Geologic Characterization of the Morrow B Reservoir in Farnsworth Unit, TX Using 3D VSP Seismic, Seismic Attributes, and Well Logs

Geologic characterization of the Morrow B reservoir in Farnsworth Unit, TX using 3D VSP seismic, seismic attributes, and well logs.

Paige Czoski

Submitted in Partial Fulﬁllment of the Requirements for the Degree of Master of Science in Geophysics

New Mexico Institute of Mining and Technology Socorro, New Mexico December, 2014 ABSTRACT

Farnsworth Field is located in Ochiltree, Texas and has been selected for a Carbon Capture Utilization and Storage (CCUS) project that is being supported by the Department of Energy, the Southwest Regional Partnership on Carbon Sequestration, and Chaparral Energy Co. LLC. One million tonnes of 100% CO2 produced from the Arkalon Ethanol Plant in Liberal KS and the Agrium Fertilizer Plant in Borger TX will be injected into the Morrow B formation and monitored using seismic methods (Grigg and McPherson, 2012). Previous geologic characterization hypothesizes that the Morrow B Formation was an incised valley depositional environment. This study focuses on the 3D Vertical Seismic Profile (VSP) survey that overlaps two injection wells covering an area of approximately 1 by 2 miles. The purpose of this study is to geologically characterize the 3D VSP using seismic attributes and well logs. The Morrow B was auto picked in the 3D VSP data using gamma ray logs to locate the formation in depth. Low amplitude lens features that resemble channels were manually picked within the Morrow B. Seismic attributes aided in the geologic characterization by providing lithologic and stratigraphic interpretations. The attributes discussed in this study are curvature, instantaneous frequency, signal envelope, sweetness, relative acoustic impedance, chaos, root mean square amplitude, and variance. An unsupervised neural network was utilized to compare the seismic attributes to find similarities that might relate to geology. The possible channel interpretation can influence CO2 flow through the reservoir and have an effect on production and storage.

Keywords: Morrow B; Farnsworth Unit; 3D VSP; Seismic Attributes ACKNOWLEDGMENTS

Funding for this project was provided by the U.S. Department of Energy’s (DOE) National Energy Technology Laboratory (NETL through the Southwest Partnership on Carbon Sequestration (SWP) under Award No. DE-FC26-05NT42591. Additional support has been provided by site operator Chaparral Energy, L.L.C., WesternGeco and Schlumberger Carbon Services. I would like to thank my committee members Robert Balch, Susan Bilek, and Peter Mozley who aided me both in my research and academics throughout the years. I would also like to thank Bob Will (Schlumberger) for helping me learn to use Petrel and aiding me in my interpretation. I also want to thank my fellow graduate students who are researching the Farnsworth Unit, especially Dylan Rose-Coss who aided in the well log interpretation, Ashley Hutton who helped me input the seismic data and interpret faults, Sara Gallagher, Evan Gragg, and William Ampomah. I want to thank all my friends and family who helped me through my education. I want to thank my parents Dana and Richard Czoski who supported me through my years of education and encouraged a love for geology by taking me to museums and other interesting geologic areas all over the world. Brooke Czoski for always being there for me as my best friend and sister. I would also like to thank my fellow graduate students and Logan Roberts for emotional support through my graduate school experience. 1 This thesis was typeset with LATEX by the author.

1 The LATEX document preparation system was developed by Leslie Lamport as a special ver- sion of Donald Knuth’s TEX program for computer typesetting. TEX is a trademark of the Ameri- can Mathematical Society. The LATEX macro package for the New Mexico Institute of Mining and Technology thesis format was written for the Tech Computer Center by John W. Shipman.

ii CONTENTS

LIST OF FIGURES v

1. INTRODUCTION 1 1.1 Research Motivation ...... 1 1.2 Farnsworth Unit History ...... 6 1.3 Carbon Capture Utilization and Storage (CCUS) or Enhanced Oil Recovery (EOR) ...... 7

2. GEOLOGIC SETTING 9 2.1 Regional Stratigraphic Framework ...... 9 2.2 Anadarko Basin Tectonic Evolution ...... 10 2.3 Depositional Environment ...... 12 2.4 Paleoﬂow Data ...... 16 2.5 Controls on Reservoir Quality and Heterogeneity ...... 17

3. METHODOLOGY 19 3.1 Seismic Survey Overview: Imaging goals ...... 19 3.2 3D VSP ...... 21 3.3 Crosswell Tomography ...... 23 3.4 Well Log Data ...... 23 3.5 Data Analysis Software ...... 23 3.6 Seismic Attributes ...... 23 3.6.1 Curvature Attributes ...... 24 3.6.2 Variance Attribute ...... 25 3.6.3 The Hilbert Transform ...... 25 3.6.4 Instantaneous Frequency ...... 27 3.6.5 Signal Envelope ...... 27 3.6.6 Sweetness ...... 28 3.6.7 Relative Acoustic Impedance (RAI) ...... 28 3.6.8 Root Mean Square (RMS) Amplitude ...... 28 3.7 Neural Network Comparison of Seismic Attributes ...... 28

iii 4. RESULTS 30 4.1 Morrow B Sandstone Delineation from 3D VSP Data ...... 30 4.1.1 Low Amplitude Lenses ...... 34 4.2 Stratigraphic and Structural Features Visible Within the Morrow B 41 4.3 Well Log Analysis ...... 44 4.4 Seismic Attributes ...... 48 4.4.1 Curvature Attributes ...... 48 4.4.2 Chaos ...... 49 4.4.3 Variance ...... 50 4.4.4 Instantaneous Frequency ...... 51 4.4.5 Signal Envelope ...... 52 4.4.6 Sweetness ...... 53 4.4.7 Relative Acoustic Impedance ...... 55 4.4.8 Root Mean Squared Amplitude ...... 56 4.5 Petrel’s Train Estimation Modeling ...... 57 4.5.1 Run #15 ...... 59 4.5.2 Run #6 ...... 60 4.5.3 Run#16 ...... 61 4.5.4 Run#4 ...... 62

5. DISCUSSION 63

6. CONCLUSIONS AND FUTURE WORK 67 6.1 Conclusions ...... 67 6.2 Suggestions for Future Work ...... 68

REFERENCES 69

A. TRAIN ESTIMATION MODEL RUNS 72

iv LIST OF FIGURES

1.1 Farnsworth Unit (pink square) located in northern Texas. Pur- ple lines represent state borders and green lines represent county boundaries. The field area is delineated by the blue outline in the blown up figure to the left. Well 13-10A is displayed by the green triangle...... 2 1.2 Paleogeography of the Morrow. Light green represents shales and mudstones, dark green represents fluvial systems, and blue represents seas. (Gallagher, 2014; Swanson, 1979)...... 3 1.3 (a) Structure map for the top of the Morrow B. The field dips towards the south east and is deeper within the middle of the field. (b) The Morrow B isopach generated from gamma ray well logs for the entire field (blue outline in Figure 1.1). The thicker sandstone runs through the middle of field. These observations support the hypothesis of the incised valley depositional environment. (Modi- fied from a figure courtesy of Dylan Rose-Coss)...... 5

1.4 Schematic diagram displaying the process of injecting CO2 and water into a reservoir. The super critical ﬂuid pushing previously unproduced oil out of pore spaces, and pushing it towards a production well. (NETL and DOE, 2010)...... 8

2.1 Stratigraphic chart showing the stratigraphic framework of the Lower Atokan-aged and Upper Morrowan strata in the FWU. Wireline log is from well 32-2. (Gallagher, 2014; Modified from Munson(1988) and Puckette et al. (2008) by Dylan Rose-Coss and Sara Gallagher). 10 2.2 Tectonic map during the deposition of the Morrow. This figure displays possible structures that could be sediment source areas (Gallagher, 2014; Modified from Sonnenburg et al., 1990 in DeVries, 2005)...... 11 2.3 Incised valley systems flowing east and southeast during late Mor- rowan deposition. (Gallagher, 2014; Puckette et al, 2008)...... 13 2.4 Model of incised valley depositional system and how it changes with sea level. LST = low stand surface of erosion. TSE = Trans- gressive surface of erosion (Gallagher, 2014; Puckette et al., 2008). . 14 2.5 Lithofacies descriptions for FWU core as characterized by Gallagher, 2014...... 15

v 2.6 Depositional model for FWU constructed by Gallagher, 2014 (Mod- ified from Wheeler et al., 1990; Puckette et al., 2008). MFS = Max- imum flooding surface. LSE = Lowstand surface of erosion. TSE = Transgressive surface of erosion. TSE = Transgressive surface of erosion...... 16 2.7 The top surface in depth of the Morrow B with wells 13-10A and 13-14 where the FMI images were taken. The rosette diagrams showing the azimuth of flow direction determined from the FMI images taken in the Morrow B formation. The paleoflow direction is mostly to the east, northeast, and southeast. (Courtesy of Dylan Rose Coss, 2014; Brown, 2014) ...... 17

