycle of oil sands-derived transportation fuels. The GreenHouse gas emissions of current Oil Sands Technologies (GHOST) model was developed within the Life Cycle Assessment of Oil Sands Technologies (LCA-OST) research group (by researchers at the University of Calgary and University of Toronto) to characterize the expected GHG emissions intensities of current oil sands operations. The model was applied to different oil sands production pathways (e.g., SAGD, CSS, and surface mining followed by upgrading or dilution) and was based on a combination of public literature and private operating data collected under non-disclosure agreements. Resulting ranges of expected GHG emissions were presented in Charpentier et al. (2011) and Bergerson et al. (2012). The initial applications of GHOST found wide but overlapping ranges of potential GHG emissions intensities of transportation fuels produced from each oil sands pathway. However, due to data and model limitations, the model was unable to predict the likelihood of a project operating at any point in the range of GHG emissions intensities reported.

Subsequently, publicly available monthly operating data (e.g., quantities of fuel and energy consumed by each project and volume of crude produced) have become available, due to mandatory reporting requirements of the Alberta Government, compiled by the Alberta Energy Regulator (AER) in annual statistical reports (see for example AER report ST39, Alberta Mineable Oil Sands Plant Statistics, AER 2018). Several models have characterized the GHG intensities of transportation fuels derived from different oil sands production pathways (e.g., the Oil Production Greenhouse gas Emissions Estimator model, OPGEE, El-Houjeiri et al. 2017; GHGenius, (S&T)2 2013; the Greenhouse gas, Regulated Emissions, and Energy use in Transportation model, GREET, Englander and Brandt 2014). Versions of GHGenius and GREET have each been employed to define the GHG intensity of fuel production pathways in existing LCFS. Each model employs data from the AER statistical series as the basis for characterizing oil sands pathways, however how the data is used differs in each model. Each model defines oil sands production pathways (e.g., a mining and upgrading pathway) based on different years of operating data and characterizing the pathways differently (e.g., the projects that are used to define each oil sands pathway). However, across all models, there remain some limitations that are addressed through this thesis. Previous models have aggregated project

operating data into oil sands production pathways, although each mining project employs its own set of technologies and operating characteristics so that a pathway may not reflect all projects that would be characterized by that pathway. Models have also used a limited number of operating years (e.g., the longest operating period characterized in a model is 2005-2012, Cai et al. 2015) to define the pathways, which may not be good indicators of a project’s lifetime GHG intensity or an indication of how a project will perform in the future. Additionally, all projects have employed simplified refinery models and used representative crude assays to represent either SCO or dilbit, not accounting for the full range of oil sands products produced by oil sands mining projects. Finally, models report results for each oil sands pathway as either point estimates (OPGEE, GHGenius), or as a point estimate with a 95% confidence interval (GREET, reported in Cai et al. 2015), that does not reflect the variability in GHG intensities from the range of oil sands projects that employ different operating technologies and produce a range of products.

The LCA modeling results presented in this thesis (Chapters 4 and 5) overcome some of the methodological limitations of previous LCAs of mined bitumen production and processing including: aggregation of projects into pathways, use of short operating time frames and incomplete data sets, and simplified refinery modeling approaches. This thesis is also the first to characterize the WTW GHG intensities of transportation fuels derived from PFT mined dilbit, using multiple years of operating data and accounting in the refining stage for differences in crude properties between in situ dilbit and dilbit from a PFT mining project. While an oil sands case study is presented here, many of the insights derived from this case study can be generalized to other energy systems and can inform policymakers and LCA practitioners regarding limitations to methods commonly employed in LCA, particularly with respect to LCAs of transportation fuel production pathways where all sources of variability (i.e., over time, across projects, in all stages of life cycle) are typically not accounted for.

Thesis Objectives

The overall objective of this thesis is to characterize GHG emissions from the production and use of transportation fuels derived from mined oil sands bitumen with more accuracy and completeness than has been done in previous studies by explicitly quantifying variability in GHG

intensities and disaggregating between temporal variability, inter-project variability, and variability in all life cycle stages. For transparency and consistency across all projects, monthly project operating data reported by the AER from 1983-2015 is employed to characterize energy use in upstream GHG intensities and refinery emissions are estimating using public crude assay data (that report crude characteristics) and a publicly-available refinery model. A secondary objective is to characterize the uncertainty associated with the future deployment of emerging technologies to assess their potential for meeting GHG intensity reduction targets and limits on industry-wide emissions. Studies described in Chapters 4 and 5, are conducted to meet that primary objective, while the study described in Chapter 6 is conducted to meet that secondary objective.

The specific objectives of each study are: 1. To characterize, on a project basis, the historic variability in upstream (mining and upgrading) GHG emissions intensities of mined dilbit and SCO production (upstream stages of life cycle). To meet this objective, temporal, inter-project, and intra-project variability are quantified and the specific operating factors that contribute to variability are identified. The findings provide insights for oil sands operators and policymakers regarding the biggest opportunities for reducing GHG emissions from oil sands mining projects. Chapter 4 presents the study related to this sub-objective. 2. To quantify the variability in GHG emissions intensities across the full well-to-wheel for transportation fuels derived from mined bitumen. For this study a statistically-enhanced WTW model is constructed by integrating GHOST-SE with a crude transportation model (Crude Oil Pipeline Emissions Model, COPTEM; Choquette-Levy et al. 2018) and a refinery model (Petroleum Refinery Life cycle Inventory Model, PRELIM; Abella and Bergerson 2012). For each mining project, variability in upstream (mining and upgrading or dilution) and downstream (crude transportation, refining, and end use) GHG emission intensities are quantified. Upstream operating decisions (e.g., the type and quality of crude to produce) have implications on downstream GHG emissions intensities (i.e., crude transport and refinery GHG emissions are dependent on crude properties determined by upstream operating decisions). By extending the analysis over the full life cycle, the implications of these upstream operating decisions on well-to-wheel GHG

emissions intensities are explored. This also facilitates a comparison to in situ oil sands pathways and North American petroleum baseline GHG intensities. Chapter 5 presents the study related to this sub-objective. 3. To capture industry knowledge regarding emerging oil sands technologies not currently available in the public literature to inform policymakers and oil sands operators of the opportunities and challenges associated with the deployment of emerging oil sands technologies and their potential for reducing the GHG emissions intensity of oil sands operations. Chapter 6 presents the study related to this sub-objective.

Publications Contained in this Thesis

Chapter 2 contains background material, including a review of relevant LCA literature, background information on oil sands technologies, and the policy and regulatory context for managing GHG emissions from the oil sands.

Chapter 3 provides an overview of the methods employed in this thesis. The International Standard Organization (ISO; ISO 2006a,b) 14040 standards for LCA are reviewed, followed by an overview of available methods for accounting for uncertainty and variability in LCA.

Chapters 4-6 have been published or are being prepared for submission to peer-reviewed journals. As first author I conducted most of the modeling, analysis, and writing of the papers. Professors Heather L. MacLean (primary supervisor) and Joule A. Bergerson (Ph.D. Advisory Committee member) are co-authors as they provided guidance on the overall thesis as well as feedback throughout the research process, including assistance with manuscript revisions. Additional co-authors for individual publications and their contributions to the research are listed below.

Chapter 4 is the first of two chapters related to characterizing variability of GHG emissions intensities from Canadian oil sands mining operations. In this chapter, the results of the development and application of a statistically-enhanced version of the GHOST model (GreenHouse gas emissions of current Oil Sands Technologies – Statistically Enhanced, GHOST-SE) to characterize variability in bitumen recovery, extraction, and upgrading or dilution (upstream) mining activities. Dr. Ian J. Laurenzi is a co-author on the publications

associated with Chapters 4 and 5 as he was involved throughout the research and contributed by providing feedback on the development of GHOST-SE, assistance with generating the histograms in the manuscript and input on manuscript revisions. This chapter has been published in Environmental Science and Technology. Citation is provided below:

• Sleep, S.; Laurenzi, I.J.; Bergerson, J.A.; MacLean, H.L. Evaluation of Variability in Greenhouse Gas Intensity of Canadian Oil Sands Surface Mining and Upgrading Operations. Environmental Science and Technology 2018, 52 (20), 11941-11951.

Chapter 5 extends the analysis of variability in upstream GHG emissions of bitumen mining presented in Chapter 4 by extending the boundary of analysis across the full life cycle (well-to- wheel). For this manuscript Dr. Ian J. Laurenzi developed some of the downstream components of the well-to-wheel model that I integrated the mining portion of GHOST-SE into. John Guo is listed as a co-author as he developed the in situ portion of GHOST-SE. This chapter is being prepared for publication in Journal of Cleaner Production. Citation is provided below: • Sleep, S.; Guo, J.; Laurenzi, I.J.; Bergerson, J.A.; MacLean. Quantifying Variability in Well-to-Wheel Greenhouse Gas Emission Intensities of Transportation Fuels Derived from Canadian Oil Sands Mining Operations In preparation for submission to Journal of Cleaner Production.

In Chapter 6, expert elicitation is employed to capture industry knowledge about emerging technologies not currently reflected in the public literature. This is the second expert elicitation conducted within this research project (the citation for the first publication related to this project is given in Section 1.5). Professor Jennifer M. McKellar is listed a co-author as she was involved in this research project overall and assisted with the development of the survey questions contained in this expert elicitation as well as feedback on the manuscript. This chapter has been published in the Journal of Cleaner Production. Citation is provided below: • Sleep, S.; McKellar, J.M.; Bergerson, J.A.; MacLean, H.L. Expert Assessments of Emerging Oil Sands Technologies. Journal of Cleaner Production, 2017, 144, 90-99.

Publication Related to this Thesis

Related to Chapter 6 of this thesis is a publication that was prepared prior to the paper referenced in Chapter 6. Citation is provided below:

• McKellar, J.M.; Sleep, S.; Bergerson, J.A.; MacLean, H.L. Expectations and Drivers of Future Greenhouse Gas Emissions from Canada’s Oil Sands: An Expert Elicitation. Energy Policy¸ 2017, 100, 162-169.

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Chapter 2 Background and Literature Review

The production and use of petroleum-derived transportation fuels contributes substantially to anthropogenic greenhouse gas (GHG) emissions (direct emissions contributing to approximately 23% of total energy-related GHG emissions, IPCC 2013), the effects of which may be severely detrimental to both humans and ecosystems. Unconventional energy sources such as the Alberta oil sands are being increasingly exploited to meet the growing demand for transportation fuels. The development of new industries related to producing unconventional fuels provides tremendous economic opportunities for the regions and nations in which these reserves are located. Recovering unconventional resources and converting them into usable transportation fuels can be challenging and often requires a different set of technologies than those used for conventional resources. As a result, the production and processing of unconventional fuels is generally associated with higher environmental impacts compared to conventional, particularly with respect to GHG emissions (Charpentier et al. 2009). Overcoming the technical and environmental challenges associated with unconventional petroleum resources is crucial for maintaining a stable supply of transportation fuels without excessive environmental degradation.

This chapter contains background information on the oil sands and life cycle assessment and a review of relevant life cycle assessment literature. First, background information is provided on the oil sands and technologies employed in oil sands extraction and processing. An overview of policies and regulations for managing GHG emissions from the oil sands is provided. Next, life cycle assessment is defined and a review of applications and use in policy is provided. Finally, a review of the life cycle assessment literature pertaining to oil sands technologies is presented and gaps in existing literature are discussed. Additional background and literature review specific to the individual studies contained in this thesis are provided in their respective thesis chapters (Chapters 4-6).

Oil Sands Background

Located primarily in the western Canadian province of Alberta, Canada’s oil sands consist of a bitumen mixed with sand, water, and other organic materials. Bitumen is a viscous hydrocarbon which does not flow at ambient temperatures and pressures (see Section 2.1.1, Properties of Oil

Sands Bitumen). Standard recovery methods are thus inadequate for production from most oil sands reserves and specialized techniques are required to extract the bitumen and process it into usable transportation fuels. For shallower deposits (up to 75m in depth), oil sands material is mined and bitumen is extracted at a centralized extraction facility using hot water and solvents. For deeper deposits, in situ methods are employed where steam is injected into the reservoir to heat the bitumen and reduce its viscosity, then pumped to the surface. Once recovered and extracted from the sand and water mixture, bitumen is either upgraded into synthetic crude oil (SCO), which can be transported by pipeline to conventional refineries for processing, primarily into gasoline and diesel fuels. In the upgrading process, the heavy components of bitumen are converted to lighter products by removing carbon or adding hydrogen to shorten the carbon chains in the hydrocarbon molecules (Charpentier et al. 2011). The bitumen that is not put through the optional upgrading step is diluted with a lighter fuel (typical diluents include naphtha, or natural gas condensate to produce dilbit, or SCO to produce synbit) and transported directly to a refinery capable of processing diluted bitumen.

2.1.1 Properties of Oil Sands Bitumen

Petroleum liquids (including crude oils) are broadly classified by their American Petroleum Institute (API) gravity and sulfur content. API gravity is an inverse measure of specific gravity, whereby a higher API indicates a lower density product. A petroleum liquid with an API gravity of 10 has a density equivalent to that of water (1000 kg/m3). Crude oil can be classified as light, medium, heavy, or extra-heavy depending on their API gravity (see Table 2-1). To describe their sulfur content, crude oils are often denoted as either sweet (containing less than 0.5 percent by mass, wt % sulfur) or sour (containing greater than 0.5 wt % sulfur).

Table 2-1. Petroleum Classifications by API gravity Crude Classification API gravity (o) Light >31.1 Medium 22.3-31.1 Heavy 10-22.3 Extra-Heavy <10 API: American Petroleum Institute

With an API gravity ranging from approximately 8.0-10 (Gray 2015), non-upgraded bitumen is an extra-heavy oil. The properties of bitumen are compared to dilbit, synbit, SCO, and a conventional crude benchmark (West Texas Intermediate, WTI) in Table 2-2. Without dilution or upgrading, bitumen does not meet pipeline crude property specification (Choquette-Levy et al. 2013). For example, the Enbridge pipeline specifications include (among other requirements), that crude have an API gravity ≥19o, viscosity ≤350 cSt at pipeline temperatures, and a solids and water content ≤0.5 vol % (Gray 2015).

Table 2-2. Comparison of Oil Sands Products to Conventional Crude

Crude API gravity (o) Sulfur (wt %)

Bitumen 8.0-10 4.1-5.0

Dilbit 20-22 3.7-3.9

Synbit 19-21 2.8-3.0

SCO 30-35 <0.2

WTI Benchmark 41 0.3 Crude properties obtained from crudemonitor.ca (Crude Monitor 2017), Abella and Bergerson (2012), and Gray (2015). WTI: West Texas Intermediate.

The WTI benchmark price is one of the most commonly quoted North American benchmark crude prices, based on the price of WTI sold in Cushing, Oklahoma (Gray 2015). Lighter, sweeter crudes tend to be sold to refineries at a premium compared to heavy, sour crudes, as heavy, sour crudes require more complex refineries and more processing to transform the whole crude into higher-value products (e.g., transportation fuels). The price difference between light, sweet crudes and heavy, sour crude (known as the light-heavy differential) is often reported as the price difference between WTI and the Western Canada Select (WCS), a blend of bitumen, heavy oil, and condensate sold in Hardisty, Alberta (Gray 2015). Light/heavy price differentials vary over time, depending on market conditions such as supply of different types of crude, capacities of refineries capable of processing different types of crudes, and access to markets for oil sands products (Deloitte 2017). The Canadian Energy Research Institute (CERI; CERI 2018) report that, since sales of WCS began in 2009, that WTI/WCS price differential has ranged from US$6/bbl in April 2009 to US$37/bbl in February 2013. Volatility in light-heavy price

differentials remain, with the WTI/WCS price differential ranging from US$16/bbl (June 2018) to US$27/bbl (March 2018) from January to June 2018 (AB 2018). Wider light/heavy oil differentials provide greater financial incentives to oil sands operators to upgrade bitumen to lighter, sweeter SCO over producing dilbit.

2.1.2 Current Oil Sands Technologies

Surface Mining. With surface mining, diesel-powered and electric shovels mine bitumen, which is trucked to a crushing facility, where rotary breakers break up large chunks of oil sands material. Crushed material is mixed with warm water (approximately 70 wt % solids; Ordorica- Garcia et al. 2007) and small amounts of sodium hydroxide. The resulting slurry is sent through hydrotransport pipelines where bitumen is released from the sand and transported to a centralized extraction facility. Operating temperatures typically range from 40-55oC, with one project operating at a temperature as low as 35oC (Long et al. 2005). Previously, tumblers had been used instead of hydrotransport, however, higher operating temperatures (up to 75oC) were required for the tumblers (Masliyah et al. 2004). The slurry is aerated, creating a bitumen froth that is separated from the sand using gravity separation; this takes place at a centralized extraction facility (Romanova et al. 2006).

Prior to treatment, bitumen froth typically has a composition of 60 wt % bitumen, 30 wt % water, and 10 wt % mineral solids (Masliyah et al. 2004). To meet pipeline and downstream quality requirements the final bitumen product must contain less than 0.5 vol % mineral solids and water (Rao and Liu 2013). Froth treatment is used to separate the bitumen from the remaining water and solids present in the froth through the addition of a light hydrocarbon, either naphthenes or paraffins. Naphthenic froth treatment (NFT) uses a low solvent to bitumen ratio and produces a bitumen product with approximately 1-2 wt % water and 0.5 wt % solids that requires upgrading to meet pipeline or refinery specifications (Rao and Liu 2013). A newer process developed in the 1990s, paraffinic froth treatment (PFT), uses a higher solvent/bitumen ratio and results in the precipitation of asphaltenes that can be separated from the desired product using conventional settlers, resulting in some bitumen loss from the froth treatment process, approximately 8% volume (Rao and Liu 2013; Jacobs 2018). The resultant bitumen from PFT has water and solids contents approximately two orders of magnitude lower than NFT bitumen (Rao and Liu 2013).

Through PFT, bitumen API increases from 8.5 to 9.5o have been observed (Jacobs 2018). Due to the lower levels of asphaltenes and of other contaminants, bitumen produced through PFT does not require upgrading (Masliyah et al. 2004), although, two PFT projects produce bitumen (commencing in 2002 and 2010, respectively) that is sent to a stand-alone upgrader. Instead, to meet pipeline specifications for transport to refinery, a bitumen is mixed with diluent. Due to asphaltene precipitation less diluent is required for PFT bitumen compared to bitumen produced from in situ methods (approximately 23% diluent by volume for PFT bitumen versus 30% for in situ bitumen; see discussion of Mining Dilbit pathway in Chapter 5). Dilution is a less expensive and less energy-intensive process than upgrading (Choquette-Levy et al. 2013).

There are currently eight active mining projects operating in the oil sands. The first mining project commenced operation in 1967 and is still operating today; the newest mining project began producing bitumen at commercial scale in February 2018 (AER 2018a,b). Mining project characteristics are summarized in Table 2-3. All six mines constructed prior to 2013 are associated with an upgrader. Projects employing NFT (Suncor Millenium and Steepbank, Syncrude Mildred Lake, Syncrude Aurora, and Canadian Natural Resources Limited, CNRL, Horizon mines) produce bitumen that is upgraded at upgraders that are integrated with mining operations.

The oil sands are the third largest reserve globally, after Venezuela and Saudi Arabia. As of November 2018 (most recent data available), 5.1% of global oil production (which averaged 82 million bbl/day) was produced in Canada (U.S. EIA 2018); approximately 64% of Canada’s oil production is from the oil sands (NRCan 2018). From January to April 2018 (the most recent data available at the time of writing this thesis), 57% of mined bitumen (771,000 bbl/day) was produced from integrated mining and upgrading projects employing NFT, 22% (288,000 bbl/day) was produced from PFT mining projects that send bitumen to a stand-alone upgrader, and 21% (280,000 bbl/day) was produced from PFT mining projects that produce dilbit (AER 2015). The AER (AER 2018a) predicts that from 2017 to 2027 mined bitumen production will increase by 32%, mostly due to production increases at PFT mines producing dilbit (Imperial Kearl and Suncor Fort Hills). For reference, over the same period in situ production is forecast by the AER to increase by 39%. While greenfield developments are generally not economic at

current crude oil prices (as of 2018), some brownfield project expansions are currently economic (AER 2018b). CNRL announced in 2018 a proposal for a 40,000 bbl/day brownfield expansion employing PFT and producing dilbit at the CNRL Horizon mine that currently employs NFT and produces SCO (AER 2018b). The shift in the mining industry from bitumen production employing NFT and producing SCO to PFT mines that produce dilbit has implications for the GHG emissions intensities across the life cycle of transportation fuels derived from this bitumen. The GHG emissions from PFT mining operations have not been fully characterized in previous life cycle studies (e.g., Bergerson et al. 2012; (S&T)2 2013; Cai et al. 2015; El-Houjeiri et al. 2017) which have not accounted for differences in upstream energy consumption between NFT and PFT mines or the properties of PFT dilbit compared to in situ dilbit. See Section 2.3, Life Cycle Assessments of Oil Sands Technologies for a literature review of previous life cycle assessments of oil sands technologies.

Table 2-3. Summary of Oil Sands Surface Mining Projects Start-up Capacity Froth Upgrading included? Mine Year (bbl/day) Treatment Suncor Millenium and 1967 330,000 NFT Yes, integrated Steepbank Syncrude Mildred Lake 1978 150,000 NFT Yes, integrated Syncrude Aurora 2002 225,000 NFT Yes, sent to Mildred Lake upgrader CNRL Muskeg Rivera 2003 155,000 PFT Yes, stand-aloneb CNRL Horizon 2009 240,000 NFTb Yes, integrated with mine CNRL Jackpinea 2010 100,000 PFT Yes, stand-alone Imperial Kearl 2013 220,000 PFT No, produces dilbit Suncor Fort Hills 2018 194,000 PFT No, produces dilbit Adapted from El-Houjeiri et al. (2017), updated with data from AER (2018) and Alberta Oil Sands Industry Summer 2018 Quarterly Update (AB 2018). aPreviously owned and operating by Shell Canada Limited. bBitumen produced at Muskeg River and Jackpine mines sent to stand-alone Scotford upgrader. cCNRL has announced plans to construct a 35,000 bbl/day expansion to the CNRL Horizon project employing PFT and producing dilbit. NFT: naphthenic froth treatment; PFT: paraffinic froth treatment

In Situ Methods. The two most common thermal in situ methods are cyclic steam stimulation (CSS), and steam assisted gravity drainage (SAGD), both of which involve pumping steam into the reservoir to reduce the viscosity of the bitumen and separate it from the sand so that it can be

recovered by pumping. In CSS, steam is periodically injected into the reservoir, and bitumen is pumped out through the same vertical well that is used for steam injection. The SAGD process allows for a higher bitumen recovery rate than CSS (up to 70% versus 25 to 30%, respectively, CRS 2014). It involves the drilling of two horizontal wells. Steam is continuously injected into the upper well, mobilizing the surrounding bitumen and allowing it to flow into the lower well. A key indicator of the efficiency of a thermal in situ project is the steam-to-oil ratio (SOR), a measure of the volume of water in the form of steam required to produce one barrel of oil. Although many LCAs of SAGD operations assume an SOR of 2.5 for a well-performing SAGD operation (Toman et al. 2008; Lacombe and Parsons 2007), some projects are operating at slightly lower SORs, while others are operating at much higher SORs (Charpentier et al. 2011).

Upgrading. During upgrading, a series of processes are undertaken to remove impurities such as sulfur and to transform bitumen into a higher quality product, SCO. First, diluent is recovered and recycled back to the extraction facility. Vacuum distillation is used to separate bitumen into different fractions depending on viscosity (Pacheco et al. 2016). Heavy fractions undergo primary conversion, which increases the hydrogen to carbon ratio of the fraction either through the removal of carbon (coking) or the addition of hydrogen (hydroconversion). Hydroconversion is a more energy-intensive process but has a higher recovery rate than coking (Charry-Sanchez et al. 2016). Presently in the oil sands, two upgraders utilize coking, one employs hydroconversion, and one is a combination of both (Gray, n.d.). All crude fractions are sent for secondary upgrading, where hydrotreaters remove sulfur, nitrogen, and other impurities by adding hydrogen under high temperature and pressure conditions in the presence of a catalyst (Gray 2015). Crude fractions are then blended to meet pipeline specifications (Choquette-Levy et al. 2013) and downstream refinery requirements (Charry-Sanchez et al. 2016). There are three main types of SCO: premium sweet synthetic, light sweet synthetic, and heavy sour crude (Charry-Sanchez et al. 2016), although all upgraders produce SCOs with different characteristics (reported at www.crudemonitor.ca, Crude Monitor 2017). Some projects produce multiple SCOs in varying proportions over time as well as some diesel production, depending on factors such as downstream demand for these products (AER 2016). By-products from upgrading include process gas produced in primary and secondary upgrading, coke produced by cokers, sulfur from hydrotreaters, and waste heat from processing units (Pacheco et al. 2016).

While some projects include stand-alone mines or stand-alone upgraders, some sites are integrated (with mining and upgrading both occurring on-site, hereafter referred to as integrated mining projects). By-products of the upgrading process are excess steam and hot water, that are of too low quality to use in upgrading. Process integration between mining and upgrading facilities can be employed to use waste heat from the upgrader to supply hot process water to extraction facilities. Process integration reduces the overall demand for energy for integrated mines compared to standalone mines and upgraders. Process integration related energy savings for extraction have been estimated to be approximately 30% (Suncor & Jacobs 2012).

Characteristics of currently operating upgraders are reported in Table 2-4. Three of five upgraders are integrated with mining operations and one is a stand-alone upgrader that upgrades bitumen from two PFT mines previously operated by that company (operated by CNRL as of 2017). The upgrader located at the Suncor mine also processes some bitumen from the Suncor Firebag SAGD project. Previously, Nexen operated an upgrader that processed bitumen from their Long Lake SAGD project; this upgrader shut down after an explosion in 2016 and as of 2018 there are no plans to refurbish this upgrader (AER 2018). Each upgrader employs a distinct set of technologies to process bitumen and produces a distinct set of products.

Table 2-4. Characteristics of Operating Oil Sands Upgraders 2017 Average Start- SCO Technology Upgrader Products Notes Up Production Employed (bbl/day) Light Upgrades bitumen from Suncor sweet SCO Millenium and Steepbank mines

and a bitumen from Firebag SAGD Suncor 1967 326,000 Coking Heavy sour project SCO

Integrated with Suncor mine Diesel Light, Upgrades bitumen from Syncrude Coking + sweet SCO Mildred Lake and Aurora mines Syncrude 1978 253,000 hydroconversion Diesel Integrated with Mildred Lake mine Light, Upgrades bitumen from CNRL sweet SCO Muskeg River and Jackpine mines Shell Scotford 2002 277,000 Hydroconversion

Heavy, Stand-alone sour SCO Upgrades bitumen from CNRL Horizon mine Light, CNRL Horizon 2009 173,000 Coking sweet SCO Integrated with CNRL Horizon mine Sturgeon refinery upgrades raw North West Diesel bitumen, producing diesel and Redwater 2017 2,000 Hydroconversion some diluent Partnership Diluent Sturgeon Refinery Stand-alone Data from AER (2018); CrudeMonitor (2017); bbl/day: barrels per day; SCO: synthetic crude oil 2.1.3 Emerging Oil Sands Technologies

The recent rapid expansion of the oil sands industry has resulted in investment in the development of new technologies targeted towards overcoming some of the technical, economic, and environmental challenges that face the industry. These technologies have been broadly classified as: process improvements and technological changes (McKellar et al. 2016; Sleep et al. 2017; CCA 2015). To date, considerable attention has been paid to incremental improvements to existing mining and in situ technologies through improved site design and operation. Gauging expert’s expectations for commercial deployment and the potential for GHG emissions intensity reductions from the deployment of these technologies is the focus of Chapter 6 of this thesis. Since the expert elicitation was conducted (2014), the status of some emerging technologies has changed. In this section, an overview of emerging oil sands technologies is provided and

developments in emerging technology deployment since 2014 are discussed. Emerging oil sands technology processes are described in more detail in Appendix C.

Emerging technologies for the recovery and extraction of bitumen through mining are generally focused on incremental process improvements that can be implemented at existing mining operations (CCA 2015). Generally, these process improvements have focused on increasing the efficiency of mining operations to reduce energy consumption and operating costs (Sleep et al. 2017). Incremental process improvements to mining operations include: efficiency improvements from better logistical planning, heat integration, use of more efficient equipment, lower temperatures for bitumen extraction, and use of solvents to aid in bitumen separation (Sleep et al. 2017). At the time the expert elicitation documented in Chapter 6 was completed, no emerging technologies were expected to be deployed in the study period (2014-2034). In January 2018, Suncor announced plans to deploy over 150 electric autonomous haul trucks to replace current diesel haul trucks over the next six years (Suncor 2018), the first large-scale deployment of electric or autonomous vehicles in oil sands mining operations.

Compared to mining projects, in situ projects differ in many aspects that make near-term commercial implementation of emerging technologies more feasible, including: smaller operating capacity, shorter timespans for regulatory approval, and lower capital costs (CCA 2015). Bitumen production from in situ methods is also increasing more rapidly than from mining projects (CCA 2015; AER 2018), so the potential for future deployment of emerging in situ technologies is greater (CCA 2015). As a result, more attention has been given to the development of emerging in situ technologies, while emerging technology research for mining has primarily focused on incremental process improvements that can be adopted at existing mines.

Current investments in emerging in situ technologies generally target production methods that produce bitumen at lower cost by partially reducing or fully eliminating the natural gas needed for steam production either through modifications to the existing SAGD process to reduce the steam-to-oil ratio (the cold water equivalent volume of steam required to produce one volume unit of oil and is a measure of the efficiency of oil production. SOR), or new methods that preclude the requirement for steam altogether. By decreasing the need for natural gas, one of the

main contributors to GHG emissions associated with the production of oil sands products, these technologies have the potential to offer substantial reductions in the industry’s GHG emissions if widely adopted by oil sands operators. Many emerging in situ technologies are also suitable for use in reservoirs where the physical characteristics of the reservoir make existing technologies unfeasible. Technologies currently in development (predominantly at the field test or pilot scale stage of development) that are considered promising include; solvent processes, in situ combustion, and electric heating.

A review of the status of specific emerging in situ technologies through a patent review was conducted by Gates and Wang (2011), see Table 2-5. The main incentive for the development of these technologies has been to reduce capital and operating costs, however these new technologies also offer the potential to provide access to reservoirs that are not viable with existing technology. Commercial-scale performance of these technologies remains uncertain, but the potential exists for these technologies to provide significant environmental benefits compared with existing oil sands technologies, including reduced GHG emissions. Subsequent to Gates and Wang (2011) and the publication of the study presented in Chapter 6 of this thesis, the Canadian Energy Research Institute (CERI) has conducted a review of the cost and GHG impacts of emerging technologies in the oil sands (Nduagu et al. 2017). In CERI’s (2017) review, they identify steam-solvent with cogeneration and steam-solvent processes as the lowest-cost emerging in situ technologies with the greatest potential for GHG intensity reductions. Solely in terms of GHG reductions, they found that pure solvent (no steam injection) has the greatest potential for GHG intensity reductions but similar to other in situ technology options (i.e., steam with cogen, brownfield developments, steam/CO2 co-injection). The potential for adoption of emerging technologies and the implications for the GHG intensity of bitumen production are explored in the expert elicitation presented in Chapter 6.

Table 2-5. Emerging In Situ Technologies. Hybrid Steam-Solvent Technology In Situ Combustion Electro-Thermal Processes Air is injected into the reservoir to prompt Electrical current Replace some steam Mechanism for combustion or passed through injection with solvent Reducing Bitumen gasification of the electrodes in ground to to reduce bitumen Viscosity heavy portion of the heat bitumen viscosity petroleum in the reservoir Several pilot projects exist that employ this THAI pilot projects technology, for A demonstration example: SAP pilots project of the THAITM operated by Cenovus; began operation in Solvent-assisted CSS Past/Existing Projects 2006; however, the ET-DSP pilot project pilot project operated Employing Technology project has since been by CNRL; ES-SAGD shut down (Touchstone pilot project operated 2016) and no new in by Nexen (additional situ combustion examples are presented projects are planned. in Gates and Wang 2011) GHG emissions Most similar to SAGD reduction if electricity in terms of site design, obtained from low- operational Partial upgrading in situ GHG source requirements Benefits Potential for higher Access shallower Can improve SORs for bitumen recovery rates resources than existing in situ projects accessible with current Some recycling of technologies solvent recovered Require some steam injection to supplement Gasification of heavy solvent residues in situ could Heat losses can be Drawbacks Limited availability of result in higher GHG substantial low-cost solvents if emissions technology becomes widely deployed THAI: Toe-to-Heel Air Injection; ET-DSP: Electro-Thermal Dynamic Stripping Process; SAP: Solvent- Aided Process; CSS: Cyclic Steam Stimulation; SAGD: Steam-Assisted Gravity Drainage; ES-SAGD: Expanding Solvent Steam-Assisted Gravity Drainage.

For bitumen upgrading, the focus of emerging technology development has been on partial upgrading technologies. Partial upgrading involves improving the quality of bitumen through partial conversion of the heavy components of bitumen, asphaltene removal, or a combination of those processes (Jacobs 2018). The benefits to partial upgrading include: increasing access to markets for oil sands bitumen, reducing demand for diluent which decreases diluent costs to producers while increasing available pipeline capacity, and reducing capital costs compared to full upgrading (AB 2018). Most partial upgrading technologies under development expect to be applied to transform bitumen so that it can meet minimum pipeline specifications with minimal (or no) diluent required, although partial upgrading technologies can produce a range of potential products which range from the minimum to meet pipeline requirements to properties closer to a higher-quality SCO (Jacobs 2018). As of 2018, more than 10 partial upgrading technologies were under development, although to date none has been deployed at a commercial scale (AB 2018). Partial upgrading has lower capital costs than full upgrading and has the potential to be economic when oil prices are insufficient to make the construction of full upgraders economic (AB 2018; Jacobs 2018).

In 2017, the Alberta government announced its plans to invest up to $1 billion to expedite the commercialization of partial upgrading technologies (AB 2018). That investment, combined with pipeline capacity constraints that are limiting current production increases, have further accelerated investment in the development of partial upgrading technologies. However, at the time of completion of the expert elicitation (2014) presented in Chapter 6, commercial adoption of partial upgrading technologies in the forecast period considered (2014-2034) was not predicted by the experts surveyed. As such, the focus of the expert elicitation and this review of emerging technologies will focus on emerging in situ technologies.

2.1.4 Policy and Regulatory Context for Managing GHG Emissions from the Oil Sands

Alberta’s first carbon policy was enacted in 2007 with the implementation of the Specified Gas Emitters Regulation (SGER, AB 2007), under which large final emitters of GHG emissions

(facilities emitting more than 100 Mt CO2eq/year) were required to reduce their emissions intensity by 12% relative to 2007 levels (or their first year of operation for new facilities).

Facilities regulated under the SGER that exceed their reduction threshold could reach compliance by purchasing emissions credits at a price of $15/tonne CO2eq (AB 2007). Over time the reduction targets and carbon price for large emitters regulated under the SGER increased, reaching a benchmark reduction of 20% and a carbon price of $30/tonne CO2eq in 2017. From 2011 to 2016, the SGER was reported to have applied a carbon price to approximately 45% of all GHG emissions from Alberta (CLP Progress Update 2017).

A Climate Change Advisory Panel convened by the Alberta government reviewed Alberta’s climate policies and provided a set of recommendations to the Alberta Government for managing GHG emissions in the province. Based on those recommendations, the Alberta Climate Leadership Plan for reducing GHG emissions was published in November 2015, including plans for the adoption of an economy-wide carbon price and other strategies for reducing GHG emissions (AB 2016). On January 1, 2017, the Climate Leadership Act came into effect in Alberta, introducing a province-wide carbon levy (excluding large final emitters) of $20/tonne

CO2eq, increasing to $30/tonne CO2eq in 2018 (AB 2017). The carbon levy applied under the Climate Leadership Act applies to all fuels that emit GHG emissions when combusted, including transportation and heating fuels. The trends in carbon price over time and the facilities they apply to are reported in Table 2-6. As of January 1, 2018, the SGER was replaced by the Carbon Competitiveness Incentive Regulation, that shifts from intensity-based regulation to an output- based regulation. Under the Carbon Competitiveness Incentive Regulation, benchmarks are set for each sector (e.g., oil sands mining and in situ) based on the best-performing facilities in that sector.

Table 2-6. Carbon prices applied over time in Alberta Year Statute/Regulation Price ($/tonne Applied to

CO2eq) 2007 Specified Gas Emitters Regulation 15 Large final emitters 2016 Specified Gas Emitters Regulation 20 Large final emitters 2017 Specified Gas Emitters Regulation 30 Large final emitters Climate Leadership Act 20 Economy-wide (excluding large final emitters) 2018 Carbon Competitiveness Incentive 30 Large final emitters Regulation Climate Leadership Act 30 Economy-wide (excluding large final emitters) Adapted from CLP Progress Update (2017).

Besides the carbon levy implemented in the Climate Leadership Act, several complementary objectives are included in the Alberta Climate Leadership Plan. These include: 1) a phase out of coal by 2030 and an increase in renewable electricity generation capacity, 2) a limit on oil sands GHG emissions of 100 Mt per year by 2030, 3) changes to an existing carbon levy applied to large final emitters, and 4) a methane emissions cap to manage venting and flaring at oil and gas facilities. The key statutes and regulations implemented to support the Climate Leadership Plan are summarized in Table 2-7. The Oil Sands Emissions Limit Act came into effect on December 14th, 2016, granting the provincial cabinet authority to make regulations “establishing and governing mechanisms to keep greenhouse gas emissions from oil sands within the limit” (CLP Progress Update 2017).

In addition to carbon legislation enacted in Alberta, the Government of Canada is moving towards a nation-wide carbon regulation strategy with the Pan-Canadian Framework on Clean Growth and Climate Change, which sets a benchmark carbon price (either through a carbon tax or cap and trade system) that must be met by all provinces and territories (ECCC 2016). Between 2007 and 2017 carbon policies (including carbon levies or taxes, renewable fuel mandates, and low carbon fuel standards) have been adopted in markets for oil sands products across North America, as well as throughout Europe (see Section 2.2.3, Applications of Life Cycle Assessment in Energy Policy). These provide additional incentive for improving our understanding of the GHG emissions that result from producing and processing oil sands

bitumen and for exploring opportunities for reducing average GHG intensity across the life cycle of fuels derived from the oil sands.

Table 2-7. Selection of Statutes and Regulations from the Climate Leadership Plan Statute/Regulation Contents Implementation Date Climate Leadership Act and Carbon levy January 1, 2017 Climate Leadership Regulation Energy Efficiency Alberta Includes incentives for improving energy October 27, 2016 Act efficiency in Alberta. Oil Sands Emissions Limit Enables the Alberta government to pass December 14, 2016 Act regulations to limit GHG emissions from oil sands

industry to 100 Mt CO2eq in any year by 2030 with provisions for cogeneration and new upgrading capacity. Renewable Electricity Act Increase renewable electricity generation to 30% March 31, 2017 of Alberta’s grid mix by 2030. Alberta Climate Replaces SGER. Shifts from an intensity-based January 1, 2018 Competitiveness regulation to an output-based regulation with (replaces SGER) Regulation benchmarks set by best performing facilities in each sector. Adapted from CLP Progress Update (2017). Life Cycle Assessment – Development and Applications

2.2.1 What is Life Cycle Assessment?

LCA is a technique for systematically assessing the potential environmental impacts from a product’s life cycle, including acquisition of raw materials, manufacturing, use, and end-of-life treatment (recycling or disposal). Two distinct methods for LCA exist: attributional LCA (ALCA) and consequential LCA (CLCA). In ALCA, the flows into and out of a system (including, for example, energy consumption and air emissions) are quantified in relation to the provision of a specified amount of a product (e.g., 1 MJ of gasoline). CLCAs quantify the system-wide changes in flows resulting from a change in the production levels of a product (Rebitzer et al. 2004). CLCA approaches are not applied in this thesis but are an opportunity for future work when assessing the impacts of GHG emissions policies on oil sands development and are relevant to discussions about interpretation of LCA results and the implications for

policymakers. For a discussion of the strengths and limitations of each approach, see Section 3.1.1, Limitations of Life Cycle Assessment Methods. As ALCA methods are employed in this thesis, they are the focus of the remainder of this section.

The broader methodological framework upon which LCAs are based is codified in the ISO standards for LCA (ISO 2006a,b). LCA can be divided into four stages: goal and scope definition, inventory analysis, impact assessment, and interpretation. The stages and terminology associated with LCA are described below.

The goal of an LCA should include a description of the product system being modeled, study objective(s), and intended audience. Generally, the scope outlines the breadth and depth of the study sufficiently to address the goal of the LCA. Within the scope definition, the following items should be defined: product system, functional unit, system boundary, data requirements, assumptions, and limitations, allocation method, as well as the impact categories to be employed (in the case of life cycle impact assessments).

The functional unit is a measurable quantity of the function provided by the product that the LCA is being conducted upon. Inputs to and outputs from the product system are related to the functional unit. The definition of a functional unit allows for comparisons across product systems. Common functional units for LCAs of transportation fuels are one MJ of gasoline or one vehicle km travelled.

The system boundary defines the activities that will be included in the analysis. Inputs and outputs with insignificant impact on the study findings may be excluded from the study boundary (ISO 2006a). In setting the study boundary, assumptions and limitations of the study should be described including cut-off criteria, taking into consideration the study goals. The system boundary should be defined so that inputs to and outputs from the study boundary can be defined as elementary flows. In some cases, the study boundary cannot be widened sufficiently to achieve this, in which case flows must be partitioned between the product system being studied and other product system(s), a procedure known as allocation.

The ISO 14040 (2006a) provides a set of principles and procedures for allocation in LCA. When possible, the ISO standard recommends avoiding allocation by either further dividing the

processes to be allocated into subprocesses or system expansion, where the system boundary is expanded to include the functions provided by coproducts into the system being studied. If this is not feasible, the system flows should be partitioned amongst products in a way that reflects either: the underlying physical relationships between them (e.g., energy or mass basis), or, if that is not possible, other relationships between the products (e.g., based on economic value of the products).

The inventory analysis stage involves data collection and the actual modeling of the product system. In this stage, energy and material flows and environmental burdens (e.g., emissions to air) are quantified and related to the functional unit defined in the goal and scope definition stage. When needed, allocation procedures are employed to partition flows to the system’s coproducts.

The objective of the impact assessment stage is to assess the potential environmental impacts of the LCI results. Impacts may include, for example, climate change, ozone depletion, eutrophication, acidification, human toxicity, land use (EC-JRC-IES 2010). In ISO 14044, the impact assessment stage is further divided into four steps. In the first step (classification), impact categories are selected and flows defined in the LCI (e.g., air emissions) are assigned to the appropriate impact categories (ISO 2006b). Next, results within each impact category are combined and a final category indicator result is calculated by applying characterization factors to the individual flows within this impact category (characterization). For example, within the characterization step, CO2, N2O, and CH4 are often reported together as a global warming potential in carbon dioxide-equivalents (EC-JRC-IES 2010). Weighting factors reported by the

Fifth Assessment Report of the International Panel for Climate Change (IPCC) are 1 for CO2, 30 for CH4, and 265 for N2O for a 100-year warming horizon (IPCC 2013). The third and fourth (optional) steps of impact assessment involve grouping impact categories and applying weighting factors based on the relative importance of each impact category depending on study goals.

In the interpretation stage, study results are reviewed considering the goal and scope of the study. Within this stage, the limitations of the study should be discussed and the definition of functional unit, system boundary, and allocation procedures employed should be assessed. The ISO 14040

recommends that both sensitivity and uncertainty analysis be conducted during this stage, characterizing uncertainties by reporting results as ranges and/or probability distributions (ISO 2006a). Once these are complete, study conclusions should be drawn and policy recommendations may be made, as appropriate.

Historically, LCA was developed primarily for examining product packaging alternatives, with the first documented LCA being conducted by Coca-Cola Company in 1969 (Hunt and Franklin 1996). The first publicly reported LCA was commissioned by the U.S. Environmental Protection Agency (EPA), comparing beverage container alternatives (Hunt and Franklin 1996). At a 1990 workshop of the Society of Environmental Toxicology and Chemistry (SETAC) the term LCA was developed (Hunt and Franklin 1996). Following this, LCA became a more commonly used tool for quantifying resource consumption and emissions to air and water. Traditional uses of LCA were: to inform product development (e.g., through hotspot analysis, product comparisons, design improvements) and strategic planning, to inform policy, to aid in marketing, eco- labelling, and other environmental product declarations. Increasingly, LCA results are also being used to inform funding agencies and investors and to enforce policy (Guinée et al. 2011).

2.2.2 Life Cycle Assessments of Transportation Fuels

Transportation fuel/vehicle life cycle-based studies are often referred to as ‘well-to-wheel’ (WTW) studies, based on that the studies include all stages along the fuel’s life cycle from raw material extraction and refining to the use of the processed fuel in a vehicle. The WTW analysis is comprised of a well-to-tank (WTT) analysis and a tank-to-wheel (TTW) analysis. The WTT considers all portions of a WTW analysis except for the vehicle use stage (the TTW component). In a well-to-refinery entrance gate (WTR) analysis (which comprises a portion of the WTT analysis), the system boundary is drawn at the refinery entrance gate, so refining and transport of the final fuel products are excluded. Figure 2-1 shows the life cycle stages included in a LCA of fuels derived from the oil sands.

