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Integrated Reservoir Modelling in Carbonates

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Integrated Reservoir Modelling in Carbonates Primary funding is provided by The SPE Foundation through member donations and a contribution from Offshore Europe The Society is grateful to those companies that allow their professionals to serve as lecturers Additional support provided by AIME Society of Petroleum Engineers Distinguished Lecturer Program www.spe.org/dl Integrated Reservoir Modelling for Carbonates Quo Vadis? Dr. Jürgen Grötsch Shell Global Solutions International B.V. Society of Petroleum Engineers Distinguished Lecturer Program www.spe.org/dl 2 Outline • Integrated Reservoir Modelling (IRM) in Carbonates • Carbonate versus clastic reservoirs • Past - A brief history of IRM in Carbonates – 1990’s – Example: Malampaya Buildup – 2000’s – Example: Jurassic Arab Formation – 2010’s – Example: North Sea Chalk • Present – Current challenges • Future – Where are we going? Grötsch, 2016 3 Why Do We Build Reservoir Models? • To facilitate exploration, development and reservoir management decisions: – Support of exploration and appraisal activities – Field development planning – Field development – additional phases – Volumes in-place and Reserves reporting – Uncertainty estimation and management – Well Planning and Operations support – Equity determinations (re-determinations) – Farm in opportunities – Joint venture or governance obligations – Visualisation Grötsch, 2016 4 Integrated Reservoir Modelling • What is Integrated Reservoir Modelling? – Structural Model – Facies Model – Property Model – Fluid Model – Flow Model • What else do we need from IRM? Grötsch, 2016 5 IRM – Input Data Field Analogue Survey Outcrop Analogue Studies Core Measurements Seismic Interpretation ? Natih E Channel Cut/Fill Field Performance Review Integrated Core Review Field Correlation Ekulama F1000 Production History 20000 900 Oil Rate bbl/d 18000 BHP psia 800 Water Cut % 16000 GOR scf/bbl 700 14000 600 12000 500 10000 400 8000 300 GOR scf/bbl, Watercut % Watercut scf/bbl, GOR Oil Rate bbls/d, BHP psia BHP bbls/d, RateOil 6000 200 4000 2000 100 0 0 Well Review Time Recovery Mechanism Conceptual Geological Model Sor og 15?% Current state Vertical Equilibrium Sor wog 15?% WI Gascap High Sweep? Rim volume Y mil bbl ROS WF 30 -50% WOGD and viscous (fracflow) Sor wo 25 -30% Current RF 35%, Ult. RF 45%? Grötsch, 2016 6 Carbonate Reservoirs • What controls reservoir • Modelling workflow architecture? commonly includes: – Depositional facies – Discrimination of depositional and diagenetic controls – Diagenetic overprint(s) – Pore typing and rock typing – Fracturing to model permeability and saturation THE RESERVOIR Well A Well B – No simple N/G cut-off criteria – Fracture models (Dual porosity: Dual permeability) Grötsch, 2016 7 Porosity-Permeability Relationships • Clastic Reservoirs – Porosity-permeability relationships are usually consistent in a reservoir (mostly intergranular pores) • Carbonate Reservoirs – Complex pore systems: Often more than one porosity-permeability relationship – ‘Mega-fabrics’ (fractures and karst) are poorly characterized from core data – Correlations between core-based and well-test-derived permeability are complicated – Permeability heterogeneity is complex , varies between scales – Permeability upscaling and averaging can be important Grötsch, 2016 8 Bi- and Multi-Modal Pore Networks • Clastic Reservoirs • Carbonate Reservoirs – Unimodal pore systems are most common – Bi- and multi-modal pore – Correlation between storage (oil column) networks are common in and productivity is present. carbonates – Oil transition zones are usually short – Big differences in production behaviour between the pore systems that provide storage (volumetrics) and the pore systems that provide productivity – Microporous reservoirs have long transition zones Grötsch, 2016 9 Fracturing Influences Flow Behaviour • Clastic Reservoirs • Carbonate Reservoirs – Fractured reservoirs are – Most reservoirs are fractured. Intensity uncommon and character widely varies. (exception: tight gas) – Tiny pore volume has potential for Darcy – Faults commonly act as seals: permeability. clay smear, cataclasis and – Fractures can dominate productivity and cementation effects. Fault form high permeability pathways through compartments are common. the reservoir: Water and gas breakthrough. – Faults and fractures create permeability LS4 LS3 anisotropy. LS2 LS1 – Faults are more often associated with fractures and less often act as seals. Grötsch, 2016 10 Past: A brief history of IRM – 1990’s • Advent of 3D graphics computing • Focus on tools – everybody made his own – Subsurface disciplines (GPs, GGs, PPs, REs, PTs) – Tool proliferation, limited integration • Focus on reducing uncertainty – Linking reservoir parameters – 3D