3.1 (a) FWU map of seismic surveys. The injector wells are triangles and the producers are circles. This study focuses on the orange square on the north west corner of the ﬁeld. (b) A zoomed in area square in (a). (b)shows the location of VSP (red oval) and crosswell tomography (yellow dashed lines) used for this study (Modiﬁed from Grigg and McPherson, 2012)...... 20 3.2 A model of the 3D VSP survey as designed by WesternGeco with the geophone arrays in wells 13-10A and 14-1. The top of the Mor- row B (grey surface) with the outline of the 3D VSP data footprint (rectangle in colored by incidence angle). The yellow dots on the surface represent the vibroseis shot points and the red lines represent ray paths traveling down to where the geophones are placed at approximately 3500 ft ...... 21 3.3 Map displaying the 3D VSP shot points, the outline of the 3D VSP, and the crosswell tomography lines. The pink dot is producing well 13-16, the green dot is injection well 13-10A, the blue dot is producing well 13-14, and the yellow dot is injection well 14-1. Wells 13-10A and 14-1 had geophone arrays for the VSP data collection...... 22 3.4 Equation displaying how curvature attributes are derived (Chopra and Marfurt, 2007)...... 24 3.5 The complex trace z(t) calculated from the real trace x(t) and the imaginary trace y(t) generated from the Hilbert Transform (Hardage, 2010)...... 26 3.6 How instantaneous seismic attributes are calculated from the complex seismic trace z(t) (Hardage, 2010)...... 27

4.1 A display on how the Morrow B top and base were picked on the 3D VSP utilizing the 13-10A GR log...... 31 4.2 The Morrow B Top two way time surface in amplitude generated from the auto picked horizon. The color bar is in amplitude with yellow being high amplitude and blues being low amplitude. The contour interval is 2.5 ms...... 32

vi 4.3 The Morrow B Base two way time surface in amplitude generated from the auto picked horizon. The color bar is in amplitude with yellow being high amplitude and blues being low amplitude. The contour interval is 2 ms...... 33 4.4 An isochron showing the thickness in the Morrow B based on the picked horizons in two way time. Orange is thinner and red is thicker...... 34 4.5 A cross section through the VSP survey as shown in the box on the upper right. The yellow horizon is showing that the seismic flattened. The blue horizon is the top of the Morrow B. These low amplitude lenses were manually picked throughout the entire 3D VSP volume. The green boxes highlight the areas of low amplitude that could be interpreted as channels...... 36 4.6 A cross section through the VSP survey as shown in the box on the upper right. The pink and black lines display how the top and base of the low amplitude lenses were manually picked throughout the entire 3D VSP volume...... 37 4.7 A 3D view of the top of the low amplitude lens shown in two way time (ms) with an inline and cross line for scale...... 38 4.8 A map showing the low amplitude lenses with the wells in the area. The outline of the 3D VSP data is in black, the outline of the Morrow B top surface from the 3D VSP is in pink, the channel outline is in red with the channel filled in with elevation time. The southeast features are deeper...... 39 4.9 An isochron map showing the thickness of the low amplitude lenses in two way time. The pink outline is the outline of the Morrow B top as picked from the 3D VSP. The black line delineates the outline of the low amplitude lenses that were hand picked. This outline appears in red in Figure 4.8 and will appear in subsequent figures. 40 4.10 An isochron map showing the thickness of the low amplitude lenses in two way time. The pink outline is the outline of the Morrow B top as picked from the 3D VSP. The paleoflow directions for wells 13-10A and 13-14 as delineated by Brown (2014) as overlain on this figure. The paleo flow directions support the idea that these low amplitude lenses are overall striking towards the east...... 41 4.11 The surface of the Morrow B Base (a). The box in the south east represents the feature of interest while the lines crossing it represent the inline sections shown in (b), (c), and (d). The white box in the upper north west corner is a another similar feature. In (b), (c), and (d) the Morrow B top is delineated with the green line. Fig- ure (b) shows the largest offset which is about 70 ft. As one moves west, the surfaces come together and form a slope...... 43

vii 4.12 (a) Two way time 3D VSP ant tracking volume generated by Ash- ley Hutton with the box showing the fault like feature. (b) The light blue dashed line indicates the fault. The Morrow B top is shown in green. (Ashley Hutton, personal communication, 2014)...... 44 4.13 A well log section cross cutting the low amplitude lenses as shown by the black arrow in the map in upper left corner. The thickness of the Morrow B increases within the channel and decreases outside. The contrast between the sandstone and mudstone also increases within the low amplitude lens...... 45 4.14 The map in the lower left corner shows the cross section across the low amplitude lenses and GR logs for 13-12, 13-10A, 13-5, and 13-14...... 46 4.15 A cross section cutting through the middle of the low amplitude lenses. Wells 13-10A, 13-14, and 13-2 have thicker and cleaner sandstone. Well 13-10 is not as thick and has more mudstone but still lies within the low amplitude lens...... 47 4.16 The Curvature attribute for the Morrow B base. The yellow line delineates the low amplitude lenses...... 48 4.17 The Chaos attribute for the Morrow B base. The yellow line delineates the low amplitude lenses...... 49 4.18 The Variance attribute for the Morrow B base. The yellow line delineates the low amplitude lenses...... 50 4.19 The Instantaneous Frequency attribute for the Morrow B base. The yellow line delineates the low amplitude lenses...... 51 4.20 The Signal Envelope attribute for the Morrow B base. The yellow line delineates the low amplitude lenses...... 52 4.21 The Sweetness attribute for the Morrow B base. The yellow line delineates the low amplitude lenses...... 53 4.22 The Relative Acoustic Impedance attribute for the Morrow B base. The yellow line delineates the low amplitude lenses...... 55 4.23 The Root Mean Squared attribute for the Morrow B base. The yellow line delineates the low amplitude lenses...... 56 4.24 The attributes are listed at on the top and the different combinations in the rows. The highlighted rows are runs that displayed possible geologic information that aligned with the low amplitude lenses...... 58 4.25 Run 15 generated from the RAI, instantaneous frequency, sweetness, envelope, and the chaos attributes...... 59 4.26 Run 6 generated from the RAI, instantaneous frequency, impedance, and envelope attributes...... 60 4.27 Run 16 generated from the RAI, instantaneous frequency, sweetness, envelope, chaos, and variance attributes...... 61

viii 4.28 Run 4 generated from the RAI, impedance and instantaneous frequency attributes...... 62

5.1 A modern braided river system from the Congo (Zaire) River on top compared to the interpreted channel outline as shown from the channel isochron. Geometric similarities can be seen between the two environments...... 64 5.2 Channel interpretation based on thickness and channel orientation from the low amplitude lenses isochron map. The possible shorter lived channels running northwest-southwest are pointed out by the orange arrows. The possible longer lived channels running west-east are pointed out by the yellow arrows. The white oval delineates an area where the feature can be interpreted as either oriented northwest-southeast or west-east...... 65

A.1 Run 1 generated from the RAI, impedance, chaos attributes. . . . . 72 A.2 Run 2 generated from the RAI, chaos, and signal envelope attributes...... 73 A.3 Run 3 generated from the RAI and signal envelope attributes. . . . 74 A.4 Run 4 generated from the RAI and instantaneous frequency attributes...... 75 A.5 Run 5 generated from envelope and instantaneous frequency attributes...... 76 A.6 Run 6 generated from the RAI, signal envelope, and instantaneous frequency attributes...... 77 A.7 Run 7 generated from the RAI, instantaneous frequency, signal envelope, and chaos attributes...... 78 A.8 Run 8 generated from the sweetness and chaos attributes...... 79 A.9 Run 9 generated from the RAI and sweetness attributes...... 80 A.10 Run 10 generated from the sweetness and signal envelope attributes...... 81 A.11 Run 11 generated from the instantaneous frequency and signal envelope attributes...... 82 A.12 Run 12 generated from the RAI, instantaneous frequency, sweetness, and signal envelope attributes...... 83 A.13 Run 13 generated from the variance and sweetness attributes. . . . 84 A.14 Run 14 generated from the RAI, variance, and sweetness attributes. 85 A.15 Run 15 generated from the RAI, instantaneous frequency, sweetness, signal envelope, and chaos attributes...... 86 A.16 Run 16 generated from the RAI, instantaneous frequency, sweetness, signal envelope, chaos, and variance attributes...... 87

ix A.17 Run 17 generated from the RAI, instantaneous frequency, sweetness, signal envelope, chaos, and RMS...... 88

x This thesis is accepted on behalf of the faculty of the Institute by the following committee:

Robert Balch, Advisor

I release this document to the New Mexico Institute of Mining and Technology.