Figure 2-1. Well-to-tank diagram of oil sands transportation fuel production (Charpentier et al. 2009)

2.2.3 Applications of Life Cycle Assessment in Energy Policy

By far the most common application of LCA in energy policy is the regulation of GHG emissions from the transportation sector. The transportation sector is one of the largest, and growing, contributors to GHG emissions, accounting for 28% of U.S. GHG emissions in 2015 (Davis et al. 2018). Three categories of policies exist that aim to regulate the GHG emissions from transportation fuels: carbon taxes and cap and trade programs, fuel intensity standards, and renewable fuel mandates (Yeh et al. 2016)

Low carbon fuel standards (LCFS) aim to reduce GHG emissions from the transportation sector by stipulating reductions in average GHG emissions intensity of fuel mixes compared to a baseline value. The first LCFS was approved by California in 2007, with the objective of reducing the GHG intensity of transportation fuels by 10% between 2010 and by 2020. These policies are applied to regulated parties that produce fuels in, or provide fuels to, that jurisdiction, such as crude oil refineries. The policy requires that all regulated parties reduce the average GHG intensity per MJ of fuel they provide to the region. LCFS-type policies are technology-neutral, in that they do not specifically provide incentives for one technology over another, but are designed to promote the adoption of low-carbon transportation fuels (Yeh and Sperling 2010). The GHG intensity of transportation fuel mixes can be reduced by either: producing low-carbon fuels, purchasing credits from other producers of low-carbon fuels, or banking credits for future years (Yeh et al. 2016). The credit price is set by the market.

Since the California LCFS was implemented, other regions have adopted similar policies, notably British Columbia (BC Laws 2017), Oregon (DEQ 2017), and the European Union (EU 2009), as well as a forthcoming Canadian Clean Fuel Standard (CCFS; ECCC 2017). The characteristics of existing LCFS policies are described in Table 2-8.

Table 2-8. Summary of LCFS-Type Policies Components California British Columbia Oregon EU Fuel Quality Directive (FQD) Policy goal 10% GHG intensity 10% GHG intensity 10% GHG intensity 6% GHG intensity reduction in 10 years reduction in 10 years reduction in 10 years reduction in 10 years for gasoline and diesel for gasoline and diesel for gasoline and diesel for vehicles pools pools pools Baseline year 2010 2010 2015 2010 First year of 2011 2013 2016 Voluntary compliance intermediate targets LCA Models1 CA-GREET GHGenius OR-GREET BioGrace Models for iLUC2 GTAP+AEZ-EF iLUC not included GTAP+AEZ-EF or iLUC not quantified; GTAP+CCLUB limit set on agricultural land- based fuels Credit generation Credits for on-road Credits for displaced Credits for on-road None; Germany’s displaced energy, or energy, or for displaced energy system allows for innovative methods in advancing low carbon banking credits for crude production and fuels future years refining4 Interaction with State-wide cap and Provincial carbon tax, RFS2 EU FQD other policies trade program, Renewable Fuel RFS2 Requirement Reference (ARB 2017) (BC Laws 2017) (DEQ 2017) (EU 2009) Adapted from Yeh et al. (2016); 1LCA models described in Section 3.1; 2Economic models employed in estimating iLUC described in Earles and Halog (2011). 3Innovative methods in crude production and refining include, e.g., carbon capture and sequestration Yeh et al. (2016). iLUC: indirect land use change; EU FQD: European Union Fuel Quality Directive. DEQ: Department of Environmental Quality.

The LCFS policies included in Table 2-8 all interact with other carbon policies or renewable fuel mandates within their jurisdiction (Whistance et al. 2017). While economy-wide carbon pricing such as carbon taxes and cap and trade programs are believed to be the most economically- efficient methods for achieving GHG emissions reductions, Yeh and Sperling (2010) argue that market failures exist within the transportation sector that necessitate complementary policies that further incentivize the adoption of low-carbon fuels and vehicles.

All LCFS and most biofuels mandates are informed in some capacity by LCA results. Life cycle emissions along the supply chain are quantified using process-based LCA models. Several

policies (e.g., California and Oregon LCFS, U.S. RFS2) also quantify indirect land use change emissions, those arising from changes in land use that occur when policies incentivize the conversion of additional land for biofuel production (Yeh et al. 2016). The market changes that result in iLUC are typically quantified in those policies using the economic equilibrium models that will be discussed in Section 3.1.3, Consequential Life Cycle Assessment. While most programs employ some method to account for GHG emissions from iLUC, there remains significant uncertainty with respect to the GHG emissions from iLUC. As a result, there remains uncertainty with respect to whether GHG emissions reductions can be achieved by biofuel mandates and LCFS policies that promote the use of biofuels (Searchinger et al. 2008; Rajagopal 2013; Plevin et al. 2013; Lemoine et al. 2010).

2.2.4 Variability and Uncertainty Analysis in Life Cycle Assessment

Uncertainty in LCA results from the limited means with which real-world processes can be represented in a model. While uncertainty in LCA is associated with difficulties in capturing an accurate picture of real world operations using simplified models and limited data collection options, variability is associated with real-world differences in processes, technologies, and projects. Uncertainty analysis is defined in the ISO 14040 Standard as the “systematic procedure to quantify the uncertainty introduced in the results of a life cycle inventory analysis due to the cumulative effects of model imprecision, input uncertainty and data variability” (ISO 2006a).

Several typologies for categorizing and discussing uncertainty and variability have been developed (e.g., Bevington and Robinson 1992; Funtowicz and Ravetz 1984; Huibregts et al. 2001; Morgan and Henrion 1990); further discussed in Heijungs and Huijbregts (2004). In this report, the typology employed is that first developed by Huijbregts et al. (2001) and employed in other studies (e.g., Huijbregts et al. 2003; Lloyd and Ries 2008; Venkatesh 2012). Generally, this typology makes the distinction between three types of uncertainty: uncertainty in input data (parameter uncertainty), uncertainty with respect to normative choices (scenario uncertainty), and uncertainty in mathematical relationships (model uncertainty; Huijbregts et al. 2003; Lloyd and Ries 2008).

Variability tends to result from heterogeneity in terms of time, space, and individuals (Plevin 2010). Unlike uncertainty, variability cannot be reduced by additional research or better knowledge about a system. For example, the emissions from the U.S. electricity grid vary both temporally (over time), spatially (by region), and technologically (by generation technology).

Parameter uncertainty is associated with the values assigned to model inputs and outputs. Sources of parameter uncertainty include imprecise measurements, reliance on expert judgement where no consensus is reached between experts, approximation, and lack of data (Huijbregts et al. 2001; Lloyd and Ries 2008). Due to the process-based approach employed in many LCA models, LCA results are generated using simplified models to represent the very complex processes occurring in the real world (simplification) and extrapolating relationships from well- studied processes to similar processes (extrapolation), classified as model uncertainty. Scenario uncertainty results from developing scenarios based on past trends, using value judgment or simplified characteristics, choice of functional unit, allocation method, and system boundaries (Lloyd and Ries 2008).

In 2008, Lloyd and Ries (2008) conducted a survey of 24 studies that employed quantitative uncertainty analysis. While all studies considered parameter uncertainty, fewer (38%) considered scenario uncertainty and only 33% considered model uncertainty. Of the 24 studies, 16 employed stochastic modeling techniques. Ten of those studies used Monte Carlo simulation, two employed Latin hypercube sampling, and one combined Monte Carlo simulation with random sampling and nonparametric sampling. Seven studies made use of scenario analysis and four employed fuzzy data sets.

Subsequent to Lloyd and Ries (2008), several studies have quantified variability and uncertainty in GHG emissions intensities of the life cycle of transportation fuels. Mullins et al. (2010) and Venkatesh et al. (2011) incorporated uncertainty in life cycle GHG intensity of the production and use of biofuels and petroleum, respectively, in the U.S. and found that, generally, uncertainty bounds exceed the 10% emissions reduction targets set by LCFS-type policies. In both studies, probability distributions of the WTW GHG intensities of different fuel production pathways are developed by fitting uncertain input parameters to available data sets. Characterizing several broad categories of fuels production pathways (e.g., Venkatesh et al. model WTW GHG

intensities from all petroleum-based fuels consumed in the U.S.), both studies employ operating data from a subset of projects to define fuel production pathways. By defining input parameter distributions based on limited data sets (i.e., that are not inclusive of all projects) and selecting operating data from a limited number of operating years, some sources of variability are not captured by these studies. Aggregating data into pathways rather than characterizing emissions on a project basis limits some of the conclusions that can be drawn from the results e.g., the specific operating factors or technologies that a low-GHG project employs and how policymakers can target industry-wide GHG intensity reductions by promoting the adoption of these technologies). Additionally, uncertainty and variability are combined in such a way that limits identification of the drivers of variability (e.g., whether variability is driven by variability over time or variability across projects).

Steinmann et al. (2014) present a framework for separating uncertainty and variability and present an application of this framework to coal-fired power generation in the U.S. Hauck et al. (2014) apply a similar method to characterize variability and uncertainty in life cycle GHG emissions of conventional gas-fired power generation in the U.S. Steinmann et al. (2014) and Hauck et al. (2014) base their studies on limited operating data (2009 and 2011 operating data, respectively) and do not explore temporal trends in GHG emissions. While Steinmann et al. (2014) present disaggregated, project-level results, Hauck et al. (2014) present aggregate results into simple cycle and combined cycle power plants rather than modeling individual power plants. In separating uncertainty from variability, Steinmann et al. find that variability contributes more the breadth of GHG intensity distributions than uncertainty, which has been the primary focus of previous studies that combine sources of variability and uncertainty (e.g., Venkatesh et al. 2011).

Life Cycle Assessments of Oil Sands Technologies

The oil sands industry has been criticized for its environmental performance, particularly with respect to GHG emissions, water use, and habitat disturbance. The GHG emissions associated with current oil sands operations has been evaluated through several life cycle studies (e.g. Jacobs 2009; TIAX 2009; Charpentier et al. 2011), which have estimated the GHG emissions across the WTW of an oil sands-derived fuel’s life cycle, from production to transport, processing, and final combustion in a vehicle. Generally, studies have focused on GHG

emissions estimates and to a lesser extent energy use. Other environmental impacts have received far less attention in LCAs of oil sands-derived fuels.

2.3.1 Life Cycle Studies of Oil Sands Prior to 2010

Charpentier et al. (2009) completed a review of 13 studies of GHG emissions associated with oil sands activities to compare the divergent estimates of life cycle GHG emissions performance estimates based on existing literature, determine the investigate the major differences between studies which leads to the wide range of emissions estimates and provide guidance for future work. Compared to conventionally-derived fuels, oil sands-derived fuels were found to be reported to be 10 to 20% more GHG-intensive on a well-to-wheel basis, which includes bitumen extraction, processing, distribution and combustion in a vehicle, as well as the upstream emissions associated with the production of natural gas and electricity inputs (Charpentier et al. 2009). When comparing the studies, Charpentier et al. (2009) found a large variation in GHG emissions performance among different oil sands projects, even for those employing similar technologies, as studies were found to rely heavily on limited publicly available data typically representative of only one or two operating projects. Reliance on few data points in order to estimate industry activities can be problematic in the oil sands where variability between projects, even those employing similar technologies, is substantial (Charpentier et al. 2011). The studies also employed various analysis boundaries and methods, relied on different sources of data often of poor quality, and provided inconsistent documentation which made elucidation of the differences between studies difficult (Charpentier et al. 2009).

Following the publishing of Charpentier et al. (2009), two additional LCA studies were completed through funding from the Alberta government (Jacobs 2009; TIAX 2009). These studies presented life cycle GHG emissions estimates from oil sands-derived fuels as well as conventional fuels produced in the U.S. from local and imported crudes. For oil sands recovery and extraction technologies, specific projects were evaluated (rather than oil sands production pathways), instead of characterizing the entire industry (Brandt 2012). For refinery modeling, Jacobs (2009) employ more detailed refinery modeling than previous studies but rely on confidential data and do not characterize all crude types produced by oil sands operations (Brandt 2012).

Another study (Brandt 2012) examined the causes of variability and uncertainty in recently published LCAs of current oil sands operations (including the Jacobs and TIAX reports). Choice of system boundary and data input type (e.g., reliance on data from specific projects as opposed to industry averages), differences in assumptions regarding energy intensities of extraction and upgrading, as well as fuel mix types, and treatment of non-combustion (e.g., flaring emissions) and ecological (e.g., land use change) emissions sources were identified as the main sources of variability between studies. Brandt et al (2012) recommend that future LCAs of crude produced from the oil sands provide more transparency with respect to the data sources employed and processes modeled and better indication of how representative the processes studied are of industry-wide operations.

2.3.2 Current Models that Assess Life Cycle Emissions of Oil Sands Technologies

Several LCA models currently exist that quantify the life cycle GHG emissions from oil sands crude production. In this section, the following models will be reviewed: the GreenHouse gas emissions of current Oil Sands Technologies (GHOST; Charpentier et al. 2011; Bergerson et al. 2012) model, the Oil Production Greenhouse gas Emissions Estimator (OPGEE version 2.0; documented in El-Houjeiri et al. 2013; El-Houjeiri et al. 2017), GHGenius (version 5.0; documented in (S&T)2 2013), and the Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREET 2017 version; oil sands pathway definitions documented in Englander et al. 2014; Cai et al. 2015). Each model varies in terms of objectives, scope, life cycle boundary, and data and methods employed for characterizing oil sands GHG emissions intensities. While Charpentier et al. (2009) and Brandt (2012) both include GHGenius and GREET models in their literature reviews, they consider older versions of the model that were current when those studies were published, which have since updated their methods and data employed for modeling oil sands pathways. The characteristics of each LCA model are summarized in Table 2-9. The methods and data sources employed in characterizing the GHG emissions of oil sands pathways are summarized in Table 2-10. Limitations of existing LCA models of oil sands-derived transportation fuels are discussed in Section 2.3.2, Current Models that Assess Life Cycle Emissions of Oil Sands Technologies.

GHOST. To improve upon the literature existing at that time (2010), a model was developed within the Life Cycle Assessment of Oil Sands Technologies (LCA-OST) research group, a collaboration between researchers at the University of Calgary and the University of Toronto. The objective with the development of GHOST is to characterize current GHG emissions performance in the oil sands using a comprehensive set of both publicly available and private operations data, validated by experts in industry and academia (Charpentier et al. 2011). The model, greenhouse gas emissions of current oil sands technologies (GHOST) relied upon an extensive inventory of existing operations to estimate the life cycle GHG emissions associated with bitumen recovery and extraction, upgrading, transport, and refining. The scope of the project to date has been limited to current technologies (surface mining, SAGD, and CSS within the recovery and extraction stage of the life cycle. GHOST was used to develop a range of emissions performance estimates for each technology included in the model; it was found that emissions varied significantly even within one technology category due to differences in operating conditions and product characteristics (Bergerson et al. 2012). Charpentier et al. (2011) provide documentation for the application of GHOST to SAGD; mining and CSS applications are demonstrated in Bergerson et al. (2012).

OPGEE. The Oil Production Greenhouse Gas Emissions Estimator (OPGEE; El-Houjeiri et al. 2013) was created by Environmental Assessment and Optimization (EAO) group at Stanford University, with the objective of estimating GHG emissions from a range of petroleum resources and has an option to model bitumen production and upgrading.

GHGenius. GHGenius is a Canadian fuel and vehicle-cycle model developed for Natural Resources Canada ((S&T)2 2013). It has been used in the BC LCFS (BC Laws 2017). Documentation for an older version of GHGenius (v4.03) is referenced for this comparison as documentation for the current model version (v5.0) was unavailable at the time of writing this thesis (August 2018). As oil sands pathways remain unchanged between GHGenius model versions 4.03 and 5.0 the documentation for 4.03 the documentation provided for v4.03 applies to how oil sands pathways are modeled in v5.0.

GREET. Developed by Argonne National Laboratory, GREET is a public WTW model that simulates energy use and GHG emissions from a range of fuel and vehicle pathways. A California version of the GREET model is used to define fuel production pathways in the California LCFS.

Table 2-9. Characteristics of LCA Models Characterizing Oil Sands Crude Production Model, version Life Cycle Availability Developed by Objective Scope (website) Boundary LCA-OST group, GHOST, N/A Characterize industry-wide University of (ucalgary.ca/lcaost/ GHG emissions Current oil sands Private Calgary and WTRa lcaost-models) from current oil technologies University of sands technologies Toronto Environmental Increase transparency of OPGEE, v2,0 Assessment and LCA of crude oil (eao.stanford.edu/research- Public Optimization production using Crude oil production WTR areas/opgee) group, Stanford engineering University fundamentals Characterize emissions from the GHGenius, v5.0 (S&T)2 for Natural production and Fuel and vehicle (ghgenius.ca/index.php/ Public Resources use of traditional WTW pathway downloads) Canada and alternative transportation fuels in Canada Characterize the energy use and GHG GREET, 2017 Argonne National emissions of Fuel and vehicle Public WTW (greet.es.anl.gov/) Laboratory various vehicle pathways and fuel combinations a WTR boundary from GHOST is extended to WTW in Bergerson et al. (2012) by employing refinery GHG emissions estimates from TIAX (2009) and vehicle use emissions from GHGenius. GHOST: GreenHouse gas emissions of current Oil Sands Technologies; OPGEE: Oil Production Greehouse gas Emissions Estimator; GREET: Greenhouse gas, Regulated Emissions, and Energy use in Transportation; LCA-OST: Life Cycle Assessment of Oil Sands Technologies; GHG: greenhouse gas; WTR: well-to-refinery entrance gate; WTW: well-to-wheel.

2.3.3 Gaps in LCA-OST Literature

Each LCA model in Section 2.3.2 employs a distinct approach for characterizing GHG emissions intensities from the life cycle of oil sands-derived fuels. While all four models described above provide quantify oil sands GHG emissions in some capacity, there are limitations to the methods and data employed by each model that affect the robustness of results and limit the types of insights that policymakers can derive from model outputs. There are five main limitations of previous LCAs of oil sands-derived fuels that are addressed in this thesis: aggregation of projects into pathways, use of limited data, use of simplified refinery modeling approaches, incomplete modeling of PFT dilbit projects, and reporting LCA results as point estimates or ranges (i.e., no probability distributions associated with the ranges presented).

Aggregation of projects into pathways. Each of the four LCA models described in Section 2.3.2 aggregate oil sands operations into a range of crude production pathways that combine different bitumen production methods (e.g., mining, in situ) and types of crude produced (e.g., dilbit, SCO). Each model characterizes pathways from project data differently; for example, GHGenius ((S&T)2 2013) and GREET (Englander and Brandt 2014) use operating data from the Syncrude Aurora mine (which sends bitumen froth to the Mildred Lake mine for froth treatment and upgrading), while OPGEE incorporates operating data from the Syncrude Aurora mine into its integrated NFT mining and upgrading pathway (El-Houjeiri et al. 2017). In each model, at least one pathway is characterized based on a limited number of projects (i.e., does not reflect all projects operating in the oil sands as of 2015).

Use of Limited Data. No existing LCA models (as of 2018) that characterize GHG emissions intensities of oil sands crude production are inclusive of all mining projects operating as of 2013 and base their pathway definitions on multiple years of operating data. While all models except GHOST rely on AER data to characterize energy consumption, models define energy consumption based on a limited number of operating years (e.g., OPGEE: 2014 data; GREET: 2005-2012 data for mining and 2009-2012 data for in situ). See Table 2-10 for additional limitations to the data employed in each model. Additionally, all models present GHG intensity results as either point estimates (OPGEE, GHGenius), ranges of emissions (GHOST), or point estimates with 95% confidence intervals associated with the estimate (Cai et al. 2015, upon

which GREET estimates are based). No model characterizes the full range of emissions as well as the probability distribution associated with the range. As a result, the likelihood of a project having a particular GHG intensity within the range presented cannot be predicted by these models.

Simplified refinery modeling approaches. GHGenius and GREET both quantify GHG emissions from refining oil sands products, however in both cases simplified refinery modeling methods are employed that model whole crude properties (namely, API gravity and sulfur content), which can fail to accurately model refinery emissions (Abella and Bergerson 2012). Additionally, when modeling refinery emissions, both models characterize refinery emissions from representative dilbit and SCO assays and do not account for variability in crude quality, which is quite variable across the oil sands, particularly for mining operations. The GHG emissions from refining PFT dilbit (which is distinct from in situ dilbit due to the asphaltene precipitation that increases crude API gravity and reduces impurities such as sulfur) have not previously been quantified in either the LCA models reviewed in this section or more detailed refinery models (e.g., Abella and Bergerson 2012).

Incomplete Modeling of PFT Pathway. As the majority of growth in mined dilbit production is expected to be from PFT mining projects (Imperial Kearl and Suncor Fort Hills) and new mining projects (or project expansions, in the case of the proposed expansion to the CNRL Horizon mine), accurately characterizing this pathway is important for assessing both current and future GHG emissions from the oil sands mining industry. All LCA models reviewed in this thesis are in some respect limited in the methods and data employed for modeling this pathway. For example, GHGenius includes a stand-alone mining pathway but combines an NFT project (the Syncrude Aurora project, where bitumen produced at the mine is sent to the nearby Syncrude Mildred Lake project for extraction) with PFT projects (CNRL Muskeg River and Jackpine), all projects which produce bitumen that is ultimately upgraded and that do not reflect current PFT mining operations that produce dilbit. As discussed above, no study has explored the downstream GHG emissions implications of refining PFT dilbit.

Reporting LCA results as point estimates or ranges. All LCA models discussed in this review present aggregate GHG intensity results for a set of oil sands pathways (e.g., NFT

mining integrated with upgrading) or crude production technologies (e.g., upgrading). LCA results are presented as either point estimates (OPGEE; GHGenius), ranges (GHOST), or a point estimate with 95% confidence intervals reported (GREET, as reported in Cai et al. 2015). No existing models present probability distributions associated with the GHG intensities reported, which provide better indications of the likelihood that crude production from a specific oil sands project will be at one of the extreme ends of the range. As both GHOST and GREET (reported in Cai et al. 2015) characterize broad ranges of possible emissions that overlap across oil sands pathways, understanding how likely a project is to have especially low or high GHG intensities compared to the range presented can help inform decision-making with respect to prioritizing low-GHG intensity pathways.

How this thesis addresses these literature gaps. Chapters 4 and 5 of this thesis improve upon previous LCAs by conducting facility-level analyses using decades of operating data, relying only on public data that is reported consistently across projects. Further, by using detailed refinery modeling and modeling refinery emissions based on public crude assays (that report the detailed characteristics of a crude) for each mining project that reflect the full range of crude properties produced from oil sands mining projects. Analysis on a project-basis (rather than aggregating projects into representative pathways) allows for more insights to be drawn from the work, including, for example, how upstream operating decisions about the properties of crude to produce affect downstream refinery emissions. The studies presented in Chapters 4 and 5 of this thesis test whether pathways can accurately be characterized that accurately reflect the range of technologies and operating characteristics of existing mining projects. By disaggregating temporal variability within individual projects from variability across projects, this thesis tests whether upstream GHG intensities from previous operating years can be used to predict future GHG intensities, the current method employed in LCFS policies. While the focus of this thesis is a case study of the oil sands, these general findings can be applied to other LCAs where similar approaches have been taken to aggregate and simplify LCA modeling approaches.

Table 2-10. Methods Employed by LCA Models for Characterizing GHG Emissions from Oil Sands Crude Production Pathways Model, version (references for Oil Sands Approach to modeling oil Pathways Defined Data Sources GHG Emissions Reporting Quantifying Limitations sands in Model Refinery Emissions pathways) 9 pathways:

Mining: Mining + Dilbit Mining + Synbit Reliance on private data Mining + SCO sets; not comprehensive of For all pathways, private all operations. In Situ: data collected from 5 oil Refinery emissions

GHOST SAGD + Dilbit sands companies, not modeled in Assumed no correlation (Charpentier et SAGD + Synbit supplemented with public Ranges of GHG for each oil GHOST. Bergerson et between input parameters. al. 2009; SAGD + SCO data and used to generate sands pathwaya + example al. (2012) combined

Bergerson et al. CSS + Dilbit ranges of inputs. Expert project for each pathway GHOST with refinery No distinction between 2011) CSS + Synbit elicitation employed to GHG estimates from NFT and PFT projects. CSS + SCO verify ranges of input TIAX (2009)

parameters. Refinery GHG not Upgrading: 3 accounted for. technologies: Delayed Coking + Hydrotreating Hydrocracking + Hydrotreating 4 pathways: Mining: Aggregation of data into OPGEE, v2.0 Average 2014 monthly pathways, not all projects (El-Houjeiri et Mining: operating data from AER Report point estimate of accounted for. al. 2014; El- PFT Mining + Refinery GHG not ST39, average of all PFT GHG for each oil sands Houjeiri et al. Bitumen modeled in OPGEE. projects for Mining + pathway Reliance on hypothetical 2017) Integrated NFT Bitumen pathway COSIA Mine Templates Mining + SCO and limited operating data

In Situ: Integrated NFT mining from AER (only 2014 In Situ + Bitumen energy consumption from operating year considered). In Situ + SCO COSIA NFT Mine Templates, less 30% heat Refinery GHG not Upgrading: 3 integration efficiency accounted for. OPGEE, v2.0 technologies: factor. (El-Houjeiri et Delayed Coking al. 2014; El- Delayed Coking + In Situ: Houjeiri et al. Hydroconversion Data from AER ST53 and 2017) Hydroconversion yearly operating reports for 24 projects, 2010-2011 operating period; supplemented with COSIA In Situ Template

Upgrading: 3 upgrading technologies modeled using OSTUMb Aggregation of projects 4 pathways plus Mining: into pathways. option to upgrade 3-year production-weighted bitumen at stand- average of monthly Does not include all alone upgrader: operating data from AER projects operating in the oil

ST39 (2007-2009) sands as of 2013. Mining:

Stand-Alone Mining In Situ: Employs general Simplified representation of + Bitumen Operating data obtained Report point estimate of estimates of refinery PFT GHGenius, Integrated Mining + from AER ST53; Data GHG for each pathway as GHG intensities v5.0 SCO collected for January 2009- well as GHG estimate for based on whole-crude Use of limited data and ((S&T)2 2013) July 2010 operating period. stand-alone upgrader. API gravity and short operating periods to In Situ: sulfur content. characterize pathways (e.g., SAGD + Bitumen Upgrading: integrated NFT mining CSS + Bitumen 3-year production-weighted pathway represented by 2

average of AER ST39 projects, although 4 are Upgrading: operating data (2007-2009) currently active in the oil Integrated with mine for Nexen Long Lake and sands). Stand-alone CNRL Horizon upgraders upgrader

Mining: Monthly operating data from AER over 2005-2012 Employs refinery 6 pathways: operating period efficiency formula Report production-weighted that estimates refinery Mining: average GHG estimate for In Situ: GHG based on Mining + Bitumen each pathway as well as 95% Simplified refinery model GREET, 2017 Monthly operating data whole-crude API and Mining + Dilbitc confidence intervals that employed; limited range of (Englander and from AER over 2009-2012 sulfur content. Mining + SCO represent monthly variability crude properties Brandt 2014; operating period. in upstream energy considered; refining PFT Cai et al. 2015) Considers operating data 3 refinery input In Situ: consumption over the dilbit not modeled. from 15 In Situ + Bitumen crudes modeled: In Situ + Bitumen operating period considered projects and 2 In Situ + bitumen, dilbit Mining + Dilbitc (i.e., 2005-2012 for mining) SCO projects (produced from in In Situ + SCO situ project), SCO Includes both SAGD (51%) and CSS (49%) a Ranges of GHG emissions intensities obtained by taken minimum of all input parameters to obtain the low GHG emissions estimate and the maximum of all input parameters to obtain the high GHG emissions estimate. b Documentation of the development of OSTUM provided in Pacheco et al. (2016). c Mining + Dilbit pathway presented in Supporting Information of Cai et al. (2015). AER operating data is monthly quantities of fuels and electricity consumed, reported for each operating oil sands mining (AER ST39) and in situ (AER ST53) project. GHOST: GreenHouse gas emissions of current Oil Sands Technologies; OPGEE: Oil Production Greehouse gas Emissions Estimator; GREET: Greenhouse gas, Regulated Emissions, and Energy use in Transportation; SCO: synthetic crude oil; AER: Alberta Energy Regulator; COSIA: Canadian Oil Sands Innovation Alliance; OSTUM: Oil Sands Technologies for Upgrading Model; GHG: greenhouse gas; NFT: naphthenic froth treatment; PFT: paraffinic froth treatment

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Chapter 3 Methods

In this chapter, an overview of the methods employed in this thesis is provided. For specifics of how these methods are adapted and applied in each study contained within this thesis, see Methods sections in Chapters 4-6.

Life Cycle Assessment Methodology

The LCA methodology as defined ISO Standards 14040 and 14044 (ISO 2006a,b) is described in detail in Section 2.2.1. As such, the focus of this section is on limitations of LCA methodologies and available techniques for addressing these limitations.

3.1.1 Limitations of Life Cycle Assessment Methods

The limitations of existing LCA methods have been widely discussed in the literature (e.g., Björklund 2002; Finnveden 2000; Plevin 2010; Reap et al. 2008a,b). Plevin (2010) provides a critique of the methodological issues associated with traditional (Attributional) LCA (ALCA) with a specific focus on uncertainty associated with GHG emissions from biofuels and policies and the implications for LCFS-type policies. Reap et al. (2008a,b) categorize the methodological issues associated with each stage of an LCA (summarized in Table 3-1). In this section, two key methodological issues identified in the literature will be discussed: uncertainty in LCA results due to data gaps, definition of a system boundary, and truncation; and limitations of analyses conducted on a functional unit basis.

At each stage in an LCA, uncertainties are introduced into the study that impact how LCA results can be interpreted (examples provided in Table 3-1). At each stage in the analysis, data may be limited or of uncertain quality; this necessitates that the LCA practitioner make assumptions to obtain a complete data set. Additionally, certain steps taken when conducting an LCA introduce uncertainties as the LCA practitioner must make decisions about how to model a system that may not reflect the real-world behaviour of the system. For example, when defining a system boundary, a decision must be made about which activities to include. Typically, activities that can be demonstrated to be insignificant can be excluded from the analysis (ISO 2006a). However, lack of knowledge about the system and its interactions with other systems may result in activities being excluded that have significant impacts on the environment. As well, small

60 impacts excluded from a system boundary due to truncation may accumulate across a life cycle to collectively have a significant impact (Plevin 2010). Increasingly, uncertainty and variability are explicitly characterized in LCAs. This is addressed in Section 2.2.4, Variability and Uncertainty Analysis.

Because impacts are assessed on a functional unit basis, large-scale indirect shifts due to, for example, land use change and other market impacts are excluded from the scope of analysis (Guinée et al. 2011; Plevin 2010; Rebitzer et al. 2004). The assumption that impacts defined on a functional unit basis can be scaled linearly to reflect broader shifts carries with it the implicit assumption that the indirect market effects described above are insignificant compared to the impacts included in the study, which previous LCAs (particularly those focused on biofuel policies, e.g., Searchinger et al. 2008; Rajagopal 2013; Plevin et al. 2013; Lemoine et al. 2010) have demonstrated can far exceed the impacts that can be defined on a functional unit basis. Bouman et al. (2000) suggest that LCAs defined on a functional unit basis are best employed for identification of hot spots within a supply chain, or comparative assessments of competing systems, where indirect effects associated with the choice will be limited.

Table 3-1. Limitations of Life Cycle Assessment Methods by Stage

Stage Description (ISO 2006a) Methodological Issue (Reap et al. 2008a,b)

Goal and Scope Definition Defines the objectives of the study and Functional unit definition describes the methods that will be employed and processes included Boundary selection (system boundary). Social and economic impacts

Alternative scenario considerations

Inventory Analysis Data collection and quantification of Allocation inputs and outputs within the system boundary. Negligible contribution criteria

Local technical uniqueness

Impact Assessment Evaluation of the significance of LCI Impact category and methodology results in terms of environmental selection burdens by assigning impact factors to inventory data. Spatial variation

Local environmental uniqueness

Dynamics of the environment

Time horizons

Interpretation Review of inventory and impact Weighting and valuation assessment results considering the objectives set out in Goal and Scope Uncertainty in the decision process Definition stage.

All Data quality and availability Adapted from Reap et al. (2008a,b)

3.1.2 Developments in Life Cycle Assessment Methods

The adoption of policies in North America and Europe (e.g., the California LCFS, ARB 2017; the EU FQD, EU 2009; and the U.S. RFS, EPA 2010) that utilize LCA findings to enforce policy, along with other developments, have brought the traditional LCA methodology, known as

62 attributional LCA, under scrutiny, pushed forward the LCA field and resulted in more robust methods for ALCA as well as novel approaches to LCA such as Consequential LCA (described below). One improvement is the increasing emphasis on incorporating explicit variability and uncertainty analysis into LCA (Section 3.2), which provides more information to decision- makers (e.g., policymakers, operators, technology developers), helping them make more informed decisions about different pathways considering the possible range of LCA results (Finnveden et al. 2009; Reap et al. 2008a,b). The introduction of more standardized methods, both by the ISO (ISO 14040 and ISO 14044; ISO 2006a,b) and by other groups (e.g., ILCD Handbooks; EC-JRC-IES 2010), have been motivated by a need for LCA results to be more consistent, transparent, and reproducible (Guinée et al. 2011; Plevin 2010). Attributional LCA, which has been defined as the “determination of all environmental problems related to a certain unit of product” (Bouman et al. 2000), has been criticized for its structural limitations due to the difficulty in interpreting functional-unit-based impact estimates to real-world applications (Guinée et al. 2011). By placing the results on a functional unit basis (i.e., assigning GHG emissions to a product based on the life cycle of one unit of that product), broader market and societal effects are left out of the analysis. Plevin et al. (2013) and others have gone so far as to say that presenting estimations of climate change mitigation benefits based on attributional LCA results is misleading to policymakers. Another approach, consequential LCA has been proposed as an alternate, and according to some, superior methodology to support decision-making and inform policy formation. Consequential LCA accounts for indirect changes to supply and demand for products resulting from a change (known as market mediated effects) into consideration and focuses more broadly on how a decision, such as the implementation of a policy, will affect process flows and environmental impacts (Zamagni et al. 2012). ALCA is still the basis of most LCA models that are employed in LCFS standards, although some consequential effects such as indirect land use changes are incorporated in most cases. Consequential LCA approaches are not applied in this thesis but are an opportunity for future work when assessing the impacts of GHG emissions policies on oil sands development and are relevant to discussions about interpretation of LCA results and the implications for policymakers.

3.1.3 Consequential Life Cycle Assessment

Incorporation of market effects into LCA-based frameworks is a growing area of research (e.g., Earles and Halog 2011; Guinée et al. 2011). Earles and Halog (2011) broadly describe

63 consequential LCA (CLCA) as the convergence of LCA and economic modeling approaches. First applications of CLCA employed a step-wise, non-computational system expansion approach described in Schmidt (2008) and demonstrated in several other studies (e.g., Reinhard and Zah 2009; Thomassen et al. 2008). In this approach, the marginal technology displaced is identified manually based on historic data. Others have used more sophisticated economic modeling tools to examine aspects of the economy (in the case of partial equilibrium, PE models) or the full set of economic consequences when a policy is implemented (in the case of computable general equilibrium, CGE models). A focus to date has been on estimating indirect land use change emissions from policies that promote bioenergy production in either the U.S. or Europe. Some (e.g., Plevin et al. 2013) have argued that, for policy assessment, the incorporation of market effects in CLCA make this a preferred methodology when compared to ALCA, which does not account for these effects. Others (e.g., Suh and Yang 2014) have criticized the arguments in the literature that one modeling approach is conceptually superior to the other, due in part to the limitations of the economic models employed to characterize indirect market effects and their failure to fully capture the complex real-world interactions resulting from new GHG emissions regulation, among other factors. The role that each modeling approach can play in informing policy assessments remains a topic of discussion.

Interest in consequential LCA has grown substantially in recent years, with a notable increase in publications on the topic from 2007 onwards (Zamagni et al. 2012). When describing the two approaches, the same set of terms are frequently associated with each: while attributional LCA is often described as static, average, context-independent, and current, consequential LCA is conversely described as dynamic, marginal, context-dependent, and predictive. The International Reference Life Cycle Data (ILCD) System recommends consequential LCA when the decision or change will result in large-scale impacts are involved, particularly market effects resulting from policy implementation (EC-JRC-IES 2010). Attributional LCA results are presented on a functional unit basis (e.g., CO2-eq/MJ fuel), whereas for consequential LCA results are reported in the aggregate, as result of a decision (e.g., X% emissions reduction from Y% increase in output of a product). By extending analysis beyond the functional unit, large-scale, indirect impacts can be incorporated into the analysis.

More extensive literature reviews of consequential applications have been conducted, for example, by Earles and Halog (2011), Zamagni et al. (2012), and Marvuglia et al. (2013).

Marvuglia et al. (2013) and Earles and Halog (2011) conducted reviews on the growing body of CLCA research and identified the intersection between, and integration of, PE and CGE modeling with LCA as an area for further research. While several CLCA examples exist that have utilized both LCA and CGE models to develop their results, a broader understanding of the role of these modeling approaches for GHG emissions policy analysis is lacking (Marvuglia et al. 2013). Zamagni et al. (2012) suggested the use of scenario modeling for evaluating policy alternatives but noted that further research is required to develop a sound methodology for framing research questions based on the types of market information relevant to the analysis.

Market-based regulations (e.g., carbon taxes) employ market mechanisms to reduce GHG emissions by adding an additional financial cost to producers for the carbon emitted by a project. ALCA on its own cannot capture the anticipated effects of market-based GHG emissions regulations, a major criticism of the use of ALCA as a policy tool (Plevin et al. 2013). Indirect or market-mediated emissions are those resulting from an activity but that are not directly part of the supply chain of the activity. The primary economic tools being used to explore the market impacts of environmental policies are equilibrium models, notably partial equilibrium (PE) and CGE models. PE models can be used to evaluate how a small set of markets or a sector of the economy will respond to a policy, while CGE models include all sectors of the economy, typically represented in less detail than the sectors included in PE models (Earles and Halog, 2011). PE modeling was used, for example, in Earles et al. (2013) and Vasquez-Rowe et al. (2013). The MIT Emissions Prediction and Policy Analysis (EPPA) model is a CGE model of the world economy that has been applied to examine how overall production levels from the oil sands are expected to respond to global climate policies (Chan et al. 2012). CGE and hybrid modeling approaches have been used to assist in assessing the impacts of, for example, rebound effects, petroleum price impacts, and other effects into LCA-based analyses (Rajagopal and Zilberman 2013). However, incorporation of more sophisticated modeling tools come with all the uncertainty associated with those modeling approaches (Kendall and Yuan 2013), which can in some cases be orders of magnitude greater than the goals of the policy being evaluated.

Methods for Accounting for Variability and Uncertainty in Life Cycle Assessment

Three types of uncertainty are defined in Section 2.2: parameter, model, and mathematical uncertainty. Huijbregts (2001) proposes specific methods for dealing with each type of uncertainty. These methods are described in Sections 3.2.1 through 3.2.4.

Parameter uncertainty may be quantified through: • Reliance on expert judgment to inform the set of values assigned to input parameters and outputs associated with the process under consideration; • Probabilistic simulation, whereby each input parameter is assigned a set of possible values, and each value is associated with a probability to reflect the likelihood of its occurrence. Specific examples of probabilistic simulation include the development of stochastic models through Monte Carlo simulation and the application of Bayesian statistics. • Reporting a range of possible outputs from the LCA rather than a point estimate to reflect the uncertainty associated with model inputs as evaluated using the above methods.

Scenario and model uncertainty may be dealt with using scenario analysis, where the possible life cycle outcomes from different production pathways, sets of technology options, or use of different allocation methods are considered as several different scenarios. Within each scenario the uncertainty associated with the parameters associated with that scenario may be accounted for using the methods suggested above (see Huijbregts et al. 2003 for an application of this approach).

3.2.1 Sensitivity Analysis

Sensitivity analysis is broadly defined as a systematic procedure for estimating how the methods or data employed in a study impact study results and is recommended by ISO 14040 for all LCAs (ISO 2006a). Sensitivity analysis may be used to justify the exclusion of life cycle stages shown to be insignificant to study results (ISO 2006b). Typically, uncertainty is assessed by a local or one-way sensitivity analysis, whereby one model parameter is varied, holding other parameters constant, and the change in output is assessed (Plevin 2010; Björklund 2002). A limitation of this type of sensitivity is that it tends to underestimate the uncertainty in a model because, in most

66 situations, linkages exist between input parameters so that varying multiple input parameters simultaneously will result in a wider uncertainty range than when only variations in input parameters are considered as discrete cases (Plevin 2010). Huijbregts et al. (2001) suggest conducting a sensitivity analysis prior to performing Monte Carlo simulations to identify the parameters with the greatest significance on study results so that they can be targeted in the Monte Carlo simulation.

3.2.2 Scenario Analysis

Scenarios in LCA are defined by Björklund (2002) as sets of possible future scenarios that depend on assumptions made about the future with respect to definition of system boundary, allocation methods, technology, time, space, characterization methods, and weighting methods. Scenario analysis is employed within LCA to explore the effects that these different data sets, models, and other choices made in conducting the analysis have on study results (Heijungs and Huijbregts 2004). Lloyd and Ries (2007) recommend scenario analysis for situations when normative choices or model formulations cannot realistically be represented by distributions. For an example of an LCA quantifying parameter, scenario, and model uncertainty see Huijbregts et al. (2003).

3.2.3 Analytical Methods

Analytical methods can be used to calculate the uncertainty of model results given uncertainty of input parameters using error propagation equations (Heijungs and Huijbregts 2004). Analytical methods may not be sufficient to capture the uncertainties associated with the complex real- world situations modeled in LCAs and may lead to inaccurate approximations (Morgan and Henrion 1990; Lloyd and Ries 2007). To achieve accurate results using analytical methods uncertainties must be uncorrelated and normally distributed, with standard deviations less than 30% of the mean (Plevin 2010), conditions which are uncommon in LCA applications.

3.2.4 Statistical Methods

Statistical methods aim to incorporate uncertainty into LCA by quantifying the uncertainty associated with input parameters (Heijungs and Huijbregts 2004; Finnveden et al. 2009). The statistical methods that have been incorporated into LCA include fuzzy data sets and Bayesian

67 statistics, and stochastic methods (Monte Carlo and Latin Hypercube simulation). Each method and examples of past applications in LCA are described below.

Fuzzy Data Sets and Bayesian Statistics. The use of fuzzy data sets for variability and uncertainty analysis in LCA is based on the premise that there is a distinction between probability (uncertain parameters defined by real-world data) and possibilty (uncertain parameter with ranges proposed by experts, based on their beliefs about a parameter’s possible values), and that distinction has implications for the interpretation- of LCA results (Tan 2008). Compared to specifying intervals for input parameters, fuzzy numbers include a degree of certainty with each value within the interval provided. This approach is proposed by Tan (2008) as an alternative to Monte Carlo simulation, when probability distributions are difficult to define due to the lack of information or the subjective nature of the input parameters being defined. Bayesian statistics, which also incorporate subjective estimates of uncertainty into parameter estimation (Björklund 2002), have also been proposed for uncertainty analysis in LCA, although the application of this method within LCA is very limited (Heijungs and Huijbregts 2004; see Shipworth 2002 for an example).

Stochastic Methods. Monte Carlo simulation is a common statistical method that can be applied to account for parameter uncertainty in LCA (Huijbregts et al. 2001). Monte Carlo simulation provides a frequency distribution that predicts the probability that each model output will have that value, accounting for the combined uncertainty of each input parameter. To conduct a Monte Carlo simulation, probability distributions are defined for each input parameter (Björklund 2002). Each input parameter is randomly sampled based on its assigned probability distribution. The input parameters are sampled randomly based on their probability distributions to obtain an estimate of the model output. This process is repeated and a probability distribution is created that reflects the expected model output values. Oracle’s CrystalBall (Oracle 2017) is an add-in to Microsoft Excel that can be employed to conduct Monte Carlo simulations and explore the sensitivity of model results to variations in input parameters.

Latin Hypercube simulation is a stochastic method similar to Monte Carlo simulation that has also been applied to account for uncertainty in LCA (see Huijbregts 1998). In this method, parameter uncertainty distributions are defined as non-overlapping intervals of equal probability

68 from which random samples are selected based on the probability within that interval (Bjöklund 2002).

Expert Elicitation

In some cases, no reliable data is available to define distributions for uncertain parameters in an LCA. In such cases, expert elicitation may be employed to fill data gaps (Huijbregts et al. 2001; Björklund 2002). Expert elicitation is a formal method for collecting quantitative, subjective judgments from a group of experts when uncertainty exists due to insufficient data, or when data is unattainable due to lack of resources or other physical constraints (Slottje et al. 2008; Morgan et al. 1985; Morgan and Henrion 1990). While expert elicitation cannot be considered a full replacement for scientific research (Morgan et al. 2006), it can support more robust decision- making by collecting and synthesizing expert knowledge that is otherwise unavailable or uncertain (Slottje et al. 2008; Morgan 2013).