seismic Close-the-Loop – industry first • Example: Malampaya, Philippines (appraisal & development) Grötsch, 2016 11 Example: Malampaya, Philippines • Conceptual geological model Seismic constrained reservoir modelling • 3D Seismic constrained reservoir models • Multiple realisations • Rock type based • 3D Seismic Close-the-Loop Real vs. synthetic seismic Ref.: Grötsch & Mercadier, 1999: AAPG Bulletin Grötsch, 2016 12 Example: Malampaya, Philippines • Velocity = f (POR, Rock type) • 3D Time/Depth conversion • Reduce Uncertainty RRT-1 RRT-2 RRT-3 RRT-4 RRT-5 Pessimistic Most Likely Optimistic Ref.: Grötsch & Mercadier, 1999: AAPG Bulletin Grötsch, 2016 13 Past: A brief history of IRM – 2000’s • Hardware gets more powerful – bigger models, more detail or area • Focus on adding functionality – Integration via using common 3D visualisation tools – Geostatistics – how can it help? – Carbonates require rock typing – how can we model this? • Example: Arab Formation, UAE (brown field) Grötsch, 2016 14 Example: Arab Formation, UAE • From conceptual geological models ..… Depositional facies distribution Ref.: Grötsch et al, 2003, Geoarabia Grötsch, 2016 15 Example: Arab Formation, UAE … to regional 3D reservoir models: Static full-field model Dynamic full-field model Facies Year 2 • Complex architecture Porosity Year 7 • Complex fluids • Rock type Perm Year 17 based model Pressure • Dynamic simulation Rock Type Year 40 Ref.: Grötsch et al, 2003, Geoarabia Grötsch, 2016 16 Past: A brief history of IRM – 2010’s • Major steps forward in technology • Tools get more complex and cumbersome – Advent of “Frankenstein” models – one model fits all – Anchoring on best guess models – Modelling for comfort rather than analytical rigor – Carbonates require “grain scale” models (i.e. pore networks) – Unconventionals cannot be handled • Conclusion: IRM did not address root cause challenges • Example: North Sea Chalk (unconventionals, subtle traps) Grötsch, 2016 17 Example: Unconventional Carbonates • Emmons (1921): Not all hydrocarbons are in anticlinal structures … • Paradigm shift: In last ten years proven by production: IRM rules do not apply. Thick source rock potential shale gas opportunity Modified after Schenk & Pollastro, 2001 • “Unconventionals” are not new: Not followed up – until recently – Example: “Tiroler Steinöl” in Austria: Triassic Seefeld Fm., Achensee National Park, carbonates mined >100 years – Example: North Sea Chalk: Discovered after many years of conventional development Grötsch, 2016 18 Example: North Sea Chalk, Denmark • HC accumulations in Chalk 1 • Originally: 4-Way closures only... • Study 1999: Halfdan discovery – no closure Area • HC distribution in low K Carbonates: Poorly understood 2 A 3 A B B Ref.: S. Back, H. van Gent, L. Reuning, J. Grötsch, J. Niederau, P. Kukla (2011) Ref.: Fabricius et al., 2007 Grötsch, 2016 19 Example: North Sea Chalk, Denmark Mass-transport Complex Northern Valley Channel 3 2 Bo-Jens Ridge Halfdan: Mound 1 linked to slumping Iso-surface 2: Chaos attribute 0 0.5 1.0 1.5 2.0 2.5 km Ref.: S. Back, H. van Gent, L. Reuning, J. Grötsch, J. Niederau, P. Kukla (2011) Grötsch, 2016 20 IRM – Where are we now? • Base case assumption persists: We build on it with ever increasing detail • Narrow range of production forecasts • Model does not address key development decisions: Ill-defined decision criteria • Perception of realism from model complexity Single Make subsurface Complex linear Base case with Narrow range investment concept workflow perturbations Dynamic of forecasts decision STOIIP based Uncertainty only $ Grötsch, 2016 21 Present – Current Challenges • Personal biases: Geological concepts & RE multipliers • Making the right models: Scaling • Uncertainty management: Parameterization, end-to-end • Linear versus iterative workflows: Enabler for integration • Technical data management: Manage models, audit trails • (T)ECOP: Neglecting more important value drivers and risks – Non-technical risks (Economic, Commercial, Operational, Political) – Access to reserves – Short-term gains versus long-term success Grötsch, 2016 22 The Challenges of the Future • Carbonate technology development IRM building blocks – Model scaling – Unconventional & subtle traps Scaled, – Grain scale & pore network modelling Integrated – End-to-end Uncertainty handling and management Workflows • Process development Decision – Focus on business decisions Driven – End-to-end integration Modelling Strategy – Scenario management • Collaboration platform Iterative – Decision Framework Tool (DFT) Model Testing – Decision Framework Model (DFM) – Technical Data Management (TDM) Grötsch, 2016 24 Unconventionals & Subtle Traps Eocene carbonate slope, Browse Basin, NWS, Australia • Carbonate Slope Geometries Map view 3D view (x5 vertical) • Reservoir property Cross section along channel axis
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