Paige Czoski Date CHAPTER 1

INTRODUCTION

1.1 Research Motivation

Farnsworth Unit (FWU), located in north Texas and operated by Chaparral Energy Co. LLC, is a commercial scale Carbon Utilization and Storage (CCUS) project supported by both the Department of Energy (DOE) and the Southwest Regional Partnership on Carbon Sequestration (Figure 1.1). The primary goal includes monitoring the injection of at least 1 million tonnes of CO2 into the Mor- row B formation over the next ﬁve years (Grigg and McPherson, 2012). Seismic monitoring over time will aid in understanding the CO2 migration within the reservoir. Each well can have injection rates of up to 0.2 million tonnes per year (Grigg and McPherson, 2012). This project can be blueprint for future commercial sequestration at active CCUS projects (Grigg and McPherson, 2012).This study focuses on geologic characterization of the Morrow B reservoir utilizing the 3D Vertical Seismic Proﬁle (VSP) study.

1 Figure 1.1: Farnsworth Unit (pink square) located in northern Texas. Purple lines represent state borders and green lines represent county boundaries. The ﬁeld area is delineated by the blue outline in the blown up ﬁgure to the left. Well 13-10A is displayed by the green triangle.

2 Figure 1.2: Paleogeography of the Morrow. Light green represents shales and mudstones, dark green represents ﬂuvial systems, and blue represents seas. (Gal- lagher, 2014; Swanson, 1979).

Recent thesis research by Sara Gallagher (2014) concluded that the depositional environment for the Morrow B is an incised valley system based on core data. Figure 1.2 displays the paleogeography and location of FWU. Previous work in the area supports Gallagher’s conclusions of an upper Morrow fluvial system (Gallagher, 2014; Sonnenburg et al., 1990; Krystinik and Blakeney, 1990; Wheeler et al. 1990; Al-Shaieb et al., 1995). The Morrow B isopach and formation top map (Figure 1.3), could be interpreted as an incised valley based on geometries. Channel features might be imaged by interpreting the 3D VSP data using seismic attributes. Understanding the channel geometries and the distribution of lithologies could lead to estimations of where the CO2 will accumulate and migrate within the reservoir. Reservoir heterogeneity, reservoir and seal structural geometry, stratigraphic features, permeability, fractures/faults, pressure and temperature, mineralogy, and in-situ formation fluids influence CO2 once it is injected into the reservoir (Gibson-Poole et al., 2009). Maps of reservoir geometry are needed in order to describe the reservoir adequately for simulation (Ebanks Jr., 1987). The 3D VSP survey could provide a map of reservoir geometry for a small area. Any channel- like features resolved in the 3D VSP could become potential pathways for CO2 3 and could effect oil production and CO2 storage. Understanding where stratigraphic heterogeneities lie within the reservoir is important to understand the migration pathway for CO2 (Gibson-Poole et al., 2009). The buoyancy of CO2 will make it migrate to the highest point in a reservoir (Gibson-Poole et al., 2009). Stratigraphic heterogeneities such as intrafor- mation silts and shales, could reduce the effective vertical permeability and generate a more complicated fluid pathway (Gibson-Poole et al., 2009). A positive for CO2 storage security is that stratigraphic discontinuities create localized traps reducing the reliance on the top seal (Gibson-Poole et al., 2009). Most of these stratigraphic heterogeneities will be below the level of resolution for the 3D VSP. Fractures/faults can also have a strong and unpredictable effect on EOR production (Ebanks Jr., 1987). Faulting can lead to complicated CO2 flow paths and can act as barriers, conduits, or a combination of the two (Pasala et al., 2003). Faults can be paths of high permeability allowing CO2 to escape the reservoir (Ebanks Jr., 1987). On the other hand, low permeability faults can limit production efficiency (Pasala et al., 2003). Faulting within the reservoir should be resolvable in the 3D VSP survey.

4 Figure 1.3: (a) Structure map for the top of the Morrow B. The field dips towards the south east and is deeper within the middle of the field. (b) The Morrow B isopach generated from gamma ray well logs for the entire field (blue outline in Figure 1.1). The thicker sandstone runs through the middle of field. These observations support the hypothesis of the incised valley depositional environment. (Modified from a figure courtesy of Dylan Rose-Coss).

Seismic attributes can aid in geologic interpretation and reservoir characterization of the Farnsworth Unit in Ochiltree County, Texas. ”Seismic Attributes are all the information obtained from seismic data, either by direct measurements or by logical or experience-based reasoning” (Taner, 2001). The main objective for attributes is to give detailed and accurate structural, stratigraphic, and lithologic data to the interpreter after performing mathematical calculations on the data

5 (Taner, 2001). A detailed geologic model for the ﬁeld was generated using seismic attributes from a baseline 3D Vertical Seismic Proﬁle (VSP) and correlated with well log data.

1.2 Farnsworth Unit History

Farnsworth Unit (FWU) produces from the upper Morrow and is the largest Morrowan oil field in the western part of the Anadarko Basin (McKay and Noah, 1996). The uppermost sandstone, called the ”Morrow B” or the ”Buckhaults”, is the primary target for oil production (Munson, 1988) and also the target for the CCUS project. The sandstone ranges in thickness from 0 to 54 feet within in the field with the average being 29 feet (Munson, 1988). FWU was originally exploited as a gas discovery by the J.M. Huber Corporation in the Pazoureck formation in May 1952 (Parker, R.L., 1956). The first well was drilled to 8,096 ft. and produced 1,219,664 thousand cubic feet of natural gas (MCF) and 10,643 barrels of distillate cumulative until June 1956 (Parker, R.L., 1956). Several other wells were drilled in the surrounding area from 1952-1955 (Parker, R.L., 1956). On July 24, 1955, Union Oil Company of California drilled the first oil well into the Mor- row B at an interval of 7,960-7,970 feet (Parker, R.L., 1956). Their second well in the same field produced at 332 barrels per day (Parker, R.L., 1956). Past operators in the field include Sinclair Oil and Gas Company, Union Oil Company of Cali- fornia, J.M Huber Corporation, and Shamrock Oil and Gas Corporation (Parker, R.L., 1956). The current operator in the field is Chaparral Energy, L.L.C. Water flooding began in 1964 (Munson, 1988; McKay and Noah, 1996). The eastern side of the field produced more oil than the western side before the implementation of water flooding, however, after its implementation the western side produced more (Munson, 1988). Peak production during water flooding was reached in 1972 with 7,967 barrels of oil per day (BOPD) produced (McKay and Noah, 1996).

Currently, Chaparral Energy Co. LLC is utilizing CO2 flooding in the Farnsworth Unit using one hundred percent anthropogenic CO2 captured from the Arkalon Ethanol Plant in Liberal, Kansas and the Agrium Fertilizer Plant in Borger, Texas (Grigg and McPherson, 2012). Up to 25 injection wells will be utilized for CO2 flooding of the field. CO2 injection rates of up to 0.2 million tonnes per year will be injected into the field over the next five years (Grigg and McPher- son, 2012). In the field, the well pattern consists of one injector in the middle of four producers. Once the fluid is produced, the oil, water, and CO2 is separated above ground and the resulting produced CO2 is reinjected to supplement new CO2. In order to improve the efficiency of the recovery process, Farnsworth Unit is implementing water alternating gas (WAG) cycles to deter the lower viscosity CO2 from moving ahead of the displaced oil as it migrates to producing wells which improves reservoir sweep efficiency(NETL and DOE, 2010).