Slottje et al. (2008) provide an overview of procedures involved in conducting an expert elicitation. First, the set of questions for the experts must be something that may reasonably be estimated by a group of experts. Once a group of experts is selected, they are asked to provide probability distributions that reflect their belief of an uncertain quantity (Morgan 2013). In some cases, the distributions obtained by experts are combined before final results are reported. A panel discussion of experts experienced in conducting expert elicitations (see Cooke and Probst 2006) provided recommendations regarding: the minimum number of experts to include (six), selection of experts, preference for group or individual assessments (individual assessments are recommended), and combining expert judgments versus reporting individual responses (preference given to reporting of individual responses). Morgan (2014) provides further suggestions for when and how to design an expert elicitation while minimizing cognitive bias and overconfidence in expert’s responses.

Expert elicitations have been used to estimate the health impacts of various air pollutants (Morgan et al. 1978; Morgan et al. 1984; Cooke et al. 2007; Roman et al. 2008; Knol et al. 2009; Hoek et al. 2009; additional examples provided in Cooke and Probst 2006). Expert elicitation has also been applied to gather information from experts about climate change and its impacts (Morgan and Keith 1995; Morgan et al. 2006; Zickfeld et al. 2007; Zickfeld et al. 2010). With respect to energy systems, expert elicitation methods have been employed to assess the costs

(e.g., Abdulla et al. 2013; Chan et al. 2011; Rao et al. 2006) and technical performance parameters (e.g., Chan et al. 2011; Curtright et al. 2008) of a range of emerging technologies.

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Chapter 4 Evaluation of Variability in Greenhouse Gas Intensity of Canadian Oil Sands Surface Mining and Upgrading Operations

Chapter 4 is the first of two chapters on assessing variability in WTW GHG intensities of transportation fuels derived from mined bitumen. This chapter outlines the development of a statistically-enhanced version of the GHOST model (GHOST-SE) and its use to characterize variability in GHG intensity both within and across all oil sands mines operating as of 2015.

Abstract

I present a statistically enhanced version of the GreenHouse gas emissions of current Oil Sands Technologies model that facilitates characterization of variability of greenhouse gas (GHG) emissions associated with mining and upgrading of bitumen from Canadian oil sands. Over 30 years of publicly available project-specific operating data are employed as inputs, enabling Monte Carlo simulation of individual projects and the entire industry, for individual years and project life cycles. I estimate that median lifetime GHG intensities range from 89 to 137 kg CO2eq/bbl synthetic crude oil (SCO) for projects that employ upgrading. The only project producing dilbit that goes directly to a refinery has a median lifetime GHG intensity of 51 kg CO2eq/bbl dilbit. As SCO and dilbit are distinct products with different downstream processing energy requirements, a life cycle assessment (“well to wheel”) is needed to properly compare them. Projects do not reach steady-state in terms of median GHG intensity. Projects with broader distributions of annual GHG intensities and higher median values are linked to specific events (e.g., project expansions). An implication for policymakers is that no specific technology or operating factor can be directly linked to GHG intensity and no particular project or year of operation can be seen as representative of the industry or production technology.

Introduction

Since the ﬁrst Canadian oil sands project began commercial production in 1967 operations have expanded signiﬁcantly, reaching 2.54 million barrels per day (bbl/day) in 2016 (AER 2017). As of year-end 2016, 58% of all bitumen was produced through surface mining (AER 2016, 2017), the focus of this study. For surface mining operations, oil sands material is mined and bitumen is extracted from the ore and either diluted to produce dilbit or upgraded to higher quality synthetic crude oil (SCO) and shipped to refineries for further processing.

Bitumen mining and upgrading operations utilize both natural gas and diesel as fuels in addition to consuming electricity – 90% of which was derived from fossil fuels in Alberta in 2015 (AUC 2016). The resulting greenhouse gas (GHG) emissions from Canada’s oil sands operations are significant: bitumen mining and upgrading reportedly accounted for 22% of Alberta’s and 8.7% of Canada’s GHG emissions in 2013 (EC 2015). This is an increasing challenge given growth projections and regulatory changes planned by the Alberta government that will limit industry- wide GHG emissions at 100 Mt/year (AB 2015). Moreover, low carbon fuel standards (LCFS) set reduction targets for the life cycle GHG intensity of transportation fuel mixes (e.g., California; ARB 2017, British Columbia; BC Laws 2017, Oregon; (DEQ 2017), and a forthcoming Canadian national Clean Fuel Standard, (ECCC 2017)).

A review of studies reporting GHG intensity from oil sands mining and upgrading projects found widely varying results ranging from 62 to 164 kg CO2eq/bbl SCO (Charpentier et al. 2009). Historic trends in energy return ratios, energy intensity, and GHG emissions of mining projects were explored in Brandt et al. (2013), Englander et al. (2013), and Englander et al. (2015), respectively. GHG intensities of the oil sands have also been quantified in other models (e.g., the GreenHouse Gas emissions of current Oil Sands Technologies (GHOST) model documented in Charpentier et al. 2011 and Bergerson et al. 2012; the GHGenius model developed for Natural Resources Canada, (S&T)2 2013a; the Oil Production Greenhouse gas Emissions Esimator (OPGEE) model, El-Houjeiri et al. 2013; the Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) model developed by Argonne National Laboratory, GREET 2016). Most past studies represent GHG intensity from mining and upgrading projects as a single point estimate based on data from individual projects or industry-average data. However, this does not fully capture the range of operations from the eight operating mining

77 projects (as of 2018) which have distinct characteristics (e.g., years of operation, site layout, integration with upgrading) and employ different technologies.

Accounting for variability (real-world differences in processes, technologies, and projects as well as temporal variability) and uncertainty (associated with modeling methods and limited available data) in LCAs helps support more robust decision-making when considering results. By identifying the sources and magnitude of variability and uncertainty, LCA results can better identify the key drivers of GHG emissions than when only industry-average or project-specific data are considered. With respect to LCFS-type policies, accounting for variability and uncertainty has in other cases resulted in GHG intensity ranges for fuels derived from a single resource that exceed the 10% reduction target set by most LCFS-type policies (Mullins et al. 2011; Venkatesh et al. 2011). Having LCA results that explicitly account for the variability and uncertainty in GHG emissions can help operators to reach GHG emission targets more effectively.

The objective of this study is to improve on previous assessments of the GHG emissions from oil sands mining and upgrading operations by explicitly (a) characterizing the variability among GHG intensities of all mining and upgrading projects and (b) identifying key technical and operating factors that may impact those intensities. A statistically-enhanced version of the GHOST model that to date focused on in situ technologies (GHOST-SE; Orellana et al. 2017) is expanded to include mining and upgrading. This version of GHOST-SE is used to generate GHG intensity distributions for each mining project using publicly available data to examine historic trends in GHG emissions, variability between projects, and the factors that contribute to this variability. Consideration of these types of variability can help evaluate whether technology adoption has reduced GHG emissions over the history of mining operations and benchmark current GHG intensities. Distributions are compared to literature GHG emissions estimates. Implications in terms of regulatory, technology, and operating decisions are discussed, providing insights for policymakers, technology developers, and oil sands operators.

Methods

At the time the original GHOST model (hereafter, GHOST) was developed, consistent public reporting of energy (fuels and electricity) inputs to oil sands projects was not available, so statistical methods could not be used to characterize energy consumption or GHG emissions. In

78 subsequent work, the in situ module of GHOST was redeveloped to account for variability, resulting in GHOST-SE (Orellana et al. 2017). In the current study, the mining and upgrading modules of GHOST are redeveloped to incorporate variability analyses based on more comprehensive public data sets than were previously available and then integrated into GHOST- SE. Statistical distributions are developed for input parameters based on publicly-available monthly operating data from all mining and upgrading projects so that the variability associated with mining and upgrading GHG intensities across the industry and over time is captured. This module of GHOST-SE assesses the upstream GHG emissions of mining and either upgrading or diluting of the produced bitumen for each mining project. Both direct (those released on-site through project operation) and indirect (those resulting from the supply chain of inputs to the project) emissions are included, yielding an estimate of the upstream GHG intensity in units of kg CO2eq/bbl crude product, that is, SCO or dilbit. 100-year global warming potentials for CO2

(1), CH4 (30), and N2O (265) are obtained from the Fifth Assessment Report of the IPCC (IPCC 2013). A diagram of the processes included within the study boundary is included in Appendix A (Figure A-1). Land use change-related GHG emissions have been estimated in another life cycle assessment of oil sands-derived fuels (Cai et al. 2015) but were excluded from this study due to lack of project-specific data over the 1983-2015 period, particularly for newer oil sands projects and recent operating years.

4.3.1 Processes Involved with Mining and Upgrading Oil Sands Bitumen

Bitumen is mined with electric and diesel-powered shovels and trucked to a crushing facility where material is crushed and mixed with warm water (typically 40-55oC; Long et al. 2005) to form a slurry which undergoes aeration to create a bitumen froth (typically 60 wt % bitumen, 30 wt % water, and 10 wt % mineral solids; Masliyah et al. 2004) from which bitumen is extracted. Once bitumen is separated, process water is treated and recycled for reuse, with some water going with the remaining solids to tailings ponds. Naphthenic froth treatment (NFT) produces a bitumen product that requires upgrading to meet pipeline or refinery specifications. A newer process developed in the 1990s, paraffinic froth treatment (PFT) produces a higher quality bitumen that does not require upgrading (Masliyah et al. 2004). A diluent is added to the bitumen to yield a dilbit product (typically 30% diluent, 70% bitumen), to meet pipeline specifications for transport to an upgrader or refinery.

During upgrading, a series of conversion processes are undertaken to remove species such as sulfur, nickel and vanadium to transform bitumen into SCO. Heavy crude fractions undergo primary conversion, which increases the hydrogen to carbon ratio of the fraction either through the removal of carbon (coking) or the addition of hydrogen (hydroconversion). All crude fractions are sent for secondary upgrading, where hydrotreaters remove sulfur, nitrogen, and other impurities by adding hydrogen under high temperature and pressure conditions in the presence of a catalyst (Gray 2015). Crude fractions are then blended to meet pipeline specifications (Choquette-Levy et al. 2013) and downstream refinery requirements (Charry- Sanchez et al. 2016). Byproducts from upgrading include process gas, coke, sulfur, and waste heat (Pacheco et al. 2016). While some projects include standalone mines or upgraders, some sites are integrated (with mining and upgrading both occurring on-site, hereafter referred to as integrated projects). Waste heat from the upgrader can be used to supply hot process water to extraction facilities (known as process integration). On-site demand for steam and hot water is met through the combustion of natural gas (or by-products to the upgrading process) in either boilers or cogeneration systems (Doluweera et al. 2011), which also produce electricity to either be consumed on-site or exported to the grid. Demand for hydrogen is typically met on-site through steam-methane reforming (SMR) of natural gas (Pacheco et al. 2016).

4.3.2 Mining and Upgrading Projects

At year-end 2015 (the last year of operating data included in this study) there were six mining projects active within the oil sands operated by ﬁve companies (see A-2). All projects that have commenced operation are still operating today. All but one surface mining project is associated with an upgrader (either on-site or off-site). Projects are numbered 1 (oldest; operating since 1967) through 6 (newest; operating since 2013). Projects 1, 2, and 4 are integrated projects, with the upgrader located adjacent to the mine. Project 2 consists of two mines; one with an on-site upgrader and the other that sends bitumen mined at that location for upgrading to the other location. Projects 3 and 5 are standalone mines that send the bitumen they produce to an off-site upgrader (Pacheco et al. 2016). In this study, the energy consumed by the upgrader is allocated to Projects 3 and 5 based on the relative volume of bitumen produced by each project. All projects use some level of cogeneration to meet at least a portion of electricity demand and some use cogeneration to meet all electricity demand while exporting additional electricity to the grid.

Projects 1-5 all generated surplus electricity during at least one operating year although the amount exported tended to vary over time.

4.3.3 Data Collection

A summary of the data collected by source and the types of statistical distributions employed for each input parameter is provided in Table 4-1. The procedure for developing statistical distributions based on the data collected from each literature source is documented below.

Table 4-1. GHOST-SE input parameters for oil sands mining and upgrading projects. Source Distribution Years of Input Notes Data Included

Fuel Inputs to Meet Demand for Steam, Hot Water, and Electricitya

AER Direct data 1983-2015 Natural gas Probability of selecting energy ST39 samplingb (monthly) consumption data for a particular Process gas month weighted by the fraction of Coke total crude produced that month. All inputs linked through lookup Grid electricity tables in GHOST-SE model. Electricity surplus export

Inputs for Surface Mining Operations

COSIA Uniform N/A Diesel One range reported for each of NFT consumption and PFT mines.c

AEMERA Direct data 2011-2014 Fugitive GHG Annual GHGs reported for each samplingb (annual) emissions company operating at least one mine.

AER Direct data 2013-2015 Fraction of diluent Reported monthly for Project 6 ST39 samplingb (monthly) in dilbitd (produces dilbit; no upgrading).

Upgrading Parameters

AER Direct data 1983-2015 Natural gas for H2 Natural gas for H2 production and b ST39 sampling (monthly) production process gas for H2 production linked through lookup table in GHOST-SE Process gas for H2 model. production

Process gas flared

Bitumen/SCO Bitumen/SCO ratio used to convert ratio diesel consumption reported by COSIA.

EMISSIONS FACTORS

GHOST Discrete N/A Natural gas and Direct (combustion) and indirect process gas (upstream; natural gas only) emissions factors included. Coke

Variese Uniform N/A Diluent Life cycle GHG emissions for diluent

(26-79 kg CO2eq/bbl diluent)

AUC; EC Point estimate 1983-2015 Alberta electricity Annual grid mix from AUC (2016); grid intensity emissions factors from EC (2015).

One average grid emissions factor calculated for each year over the study period.

COSIA Point estimate N/A Deemed GHG emissions credit for electricity electricity from produced by cogeneration system cogeneration that is exported to the grid. aSome energy inputs reported by the AER are aggregated with energy consumed for bitumen mining and extraction and that consumed for upgrading. Inputs that are combined between the two processes are presented here. bFor parameters with distributions obtained through direct data sampling, distributions are defined as discrete distributions where the likelihood of selecting the energy intensity of a month is proportional to the crude produced in that month relative to the total crude produced over the simulation period. cNFT range was employed for Projects 1, 2, and 4; PFT range was employed for Projects 3, 5, and 6. dVolume of diluent blended with bitumen produced by Project 6. eA uniform distribution is assumed for the upstream GHG intensity for diluent, based on a range of emissions factors reported by several literature sources (see Appendix A for documentation). AER: Alberta Energy Regulator; SCO: synthetic crude oil; GHOST-SE: GreenHouse gas emissions of current Oil Sands Technologies – Statistically Enhanced; COSIA: Canada’s Oil Sands Innovation Alliance; N/A: not applicable, as data is not associated with specific operating years (includes diesel consumption reported by COSIA and emissions factors for all inputs except electricity grid); NFT: naphthenic froth treatment; PFT: paraffinic froth treatment; AEMERA: Alberta Environmental Monitoring, Evaluation and Reporting Agency; AUC: Alberta Utilities Commission; EC: Environment Canada.

Input Parameters Reported by the Alberta Energy Regulator. Monthly energy consumption and crude production data for mining and upgrading projects are collected from AER Statistical Series ST39 (AER 2015) and ST43 (AER 2007) from 1983 to 2015, based on mandatory reporting by oil sands operators under Alberta’s Specified Gas Emitters Regulation (SGER; AB

2007). Data in the ST39 and ST43 datasets is reported on a facility basis and is the aggregate of all energy demands for the project (i.e., including upgraders if located on-site, as well as energy consumed for water treatment and disposal; disaggregated data are not available). For integrated projects (those co-located with an upgrader, Projects 1, 2, and 4), as energy consumption and GHG emissions data are not reported separately for mining and upgrading, some additional steps had to be taken to separate the data so that the input parameters accurately reflect a complete mining and upgrading/dilution pathway for each project. Documentation of these steps is in Appendix A Section A.2.

Diesel Consumption. Diesel consumption by project is not reported by the AER or other data sources. The Canadian Oil Sands Innovation Alliance (COSIA) has published two mine templates of generic, hypothetical mines employing current technologies, one for NFT (COSIA 2017a) and one for PFT mines (COSIA 2017b). These templates present energy flows for low (9% wt ore) and high (12% wt ore) grade mined oil sands material from which diesel consumption estimates are obtained.

Fugitive GHG Emissions. Fugitive GHG emissions (including mine face and tailings ponds emissions) for each company operating a mining project are published by the Alberta Environmental, Monitoring, Evaluation and Reporting Agency (AEMERA) for the 2011 to 2014 period (AEMERA 2015). For companies operating more than one mining project included in this study, fugitive GHG emissions are allocated to the individual projects based on the relative bitumen production from each project.

Greenhouse Gas Emissions Factors. GHOST-SE primarily employs emissions factors from the GHOST model, which includes a set of emissions factors for each input parameter from a variety of literature sources. For a given emissions factor, estimates from the literature vary due to both uncertainty in data collection and variability in both the resources from which fuels are extracted and the methods employed for processing fuels and transporting them to the oil sands. For simplicity, in this study uncertainty and variability is considered solely as a source of variability. GHG emissions from grid electricity consumption are updated with annual data from 1983-2015 reported by the Alberta Utilities Commissions (AUC; AUC 2016). A credit for surplus electricity exported to the grid is obtained from the COSIA Mine Template (COSIA 2017), which employs the same allocation method to account for surplus electricity generation as the SGER (AB 2007),

83 see Appendix A Section A.2.4. Different methods for allocating an emissions credit for surplus electricity are explored in a sensitivity analysis (e.g., displace average Alberta grid emissions, displace natural gas).

Diluents are often sourced from the Enbridge Condensate Blend (CRW) stream, a pool of several light hydrocarbons including field condensates, ultra-light sweet crudes, and upgrader and refinery naphtha streams (CAPP 2017). In this study, an upstream emissions factor for diluent is assigned a uniform distribution, ranging from 26-79 kg CO2eq/bbl diluent, based on a range of emissions factors obtained from the literature (documented in Table A-4) for diluent supply.

4.3.4 Monte Carlo Simulations of GHG Emissions from Oil Sands Mining and Upgrading Projects

Monte Carlo simulations are conducted using the Oracle Crystal Ball add-in for Excel using the Excel-based GHOST-SE model. Each simulation consists of 20,000 Monte Carlo runs, used to develop GHG intensity distributions. For each run, Crystal Ball randomly selects an input parameter based on the distributions for each project input to GHOST-SE. For input parameters reported either monthly or annually for each project that can be linked to production volumes in the original datasets (i.e., those derived from AER and AEMERA data sources, which includes natural gas and process gas consumed as a fuel, natural gas and process gas consumed for hydrogen production, coke consumption, electricity production, on-site consumption, and export to the grid, and flaring and fugitive emissions, see Table 4-1), distributions are defined by sampling directly from the dataset, so that input parameter values are linked to relative bitumen or SCO production. For example, if 5% of a project’s crude is produced in a given month, there will be a 5% probability that the energy intensity from that month will be selected in the simulations. For each energy consumption parameter (e.g., natural gas consumption, electricity consumption), simulation period (i.e., project lifetime or a specific operating period), and project, a new distribution is defined based on the data reported by the AER for that operating period. For example, for the project lifetime simulations each process energy parameter distribution for Projects 1 and 2 is defined considering data reported monthly over the 33-year study period (396 data points). For simulations that consider only one year of operating, process energy parameter distributions are defined based on 12 data points. Some of these input parameters (e.g., natural gas and process gas for hydrogen production) are statistically dependent so are linked through lookup tables within GHOST-SE (see Appendix A Section A.2.7). Uniform distributions are

84 assumed for diesel consumption based on the range provided in the COSIA Mine Templates for each of NFT and PFT mines (COSIA 2017). Emissions factors from GHOST are randomly selected from GHOST’s ranges of emissions factors. Grid emissions factors are modeled as point estimates for each year included in this study. A point estimate is also used for the emissions credit applied for electricity exported to the grid. A comparison of the input parameter ranges in GHOST and the distribution characteristics in GHOST-SE is provided in Table A-5.

Simulations are run using GHOST-SE for mining and upgrading operations to develop distributions of the GHG intensity of SCO (Projects 1-5) or dilbit (Project 6) produced from each project over the lifetime of the project to evaluate inter-project variability. Simulations are also completed for each project for each year of operation so that temporal and intra-project variability can be explored. The same set of simulations are also completed for industry-wide mining and upgrading (including Projects 1-5) operations to facilitate a comparison to literature values, where the likelihood of each project being selected is weighted by that project’s relative SCO production.

A sensitivity analysis is performed to identify the input parameters that contribute most to intra- project variability. Input parameters are varied from the fifth to 95th percentiles (incorporating most of the input parameter’s range while excluding extreme values) within their assigned distributions and the resulting GHG emissions are compared to a baseline estimated using mean values from the input parameter distributions. Tornado diagrams are generated to show the relative contribution of each parameter to total GHG emissions of each project. A sensitivity analysis was also performed on the GHG distributions generated using only 2015 operating data (most recent available, presented in Appendix A Figure A-2) to show how variability in current input parameters compares to lifetime parameter values.

Results 4.4.1 Variability of GHG intensity Across Mining and Upgrading Projects

Distributions of the GHG intensities of each oil sands mining and upgrading/dilution project from 1983-2015 (Figure 4-1) show two interesting trends: more intra-project variability than inter-project variability and less variability in GHG emissions distributions for newer projects with decreasing lower bound (10th percentile, or “P10”) values. Median GHG intensities from

mining projects that include upgrading (Projects 1-5) range from 90 to 138 kg CO2eq/bbl. Four of the five projects have median GHG emissions that are within 18 kg CO2eq/bbl SCO of each other. Besides Project 1, which over the life of the project consumes significantly more coke than other projects (see Appendix A Table A-5), and Project 6, which does not upgrade bitumen to SCO, the other projects have visually similar GHG intensity distributions. Lower bound (P10) values have decreased for newer projects. Project 1 (which commenced operations in 1967) has a p10 of 91 kg CO2eq/bbl SCO. By contrast, Project 5 (which commenced operations in 2010) has a p10 of to 72 kg CO2eq/bbl SCO. Contributing factors are investigated further in our discussion of “Influence of Drivers of Variability in GHG Intensity” (Section 4.4.3).

Figure 4-1. Mining and upgrading/dilution GHG intensity distributions for oil sands mining projects over the life of the project and industry average comparison with literature. Projects are listed in order from oldest (Project 1) to newest (Project 6). Projects 1-5 include upgrading and produce SCO, while Project 6 does not include upgrading and produces dilbit. Industry (SCO) includes Projects 1-5. Blue vertical lines represent the 10th and 90th percentiles of the Monte Carlo Simulation generated GHG emissions ranges obtained from GHOST-SE, while the black vertical line represents the median value for each project. Mean GHG emissions are in red. Literature values for each of mining and upgrading and mining and dilution from the following sources are included in the figure: GHOST (green), GHGenius (purple), and GREET (orange). 4.4.2 Temporal Variability of GHG Intensity

The annual GHG intensities of each project and an Industry (SCO) case over their lifetimes are presented in Figure 4-2. Projects 1, 2, 4, and 6 show some trend of decreasing median GHG

87 intensity compared to their start-up years with variability (distance between upper and lower whiskers on the boxplot) also decreasing over time.

The GHG intensity associated with project start-up tends to be higher than the intensity in subsequent years (e.g., lifetime median) due to low crude production in a project’s first few months of operation, which is observed in all projects except Project 3 (and Project 1, which began operating in 1967, before the operating period included in this study). Information regarding project expansions and technological changes described in the current and subsequent paragraph is obtained from 1) company websites and annual reports (company websites, listed alphabetically: CNRL, Imperial, Shell, Suncor, Syncrude 2017); and 2) Alberta Oil Sands Industry Quarterly Updates (AB 2017). Disruptions leading to temporary shutdowns or drops in production (e.g., fires at Projects 1 and 4 forced temporary shutdowns in 1995 and 2011, respectively) have resulted in very high GHG intensities in those months as energy consumption tends to remain consistent even when production drops. Older projects (Projects 1 and 2) have also undertaken significant expansions over their lifetimes, during which higher median GHG intensities and greater variability in GHG intensities were generally observed compared to prior and subsequent years (e.g., expansion of Project 1 from 1999-2001; expansion of Project 2 with production commencing in 2006) and less variable GHG intensities over time, except in years with especially low production. Aside from Projects 3 and 5, all projects show general trends of decreasing median production as discussed above. Newer projects (Projects 4 and 5; operating since 1999) have lower median GHG intensities in 2015 (86 and 81 kg CO2eq/bbl SCO, respectively, see Figure A-2) compared to projects that began producing bitumen prior to 1999

(112-137 kg CO2eq/bbl SCO for Projects 1-3) and narrower GHG emissions distributions.

Between 1990 and 2000, several major technological changes were implemented at Projects 1 and 2 that are expected to have led to reduced energy demand. Beginning in 1993 ( ) and 1997 (Project 2), truck and shovel mining operations began replacing bucketwheel and dragline mining systems. During the 1990s, hydrotransport lines replaced conditioning drums, which allowed for lower water temperatures for bitumen extraction (Masliyah et al. 2004). Due to a combination of regulatory changes (primarily deregulation of the Alberta electricity grid) and favorable economics over the 1990s, sizing cogeneration for surplus electricity generation became more attractive (Solas 2014). Beginning in 2000, both projects operating at that time

88 began exporting substantial amounts of electricity to the grid, which they receive a GHG emissions credit for as other sources of electricity are displaced.

In this study, a conservative GHG emissions credit (163 g CO2eq/kWh) for electricity exported to the grid is assumed, adapted from the COSIA Mine Templates (COSIA 2017). This is substantially smaller than the average annual grid emissions factor in Alberta, which ranges from

717-957 g CO2eq/kWh over the period from 1983-2015, and electricity produced from natural gas, which in Alberta has an average emissions factor of 494 g CO2eq/kWh (see Appendix A Section A.2.5). A larger credit for surplus electricity generation (e.g., assuming that electricity is displacing the average Alberta grid GHG emissions intensity) would result in lower GHG intensities for projects exporting electricity to the grid (particularly Projects 1 and 2, seeAppendix A Section A.6). Over the 1983-2015 period, applying a credit for surplus electricity generation equal to the average grid emissions factor in that operating year reduces the median upstream GHG intensity for mining and upgrading projects (Industry SCO case) by 6 kg

CO2eq/bbl SCO (from 110 to 104 kg CO2eq/bbl SCO, see Figure A-3 for lifetime GHG intensity distributions for each project where the credit for surplus electricity generation is equivalent to the average Alberta grid GHG intensity), but varies across projects depending on the amount of cogeneration employed by that project. An additional set of model results are presented in Figure A-4, where the credit for surplus electricity generation is equivalent to the emissions factor for electricity produced from natural gas in Alberta, which results in a reduction in median GHG intensity for mining and upgrading projects of approximately 5 kg CO2eq/bbl SCO compared to the base case results.

Over time, companies have implemented more process integration at their operations to further reduce energy demands. Due to the number of changes being adopted in this period and the level of aggregation of input data, the adoption of specific technologies cannot be linked directly to a reduction in GHG intensities for the two projects. Generally, however, the GHG intensity of SCO production was reduced at both projects during the period 1990-2000 (by 40% from 1990- 1998 for Project 1 and 13% from 1990-2000 for Project 2; 1999/2000 not considered for Project 1 due to mine expansion underway at that time), except in specific years with unusually high GHG emissions discussed above. Over the projects’ operating periods, the adoption of new regulations such as those regarding wastewater treatment and disposal (e.g., the Water Act, adopted in 2000, AEP 2009; Directive 074: Tailings Performance Criteria and Requirements for

Oil Sands Mining Schemes implemented in 2009, AER 2009) and the SGER (AB 2007) may have led to changes in operations that contribute to temporal variability of GHG intensity. As GHG intensity for each project varies considerably year to year, assigning a GHG intensity value to SCO production based on one project or one year of operating data may not result in a representative estimate of the GHG emissions typically resulting from that production method. Due to continuous changes in operating decisions (e.g., amount of cogeneration), disruptions (e.g., unexpected shutdowns, plant maintenance), and project expansions, operations may not appear to reach a steady state in terms of energy intensity, so GHG emissions per unit of production from the previous year of operations may not be an accurate predictor of the GHG intensity of future operations.

Additionally, each project has a unique configuration and operates under a distinct set of operating conditions, making it difficult to identify the drivers of GHG emissions from each project. For example, while Projects 4 and 5 began operating around the same time (2009 and 2010, respectively) and have similar GHG intensity distributions in 2015 (median GHG emissions of 86 and 81 kg CO2eq/bbl SCO, respectively), they have several technological differences: The respective projects differ in terms of mine configuration (integrated versus standalone), froth treatment technology (NFT versus PFT), upgrading technology (delayed coking versus hydroconversion), and cogeneration scenario (net exporter versus net importer of electricity). Except in the case of Projects 3 and 5, which are operated by the same company and upgrade bitumen at the same upgrader, no two projects have a set of conditions similar enough to compare specific technologies or operating decisions. As such, no single project can be considered representative of a production technology (e.g., delayed coking) or technology pathway (e.g., integrated mining). From a regulatory perspective, setting GHG intensity reduction targets becomes difficult as the historic operating data provides no clear benchmarks upon which to base future GHG intensity targets.

Figure 4-2. Time series of oil sands mining and upgrading/dilution GHG intensity distributions for all major oil sands mining projects. Emission data utilize the left axes, and production data utilize the right axes. Boxes (blue) delineate the 25th and 75th percentiles (p25 and p75) of the emission distributions. Whiskers extend to the 10th and 90th percentiles (P10 and P90) of the emission distributions. Solid red lines illustrate the median emissions. Dotted red lines illustrate the median emissions over the life cycles of the projects (all years); See Figure 4-1 (black lines). Projects 1-5 include upgrading and produce SCO whereas Project 6 does not upgrade and produces dilbit. Industry (SCO) includes Projects 1-5. Outliers (those outside of the P10/P90 range) are not illustrated in the figure for readability (see Appendix A). Dotted red horizontal lines represent the median GHG emissions for each project over the lifetime of that project. The green line tracks annual SCO or dilbit produced by each project based on the scale on the right-hand axis. 4.4.3 Drivers of Variability in GHG Intensity

The results of the sensitivity analysis for three projects (Projects 1, 3, and 6) are presented in Figure 4-3; results for remaining projects are presented in Figure A-7. The 10 most influential input parameters are displayed in Figure 4-3, with the variable names on the y-axis. The results of the sensitivity analysis performed using 2015 operating data are presented in Figures A-8 and A-9. Contributions of each parameter to overall upstream GHG intensity are presented in Chapter 5 Section 5.4.1.

Coke, consumed on-site at Project 1 to meet demands for steam, hot water, and electricity, is the biggest contributor to the project’s GHG emissions and is the primary input that contributes to Project 1 having greater variability in, and higher overall, GHG emissions than other projects. For Project 2, the only other project in which coke is consumed, coke consumption is the second largest contributor to overall GHG emissions after natural gas consumption (see Figure A-3), although for Project 2 the amount of and range for coke consumption is much smaller (32-82 kg coke/m3 SCO) compared to that for Project 1 (45-654 kg coke/m3 SCO). Besides coke consumed in Project 1, natural gas or process gas consumption is the most significant contributor to variability in GHG emissions for all projects. Over time, coke consumption at Projects 1 and 2 is decreasing (28-77 kg coke/m3 SCO and 33-51 kg coke/m3 SCO for Projects 1 and 2, respectively; see Figures A-4 and A-5 for 2015 tornado diagrams), however this is partially off-set by increases in natural gas consumption. For other projects, natural gas and process gas consumption vary depending on several factors (e.g., decisions made about the level of cogeneration to employ). However due to the number of other factors that affect natural gas and process gas consumption and a project’s GHG intensity, no definitive conclusions can be drawn from these results regarding the drivers of this variability.

Natural gas and process gas consumed for hydrogen production also vary considerably across projects with upgrading, with the lowest demand for Project 1 (22-60 m3 NG/m3 SCO, no process gas consumed for hydrogen production), which relies solely on delayed coking for primary upgrading and highest for Projects 3 and 5 (30-106 m3 PG/m3 SCO and 15-44 m3 NG/m3 SCO for Project 3) which employ only hydroconversion (hydroprocessing) for primary upgrading. The type of SCO (e.g., light/heavy based on API gravity; sweet/sour depending on sulfur content) being output from upgraders is not accounted for in this study but will result in at least a small differential in downstream refining GHG emissions. In 2015, the GHG intensity for Projects 1 and 2 are very sensitive to the method employed for crediting projects for surplus electricity exported to the grid (see Appendix A). For Project 6, the choice of emissions factor for diluent is the third most impactful parameter in upstream GHG intensity and reflects the variety of diluent sources and emissions factors reported in the literature for diluent supply. For Project 6, due to one month out of 36 data points where flaring is significant which is otherwise limited, the upper limit GHG intensity is lower than the baseline value. Compared to other input parameters, which are derived from monthly operating data, the available data for diesel consumption is much more

93 limited and not project-specific. While the diesel consumption range used in GHOST-SE is not a primary contributor to inter-project variability in GHG emissions for any of the mining projects, a complete representation of historic diesel consumption is not available and could potentially increase variability.

Figure 4-3. Tornado diagrams showing sensitivity of oil sands mining and upgrading/dilution GHG intensities to model inputs, Projects 1, 3, and 6. Projects are listed in order from oldest (Project 1) to newest (Project 6). Projects 1 and 3 include upgrading and produce SCO; a portion of the coke produced from the upgrader at Project 1 is consumed

95 on-site; dilbit is produced by Project 6. The ranges for each input parameter are presented on the figure while the bars represent the variation in GHG emissions as input parameters are varied from their mean values. Each part of the figure has its own scale to improve readability of the individual figures. Note that baselines are LCA(,,…,), whereas mean values of distributions are <(LCA(x1,x2,…,xN)>, hence slight differences between mean GHG intensities in Figure 4-1 versus Figure 4-3. 4.4.4 Comparison of GHOST-SE GHG Emissions for Mining and Upgrading Projects to those Reported in the Literature

GHG intensities for mining and upgrading projects reported in the literature (GHOST: Bergerson et al. 2012; GREET: see GREET 2016, Cai et al. 2015, and Englander et al. 2014; and GHGenius v4.03: (S&T)2 2013a,b; adjusted to be consistent with this study in terms of study boundaries, etc., see Appendix A) generally fit within the statistical distribution for industry- average (Projects 1-5; see Figure 4-1) mining and upgrading projects from the current analysis,

GHOST-SE. The industry-average GHOST-SE P90 estimate (176 kg CO2eq/bbl SCO) for mining and upgrading projects is higher than that of GHOST (166 kg CO2eq/bbl SCO) due to the high upper bound GHG intensity for Project 1 (293 kg CO2eq/bbl SCO) compared to other projects (maximum 159 kg CO2eq/bbl SCO). GHOST assumes that coke generated through upgrading is stockpiled, whereas a major contributor to the higher GHG emissions reported for Project 1 is from coke combustion. The minimum GHG intensity estimate for mining and upgrading reported from GHOST (64 kg CO2/bbl SCO) is below the industry average P10 estimate (89 kg CO2eq/bbl SCO) as GHOST includes a much higher upper bound estimate for surplus electricity than that reported for any project in the AER data (2765 kWh/m3 SCO versus 324 kWh/m3 SCO, respectively; see Table A-5), which would correspond to a larger credit for surplus electricity.

The GREET estimate of the GHG intensity of mining and upgrading operations (160 kg

CO2eq/bbl SCO; see Appendix A Table A-6) is developed from multi-year production-weighted average data over 2009-2012 (Cai et al. 2015), a shorter operating period than that considered in GHOST-SE. GHGenius reports two energy consumption values for mining and upgrading: one for standalone mines and upgraders (155 kg CO2eq/bbl SCO), and one for integrated projects 2 (102 kg CO2eq/bbl SCO; (S&T) 2013b). The GHGenius values are within the industry-wide P10/P90 range, but do not correspond with the median GHG emissions estimates from standalone (110 and 89 kg CO2eq/bbl SCO for Projects 3 and 5, respectively) and integrated

(137, 108, and 99 kg CO2/bbl SCO for Projects 1, 2, and 4, respectively) mining and upgrading projects from this study. Both the standalone GHGenius estimate and the GREET estimate are

96 well above the median industry-average GHG emissions estimate for mining and upgrading projects, falling around the 85th percentile in GHOST-SE’s statistical distribution.

The GHOST-SE GHG intensity distribution for Project 6, which does not have an upgrader, has lower median emissions (51 kg CO2eq/bbl dilbit) and a narrower P10/P90 range (23 kg

CO2eq/bbl dilbit) than other projects. GHOST’s range of GHG emissions from mining and dilution is 28-59 kg CO2eq/bbl dilbit, although again GHOST predicts a large GHG emissions credit for surplus electricity exported to the grid (Bergerson et al. 2012), while Project 6 has not exported any electricity to the grid over its three years of operation (2013-2015). The GHG emissions reported in GREET are 64 kg CO2eq/bbl dilbit (Englander et al. 2014), slightly lower than the P90 estimate for Project 6. The GHG emissions from GHGenius for standalone mining 2 with dilution are 47 kg CO2eq/bbl dilbit ((S&T) 2013b), slightly lower but still within GHOST- SE’s reported range for Project 6. All literature comparisons for the dilbit pathway are calculated assuming mined bitumen is diluted 30% with naphtha (see Appendix A).

Discussion

The mining and upgrading modules of the GHOST-SE model have been developed to assess variability in GHG intensities from oil sands mining and upgrading projects in Alberta using publicly-available, facility-level data over 33 years of operation. Over time, projects’ median

GHG intensities have been decreasing, from 89-137 kg CO2eq/bbl SCO over lifetime for Projects

1-5 to 80-138 kg CO2eq/bbl SCO in 2015. Variability is also decreasing over time, both when comparing newer and older projects as well as within projects operating over time. If these trends continue with future production, both intra- and inter-project variability in GHG emissions are likely to decrease in addition to median GHG intensity for mining and upgrading across the industry. However, after more than 30 years of operation for some projects, there remains annual variation in GHG intensity distributions for those projects. Based on our analysis, projects are most sensitive to: the amount of coke consumption, hydrogen demand for upgrading, and decisions about cogeneration. Hydrogen demand for upgrading varies significantly over time and across projects and this may be due to two factors: the upgrading technology employed (e.g., whether delayed coking is employed or only hydroconversion processes) as well as the type of SCO produced (and thus the level of upgrading).

Because the observed intra-project variability in GHG intensity over time is bigger than inter- project variability, technology (e.g., type of froth treatment) and operating (level of cogeneration) factors may have a greater impact on a project’s GHG emissions than location- specific factors such as mined ore quality and site design. However, due to the small number of projects, their unique characteristics and the level of aggregation of publicly-available data, the technologies or operating decisions cannot be directly linked to changes in GHG emissions over time.

These results highlight the challenges in achieving year over year decreases in GHG intensity, both across the industry and at individual projects. Factors that may influence temporal GHG intensity include: project expansions and shutdowns, changes in ore quality as mine sites expand, adoption of new regulations, technology development, and changes to operating decisions (e.g., level of cogeneration to employ). Additional effort towards reducing GHG emissions of existing projects should be directed at reducing key inputs (e.g., coke, hydrogen) and/or utilizing lower GHG intensity inputs (e.g., hydrogen produced through new methods). For policymakers, due to significant variation in annual GHG emissions from oil sands mining and upgrading projects, annual updating of emissions estimates by regulatory bodies is suggested to reflect current operations. As each project comprises its own set of technologies and operating characteristics, no specific project or type of crude (e.g. SCO or dilbit), project (e.g., integrated mining) or technology (e.g., type of froth treatment) should be used to represent a production pathway in LCFS-type policies. Use of industry-wide data (versus that of specific projects to represent a production pathway) should be utilized to fully represent current operations, and regularly verified and updated. Further work and additional availability of required data to support the determination of process level energy demands for standalone and integrated mining operations would allow for more specific identification of the drivers of GHG emissions and better identification of opportunities for reducing GHG emissions. Due to data limitations the current analysis did not include land use change-related emissions; this emissions source should be included in analyses of life cycle GHG emissions as more data becomes available for all mining projects.

This study quantifies the impacts associated with mining and upgrading or dilution of oil sands bitumen but does not consider transport, refining, or vehicle use stages of the life cycle. Each mining project produces a crude with different properties that will affect the product’s

98 downstream GHG emissions. By quantifying, at a facility level, the upstream GHG emissions this work will facilitate investigations that consider the full cradle-to-gate life cycle of oil sands mining projects.

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Chapter 5 Quantifying Variability in Well-to-Wheel Greenhouse Gas Emission Intensities of Transportation Fuels Derived from Canadian Oil Sands Mining Operations

In Chapter 5 the GHOST-SE model introduced in Chapter 4 is integrated with a crude transportation model (COPTEM) and a refinery model (PRELIM) to assess variability in GHG intensity of mined bitumen across the full well-to-wheel (WTW).

This chapter is based on a manuscript that is being prepared for submission to a peer-reviewed journal for publication, citation below: • Sleep, S.; Laurenzi, I.J.; Bergerson, J.A.; MacLean, H.L. Quantifying Variability in Well- to-Wheel Greenhouse Gas Emission Intensities of Transportation Fuels Derived from Canadian Oil Sands Mining Operations.

Abstract

We quantify, on a project basis as well as across the industry, variability in well-to-wheel (WTW) greenhouse gas (GHG) intensities of transportation fuels from mined oil sands bitumen using detailed upstream, crude transport, and refinery models. We include downstream sources of variability not accounted for in previous studies. Across projects, the mining project producing dilbit has lower median WTW GHG intensity per MJ gasoline (median: 96; 80% confidence interval: 93-101 g CO2eq/MJ gasoline) versus SCO projects (median: 108-114; 80% confidence interval across all SCO projects: 106-123 g CO2eq/MJ gasoline) but is strongly influenced by assumptions regarding refinery configuration and allocation to refinery projects. Intraproject variability exceeds interproject variability, with 80% confidence intervals for individual projects varying up to 16 g CO2eq/MJ, despite differences in operating lifetime, technologies, crude properties, and site design. Intraproject variability is driven in approximately equal proportions by the upstream and refining stages, although the relative contribution varies across projects. Emissions from refining SCOs (which in previous studies have been aggregated into a single representative SCO) vary from 48-70 kg CO2eq/bbl SCO across all SCOs and refinery configurations. For a specific crude, variability in refinery emissions is driven by refinery configuration (especially for crudes with high proportions of heavy fractions, i.e., dilbit and low- API gravity SCO). Industry-wide (mining), median WTW GHG intensities of gasoline (108 g

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CO2eq/MJ) and diesel (102 g CO2eq/MJ) are 16% and 9.7% higher, respectively, than RFS2 baselines. Allocation of refinery emissions to products (gasoline and diesel) strongly influences WTW GHG intensity when emissions are allocated on a process-unit basis. We identify for policymakers, oil sands operators, and LCA practitioners key parameters affecting WTW GHG intensities of fuels produced from mined bitumen, insights on modeling choices affecting intensities, and implications for meeting intensity-based targets.

Introduction

Fuels produced from the Canadian oil sands have varying life cycle greenhouse gas (GHG) intensities (emissions per unit of fuel, e.g., g CO2eq/MJ gasoline) because oil sands projects employ a variety of production technologies, produce products with varying properties, and therefore have differing refining requirements. The emissions intensities of oil-sands based fuels are generally higher than those of transportation fuels from conventional petroleum resources (Bergerson et al., 2012; Cai et al., 2015; Charpentier et al., 2009). The production of bitumen, the extra heavy oil present in the Canadian oil sands, was 2.8 million bbl/day in 2017 and is expected to reach 3.9 million bbl/day in 2027 (AER, 2018). The GHG emissions of oil sands-derivedz fuels have implications for meeting North American low carbon fuel standards (LFCS; e.g., CA- LCFS, ARB, 2018; BC LFCS, B.C. Laws, 2018) and other GHG mitigation targets.

In 2015, 46% of oil sands bitumen production was through surface mining (AER, 2016), a technique in which overburden is removed, oil sands ore is mined, and bitumen is extracted using hot water and solvents, either naphthenes (for naphthenic froth treatment, NFT) or paraffins (for paraffinic froth treatment; PFT). Currently all bitumen produced through NFT is upgraded to synthetic crude oil (SCO). During upgrading hydrocarbon chains are broken down either by removing carbon (coking), adding hydrogen (hydroconversion), or a combination of both processes, while hydrotreating removes sulfur, nitrogen, and other species (Gray, 2015). Upgrading produces a higher quality crude (defined by the crude’s physical and chemical properties such as API gravity and sulfur). PFT requires more solvent but causes asphaltene precipitation, producing lower viscosity bitumen with fewer contaminants. This bitumen can be either upgraded to SCO or diluted with condensate (producing dilbit) to meet specifications for pipeline transport (Rao and Liu, 2013).