6 1.3 Carbon Capture Utilization and Storage (CCUS) or Enhanced Oil Recov- ery (EOR)

Large amounts of CO2 are emitted into the atmosphere by burning fossil fuels from power plants, factories, and both commercial and residential build- ings. Carbon Capture and Storage (CCS) could be applied to large point sources of CO2 such as power plants or large industrial facilities where the largest quan- tity of gas can be collected and stored. The CO2 would then be compressed to a supercritical ﬂuid and transported by pipeline to the ﬁeld where it is injected into a geologic reservoir such as depleted oil and gas reservoirs, saline aquifers, and unmineable coal seams (IPCC, 2005). Fortunately, tools developed by the oil and gas industry such as pipeline construction, compression, well drilling technology, injection technology, and monitoring methods can be adapted for geologic storage of CO2 (IPCC, 2005).

Enhanced Oil Recovery (EOR) (also known as CO2 flooding) has been in- creasingly utilized since the early 1970s as a means to increase oil production. EOR utilizing CO2 injection can greatly increase the amount of production within an older oil field. Primary production often yields 5-40% of the original oil in place (IPCC, 2005). Water flooding will produce an additional 10-20% and CO2 flooding can recover 7-23% of the original oil in place (IPCC, 2005). Permanent CO2 storage can be accomplished while increasing oil production. Between 33% and 50% of injected CO2 remains permanently in the reservoir while the balance is collected with the oil, separated, and then re-injected (IPCC, 2005). Over long project periods 100% of the originally injected CO2 will remain in the reservoir.

When supercritical CO2 is injected into an oil reservoir, it becomes mutu- ally soluble (miscible) with the crude oil, meaning they both dissolve into one another (NETL and DOE, 2010). When they encounter each other, the physical forces holding the two phases apart disappears, resulting in reduced viscosity and swelling of the miscible ﬂuid, which results in the removal of residual oil from the pore spaces (NETL and DOE, 2010). The crude oil and CO2 mixture migrates towards the producing well due to the pressure differential between the injector and producing well. Figure 1.4 represents this process schematically. It is important to geologically characterize the reservoir to understand the potential ﬂuid pathways for the CO2 oil mixture. For example, Morrow B sand channel geometries affect where the highest production will be and where future injector and producing wells should be drilled.

7 Figure 1.4: Schematic diagram displaying the process of injecting CO2 and water into a reservoir. The super critical ﬂuid pushing previously unproduced oil out of pore spaces, and pushing it towards a production well. (NETL and DOE, 2010).

8 CHAPTER 2

GEOLOGIC SETTING

2.1 Regional Stratigraphic Framework

The Atoka-aged Thirteen Finger Limestone overlies the Morrow with Mis- sissippian strata below forming an unconformable contact (Figure 2.1)Gallagher, 2014). The Morrow Formation can be subdivided into the upper and lower Mor- row where the boundary between the two is a thin limestone bed (Gallagher, 2014). The Morrow consists of multiple sandstone units separated by mudstone intervals (Munson, 1988). Upper Morrow reservoirs in Oklahoma, the Texas Pan- handle, southeastern Colorado, and western Kansas have produced greater than 320 million barrels of oil and 3.5 trillion cubic ft. of gas from reservoirs less than 6,000 ft. deep (Puckette and Shaieb, 2008). However, drilling depths can reach up to 21,500 ft. in the deeper part of the basin (Ball et al., 1991). Although the traps in the Morrow are mainly stratigraphic, there are also some structural traps (Ball et al., 1991). The Morrow likely entered the oil window during the Permian time when migration was occurring (Ball et al., 1991). Average porosity and permeability for the upper Morrow sandstones are 13.4% and 50.6 millidarcies (Puckette and Shaieb, 2008). The depositional environment for the Morrow was dependent on sea level ﬂuctuations.

9 Figure 2.1: Stratigraphic chart showing the stratigraphic framework of the Lower Atokan-aged and Upper Morrowan strata in the FWU. Wireline log is from well 32-2. (Gallagher, 2014; Modiﬁed from Munson(1988) and Puckette et al. (2008) by Dylan Rose-Coss and Sara Gallagher).

2.2 Anadarko Basin Tectonic Evolution

The Anadarko Basin includes an area of 50,000 square miles covering the western part of Oklahoma, the southwestern part of Kansas, and the northeastern part of the Texas Panhandle (Henry and Hester, 1995). The Anadarko is also one of the deepest basins (40,000 ft.) containing sedimentary rocks from the Cambrian

10 through the Permian (Ball et al., 1991; Perry, 1989). The Wichita orogeny resulted from the collision of the North American and South American plates in the early Pennsylvanian period (Roscoe and Alder, 1983; Gallagher, 2014). The possible sediment source areas that could account for the upper Morrowan rocks are the Cimarron arch and Keyes Dome to the west and northwest, the Sierra Grande Uplift to the west, the Central Kansas Uplift to the northeast, the Bravo Dome of the west-southwest, Dalhart Basin to the west, and the Plainview Basin and Nemaha Ridge to the south and west (Figure 2.2; Gallagher, 2014; Munson, 1988; Sonnenburg et al., 1990; Puckette and Shaieb, 2008).

Figure 2.2: Tectonic map during the deposition of the Morrow. This ﬁgure displays possible structures that could be sediment source areas (Gallagher, 2014; Modiﬁed from Sonnenburg et al., 1990 in DeVries, 2005).

11 2.3 Depositional Environment

Munson (1988) concluded that the Morrow B deposition in the FWU was controlled by a fluvial-deltaic system. His conclusions were based on sand-body geometry, petrographic log signatures, and grain size analysis methods that are now obsolete (Ehrlich, 1983; Gallagher, 2014). Based on more modern techniques and data collected from the FWU and Morrow B reservoirs in the region, geologists concluded that the deposits are from incised valleys that covered the area during Morrowan times (Figure 2.3; Wheeler et al., 1990; Puckette et al., 2008; Gallagher, 2014). The lithofacies studied in cores from the FWU are similar to other Morrowan deposits in southeastern Colorado, Southwestern Kansas, and the Oklahoma Panhandle (Wheeler et al., 1990; Puckette et al., 2008; Gallagher, 2014). The deposition of Morrowan rocks was controlled by sea level fluctuations in the Pennsylvanian time (Sonnenburg et al., 1990; Krystinik and Blakeney, 1990; Wheeler et al. 1990; Al-Shaieb et al., 1995; Gallagher, 2014). The Upper Morrowan deposits were formed from valley incisions during episodes of regression and in- filling during transition (Wheeler et al., 1990; Al-Shaieb et al., 1995; Gallagher, 2014). When the sea level was low, erosion dominated and sediment transport was towards the southeast (Figure 2.3; Figure 2.4; Wheeler et al., 1990, Gallagher, 2014). During a lowstand systems tract when sea levels were low, the valleys were filled with fluvial sediments (Figure 2.4; Gallagher, 2014). As the seas rose, the valleys become flooded and estuarine and floodplain sediments were deposited. Subsequently these deposits were covered with near shore to offshore marine muds (Figure 2.4; Wheeler et al., 1990; Gallagher, 2014).

12 Figure 2.3: Incised valley systems ﬂowing east and southeast during late Mor- rowan deposition. (Gallagher, 2014; Puckette et al, 2008).

13 Figure 2.4: Model of incised valley depositional system and how it changes with sea level. LST = low stand surface of erosion. TSE = Transgressive surface of erosion (Gallagher, 2014; Puckette et al., 2008). 14 The general stratigraphy from the FWU core, from deeper to shallower, is: marine mudstone, channel lag conglomerate, fluvial coarse-grained sandstone, estuarine fine-grained sandstone, and marine mudstone (Figure 2.5; Figure 2.6; Gallagher, 2014). The Morrow B in the FWU has lithology consistent with the incised valley model of deposition that also fit a basin-wide sequence stratigraphic model (Figure 2.6; Gallagher, 2014). The conglomerate facies were deposited during a sea level lowstand (Gallagher, 2014). The Morrow B sandstone was deposited by fluvial processes during transgression (Gallagher, 2014). The marine or marine-influenced mudstones above and below represent highstand systems (Gallagher, 2014).

Figure 2.5: Lithofacies descriptions for FWU core as characterized by Gallagher, 2014.

15 Figure 2.6: Depositional model for FWU constructed by Gallagher, 2014 (Modi- ﬁed from Wheeler et al., 1990; Puckette et al., 2008). MFS = Maximum ﬂooding surface. LSE = Lowstand surface of erosion. TSE = Transgressive surface of erosion. TSE = Transgressive surface of erosion.