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Most oil produced in Western Canada (96% in 2015; CAPP, 2018a) is transported via pipeline to refineries; 75% of this crude is refined in the U.S., typically in the mid-west (CAPP, 2018b). Heavier crudes are more GHG-intensive to transport than lighter crudes (Choquette-Levy et al., 2018; Tarnoczi, 2013). Refining oil sands crude is typically more GHG-intensive than refining conventional oil (Karras, 2010), but depends on several factors including crude quality, refinery configuration, product slate, fuels combusted for process energy, and process capacity utilization (Abella and Bergerson, 2012; Karras, 2010). Several studies quantify refinery emissions from a range of crudes using detailed refinery models and a variety of allocation methods (e.g., Abella and Bergerson, 2012; Elgowainy et al., 2014; Forman et al., 2014; Han et al., 2015; Moretti et al., 2017; Wang et al., 2004), but none have combined this modeling with life cycle assessment (LCA) to quantify, on a project basis, the variability in upstream emissions across all oil sands projects.

From bitumen extraction and processing to fuel use in a vehicle (across the well-to-wheel, WTW), a complex set of operating and technology decisions lead to a wide range of potential GHG intensities (Bergerson et al., 2012). Understanding the WTW drivers of variability supports more robust decision-making of oil sands and refinery operators targeting emissions reductions. It also informs policymakers aiming to reduce industry-wide GHG emissions from the oil sands and the GHG intensity of bitumen-derived transportation fuels. Variability, observed variations in real-world activities, and uncertainty, due to missing or poor quality data, have previously been quantified for conventional petroleum (Venkatesh et al., 2011) and biofuels (Mullins et al., 2011). The studies found confidence intervals for GHG intensities of gasoline produced from a single fuel pathway often exceed LCFS intensity reduction targets. No study explicitly characterizes variability and/or uncertainty of GHG intensities of transportation fuels produced from oil sands bitumen across the full WTW.

Several previous LCAs report point estimates or ranges of WTW GHG intensities from bitumen mining, deriving their GHG intensity estimates from a range of data sources (e.g., (S&T)2 Consultants Inc., 2013; Bergerson et al., 2012; Cai et al., 2015; Jacobs, 2009; Ordorica-Garcia et al., 2007; TIAX, 2009; Wang, 2016). Jacobs (2009) and TIAX (2009) derive their GHG intensity estimates from case studies or project-specific data. Bergerson et al. (2012) use confidential and public data up to 2010 to develop ranges of emissions for then-current operations in the GreenHouse gas emissions of current Oil Sands Technologies (GHOST) model (see also

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Charpentier et al., 2011). (S&T)2 (2013a), Cai et al. (2015), and Wang (2016) employ public production-weighted industry-average data from existing projects up to 2012, using monthly operating data reported by the Alberta Energy Regulator (AER; AER 2015, 2007). Cai et al. (2015) account for variability in upstream energy consumed and report 95% confidence intervals alongside the GHG intensities for each oil sands pathway. Our prior work develops a statistically-enhanced version of the GHOST model (GHOST-SE) that accounts for historic variability in upstream (bitumen production and upgrading or dilution) GHG intensities from mining (Sleep et al., 2018) and in situ (Orellana et al., 2018) projects. GHOST-SE’s upstream module employs AER data reported for each oil sands project over the 1985-2015 operating period (AER 2015, 2007), distinguishing between variability across projects and over time. Only Cai et al. (2015) and Sleep et al. (2018) quantify variability in upstream GHG intensities from mining, and neither study captures variability in downstream (crude transport and refining) emissions.

This study extends the study boundary in Sleep et al. (2018) to include the full set of WTW activities. As upstream emissions account for approximately 13-23% of WTW emissions (Bergerson et al., 2012), including the full life cycle provides a more comprehensive characterization of variability than has been done in previous studies. Importantly, it facilitates “apples-to-apples” comparisons of transportation fuels produced from oil sands projects that produce crudes with distinct downstream processing requirements, as well as comparisons to transportation fuels derived from other resources. A WTW approach is required to avoid making suboptimal decisions that reduce upstream emissions but result in a net increase in GHG intensity by increasing downstream emissions.

The objective of this study is to quantify the variability in WTW GHG intensity of transportation fuels derived from mined bitumen to identify the drivers of variability and the effects of modeling choices (e.g., decisions about how to model refineries) on WTW results. We develop a statistically-enhanced WTW model, presenting resolution on a project basis for all mining projects and differentiate between inter- and intraproject variability (variability between and within projects, respectively). The WTW model includes additional sources of variability (e.g., variability in refinery emissions) not accounted for in previous LCAs of oil sands-derived products. Mining WTW GHG intensity distributions are compared to distributions for in situ oil sands pathways and North American petroleum baseline GHG intensities. We identify for

107 policymakers, oil sands operators, and LCA practitioners the key parameters affecting WTW GHG intensities of fuels produced from mined bitumen, insights on the modeling choices affecting GHG intensities, and implications for meeting GHG intensity-based targets.

Material and Methods

We construct a WTW model, comprised of a set of submodels, that employs Monte Carlo analysis to generate statistical distributions of the WTW GHG intensities of transportation fuels produced by each mining project (see Figure 5-1). The WTW model includes the following life cycle stages within the system boundary: upstream (bitumen extraction and upgrading or dilution), crude transport to refinery, refining crude into products, transport of products to vehicle refueling stations, and vehicle use emissions. The functional units are one MJ of gasoline and one MJ of diesel fuel produced (on a lower heating value basis). Across the life cycle, the WTW model accounts for: direct and indirect (supply chain) emissions from fuel combustion, natural gas and process gas consumed at mine sites, upgraders and refineries for hydrogen production (an input to upgrading and refinery processes), emissions from electricity consumption (or credit for surplus electricity exported to the grid), flaring and fugitive emissions at mine and upgrader sites, and combustion emissions from vehicle use. GHG emissions are reported in g CO2eq, based on 100-year global warming potentials provided by the Fifth Assessment Report of the IPCC (AR5; IPCC, 2013).

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Figure 5-1. Submodels Employed in Quantifying WTW GHG Intensity of Transportation Fuels from Mined Bitumen. See Appendix B for additional detail on model inputs. GHOST-SE: Statistically Enhanced version of the Greenhouse Gas Emissions of current Oil Sands Technologies Model (Sleep et al., 2018); COPTEM: Crude Oil Pipeline Transport Emission Model (Choquette-Levy et al., 2018); PRELIM: Petroleum Refinery Life Cycle Inventory Model (Abella et al., 2017); NETL: National Energy Technology Laboratory (NETL, 2009); GREET: Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation Model (2016 model version employed; Wang, 2016); AB: Alberta; U.S.: United States; P10: 10th percentile; P50: median; P90: 90th percentile; RFS2: Renewable Fuel Standard petroleum baseline (Environmental Protection Agency, 2010).

All mining projects operating as of 2015 are modeled in this study. Characteristics of these projects are summarized in Table 5-1. Projects are numbered from oldest (Project 1; operating since 1967) to newest (6; operating since 2013). Project 2 includes two mines that process bitumen at the same mine site. A single stand-alone upgrader processes bitumen produced by Projects 3 and 5. Project 6 produces dilbit. The WTW model is run for each mining project, a Mining SCO pathway (includes all five mining and upgrading projects), and an All Projects pathway that includes the entire mining industry (includes all six projects). The Mining SCO and

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All Projects pathways assume equal probability of selecting each project in the pathway. The Mining Dilbit pathway includes Project 6 (the only mining project producing dilbit). Each mining WTW model run consists of 10,000 Monte Carlo simulations (run in the Oracle Crystal ball add-in to Excel; Oracle, n.d.). For each simulation, the WTW model samples from the output of three separate submodels, representing the upstream (GHOST-SE: Orellana et al., 2018; Sleep et al., 2018), crude transport (Crude Oil Pipeline Transport Emission Model, COPTEM: Choquette-Levy et al., 2018) and refining (Petroleum Refinery Life Cycle Inventory Model, PRELIM, available at http://www.ucalgary.ca/lcaost/prelim; Abella et al., 2017) stages. The respective submodel outputs capture variability in emissions from each life cycle stage. Sections 5.3.1-5.3.3 summarize modifications made to each submodel for this work. Refined product transport and vehicle use emissions estimates are based on the methods presented in NETL (2009), see Section 5.3.4 and GREET 2016 (Wang, 2016), see Section 5.3.5, respectively. As most oil sands crude is processed in U.S. refineries, U.S. models (i.e., NETL) are employed for modeling transport of refined products to vehicle refueling stations. Each submodel is run for a Base Case (defined in Sections 5.3.1 to 5.3.5); Section B.10 presents a sensitivity analysis on Base Case assumptions.

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Table 5-1. Summary of Mining Project Characteristics. Project 1a Project 2 Project 3b Project 4 Project 5 Project 6 First year of 1967 1978 2003 2009 2010 2013 operation Time period 2005-2015 2005-2015 2005-2015 2009-2015 2010-2015 2013-2015 included in this study Froth NFT NFT PFT NFT PFT PFT treatment Upgrading Delayed Coking + HC HC Delayed HC Dilution technology coking (integrated) (standalone) coking (standalone) (with (integrated) (integrated) condensate) Crude type Medium-API High-API High-API High-API High-API Dilbit for pipeline gravity SCO gravity SCO gravity SCO gravity SCO gravity SCO transport Summary of whole crude properties input to PRELIM S 2.09 0.14 0.81 0.08 0.81 3.96 (wt%) API gravity 24.6 31.5 27.1 35.0 27.1 22.0 H 11.8 12.5 12.0 12.9 12.0 12.1 (wt%) MCR 0.40 0.05 3.87 0 3.87 8.42 (wt%)

~Kw 11.5 11.8 11.4 11.7 11.4 12.0

Tb5o 365 321 317 276 317 480 (oC) a Crude properties presented for 2010 blended assay; blend ratio (described in Section B.6.2) varies by year (see Table B-8 and Table B-9). b Blended assay for Project 3/5 upgrader presented. Summary of whole crude properties for individual crudes produced (not blended) are presented in Table B-10. NFT: naphthenic froth treatment; PFT: paraffinic froth treatment; HC: hydrocracking; SCO: synthetic crude oil;

S: sulfur content; API gravity: gravity; H: hydrogen content; MCR: micro carbon residuum; ~Kw: approximated Watson characterization factor, with Tb50 in wt; Tb50: temperature where 50% of the mass of the whole crude is recovered through distillation.

5.3.1 Quantifying Upstream GHG Emissions: GHOST-SE

This study uses GHOST-SE (previously applied to estimate historic upstream GHG intensities for mining and in situ projects in Sleep et al. 2018 and Orellana et al. 2018, respectively) to estimate upstream GHG intensities of mining projects operating as of 2015. For the Base Case, GHOST-SE is run for each project and operating year from 2005-2015 (up to 11 model runs total per project). Each run consists of 10,000 simulations. GHOST-SE is run for the 2005-2015 period 1) as public production volumes for projects producing multiple upgrader products were unavailable prior to 2005, 2) to generally reflect current technologies (i.e., excluding operating years from older operating projects that have adopted new technologies) and 3) to include

111 enough operating years to capture the significant annual variability in upstream GHG intensities within individual projects observed in Sleep et al. (2018). Results are reported as kg CO2eq/bbl crude (SCO or dilbit).

5.3.2 Pipeline Transportation Model: COPTEM

The Base Case employs COPTEM to model crude pipeline transport (Choquette-Levy et al., 2018). COPTEM input parameters are fit to statistical distributions using data from an inventory of 147 pipelines that transport oil sands products across North America (Guo et al., n.d. document statistical enhancement of COPTEM). The crude produced by each mining project is categorized as either dilbit, medium-API gravity SCO, or high-API gravity SCO depending on the crude’s API gravity (see Table 5-1). COPTEM is run for each crude category, generating a custom distribution of crude pipeline GHG intensity from 10,000 Monte Carlo simulations, reported in kg CO2eq/bbl crude. COPTEM results are compared to other pipeline transport models (described in Section B.5 and transport modes (pipeline versus rail) in a sensitivity analysis (Section B.10).

5.3.3 Refining of Oil Sands Products: PRELIM

PRELIM is an open-access model that employs refinery linear programming modeling methods to quantify refinery GHG intensities (see Figure B-2) from current North American refineries, producing a crude slate that includes gasoline, diesel, jet fuel, heating fuel oil, and heavy fuel oil (Abella et al., 2017). Crude properties are defined in PRELIM from public crude assays that report the characteristics of crudes produced by each project, obtained from Crudemonitor (Crude Monitor, 2018). For Projects 1, 3, and 5 (whose upgraders produce multiple crudes with different properties), PRELIM’s blending tool generates a blended assay for each project, representing the mix of crudes produced by those upgraders (blended using a mass ratio, see Section B.6.2). See Table 5-1 for whole crude properties of the blended assays.

In PRELIM v1.2.1 (modified in this study), the hydrogen content of the fuels produced by the refinery varies based on the blend of refinery product streams (e.g., naphtha) in the final fuel products (e.g., gasoline, diesel). This study employs vehicle use emissions from GREET 2016 (Wang, 2016), based on the combustion of fuels (i.e., gasoline and diesel) with set properties. We modify PRELIM by fixing the hydrogen content in the refinery product streams to match the

112 hydrogen content of fuels produced by the refinery to GREET’s gasoline and diesel fuel properties. See Section B.8 for more information.

This study assumes all crudes are processed in either medium or deep conversion refineries (representing 19% and 73% of U.S. refining capacity respectively; Cooney et al., 2017). For each category of refinery (medium or deep conversion), three possible configurations are considered: Fluid Catalytic Cracking (FCC), Gas Oil Hydrocracking (GO-HC), and combined Fluid Catalytic Cracking and Gas Oil Hydrocracking (FCC+GO-HC). The Base Case assumes an equal probability of selecting each configuration. For each crude assay (or blended assay for Projects 1, 3, and 5), PRELIM is run for the six refinery configurations with a hydrotreater either upstream or downstream of the FCC. Approximately 50% of U.S. refinery configurations include FCC- feed hydrotreaters (U.S. EIA, 2010), selected for the Base Case in this analysis.

PRELIM predicts a different product slate depending on the assay and refinery configuration. PRELIM is run for each combination of crude and refinery configuration. For each run PRELIM predicts refinery GHG intensities in kg CO2eq/bbl crude. The precision and accuracy of PRELIM is tested by simulating in PRELIM the direct GHG emissions from 22 refineries operated by ExxonMobil and affiliates in 2012. Section B.6.3 compares simulation results to actual direct emissions from each refinery. PRELIM predicts refinery emissions from the 22 refineries with a mean squared error of 21%. To reflect the uncertainty of PRELIM’s performance relative to reported refinery emissions, each PRELIM run is assigned a log-normal distribution with a mean equal to PRELIM’s refinery emissions estimate (in kg CO2eq/bbl crude). Standard deviations for the log-normal distributions are derived by multiplying the mean of the distribution by the 21% mean squared error described above.

Upstream and crude transport emissions are allocated to refinery products based on PRELIM’s estimate of the energy content of each refinery product. Refinery emissions can be allocated to refinery products using either process-level allocation or refinery-level allocation. Process-level allocation assigns refinery emissions to each refinery product based on the input (crude fraction) to and output (GHG emissions, refinery product) from individual process units within the refinery. Process-level allocation can be done on an energy content, hydrogen content, or mass basis (Abella and Bergerson, 2012). Refinery-level allocation assigns refinery emissions to products based on net refinery outputs. The Base Case employs process-level allocation on an

113 energy basis (HHV) to quantify refinery emissions, as recommended by Wang et al. (2004). The sensitivity analysis (Section B.10) explores the effects of modeling choices (e.g., allocation method, refinery configuration, percentage of refineries with FCC-feed hydrotreater) on WTW GHG intensity distributions.

5.3.4 Refined Products Transportation

For the Base Case, NETL's (2009) method for quantifying GHG emissions of transporting products from refineries to refueling stations (via pipeline, train, or truck) and for vehicle refueling is employed, updated with more recent data (Davis, S.C.; Diegel, S.W.; Boundy, R.G.; Moore, 2014; Davis, S.C.; Diegel, S.W.; Boundy, 2012; U.S. Energy Information Amdinistration (EIA), 2011). See Section B.7 for more information.

5.3.5 Vehicle Use Emissions

The vehicle use (tank-to-wheel, TTW) stage of the life cycle for the Base Case is adapted from GREET 2016 (Wang, 2016) as described in Section 5.3.3. The possible effect of variations in properties of fuels produced on vehicle use emissions is discussed in Section B.14.

5.3.6 Comparison of WTW GHG Intensities of Transportation Fuels Derived from Mined Bitumen to Literature

Our prior work (Guo et al., n.d.) develops an in situ WTW model that characterizes statistical distributions of WTW GHG intensities of transportation fuels derived from two in situ technologies: steam-assisted gravity drainage (SAGD) and cyclic steam stimulation (CSS). In the current study, the in situ WTW model is run for four in situ pathways (SAGD SCO, CSS SCO, SAGD Dilbit, CSS Dilbit). In situ WTW GHG intensity distributions are compared to those of the Mining SCO and Mining Dilbit pathways. As public assays are unavailable for some in situ projects, the in situ WTW model employs representative assays for dilbit and SCO from PRELIM’s assay inventory rather than assays for specific projects. While all bitumen produced from CSS is currently diluted, both Guo et al. (n.d.) and this study include a hypothetical CSS SCO pathway for comparison with other pathways.

Each mining project’s WTW GHG intensity distributions for gasoline and diesel production are compared to the U.S. WTW petroleum baseline (the Renewable Fuel Standard Baseline: RFS2; EPA, 2010), adjusted to 100-year GWPs reported in AR5 (IPCC, 2013). Pathway distributions

114 are also compared to GHOST’s previous results (Bergerson et al., 2012), and other literature sources (Cai et al., 2015; Jacobs, 2009; TIAX, 2009). For all literature comparisons, constant vehicle use emissions from GREET 2016 (Wang, 2016) are applied (with no variation due to possible variations in H2 content of fuels produced at the refinery).

Results 5.4.1 Inter- and Intraproject Variability of WTW GHG Intensities of Transportation Fuels Derived from Mined Bitumen

Figure 5-2 shows probability distributions of the WTW GHG intensities of gasoline and diesel fuels derived from mined bitumen for the Base Case. The Base Case includes process-level, energy allocation and pipeline transportation. Across all mining projects, median WTW GHG intensity is 108 g CO2eq/MJ gasoline (80% confidence interval, CI: 96-119), 16% higher than the RFS2 petroleum baseline (93.7 g CO2eq/MJ gasoline; adjusted to AR5 100-year GWPs, IPCC, 2013). The results are generally consistent with prior studies (Bergerson et al., 2012; Cai et al., 2015; for a detailed comparison to literature see Section 5.4.4). Median WTW GHG intensity is 102 g CO2eq/MJ diesel (80% CI: 94-110), 9.7% greater than the RFS2 baseline (92.6 g CO2eq/MJ diesel); impacts of allocation to gasoline and diesel are discussed in Section 5.4.2.

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Figure 5-2. WTW GHG intensity of gasoline and diesel produced by oil sands mining projects for Base Case. Projects are listed in order from oldest (Project 1) to newest (Project 6). Projects 1-5 produce SCO; Project 6 produces dilbit. Left and right panels present results per MJ gasoline and per MJ diesel, respectively. Blue vertical lines represent the 10th and 90th percentiles of the Monte Carlo simulation generated GHG emissions ranges. Black vertical line represents the median value for each project. Mean results are marked by red “x” markers. Green vertical lines represent RFS2 baseline results (93.7 g

CO2eq/MJ gasoline, 92.6 g CO2eq/MJ diesel, adjusted to 100-year GWPs reported in AR5). Percentage values on the right-hand side of the figure represent the difference between median GHG intensity and the RFS2 baseline value.

Despite different operating lifetimes, froth treatments, upgrading technologies, and operating decisions (e.g., level of cogeneration, treatment of upgrading by-product coke), intraproject variability in WTW GHG intensity distributions of SCO projects exceeds interproject variability (indicated by differences in median WTW GHG intensities across projects). Per MJ diesel, 80%

CIs vary up to 16 g CO2eq/MJ (80% CI: 101-117 g CO2eq/MJ diesel, Project 1), which is more than the variability across SCO projects (median GHG intensities range from 95-106 g

CO2eq/MJ, a difference of 11 g CO2eq/MJ diesel). Both upstream and refinery stages drive

116 variability, although the relative contribution of each stage to WTW variability depends on the project (see Section 5.4.2). Results for refinery-level allocation are shown in Section B.9 and those for the sensitivity analysis to Base Case assumptions are in Section B.10.

5.4.2 Contribution of Life Cycle Stages to WTW Variability in GHG Intensity of Fuels Produced by Mining and Upgrading Projects

Mining SCO (Projects 1-5) and Mining Dilbit (Project 6) GHG intensity distributions for each life cycle stage per MJ of gasoline for the Base Case are shown in Figure 5-3 (results per MJ diesel and for individual Mining SCO projects are presented in Section B.11). Overall, on a per MJ gasoline basis, upstream and refining emissions are the most GHG-intensive life cycle stages and contribute similarly in terms of both magnitude and variability, to WTW GHG intensity. Crude pipeline emissions are minor relative to upstream and refinery emissions, accounting for less than 1% of mean WTW GHG intensities. Refined products transport are both small (less than 1% of mean WTW GHG intensities) and consistent across projects/pathways so are excluded from the figure. The remainder of this section focuses on drivers of variability in GHG intensity in upstream and refining stages due to the importance of these two life cycle stages.

Figure 5-3. WTW GHG intensity distribution for gasoline produced through oil sands mining pathways (Mining SCO and Mining Dilbit) disaggregated by life cycle stage.

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The left panel represents results for the Mining SCO pathway (Projects 1-5) and the right panel for the Mining Dilbit pathway (Project 6). Mean results are marked by red “x” markers.

Variability in Upstream Emissions. Mean upstream GHG intensities are lower for the project producing dilbit (Project 6: 9.3 g CO2eq/MJ fuel) than for projects producing SCO (Mining SCO pathway: 21 g CO2eq/MJ fuel). Upstream, dilution is less GHG-intensive than upgrading as it involves blending with a lighter hydrocarbon (e.g., condensate), while upgrading requires considerable energy consumption (primarily natural gas and process gas), producing a higher market value and higher quality product (i.e., vacuum residues transformed to lighter fractions, sulfur and other species removed). Upstream, intra-project variability exceeds interproject variability, especially for projects producing SCO (see Section B.11). Upstream GHG intensities for most projects are positively skewed due to months of low production (e.g., due to shutdowns for maintenance), during project expansions, and, for Project 1, in months where large quantities of coke are consumed. Sleep et al. (2018) explore drivers of upstream variability in GHG intensity in more detail. As Project 6 (representing the Mining Dilbit pathway) began producing dilbit only in 2013 and this study includes the 2005-2015 operating period, this pathway includes fewer operating years (3) than other projects (6-11), one factor that contributes to less upstream variability in GHG intensity for this project (and less variability for the Dilbit versus SCO Mining pathways).

Variability in Refinery Emissions. Each mining project produces a crude (or slate of crudes) with distinct characteristics affecting both refinery emissions and allocation of refinery emissions to products. Mean refinery emissions are presented for medium and deep conversion refineries with different functional units (Table 5-2). This section is organized as follows: first, variability of refinery GHG intensity, reported in kg CO2eq/bbl crude with no allocation to products, is discussed. Next, results per MJ gasoline and per MJ diesel are presented and compared to show the impacts of allocation to refinery products. Finally, the overall impact of variability in refinery emissions on WTW GHG intensities for individual mining projects is discussed.

For a specific crude, the biggest driver of variability in refinery emissions per bbl of crude is refinery configuration. We assume a uniform probability of selecting medium and deep conversion refineries due to lack of public reporting on the specific refinery each crude is processed in. For medium conversion refineries, emissions are lowest for Project 6 (34-37 kg

CO2eq/bbl dilbit, versus 48-67 kg CO2eq/bbl SCO across Projects 1-5). Dilbit contains larger

118 fractions of heavy molecules (vacuum residues) than SCOs. These heavy fractions leave the refinery unprocessed, leading to lower overall refinery emissions per barrel of crude but also lower gasoline and diesel yields (see Figure B-12). In deep conversion refineries, heavy fractions are processed in GHG-intensive units such as the FCC and coker, generally leading to higher refinery emissions for refining dilbit versus SCOs. An exception is Project 1 (the lowest-API

SCO, API gravity 25), which has higher refinery emissions (62-70 kg CO2eq/bbl crude) than

Project 6, (dilbit 54-59 kg CO2eq/bbl crude) in a deep conversion refinery. Across medium and deep conversion refineries, Project 6 refinery emissions vary by 25kg CO2eq/bbl dilbit, considerably more than variability for refining any individual SCO (maximum variability across refinery configurations is 10 kg CO2eq/bbl SCO, Project 1). Section B.12 explores drivers of refinery emissions for each refinery configuration.

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Table 5-2. API gravity, sulfur, and mean refinery GHG intensities for mining projects and in situ pathways across refinery configurations (FCC+GO-HC configuration). Project 1 Project Project Project Project Project In Situ In Situ 2 3 4 5 6 SCOb Dilbitb API gravity 25a 32 27 35 27 22 30 21 S (wt%) 2.1a 0.14 0.81 0.08 0.81 4.0 1.0 3.9 Medium Conversion Refinery FCC, kg CO2eq per bbl crude 67 56 51 53 51 37 59 41 MJ gasoline 16 17 17 21 17 12 16 14 MJ diesel 12 8.4 8.3 6.1 8.3 8.4 9.6 7.8 GO-HC, kg CO2eq per bbl crude 60 52 48 52 48 34 54 37 MJ gasoline 19 20 21 24 21 12 19 16 MJ diesel 10 6.1 6.2 4.5 6.2 6.7 7.1 6.4 FCC+GO-HC, kg CO2eq per bbl crude 64 54 49 52 49 36 57 39 MJ gasoline 17 18 18 22 18 12 17 15 MJ diesel 10 6.8 6.8 5.0 6.8 7.2 8.3 6.9 Deep Conversion Refinery FCC, kg CO2eq per bbl crude 70 57 53 54 53 59 61 61 MJ gasoline 16 16 15 20 15 13 16 15 MJ diesel 12 8.7 8.4 6.5 8.4 11 10 10 GO-HC, kg CO2eq per bbl crude 62 53 50 53 50 54 56 56 MJ gasoline 18 20 19 23 19 15 19 17 MJ diesel 10 6.4 6.4 4.8 6.4 8.2 7.4 7.8 FCC+GO-HC, kg CO2eq per bbl crude 67 55 51 54 51 57 59 59 MJ gasoline 16 18 17 21 17 14 17 16 MJ diesel 11 7.0 7.0 5.4 7.0 9.1 8.5 8.6 Refinery GHG intensities reported per bbl of crude (either SCO or dilbit; no allocation to products), as well as per MJ gasoline or diesel (refinery emissions allocated to each of those refinery products). aProject 1 results are presented for 2010 blend. FCC+GO-HC: Fluid Catalytic Cracking + Gas Oil Hydrocracking. Projects 3 and 5 send bitumen to the same upgrader so have the same refinery emissions. Product properties obtained from CrudeMonitor (2017) assays.

The above suggests that to minimize WTW emissions intensity, dilbit should be refined in medium conversion refineries. However, this combination of refinery and crude produces smaller quantities of high-value products (i.e., gasoline and diesel), increasing overall crude volumes required to meet demand for these products. Most heavy refinery products (i.e., those excluded from WTW boundaries when reporting results on a per MJ gasoline or diesel) are still combusted, emissions not accounted for when reporting results for a subset of refinery products, although it is common practice in LCA to report emissions in this manner (i.e., per MJ gasoline).

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The proportion of the refinery emissions allocated to refinery products varies widely across crudes and refinery configurations, depending on the product yield and emissions from each process unit in the refinery. For all crudes and refinery configurations, gasoline is more GHG- intensive to produce than diesel as gasoline is generally produced by more emissions-intensive process units in the refinery. This effect is most pronounced for Project 4, which produces the highest-API gravity SCO and has the highest refinery emissions for gasoline (24 g CO2eq/MJ) but the lowest emissions for diesel (4.5 g CO2eq/MJ), due to relatively low gasoline yields, produced by emissions-intensive process units in the refinery. The biggest driver of refinery emissions is refinery fuel gas and natural gas combustion to meet demands for heat (accounts for 50-75% of overall refinery emissions, see Figure B-12). Emissions from hydrogen production are lowest for projects producing relatively high-API gravity, low sulfur SCO (i.e., Project 4: 10% of refinery emissions) and highest for projects producing relatively low-API gravity, high-sulfur SCO (i.e., Project 1: 28% of refinery emissions). Impacts of allocation of refinery emissions to products is observed across the full WTW, although this varies across projects. Overall, the effect across the WTW is that Project 4 has the highest median WTW GHG per MJ gasoline (114 g CO2eq/MJ) but lowest per MJ diesel (99 g CO2eq/MJ). For dilbit (Project 6, API gravity 22), median WTW GHG intensities are similar when reported for different refinery products (96 and

95 g CO2eq/MJ gasoline and diesel, respectively). The implications of allocation on interpreting LCA results is discussed in Section 5.5 (Discussion).

Sensitivity of Vehicle Use Emissions to Refinery Product Properties. In the Base Case, vehicle use emissions are held constant at 73.2 g CO2eq/MJ gasoline and 75.6 g CO2eq/MJ diesel. We modified PRELIM to match the H2 content of gasoline and diesel reported in GREET 2016 (as vehicle use emissions were derived from this source; Wang 2016); see Section 5.3.3, however, the unmodified version of PRELIM (v1.2.1) predicts distinct fuel properties for the gasoline and diesel produced by each combination of crude and refinery configuration. In this sensitivity analysis, vehicle use emissions accounting for these variations in fuel properties range from 70-75 g CO2eq/MJ gasoline. Modeling the same crude but varying refinery configuration can affect vehicle use emissions by up to 3 g CO2eq/MJ fuel. Future work should investigate more explicitly how variations in refinery product properties affect WTW analyses.

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5.4.3 Variability of WTW GHG Intensities of Transportation Fuels Across Oil Sands Mining and In situ Pathways

WTW GHG intensity distributions for transportation fuels produced through the mining and in situ pathways are shown in Figure 5-4. Currently, most mined bitumen is upgraded (five of six projects), most bitumen produced from SAGD is diluted (10 of 12 projects), and all bitumen produced using CSS is diluted. Historically, all mined bitumen was upgraded and most in situ bitumen was diluted. As new mining projects (those operating after 2012) produce dilbit and some in situ projects produce SCO, in this study mining and in situ pathways producing the same type of crude are compared (i.e., SCO or dilbit).

In situ pathways have wider 80% CIs (in part because there are more in situ than mining projects), and higher GHG intensities, results consistent with prior literature (e.g., Bergerson et al., 2012; Brandt, 2012; Cai et al., 2015). For SCO pathways, median GHG intensities are 7- 9 g

CO2eq/MJ gasoline higher for in situ than mining. For dilbit, median intensities are 6-8 g

CO2eq/MJ gasoline higher for SAGD and CSS than mining but only 2-4 g CO2eq/MJ diesel higher. Differences in the properties of mined and in situ dilbit (as asphaltene precipitation occurs during bitumen extraction at PFT mines) cause a greater fraction of emissions for refining mined dilbit to be allocated to diesel than gasoline. Mined and in situ dilbit refinery emissions are compared in Section B.13. In situ projects consume more natural gas in the upstream stage for steam production, leading to higher upstream GHG intensities compared to mining projects for both Dilbit and SCO pathways. Additionally, most upgraders processing mined bitumen are co-located with mines that utilize waste heat from the upgrading process for bitumen extraction, potentially reducing energy demand across the life cycle compared to in situ projects with standalone upgraders.

As shown in Figure 5-4, for all bitumen production methods, dilbit pathways have lower median

GHG intensities (96, 102, and 104 g CO2eq/MJ gasoline for mining, SAGD, and CSS pathways, respectively) than their respective SCO pathways (112, 119, and 121 g CO2eq/MJ gasoline), due to demand for hydrogen, steam, and electricity for projects that include upgrading (demonstrated for mining projects in Sleep et al. 2018). Both mining and in situ pathways have higher GHG intensities per MJ gasoline than per MJ diesel due to differences in product yields, refinery emissions for individual process units, and the allocation of refinery emissions to refinery products. Dilution pathways in some cases have the lower end of the WTW GHG intensity

122 distributions below the RFS2 petroleum baseline, although for all pathways median WTW GHG intensities are higher (3-29%) than the baseline.

Figure 5-4. Comparison of WTW GHG emissions for gasoline and diesel production from oil sands mining and in situ pathways. Left and right panels present results per MJ gasoline and per MJ diesel, respectively. Blue vertical lines represent the 10th and 90th percentiles of the Monte Carlo simulation generated GHG emissions ranges. Black vertical line represents the median value for each project. Mean results are marked by red “x” markers. Green vertical lines represent RFS2 baseline results (93.7 g CO2eq/MJ gasoline, 92.6 g

CO2eq/MJ diesel, adjusted to 100-year GWPs reported in AR5). Percentage values on the right-hand side of the figure represent the difference between median GHG intensity and the RFS2 baseline value.

5.4.4 Comparison of Oil Sands Mining and In Situ Pathway GHG Intensities to Literature Values

This study improves upon previous assessments of the GHG intensities of fuels derived from mined bitumen by characterizing, on a project basis, the GHG intensity distributions and accounting for downstream variability in GHG intensity. Overall, oil sands pathway WTW GHG intensity distributions from this study have similar ranges to those presented in the literature (results per MJ gasoline are compared in Table 5-3, per MJ diesel in Table B-14; Bergerson et al. 2012; Cai et al. 2015; Jacobs 2009; TIAX 2009). GHOST (reported in Bergerson et al. 2012) found that the low end of WTW GHG intensity ranges across all oil sands pathways, 93-125 g

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CO2eq/MJ gasoline (versus 80% CI of 93-133 g CO2eq/MJ gasoline in this study), are in some cases higher than high-GHG intensity conventional pathways. GREET (GHG intensity estimates derived from Cai et al. 2015) found that across oil sands pathways, WTW GHG intensity is 9- 26% higher than conventional, slightly narrower than the difference between median GHG intensities for oil sands pathways and the RFS2 baseline for gasoline employed in this study (3- 29% higher). Jacobs (2009) and TIAX (2009) are also compared in Table 5-3 and B-14, and discussed in Section B.15.

Primarily, differences between this study and the literature arise from different refinery modeling approaches: both the method employed for quantifying refinery emissions and how refinery emissions are allocated to products (i.e., fraction of refinery emissions allocated to gasoline versus diesel). GREET estimates refinery emissions using a refinery efficiency formula that accounts for the API gravity and sulfur content of the whole crude (Cai et al. (2015). The formula employed by GREET predicts higher refinery emissions for dilbit (14 versus 12 g

CO2eq/MJ gasoline; 9.6 versus 7.3 g CO2eq/MJ diesel for refining SCO and dilbit, respectively). GHOST adapts the refinery GHG intensities from TIAX (2009), reporting GHG intensities ranging from 7.2-11 and 15-16 g CO2eq/MJ gasoline for refining SCO and dilbit, respectively (emissions per MJ diesel not reported; see Bergerson et al. 2012 for details). Employing PRELIM in the current study, we find higher median emissions for refining SCO than dilbit across all refinery configurations per MJ gasoline (17-18 versus 13-15 g CO2eq/MJ gasoline, respectively) but lower per MJ diesel (7.1-8.3 versus 7.8-8.2 g CO2eq/MJ diesel for refining SCO and dilbit, respectively). Our results support Abella and Berg erson’s findings that whole-crude API gravity and sulfur are not the sole drivers of refinery emissions (Abella and Bergerson, 2012). Additional literature comparisons are provided in Section B.15).

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Table 5-3. Comparison of GHOST-SE WTW GHG intensity distributions to literature values (g CO2q/MJ gasoline).

Life cycle stage GHG intensity (g CO2eq/MJ gasoline) Pathway Model/Report Crude Refining and Vehicle Upstream WTW pipeline transport usea 113 GHOST-SE mean (P10- 21 0.44 19 73 (106- P90) (15-30) (0.37-1.7) (14-23) 123) GHOSTb 104 Mining 17 2.7 11 73 (range) (97-116) SCO GREETc 20 2.7 12 73 109 Jacobsd 20 1.0 13 73 108 TIAXe 11-13 1.2-1.8 12.4-14.8 73 97-103 PADD 2/3 range GHOST-SE mean (P10- 9.3 0.76 13 97 73 P90) (7.2-11) (0.38-2.5) (10-16) (93-101) GHOSTb 97 6.0 4.0 15 73 Mining (range) (93-105) Dilbit GREETc 7.8 3.9 14 73 100 Jacobsd 9.0 1.1 15 73 98 TIAXe N/A N/A N/A N/A N/A PADD 2/3 range 121 GHOST-SE mean (P10- 27 0.66 17 73 (112- P90) (22-41) (0.38-2.0) (14-21) 133) 111.9 GHOSTb 25 2.7 10.7 73 (107- SAGD SCO (range) 125) GREETc 26 2.7 12.2 73 115 Jacobsd 28 1.0 13.0 73.2 116 TIAXe 27 1.2-1.8 10-12 73 111-113 PADD 2/3 range GHOST-SE mean (P10- 12 0.79 15 104 73 P90) (9.3-22) (0.44-2.5) (13-19) (98-112) GHOSTb 103.2 11 3.5 15 73 SAGD (range) (99-112) Dilbit GREETc 17 3.9 14 73 108 Jacobsd 16 1.1 15 73 105 TIAXe 10-13 1.2-1.8 15 -16 73 103-104 PADD 2/3 range aA consistent value for vehicle use emissions from GREET 2016 (Wang, 2016) is applied to all literature values; bSource: Bergerson et al. (2012); c Source: Cai et al. (2015); dDownstream GHG for Mining Dilbit pathway adapted from SAGD Dilbit pathway; Source: Jacobs (2009); eMining pathway presented includes upgrading (no mined dilbit pathway). Range derived from transport and refinery results presented for PADD2 and PADD3; Source: TIAX (2009).

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Discussion

This study improves our understanding of WTW GHG intensities of transportation fuels derived from mined bitumen by accounting for variability in both upstream and downstream GHG emissions and tracking bitumen produced at individual mining projects across the full life cycle. Further, we present probability distributions of the WTW emissions for mining pathways and compare them to in situ pathways as well as the RFS2 petroleum baseline, thereby providing an understanding of the likelihood of a project (or the industry) having an emissions intensity higher or lower than a baseline.

By modeling each individual SCO in PRELIM we find that the highest-API gravity, lowest- sulfur SCO does not have the lowest refinery emissions, either per bbl of crude processed or per MJ of gasoline produced. Refinery emissions modeling that has been employed in some prior studies, that accounts only for whole-crude API gravity and sulfur therefore does not accurately reflect the relationship between refinery emissions and crude properties for oil sands crudes. We find that emissions for refining SCO, which previous studies represent as a single point estimate, vary considerably, from 48-70 kg CO2eq/bbl SCO (or, once allocated to products: 15-24 g

CO2eq/MJ gasoline, 4.5-12 g CO2eq/MJ diesel) across all SCOs and refinery configurations. Assumptions made within an LCA about how to model refineries and allocate emissions to products strongly impacts the relative GHG emissions of different oil sands pathways, even across the full life cycle. Careful accounting and reporting of emissions should be employed to prevent incentivizing actions that shift emissions to refinery products not regulated under LCFS without reducing overall refinery emissions. This may incentivize upstream decision-making (i.e., the properties of upgraded crude) that increase emissions over the full WTW.

As of 2018, market conditions such as light/heavy price differentials may incentivize further upgrading of oil sands bitumen with no reduction in downstream refinery emissions. There may be an opportunity to reduce the WTW GHG intensity of transportation fuels derived from SCO by adjusting the properties of the crude produced to minimize refinery emissions. Changing demands for refined products (e.g., increased demand for diesel relative to gasoline) may impact the relative emissions of refining crudes with different properties (Motazedi et al., 2018). Future work should investigate more explicitly how upstream operating decisions can be made to optimize the quality of crude produced to minimize WTW GHG intensity. As public reporting of

126 upstream energy consumption for mining projects is on a project-wide basis (e.g., energy consumed for mining and upgrading are reported together for integrated projects) this type of analysis is limited by the public data available. Public reporting of the properties of crudes being processed by each type of refinery would enable more accurate refinery modeling and reduce variability in WTW GHG intensity.

This study compares upgrading to a dilbit pathway where diluent is refined alongside bitumen in the refinery. Alternatively, diluent can be recovered at the refinery and transported back to the oil sands project for reuse (with bitumen refined separately). Cai et al. (2015) found WTW GHG intensities for gasoline and diesel produced from dilbit were 3-5% and 4-6% lower, respectively, than for a bitumen pathway where diluent is recovered from the dilbit at the refinery and is thus excluded from the life cycle (Cai et al., 2015). Cai et al. found WTW GHG intensities (reported per MJ gasoline or diesel) for bitumen pathways (no upgrading) were still lower than SCO pathways employing the same bitumen production method (i.e., mining or in situ). Future work could take a consequential LCA approach to compare tradeoffs between diluent recovery and reuse and processing diluent with bitumen (as dilbit) in a refinery. This approach could also assess the GHG emissions implications of the deployment of partial upgrading technologies, an emerging and potentially lower-cost alternative to the construction of full upgraders that reduces (or eliminates) the need for diluent blending to meet pipeline specifications (Fellows et al., 2017).

This study answers important questions about how WTW GHG intensities of distinct mining projects and oil sands production pathways compare and facilitates comparisons to other fuel production pathways as well as benchmark petroleum baselines. This perspective can inform consumers and parties regulated under LCFS about the lowest-GHG oil sands production pathways and opportunities for reducing GHG intensities along the life cycle of those pathways. For mining projects, this includes comparisons between mining projects employing different froth treatment technologies (i.e., NFT or PFT) and producing different products (i.e., a range of SCOs, dilbit). Per MJ gasoline, dilution pathways are typically less GHG intensive than SCO pathways across the WTW for both in situ and mining. For the purposes of a LCFS, where the goal is to reduce the GHG intensity of the transportation fuel mix in the region where fuel is consumed, reporting WTW GHG intensities per MJ of fuel (i.e., gasoline or diesel) provides sufficient information about the lowest-GHG options. From the perspective of an oil sands

127 operator or Alberta regulator aiming to maximize yields of high-value transportation fuels derived from oil sands bitumen while minimizing environmental burdens (both upstream and across the life cycle), other functional units or life cycle boundary definitions may be more appropriate and warrant further analysis. For example, Choquette-Levy et al. (2013) compare life cycle GHG intensities for diluting versus upgrading bitumen produced by a representative SAGD project and find that dilution generally is less GHG-intensive per MJ of transportation fuel but more GHG-intensive per bbl bitumen produced. Future work could extend Choquette-Levy et al.’s comparison to explore tradeoffs between mined (PFT) and in situ dilbit and different dilbit processing options (e.g., refining dilbit versus recovering diluent prior to refining). Nevertheless, the current study contributes to the literature by presenting results with more granularity (presenting results on a project-basis), accuracy (using detailed pipeline and refinery modeling), and transparency (using only public data) than previous studies that quantify the WTW GHG intensity of transportation fuels derived from oil sands bitumen. Future studies should account for variability in refinery GHG emissions due to crude properties and carefully select boundaries and assumptions regarding refinery configuration and allocation procedures to produce robust LCA results that can inform operators and policymakers of the life cycle impacts of mining oil sands bitumen.

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Chapter 6 Expert Assessments of Emerging Oil Sands Technologies

This section contains the findings of an expert elicitation on the deployment of emerging oil sands technologies and their potential for reducing energy use, with implications for greenhouse gas emissions. This chapter has been published in the Journal of Cleaner Production; citation is provided below: • Sleep, S.; McKellar, J.M.; Bergerson, J.A.; MacLean, H.L. Expert Assessments of Emerging Oil Sands Technologies. Journal of Cleaner Production, 2017, 144, 90-99.

Abstract

Emerging oil sands technologies could influence industry-wide greenhouse gas emissions, however projecting future emissions is difficult due to limited public reporting of expected performance and deployment of emerging technologies. An expert elicitation was conducted to gauge how experts anticipate emerging in situ, surface mining and upgrading technologies will be deployed and perform compared to current technologies. All experts project the majority (60- 98%) of in situ bitumen production in 2034 will be produced using current technologies or hybrid steam-solvent processes. Experts built boxplots to show how they project commercial projects employing emerging technologies would perform in 2034 compared to a current project employing steam-assisted gravity drainage. Across experts, the median reduction in steam-to-oil ratio for hybrid steam-solvent projects and current in situ projects employing process changes (e.g., better well placement) ranged from 3-30% and from 12-14%, respectively. Median projections from experts about the change in bitumen recovery rate compared to a current (2014) steam-assisted gravity drainage project ranged from 3-30% for hybrid steam-solvents and up to 15% for electro-thermal and in situ combustion projects. The responses show that a slight reduction in energy consumption is expected by experts from the adoption of hybrid steam- solvent processes. Experts projected that emerging in situ technologies, which have the largest potential for adoption, will be used primarily for accessing marginal resources and increasing overall production levels, rather than targeting greenhouse gas emissions reductions. Therefore, deployment of emerging technologies is not expected to contribute substantially to meeting greenhouse gas emissions reduction targets for the industry by 2034 under the regulatory conditions at the time of the elicitation, a key insight for policy makers.