2.4 Paleoﬂow Data

Based on structure and isopach maps, multiple channel-like features can be visualized. From the structure map of the top of the Morrow B (Figure 1.3(a)), the channel feature is visible flowing in a east or southeast direction. This is consistent with Puckette and Shaieb’s (2008) prediction of flow direction for the Mor- row streams (Figure 2.3). The gross sandstone isopach (Figure 1.2(b)) maps the Morrow sandstone thickness in the field and is also consistent with the flow and channel direction. Generally, the thickest sections of Morrow lie within the middle of the field. Ambiguities that do not support an incised valley model within the structure map and isopach map are probably due to large faults in the east and south west sides of the field that have been mapped by Ashley Hutton (personal communication, 2014). Brown (2014) from Schlumberger’s Petrotechnical Services recently identified paleoflow directions using Formation Micro-Imager (FMI) logs to image the Morrow B in wells 13-10A and 13-14 (Figure 2.7). An FMI log measures real-time mircrosensitivity images and dip data in the formation of interest (Brown, 2014). Brown (2014) found that the paleoflow directions are

16 roughly east with some components of flow to the northeast and southeast (Fig- ure 2.7). Brown (2014) analyzed how paleoflow changed throughout the Morrow B formation in well 13-10A. Starting from the deepest (earliest) flow was east northeast to east southeast, east southeast, northeast, east east southeast, and flowing eastwards at the end of deposition (Brown, 2014).

Figure 2.7: The top surface in depth of the Morrow B with wells 13-10A and 13-14 where the FMI images were taken. The rosette diagrams showing the azimuth of ﬂow direction determined from the FMI images taken in the Morrow B formation. The paleoﬂow direction is mostly to the east, northeast, and southeast. (Courtesy of Dylan Rose Coss, 2014; Brown, 2014)

2.5 Controls on Reservoir Quality and Heterogeneity

The Morrow B on the western side of FWU produced more under water ﬂood then the eastern side (Munson, 1988). The western side also has higher average (mean and median) permeability than the reservoir in eastern side of the ﬁeld (Munson, 1988; Gallagher, 2014). Gallagher (2014) determined that the reservoir quality in FWU does not appear to be controlled by depositional processes. The eastern side is paleogeographically downstream (Figure 1.3 (a)) and may represent a transition from braided to meandering processes (Gallagher, 2014). How- ever, Gallagher (2014) points out that there is no decrease in grain size, sorting,

17 and no increase in detrital clay that could account for the lower permeability in the eastern wells. Diagenetic processes have a much greater effect on the reservoir quality compared to the depositional processes (Gallagher, 2014). The diagenetic processes that had the greatest effect are the dissolution of feldspars and lithics, precipitation of authigenic clay, carbonate, and quartz, and compaction (Gallagher, 2014).

18 CHAPTER 3

METHODOLOGY

3.1 Seismic Survey Overview: Imaging goals

Objectives for acquired seismic data is to improve the geologic understanding and to directly monitor the CO2 plume movement in the reservoir over time. The full field 3D seismic UniQ survey, 3D Vertical Seismic Profiles (VSP) surveys, and crosswell tomography will be combined with the well logs, core, and other physical data to generate a facies-based geomodel that will aid in simulation models and understanding the geology and to model CO2 movement in the reservoir. Figure 3.1(a) shows an overview of the layout of the seismic surveys at Farnsworth Unit. Triangles represent the injection wells and circles represent production wells. There are two 3D VSP and cross well surveys on the east and west side of the field. The two 3D VSP surveys and crosswell tomography are centered on, or occur on, transects that include injection wells to image CO2 flow through the reservoir. The UniQ Survey imaged the entire 40 square miles of the field. The data used for this study focuses on the west side 3D VSP baseline which was collected in February of 2014 (Figure 3.1 (b); Figure 3.3).

19 Figure 3.1: (a) FWU map of seismic surveys. The injector wells are triangles and the producers are circles. This study focuses on the orange square on the north west corner of the ﬁeld. (b) A zoomed in area square in (a). (b)shows the location of VSP (red oval) and crosswell tomography (yellow dashed lines) used for this study (Modiﬁed from Grigg and McPherson, 2012).

20 3.2 3D VSP

3D Vertical Seismic Profiles (VSP) for this project have a dual purpose, first to aid in geological characterization and for time lapse monitoring of the evolution of the CO2 plume as it is injected into the reservoir. 3D VSP surveys have some advantages,first it is possible to obtain better vertical and horizontal resolution which provides greater detail of the reservoir characteristics (Muller et al., 2010). The reason for this is that source energy only passes through near surface materials (in the case of Farnsworth, thick weathered and anhydrite layers) in one direction therefore reducing attenuation effects which results in better imaging of the reservoir (Daley et al., 2008). The resolution increases from 30-100 m for a surface seismic survey to 10-30 m for VSP (Daley et al., 2008). The second reason is that this type of survey is precisely repeatable in order to understand reservoir dynamics over time (Muller et al., 2010).

Figure 3.2: A model of the 3D VSP survey as designed by WesternGeco with the geophone arrays in wells 13-10A and 14-1. The top of the Morrow B (grey surface) with the outline of the 3D VSP data footprint (rectangle in colored by incidence angle). The yellow dots on the surface represent the vibroseis shot points and the red lines represent ray paths traveling down to where the geophones are placed at approximately 3500 ft .

21 Figure 3.3: Map displaying the 3D VSP shot points, the outline of the 3D VSP, and the crosswell tomography lines. The pink dot is producing well 13-16, the green dot is injection well 13-10A, the blue dot is producing well 13-14, and the yellow dot is injection well 14-1. Wells 13-10A and 14-1 had geophone arrays for the VSP data collection.

The baseline 3D VSP data was collected in February of 2014 with additional surveys planned for the future. WesternGeco designed the survey to focus on and get high resolution data for Morrow B. Figure 3.2 is the survey model designed by WesternGeco to best image the reservoir. The grey surface shown on the bottom is the Morrow B top surface with the estimated seismic image that will be collected. The survey overlaps two injection wells, the 13-10A and 14-1, and was designed to image the Morrow B (Figure 3.1; Figure 3.3). Both the 13-10A and the 14-1 wells had 40 tri-component geophones at depths of approximately 3,500 ft. The survey covered a surface area of approximately 12,000 X 8,000 feet (Figure 3.3). The survey consisted of 1,763 vibroseis shot points, with a sweep frequency of 80-180 Hz, recorded over the area (Figure 3.3). WesternGeco completed the basic data processing for New Mexico Tech in May of 2014.

22 3.3 Crosswell Tomography

Crosswell tomography is a technique in which a signal is produced from within a well and it is received by other geophones in different wells. The geophone array stays stationary as the source moves upwards. The geophone is later moved to a different position in the well and the process is repeated. Hav- ing geophones within the well will aid in achieving high-resolution images of the reservoir without the inﬂuence of near surface anhydrite layers and weathered zones (Daley et al., 2008). Crosswell tomography is being conducted in order to time lapse image the CO2 moving between injector and producers. The baseline crosswell acquisition also occurred in February 2014 with additional surveys planned. The survey runs from wells 13-6, 13-10A, 13-14, and 14-1 with the borehole arrays in 13-10A and 14-1 (Figure 3.1; Figure 3.3). West- ernGeco completed the basic data processing for the New Mexico Tech study in May of 2014. This study interprets the 2D crosswell tomography lines and compares them to well logs in order to constrain the lithology.

3.4 Well Log Data

Ten well logs within the VSP area were analyzed to compare the seismic with the lithology. Gamma Ray (GR) logs were utilized for this study. The well log data was collected at different times with different operators so there can be some ambiguity when comparing them.

3.5 Data Analysis Software

All the data analysis was done in Schlumberger’s Petrel 2013 Software. Petrel is powerful suite of software that allows interpretation of seismic data, well logs, seismic attributes, and generation of neural networks to compare the seismic attributes.