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Introduction

The Canadian oil sands, with 166 billion barrels of remaining established reserves (AER 2015), are a vast oil resource whose extraction and processing have significant environmental and economic consequences (RSC 2010). Several oil sands technologies currently under development have the potential to influence industry-wide greenhouse gas (GHG) emissions. A limit on GHG emissions is anticipated (AB 2015) and the industry faces external pressures to limit or reduce its emissions. While experts within the industry possess knowledge about the potential performance and deployment of these emerging technologies, limited public information is available, which limits policymakers’ ability to effectively target the deployment of technologies for reducing the GHG emissions intensity of bitumen production. To address this, an expert elicitation was carried out to obtain input from experts on how they predict emerging technologies will be deployed in the oil sands over the next 20 years and the associated implications for GHG emissions. Below, background information is provided on oil sands production processes, expectations about future oil sands production, the industry’s contribution to GHG emissions and associated regulations, literature on emerging oil sands technologies and GHG emissions, and the expert elicitation method.

Bitumen, the extremely viscous oil present in oil sands deposits, can either be extracted through surface mining, or, for deeper deposits, in situ methods. In surface mining, oil sands are dug using shovels and transported to a facility where hot water is used to extract bitumen from the sand. Current in situ methods include steam-assisted gravity drainage (SAGD) and cyclic steam stimulation (CSS), SAGD being the more common method for recent oil sands projects. Both in situ methods involve injection of steam, generated primarily through the combustion of natural gas, into the reservoir to mobilize the bitumen in place (for additional information see Appendix C). The GHG emissions from oil sands projects is closely linked to the project’s energy consumption. For in situ methods the ratio of steam injected to bitumen produced, the steam-to- oil ratio (SOR), is a key indicator of a project’s environmental and economic performance, accounting for up to 76% of recovery, extraction, and upgrading GHG emissions (Charpentier et al. 2011).

Once bitumen has been recovered it is either upgraded to synthetic crude oil (SCO) or diluted with a lighter hydrocarbon to produce dilbit so that it can be transported by pipeline to a refinery.

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Current upgrading technologies convert bitumen to SCO either by adding hydrogen (hydrocracking) or removing carbon (coking) to break up large molecules and remove some or most of the sulfur. Upgrading bitumen to SCO is a more energy and GHG-intensive process than dilution, however across the life cycle this increase is slightly offset by higher refining GHG emissions for processing dilbit compared to SCO. Life cycle GHG emissions for upgrading and dilution were found to range from 98-121 g CO2eq/MJ gasoline and 94-113 g CO2eq/MJ gasoline, respectively (Choquette-Levy et al. 2013).

In 2014, 0.91 million barrels per day (bpd) of bitumen were produced through mining and 1.24 million bpd from in situ operations (CAPP 2015). Even with today’s low oil prices, expansion of production is expected to continue, although at a more moderate rate than previously projected. Current production forecasts predict that in the near-term (2015-2019) most approved projects will continue to be built but no new projects will be announced; over the longer-term (2020- 2030) growth in bitumen production will recommence, with bitumen production reaching 3.95 million bpd by 2030 (CAPP 2015).

The oil sands were responsible for approximately 22% of Alberta’s GHG emissions in 2013 (AB

2015); 8.5% of Canada’s 726 Mt CO2eq of GHG emissions in that year (EC 2015). The Alberta government has adopted legislation that by 2030 would cap total GHG emissions from the oil sands at 100 Mt CO2eq per year (with some exemptions considering cogeneration and new upgrading capacity), and would include a $30/tonne CO2eq carbon tax on oil sands facilities (AB 2015), imposing both a limit on absolute industry-wide GHG emissions and providing an incentive for individual producers to reduce the GHG emissions intensity of their projects.

Since the first deployment of SAGD in 1996, most investment in the development of new technologies has been targeted at making incremental improvements to overcome some of the technical, economic, and environmental challenges faced by the industry such as declining reservoir quality (AB 2015). Many emerging technologies are suitable for use in reservoirs where the physical reservoir characteristics make existing technologies infeasible. Much of the recent investment has focused on new in situ methods that produce bitumen at lower cost by reducing natural gas required for steam production. By decreasing (or eliminating) the need for natural gas, these technologies have the potential to reduce the industry’s GHG emissions. In situ technologies currently under development (predominantly at the field test or pilot scale stages)

136 that are considered promising include; hybrid steam-solvent processes, in situ combustion, and electro-thermal (see Gates and Wang (2011) and Appendix C for descriptions of these technologies).

While emerging oil sands technologies may facilitate GHG emissions reductions, limited work has been undertaken to estimate this potential. While Bergerson and Keith (2010) completed an initial, qualitative comparison of emerging technologies being developed and implemented in the oil sands, a more detailed understanding of the anticipated operating parameters of each technology is required. Jacobs and Suncor (2012) used individual existing SAGD, mining, and upgrading projects as representative of typical facilities and found that operational, project, and technology improvements can contribute 2, 9, and 20%, respectively, to GHG emissions reductions from in situ projects (2, 5, and 30% for mining and two, six, and 10% for upgrading, respectively). In CERI (2015), projections of GHG emissions from all oil sands activities were developed to 2050. Point estimates of emissions for emerging technologies relied upon public data from company reports. CERI (2015) projected that increased energy efficiency from technology learning and innovation can reduce energy consumption from the industry by up to 30% from the business as usual scenario, but no process changes or specific emerging technologies were identified as contributing to the potential increase in energy efficiency predicted. An expert panel convened by the Council of Canadian Academies published a report (CCA 2015) based on publicly-available sources on the potential for current and emerging technologies to reduce the environmental footprint of oil sands development. The panel prioritized technologies with the greatest potential to reduce the footprint in the next 15 years (CCA 2015). Although some technologies were identified as having the potential to reduce the GHG emissions intensity of oil sands operations, no combination of emerging bitumen production and upgrading technologies were found to provide the opportunity to reduce absolute industry-wide GHG emissions in the next 15 years at current rates of industry expansion. The report’s scope is broad in that a wide set of technologies were examined. While the report reflects experts’ judgments, this was not done systematically across all technologies so ranges of expert opinions on each technology were not obtained. There remains a subset of expert knowledge across industry, academia, and government about emerging oil sands technologies that has not been captured in previous studies.

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Expert elicitation is a formal method that allows data to be collected consistently across experts. Applying this method allows collection and dissemination of some of the knowledge possessed by the industry about the potential performance of emerging technologies. The results of the expert elicitation can inform effective regulatory development, enabling governments to meet GHG emissions caps or reduction targets. Expert elicitation has been used to assess the anticipated costs (e.g., Abdulla et al. 2013) and technical performance parameters (e.g., Curtright et al. 2008) of emerging technologies to complement limited available data or when uncertainty associated with a prediction is high but has not been used to assess emerging oil sands technologies. Rainville et al. (2015) used similar methods to evaluate the potential for a Canadian product standard to increase the consistency across and credibility of life cycle emissions estimates of oil sands products.

Prior work (McKellar et al. 2017) included an expert elicitation that obtained judgments on the direction the oil sands industry is heading over the next twenty years (2013-2033), including the future industry average, cumulative steam-oil ratio (cSOR), expected GHG emissions intensity reductions, and the drivers responsible for those changes. The elicitation did not focus on specific technologies but did distinguish between technology changes and incremental process changes. One of the key findings was that technology changes are expected to play a large role in reducing the GHG emissions intensity of oil sands projects, particularly in the in situ area (see McKellar et al. 2017).

The objective of the current paper is to inform policymaking, technology development, and stakeholder discourse on the potential of specific emerging technologies to reduce the GHG emissions intensity of oil sands projects in the next 20 years. Using expert elicitation to capture some of the industry knowledge that is not currently reflected in the public literature, the following is presented: (1) insights on how a set of industry experts anticipate emerging recovery, extraction and upgrading technologies to be deployed in the oil sands over the next 20 years; (2) quantitative estimates of how the experts anticipate these technologies will perform compared to current technologies and existing, publicly-available pilot data for emerging in situ technologies; (3) opportunities for and barriers to the deployment of emerging technologies in the industry; and (4) recommendations for how decision-making can be informed by insights from the experts’ responses. The results of this study should not be taken as a forecast of how specific technologies will be deployed or perform, but instead viewed as an indication of the

138 challenges and opportunities involved in deploying emerging technologies in the industry at this point in time.

Material and Methods

An introductory stakeholder workshop was held in January 2012, with 15 representatives from seven oil sands companies in attendance. The goal was to introduce the overall project objectives, and the expert elicitation methodology to a sub-set of the experts who would be invited to participate in the project. The overall project consists of the work presented in McKellar et al. (2017) and that presented in this paper. In McKellar et al. (2017), experts were asked about the role of technology in driving GHG emissions intensity changes, but no questions were asked about the specific technologies that would contribute to those changes. The potential roles of specific emerging technologies are investigated in a subsequent survey, the subject of this paper.

6.3.1 Survey 2 Development

Survey questions about emerging technologies were developed for each of: in situ bitumen production, surface mining, and upgrading, with the majority of the questions being directed towards in situ operations, the area most targeted by developers of emerging oil sands technologies. Experts could choose to answer questions about any or all of the three areas and could choose to skip any individual question within an area that they did not feel comfortable providing a response. The full survey is included in Appendix D.

A summary of the current and emerging technologies included in this study is shown in Table C- 1 (for additional information about the specific technologies see Appendix C). Technologies were then divided into two categories: 1) incremental process changes, which are currently available but not yet widely deployed; and 2) emerging technologies, which are not yet commercially available or deployed (similar to categories in Suncor and Jacobs 2012). The survey requested input from experts on three emerging in situ technologies: hybrid steam-solvent processes, in situ combustion, and electro-thermal processes (see Appendix C for explanation of how these technologies were selected). The in situ emerging technologies included in the survey are similar to those included in literature assessments of emerging technologies (e.g., Bergerson and Keith 2010; Boone et al. 2012) and the upgrading and mining technologies are a subset of

139 those discussed in CCA (2015). For each technology-related question, experts had the option to specify an “other” technology that they believed should be included (e.g., biological processes). This technology could be specified in the optional comment box at the bottom of each question page.

Thirty questions were posed to experts, 21 about in situ operations, six about surface mining, and three about upgrading. For each question, experts could choose to add comments to clarify or expand upon the responses they provided. Experts could choose to answer questions in any or all of the three areas and could choose to skip any individual question for which they did not feel comfortable providing a response. For each of in situ, mining, and upgrading, three types of questions were asked of experts to obtain: i) rankings of technologies and process changes based on the level of impact each is expected to have on energy consumption; ii) rankings of barriers to the adoption of emerging technologies and incremental process changes; and iii) feedback on required and expected project economics. For in situ, a fourth type (iv) of question was asked to obtain quantitative forecasts of the performance of specific technologies. For mining and upgrading, experts were asked to respond to more general questions and no specific feedback was asked about particular emerging technologies, since no emerging mining or upgrading technologies have been developed (i.e., tested through pilot or demonstration projects) to the extent that in situ technologies have been. More detail on each question type is provided below.

When experts were asked to forecast quantitatively the performance of in situ technologies, they were asked to show how they believe energy consumption will compare to that of current SAGD operations (when the technology is comparable) or to existing pilot projects (when a comparison to SAGD could not be readily made). The GHG emissions intensity of a project is closely tied to its energy consumption. Experts were asked to build projections of the energy consumed by projects employing emerging technologies. The rationale is that information about energy consumption is more likely to be available than projections of a project’s GHG emissions. Based on the experts’ projections of energy consumption of projects employing emerging technologies, insights are derived about the potential for these technologies to reduce the GHG emissions intensity of bitumen production and upgrading.

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6.3.2 Ranking technology and process changes (Type i)

Experts were asked to identify and rank (separately), the emerging technologies and incremental process changes they felt would have the biggest impact on reducing the energy consumption (e.g., SOR, electricity consumption) of a project in the next 20 years. This type of question was asked for: in situ incremental process changes, surface mining emerging technologies, and surface mining incremental process changes. Experts could rank technologies/process changes from a list provided, and add additional technologies/process changes to the lists using the “other” ranking card. For emerging in situ and upgrading technologies, experts were instead asked to predict the fraction (percentage of total bitumen production) that each technology (including current and emerging technologies) will contribute by 2034.

6.3.3 Identifying barriers to the adoption of emerging technologies and incremental process changes (Type ii)

Experts were asked to “identify and rank the primary barrier(s) [they] think are preventing or delaying the commercial deployment of this [technology or incremental process change]”. This was asked for the following categories: in situ incremental process changes, emerging in situ technologies (hybrid steam-solvent processes, in situ combustion, electro-thermal processes), mining incremental process changes, and emerging mining technologies. For each category experts could identify “other” barriers not included in the list provided in the survey and include those in the overall ranking.

6.3.4 Project economics (Type iii)

For each category, experts were asked what internal rate of return (IRR) they would require, if they were investing at the time the survey was deployed, in a new oil sands project employing an emerging technology or process change (e.g., electro-thermal processes, one or more incremental process changes in a mining project). To establish a baseline for comparison, experts were also asked the IRR they would require to invest in a SAGD (for in situ technology questions) or mining (for surface mining emerging technology and incremental process change questions) project. Experts were then asked what production cost they thought would be incurred by a project employing the emerging technology or process change, compared to an average (2014) SAGD production.

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6.3.5 Quantitative responses on specific technologies (Type iv)

Experts were asked specific questions about the anticipated performance of the three emerging technologies identified in Section 6.3.1: hybrid steam-solvent processes, in situ combustion, and electro-thermal processes, with an additional “other” technology category where experts could provide input on a technology of their choosing. Whenever possible, experts were asked to compare an emerging technology’s performance to that of a current (2014) SAGD project (e.g., SOR for a project employing hybrid steam-solvent processes compared to the SOR for a SAGD project). When no comparable metrics (except bitumen recovery rates) to SAGD could be used for emerging technologies, experts were asked to compare commercial projects employing the emerging technology to published data on existing pilot projects employing that technology. The pilot project data were included within the survey questions. For example, for electro-thermal operations experts were asked what electricity consumption they expected to be achievable at more than one commercial project by 2034; published electricity consumption from an existing electro-thermal pilot project was provided in the survey question to give experts a baseline from which to provide their input.

The phrasing “more than one commercial project” was employed to exclude pilot projects or other unique scenarios where performance of the technology may not be reflective of that likely to be obtained in multiple commercial deployments due to project-specific conditions such as location (e.g., optimal reservoir conditions for that technology) or operating conditions (e.g., especially high energy consumption per barrel of bitumen produced due to unexpected project shutdowns).

6.3.6 Survey Deployment

The survey was deployed using Near Zero’s online elicitation tool. Fifty-five experts were invited to participate in Survey 2. The first experts were invited on April 23, 2014 and the last survey was completed on June 2, 2014.

6.3.7 Analysis

For questions that asked experts to rank barriers or technologies (types i and ii), rankings from all the experts who responded to each question were aggregated using the Global Rank Method presented in Zickfeld et al. (2007). For each factor (barrier or technology), a global rank was

142 assigned based on the number of times a factor was ranked ‘1’ by experts. The factor with the most ‘1’ rankings by different experts was ranked ‘1’ overall, with the factor with the second most ‘1’ rankings assigned a ‘2’ ranking overall, and so forth, until all factors were assigned a global rank. If two factors had the same number of ‘1’ rankings, the factor with the most ‘2’ rankings would be assigned the higher ranking.

For quantitative questions (type iv), experts used the Near Zero survey tool to generate box plots to represent the minimum, 25th percentile, median, 75th percentile, and maximum values that they expect would be achieve by 2034. For the commercial SAGD projects in 2034 employing process changes, instead of boxplots, experts were asked for their input on what they expect the average SOR would be as well as an 80% confidence interval for the SOR that they expect. For the questions about project economics (type iii), data were compiled by the authors across all technologies and categories and general insights were provided about the project economics experts believe would be required for a project employing an emerging technology to go forward. Survey responses related to in situ technologies are presented and discussed below, while responses related to surface mining and upgrading technologies are presented in the Appendix C.

Results and Discussion

Eleven experts completed the survey (response rate of 20%): six from industry (representing four oil sands companies), three from academia, and two from government agencies. While it would have been optimal to have a higher response rate, recruiting experts to participate was challenging due to several factors: 1) not many experts felt they had the expertise to answer quantitative questions about the future performance of specific emerging technologies; 2) many experts identified by the authors were unable to participate due to confidentiality constraints imposed by their organizations; 3) to lessen bias, experts with more general expertise on emerging technologies were targeted rather than those who work at companies directly involved in the development of any of the emerging technologies being considered, which may have exacerbated the first challenge. Other expert elicitations of emerging technologies have also obtained similar or slightly higher numbers of responses (12: Rao et al. 2006; 13: Chan et al. 2011; 16: Abdulla et al. 2013; 18: Curtright et al. 2008) and similar response rates (10.7%: Subramanian and Golden 2016). According to a panel on expert judgment, based on the

143 panelists’ experience the targeted number of experts for most studies they conducted was between six and 12, and that beyond 12 experts the additional benefit of including more experts declines significantly (Knol et al. 2010; Cooke and Probst 2006). Survey participants ranked their oil sands-related experience over a range from five years or less to 11-15 years. A summary of characteristics of the participants is provided in . The names and affiliations of the experts have been kept confidential, both to respect confidentiality concerns raised by experts but also to prevent attribution of the views expressed to specific organizations. The views expressed are those of the experts who participated and are not representative of an entire organization’s views or expectations of the future. Other expert elicitations have kept expert identities confidential (Abdulla et al. 2013; Bates et al. 2016).

Projections made about emerging technologies are inherently uncertain and the expert elicitation method has its own limitations. The survey responses capture a range of perspectives from individuals at several different organizations. The results improve on the current information available about these technologies. However, as only a subset of those involved in the oil sands industry were targeted, results should not be interpreted as a reflection of the expectations of the entire industry. The term “expert” has been used to refer to survey participants with experience and expertise in the oil sands industry with a broader range of knowledge about emerging oil sands technologies rather than experts on particular emerging technologies. As such, the survey results largely reflect industry perspectives on technologies close to commercialization. The results of this study can be used to provide insights about perceived trends in technology development, fill existing gaps in engineering and LCA studies, and provide a framework to allow for regular updating as industry conditions change. Other studies (e.g., Subramanian and Golden 2016) have used expert elicitation to fill data gaps in life cycle inventories and have identified similar study limitations.

The responses to the survey questions are reported and discussed in Sections 6.4.1 to 6.4.6 below. For in situ combustion only one expert provided responses to the quantitative survey questions so that information is included in Appendix C.

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6.4.1 Technology and Process Changes Ranking: In Situ Bitumen Production

Experts were asked what they expect to be the contribution to overall bitumen production by 2034 of current and emerging in situ technologies. Experts were provided with a list of technologies to choose amongst: current, hybrid steam-solvent processes, in situ combustion, and electro-thermal (the latter three being the most promising emerging in situ technologies identified in Section 2.1). Experts also had the option of identifying other emerging technologies and in completing the survey suggested adding the following to the list: electromagnetic, shallow SAGD, SAGD in carbonates, gas co-injection, alternate well architecture, solvent-only processes, and “steam-additive (additive=surfactant, alkaline)” processes. Further, two experts suggested removing in situ combustion from the list of technologies with the potential for commercial deployment in the next 20 years.

As shown in Figure 6-1, all but one expert felt that hybrid steam-solvent processes would account for the widest deployment of an emerging technology, but still would only comprise 9- 40% of total production. Hybrid steam-solvent processes are the emerging technology most similar to current steam-based processes, so there are limits to the GHG emissions reductions possible through deployment of this technology. Only three of the 11 experts felt in situ combustion would contribute to overall bitumen production. Most (nine) experts believed that electro-thermal will contribute a small fraction (typically 5-10%) of overall bitumen production, with one expert responding that electro-thermal would contribute a more significant fraction (40%) by 2034.

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100

40 Production(%)

20 Percent Percent Overall of InSitu Bitumen 0 1 2 3 4 5 6 7 8 9 10 11 12 Expert

Current Hybrid Steam Solvent Electrothermal In Situ Combustion Other

Figure 6-1. Experts’ responses on the percentage of in situ bitumen production from current and emerging technologies in 2034 Survey question: In 20 years, what percentage do you think each technology (including current and emerging technologies) will contribute in terms of overall in situ bitumen production?

Experts were asked to identify and rank the solvent(s) they thought would be most widely used to assist with in situ. Using the global rank method, the overall ranking for solvents from highest to lowest was: gas condensate, butane, naphtha, propane, Jet B, xylene, and CO2 (CO2 identified as an “other” solvent by one expert). For each solvent, experts were asked if they believed solvent recovery and recycling (for co-injection) would be employed. Solvent recovery and recycling was expected by most experts (ranging from four of seven experts responding yes for gas condensate to all experts responding yes for butane, propane, and naphtha). Factors that the study authors felt could impact the choice of solvent were listed and experts were asked to rank them. The overall global rank for the different factors (from most to least impactful) were as follows: solvent cost, reduction in SOR, improved overall reservoir recovery rate, solvent availability, solvent recovery rate, and “other,” identified as “solvent recovery rate and selective partial upgrading of mobilized bitumen” by one expert.

For in situ process changes, experts were asked to rank those that they expected to have the biggest impact on energy consumption of a commercial in situ project by 2034 (responses summarized in Table 6-1). The in situ process change expected to have the biggest impact on energy consumption was better well placement. This change would allow the steam injected to

146 more efficiently mobilize bitumen in the reservoir. The next highest in situ process changes related to efficient steam generation (improved boiler efficiency and maximized steam quality) and better handling of produced water on site.

Table 6-1. Experts’ ranking of the technology or process change that will have the biggest impact on energy consumption of a commercial oil sands in situ project by 2034 In Situ In Situ Process Changes Emerging Technologies 1. Better well placement 1. Hybrid steam-solvent processes 2. Improved boiler efficiency 2. Electro-thermal 3. Improved handling of produced water 3. In situ combustion 4. Maximize steam quality 4. Other 5. Low-carbon alternatives to natural gas i. Electromagnetic 6. Other ii. Shallow SAGD i. Solvents iii. SAGD in carbonates ii. Better facility heat integration and utilization iv. Gas co-injection of waste heat streams v. Alternate well architecture iii. In-well control devices vi. Solvent-only processes iv. Low-carbon alternatives vii. Steam-additive (additive=surfactant, v. Solvents and surfactants as additives to steam alkaline) processes

6.4.2 Barriers to the Adoption of Emerging Technologies

Technology uncertainty, uncertainty about the potential performance of an emerging technology once deployed commercially, was consistently ranked as the biggest barrier preventing the adoption of emerging in situ technologies. Availability of capital to fund research and development, regulatory issues, and “other” (write-in) barriers were 2nd, 3rd, and 4th, respectively for the adoption of any emerging in situ technology (not specific to a technology). The full set of barriers ranked by experts is presented in Table 6-2, including 19 “other” barriers experts identified. For the specific in situ technologies (hybrid steam-solvent processes, in situ combustion and electro-thermal), highly-ranked barriers included level of technology development (how close a technology is to commercial deployment), cost factors including solvent/electricity costs and availability (for solvent processes and electro-thermal), and “other” barriers identified by experts. “Other” barriers provided by experts included: uncertainty/unpredictability of new technologies combined with risk of failure; culture of non- innovation and reluctance to entertain new ideas; reservoir uncertainty difficulty modeling emerging technologies and high costs associated with field tests; and cost for key inputs (e.g., solvents, electricity).

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Table 6-2. Experts’ ranking of the primary barriers to the adoption of emerging technologies and incremental process changes Technology Ranking of barrier In situ – 1. Technology uncertainty process 2. Availability of capital to fund research and development changes 3. Regulatory issues 4. Other: Competing capital for technology deployment, not just R&D Other: Uncertainty/unpredictability of new technologies Other: Limited number of opportunities for testing Other: Willingness to entertain new ideas In situ – 1. Technology uncertainty technology 2. Availability of capital to fund research and development 3. Regulatory issues 4. Other: Lack of operational experience leads to reluctance Other: Risk of failure too high due to technology uncertainty Other: Development cost Other: Lack of effort toward conceptualizing novel technologies Other: Culture of non-innovation Solvents 1. Solvent cost 2. Reduction in SOR 3. Improved overall reservoir recovery rate 4. Solvent availability 5. Solvent recovery rate 6. Other: Solvent cost is the primary barrier. If it were cheaper, It would help off-set the risk around solvent returns, which is the key technology uncertainty Other: Regulatory uncertainty In situ 1. Technology uncertainty combustion 2. Technology development 3. Cost factors 4. Other: Reservoir uncertainty Other: Limited body of knowledge on in situ combusion Other: Lack of ability to properly model the process (and high cost of field pilots) Other: Quality of oil produced (LTO dominated products) Other: Uncertainty about what the concept is Electro- 1. Technology uncertainty thermal 2. Technology development 3. Electricity pricing 4. Electricity availability 5. Other: Reservoir response Other: Electricity pricing is a large barrier if one of these processes is used to completely replace SAGD. However, I rank technology development and uncertainty even higher because the commercial concept(s) aren’t fully defined yet. I expect solutions in this space to augment rather than replace SAGD, in which case electricity pricing will be less of a barrier

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Other: Impact on cap rock Othera 1. Technology uncertainty 2. Cost factors 3. Technology development 4. Other: performance of NCG coinjection is impaired by inability to properly model, so performance uncertainty is large. However, the field piloting and likely commercial implementation cost is relatively low compared to other technologies, so cost factors are less of an issue. Barriers noted as “Other” were written in by experts on the survey response page, remaining barriers listed were identified in the survey. aOther technology: non-condensible gas co-injection. 6.4.3 Project Economics

Generally, experts responded that they would require the same minimum IRR to invest in an emerging in situ technology project, as they would require to invest in a SAGD project, regardless of the technology employed (see Table C-2). About project economics, one expert said, “I would only slightly reduce the production cost from the current average, because new technologies will be applied to both improving competitiveness of current reserves and for enabling the production of more marginal resources. In other words, I believe the technologies will be used more to increase production volume than to drive cost out of production of higher quality reservoirs.” Experts were also asked what they expected production costs to be for commercial projects employing process changes or emerging technologies, compared to a reference SAGD price (see Table C-2). Experts generally expected production costs for in situ projects employing incremental process changes or emerging technologies to have similar or slightly lower production costs compared to current (2014) SAGD production costs, indicating that several experts shared the belief that emerging technologies will be used primarily to boost production volumes by accessing marginal resources rather than as lower-cost alternatives to current technologies. Experts seemed to be indicating that they perceive technology development to remain focused on economic competitiveness by lowering extraction costs for projects that would not be economic using SAGD, rather than technologies that result in large absolute reductions in GHG emissions. That is, the GHG emissions mitigation policy forecasts that presumably underlay the experts’ judgments were not expected to be stringent enough to prompt technology development that might be costlier but more effective at reducing emissions.

Since the last expert completed Survey 2 (June 2, 2014) oil prices have decreased substantially (from $95.55 on June 1st, 2014 to$43.56 on October 31st, 2016 for Canadian Heavy Hardisty, prices in Canadian dollars) (NRCan 2016). The lower oil prices have implications for operating

149 costs and revenues of oil sands projects, impacting IRR. However, the relative difference in the experts’ required IRR between existing technologies and emerging technologies remains relevant at different oil prices (e.g., lower oil prices are not expected to change experts’ comments about the similarity in minimum IRR required for current and emerging technologies).

Similarly, the recent changes in industry production forecasts are not expected to significantly alter experts’ forecasted contributions of different technologies for new oil sands projects. The change in oil prices has prompted several industry stakeholders to adjust their projections of oil production rates over the time horizon considered in this analysis (up to 2034). For example, CAPP (2015) reduced their projections for total Canadian oil production by 1.1 million bbl/day by 2030 compared to their 2014 forecasts with relatively minor reductions in growth projections until 2020. Because of the long time horizon for developing oil sands projects and their considerable capital costs (particularly for mining), changes in bitumen production rates in response to fluctuations in oil prices tend to be smaller than for conventional oil projects. CAPP (2015) forecasts that existing projects and those currently under construction will continue but that future projects are more uncertain. As low oil prices would not likely change the rank of emerging technologies (they are already ranked by cost competitiveness), projections about relative level of deployment (as a percentage of total bitumen produced in a given year) of the different emerging technologies today compared to 2014 when the survey was conducted are expected to be similar. However, with operations continuing at existing projects and fewer new projects coming online, deployment of emerging technologies is likely to slow down as a fraction of the total volume of bitumen produced.

6.4.4 Quantitative Responses about Specific In Situ Technologies

For incremental process changes to in situ projects (e.g., better well placement), experts were asked to provide input on the extent to which these changes could impact SOR in 2034, given that the SOR range for current (2014) projects was generally between 2.2 and 3.3 (Charpentier et al. 2011). The responses are summarized in Figure 6-2. The average expected industry-wide SOR in 2034 estimated by the experts was 2.4, and the 80 percent confidence interval was 1.9 to 2.9, a slight decrease from the range provided for SOR of current (2014) projects. According to one expert, “gains will be offset somewhat by declining reservoir quality.” Approximately half the experts believed the SOR range will be reduced, the other half felt it will be similar or slightly higher.

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Figure 6-2. Experts’ projections of expected industry-average anticipated SOR achievable in 2034 once process changes are adopted. Error bars represent 80% confidence interval. Dashed horizontal lines represent the SOR range for current (as of 2014) projects provided to the experts for reference

All but one expert forecast that SOR for hybrid steam-solvent projects will be at least 15% lower than that of a current (2014) SAGD project (see Figure 6-3a). Significant variability was seen across experts in terms of their projections of the expected reductions in SOR. The median reduction in SOR projected by individual experts ranged from 3-30% (or an average of 20% across all experts. Expert 6 projected the largest range in expected change in SOR (from a maximum reduction of 40% to a 30% increase in SOR, however this expert also specified in other survey questions that they expect the deployment of emerging technologies to be targeted at accessing marginal resources. Most experts anticipate solvent losses to the reservoir, which decreases the overall financial and GHG emissions performance of this technology, will be less than 50% of the total solvent injected into the reservoir (Figure 6-3b). The bitumen recovery rate (fraction of bitumen recovered from a reservoir out of the total bitumen present in the reservoir) for hybrid steam-solvent processes is expected, on average, to be higher than that of a SAGD project (Figure 6-3c). According to the expert who included the lowest percentage change on the lower bound of the range, “I believe some solvent processes will have worse performance than today’s SAGD projects (higher SOR, lower recovery rate) because of the ability of technologies to enable production from more marginal resources.” No direct conclusions can be drawn from this about an expected change to GHG emissions from the adoption of hybrid steam-solvent

151 processes, as lower GHG emissions from a reduced natural gas consumption due to lower SOR will be offset by the indirect upstream GHG emissions from solvents injected to the reservoir that are not recovered during operation. Experts who were more optimistic about the SOR reductions achievable by hybrid steam-solvent projects tended to also expect lower rates of solvent loss to reservoir, both factors which would result in lower GHG emissions and more favourable economic performance of that technology.

For quantitative electro-thermal and in situ combustion questions, experts were asked to fill in the ranges of expected values for a few key operating parameters to be achieved by 2034 at more than one commercial project. For comparison, experts were provided with operating data for these parameters based on public pilot data. For electro-thermal, the E-T Energy ET-DSP pilot project was selected (McGee 2009). For in situ combustion, the reference data provided were obtained from the design data in the December 2008 Application for Approval for Petrobank's May River THAI project (Petrobank 2008).

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a) Percentage change in SOR Percentage change in SOR achievable by 2034 at more than one commercial hybrid steam-solvent project compared to a current (2014) operating SAGD project.

b) Rate of solvent loss to reservoir Rate of solvent loss to the reservoir (solvent not recovered with bitumen) achievable at more than one commercial hybrid steam- solvent project by 2034.

c) Change in bitumen recovery rate Percentage change in bitumen recovery rate achievable at more than one commercial hybrid steam- solvent project by 2034 compared to a current (as of 2014) operating SAGD project.

Figure 6-3. Experts’ projections of the expected performance of hybrid steam-solvent processes. Box plots show the minimum, maximum, median, and 25th and 75th percentiles reported by each expert.

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Expert responses to the electro-thermal questions are shown in Figure 6-4a (electricity consumption during the operating stage compared to the pilot project) and Figure 6-4b (change in bitumen recovery rate compared to a currently operating SAGD project). Expert responses for anticipated electricity consumption were higher than the pilot project for two of the three experts. The third expert had a very wide range (0-800 kWh/m3 oil) with the average slightly below the pilot (400 versus 434 kWh/m3 oil), and included the justification that, “I put zero as the lower bound on electricity during the operating stage given that these processes may be used ahead of the operating stage”. Comparing the expected bitumen recovery rate of electro-thermal projects to that of current SAGD, experts projected that it will be similar, or slightly better. For electro- thermal bitumen recovery rates, the same expert who commented on hybrid steam-solvent process recovery rates said: “again, my dive into negative territory on rate is because the technology will be applied to more marginal resources than today’s producing projects”.

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a) Electricity input during operating stage Expected electricity input during operating stage achievable at more than one commercial electro- thermal project by 2034.

b) Change in bitumen recovery rate Percentage change in bitumen recovery rate achievable at more than one commercial electro- thermal project by 2034 compared to a current (as of 2014) operating SAGD project.

Figure 6-4. Experts’ projections of the expected performance of electrothermal projects Box plots show the minimum, maximum, median, and 25th and 75th percentiles reported by each expert. Dashed horizontal line in Figure 4a shows the reported electricity input for a pilot eletro-thermal project provided to the experts for reference.

One expert provided a response to questions about the potential operating performance of in situ combustion (response recorded in Table C-4). While no explanation was provided for the factors that would contribute to these changes in energy consumption for a commercial in situ combustion project compared to the pilot data provided, the expert forecasted that the May River pilot project represented the lower end of the natural gas and electricity consumption that would be expected at future commercial in situ combustion projects, but that commercial in situ combustion projects will have significantly lower produced gas compared to the pilot project (between 108 and 493 m3 produced gas per m3 of oil for commercial projects compared to 1483 for the pilot). Other experts provided responses to the question about the barriers to the

155 commercial deployment of in situ combustion. According to one expert who did not provide a response about the expected operating performance of in situ combustion, “the limited body of knowledge of in-situ combustion, combined with the lack of ability to properly model the process (thus requiring large expensive field pilots), combined with the large uncertainty about what the technology concept actually is combined with the unknown about what the produced oil properties are or how they will vary, makes even the smallest barrier here larger than the largest barrier for solvent processes. Just to make the point that 'biggest' and 'smallest' [barriers] may not be the same across all the technologies.” The lack of quantitative estimates on the anticipated performance of in situ combustion may reflect a lack of exposure across the industry to this technology and larger uncertainty about its potential performance compared to other emerging in situ technologies included in this survey. These aspects were identified by the expert who responded about barriers to the adoption of in situ combustion (above). Five experts responded to a question about the barriers to the commercial deployment of in situ combustion but only one provided a response regarding the quantitative performance of the technology. These response rates are consistent with the opinion of the expert above that the barriers to the deployment of this technology are currently more significant than those faced by other emerging in situ technologies included in this survey.

In 2014, the year the survey was conducted, the industry faced an intensity-based carbon levy of

$15/tonne CO2eq on large industrial facilities emitting over 100,000 tonnes CO2eq/year. Experts would have considered this regulation and likely the potential for more stringent regulations in the projections they made during the elicitation. For example, Royal Dutch Shell PLC already considers a $40/tonne CO2 carbon tax when evaluating the economics of new oil sands projects

(Lewis 2015). The planned $30/tonne CO2eq carbon tax (AB 2015) is likely not sufficient to trigger a greater focus on development and deployment of emerging technologies. Therefore, to meet the 100 Mt cap on absolute oil sands GHG emissions and make progress towards other climate targets, other regulatory options (e.g., more stringent intensity-based targets or more direct incentives associated with technology development) should be considered. These results are in general agreement with CCA (2015), which did not identify any bitumen production or upgrading technologies that would significantly affect absolute GHG emissions of the industry by 2030. These findings quantified the variability in expert judgment about specific technologies which can vary by up to 27% in emissions intensity expectations and 66% in extent of

156 deployment of any given technology. These results are also consistent with the results in McKellar et al. (2017), which found that while new technology was expected to drive GHG emissions intensity reductions, policy interventions (in the form of limits or higher prices on GHG emissions) were also highly important.

Conclusions

The expert elicitation yields relevant findings for policymakers. Experts believe emerging technologies will primarily be deployed to access marginal oil sands resources rather than to reduce GHG emissions. Experts do not believe ground-breaking technologies (i.e., a transition from steam and solvent-based bitumen production to bitumen produced by technologies that require no steam injection) will be widely deployed in the next 20 years. The emerging in situ technology identified as having the most potential for near term adoption is hybrid steam-solvent processes; however, experts felt that uncertainty about potential solvent losses to the reservoir and the high cost of solvents prevents this technology from being deployed more broadly without further motivation (e.g., economic or policy incentives). The experts’ projected performance of the technologies varied but generally most anticipate slightly lower energy consumption (e.g., median SOR reductions of 3-30% at commercial hybrid steam-solvent projects) and higher bitumen recovery rates (median increase in bitumen recovery rate of 3-30% for hybrid steam- solvent projects, 0-15% for electro thermal projects, and 15% for in situ combustion) compared to current (2014) SAGD projects or the provided pilot project data. The experts projected smaller reductions in SOR compared to current (2014) SAGD projects from the adoption of process changes, reducing the range of expected industry-wide SOR by 12-14% between 2014 and 2034. Experts’ responses indicate that they do not see changes to upgrading processes significantly affecting industry-wide energy consumption in the next 20 years. While some of the ranges of expert responses are fairly wide they are valuable for policymakers in terms of indicating future industry directions, and the basis upon which industry decisions are being made. Without significant policy intervention, technology is expected to play a minor role in reducing industry-wide GHG emissions; in which case emissions will only be reduced through reducing industry-wide bitumen production.

Experts identified several barriers that make wide scale deployment of emerging oil sands technologies challenging including: technology uncertainty, culture of non-innovation, high sunk

157 capital costs, and long planning horizons for developing new projects. These barriers make the risks associated with deploying a new technology more of a concern for investors and prevent a greater shift in technology away from those deployed commercially. The ranking of the barriers to adoption of the emerging technologies provides insights for policymakers by identifying what experts believe to be the primary obstacles to widespread commercial adoption. To promote the commercial deployment of emerging technologies, policymakers can focus on developing policies that address these barriers or provide incentives to companies who choose to employ emerging technologies. Today, with stricter upcoming climate regulations and low oil prices, oil sands companies face even greater challenges in deploying new technologies. Future expert elicitations can be employed to identify the potential benefits/costs of these technologies.

Data regarding emerging technologies are by their nature scarce and uncertain. Confidentiality about existing operations and emerging technologies adds further challenge when making projections about the industry’s future GHG emissions. It is therefore difficult to assess whether the Alberta and Canadian governments are likely to meet provincial and national GHG emissions reduction targets. However, insights into the expected contributions of emerging technologies and process changes are needed to inform policymaking at both levels, research and development investments and stakeholder discourse. By employing expert elicitation, this study was able to gather insights into; the expected contributions of emerging technologies and process changes, the most promising options, and, just as importantly, the barriers that are seen as impeding their deployment. The survey findings indicate that few technologies not yet commercially deployed may be adopted in the next 20 years in the industry and that experts generally anticipate that these technologies will perform similarly to, or slightly better than, current technologies in terms of energy consumption and operating costs or IRR. This suggests that policymakers should not rely on business-as-usual deployment of emerging technologies as the sole driver of desired GHG emissions reductions. Given the recent developments internationally (e.g., low oil prices, international climate change agreements) and the sensitivity of the oil sands industry to large- scale market and geopolitical forces, it is recommended that the expert elicitation framework applied here be updated and used regularly to gauge how experts’ judgments about emerging technologies change in the face of new policies and/or technology developments.

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Chapter 7 Conclusions

Production and use of transportation fuels derived from the oil sands are generally associated with higher life cycle greenhouse gas (GHG) emissions than those derived from conventional petroleum resources. This has implications for meeting GHG intensity-based targets and limits on industry-wide GHG emissions. Improved characterization of the GHG emissions associated with different oil sands projects and operating technologies and the potential for emerging technologies to influence emissions intensities helps inform the oil sands industry and policymakers about the impacts of oil sands development and opportunities to manage industry- wide GHG emissions. Changing market conditions (e.g., widening light/heavy crude differentials), evolving technologies (e.g., adoption of paraffinic froth treatment, PFT), and the adoption of new regulations (e.g., low-carbon fuel standards) all affect Alberta oil sands projects. As a result, oil sands operators are continuously adapting their operations and technologies employed, resulting in emissions intensities that vary both over time and across projects. Accurate characterization of the GHG intensity of crude production from the oil sands requires that sources of variability across the full well-to-wheel (WTW) be accounted for, to prevent unintended consequences where policies or the industry target emissions reductions in one stage of the life cycle that result in a net overall increase in life cycle emissions (burden shifting).

In this thesis, I present three studies that improve our understanding of the GHG emissions intensities of the life cycle of oil sands-derived transportation fuels. Key findings from each study are presented in Section 7.1 along with recommendations for oil sands operators, policymakers, and the broader life cycle assessment (LCA) community. In Section 7.2 I present opportunities for future work that extend or improve upon the work in the thesis.

Key Findings

For mining projects, historic emissions intensities of production are of interest due long project timelines (i.e., the oldest mining project began producing bitumen in 1967 and is still operating today) and wide variety of operating technologies employed, and range of oil sands products produced. In Chapter 4, I document the development and application of the mining and upgrading module of the GreenHouse gas emissions of current Oil Sands Technologies-

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Statistically-Enhanced (GHOST-SE) model. Despite the differences between mining projects, I find that intra-project variability in upstream GHG intensity exceeds interproject variability across all mining and upgrading projects, with median lifetime GHG intensities ranging from 89-

137 kg CO2eq/bbl SCO for projects that employ upgrading (versus 51 kg CO2eq/bbl dilbit). Temporal variability in GHG intensity over the 1983-2015 period was significant, and no project reached a steady-state in terms of GHG intensity in that time period, even projects that have been operating for several decades.

These results highlight the challenges in achieving year over year GHG intensity reductions, across the industry as well as at individual projects. For operators, a significant finding is that significant monthly variability in GHG intensity exists, even over a single operating year for a single project. In several cases, unusually high median GHG intensity within a specific operating year compared to previous and subsequent years can be directly linked to years where crude production drops (e.g., due to a project shut-down) or where project expansions are underway. Operators could potentially reduce both upstream median GHG intensity as well as variability in GHG intensity of their projects by focusing on minimizing the effects of those events on their projects. Year-over-year variability in upstream GHG intensity within an individual project has three significant implications for policymakers. First, no single operating year should be taken as representative of a project’s overall (lifetime) operating performance. Further, unless all other factors are accounted for (e.g., project expansions or shutdowns for maintenance), consistent year-over-year reductions in upstream GHG intensity may not be achievable by mining projects due to the effects these impacts have on median GHG intensities. Finally, projections of future industry-wide GHG emissions may be difficult to make due to uncertainty with respect to how projects will perform in the future relative to their current (or historic performance). Rather than taking a single, point estimate of GHG intensity from either an individual project or a single operating year as representative of an oil sands project or pathway (as has been done in some prior studies), policymakers should set the boundaries of their studies so that they are in line with their policy goals, to avoid inaccurate representations of a project or pathway in their study. Compared to past LCAs of oil sands mining and upgrading (and LCAs of petroleum resource extraction more generally) which typically aggregate results of individual projects into a pathway (e.g., a mining and upgrading pathway), my analysis disaggregates temporal and inter-project variability and demonstrates the utility of conducting LCAs on a project basis.

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To compare bitumen dilution and upgrading pathways on a common functional unit, and to compare the GHG intensities of oil sands crudes with a conventional petroleum baseline, in Chapter 5 the upstream model is integrated with crude transport and refinery models and vehicle use emissions estimates to complete the WTW analysis. As public crude assays are available for each mining project, the link between upstream operating decisions that dictate the properties of crude to produce and downstream processing emissions can be explored. Incorporating variability into LCAs of transportation fuels derived from the oil sands supports robust decision- making by oil sands operators, refiners, and policymakers by helping them target development of technologies with the greatest potential for life cycle emissions intensity reductions and incentivize production pathways with the lowest GHG intensity across the full WTW, without risk of unintended consequences due to lack of information.

The most significant findings of the WTW study are; 1) how variability in emissions from refining the full range of crudes produced from mining projects affects inter-project variability in WTW GHG intensities, and, 2) how crude quality and the impacts of allocating refinery emissions to products affect WTW results. Previous studies (e.g., Cai et al. 2015) that evaluated the WTW GHG intensities of oil sands crudes modeled refinery emissions using simplified refinery models that considered only whole crude API and sulfur content and considered only one representative SCO or dilbit (instead of accounting for variability in crude quality produced) and did not distinguish between froth treatment technologies in mining. I find that, across all crudes produced by oil sands mining projects, API gravity and sulfur content are not the sole indicators of a refinery’s GHG emissions, even amongst projects producing SCO (i.e., the highest-API SCO does not have the lowest refinery emissions per bbl crude). Allocation to refinery products also has a strong impact on WTW GHG emissions. Allocation to products is highly dependent on crude properties; most SCO refinery emissions are allocated to gasoline, while for dilbit, refinery emissions per MJ gasoline and diesel are similar. In the most extreme case, Project 4 has the highest median WTW GHG intensity across upgrading projects per MJ gasoline (114 g CO2eq/MJ) but the lowest per MJ diesel (99 g CO2eq/MJ), due to the higher refinery emissions for producing gasoline from this crude.

Based on these findings, I recommend reporting refining emissions for multiple refinery products (or the whole crude) to avoid incentivizing the production of a crude where refinery emissions are primarily allocated to products that are not included in the reporting. Emissions reductions

164 over the life cycle can be achieved by adjusting the properties of SCOs produced by upgraders so to minimize WTW emissions (optimizing crude properties that minimize net upgrading and refinery emissions). As operating data is reported for entire mining projects (i.e., not disaggregated between energy consumed for bitumen extraction versus upgrading), further analysis of the link between upstream GHG intensities and crude quality is constrained by the availability of public upstream energy consumption data for mining projects. Several opportunities for future work exist (see Section 7.2) that could improve these characterizations for each life cycle stage. Regardless, this study presents the most detailed and comprehensive characterization of variability in WTW GHG intensities of transportation fuels derived from mined bitumen.

To inform operators and policymakers of the role emerging technologies may play in meeting future GHG intensity-based targets or limiting industry-wide GHG emissions, an expert elicitation was conducted to identify the opportunities and challenges with the deployment of emerging technologies between 2014 and 2034. Compared to mining projects, in situ projects generally have lower capital costs, shorter timelines for development, and higher GHG intensities (due to natural gas consumed for steam generation). Several emerging technologies are under development that reduce (or eliminate) steam demand by reducing bitumen viscosity in situ through the use of solvents (or a combination of solvent and steam injection), electro- thermal heating, or in situ combustion. All experts surveyed expect that more than half (60-98%) of in situ bitumen production in 2034 will employ either current in situ technologies or hybrid steam solvent processes, with reductions in steam-oil-ratios of no more than 30%. Further, they predicted that emerging in situ technologies will primarily be employed to access marginal resources not economical to extract using current technologies, rather than being deployed to reduce the GHG intensity of bitumen production. These expert responses indicate that, by 2034, deployment of emerging technologies will play a minimal role in achieving GHG intensity reductions and as such cannot be relied upon for meeting GHG intensity reductions or industry- wide GHG emissions limits. This expert elicitation was conducted prior to the announcement of the 100 Mt CO2eq/year emissions limit proposed in the Alberta Climate Leadership Plan, which may provide additional incentives to industry to apply emerging technologies for GHG intensity reductions. To my knowledge, this is the first application of expert elicitation (alongside the related publication McKellar et al. 2017) to evaluate emerging technologies in the oil sands.

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There is currently much discussion within the LCA community about developing robust methods for characterizing the environmental burdens of emerging technologies once deployed at commercial scale that accounts for the uncertainty regarding future performance of emerging technologies. Chapter 6 demonstrates one possible approach for quantifying uncertainty with respect to the anticipated future performance of emerging technologies compared to current projects (or existing pilot projects), which could be extended to a full LCA of each of those technologies.

Future Work

Below, I outline several opportunities for future work that would improve characterizations of environmental impacts from oil sands development and help identify opportunities for GHG emissions reductions across the life cycle of oil sands-derived transportation fuels.

7.2.1 Quantifying Non-GHG Environmental Impacts

The scope of this thesis is restricted in terms of environmental impacts to global warming potential. However, producing and processing bitumen from Alberta’s oil sands has impacts on other aspects of the environment, including land use degradation, and the release of air and water pollutants that can impact wildlife and biodiversity. Oil sands projects or emerging technologies with the lowest life cycle GHG emissions may also be associated with higher overall environmental burdens when other metrics are considered and could be incorporated into future assessments.

7.2.2 Improvements to Chapter 4: Improved Characterization of Mining Project Fugitive GHG Emissions and Diesel Consumption

In Chapter 4, upstream variability in GHG intensity of mined bitumen is characterized on a project basis, employing monthly operating data from the AER’s statistical series (AER ST39/43; AER 2015), the most disaggregated data available for mining projects. As diesel consumption and fugitive GHG emissions are not reported by the AER, for these emissions sources, other operating data reported for different time horizons was employed. Although both diesel and fugitive GHG emissions are relatively small relative to total upstream GHG emissions (between 1-11% and 6-7% of upstream GHG emissions per bbl of crude produced for diesel and

166 fugitive emissions, respectively) due to data limitations the current analysis may be underestimating variability in emissions from diesel consumption and fugitive GHG emissions.

In GHOST-SE, fugitive GHG emissions for each project are obtained from annual fugitive GHG emissions reported for 2011-2014 operating years by the Alberta Environmental Monitoring, Evaluation, and Reporting Agency (AEMERA; AEMERA 2015). GHOST-SE samples directly from this annual data, with the probability of selecting each operating year equivalent to the fraction of total bitumen produced in that year (the same approach taken to define distributions based on the monthly AER data). AEMERA reports three sources of fugitive emissions: mine face, tailings ponds, and “other” (e.g., pipe fittings and equipment leaks). Fugitive emissions from tailings ponds primarily result from microbial fermentation of lost diluent, releasing both

CO2 and CH4. For other GHOST-SE parameters (besides fugitive GHG emissions from tailings ponds) monthly inputs are more directly related to the crude produced in that month. For tailings pond fugitive emissions there is some time lag between when diluent from bitumen extraction is released into the tailings ponds and when fugitive emissions are released from the tailings (up to 15 years; Foght et al. 2017). As a result, the treatment of fugitive emissions in GHOST-SE may under-report fugitive emissions from tailings ponds of newer projects and over-estimate fugitive emissions for tailings ponds where the fugitive emissions reported in one year are the result of many years of bitumen production and not directly related to the bitumen produced in that year. As more project-specific fugitive emissions data become available, GHOST-SE should be updated so that the time lag of fugitive emissions is accounted for. Currently in the model, median GHG emissions from fugitive GHG range from 1-4% of total upstream emissions for projects that began operating after 2000 to 8-11% of total upstream emissions for older projects. As these contributions are relatively small compared to total upstream emissions, the current treatment of fugitive emissions was deemed sufficient for this analysis.

Currently GHOST-SE estimates for diesel fuel consumption are obtained from Canadian Oil Sands Innovation Alliance (COSIA) mine templates, and not modeled on a project basis. As a result, GHOST-SE may be under-estimating both inter-project variability in GHG emissions resulting from different site designs as well as temporal variability in GHG emissions (which may increase over time as project expansions occur, increasing distances travelled from mine face to extraction facility). Improved data for diesel fuel consumption at individual projects (which was not currently available at the time of writing this thesis) would improve GHOST-SE

167 and allow for more detail investigation about how mines are designed and the implications for GHG emissions reductions from diesel consumption at mining projects.

7.2.3 Extensions to Chapter 4: Distinguishing between Resource Characteristics, Technology Choices, and Operating Decisions

For bitumen produced through in situ methods, steam-to-oil ratio (SOR) and, by proxy, GHG emissions, are largely determined by reservoir characteristics, which can be used to predict SOR with high accuracy (R2~0.8; Akbilgic et al. 2015). To date, no study has evaluated the possible relationship between resource quality and mining project GHG emissions. While Chapter 4 distinguishes between types of variability (i.e., temporal, inter/intra-project) and identifies the drivers of variability (e.g., coke consumption, natural gas consumption), further analysis is required to determine the specific resource characteristics, technology choices, and operating decisions that would lead to lower GHG intensities of mined bitumen production. Future work should distinguish between drivers within an operator’s control (e.g., choices about process fuel consumption to meet demands for steam, hot water, and electricity), technology choices (those locked in at the planning stage of the project: e.g., froth treatment technology, site design), and resource properties beyond the operator’s control (i.e., ore quality). Specifically, cogeneration has been identified as an opportunity for oil sands operators to reduce the GHG intensity of their operations. Further analysis of this historic operating data would enable an assessment of the extent to which cogeneration has contributed to reductions in the GHG intensity of mining and upgrading oil sands bitumen. Other trends could be investigated; for example, how monthly energy consumption relates to the total volume of crude produced in that month or cumulative crude produced by that project. Additionally, future work could identify drivers of monthly variability (e.g., are there seasonal trends in GHG intensity?). A closer look at the factors that contribute to variability within projects would inform opportunities for GHG intensity reductions within the project (e.g., level of cogeneration), while drivers of interproject variability (e.g., ore quality, froth treatment) would help guide operators and policymakers towards resources and technology choices that lead to lower GHG intensities of bitumen production. As the AER reports energy consumption for an entire facility, additional data or process-modeling would be required to differentiate between the different energy demands within a site.

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7.2.4 Improved Characterizations of Emissions from Refining

The application of Petroleum Refinery Life Cycle Inventory Model (PRELIM) in Chapter 5 is the first to incorporate into a WTW analysis of oil sands-derived transportation fuels the full variability in refinery GHG emissions resulting from both variations in crude properties and refinery configurations, considering a range of allocation methods for allocating refinery emission to final products. Several aspects of this work merit further investigation. First, I assume in Chapter 5 that electricity supplied to the refinery is generated from a natural gas-fired power plant. While GHG emissions from electricity consumed in the refinery are a relatively small fraction of total refinery emissions (contributing 5-7% of total refinery emissions per bbl of crude processed in the refinery, assuming electricity is derived from natural gas), variability exists here that is not captured in the model. Oil sands products are generally refined in PADD 2, which aligns with regions of the U.S. where a relatively large portion of the electricity grid is derived from coal. Future work could improve the refinery modeling employed in Chapter 5 by mapping the locations of North American refineries that process oil sands products to North American Electricity Reliability Corporation (NERC) regions to account for the effects of regional variability in electricity grid emissions on refinery emissions.

In PRELIM v1.2.1, the version modified for use in Chapter 5, product slates vary across crudes and refinery configurations. As some refinery products (e.g., gasoline) require more energy and are thus more GHG-intensive to produce, crudes and refinery configurations that produce relatively large quantities of these products will likely have higher overall refinery emissions (per bbl of crude processed). Future work could investigate the implications for WTW GHG intensities of different oil sands pathways if product slates from the refinery are fixed, so that pathways are not assigned higher GHG intensities solely because they are generating larger quantities of high-value products.

7.2.5 Variability in Vehicle Use Emissions

Most LCA studies and models treat vehicle use emissions from the combustion of petroleum- derived fuels as a fixed value that is constant across all fuel production pathways, even while accounting for variability and uncertainty in other life cycle stages along the WTW. In Chapter 5, PRELIM was initially run (unmodified) for all SCO and dilbit assays. PRELIM generates a final product slate by blending product streams from the different process units in the refinery.

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The PRELIM results predicted that each combination of crude properties and refinery configuration would produce refinery products (i.e., gasoline and diesel) with distinct properties, including carbon content, hydrogen content, and lower heating value (LHV). Adjusting vehicle use emissions to account for this variability in fuel properties can result in emissions varying by up to 5 g CO2eq/MJ fuel produced. As these results could not be validated with data from refineries or vehicles (i.e., what are reasonable ranges of fuel properties produced at refineries? How much variability in vehicle use emissions is observed in the real world?), in Chapter 5 PRELIM is modified by fixing the hydrogen content of the refinery product streams so that vehicle use emissions would remain constant for all model runs.

Future work should investigate the relationship between crudes properties, properties of refinery products, and the effects on vehicle use emissions to test the assumption in most LCAs that vehicle use emissions are constant across all petroleum fuel production pathways. Theoretically, fuels with higher carbon content will have higher combustion emissions. Fuel standards typically report a set of fuel properties (e.g., octane rating) but do not report or regulate carbon and energy contents of fuels, which cannot be readily determined from the properties reported. By identifying the relationship between crude properties, the properties of refinery products, and final vehicle use emissions, this work could identify an additional source of variability not previously accounted for in LCAs of petroleum-derived transportation fuels. This variability could affect WTW GHG intensity comparisons between transportation fuels derived from different types of crude oil as well as comparisons to alternative fuels or vehicle propulsion technologies (e.g., biofuels, electric vehicles).

7.2.6 How Best to Develop the Oil Sands Resource? A Conceptual Framework for Comparing Bitumen Production and Processing Pathways

In Chapter 5, WTW GHG intensity distributions are reported per MJ of transportation fuel produced (i.e., emissions are reported for each project in g CO2eq/MJ gasoline and g CO2eq/MJ diesel). For fuel consumers and regulation parties under low carbon fuel standards (LCFS), quantifying GHG emissions on a per MJ transportation fuel produced may provide enough information about the lowest-GHG transportation fuel production pathway and incentivize producers to reduce the GHG intensity of their pathways. Other functional units or life cycle boundary definitions should be investigated further to inform oil sands operators or Alberta

170 regulators about how to maximize yields of high-value transportation fuels while minimizing emissions along the life cycle, including in the boundary activities that may be excluded from a WTW LCA reporting results solely per MJ gasoline or diesel.

In Chapter 5, I find that processing mined PFT dilbit has the lowest WTW GHG intensity per MJ of gasoline, but also has lower yields of bitumen per tonne of oil sands ore mined (due to asphaltene precipitation from the PFT process) and lower yields of high-value refinery products when processed in a medium conversion refinery (due to relatively large fractions of vacuum residues in dilbit) compared to SCO. By including diluent (a light hydrocarbon that is less GHG- intensive to produce and process than bitumen) in the life cycle boundary of the dilbit and modeling refining of dilbit (rather than raw bitumen), refinery emissions for producing gasoline are relatively low as diluent fractions (which account for approximately 23-30% of the total volume of dilbit) require little processing at the refinery to produce gasoline. This is one of the factors that contributes to dilbit pathways having generally lower WTW GHG intensities than SCO pathways. Currently, most dilbit is processed at refineries in this manner, rather than diluent being recovered at the refinery and transported back to the oil sands for reuse, making this a reasonable default pathway.

One possible argument is that a better comparison between upgrading and dilution pathways would be a comparison between upgrading and a bitumen pathway where diluent is recovered rather than being processed with the bitumen as dilbit. WTW GHG intensities for gasoline and diesel produced from dilbit were found in a previous study to be 3-5% and 4-6% lower, respectively, than for a bitumen pathway with diluent recovery and recycling (Cai et al. 2015). In that study, WTW GHG intensities (reported per MJ gasoline or diesel) for bitumen pathways (no upgrading) were still found to be lower than SCO pathways when the same bitumen production (i.e., mining or in situ) method was employed. Another study, Choquette-Levy et al. (2013) compare tradeoffs between upgrading and dilution for a SAGD project under various carbon prices from a variety of stakeholder perspectives, modeling a dilution pathway where the full dilbit is processed. They found that dilution is typically less GHG-intensive over the WTW than upgrading per MJ of gasoline produced but more GHG-intensive per barrel of bitumen produced. However, their analysis did not include mining projects or explore the implications of recovering diluent from dilbit prior to refining.

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A possible extension to Chapter 5 would be to take a consequential approach, comparing diluent recovery and reuse (bitumen refining) and processing diluent with bitumen (dilbit refining). Using this framework, partial upgrading technologies could be compared to different dilution pathways. Partial upgrading is currently being promoted as lower-cost alternative to full upgrading with low (or no) requirement for diluent to meet pipeline specifications (Fellows et al. 2017).

Another possible future project could compare bitumen production methods (SAGD, CSS, NFT mining, PFT mining) and upgrading/dilution pathways (integrated mine and upgrader, stand- alone upgrader, dilution with diluent recovery, dilution without diluent recovery) on the bases of additional functional units (i.e., other than per MJ of gasoline or diesel). For example, pathway GHG emissions could be compared from a “barrel forward” approach, including all emissions from producing, refining, and consuming all products from a barrel of crude (Brandt et al. 2018). Alternatively, life cycle emissions for different pathways could be compared on the basis of one barrel of bitumen produced. Comparing pathways from a range of perspectives would help inform Alberta regulators and operators about the most efficient ways to develop the resource and would contribute to broader methodological discussions of how to model fuel production pathways.

7.2.7 Extensions to Chapter 6: Evaluating the GHG Emissions Intensity Reduction Potential of Emerging Oil Sands Technologies

The quantitative responses by experts in Chapter 6 regarding the anticipated performance (in terms of energy consumption, e.g., SOR) of emerging technologies could be incorporated into a prospective LCA that quantifies the GHG intensity mitigation potential of the adoption of emerging oil sands technologies. Specifically, hybrid steam-solvent processes were identified by many experts who participated in the elicitation as having the greatest potential for widespread commercial deployment in by 2034 and merit further investigation. Expert responses could be used to develop a range of adoption and GHG intensity scenarios for hybrid steam-solvent processes, and to quantify uncertainty related to the life cycle emissions of bitumen produced by projects employing this emerging technology.

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References

AER. ST39: Alberta Minable Oil Sands Plant Statistics Monthly Supplement; Alberta Energy Regulator: Calgary, Alberta, 2015.

Akbilgic, O.; Zhu, D.; Gates, I. D.; Bergerson, J. A. Prediction of steam-assisted gravity drainage steam to oil ratio from reservoir characteristics. Energy 2015, 93, 1663-1670.

Brandt, A. R.; Masnadi, M. S.; Englander, J. G.; Koomey, J.; Gordon, D. Climate-wise choices in a world of oil abundance. Environ. Res. Lett. 2018, 13 (4), 044027.

Burkus, Z., Wheler, J., Pletcher, S. GHG Emissions from Oil Sands Tailings Ponds: Overview and Modelling Based on Fermentable Substrates. Part I: Review of the Tailings Ponds Facts and Practices. Alberta Environment and Sustainable Resource Development: Edmonton, AB, 2014.

Cai, H.; Brandt, A.R.; Yeh, S.; Englander, J.G.; Han, J.; Elgowainy, A.; Wang, M.Q. Well-to- Wheels Greenhouse Gas Emissions of Canadian Oil Sands Products: Implications for U.S. Petroleum Fuels. Environ. Sci. Technol. 2015, 49, 8219-8227.

Foght, J. M.; Gieg, L. M.; Siddique, T. The microbiology of oil sands tailings: Past, present, future. FEMS Microbiology Ecology 2017, 93 (5).

Jacobs. Bitumen Partial Upgrading 2018 Whitepaper; AM0401A; Jacobs Consultancy and Life Cycle Assoc. for the Alberta Energy Research Institute: Chicago, IL, 2018.

Jordaan, S. M.; Keith, D. W.; Stelfox, B. Quantifying land use of oil sands production: A life cycle perspective. Environ. Res. Lett. 2009, 4 (2).

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Fellows, G. K.; Mansell, R.; Schlenker, R.; Winter, J. Public-Interest Benefit Evaluation of Partial-Upgrading Technology. SPP Res. Pap. 2017, 10 (1).

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Appendix A Supporting Information for Chapter 4

This appendix is based on Supporting Information published for: • Sleep, S.; Laurenzi, I.J.; Bergerson, J.A.; MacLean, H.L. Evaluation of Variability in Greenhouse Gas Emissions Intensity of Canadian Oil Sands Surface Mining and Upgrading Operations. Environmental Science and Technology 2018, 52 (20), 11941- 11951.

A.1 Methods: Process Diagram of Oil Sands Mining and Upgrading/Dilution Pathways

Figure A-1. Process diagram of oil sands mining and upgrading/dilution pathways

A.2 Methods: Additional Steps Taken to Develop Distributions for GHOST-SE

A.2.1 In Situ Bitumen Upgraded at Integrated Mining and Upgrading Project

In 2004, an in situ project began producing bitumen that was sent for upgrading at the Project 1 upgrader. The energy consumed to upgrade that bitumen is included in the total energy consumption reported for the site in the AER dataset (AER 2007, 2015). As the energy consumed for upgrading this bitumen is outside the scope of this study, the fraction of energy consumed for upgrading the in situ-produced bitumen is subtracted from the energy consumption

175 reported to the AER for Project 1. Natural gas and process gas demand for upgrading this bitumen is approximated as a uniform distribution between 95 and 115 m3/m3 SCO, based on the range of energy consumption reported in Bergerson et al. (2012) It is assumed that electricity demand is met through cogeneration, and no credit for surplus electricity generation is attributed to upgrading this bitumen.

A.2.2 Procedure for Filling Gaps in AER Data Some gaps exist in the monthly operating data reported by the AER (e.g., no reporting of certain input parameters for months where SCO production and other energy consumption parameters are reported to be greater than zero), see Table A-1.

Table A-1. Percentage of missing data in AER dataset (AER 2007, 2015) Project Total percentage of Energy inputs reported by AER with >1% of data missing data across missing over study period (% of total crude all AER data (%)a produced in months with missing data) b 1 2.3 Process gas consumption (18) 2 5.9 Natural gas consumption (2.3) Process gas consumption (26) Coke consumption (24) 3 0.20 Grid electricity (0.87) 4 2.6 Process gas consumption (1.4)

Process gas for H2 production (17) 5 0.15 Process gas consumption (1.9) 6 0 None aTotal percentage of missing data is calculated by dividing the total number of months of missing data across all energy inputs by the total number of data points reported for that project. b Percentage of total crude produced in months with missing data is calculated for each input with missing data by dividing the crude produced in months where that data is not reported by the total crude produced for that project over the study period.

The following procedure was used for filling gaps in the AER dataset. For each input parameter, a correlation between that input parameter and monthly SCO production is tested using linear regression for months with data reported. If a correlation exists, data gaps are filled based on the correlation found. If no correlation is found, gaps are filled with random numbers within the range of data reported for that input parameter. The difference between mean values for the reported data (with gaps) and the final dataset were found to be small (<1%).

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A.2.3 Summary of Mining Project Characteristics

Table A-2. Summary of mining project characteristics Project Capacitya Start- Froth Upgrading Final Cogeneration (bbl/day) Up Treatment Technologyb Product Scenarioc Yeara Technology Project 1 501,000 1967 NFT Delayed coking Diesel, Starting in 2000, net (integrated) SCO electricity exported. Coke consumed Project 2 407,000 1978 NFT Coking and SCO Starting in 2000, cogen hydrocracking mix meets on-site demand (integrated) for electricity. Coke combusted. Project 3 155,000 2003 PFT Hydroconversion SCO Net electricity (stand-alone)d exported. Project 4 197,000 2009 NFT Delayed coking SCO Variable. 2009, 2011, (integrated) 2012: net electricity exported.

Project 5 100,000 2010 PFT Hydroconversion SCO Net electricity (stand-alone)d imported. Project 6 225,000 2013 PFT None (dilbit Dilbit Net electricity produced) imported. aSource: COSIA (2017). bSource: Gray (n.d.) cSource: AER (2016). dBitumen produced at Projects 3 and 5 is upgraded at a stand-alone upgrader operated by the same company. NFT: naphthenic froth treatment; PFT: paraffinic froth treatment; Cogen: cogeneration. A.2.4 Emissions Associated with Grid Electricity Consumption Annual Alberta electricity generation data by resource type (e.g., coal, natural gas, renewables) for the period of 1985 to 2015 is reported in by the Alberta Utilities Commission (AUC; AUC 2016). The GHG intensity of electricity produced by each resource reported by the AUC (g CO2eq/kWh produced by a given resource) is obtained from Environment Canada’s National Inventory Reports (EC 2008). This data is used to calculate the average grid electricity GHG emissions used in this study, from 1983 to 2015, presented in Table A-3.

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Table A-3. Annual Alberta electricity grid GHG emissions intensity Year AB Grid Intensity Percentage of Alberta Electricity Generated by Resource Type (%) (g CO2eq/kWh) Coal Natural Gas Renewables Other 1983 952 83.3 11.4 5.3 0.001 1984 952 83.3 11.4 5.3 0.001 1985 952 83.3 11.4 5.3 0.001 1986 949 83.6 10.1 6.3 0.001 1987 957 83.8 11.3 4.9 0.001 1988 950 82.3 13.2 4.5 0.001 1989 926 78.5 16.9 4.5 0.001 1990 933 80.9 12.9 6.2 0.001 1991 942 82.3 11.5 6.1 0.001 1992 946 80.6 14.2 5.2 0.001 1993 944 80.5 13.9 5.6 0.001 1994 928 79.9 14.1 6.0 0.003 1995 922 81.3 11.9 6.7 0.002 1996 920 79.3 13.7 6.9 0.001 1997 927 79.4 14.1 6.5 0.001 1998 903 74.2 19.1 6.7 0.001 1999 880 73.4 19.4 7.2 0.001 2000 857 69.9 24.1 6.0 0.001 2001 810 68.8 25.6 5.3 0.3 2002 807 69.6 23.9 6.0 0.4 2003 842 66.5 27.1 5.9 0.4 2004 799 64.4 28.7 6.6 0.4 2005 804 66.3 25.9 7.4 0.4 2006 799 64.6 28.2 6.9 0.4 2007 789 63.5 28.4 7.8 0.3 2008 778 61.4 30.4 8.0 0.2 2009 771 59.5 32.8 7.4 0.3 2010 765 58.3 34.1 7.3 0.4 2011 739 55.0 35.5 9.1 0.5 2012 724 52.5 37.4 9.7 0.5 2013 772 51.6 38.2 9.7 0.5 2014 741 55.3 35.0 9.2 0.5 2015 717 50.7 39.5 9.4 0.4 Grid mix from AUC (2016). Grid mix intensity from Environment Canada’s National Inventory Report (EC 2008). A.2.5 Emissions Credit for Surplus Electricity Exported to Grid from Upstream Projects

A credit of 163 g CO2eq/kWh for surplus electricity exported to the grid is employed in this study, obtained from COSIA (2017). In the sensitivity analysis, different credits are applied (i.e., a credit for emissions equivalent to the emissions intensity of electricity produced from natural

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gas in Alberta, reported in EC 2008: 494 g CO2eq/kWh; a credit for emissions equivalent to the average emissions intensity of the Alberta grid in that operating year, see Table A-3).

A.2.6 Diluent Emissions Factors.

The upstream emissions factor for diluent is assigned a uniform distribution ranging from 26-79 kg CO2eq/bbl diluent. This emissions factor range varies from the low to high ends for diluent emissions factors reported in the literature (see Table A-4).

Table A-4. GHG emissions factors for the supply of diluent to Alberta’s oil sands Literature Source Original value Diluent type Emissions factor (kg CO2eq/bbl diluent) a EcoInvent 2 5.0 kg CO2eq/GJ (LHV) Naphtha b GREET 2017 10505 g CO2eq/mmbtu NGL from U.S. shale 57 wells

8478 g CO2eq/mmbtu NGL from Western 46 Canada

10660 g CO2eq/mmbtu Crude naphtha 58 Laurenzi et al.c 10th percentile: 32 kg Bakken tight oil 10th percentile: 32

CO2eq/bbl Median: 44 th Median: 44 kg CO2eq/bbl 90 percentile: 71 90th percentile: 71 kg

CO2eq/bbl d IET 14.1 kg CO2eq/GJ (HHV) Naphtha aSource: EcoInvent (2013); bSource: GREET (2017); c Source: Laurenzi et al. (2016); dSource: IET (2014); Heating values obtained from GREET 2017: diluent LHV employed for transforming GREET 2017 values (128,449 btu/gal), naphtha LHV employed for transforming EcoInvent 2.2 values (32.9 GJ/m3) and naphtha HHV employed for transforming IET WTT values (35.2 GJ/m3).11 LHV: lower heating value; HHV: higher heating value; NGL: natural gas liquids. A.2.7 Linking Statistically-Dependent Input Parameters Using Lookup Tables Some input parameters reported by the AER are statistically dependent. For example, natural gas, process gas, and coke are all combusted on-site to generate steam, hot water, and electricity to meet the demands of the mining and upgrading operations. Demand for electricity not met by on-site cogeneration is met by electricity imported from the grid. Surplus electricity generated on- site is exported to the grid. Steam and electricity demands can be met from any of the above inputs, the proportion of which is consumed at any individual project will vary depending on a variety of operating factors and has shifted over time as economic and policy conditions have changed. Due

179 to their statistical dependence, these factors are linked in the GHOST-SE model through lookup tables in Excel so that each input is selected from the same month of operation. For upgrading, both natural gas and process gas are consumed by a steam-methane reformer to produce hydrogen used in the upgrading process and as such are also linked through a lookup table. The GHG intensity of the Alberta grid has decreased over time (from approximately 952 g CO2eq/kWh in

1985 to 717 g CO2eq/kWh in 2015). The emissions from grid electricity consumption calculated by the model link the month of grid electricity consumption with the emission factor for grid electricity from that year using a lookup table.

A.3 Methods: Comparison of GHOST and GHOST-SE Input Parameters

Table A-5. Comparison of GHOST-SE model input parameters to GHOST input ranges Input Parameter GHOST P10-P90 range from statistical distributions used in GHOST-SE range (production-weighted mean)b (default)a Project 1 Project 2 Project 3 Project 4 Project 5 Project 6 NG consumption 24-954 25-139 24-95 90-191 32-105 6.0-99 77-125 (m3/m3 crude) (482) (84) (57) (136) (76) (44) (103) PG consumption 55-115 50-133 88-152 79-170 82-101 31-268 N/A (m3/m3 crude) (70) (90) (118) (116) (93) (132)

NG for H2 production 28-87 26-53 60-80 15-39 54-87 23-44 N/A (m3/m3 crude) (35) (41) (71) (26) (70) (36)

PG for H2 production 0 0-5.3 0 32-95 16-85 30-92 N/A (m3/m3 crude) (0) (1.5) (0) (55) (47) (47) Flared NG and PG 2.6-14 0-9.9 0.3-1.2 0-5.4 2.8-17.7 0-0.3 0.8-7.5 (m3/m3 crude)4 (4.6) (5.5) (0.7) (2.1) (9.0) (0.4) (11) Grid Electricity 99-188 0-233 9-52 0.6-35 7-166 56-99 50-137 (kWh/m3 crude) (126) (59) (32) (12) (64) (74) (104) Surplus electricity 0-2,765 0-324 0-72 10-95 0-171 0.4-10 0 (kWh/m3 crude) (1341) (120) (29) (57) (138) (3.3) (0) Coke consumption 0 49-381 33-66 0 0 0 N/A (kg/m3 crude) (0) (181) (48) (0) (0) (0)- Diesel consumption 7-15 9-13 9-13 9-16 9-13 9-16 9-16 (L/m3 bitumen) (10) (11) (11) (12.5) (11) (12.5) (12.5) Fugitive GHGs (kg 3.5-30 42-148 66-97 22-159 5.6-25 22-159 5-27

CO2eq/bbl crude) (13) (78) (76) (53) (14) (50) (10) a GHOST range represents minimum and maximum parameter values that are expected at current oil sands operations. GHOST default represents a default case study value, as defined in Bergerson et al. (2012) and Charpentier et al. (2011). Delayed coking is assumed for the GHOST pathway and mining

180 inputs are converted to per m3 of SCO assuming a bitumen to SCO ratio of 1.18. b Inputs for Projects 1-5 are per m3 SCO, inputs for Project 6 are per m3 bitumen. A.4 Results: Variability Across Projects

Figure A-2. GHG emissions intensity distributions for oil sands mining and upgrading/dilution projects based on 2015 operating data. Projects are listed in order from oldest (Project 1) to newest (Project 6). Projects 1-5 include upgrading while dilbit is produced for Project 6. Industry (SCO) includes Projects 1-5. Blue vertical lines represent the 10th and 90th percentiles of the Monte Carlo Simulation generated GHG emissions ranges obtained from GHOST-SE, while the black vertical line represents the median value for each project. Mean GHG emissions are in red.

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A.5 Results: Outliers for Time Series GHG Emissions Intensity Distributions (Figure 4-2) For readability, outliers to the boxplots presented in Figure 4-2 are not included in the figure.

The upper end outliers (those above the P90 value) are in some simulations several times larger than the P90 value, especially in years where a project shutdown has led to very low production

(1/10 or less) in one month compared to prior and subsequent months as total energy consumption tends to remain steady in those months. Generally, however, outliers are within 10% of the P90 value and have no effect on the interpretation of data findings.

A.6 Results: Credit for Surplus Electricity Exported to Grid Additional GHOST-SE runs presented below for both lifetime (Figures A-3 and A-4) and 2015

(Figures A-5 and A-6) operating periods. Figures A-3 and A-5 present upstream GHG intensity distributions for all projects where projects an emissions credit for surplus electricity generation is applied based on the Alberta grid emissions intensity. Projects 1 and 2 are most sensitive to the size of the credit, particularly in 2015 where more electricity is exported to the grid compared to the lifetime average. Figures A-4 and A-6 present upstream GHG intensity distributions where an emissions credit for surplus electricity generation is applied based on the emissions intensity for electricity produced from natural gas in Alberta.

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Figure A-3. Lifetime GHG emissions intensity distributions for oil sands mining and upgrading/dilution projects with credit for surplus electricity generation equivalent to Alberta grid GHG intensity Projects are listed in order from oldest (Project 1) to newest (Project 6). Projects 1-5 include upgrading while dilbit is produced for Project 6. Industry (SCO) includes Projects 1-5. Blue vertical lines represent the 10th and 90th percentiles of the Monte Carlo Simulation generated GHG emissions ranges obtained from GHOST-SE, while the black vertical line represents the median value for each project. Mean GHG emissions are in red.

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Figure A-4. Lifetime GHG emissions intensity distributions for oil sands mining and upgrading/dilution projects with credit for surplus electricity generation equivalent to Alberta GHG intensity for producing electricity from natural gas. Projects are listed in order from oldest (Project 1) to newest (Project 6). Projects 1-5 include upgrading while dilbit is produced for Project 6. Industry (SCO) includes Projects 1-5. Blue vertical lines represent the 10th and 90th percentiles of the Monte Carlo Simulation generated GHG emissions ranges obtained from GHOST-SE, while the black vertical line represents the median value for each project. Mean GHG emissions are in red.

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Figure A-5. GHG emissions intensity distributions for oil sands mining and upgrading/dilution projects with credit for surplus electricity generation equivalent to Alberta grid GHG intensity based on 2015 operating data. Projects are listed in order from oldest (Project 1) to newest (Project 6). Projects 1-5 include upgrading while dilbit is produced for Project 6. Industry (SCO) includes Projects 1-5. Blue vertical lines represent the 10th and 90th percentiles of the Monte Carlo Simulation generated GHG emissions ranges obtained from GHOST-SE, while the black vertical line represents the median value for each project. Mean GHG emissions are in red.

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Figure A-6. GHG emissions intensity distributions for oil sands mining and upgrading/dilution projects with credit for surplus electricity generation equivalent to Alberta GHG intensity for producing electricity from natural gas based on 2015 operating data. Projects are listed in order from oldest (Project 1) to newest (Project 6). Projects 1-5 include upgrading while dilbit is produced for Project 6. Industry (SCO) includes Projects 1-5. Blue vertical lines represent the 10th and 90th percentiles of the Monte Carlo Simulation generated GHG emissions ranges obtained from GHOST-SE, while the black vertical line represents the median value for each project. Mean GHG emissions are in red.

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A.7 Results: Sensitivity to Input Parameters, Other Projects The results of the sensitivity analysis (performed using lifetime operating data) for Projects 2, 4, and 5 are presented in Figure A-7.

The results of the sensitivity analysis performed using 2015 operating data are presented in

Figures A-8 (Projects 1, 3, and 6) and A-9 (Projects 2, 4, and 5).

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Figure A-7. Tornado diagrams showing sensitivity of oil sands mining and upgrading/dilution GHG intensities to model inputs, Projects 2, 4, and 5. Projects are listed in order from oldest (Project 2) to newest (Project 5). All projects include upgrading. A portion of the coke produced from the upgrader at Project 2 is consumed on-site.

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Figure A-8. Tornado diagrams showing sensitivity of oil sands mining and upgrading/dilution GHG intensities to model inputs based on 2015 operating data, Projects 1, 3, and 6.

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Projects are listed in order from oldest (Project 1) to newest (Project 6). Projects 1 and 3 include upgrading. A portion of the coke produced from the upgrader at Project 1 is consumed on-site. Dilbit is produced by Project 6.

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Figure A-9. Tornado diagrams showing sensitivity of oil sands mining and upgrading/dilution GHG intensities to model inputs based on 2015 operating data, Projects 2, 4, and 5. Projects are listed in order from oldest (Project 2) to newest (Project 5). All projects include upgrading. A portion of the coke produced from the upgrader at Project 2 is consumed on-site. A.8 Results: Comparison of GHOST-SE Results with Literature GHG Intensity Estimates To allow for a direct comparison of GHOST-SE results with literature estimates of GHG emissions intensities, some steps were taken to modify the values so that comparisons were consistent (see Table A-6). GREET’s GHG emissions intensity estimates were obtained from

Englander and Brandt (2014) and Cai et al. (2015). GHGenius reports energy consumption (coke, natural gas, process gas, net electricity consumed) for: standalone mining, standalone upgrading, and integrated mining and upgrading ((S&T)2 2013). Three GHG emissions intensity estimates are obtained from the data reported by GHGenius to compare to GHOST-SE results: integrated mining and upgrading, standalone mining and upgrading, and mining and dilution. The energy consumption rates reported by GHGenius are multiplied by the default emissions factors employed in GHOST (Bergerson et al. 2012; Charpentier et al. 2011), so that GHG emissions intensities can be compared to GHOST-SE results. For electricity consumption, GHGenius reports net electricity consumption and for all cases, models projects as net exporters of electricity. For the literature comparison, the same credit for surplus electricity generation (163 g CO2eq/kWh) as employed in this study is applied. For the mining and dilution case, for all literature comparisons it is assumed that bitumen is diluted 30% with naphtha, the same approach employed in GHOST-SE for modeling Project 6.

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Table A-6. Literature GHG emissions intensity estimates Pathway Literature Original Value Transformation Value in Figure 4-1 Source (kg CO2eq/bbl crude)

Mining + GHOST 11.6-32.4 g CO2eq/MJ Transport (to 64-166 Upgrading SCO (including upgrader/refinery) emissions transport) removed.

Transport emissions: 1.3- HHV for bitumen and SCO a 5.6 g CO2eq/MJ SCO obtained from GHOST

Assume diluted 30% with naphtha. GHGenius Mining (per m3 Emissions factors from 155 (stand-alone); bitumen): b GHOST employed to Diesel: 35 L/m3 transform energy consumption NG: 73.4 m3/m3 into GHG emissionsa Electricity: -70 kWh/m3 Assume diluted 30% with 102 (integrated) Upgrading (kJ/tonne naphtha. SCO): Stand-alone: NG: 5,340,000 Electricity: 217,000 Coke: 1,300,000 PG: 4,660,000 Integrated: Diesel: 605,000 NG: 5,200,000 Electricity: -100,000 Coke: 1,600,000 PG: 3,450,000 GREET Mining + SCO pathway Assume diluted 30% with 160 naphtha.

Mining + GHOST 3.5-12.7 g CO2eq/MJ Transport (to 28-59 Dilution dilbit (including upgrader/refinery) emissions transport) removed.

HHV for bitumen and SCO Transport emissions: 1.1- obtained from GHOSTa

4.7 g CO2eq/MJ dilbit Assume diluted 30% with naphtha. GHGenius Mining (per m3 Assume diluted 30% with 47 bitumen):b naphtha. Diesel: 35 L/m3 NG: 73.4 m3/m3 Emissions factors from Electricity: -70 kWh/m3 GHOST employed to (export) transform energy consumption into GHG emissionsa

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COSIA credit for surplus electricity generation applied. GREET Mining + bitumen 64 pathway aSee Table A-7. bEmissions from leaks and flares also included: 5,480 L/tonne bitumen (mining), 5,559 L/tonne SCO (stand-alone mining and upgrading), 6948 L/tonne SCO (integrated mining and upgrading). NG: natural gas; PG: process gas; HHV: higher heating value; SCO: synthetic crude oil.

Table A-7. Default emissions factors and fuel properties employed in transforming literature values

Product Higher Heating Value (MJ/L) Emissions Factor (g CO2eq/L) Diesel 38.7 2,804 (direct) + 710 (indirect) Natural gas 0.0378 1,937 (direct) + 260 (indirect) Coke 40.0 3,578 (direct) Dilbit 40.6 N/A SCO 39.0 N/A

A.9 References

AER. ST98-2016: Alberta’s Energy Reserves 2015 & Supply/Demand Outlook 2016-2025 Report Data; Alberta Energy Regulator: Calgary, Alberta, 2016; www.aer.ca/data-and- publications/statistical-reports/report-data (accessed November 22, 2017).

AER. ST39: Alberta Minable Oil Sands Plant Statistics Monthly Supplement; Alberta Energy Regulator: Calgary, Alberta, 2015.

AER. ST43: Alberta Minable Oil Sands Plant Statistics Annual Supplement; Alberta Energy Regulator: Calgary, Alberta, 2007.

AUC. Annual Electricity Data Collection; Alberta Utilities Commission (AUC): Calgary, Alberta, November 22, 2016; www.auc.ab.ca/market-oversight/Annual-Electricity-Data- Collection/Pages/default.aspx (accessed November 22, 2017).

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Cai, H.; Brandt, A.R.; Yeh, S.; Englander, J.G.; Han, J.; Elgowainy, A.; Wang, M.Q. Well-to- Wheels Greenhouse Gas Emissions of Canadian Oil Sands Products: Implications for U.S. Petroleum Fuels. Environ. Sci. Technol. 2015, 49, 8219-8227.

COSIA. Development of a Static Oil Sands Mine and Extraction Reference Facility; Tetra Tech Canada Inc.; presented to Canadian Oil Sands Innovation Alliance (COSIA): Calgary, Alberta, 2017.

EcoInvent. EcoInvent 2.2 U.S. Database; Ecoinvent Centre; Swiss Centre for Life Cycle Inventories, 2013; www.ecoinvent.ch/ (accessed May 11, 2018).

GREET. GREET Model 2017; Argonne National Laboratory: Lemont, Illinois, 2017; greet.es.anl.gov (accessed May 11, 2018).

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IET. JEC Well-To-Wheels Analysis: Well-to-Wheels Analysis of Future Automotive Fuels and Powertrains in the European Context; European Union Institute for Energy and Transport (IET); Joint Research Centre of the European Commission: Luxembourg, European Union, April 2014. http://iet.jrc.ec.europa.eu/about-jec/sites/iet.jrc.ec.europa.eu.about- jec/files/documents/report_2014/wtt_report_v4a.pdf (accessed May 11, 2018).

Laurenzi, I.J.; Bergerson, J.A.; Motazedi, K. Life Cycle Greenhouse Gas Emissions and Freshwater Consumption Assocaited with Bakken Tight Oil. PNAS 2016, 113 (48), E7672- E7680.

Appendix B Supporting Information for Chapter 6

This appendix is based on supporting information prepared for:

• Sleep, S.; Laurenzi, I.J.; Guo, J.; Bergerson, J.A.; MacLean, H.L. Quantifying Variability in Well-to-Wheel Greenhouse Gas Emission Intensities of Transportation Fuels Derived from Canadian Oil Sands Mining Operations.

B.1 Methods: Global Warming Potentials

GWPs employed in this study are 100-year GWPs from the Fifth Assessment Report of the

Intergovernmental Panel on Climate Change (IPCC’s AR5; IPCC, 2013): 1 for CO2, 30 for CH4, and 265 for N2O.

B.2 Methods: Emissions Factors Employed in GHOST-SE Model

B.2.1 Non-Electricity Emissions Factors

Emissions factors for GHOST-SE are obtained from the original GHOST model documented in Bergerson et al. (2012). For Monte Carlo simulations in this study emissions factor parameters are defined as custom distributions with equal probability of obtaining the emissions factor presented by each source. The average of each emissions factor is presented in Table B-1.

Table B-1. Average emissions factors employed in GHOST-SE. Fuel Type Direct Indirect

3 Natural Gas (g CO2eq/m NG) 1938 260

3 Process Gas (g CO2eq/m PG) 1938 N/A

Coke (g CO2eq/kg Coke) 3139 N/A

Diesel (g CO2eq/L Diesel) 2804 710

Diluent (Condensate; g CO2eq/L Diluent) N/A 51 NG: natural gas; PG: process gas; Direct: combustion emissions; Indirect: supply chain emissions. Source: (J. Bergerson et al., 2012)

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B.2.2 Emissions Associated with Grid Electricity Consumption

Annual Alberta electricity generation data by resource type (e.g., coal, natural gas, renewables) for the period of 1985 to 2015 is reported in by the Alberta Utilities Commission (AUC; AUC, 2018). The GHG intensity of electricity produced by each resource reported by the AUC (g

CO2eq/kWh produced by a given resource) is obtained from Environment an Climate Change Canada’s National Inventory Report (ECCC; ECCC, 2006). This data is used to calculate the average grid electricity GHG emissions used in this study, from 2005 to 2015, presented in Table B-2.

Table B-2. Annual Alberta electricity grid GHG intensity AB Grid Intensity Percentage of Alberta Electricity Generated by Resource Type (%) Year (g CO2eq/kWh) Coal Natural Gas Renewables Other 2005 804 66.3 25.9 7.4 0.4 2006 799 64.6 28.2 6.9 0.4 2007 789 63.5 28.4 7.8 0.3 2008 778 61.4 30.4 8.0 0.2 2009 771 59.5 32.8 7.4 0.3 2010 765 58.3 34.1 7.3 0.4 2011 739 55.0 35.5 9.1 0.5 2012 724 52.5 37.4 9.7 0.5 2013 772 51.6 38.2 9.7 0.5 2014 741 55.3 35.0 9.2 0.5 2015 717 50.7 39.5 9.4 0.4 Grid mix from AUC (AUC, 2018); Intensity from ECCC’s National Inventory Report (ECCC, 2006).