3.6 Seismic Attributes

Seismic attributes were introduced in the early 1970’s as a display form and later combined with seismically-derived measurements to become an ana- lytical tool for interpreters to study reservoir characteristics (Taner, 2001). In the mid 1970’s there were three attributes and today there are over 300 deﬁned attributes (Taner, 2001). Attributes can be broken down into physical and geometric attributes. Physical attributes are directly related to the wave propagation, lithology and other parameters (Subrahmanyam and Rao, 2008). For example,

23 the magnitude of the trace envelope is related to acoustic impedance contrast, instantaneous and average velocities relate to rock properties, and frequencies can relate to bed thickness. (Taner, 2001). Physical attributes subdivided into instantaneous and wavelet attributes (Subrahmanyam and Rao, 2008). Instantaneous attributes are computed sample by sample and indicate instantaneous variations of various parameters (Subrahmanyam and Rao, 2008; Taner, 2001). Wavelet attributes are computed at the peak of the trace envelope (Taner, 2001). Geometri- cal attributes relay information about spatial and temporal relationships (Taner, 2001). Geometrical attributes are useful for stratigraphic interpretation and depositional characteristics (Taner, 2001). Twenty-four attributes were generated but only seven were analyzed more deeply since they aligned best with the low amplitude lenses. The attributes were generated for the entire 3D VSP volume and then applied to the Morrow B top and base surfaces. The attributes that were most useful are instantaneous frequency, signal envelope, sweetness, relative acoustic impedance, chaos, root mean square amplitude, and variance.

3.6.1 Curvature Attributes

This attribute is useful in identifying faults and channel like features. This attribute calculation is a black box operator in Petrel so the actual mathematical process done on the data is unknown. However, the basic process will be out- lined here. ”Curvature can be defined as the reciprocal of the radius of a circle that is tangent to the given curve at a point” (Chopra and Marfurt, 2007). Pe- trel is most likely calculating curvature by fitting a quadratic surface z(x,y) to an interpreted horizon using least-squares or some other approximation method( Equation seen in Figure A1; Chopra and Marfurt, 2007). This equation yields the coefficients from which other curvature attributes can be calculated including minimum and maximum curvatures, principal curvatures, most-positive, most- negative, dip curvature, strike curvature, curvedness, and shape index (Chopra and Marfurt, 2007). If the horizon is picked on a noisy surface then this attribute can lead to incorrect curvature interpretations (Chopra and Marfurt, 2007). The Morrow B surface could have been too noisy to have generated accurate curvature attributes.

Figure 3.4: Equation displaying how curvature attributes are derived (Chopra and Marfurt, 2007).

24 3.6.2 Variance Attribute

This attribute is useful in for edge detection and discontinuities. This attribute calculation is a black box operator in Petrel so the actual mathematical process done on the data is unknown. The attribute was generated with a short window in order to bring out the channel edges.

3.6.3 The Hilbert Transform

Before the remaining attributes can be discussed, a concept called the Hilbert Transform must be introduced. Taner and Sheriff (1977) introduced using the Hilbert transform to calculate instantaneous amplitude, phase, and frequency. The Hilbert transform is used to calculate the seismic amplitude, phase, and frequency instantaneously and is used to calculate attributes in most seismic interpretation software (Hardage, 2010). The complex trace z(t) is comprised of the real seismic trace x(t) and an imaginary seismic trace y(t) (Hardage, 2010). The imaginary trace y(t) is calculated using the Hilbert Transform. n other words, the Hilbert transform applies a 90 degrees phase shift to every sinusoidal component of a signal. The real trace x(t) and the imaginary trace y(t) calculated using the Hilbert transform are added to generate the helical complex trace z(t) (Figure A2; Hardage, 2010). In Figure A2, the traces are shown in 3 dimensional space (x,y,z), t is time, x is the real data plane, and y is the imaginary data plane (Hardage, 2010). At any time on this trace, a vector a(t) can be calculated that extends perpendicularly away from the time axis to intercept z(t) (Figure A3; Hardage, 2010). From this point, instantaneous amplitude, instantaneous phase, and instantaneous frequency can be calculated (Figure A3). These calculations are utilized for the discussed attributes in this study.

25 Figure 3.5: The complex trace z(t) calculated from the real trace x(t) and the imaginary trace y(t) generated from the Hilbert Transform (Hardage, 2010).

26 Figure 3.6: How instantaneous seismic attributes are calculated from the complex seismic trace z(t) (Hardage, 2010).

3.6.4 Instantaneous Frequency

The instantaneous frequency is the time derivative of the instantaneous phase and is measured in hertz (Figure A2; Subrahmanyam and Rao, 2008). The instantaneous phase is generated by taking the arctangent of the imaginary trace y(t) by the real trace x(t) (Figure A2; Hardage, 2010).

3.6.5 Signal Envelope

Also known as reﬂection strength is amplitude independent of phase. Seis- mic envelope is useful in highlighting discontinuities, changes in lithology, and changes in deposition ( Subrahmanyam and Rao, 2008). The signal envelope is generated by taking the square root of the real trace x(t) plus the imaginary trace y(t) (Subrahmanyam and Rao, 2008).

27 3.6.6 Sweetness

Sweetness is calculated by dividing the signal envelope (reﬂection strength) by the square root of instantaneous frequency (Hart, 2008). The units for sweetness is the amplitude divided by the square root of hertz but is best though of a relative value for ease of understanding physically (Hart, 2008).

3.6.7 Relative Acoustic Impedance (RAI)

Relative acoustic impedance is a black box operator in Petrel so the actual mathematical process done on the data is unknown. Petrel is probably calculating the running sum of the trace and a low cut ﬁlter is applied (Subrahmanyam and Rao, 2008). The calculated attribute is the integration of the complex trace z(t) and approximates the relative acoustic impedance Subrahmanyam and Rao, 2008.

3.6.8 Root Mean Square (RMS) Amplitude

Root Mean Square (RMS) Amplitude is a black box operator in Petrel so the actual mathematical process done on the data is unknown. ”RMS value of a waveform represents a squaring of the amplitude of each point of a waveform and then taking its mathematical average” (Hass, 2003).

3.7 Neural Network Comparison of Seismic Attributes

Artificial neural networks aid in classifying multiple sets of input data that would be to complex for conventional statistical methods (Strecker et al., 2002). Neural networks are based on the mammalian brain’s ability to take large inputs of data and unknowns and then classify the data and find patterns (Strecker et al., 2002). In this study, an unsupervised neural network is free to search, rec- ognize, and classify structural patterns spanning the entire 3D VSP seismic data set (Strecker et al., 2002). The unsupervised neural network compares multiple attributes in order to find similarities between them based on three classes. In Petrel, the train estimation model was used for the unsupervised neural network seismic attribute comparison. The train estimation model will find similarities within multiple input data. The train estimation model took selected in- puted attributes and characterized them into three different classes. Three classes were chosen based on the estimated lithology of the area: mudstone, shale, and sandstone. The three classes did not have any physical constraints pertaining to lithology applied in order to classify them so no quantitative information can be ascertained. The unsupervised network is showing similarities in the combinations of attributes based on three classes not constrained by physical parameters.

28 Hypothetically, the similarities in the data do correspond to changes in lithology and bed thickness.

29 CHAPTER 4

RESULTS

4.1 Morrow B Sandstone Delineation from 3D VSP Data

The Morrow B was picked in the depth converted 3D VSP data set from tops picked in the 13-10A well. The top and base horizons were then picked in two way time (TWT) since seismic attributes are generated in time domain. The top of the Morrow B was picked at a zero crossing with the base picked at the trough (Figure 4.1; Figure 4.2; FIgure 4.3). The Morrow B could have been picked on the next zero crossing, however no amplitude interpretations can be made on a zero crossing for seismic attributes so it was picked at the trough (Figure 4.1; Figure 4.3). The 3D auto picker Petrel tool was used to pick the Morrow B top and base surfaces throughout the entire 3D VSP volume. The auto picker horizon was manually checked for accuracy afterwards. The Morrow B base surface appears to show differences in amplitude which could indicate differences in lithology (Figure 4.3). An isochron map displays how thickness varies in the ﬁeld (Figure 4.4). The edge of the survey are thinner then the interior portions. The northwest side of the survey does not vary much in thickness while the southeast side of the survey is thicker.

30 Figure 4.1: A display on how the Morrow B top and base were picked on the 3D VSP utilizing the 13-10A GR log.

31 Figure 4.2: The Morrow B Top two way time surface in amplitude generated from the auto picked horizon. The color bar is in amplitude with yellow being high amplitude and blues being low amplitude. The contour interval is 2.5 ms.

32 Figure 4.3: The Morrow B Base two way time surface in amplitude generated from the auto picked horizon. The color bar is in amplitude with yellow being high amplitude and blues being low amplitude. The contour interval is 2 ms.