B.2.3 Emissions Credit for Surplus Electricity Exported to Grid from Upstream Projects

A credit of 163 g CO2eq/kWh for surplus electricity exported to the grid is employed in this study, obtained from the Canadian Oil Sands Innovation Alliance’s Mine Template (COSIA; documented in Tetra Tech Canada Inc., 2017). More information about the development of the COSIA Mine Templates is presented below.

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B.3 Methods: Electricity Grid GHG intensity for Refining and Refined Products Transportation

Average U.S. grid electricity GHG intensity is estimated based on the 2013 U.S. grid mix and GHG emissions estimates from LCAs of electricity generation by resource from the National Energy Transportation Lab (NETL; Skone, T.J.; Adder, 2012), adjusted for 100-year GWPs reported by IPCC (Table B-3). Grid mixes for the U.S. in 2013 are reported in by the U.S. Energy Information Administration (EIA; U.S. EIA, 2013, see Table B-4) for the North American Electric Reliability Corporation (NERC) regions (see Figure B-1). Resulting regional electricity grid mix GHG intensities are presented in Table B-5. The weighted-average U.S. grid mix GHG intensity employed in this study is 691 g CO2eq/kWh electricity consumed. The statistical distribution for the grid GHG intensity is defined as a custom distribution where the probability of electricity being consumed from a given region is proportional to the fraction of total U.S. electricity produced from that region.

Table B-3. GHG intensity by electricity generation technology

Generation Technology GHG Emissions (g CO2eq/kWh) NGCC 514 GTSC 790 Nuclear 39 Conventional Wind 22 Advanced Wind 16 Coal 1,115 Hydro, Eastern Interconnect 5 Geothermal 63 NGCC: Natural Gas Combined Cycle; GTSC: Gas Turbine Simple Cycle; GHG emissions adjusted to 100-year GWPs based on AR5. (Source: Skone, T.J.; Adder, 2012).

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Table B-4. Electricity generation by technology and NERC region Generation in 2013 TRE WECC FRCC MRO RFC NPCC SERC SPP (MWh) Nuclear 38,314,996 57,803,913 26,525,855 24,627,385 270,277,700 81,938,883 282,359,477 7,168,301 Coal, Steam 121,960,046 206,795,575 41,272,626 131,122,356 466,900,862 10,726,292 440,145,202 126,734,972 Turbine NG, CCGT 137,520,997 173,808,742 119,679,754 8,182,475 118,618,106 81,416,723 222,551,929 43,323,318 NG, GT 8,668,828 10,791,421 2,461,100 1,077,751 7,558,187 4,859,615 18,611,817 2,598,136 NG, Steam 6,405,474 10,667,637 5,658,322 486,381 4,396,695 11,296,576 23,626,196 15,779,984 Turbine Hydropower 22,042 137,019,338 - 5,951,208 5,202,250 23,165,985 32,243,706 3,402,654 Wind 32,285,300 43,981,118 - 33,707,659 18,441,023 5,100,619 3,414,106 23,673,711 Geothermal - 10,240,177 ------NG: natural gas; CCGT: Combined Cycle Gas Turbine; TRE: Texas Reliability Entity; WECC: Western Electric Coordinating Council; FRCC: Florida Reliability Coordinating Council; MRO: Midwest Reliability Organization; RFC: ReliabilityFirst Corporation; NPCC: Northeast Power Coordinating Council; SERC: SERC Reliability Corporation; SPP: Southwest Power Tool, RE. (Source: U.S. EIA), 2013).

Table B-5. Grid GHG intensity by region NERC Total Power from Region Fraction of Power from Region Regional Carbon Region (MWh) (%) Footprint

(g CO2eq/kWh) TRE 345,177,684 9 715 WECC 651,107,921 17 579 FRCC 195,597,657 5 645 MRO 205,155,216 5 853 RFC 891,394,822 24 753 NPCC 218,504,694 6 349 SERC 1,022,952,434 27 702 SPP 222,681,076 6 913 NERC: North American Electric Reliability Corporation; TRE: Texas Reliability Entity; WECC: Western Electric Coordinating Council; FRCC: Florida Reliability Coordinating Council; MRO: Midwest Reliability Organization; RFC: ReliabilityFirst Corporation; NPCC: Northeast Power Coordinating Council; SERC: SERC Reliability Corporation; SPP: Southwest Power Tool, RE.

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Figure B-1. North American Electric Reliability Corporation (NERC) Regions. TRE: Texas Reliability Entity; WECC: Western Electric Coordinating Council; FRCC: Florida Reliability Coordinating Council; MRO: Midwest Reliability Organization; RFC: ReliabilityFirst Corporation; NPCC: Northeast Power Coordinating Council; SERC: SERC Reliability Corporation; SPP: Southwest Power Tool, RE. (Source: NERC, 2017). B.4 Methods: Modeling Upstream GHG Emissions: Defining GHOST-SE Input Parameters

B.4.1 Summary of Oil Sands Mining Projects

All seven mines that have opened in the oil sands are in operation today, with projects opening as early as 1967. Five companies operate these seven mines. In this study, two of the mines operated by one project are combined, as the bitumen mined at one site is transported to the other for extraction. Projects are numbered from oldest (1; bitumen production began 1967) to newest (6; bitumen production began 2013). Projects 3 and 5 are operated by the same mining company and both produce bitumen which is upgraded at a nearby upgrader, also operated by that company. Energy consumed for upgrading is allocated to these two projects based on the relative quantity of bitumen produced monthly by each company. Project 6 produces dilbit and is not associated with an upgrader.

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B.4.2 Mining and Upgrading Project Historic Energy Consumption Reported by the Alberta Energy Regulator

All oil sands mining projects are required to report their monthly energy consumption and flaring to the Alberta Energy Regulator (AER) which is made available to the public in the AER’s Statistical Series ST39 (1983-2001; 2008-2015; AER, 2015) and ST43 (2002-2007; AER, 2007). Energy consumption is reported by fuel type for the entire site, with no disaggregation between mining and upgrading operations for projects with integrated on-site upgraders. For this study energy consumption (and flaring) is converted into intensity values (e.g., kWh electricity consumed/m3 SCO produced) by dividing energy consumption by the crude (either SCO or dilbit) produced by the project in that month, also reported in the AER Statistical Series (ST39 or ST43). As the grid mix and thus GHG emissions from the electricity grid in Alberta has changed over the 2005-2015 operating period (from 804 to 717 g CO2eq/kWh), GHG emissions from electricity consumed at mining projects are linked to the year of operation. Due to the reporting structure, some additional steps had to be taken to modify the data so that each project is accurately represented in this study; see Sleep et al. (2018) for documentation of these steps. For the Monte Carlo simulations, distributions are defined by sampling directly from the monthly AER data provided. A summary of the statistical distributions developed for each project is reported in Table B-6.

Distributions for coke, natural gas, process gas, grid electricity consumption, flaring GHGs, and electricity export are developed based on monthly operating data from 2005-2015 reported for each mining and upgrading project to the Alberta Energy Regulator (AER), with parameters linked to the relative crude production in that month compared to total crude produced by that project (AER, 2015, 2007). Diesel consumption is estimated based on that reported in the COSIA Mine Templates (Tetra Tech Canada Inc., 2017) and fugitive GHG emissions are obtained from the Alberta Environmental Monitoring, Evaluation, and Reporting Agency (AEMERA; AEMERA, 2017). Emissions factors for energy consumed in upstream activities are based on those employed in GHOST-SE (see Sleep et al., 2018).

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B.4.3 Diesel Consumption for Mines from COSIA Mine Templates

No project-specific data for diesel consumption is available in public datasets (it is not included in the AER dataset). The Canadian Oil Sands Innovation Alliance (COSIA) publishes two templates of oil sands mining operations: one to represent naphthenic froth treatment (NFT; COSIA, 2015a) projects and one to represent paraffinic froth treatment (PFT; COSIA, 2015) projects. The Mine Templates, which include only the mine and no integration with an upgrader, are intended to represent a hypothetical mine operating using current technologies but does not represent any specific mines in operation today. For each Mine Template (NFT and PFT), energy consumption is reported for two cases: low grade (nine percent by weight) ore and high grade (12 percent by weight) ore. The reported COSIA Mine Template values for diesel consumption are used in this study, assuming a uniform distribution between the low (high grade ore) and high (low grade ore) for diesel consumption. The ranges for diesel consumption are: 9-13 L/m3 bitumen produced (NFT projects) and 9-16 L/m3 bitumen produced (PFT projects).

B.4.4 Fugitive GHG Emissions from the Alberta Environmental, Monitoring, Evaluation and Reporting Agency

Annual fugitive GHG emissions by project from 2011 to 2014 are collected by the Government of Alberta and published by AEMERA (AEMERA, 2017). These include fugitive GHG emissions from the tailings ponds as well as mine-face emissions. In GHOST-SE fugitive GHG emissions are defined as custom distributions based on the relative crude production in each year of operation in which fugitive emissions are reported.

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Table B-6. Summary of statistical distributions for input parameters to GHOST-SE. Average Parameter Value Data Distribution Input Parameter Project Project Project Project Project Project Source Type 1 2 3 4 5 6 NG consumption Custom 84 57 136 76 44 103 (m3/m3 crude)2 PG consumption Custom 90 118 116 93 132 N/A (m3/m3 crude) NG for H2 production (m3/m3 Custom 41 71 26 70 36 N/A crude) PG for H2 production (m3/m3 Custom 1.5 0 55 47 47 N/A AER crude) Flared NG and PG Custom 5.5 0.7 2.1 9.0 0.4 11 (m3/m3 crude)4 Grid Electricity Custom 59 32 12 64 74 104 (kWh/m3 crude) Surplus electricity Custom 120 29 57 138 3.3 0 (kWh/m3 crude) Coke consumption Custom 181 48 0 0 0 N/A (kg/m3 crude) Diesel consumption COSIA Uniform 11 11 12.5 11 12.5 12.5 (L/m3 bitumen) Fugitive GHGs (kg AEMERA Custom 78 76 53 14 50 10 CO2eq/bbl crude) Probability for custom distributions is based on relative crude production in that period. AER data reported monthly by project from 1983 to 2015. AEMERA data is reported annually by project from 2011 to 2015. B.5 Methods: Crude Transportation Models

The Base Case assumes crude transport via pipeline, modeled with the Crude Oil Pipeline Transportation Emissions Model (COPTEM) described in Choquette-Levy et al. (2018). Other pipeline transport models and rail transportation are considered in the scenario analysis. The GHG intensity of transport by each mode and pipeline model are presented in Table B-7. Detail about each model is provided below.

B.5.1 COPTEM

COPTEM was developed to estimate the energy consumption and GHG emissions from transporting crude oil by pipeline based on fundamental fluid mechanics. COPTEM takes into consideration pipeline properties (diameter, roughness factor, pump efficiencies), power grid emissions factors, and elevation change.

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Electric pumps operating over the length of the pipeline overcome head losses due to friction as the crude is transported through the pipeline. The statistical distributions of input parameters to the pipeline transport model are developed based on the properties of 157 operating pipelines that transport oil sands crude within and outside of Alberta. For pipeline properties, custom distributions are used when sufficient data is available (more than 10 data point); elsewise fitted distributions (either uniform or normal) are used to represent the data. For this study, an operating flow rate at 90 percent of pipeline capacity is assumed along with a transport distance of 3,000 km. GHG emissions from electricity consumption range from 647-882 kg CO2eq/kWh electricity consumed, based on regional grid emissions factors obtained from the Alberta Electricity System Operator (AESO; AESO, n.d.).

One COPTEM run is completed for each of dilbit, low-API SCO, and high-API SCO, with

10,000 simulations per run. The output from the pipeline transportation model (kg CO2/bbl crude) is used as an input to the WTW mining model. For Project 1, the blended SCO produced by the upgrader is a medium-API SCO which is assumed to be an average of the low- and high- API SCO GHG intensity estimate. The results are consistent with the GHG emissions for crude transportation reported by Enbridge Inc. (2016).

A sensitivity of WTW results to the choice of transportation mode (pipeline versus rail, rail GHG emissions estimate from Tarnoczi (2013) and model for estimating pipeline GHG emissions (COPTEM versus others in the literature: Enbridge Inc., 2016; Hooker, 1981; Tarnoczi, 2013) is evaluated.

B.5.2 Enbridge 2015

The 2015 Enbridge Sustainability Report (Enbridge Inc., 2016) presents the total GHG emissions from all crudes transported via pipelines operated by Enbridge for 2012, 2013, and 2014 (denoted as “liquids pipelines”). For this study, GHG intensities are calculated based on this data considering the annual volumes of crude transported by Enbridge via pipeline reported in Enbridge Inc. (2014). This is modeled as triangular distribution with the most likely value being the average of the 2012 to 2014 values and the minimum and maximum being the 2012 and 2014 values, respectively.

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B.5.3 Hooker 1981

Hooker (1981) reports the energy consumption for five crude transportation pipelines: 0.050, 0.025, 0.052, 0.53, and 0.77 kWh/ton-mile. For this study, values are converted to kg CO2eq/bbl crude assuming 3,000 km transport distance and crude densities from COPTEM (Choquette- Levy et al., 2018) with the GHG intensity of the U.S. grid mix (average 691 kg CO2eq/MWh electricity consumed, see above). For the Monte Carlo simulations Hooker’s crude pipeline transport GHG emissions are modeled as a uniform discrete distribution, with equal probability of obtaining the GHG intensity reported for each pipeline.

B.5.4 Tarnoczi 2013

Tarnoczi (2013) quantified the expected energy consumption and GHG emissions from several proposed and existing pipeline and rail transport routes. For this study, the GHG intensities taken from the Tarnoczi model are based on a 3,340 km pipeline and the average of four rail transport routes (three to British Columbia, one to Texas). Results are presented in Table B-7.

Table B-7. Comparison of crude transportation models. Transportation Pipeline Rail Mode COPTEM Low- Med- High- Hooker Enbridge Tarnoczi Tarnoczi Model Dilbit API API API 1981a 2015 2013 2013 SCO SCO SCO MC Distribution Point Point Custom Custom Custom Custom Custom Triangular Type Estimate Estimate Average GHG 4.94 (three- Emissions (kg 4.51 3.73 2.89 2.05 10.75 year 46.0 11.67

CO2eq/bbl crude) average) P10/Minimum GHG Emissions 0.19 0.17 0.12 0.07 4.93 4.65 (2012) N/A N/A (kg CO2eq/bbl crude) P90/Maximum GHG Emissions 10.54 8.76 6.76 4.76 16.08 5.20 (2014) N/A N/A (kg CO2eq/bbl crude) P10 and P90: 10th and 90th percentiles, respectively, reported for COPTEM (Choquette-Levy et al., 2018). Minimum and maximum values reported for other models (Hooker and Enbridge). Each COPTEM run consists of 10,000 simulations, used as input to the WTW model. aHooker (1981) reports electricity

205 consumption in kWh/ton-mile, converted in this study to kg CO2eq/bbl crude based on 3,000 km transport distance and U.S. grid electricity emissions intensity. B.6 Methods: Modeling the Refining of Oil Sands Crudes with PRELIM

PRELIM is a spreadsheet-based mass and energy-based process unit-level tool that estimates the energy use and GHG emissions from refinery GHG emissions based on crude quality, refinery configuration, and allocation method (Abella et al., 2017). PRELIM data are derived from the literature, compared with confidential operating data, and verified by industry experts (Abella and Bergerson, 2012). The system boundary includes the energy consumed and GHG emissions from all major process units used in a refinery, as well as upstream energy use and GHG emissions from the supply chain of those fuels. All configurations include hydrotreating (to remove species such as sulfur), naphtha catalytic reforming (from naphtha fraction of crude input to refinery, for hydrogen production), and steam methane reforming (steam methane reformer, to meet additional hydrogen demand beyond that supplied by naphtha catalytic reforming). The process units included in PRELIM are shown in Figure B-2.

When a crude enters a refinery, it is distilled into different fractions (or cuts) depending on boiling point, then sent through a variety of processing units depending on the refinery configurations, which are split into three major categories. Lighter crude fractions require less refining and undergo hydroskimming, which is limited to hydrotreating and naphtha catalytic reforming. For medium and deep conversion refineries, more processing is required to process heavier crude fractions. At medium conversion refineries, light and heavy vacuum gas oils are sent either to gas oil hydrocracking (GO-HC) or fluid catalytic cracking (FCC). Deep conversion refineries include all the processing units included in medium conversion refineries but also upgrade vacuum residues using either delayed coking or hydrocracking that can be further processed.

Multiple allocation scenarios are considered for crude refining, which can change GHG intensity estimates for different refinery products significantly (Abella and Bergerson, 2012). Process- level allocation is employed in the Base Case Scenario. In this allocation method, the GHG emissions from each individual refining process is allocated to the product streams produced by

206 that refinery process. An alternative to process-level allocation is refinery-level allocation, whereby GHG emissions from refining are allocated to refined products based on their relative production compared to all products output from the refinery. Allocation can be in terms of energy content (HHV), hydrogen content, or mass of the products. For the Base Case scenario allocation is done in terms of energy content of the products with other methods explored in a scenario analysis.

Figure B-2. PRELIM model structure (Source: Abella and Bergerson, 2012).

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B.6.1 Crude Assays for Oil Sands Mining Projects

This section contains information about the crude properties (distillation yield, Figure B-3) of the crudes modeled in PRELIM in this study, based on crude assays obtained from Crudemonitor (“Crude Monitor,” 2018).

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Distillation Yield (vol%) Yield Distillation 20

0 1 2 3 4 5 6 SCO Dilbit Mining Project In Situ Pathway

VR HVGO LVGO AGO Diesel Kerosene Naphtha LSR

Figure B-3. Distillation yield for crudes produced by each mining project and in situ pathway. Data from project’s crude assay, obtained from Crude Monitor (2017). Distillation yields presented for blended assays for Projects 1 (2010 blend), 3, and 5. VR: Vacuum Residue; HVGO: Heavy Vacuum Gas Oil; LVGO: Light Vacuum Gas Oil; AGO: Atmospheric Gas Oil; LSR: Light Straight Run naphtha. B.6.2 PRELIM Assay Blending Tool

PRELIM includes a crude blending tool that allows the user to blend up to 10 crudes. Crude assays are selected from the assay inventory (or input as custom assays) and blended using a mass ratio. Blended crudes are assumed to be mixed using thermodynamic mixing rules to generate the mixture properties (Abella et al., 2017). Project 1 produces two main products: a light, sweet SCO and a heavy, sour SCO in proportions that vary over time in response to market conditions such as light/heavy differentials in crude prices. For Project 1, the two SCOs products (a light, sweet crude and a heavy, sour crude) are blended in proportion to the mass of crude

208 produced in each year, from 2005 to 2015. Production volumes are presented in Table B-8 and whole-crude properties modeled in PRELIM for Project 1 for each year from 2005 to 2015 are presented in Table B-9. For SCO produced by Projects 3 and 5, the assays (a light, sweet crude and a heavy, sour crude) are blended in proportion to the production rates presented in company annual reports: 150,500 and 76,900 bbl/day for a light, sweet crude and heavy, sour crude, respectively. Public time series data for this upgrader were not available at the time this study was completed. A summary of the crude properties produced by each project (taken from crude assays or blended assays) are presented in Table B-10.

Table B-8. Project 1 annual SCO production volumes, 2005-2015. SCO Production from Annual Report Relative Production for Blended Assays Light, sweet crude Heavy, sour crude Light, sweet crude Heavy, sour crude (1,000 bbl/day) (1,000 bbl/day) (wt%) (wt%) 2005 73 60 53 47 2006 110 118 46 54 2007 102 102 48 52 2008 77 129 35 65 2009 100 136 40 60 2010 82 145 34 66 2011 85 171 32 68 2012 94 161 35 65 2013 91.5 166 34 66 2014 100 159 37 63 2015 107 182 35 65 Production volumes (1,000 bbl/day) of two types of SCOs are reported in annual reports: light sweet SCO and light sour SCO. While the upgrader at Project 1 produces a variety of SCO qualities, the two SCO types reported can roughly be divided into two SCO types with public assay data available: a light, sweet crude, and a heavy, sour crude. SCO production is reported by volume. To generate blended assay, the relative production of each SCO type by mass is required. SCO production volumes are converted into mass assuming the density of each product in PRELIM: light, sweet crude 856.7 kg/m3; heavy, sour crude: 933.6 kg/m3. Source: company annual reports

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Table B-9. Summary of whole crude properties for Project 1 assay blends.

S API H MCR ~Kw Tb5o (wt%) (wt%) (wt%) (oC) 2005 1.55 27.1 12.1 0.29 11.6 350 2006 1.75 26.2 12.0 0.33 11.6 355 2007 1.70 26.4 12.0 0.32 11.6 354 2008 2.05 24.7 11.8 0.39 11.5 364 2009 1.92 25.4 11.9 0.37 11.6 360 2010 2.09 24.6 11.8 0.40 11.5 365 2011 2.07 24.7 11.8 0.40 11.5 365 2012 2.07 24.7 11.8 0.40 11.5 365 2013 2.10 24.5 11.8 0.40 11.5 366 2014 2.02 24.9 11.8 0.39 11.5 363 2015 2.07 24.7 11.8 0.39 11.5 365 S: sulfur content; API: gravity; H: hydrogen content; MCR: micro carbon residuum; ~Kw: approximated Watson characterization factor, with Tb50 in wt; Tb50: temperature where 50% of the mass of the whole crude is recovered through distillation.

Table B-10. Summary of whole crude properties for each mining project.

S H MCR Tb5o API ~Kw (wt%) (wt%) (wt%) (oC) Project 1 2.09 24.6 11.8 0.40 11.5 365 (2010 blend) Light SCO 0.16 33.1 12.7 0.02 11.9 315 Heavy SCO 3.06 19.9 11.1 0.59 11.4 393 Project 2 0.14 31.5 12.5 0.05 11.8 321 Project 3 0.81 27.1 12.0 3.87 11.4 317 (blend) Light SCO 0.03 31.3 12.6 0 11.2 245 Heavy SCO 2.24 19.5 10.7 10.9 11.6 448 Project 4 0.08 35.0 12.9 0.01 11.7 276 Project 5 0.81 27.1 12.0 3.87 11.4 317 (blend) Light SCO 0.03 31.3 12.6 0 11.2 245 Heavy SCO 2.24 19.5 10.7 10.9 11.6 448 Project 6 3.96 22.0 12.1 8.42 12.0 480

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S: sulfur content; API: gravity; H: hydrogen content; MCR: micro carbon residuum; ~Kw: approximated Watson characterization factor, with Tb50 in wt; Tb50: temperature where 50% of the mass of the whole crude is recovered through distillation; (Source: “Crude Monitor,” 2018). B.6.3 Uncertainty of PRELIM Emissions Estimates

The accuracy of PRELIM in predicting actual refinery emissions from ExxonMobil’s refineries is tested. PRELIM’s estimates of the refinery emissions for each of the refineries modeled is compared to observed emissions from those refineries in Figure B-4.

Figure B-4. Assessment of the precision and accuracy of PRELIM 1.2.1 based on the actual GHG emissions of 22 ExxonMobil refineries in 2012. Direct GHG emissions (actual and modeled) are compared, i.e. GHG emissions associated with purchased electricity are not included in the PRELIM estimates illustrated here. Refinery GHG emissions (kg CO2eq/bbl crude input) are normalized with respect to the average ExxonMobil refinery intensity. The “PRELIM Average” signifies that PRELIM did not demonstrate a systematic bias at the fleet level when runs explicitly accounted for configuration including the presence or absence of FCC-feed hydrotreating. However, PRELIM was not generally predictive for individual refineries. B.7 Methods: Refined Products Transportation

GHG emissions from combustion of diesel for truck and rail transport are based on those reported in the Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation Model (GREET; Wang, 2016), adjusted using 100-year GWPs reported in AR5 (IPCC, 2013). Lower and higher heating values for gasoline and diesel are adopted from GREET (Wang, 2016). Fuel economy for truck transport is modeled as a uniform distribution ranging from 115 to 154

211 ton-miles/gal diesel, based on the lower and upper bound for trucks reported in the 2013 Vehicle Technologies Market Report (Davis et al., 2014). For this study rail fuel efficiency of 289 Btu/ton-mile is applied (479 ton-miles/gal diesel converted using GREET 2013’s HHV for diesel), obtained from the 31st Edition of the Transportation Energy Data Book (Davis et al., 2012). Hooker (1981) reports electricity consumption values for four different pipelines transporting refined products: 0.036, 0.031, 0.087, and 0.037 kWh/ton-mile. In this study pipeline electricity consumption for refined products transport is modeled assuming equal probability of achieving each of the electricity consumption values from Hooker, converted to kg

CO2eq/bbl crude given a transport distance of 3,000 km and the U.S. average grid electricity GHG intensity described above.

B.8 Methods: Method Employed for Estimating GHG Emissions from Vehicle Use

For this study, greenhouse gas (GHG) emissions from vehicle use are adapted from GREET 2013, see Table B-11 (Wang, 2016). For the Base Case, combustion emissions are taken directly from GREET (73.2 g CO2eq/MJ gasoline, 75.6 g CO2eq/MJ diesel).

Table B-11. Vehicle use GHG intensities reported by GREET. Gasoline Diesel

CO2 (g/mile) 368 316

CH4 (g/mile) 0.0139 0.0006

N2O (g/mile) 0.0068 0.0007 Fuel Economy (Btu LHV/mile) 4,795 3,995 Vehicle Use (g CO2eq/MJ fuel) 73.2 75.6

In a sensitivity analysis, combustion emissions (CO2 and CH4) are scaled up based on the carbon and energy contents of the fuel, assuming the CO2 and CH4 are emitted in the same relative quantities for both fuels. Nitrous oxide (N2O) emissions from vehicle use are significantly affected by: driving cycle, sulfur content of the fuel being consumed, catalyst type, presence of a precatalyst, and vehicle type (Behrentz et al., 2004). A significant portion of N2O emissions from vehicle use are formed in a vehicle’s catalyst as an intermediate product when reducing NO to nitrogen (N2) at lower operating temperatures (Dasch, 2012). Some studies (e.g., Dasch, 2012)

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have measured N2O emissions for U.S. vehicle fleets based on catalyst type but not fuel properties. In one study, N2O emissions were linked to CO2 emissions, however the vehicle fleet upon which that study was based is not similar to the current vehicle fleet (Becker et al., 1999). Behrentz et al. (2004) selected a set of test vehicles to represent California’s vehicle fleet and attempted to establish correlations between N2O and other vehicle exhaust emissions. They found the highest correlation between N2O and NOx and a weak correlation between N2O and

CH4, depending on catalyst type, vehicle type, and drive cycle. GREET vehicle use emission factors (published in g/mile) are based on emissions estimates developed from simulations of the MOVES model for different classes of vehicle based on model year and average emissions over a vehicle’s operating life (Cai et al., 2013). The MOVES model’s vehicle use GHG emissions are developed based primarily on the Federal Test Procedure, a series of tests of vehicle tailpipe emissions based on different driving cycles, vehicle types, and emissions control technologies

(U.S. EPA, 2012). The MOVES model includes estimates of N2O emission (in terms of g/mile travelled) based on vehicle year and technology, and specifies the fuel type (i.e., whether gasoline or diesel fuel is combusted) but not the characteristics (e.g., carbon and energy content) of the fuel. As the current N2O estimate for vehicle use employed in the GREET model is not specified based on fuel characteristics, and no clear relationship between N2O emissions and other GHG emissions has been established in the literature, the GREET model emissions for

N2O are used directly in this study, despite the different fuel characteristics. The fuel properties predicted by PRELIM and those employed in GREET are compared in Table B-12. As the variability in fuel properties predicted by PRELIM could not be verified with real-world fuel property data, for this study vehicle use emissions are fixed at the GREET values in Table B-11.

Table B-12. Example properties of gasoline and diesel produced by each project. Project 1 Project 2 Project 3 Project 4 Project 5 Project 6 GREET Carbon Content (g C/MJ LHV) Gasoline 20.2 20.2 19.8 20.4 19.8 19.3 20.4 Diesel 21.0 20.9 21.0 21.0 21.0 21.0 20.6 Fuel Lower Heating Value (MJ LHV/bbl) Gasoline 4931 4915 5060 4969 4915 4706 5143 Diesel 5458 5417 5606 5425 5417 5461 5737 LHV: lower heating value; bbl: barrel; MJ: megajoule; g: gram; C: carbon.

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B.9 Results: Additional WTW Model Runs Employing Refinery- Level Allocation

Total GHG emissions (when all refinery products are considered) are the same for process-level and refinery-level allocation methods. However, refinery-level allocation results in a greater percentage of GHG emissions allocated non-road transport refinery products (which are produced by process units with lower hydrogen demand). As a result, compared to process-level allocation, refinery-level allocation reduces median GHG intensity distributions by 3-4 g

CO2eq/MJ gasoline and 2-3 g CO2eq/MJ diesel; distributions for each project derived using refinery-level allocation are presented in Figure B-5). Process-level allocation accounts for the different GHG intensities of producing different products within the refinery itself, leading to higher GHG emissions for gasoline, and to a lesser extent diesel. While overall, emissions from the refinery are unchanged, modeling decisions about how to allocate these emissions (i.e., in this case, by considering overall refinery inputs and outputs versus disaggregating the refinery into individual process units and allocating on that basis) impact the GHG emissions performance of the oil sands mining industry.

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Figure B-5. WTW GHG intensity of transportation fuels derived from oil sands mining projects using refinery-level allocation. Projects are listed in order from oldest (Project 1) to newest (Project 6). Projects 1-5 produce SCO; Project 6 produces dilbit. Left and right panels present results per MJ gasoline and per MJ diesel, respectively. Blue vertical lines represent the 10th and 90th percentiles of the Monte Carlo simulation generated GHG emissions ranges. Black vertical line represents the median value for each project. Mean results are marked by red “x” markers. Green vertical lines represent RFS2 baseline results (93.7 g CO2eq/MJ gasoline, 92.6 g CO2eq/MJ diesel, adjusted to 100-year GWPs reported in AR5). Percentage values on the right-hand side of the figure represent the difference between median GHG intensity and the RFS2 baseline value.

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B.10 Results: Sensitivity of WTW GHG Intensity to Variations in Model Inputs

Figure B-6. Sensitivity of WTW GHG intensity to variations in model inputs, Projects 1 (top), 2 (middle), and 3 (bottom). The ranges for each input parameter are presented on the figure next to the bars. The bars represent the variation in GHG emissions as the input parameters are varied one at a time from their mean values, holding all other parameters constant at their mean values. Modeling choice parameters are identified with an asterisk. NG: natural gas; FCC: Fluid Catalytic Cracking; HT: hydrotreating; GO-HC: Gas Oil Hydrocracking; H: hydrogen. COPTEM: Crude Oil Pipeline Transport Emissions Model.

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Figure B-7. Sensitivity of WTW GHG intensity to variations in model inputs, Projects 4 (top), 5 (middle), and 6 (bottom). The ranges for each input parameter are presented on the figure next to the bars. The bars represent the variation in GHG emissions as the input parameters are varied one at a time from their mean values, holding all other parameters constant at their mean values. Modeling choice parameters are identified with an asterisk. NG: natural gas; FCC: Fluid Catalytic Cracking; HT: hydrotreating; GO-HC: Gas Oil Hydrocracking; H: hydrogen. COPTEM: Crude Oil Pipeline Transport Emissions Model.

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B.11 Results: Contribution of Each Life Cycle Stage to WTW GHG Intensity, Additional Figures

Figure B-8. WTW GHG intensity distribution of mining pathways disaggregated by life cycle stage, Mining SCO and Mining Dilbit pathways, reported per MJ diesel. The left panel represents results for the Mining SCO pathway (Projects 1-5) and the right panel for the Mining Dilbit pathway (Project 6). Mean results are marked by red “x” markers.

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Figure B-9. WTW GHG intensity distribution of mining pathways disaggregated by life cycle stage, Projects 1-6, reported per MJ gasoline.

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Figure B-10. WTW GHG intensity distribution of mining pathways disaggregated by life cycle stage, Projects 1-6, reported per MJ diesel.

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B.12 Results: Drivers of Refinery Emissions Across Oil Sands Projects and Refinery Configurations

100

40 Product Yield (vol%) 20

Deep Deep Deep Deep Deep Deep Deep Deep

Medium Medium

Medium Medium Medium Medium Medium Medium Project 1 Project 2 Project 3 Project 4 Project 5 Project 6 In Situ SCO In Situ Dilbit

Blended Gasoline ULSD Jet-A/AVTUR Liquid Heavy Ends Coke

Figure B-11. Product slate for each mining project for medium and deep conversion, FCC refineries.

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10 Refinery Refinery GHG CO2eq/bbl (kg crude)

Deep Deep Deep Deep Deep Deep Deep Deep

Medium Medium

Medium Medium Medium Medium Medium Medium Project 1 Project 2 Project 3 Project 4 Project 5 Project 6 In Situ SCO In Situ Dilbit Electricity Heat Steam Hydrogen via SMR FCC catalyst regeneration

Figure B-12. Detailed PRELIM output: Breakdown of refinery GHG emissions by refinery configuration and mining project, disaggregated by process fuel consumption.

B.13 Results: Comparison of Refinery Emissions for Mined and In Situ Dilbit

Due to asphaltene precipitation in PFT, Project 6 dilbit has different properties than in situ dilbit (see Figure B-3 for the distillation yield for each crude modeled), with implications for refinery GHG intensities. Dilbit produced from Project 6 is blended with less diluent (dilbit composed of, on average, 23% diluent for mined dilbit, value derived from AER ST39 data, versus approximately 30% for the Cold Lake assay used to represent in situ dilbit, (ECCC, n.d.). Per bbl of crude, compared to in situ dilbit, mined PFT dilbit is associated with lower refinery GHG emissions for medium conversion refineries (average 33 kg CO2eq/bbl crude versus 35 kg CO2eq/bbl crude; results presented in Table 5-2) and higher GHG emissions for deep conversion refineries (average 62 versus 55 kg CO2eq/bbl crude). However, the additional refinery emissions for refining PFT mined dilbit in a deep conversion refinery are primarily allocated to diesel, so refinery emissions per MJ gasoline are similar for PFT mined and in situ dilbit.

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B.14 Results: Influence of Varying Fuel Properties on Vehicle Use Emissions

With the default version of PRELIM v1.2.1 (Abella et al., 2017), refinery product properties vary across crude qualities and refinery configuration. Vehicle use emissions from GREET, which are employed in this analysis, are based on a set of fuel properties, which are compared to predicted refinery product properties resulting from processing mined bitumen in Table B-12. Allowing the properties of the gasoline and diesel produced to vary and adjusting the GREET vehicle use emissions, results in vehicle use emissions that range from 72 g CO2eq/MJ gasoline for Project 6 to 75 g CO2eq/MJ gasoline for Projects 1 and 5 (see Table B-13). Future work should investigate the link between crude quality, refinery configuration, and fuel properties, and the resulting impacts on vehicle use emission, which have implications for LCAs over the WTW.

Table B-13. Vehicle use emissions, adjusted for fuel properties predicted by PRELIM. Project 1 Project 2 Project 3 Project 4 Project 5 Project 6 GREET Vehicle Use GHG, 75 74 73 75 73 72 73.2 g CO2eq/MJ gasoline Vehicle Use GHG, 77 77 77 77 77 77 75.6 g CO2eq/MJ gasoline

B.15 Results: Comparison of Oil Sands Pathway WTW GHG Intensities to Literature Values

In this section we compare the results of the WTW model (henceforth referred to as GHOST-SE) to other LCAs of transportation fuels derived from oil sands products. GHOST-SE model output distributions for the Mining SCO, Mining Dilbit, SAGD SCO, and SAGD Dilbit pathways are compared to Cai et al. (2015) and the original GHOST model (based on results reported in Bergerson et al. 2012) in Table 3 and Table 4 for gasoline and diesel, respectively. Cai et al. (2015) present results for an in situ pathway that includes both SAGD and CSS projects, and do not present disaggregated results for SAGD and CSS. As a result, GHOST-SE is compared to this in situ pathway. Additionally, Cai et al. present in their manuscript Mining + Bitumen (M+B) and In Situ + Bitumen (IS+B) pathways, which assume that the diluent in dilbit is

223 recovered and returned for reuse, with the refinery processing bitumen rather than dilbit. We compare GHOST-SE to the Mining + dilbit (M+dilbit) and In Situ + dilbit (IS+dilbit) pathways presented in the Supporting Information, which assume that the full crude is processed in the refinery (no diluent recovery).

Modeling methods employed in LCAs of transportation fuels derived from oil sands bitumen published prior to 2012 (Jacobs, 2009; TIAX, 2009) are compared to GHOST-SE and those employed in newer studies in Table 5-3 and Table B-14. For more information about the differences in modeling methods and results across the oil sands LCA literature, see Brandt (2011) or Charpentier et al. (2009).

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Table B-14. Comparison of GHOST-SE WTW GHG intensity distributions to literature values (g CO2q/MJ diesel).

Life cycle stage GHG intensity (g CO2eq/MJ diesel) Pathway Model/Report Crude Refining and Vehicle Upstream WTW pipeline transport usea GHOST-SE mean 19 0.60 7;1 105 76 (P10-P90) (15-30) (0.34-1.6) (5.0-10) (98-114) GHOSTb N/A N/A N/A N/A N/A Mining (range) SCO GREETc 23 3.2 7.3 76 110 Jacobsd 20 1.0 9.3 76 106 TIAXe 11-13 1.2-1.8 2.1-2.3 76 88-92 PADD 2/3 range GHOST-SE mean 9.5 0.74 8.2 95 76 (P10-P90) (7.7-11) (0.35-2.6) (6.3-11) (92-99) GHOSTb N/A N/A N/A N/A N/A 99Mining (range) Dilbit GREETc 9.0 4.5 9.6 76 Jacobsd 8.0 1.1 14 76 98 TIAXe N/A N/A N/A N/A N/A PADD 2/3 range 115 GHOST-SE mean 27 0.69 8.3 76 (106- (P10-P90) (22-42) (0.35-2.0) (6.6-11) 128) GHOSTb N/A N/A N/A N/A N/A SAGD SCO (range) GREETc 30 3.2 7.3 76 117 Jacobsd 28 1.0 9.3 76 113 TIAXe 27 1.2-1.8 3.0 76 106-107 PADD 2/3 range GHOST-SE mean 12 0.76 7.8 99 76 (P10-P90) (9.3-22) (0.35-2.1) (6.0-10) (93-107) GHOSTb N/A N/A N/A N/A N/A SAGD (range) Dilbit GREETc 20 4.5 9.6 76 110 Jacobsd 14.0 1.1 13.5 76 104 TIAXe 10-13 1.2-1.8 6.0-7.2 76 93-97 PADD 2/3 range aA consistent value for vehicle use emissions from GREET 2016 (Wang, 2016) is applied to all literature values; bGHOST results reported solely on a per MJ gasoline basis; Source: Bergerson et al. (2012); cSource: Cai et al. (2015); dDownstream GHG for Mining Dilbit pathway adapted from SAGD Dilbit pathway; Source: Jacobs (2009); eMining pathway presented includes upgrading (no mined dilbit pathway). Upstream range derived from pathways with different assumptions for treatment of coke (mining) and electricity generation (SAGD). Downstream range derived from transport and refinery results presented for PADD2 and PADD3; Source: TIAX (2009).

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B.15.1 Modeling Differences Across Literature Sources – Upstream

Several studies published after 2014 (e.g., Cai et al., 2015; Orellana et al., 2018; Sleep et al., 2018) employ the monthly operating data published in the AER statistical series to define the energy consumption for bitumen and SCO production from the oil sands. Cai et al. (2015) use the AER data to calculate production-weighted average WTW GHG emissions intensities and report results for in situ (aggregate of SAGD and CSS) and mining pathways, producing either dilbit, SCO, or bitumen. Jacobs (2009) and TIAX (2009) model specific oil sands projects rather than industry-wide oil sands crude production pathways, and, as they were published in 2009 when all mined bitumen was upgraded, do not quantify upstream GHG emissions for producing dilbit from mined bitumen.

B.15.2 Modeling Differences Across Literature Sources – Refinery

Cai et al. (2015) estimated refinery GHG intensities using a refinery efficiency formula and predicted higher GHG emissions for refining dilbit than SCO (GHG intensities of 12 and 14 g

CO2eq/MJ gasoline and 7.3 and 9.6 g CO2eq/MJ diesel for refining SCO and dilbit, respectively). Bergerson et al. (2012) adapted the refinery GHG intensities from TIAX (2009) reporting GHG intensities ranging from 7.2-11 and 15-16 g CO2eq/MJ gasoline for refining SCO and dilbit, respectively. Our analysis predicts higher GHG intensities for refining SCO than dilbit for gasoline production but lower for diesel (median GHG intensities of 13 and 18 g CO2eq/MJ gasoline, 7.1 and 8.2 g CO2eq/MJ diesel for refining SCO and dilbit from mined bitumen, respectively), due to factors mentioned previously.

Cooney et al. (2017) employ PRELIM to calculate an average 2014 U.S. petroleum. For all oil sands pathways, mean refinery emissions are 16-61% higher than the average U.S. petroleum refinery emissions for gasoline (11.2 g CO2eq/MJ gasoline) but only 1.4-19% higher than the average U.S. petroleum refinery emissions for diesel (7.0 g CO2eq/MJ diesel).

B.15.3 Modeling Differences Across Literature Sources – Vehicle Use

Vehicle use emissions also vary across studies. Cai et al. report vehicle use emissions of 73.2 g

CO2eq/MJ gasoline and 75.6 g CO2eq/MJ diesel across all pathways. Bergerson et al., (2012)

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employ vehicle use emissions from GHGenius (68.1 g CO2eq/MJ gasoline; Natural Resources Canada, 2009) in their study. In this study we utilize the same vehicle use emissions from

GREET for the Base Case (73.2 g CO2eq/MJ gasoline, 75.6 g CO2eq/MJ diesel) as those employed in Cai et al. (2015), adjusting PRELIM so that the gasoline and diesel produced by the refinery have the same properties (carbon and hydrogen content) as those modeled in GREET. In the literature comparison presented in Table 5-3 and Table B-14, vehicle use emissions from GREET are applied across all studies.

B.16 References

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Abella, J.P., Motazedi, K., Guo, J., Cousart, K., Bergerson, J.A., 2017. Petroleum Refinery Life Cycle Inventory Model (PRELIM); PRELIM v1.2; User guide and technical documentation.

Alberta Energy Regulator (AER), 2015. ST39: Alberta Minable Oil Sands Plant Statistics Monthly Supplement. Calgary, AB.

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Alberta Energy System Operator (AESO), n.d. Market and system reporting [WWW Document]. 2016. URL https://www.aeso.ca/market/market-and-system-reporting/ (accessed 11.22.17).

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Bergerson, J., Kofoworola, O., Charpentier, A.D., Sleep, S., MacLean, H.L., 2012. Life Cycle Greenhouse Gas Emissions of Current Oil Sands Technologies: Surface Mining and. Environ. Sci. Technol. 46, 7865–7874.

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Cai, H., Burnham, A., Wang, M., 2013. Updated Emissions Factors of Air Pollutants from Vehicle Operations in GREET Using MOVES. Lemont, Illinois.

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228 industry’s greenhouse gas emissions. Environ. Res. Lett. 4, 014005. https://doi.org/10.1088/1748-9326/4/1/014005

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Appendix C Supporting Information for Chapter 6

This appendix is based on supporting information prepared for:

• Sleep, S.; McKellar, J.M.; Bergerson, J.A.; MacLean, H.L. Expert Assessments of Emerging Oil Sands Technologies. J. Cleaner Production, 2017, 144(15), 90-99.

C.1 Background on Oil Sands Technologies C.1.1 Current Recovery and Extraction Technologies

The two most common thermal in situ methods are cyclic steam stimulation (CSS), and steam assisted gravity drainage (SAGD), both of which involve pumping steam into the reservoir to reduce the viscosity of the bitumen and separate it from the sand so that it can be recovered by pumping. In CSS, steam is periodically injected into the reservoir, and bitumen is pumped out through the same vertical well that is used for steam injection. SAGD involves the drilling of two horizontal wells. Steam is continuously injected into the upper well, mobilizing the surrounding bitumen and allowing it to flow into the lower well. A key indicator of the efficiency of a thermal in situ project is the steam-to-oil ratio (SOR), a measure of the amount of water in the form of steam required to produce one barrel of oil. Most projects have SORs between 2 and 5, although some projects are operating at slightly lower SORs while others are operating at much higher SORs (Brandt 2012).

The primary focus of current research and development within bitumen recovery and extraction has focused on in situ operations, which will also make up the majority of this investigation. In situ technologies can be loosely categorized based on the means of reducing bitumen viscosity (thermal, solvent, or other). Currently, only the steam process options within thermal processes section have reached significant commercial deployment; within this category, the research focus is on improvements to the existing recovery and extraction processes (particularly with respect to SAGD operations). The following sections provide an overview of each emerging technology category.

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C.1.2 Process Improvements to SAGD

Heat integration is also applicable to in situ operations, where large volumes of steam and hot water are both required for bitumen production. Steam processes can also be applied to in situ projects to reduce costs through lowering energy requirements. They involve modifications to the existing SAGD method to improve recovery efficiencies by changing well configuration and placement. The performance of these emerging steam processes at the commercial scale remains uncertain (Bergerson and Keith 2010). Once such process improvement which appears promising is the application of wedge wells, where additional wells are drilled between existing wells in order to access the ‘wedge’ of bitumen that forms in this space over the life of the project. Wedge wells are expected to improve recovery rates and reduce steam requirement by accessing bitumen that would be otherwise unavailable, which has already been partially heated by the steam chambers from the surrounding wells (Cenovus 2016).

C.1.3 Hybrid Steam-Solvent Processes

Solvent processes use similar well configurations as SAGD, however some or all of the steam typically required for SAGD is replaced with a solvent, which acts to reduce the viscosity of bitumen in the reservoir. When the bitumen is produced, a portion of the injected solvent is recovered and can be recycled for re-injection. In addition to reductions in energy and natural gas needed for bitumen production, solvent processes may also reduce or eliminate water use. A hybrid steam-solvent approach can be taken, where both steam and solvents are injected into the reservoir to increase bitumen production. Several companies are currently testing hybrid steam- solvent processes, and have found benefits, including improved recovery rates and reduced SORs, compared to typical SAGD and CSS operations (Laricina 2007). It is still uncertain whether sufficient solvent recovery and recycling rates can be obtained at commercial scale to make solvent processes viable. Large quantities of solvents lost to the surrounding reservoir may negatively affect the region’s ecosystems.

C.1.4 Electro-Thermal

Electro-thermal is also being considered as an alternative to steam injection. For one such technology, the electro-thermal dynamic stripping process (ET-DSP), a grid of electrodes is placed in the ground around a central extraction well (McGee 2009). An electrical current is

233 passed through these electrodes, which flows through the water in the formation and heats the surrounding bitumen. Pilot applications of this technology have performed well. The potential for emissions reductions from the eletro-thermal process is substantial, provided that electricity is obtained from low GHG-intensity sources.

C.1.5 In Situ Combustion

In situ combustion is an alternative in situ technology that has been under development for many years and is now moving toward commercial scale. With in situ combustion, air is injected into the reservoir to prompt combustion or gasification of the heavy portion of the petroleum in the reservoir. Bitumen viscosity is reduced by the heat generated by the combustion reaction. One advantage of this process is the potential for bitumen to be partially upgraded by the combustion process within the reservoir, reducing the need for upgrading and further processing once the bitumen is recovered (Bergerson and Keith 2010). The extent of in situ upgrading and the implications for further processing are yet to be fully determined. Higher bitumen recovery rates than current in situ methods may also be obtained, however GHG emissions from this process may be higher as natural gas is replaced with a heavier fuel. The toe to heel air injection (THAITM) process is an in situ combustion technology uses a vertical air injection well and a horizontal well for bitumen production (Petrobank 2009). A demonstration project of the THAITM began operation in 2006; however, the project has since been shut down (Touchstone 2016) and no new in situ combustion projects are planned.

C.1.6 Surface Mining Process Improvements

Improvements to surface mining operations generally have focused on incremental changes to site design and operation. The primary aim of these improvements is to improve the efficiency of the operation so that energy use is reduced and costs and GHG emissions are lowered. For example, better logistical planning at mine operations can be used to minimize the distances that mined materials must be transported within the site, from their initial removal from the ground to the separation facility and then the upgrader. Heat integration has also been identified as a means of reducing demand for outside energy sources whereby the waste heat from one process is used in another process. For mining projects with nearby upgrading operations, the excess hot water from the upgrader is pumped to the mine site where it is used in bitumen separation and

234 then recycled (Suncor & Jacobs 2012). Other companies have installed heat exchangers to recover the energy lost from process cooling water (Marathon 2010).

C.2 Procedure for Selecting Technologies to Include in Survey 2

Various steps were undertaken to identify the technologies to include in Survey 2. First, a literature search was completed to compile a list of all emerging technologies in the literature. Second, during the stakeholder workshop, participants completed a questionnaire about emerging in situ technologies. In the questionnaire, participants were provided with a list of 20 emerging in situ technologies identified by Gates and Wang (2011) and were asked if they were a) capable of assessing the technologies listed; b) if the list of technologies was comprehensive; c) if they would add or remove any technologies from the list; and d) if they were capable of assessing the technologies they suggested to include.

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Table C-1. Life cycle flow chart for oil sands derived fuels showing the current and emerging surface mining, in situ and upgrading technologies and incremental process changes included in this study. Stage Recovery and Extraction Bitumen Processing Technology Surface Mining In Situ Upgrading Emerging -Waterless separation -Hybrid steam- -Slurry hydrocracking Technologies process solvent -Novel coking -Switch to LNG vehicles processes -Other -Switch to electrical/hybrid -Electro-thermal thermal/hydrocracking drive for heavy haulers -In situ combustion -Use of biofuels Process Changes -Heat integration -Better well -Novel separation -Better site placement technologies planning/logistics -Improved boiler -Other upgrading -Use of more efficient efficiency trucks -Maximize steam or shovels quality -Reduction of utility -Improved handling requirements for buildings of -Lower water temperature produced water for ore -Low-carbon extraction alternatives -Improved bitumen/water to natural gas Separation -Use of solvents, surfactants, or other chemicals to aid in bitumen separation

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C.3 Project Economics

Table C-2. Experts’ expected internal rate of return required to invest in a project and expected operating cost that could be obtained in a project employing this technology. Expert 1 2 3 4 5 6 9 10 Technology Internal rate of return (IRR) required for a project employing this technology (%) SAGD 15 12 15 16 16 15 12 15 In situ with process 15 12 15 16 12 15 12 10 change Solvents - 12 15 16 16 15 12 10 In situ combustion - 18 15 12 Electro-thermal - 15 16 15 12 Othera technology 20 15 Surface mining 16 15 Surface mining with 12 10 process changes Technology (2012) Average production cost for a project employing this technology ($/bbl)b In situ with process 45 40 40 40 45 40 . 40 change Solvents - 40 38 40 45 47.57 40 45 In situ combustion 55 - 40 Electro-thermal 48 40 40 40 Othera technology 50 40 Surface mining with 50 40 process change a Experts 5 and 6 wrote in economics responses for an optional “other” technology. Expert 6 identified this other technology as noncondensible gas co-injection. bProduction cost for commercial SAGD project provided as reference to experts was $47.57 (current as of 2012, based on production costs presented in CERI, 2011). C.4 Participant Summary

A summary of the survey participants is provided in Table C-3. Experts were asked to report the number of years of experience they had in the oil sands field. More than half of the experts who participated in the survey had 6-10 years of oil sands experience. Note that the order of experts in the table is not linked to the expert numbers in subsequent tables/figures.

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Table C-3. Summary of experts who completed the survey. Expert Affiliation Years in Field A Academia 11-15 B Academia 6-10 C Academia 0-5 D Government Agency 6-10 E Government Agency 0-5 F Oil Sands Mining, In Situ & Upgrading Operator 6-10 G Oil Sands Mining, In Situ & Upgrading Operator 0-5 H Oil Sands In Situ Operator 11-15 I Oil Sands In Situ Operator 6-10 J Oil Sands In Situ Operator 6-10 K Oil Sands In Situ Operator 6-10

C.5 Electro-Thermal Responses

Experts were asked to “Fill in the ranges of expected values for the key operating parameters below. The ranges should represent the performance you think will be achieved by 2034 at more than one commercial project.

For reference, the following values were obtained from published reports containing operating data for E- T Energy's ET-DSP pilot project (Electricity input: 434 kWh / m^3 oil).”

Responses are summarized in Table C-4. One comment, from Expert 6: “I put zero as the lower bound on electricity during the operating phase given that these processes may be used ahead of the operating phase. Again, my dive into negative territory on rate is because the technology will be applied to more marginal resources than today's producing projects.”

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Table C-4. Experts’ projections of expected performance achievable by 2034 at more than one commercial operating project using technology. Question # Minimum 25th Median 75th Maximum (reference pilot Responses Percentile Percentile data if Avg σ Avg σ Avg σ Avg σ Avg σ applicable) Solvents Percent change in SOR compared to a currently operating SAGD project Percentage 7 -27.7 21.3 -20.9 17.8 -13.7 17.5 -8.7 19.3 2.1 24.8 Rate of solvent loss to the reservoir (solvent not recovered with bitumen) Percentage 7 13.6 7.1 20.7 10.6 23.1 11.3 27.9 14.7 39.4 23.1 Percentage change in bitumen recovery rate compared to a currently operating SAGD project Percentage 7 -1.7 15.7 9.8 15.2 18.6 12.0 25.2 13.5 37.5 24.8 In Situ Combustion Natural gas input during operating phase (23.3.) m3 natural 1 20 40 40 40 60 gas / m3 oil Percentage change in bitumen recovery rate compared to a currently operating SAGD project Percentage 1 14 14 14 14 14 Produced gas during operating phase (1485) m3 1 108 259 273 273 493 produced gas / m3 oil Natural gas input during pre-ignition heating cycle (PIHC) (3.8) m3 natural 1 3 5 5 5 7 gas / m3 oil Electricity input during operating phase (30.1) kWh / m3 1 29 40 42 42 56 oil Electro-thermal Electricity input during operating phase (434) kWh / m3 3 275.7 239.1 458.0 223.0 556.3 151.1 639.7 98.0 762.7 58.0 oil Percentage change in bitumen recovery rate compared to a currently operating SAGD project Percentage 4 -4.5a 13.2 1.0a 8.7 6.8 7.0 9.3 8.5 18.8 15.5 Upgrading What do you think is the probability that any future stand-alone upgraders with use cogeneration? b Percentage 6 35.7 33.4 40.5 33.3 49.2 33.3 54.0 34.2 59.5 33.4 a Comment from one expert who put negative values for the minimum and 25th percentile options: “my dive into negative territory on rate is because the technology will be applied to more marginal resources than today's producing projects”. b “The only reason not to use cogen is if the electrical grid does not need the power and transmission capacity is insufficient to take it somewhere else”

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C.6 Surface Mining and Upgrading Responses

Responses to survey questions related to surface mining and upgrading are presented below. When comparing responses between in situ, surface mining, and upgrading processes it is important to keep in mind the different time scales to investment. While the first commercial mining operation began in 1968 and is still in operation and there are no plans to shut down existing mining or upgrading facilities, in situ wells have much shorter lifetimes. As such the deployment of emerging in situ technologies by 2034 is more likely and would require less modification to existing operations.

C.6.1 Technology and Process Changes Ranking: Surface Mining

Seven experts answered questions related to surface mining. When asked if they thought that any emerging surface mining recovery or extraction technologies would be deployed at commercial scale by 2034, four responded with “yes” and three with “uncertain”. The emerging technologies for mining, ranked in Table C-5, were mostly related to reducing GHG emissions from the mine trucks by switching from diesel fuel to lower-carbon alternatives (e.g., liquefied natural gas vehicles) or more efficient drive systems (e.g., hybrid vehicles). An “other” emerging technology identified by one expert and not presented in the initial technology list was “slurry at mine-face and citric acid”. In the ranking of incremental process changes for mining, experts identified use of solvents, surfactants, and other chemicals to aid in bitumen separation, which would reduce hot water demand for mining projects and therefore reduce GHG emissions from natural gas combustion, as having the biggest impact on energy consumption by 2034.

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Table C-5. Experts’ ranking of the technology or process change that will have the biggest impact on energy consumption of a commercial oil sands surface mining project by 2034. Surface Mining Surface Mining Process Changes Emerging Technologies Use of solvents, surfactants, or other chemicals to Switch to LNG vehicles aid in bitumen separation Switch to electrical/hybrid drive system for heavy Use of more efficient trucks or mine shovels haulers Heat integration Waterless separation process Lower water temperature for ore extraction Use of biofuels Improved bitumen/water separation Other Reduction of utility requirements (heat, cooling, Slurry at mine face and citric acid electricity) for buildings Driverless trucks Better site planning/logistics 8. Other (not specified)

C.6.2 Barriers to the Adoption of Emerging Technologies

For surface mining, barriers identified by experts for both emerging technologies and incremental process changes were corporate inertia and technology lock-in due to the sunk costs of existing infrastructure. The full set of barriers to the adoption of emerging mining technologies is presented in Table C-6.

Table C-6. Experts’ ranking of the primary barriers to the adoption of emerging technologies and incremental process changes. Technology Ranking of barrier Surface 1. Availability of capital to fund research and development mining – 2. Technology uncertainty process 3. Regulatory issues changes 4. Other: Technology lock-in Surface 1. Cost issues mining - 2. Technology uncertainty technology 3. Technology development 4. Regulatory issues 5. Other: Sunk costs in existing infrastructure – technology lock-in Other: Corporate inertia Barriers noted as “Other” were written in by experts on the survey response page, remaining barriers listed were identified in the survey. a Other technology: non-condensible gas co-injection

Experts were asked three questions about upgrading oil sands bitumen. Between five and seven responses were obtained for each question. Responses are summarized in Table C-7. Most

241 experts felt that emerging technologies (and current technologies employing process changes) would contribute 40-70% of all upgrading in 2034, with fairly even contributions from “other thermal/hydrocracking” and “novel coking”. The only expert who did not feel that emerging technologies would be employed for bitumen upgrading by 2034 wrote, “I don't see there being any changes from the current mix of technologies because I don't believe that there will be any new upgrading facilities added in Alberta during this timeframe”.

Table C-7. Experts’ responses on the percentage of upgrading by current and emerging upgrading technologies in 2034. Expert 3 4 5 6 8 Current 65 30 40 50 60 Slurry hydrocracking 0 0 10 10 20 Novel coking 0 0 25 10 10 Other thermal/hydrocracking 15 50 10 15 2 Process change, novel separation 10 0 10 0 5 technologies Process change, other upgrading 10 20 5 15 3 Survey question: In 2034, what do you think the breakdown of bitumen upgrading technologies will be? Technology options are current upgrading technologies, new technologies including slurry hydrocracking, novel coking, and other thermal/hydrocracking, and process changes such as separation technologies, or other upgrading process changes. Adjust the input values so that the given percentage in each category represents the fraction that technology will contribute to total upgrading.

Experts were then asked about hydrogen production, a key energy input (and significant contribution to overall upgrading GHG emissions) to the bitumen upgrading process. Experts were asked which fraction of industry-wide hydrogen production they thought would be supplied by the different hydrogen production approaches (steam methane reforming (SMR), gasification, and other) by 2034. Six experts responded and all believed that SMR would contribute to the majority of hydrogen production, ranging between 70 and 90 percent of total hydrogen production (average response 81 percent with a standard deviation of 9.2 percent). The remainder of the hydrogen production is expected by experts to be provided by gasification (average 17%, standard deviation 7.5%) with a small fraction from “other” hydrogen production methods (average 2.5%, standard deviation 4.2%). If gasification is employed, four of six respondents believe that petroleum coke (petcoke) will be used as a fuel, with one expert choosing each of coke, fuel gas, and “low carbon fuel waste” as the fuel for gasification.

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Finally, experts were asked about the probability that any stand-alone future upgraders would use cogeneration. There was significant variation in responses from each expert for this question, and the standard deviation for each quartile was between 33.3 and 34.2 percent. According to one expert who provided significantly higher percentage responses for cogeneration compared to other experts, “the only reason not to use cogen is if the electrical grid does not need the power and transmission capacity is insufficient to take it somewhere else”.

C.7 Detailed Survey Responses

For type i and type ii survey questions where experts were asked to rank barriers or technologies, expert responses were aggregated using the Global Rank Method presented in Zickfield et al. (2007). Global rankings for each type i and type ii survey response were derived from the detailed responses below. Barriers or technologies are presented in the tables in the order they appeared in survey questions presented to experts.

C.7.1 Detailed Responses – Incremental Process Changes (In Situ)

Table C-8. Experts’ rankings of incremental process changes with the biggest impact on in situ projects over the next 20 years.

Expert 1 2 3 4 5 6 7 8 9 10 11

Better well placement 1 2 1 1 2 2 1 2 1 1 2

Low-carbon alternatives to natural gas 2 6 5 6 2 6 5 5 - 6 1

Improved boiler efficiency 3 4 3 2 1 3 4 3 - 2 4

Maximize steam quality 4 3 2 5 1 4 2 3 - 4 3

Improved handling of produced water 5 5 4 3 3 5 3 4 - 3 2

Other 6 1a 6 4 3b 1 6 - 2 c 5 1d Survey question: Identify the incremental process changes you think will have the biggest impact on an in situ project over the next 20 years. “Other” write-in responses by experts: a “Solvents”; b “Better facility heat integration and utilization of waste heat streams”; c “In-well control devices”; d “Low carbon alternatives - I include alternative in-situ heating techniques like direct contact steam generation, and RF heating of reservoirs”

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Table C-9. Experts’ rankings of the primary barriers to the implementation of incremental in situ process changes.

Expert 1 2 3 4 5 6 7 8 9 10 11

Availability of capital to fund research and development 1 3 2 3 1 4 2 1 2 1 2

Technology uncertainty 2 2 1 1 2 1 1 2 1 3 1

Regulatory issues 4 1 3 2 3 3 3 3 - 4 -

Other 3 - 4 4 4a 2b 4 - 3c 2 - Survey question: What do you think are the primary barrier(s) preventing or delaying the implementation of these incremental process changes for in situ projects? List all you believe apply, in their order of a importance. “Other” write-in responses by experts: “Competing capital for technology development, not just R&D”; b “The industry has the simultaneous need to get better while also being risk averse. This contradiction in needs works against new technology testing by industries driven by short-term financial metrics. The uncertainty/unpredictability of new technology paralyzes companies even though it offers huge upside. Under 'other' I would consider a barrier to be the number of opportunities for testing. While many opportunities to test are missed due to risk aversion, I believe there is a lack of quality opportunities in the pipeline. There isn't enough effort put toward improving SAGD by the industry, academe, regulators, etc. The regulatory landscape does not seem to be much of a barrier to incremental process changes. Money for incremental changes is not generally an issue for large oil companies, but may be an issue for smaller producers.” c “Willingness to entertain new ideas”. C.7.2 Detailed Responses – In Situ Technology Barriers

Table C-10. Experts’ rankings of the primary barriers to the adoption of novel in situ recovery and extraction technologies.

Expert 1 2 3 4 5 6 7 8 9 10 11

Availability of capital to fund research and development 1 3 3 1 1 2 1 1 - 2 2

Technology uncertainty 2 1 1 2 2 1 2 1 1 1 1

Regulatory issues 3 1 2 3 3 4 3 3 - 3 -

Other 4 4 4 4 4a 3b 4 -c 2d 4 - Survey question: What do you think are the primary barrier(s) to the development and implementation of novel recovery and extraction technologies? List all you believe apply, in their order of importance. a b “Other” write-in responses by experts: “Lack of operational experience leads to reluctance”; “Technology uncertainty drives fear of change, especially for novel technologies. The risk of failure is

244 simply too high to go after the prize.”; c “Its not clear to me whether regulatory issues is meant to indicate an increase or decrease in the implementation of technologies. I believe it can act both ways.”; d “Culture of non-innovation” C.7.3 Detailed Responses – Hybrid Steam-Solvents

Table C-11. Experts’ rankings of the primary barriers to the commercial deployment of hybrid steam-solvent processes.

Expert 1 2 3 4 5 6 7 8 9 10 11

Technology uncertainty - - 1 2 1 2 - - 2 4 1

Technology development - - 2 1 2 4 - - - 3 3

Solvent availability - - 3 5 3 3 - - - 1 5

Solvent cost - - 4 4 1 1 - - 1 2 2

Other - - 5 3 - 5 - - - 5 - Survey question: Identify and rank the primary barrier(s) you think are preventing or delaying the a commercial deployment of this technology. “Other” write-in responses by experts: “Solvent cost is the primary barrier. If it were cheaper, it would help offset the risk around solvent returns, which is the key technology uncertainty.”

Table C-12. Experts’ rankings of the factors that will have a significant impact on the choice of solvent.

Expert 1 2 3 4 5 6 7 8 9 10 11

Reduction in SOR - - 1 2 2 3 2 - 3 3 2

Solvent recovery rate - - 2 ------

Improved overall reservoir recovery rate - - 3 1 1 4 4 - 4 4 3

Solvent availability - - 4 3 4 2 3 - 2 1 -

Solvent cost - - 5 4 1 1 1 - 1 2 1

Other - - - 5 3a - 5 - - 5 -

245

Survey question: Identify and rank any factor(s) you think will have a significant impact on the choice of a solvent. “Other” write-in response by experts: “Solvent recovery rate and selective partial upgrading of mobilized bitumen.” C.7.4 Detailed Responses – In Situ Combustion

Table C-13. Experts’ rankings of the primary barriers to the commercial deployment of in situ combustion/

Expert 1 2 3 4 5 6 7 8 9 10 11

Technology uncertainty - - 1 - 1 2 2 - 1 - -

Cost factors - - 2 - 2 3 4 - 2 - -

Technology development - - 3 - 1 1 1 - - - -

Other - - 4 - 3a 4b 3 - 3c - - Survey question: Identify and rank the primary barrier(s) you think are preventing or delaying the a commercial deployment of this technology. “Other” write-in responses by experts: “Reservoir uncertainty”; b “The limited body of knowledge of in-situ combustion, combined with the lack of ability to properly model the process (thus requiring large expensive field pilots), combined with the large uncertainty about what the technology concept actually is combined with the unknown about what the produced oil properties are or how they will vary, makes even the smallest barrier here larger than the largest barrier for solvent processes. Just to make the point that 'biggest' and 'smallest' may not be the same across all the technologies.”; c “Quality of produced oil (LTO dominated products)”. C.7.5 Detailed Responses – Electro-Thermal

Table C-14. Experts’ rankings of the primary barriers to the commercial deployment of electro-thermal.

Expert 1 2 3 4 5 6 7 8 9 10 11

Technology uncertainty - - 1 - 3 2 - - 3 1 1

Technology development - - 2 - 1 1 - - - 2 3

Electricity pricing - - 3 - 2 4 - - 1 3 2

Electricity availability - - 4 - 4 3 - - 2 4 4

Other - - 5 - 5a 5b - - 4c 5 5

246

Survey question: Identify and rank the primary barrier(s) you think are preventing or delaying the a commercial deployment of this technology. “Other” write-in responses by experts: “Reservoir response”; b “Electricity pricing is a large barrier if one of these process is used to completely replace SAGD. However, I rank technology development and uncertainty even higher because the commercial concept(s) aren't fully defined yet. I expect solutions in this space to augment rather than replace SAGD, in which case electricity pricing will be less of a barrier.”; c “Impact on cap rock”. C.7.6 Detailed Responses – Incremental Process Changes (Surface Mining)

Table C-15. Experts’ rankings of the incremental process changes that will have the biggest effect on energy consumption of a mining project.

Expert 1 2 3 4 5 6 7 8 9 10 11

Heat integration - - - - 1 - - 2 6 3 5

Use of more efficient trucks or mine shovels - - - - 2 - - 2 1 6 4

Lower water temperature for ore extraction - - - - 2 - - 5 2 2 6

Use of solvents, surfactants, or other chemicals - - - - 3 - - 3 4 1 1 to aid in bitumen separation

Improved bitumen/water separation - - - - 3 - - 3 3 7 2

Reduction of utility requirements (heat, cooling, - - - - 4 - - 6 5 5 3 electricity) for buildings

Better site planning/logistics - - - - 4 - - 4 4 7

Other ------8 8 Survey question: Identify and rank the incremental process changes you think will have the biggest effect on the energy consumption (electricity and natural gas) of a mining project in the next 20 years.

247

Table C-16. Experts’ rankings of the primary barriers to the implementation of incremental process changes for mining projects.

Expert 1 2 3 4 5 6 7 8 9 10 11

Availability of capital to fund research and development - - - - 1 - 2 2 2 1 1

Technology uncertainty - - - - 3 - 1 2 1 2 2

Regulatory issues - - - - 4 - 3 3 - 3 3

Other - - - - 2a - 4 - - 4 4 Survey question: What do you think are the primary barrier(s) preventing or delaying the implementation of these incremental process changes for mining projects? List all you believe apply, in their order of a importance. “Other” write-in response by expert: “Technology lock-in”. C.7.7 Detailed Responses – Surface Mining Technologies

Table C-17. Experts’ rankings of the novel technologies that will have the biggest effect on energy consumption of a mining project in the next 20 years.

Expert 1 2 3 4 5 6 7 8 9 10 11

Switch to electrical/hybrid drive system for heavy haulers - 1 - - 2 - 2 2 - 2 3

Driverless trucks - 2 ------

Switch to LNG vehicles - 3 - - 2 - 1 2 1 1 2

Waterless separation process - 4 - - 1 - 3 3 3 5 1

Use of biofuels - - - - 4 - 5 4 - 4 5

Other - - - - 3a - 4 - 2b 3 4 Survey question: Identify and rank the novel technologies you think will have the biggest effect on the energy consumption (electricity and natural gas) of a mining project in the next 20 years. “Other” write- in responses by experts: a “Slurry at mine-face and citric acid”; b “Driverless trucks”.

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Table C-18. Experts’ rankings of the primary barriers to the adoption of novel mining technologies.

Expert 1 2 3 4 5 6 7 8 9 10 11

Cost issues - - - - 1 - 2 1 1 2 2

Technology development - - - - 2 - 3 2 3 1 3

Technology uncertainty - - - - 3 - 1 3 2 3 1

Regulatory issues - - - - 4 - 4 4 - 4 4

Other - - - - 5 - 5a - - 5 5 Survey question: What do you think are the primary barrier(s) preventing or delaying the implementation of novel technologies for mining projects? List all you believe apply, in their order of importance. a “Other” write-in responses by experts: “Corporate inertia”. C.8 References

Bergerson, J.A. Keith, D.W. The truth about dirty oil: is CCS the answer? Environ. Sci. Technol. 2010, 44, 6010-6015.

Brandt, A. Variability and uncertainty in life cycle assessment models for greenhouse gas emissions from Canadian oil sands production. Environ. Sci. Technol. 2012, 46 (2), 1253-1261.

Canadian Energy Research Institute (CERI), 2011. Canadian oil sands supply costs and development projects (2010-2044). Study No. 122. Canadian Energy Research Institute, Calgary, AB.

Charpentier, A.D., Bergerson, J.A., MacLean, H.L. Understanding the Canadian oil sands industry’s greenhouse gas emissions. Environ. Res. Lett. 2009, 4, 1-11.

Cenovus Energy Ltd. Operations, Wedge Well Technology, 2016. www.cenovus.com/technology/wedge-well-tm-technology.html (accessed 16.07.05).

Gates, I.; Wang, J. Evolution of In Situ Oil Sands Recovery Technology: What Happened and What’s New? SPE Heavy Oil Conf. Exhib. 2011 2011, 1–10.

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GHGenius 3.14b, 2008. Ottawa, ON: Natural Resources Canada. Retrieved January 23, 2009, www.ghgenius.ca.

Laricina Energy Ltd., 2007. Demystifying the Reservoir. Calgary, AB. www.laricinaenergy.com/uploads/press/demystify_oilsands.pdf#zoom=100 (accessed 16.07.05).

Marathon Oil Corporation (Marathon), 2010. Oil Sands Fact Book – Marathon Oil Corporation. www.marathonoil.com/content/documents/investor.../fact_books/osm/OSM_2009.pdf (accessed 16.07.05).

Petrobank Energy Resources Ltd. (Petrobank), 2009. The THAI Process. Calgary, AB. www.petrobank.com/wp-content/uploads/2009/06/THAI-sheet-Nov-20091.pdf (accessed 10.09.16)

(S&T)2 Consultants Inc., 2008. 2008 GHGenius Update. Delta, BC.

Touchstone Exploration Inc. (Touchstone), 2016. News Release: Touchstone Announces Kerrobert Saskatchewan Disposition. Calgary, AB. www.touchstoneexploration.com/news (accessed 16.07.05).

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Appendix D Expert Elicitation Survey Questions

The following section contains the survey questions for the expert elicitation discussed in Chapter 6.

1 of39 Introduction

LCA-OST Phase 3, Part 2 Survey: Emerging Recovery, Extraction, and Upgrading Technorogies

Purpose:

1. To gauge industry views regarding emerging recovery, extraction, and upgrading technologies in the oil sands over the next 20 years; and, 2. To obtain quantitative input regarding the anticipated performance of these technologies.

Information Requested:

This survey is expected to take approximately 30 minutes to complete. We are requesting your expert judgment on specific technologies and processes and their potential to affect commercial oil sands recovery, extraction, and upgrading operations.

Expertise:

Please note that while the survey poses questions related to specific surface mining, in situ, and upgrading operations as well as specific oil sands technologies and processes, we are expecting you to answer only those within your area( s) of expertise. You may skip any questions you feel lay outside your field. 2of39 Introduction Continued

Emissions Reductions Under Investigation

Reductions achieved through incremental process changes: These process changes may include changes to current operating procedures or equipment, as well as changes requiring capital expenditures. Only technologies currently available and deployed in the oil sands industry are considered.

Reductions achieved through new technologies: These technologies may be implemented at new or existing facilities, but are not yet commercially available or deployed.

Note about new technologies: Three primary technologies categories are identified as emerging technologies (hybrid steam solvent processes, in situ combustion, and electrothermal processes). For each technology-related question, the option exists to specify an "other" technology, to be used if you believe a different type of technology (e.g. biological processes) should be included in the analysis. This technology can be specified in the optional comment box at the bottom of each slide. 3of39 In Situ

In Situ

If you do not wish to answer questions related to in situ projects, please click through these questions (without entering any data) to the mining and/or upgrading questions. 4of39 In Situ - Process Changes

2 Identify and rank the incremental___ process___ changes 1 you __ think will have the biggest impact on the SOR of an in situ project in the next 20 years.

Biggest Impact Betterwell placement Drag a cardhere.

Improved boiler efficiency Drag card here. a

Maximize steam quality I Drag card here. a

Improvedhandling of produced Drag card here. water a

Other I Drag cardhere. You may return cardshere. a SmallestImpact

Comment

optional 5of39 In Situ - Process Changes - Barriers

What do you __ think 1 are the primary barrier(s) preventing or delaying the implementation of these incremental___ process___ changes 2 for in situ projects? List all you believe apply, in their order of importance

Availabilityof capital Biggest Barrier Drag a card here.

Regulatoryissues I Drag a card here. Other I Drag a card here. You may return cardshere. SmallestBarrier

Comment

optional 6of39 In Situ - Process Changes - SOR Chang

How much can these technologies improve upon SOR for a particular project? Given that the SOR range for current projects is 1 generally between ...... 2.1 to 5.4 , fill in SOR range below that you think 2 will be reached once these improvements are adopted......

What is the average anticipated SOR?

Adjust the sliders until you think that 80% of projects will have a cumulative SOR within this range. =

Comment

optional 7of39 In Situ - Process Changes - Economics

What I_ _RR 1 (percentage) would you require to invest in a SAGO project?

What IRR (percentage) would you require to invest in a project that involved employing one or more of these process changes to an existing SAGO project?

If the averageprod_ _uction __cost 2 for a SAGO project is $30 per barrel, what production cost do you __ think 3 would be obtained in a project employing one or more of these process changes?

Comment

optional 8of39 In Situ - Technology - General

In 20 years, what percentage do you __ think 1 each technology (including -�-�t�.�-�!. 2 and .�.!!.1.�_qi!.�.9.. 3 technologies) will contribute in terms of overall in situ bitumen production? Adjust the sliders so that the given percentage in each category represents the fraction that technology will contribute to overall production.

Current Electrothermal Hybrid Steam Solv In Situ Combustio Other

20%

Comment

optional

9of39 In Situ - Technology - Barriers

What do you think are the primary barrier( s) 1 to the development and implementation of novel recovery and extraction technologies? List all you believe apply, in their order of importance.

Most Important Availabilityof capital to fund Drag a cardhere. rampd

Technology uncertainty Drag card here. a

Regulatoryissues I Drag card here. a

Other I Drag card here. You may return cardshere. a Least Important

Comment

optional 10 of39 In Situ - Technology - Selection

We have identified three categories of emerging in situ technologies (hybrid steam solvent, in situ combustion, electrothermal) currently under development but frequently mentioned as having the potential to be commercially available and deployed in the next 20 years.

Do you think any technologies should be added or removed from this list? I Select one...

Which technologies would you add or remove to this list and why? 11 of39 In Situ - Hybrid Steam Solvent Process

Hybrid Steam Solvent Processes

If you do not wish to answer questions related to in situ projects, please click through these questions (without entering any data) to the mining and/or upgrading questions. 12 of39 In Situ - Hybrid Steam Solvent ProcessE

Identify and rank the primary factor(s) 1 you __ think 2 are preventing or delaying the commercial deployment of this technology.

Technology uncertainty Biggest Barrier Drag a card here.

Solventcost I Drag a card here. Solvent availability I Drag a cardhere. Technology development Drag a cardhere.

Other I Optional: Drag a card here. You may return cardshere. SmallestBarrier

Comment

optional 13 of39 Hybrid Steam Solvents - Solvent Selecti

Identify and rank the solvent(s) 1 you think will be most widely used to assist with in situ extraction.

Will solvent recoveryand Butane rec�ling (for re-injection) be em ployedfor this I solvent? Gas condensate Most widely used Yes No Uncertain I Drag a cardhere. JetB 0 0 0 I Drag a cardhere. Naphtha 0 0 0 I Drag a card here. Propane 0 0 0 I Drag a card here. X�ene 0 0 0 I Drag a card here. Other 0 0 0 I Drag a cardhere. You may return cardshere. 0 0 0

Drag a cardhere. 0 0 0 Least widelyused

Comments

optional 14 of39 Hybrid Steam Solvents - Solvent Selecti

Identify and rank any factor(s) 1 you think will have a significant impact on the choice of solvent.

Reduction in SOR Biggest Impact I Drag a card here. Improved overall reser..oir recoveryrate Drag a card here.

Solvent cost I Drag a cardhere. Solvent availability I Drag a cardhere. Other I Drag a card here. You may return cardshere. SmallestImpact

Comments

optional 15 of39 Hybrid Steam Solvents - Operations

Fill in the ranges of expected values for the key operating parameters below. The ranges should represent the performance you think will be achieved by 2032 at more than one commercial project.

Percent change in SOR compared to a currently operating Click the axis: SAG D project • Minimum Maximum 25th Percentile 75th Percentile Median

-80 --60 -40 -20 0 20 40 60 80 Percentage (%) Change in SOR

Rate of solvent loss to the reservoir ( solvent not Click the axis: recovered with bitumen) • Minimum Maximum 25th Percentile 75th Percentile Median

10 20 30 40 50 60 70 80 90 Percentage (%) of Solvent Lost to the Reservoir Percentage change in bitumen recovery rate compared to Click the axis: a currently operating SAG D project • Minimum Maximum 25th Percentile 75th Percentile Median

-80 --60 -40 -20 0 20 40 60 80 Percentage (%) Change in Bitumen RecoveryRate

Comment

optional 16 of39 Hybrid Steam Solvents - Economics

What _I_RR 1 (percentage) would you require to invest in a SAGO project?

What IRR (percentage) would you require to invest in a project employing this technology?

If the average_production __ cost 2 for a SAGO project is $30 per barrel, what production cost do you __ think 3 would be obtained in a project employing this technology? 17 of39 In Situ - In Situ Combustion

In Situ Combustion

If you do not wish to answer questions related to in situ combustion, please click through these questions (without entering any data) to the final submission page. 18 of39 In Situ Combustion - Barriers

2 Identify and rank the primary factor(s) 1 you __ think are the primary barrier( s) to the commercial deployment of this technology

Biggest Barrier Technology uncertainty Drag card here. a

Technology development Drag a card here.

Costfactors I Drag a cardhere.

Drag a cardhere. You may return cardshere. SmallestBarrier

Comment

optional 19 of39 In Situ Combustion - Operations

Fill in the ranges of expected values for the key operating parameters below. The ranges should represent the performance 1 ...... you think will be achieved by 2032 at more than one commercial project.

For reference, the following values were obtained from the design data in the December 2008 Application for Approval 2 for Petrobank's May River THAI 3 project.

PIHC: Natural gas input: 3.8 mA3 natural gas/ mA3 oil

Operations: Natural gas input: 23.3 mA3 natural gas/ mA3 oil

Electricity input: 30.1 kWh/ mA3 oil

Produced gas: 1485 mA3 / mA3 oil

4 Click the axis: Natural gas input during pre-ignition heating cycle (PI.HC ) • Minimum Maximum 25th Percentile 75th Percentile Median

1 2 3 4 5 6 7 8 9

m"3 natural gas I m"3 oil Natural gas input during operating phase Click the axis: • Minimum Maximum 25th Percentile 75th Percentile Median

10 20 30 40 50 60 70 80 90 m"3 natural gas I m"3 oil

Electricity input during operating phase Click the axis: • Minimum Maximum 25th Percentile 75th Percentile Median

10 20 30 40 50 60 70 80 90 kWhlm"3 oil

Produced gas during operating phase Click the axis: • Minimum Maximum 25th Percentile 75th Percentile Median

200 400 600 800 1000 1200 1400 1600 1800 2000 2200 2400 2600 2800 m"3 producedgas I m"3 oil Percentage change in bitumen recovery rate compared to • a currently operating SAG D project Minimum Maximum 25th Percentile 75th Percentile Median

10 20 30 40 50 60 70 80 90 Percentage (%) change in bitumen recoveryrate 20 of39 In Situ Combustion - Economics

What _I_RR 1 (percentage) would you require to invest in a SAGO project?

What IRR (percentage) would you require to invest in a project employing this technology?

If the average_production __ cost 2 for a SAGO project is $30 per barrel, what production cost do you __ think 3 would be obtained in a project employing this technology?

Comment

optional 21 of39 In Situ - Electrothermal Processes

Electrothermal Processes

If you do not wish to answer questions related to electrothermal processes, please click through these questions (without entering any data) to the final submission page. 22of39 In Situ - Electrothermal Processes - Bar

2 Identify and rank the primary factor(s) 1 you __ think are the primary barrier( s) to the commercial deployment of this technology

Biggest Barrier Technology uncertainty Drag card here. a

Technology development Drag a card here.

Electricity pricing I Drag a cardhere.

Electricity availability Drag a cardhere.

Other I Drag card here. a You may return cardshere. SmallestBarrier

Comment

optional 23 of39 In Situ - Electrothermal Processes - OpE

Fill in the ranges of expected values for the key operating parameters below. The ranges should represent the performance you think will be achieved by 2032 at more than one commercial project.

For reference, the following values were obtained from published 1 2 reports containing operating data for E-T Energy's ...... ET-DSP pilot project.

Electricity input: 434 kWh / mA3 oil

Electricity input during operating phase Click the axis: • Minimum Maximum 25th Percentile 75th Percentile Median

100 200 300 400 500 600 700 800 900 kWh/m113 oil

Percentage change in bitumen recovery rate compared to Click the axis: a currently operating SAG D project • Minimum Maximum 25th Percentile 75th Percentile Median

Percentage (%) change in bitumen recoveryrate 24of39 In Situ - Electrothermal Processes - Ecc

What _I_RR 1 (percentage) would you require to invest in a SAGO project?

What IRR (percentage) would you require to invest in a project employing this technology?

If the average_production __ cost 2 for a SAGO project is $30 per barrel and the well-head production cost for the _ET-_OSP 3 pilot was (2007) $32.23 per barrel, what production cost do you __ think 4 would be obtained in a project employing this technology?

Comment

optional 25 of39 In Situ - Other Technology

Other In Situ Technology

Three primary technologies categories are identified as emerging technologies (hybrid steam-solvent processes, in situ combustion, and electrothermal processes). If you believe a different type of technology ( e.g. biological processes) should be included in the analysis, the following slides can be used to evaluate an emerging in situ technology not previously evaluated here.

If you do not wish to answer questions related to any additional emerging in situ technologies, please click through these questions (without entering any data) to the final submission page.

If you are answering questions in the following section, please identify the technology you are assessing below:

optional 26 of39 In Situ - Other Technology - Barriers

2 Identify and rank the primary factor(s) 1 you __ think are the primary barrier(s) to the commercial deployment of this technology.

Biggest Barrier Technology uncertainty Drag card here. a

Technology development Drag a card here.

Costfactors I Drag a cardhere.

Drag a cardhere. You may return cardshere. SmallestBarrier

Comment

optional 27 of39 In Situ - Other Technology - Operations

Fill in the ranges of expected values for the key operating parameters below. The ranges should represent the performance you think will be achieved by 2032 at more than one commercial project.

Percentage change in SOR compared to a currently Click the axis: operating SAGO project (if applicable) • Minimum Maximum 25th Percentile 75th Percentile Median

-80 --60 -40 -20 0 20 40 60 80 Percentage (%) Change in SOR

Percentage change in bitumen recovery rate compared to Click the axis: a currently operating SAG D project (if applicable) • Minimum Maximum 25th Percentile 75th Percentile Median

-80 --60 -40 -20 0 20 40 60 80 Percentage (%) Change in Bitumen RecoveryRate Percentage change in any other 1 parameter you think is Click the axis: • Minimum significant, as compared to a currently operating SAGO Maximum project 25th Percentile 75th Percentile Median

-80 -60 -40 -20 0 20 40 60 80 Percentage (%) change

Comment

optional 28 of39 In Situ - Other Technology - Economics

What _I_RR 1 (percentage) would you require to invest in a SAGO project?

What IRR (percentage) would you require to invest in a project employing this technology?

If the average_production __ cost 2 for a SAGO project is $30 per barrel, what production cost do you __ think 3 would be obtained in a project employing this technology?

Comment

optional 29 of39 Mining

Mining (No Upgrading)

If you do not wish to answer questions related to mining projects, please click through these questions (without entering any data) to the mining and/or upgrading questions. 30 of39 Mining - Process Changes - Opportunit

Identify and rank the incremental___ process___ changes 1 you __ think 2 will have the biggest effect on the energy consumption ( electricity and natural gas) of a mining project in the next 20 years.

Heat integration Biggest Item I Drag a cardhere. Better site planning I Drag a card here. Other I Drag a card here. You may return cardshere. SmallestItem

Comment

optional 31 of39 Mining - Process Changes - Barriers

What do you think are the primary barrier( s) preventing or delaying the implementation of these incrementa1... process... changes 1 for mining projects? List all you believe apply, in their order of importance

Availabilityof capital Biggest Barrier I Drag a card here. Regulatoryissues I Drag a cardhere. Other I Drag a cardhere. You may return cardshere. SmallestBarrier

Comment

optional 32 of39 Mining - Technology - Opportunities

What _I_RR 1 (percentage) would you require to invest in a mining project?

What IRR (percentage) would you require to invest in a project employing one or more of these process changes to an existing mining project?

Comment

optional If the average _ __production __ cost 2 for a mining project is $30 per 3 barrel, what production cost do ...... you think would be obtained in a project employing one or more of these process changes?

Comment

optional

U 33 of39 Mining - Technology - Barriers

What do you __ think 1 are the primary barrier(s) preventing or delaying

the implementation of these technologies 2 for mining projects? List all you believe apply, in their order of importance

Biggest Barrier I Regulatoryissues Drag a card here.

Technology uncertainty Drag a card here.

Technology development Drag a card here.

Cost issues I Drag a cardhere. Other I Drag a cardhere. You may return cardshere. SmallestBarrier

Comment

optional 34of39 Upgrading

Upgrading

If you do not wish to answer questions related to upgrading projects, please click through these questions (without entering any data) to the mining and/or upgrading questions. 35 of39 untitled

Given that *% of all bitumen produced is partially or fully upgraded 1 today, *% ...... partially upgraded , *% 2 fully ..upgraded .._ in ___a ___ stand-alone,..tacility , and *% 3 fully __ upgraded___ in ___an __ integrated...facility , what percentage of bitumen do you think undergo any upgrading in 2032, and what technology mix do you__ think 4 will be employed to upgrade the bitumen? Adjust the sliders so that the given percentage in each category represents the fraction that each process will contribute relative to overall production.

No Upgrading Partial Upgrading Full Upgrading - Stand Full Upgrading - lntegr

25%

Comment

optional 36 of39 Upgrading - Innovation

In 2032, what do you __ think 1 the breakdown of bitumen upgrading technologies will be? Technology options are current technologies ( 2 3 -�-�t�.�-�!. ), new technologies including -�-1-�t�.Y.....� .Y..9.E.�.��-�-��!.�9. , 4 5 novel ...coking , and other __ thermal/hyd rocracking , and process 6 , changes such as ...... separation technologies or other process 7 changes other • Adjust the sliders so that the given percentage in each category represents the fraction that each process will contribute relative to overall production

Current Slurry Hy Novel Co Other The Separatio Other

11%

Comment

optional 37 of39 Upgrading - Hyrdogen Production

What fraction of industry-wide hydrogen production do you think will be supplied by each process/approach by 2032? Adjust the sliders so that the given percentage in each category represents the fraction that each process will contribute relative to overall production

Steam Methane Reforming Gasification Other

fnoi. � f34%

If gasification is employed, what f.�.�-1. 1 do .Y..

Comment

optional 38 of39 Upgrading - Cogeneration

What do you think is the probability that any future stand Click the axis: • Minimum alone upgraders with use cogeneration? Maximum 25th Percentile 75th Percentile Median

10 20 30 40 50 60 70 80 90 Percent (%) Probability

Comment

optional 39 of39 Additional Expert Identification

Is there anyone 1 else you feel should be invited to participate in this expert elicitation? Name Organization Email (if known) ,------, ,------,

Thank you for participating!

Click submit to finish the survey, or else go back to make any last changes.