33 Figure 4.4: An isochron showing the thickness in the Morrow B based on the picked horizons in two way time. Orange is thinner and red is thicker.

4.1.1 Low Amplitude Lenses

Areas of lower amplitude can be delineated within the Morrow B formation and are apparent on the Morrow B base surface (Figure 4.3). These lower amplitude packages resemble lenses in some areas (Figure 4.5). The lenses often separate, come together, and separate again which possibly make them con- formable with channel features interpreted by geologic studies. These darker areas were manually picked at the top and the base (Figure 4.6). The lenses were delineated by lower amplitude then the material to the sides of it (Figure 4.5). No exact amplitude constraints were used to pick the lenses since it was manually picked. This process was repeated throughout the entire 3D VSP volume using both inlines and crosslines (Figure 4.7). Figure 4.8 shows the outline of the hori-

34 zon shown in Figure 4.7 in red and the lenses themselves in two way time with nearby wells also plotted. An isochron map was generated from the top and the base of the low amplitude lenses (Figure 4.9). The black outline of the low amplitude lenses is shown on this figure that appears on Figure 4.8 and future attribute and neural network figures. The red areas are the thickest with the thinnest areas being pink. The channels are approximately 30-35 feet in the deepest areas and 20-25 feet on the edges of the low amplitude lenses. The low amplitude lenses are overall thicker within the middle of the axis and thin as they go outwards. The thicker areas could be interpreted as long-term channels while the some of the features that are thinner (light blue) could be short-lived channels that were abandoned over time. The paleoflow data from well 13-10A and 14-1 is also overlain on Figure 4.10. The low amplitude lens orientation strikes approximately east or southeast which is consistent with Brown’s (2014) paleoflow interpretations from FMI logs (Figure 4.9; Figuer 4.10; Figure 2.2; Gallagher, 2014; Wheeler et al., 1990; Puckette et al., 2008). It is possible that there are more channels but they are not resolvable due to being too thin or distorted by edge effects near the boundaries of the 3D VSP survey. The areas with thicker lenses run approximately east and some of the thinner lens areas could be interpreted as going more southeast. It could be interpreted that channels were flowing in this area towards the east over longer periods of time as compared to the thinner channels.

35 Figure 4.5: A cross section through the VSP survey as shown in the box on the upper right. The yellow horizon is showing that the seismic ﬂattened. The blue horizon is the top of the Morrow B. These low amplitude lenses were manually picked throughout the entire 3D VSP volume. The green boxes highlight the areas of low amplitude that could be interpreted as channels.

36 Figure 4.6: A cross section through the VSP survey as shown in the box on the upper right. The pink and black lines display how the top and base of the low amplitude lenses were manually picked throughout the entire 3D VSP volume.

37 Figure 4.7: A 3D view of the top of the low amplitude lens shown in two way time (ms) with an inline and cross line for scale.

38 Figure 4.8: A map showing the low amplitude lenses with the wells in the area. The outline of the 3D VSP data is in black, the outline of the Morrow B top surface from the 3D VSP is in pink, the channel outline is in red with the channel ﬁlled in with elevation time. The southeast features are deeper.

39 Figure 4.9: An isochron map showing the thickness of the low amplitude lenses in two way time. The pink outline is the outline of the Morrow B top as picked from the 3D VSP. The black line delineates the outline of the low amplitude lenses that were hand picked. This outline appears in red in Figure 4.8 and will appear in subsequent ﬁgures.

40 Figure 4.10: An isochron map showing the thickness of the low amplitude lenses in two way time. The pink outline is the outline of the Morrow B top as picked from the 3D VSP. The paleoflow directions for wells 13-10A and 13-14 as delineated by Brown (2014) as overlain on this figure. The paleo flow directions support the idea that these low amplitude lenses are overall striking towards the east.

4.2 Stratigraphic and Structural Features Visible Within the Morrow B

There are a few areas of interest within the Morrow B that could be faulting or a stratigraphic features. The main area delineated by the box in the south east corner (Figure 4.11 (a)) has an offset of approximately 70 ft. One explanation for the feature could be that the seismic is displaying different lenses of similar lithology that were deposited at different times. As one steps through inlines headed west (Figure 4.11 (b)(c)(d)), the two layers slowly start to come together

41 until they form a slope. The other possibility is that this could be a small low angle thrust fault. The collision of the North American and South American-African plates started in the early Middle Pennsylvanian with an orientation of roughly northeast to southwest (Sonnenburg et al., 1990). Compressional stresses from the continental collision were transported deep into the continent (Walper, 1977). A low angle thrust fault orientations in the field also strike northeast to southwest, which aligns with the tectonic activity in the Morrowan (Sonnenburg et al., 1990; Evan Gragg, personal communication, 2014). The low angle fault could have occurred either syndepositional or shortly after deposition of the Morrow B. There is another similar feature in the north west corner of the VSP. The northwest corner feature also has about 70 ft, offset and also looks like a small fault (location shown by box in north west corner of Figure 4.11 (a)). Ashley Hutton (personal communication, 2014) generated an ant tracking volume for the 3D VSP to enhance fault-like features. An ant tracking volume is made by generating a variance attribute for the entire volume and then running the ant tracking volume algorithm that finds similarities in variance. There is is a fault-like feature at the location of the southeast box (Figure 4.11(a)) and Petrel automatically picked a fault surface in the ant tracking volume (Figure 4.12(a)). This fault cannot be seen within the full field 3D seismic data but Hutton believes that a fault of this size should be resolvable (personal communication, 2014). The feature does not appear very fault-like when looking at it in relation to the seismic data (4.12, (b)). It is difficult to interpret one large, continuous fault from the seismic cross section (Figure 4.12(a)). The ant tracking volume could be delineating a series of smaller faults that occurred at different times or a combination of both faults and stratigraphic features. Another possibility is that this feature could be an artifact from migration processing of the data. This feature does occur very close to well 14-1 where the geophone array was deployed. Data processing seems unlikely as a reason because a similar feature is not observed near the 13-10a which also had a geophone array.

42 Figure 4.11: The surface of the Morrow B Base (a). The box in the south east represents the feature of interest while the lines crossing it represent the inline sections shown in (b), (c), and (d). The white box in the upper north west corner is a another similar feature. In (b), (c), and (d) the Morrow B top is delineated with the green line. Figure (b) shows the largest offset which is about 70 ft. As one moves west, the surfaces come together and form a slope.

43 Figure 4.12: (a) Two way time 3D VSP ant tracking volume generated by Ashley Hutton with the box showing the fault like feature. (b) The light blue dashed line indicates the fault. The Morrow B top is shown in green. (Ashley Hutton, personal communication, 2014).

4.3 Well Log Analysis

Well logs were analyzed in order to determine any differences between the inside and outside of the low amplitude lenses. Gamma ray (GR) and spon- taneous potential (SP) logs were analyzed for lithology and thickness. A section crossing the low amplitude lenses are in Figure 4.13. The GR well logs display where the reservoir sand thickens within the low amplitude lens (well 13-14) and thins outside the lens (wells 13-5 and 14-3). The contrast between the sand and the mudstone also increases within the low amplitude lenses. A cross section displaying well logs within and outside the low amplitude lenses illustrate differences in both thickness and lithology (Figure 4.14). Wells 13-10A and 13-14 are within the low amplitude lenses and display thick course sandstone beds in the

44 GR log. Well 13-10A has a thickness of 36 ft. and well 13-14 has a thickness of 29 ft. Wells 13-12 and 13-15 fall outside the low amplitude lenses and still have sandstone, but they are not as thick. Well 13-12 has a thickness of 25 ft. and 13-5 has a thickness of 20 ft. The contrast between the sand bed and the surrounding mudstone differs between the logs within the lens and outside. The GR contrast is greater between the mudstone and sandstone within the lens. The low contrast between the reservoir and the mudstone outside the channels could be the reason why they are not evident in the seismic data.

Figure 4.13: A well log section cross cutting the low amplitude lenses as shown by the black arrow in the map in upper left corner. The thickness of the Morrow B increases within the channel and decreases outside. The contrast between the sandstone and mudstone also increases within the low amplitude lens.

45 Figure 4.14: The map in the lower left corner shows the cross section across the low amplitude lenses and GR logs for 13-12, 13-10A, 13-5, and 13-14.

Differences in well logs within the channel could be due to point bars and other internal stratigraphic differences. An example of this is from wells 13-10A and 13-10. Both wells are within the low amplitude lenses but have different GR log characteristics (Figure 4.15). Well 13-10A has a thick ( 36 ft.) and clean Morrow B sandstone compared to 13-10 where it is thinner ( 28 ft.) and appears to have more mudstone.

46 Figure 4.15: A cross section cutting through the middle of the low amplitude lenses. Wells 13-10A, 13-14, and 13-2 have thicker and cleaner sandstone. Well 13- 10 is not as thick and has more mudstone but still lies within the low amplitude lens.

47 4.4 Seismic Attributes

4.4.1 Curvature Attributes

Figure 4.16: The Curvature attribute for the Morrow B base. The yellow line delineates the low amplitude lenses.

Curvature can be mathematically described as the second-order derivative of the curve (Chopra and Marfurt, 2007). Thus a large curvature will mean that the curve is bent more and a straight line will be equal to zero (Chopra and Marfurt, 2007). Although curvature attributes can be useful in delineating channels, however, they did not prove to be useful in this data set (Figure 4.16). The most positive and negative curvature, the gaussian curvature (product of minimum and maximum curvature), and strike curvature (curvature extract along a direction perpendicular to the dip) were calculated and proved not be helpful in

48 delineating the channel boundaries (Chopra and Marfurt, 2007). Curvature attributes are very sensitive to noise and edge effects, which could be a factor in this study (Chopra and Marfurt, 2007) (Suarez et al., 2008).

4.4.2 Chaos

Figure 4.17: The Chaos attribute for the Morrow B base. The yellow line delineates the low amplitude lenses.

The chaos attribute measures the lack of organization in the dip and azimuth estimation. Chaos is similar to variance but has a black box procedure within Petrel so that the actual mathematical calculation is hidden. Chaos is scaled from 0-1 with 1 being more chaotic. The channel ﬁll appears less chaotic (4.17). The Morrow B base attribute might delineate the channel sands. The less

49 chaotic areas grey/white follow the paleo current and approximately align with the low amplitude lenses. Other less chaotic areas on the surface could be displaying more channels or more detail on the low amplitude lenses.

4.4.3 Variance

Figure 4.18: The Variance attribute for the Morrow B base. The yellow line delineates the low amplitude lenses.

Petrel’s coherence attribute is referred to as the variance attribute (Samp- son, 2005). Both variance and coherence measures the similarity between wave- forms. Variance is deﬁned as one minus the coherence value (Sampson, 2005). Variance is useful as an edge detector and can bring out depositional features

50 such as channels (Figure 4.18). The variance attribute highlights the proposed fault or stratigraphic feature discussed earlier as well as the outline of the channel areas in red and yellow. The highlighted areas on the edges of the surface might not be geological and could be due to edge effects.

4.4.4 Instantaneous Frequency

Figure 4.19: The Instantaneous Frequency attribute for the Morrow B base. The yellow line delineates the low amplitude lenses.

Instantaneous frequency is the time derivative of the phase otherwise known as the rate of change of the phase (Subrahmanyam et al., 2008). Instantaneous frequency attribute can give information about the bed thickness, bed interfaces,

51 sand/shale ratio indicator, hydrocarbon indicator, and a fracture zone indicator (Taner, 2001; Suarez et al., 2008; Subrahmanyam et al., 2008). In this study, instantaneous phase has been used as a bed thickness indicator (Figure 4.19). Higher frequencies demonstrate sharp interfaces such as those exhibited by thin shale bedding while lower frequencies indicate more massive bedding of sandstone lithologies (Taner, 2001). The Morrow B base best delineates areas of thicker sandstones and thinner shales. The lower frequency areas line up approximately with the outline of the low amplitude lenses meaning that there could be more massively bedded sandstones.

4.4.5 Signal Envelope

Figure 4.20: The Signal Envelope attribute for the Morrow B base. The yellow line delineates the low amplitude lenses.

52 The signal envelope (reflection strength) is the envelope of the seismic signal, representing the instantaneous energy of the signal that is proportional to the reflection coefficient (Subrahmanyam et al., 2008). The signal envelope attribute can aid in interpreting the following characteristics: Acoustic impedance contrast (reflectivity), bright spots due to possible gas accumulation, sequence boundaries, thin-bed tuning, major changes in depositional environment, and the spatial correlation to porosity and lithology (Taner, 2001). Figure 4.20 displays the Morrow B base signal envelope attribute. The higher amplitudes could correspond to sandstone lithologies and align with the low amplitude lenses.

4.4.6 Sweetness

Figure 4.21: The Sweetness attribute for the Morrow B base. The yellow line delineates the low amplitude lenses.

53 Sweetness is defined by dividing the reflection strength (also known as the instantaneous amplitude or amplitude envelope) by the square root of instantaneous frequency (Hart, 2008). Sweetness is used for finding isolated sand bodies surrounded by shales (Hart, 2008). If the acoustic impedance contrast between sands and shales are low or if sands and shales are highly interbedded sweetness does not work as well (Hart, 2008). Seismic with both high amplitudes and low frequency will have high sweetness while other combinations will have low sweetness (Hart, 2008). The characteristics of shale dominated lithologies include low amplitudes (low acoustic impedance contrasts) and closely spaced reflections (high frequency) (Hart, 2008). Sandy intervals have high amplitudes (high acoustic impedance contrasts) and low frequencies (broad reflections) (Hart, 2008). Sweetness is not useful when the acoustic impedance between shale and sand is low, if destructive interference from reflections above and below the sand pre- vents high-amplitude reflections from developing, or if the thickness is below the 20 m (65 ft.) tuning thickness (Hart, 2008). The higher amplitude areas for the sweetness attribute (Figure 4.21) could correspond to sandstone lithologies or correspond to thicker sandstone layers. The grey areas surrounded in blue could be indicating thinner sandstone beds with mudstone incasing them. The higher amplitudes also line up with the low amplitude lenses.

54 4.4.7 Relative Acoustic Impedance

Figure 4.22: The Relative Acoustic Impedance attribute for the Morrow B base. The yellow line delineates the low amplitude lenses.

Relative acoustic impedance (RAI) is used for lithology and as a thickness variation indicator (Suarez et al., 2008). Impedance variations within the channels can be used to delimit facies change (Suarez et al., 2008). The impedance amplitude variations may be correlated to sand/shale ratios (Suarez et al., 2008). Higher values of RAI are related to shale intervals (Suarez et al., 2008). In Suarez et al., (2008), the study focuses on channels in the Red Fork formation in the Anadarko Basin. Higher amplitudes within their study area correspond to channel facies since it enhances impedance contrast boundaries making lithology changes more evident (Suarez et al., 2008). There are higher amplitudes within the north

55 western and south eastern areas of the survey correspond to channel facies (Fig- ure 4.22). The higher amplitude areas approximately line up with the channels but not as well as other attributes.

4.4.8 Root Mean Squared Amplitude

Figure 4.23: The Root Mean Squared attribute for the Morrow B base. The yellow line delineates the low amplitude lenses.

The root mean square (RMS) attribute aids in identifying lithology. ”RMS value of a waveform represents a squaring of the amplitude of each point of a waveform and then taking its mathematical average” (Hass, 2003). High RMS values correspond to higher proportions of channel sands or better reservoir facies (Raef et al., 2010). The top of the Morrow B appears to follow the paleo ﬂow

56 of the channels (Figure 4.23). There are areas of high amplitude which might correspond to higher proportions of channel sands (Raef et al., 2010).

4.5 Petrel’s Train Estimation Modeling

Figure 4.24 lays out the different attributes and how they were combined using the train estimation model. The highlighted rows in Figure 4.24 are the combinations that generated the most useful geologic information and aligned with the low amplitude lenses. The dark blue, light blue, and pink colors in the output surfaces (Figures 4.25-4.28) correspond to the three classes as described in section 3.7. These classes hypothetically correspond to differences in lithology and/or bed thickness. The attribute combinations that were considered not useful for geologic interpretation were those predominantly characterized as usually having only one similarity to cover all of the Morrow B surfaces (Figure 4.27). However, even the ones that do not delineate the low amplitude lenses as well could be showing other geologic information as explained by the example of Run #4 below. All of the neural network runs will be displayed in Appendix A. A consistent feature that appears in most of the attributes is in the southeast edge of the survey. This could be due to the increase in thickness as seen in the isochron map or could be a similarity in lithology. Run #15, run #6, run #15, and run #4 will be analyzed in detail.

57 Figure 4.24: The attributes are listed at on the top and the different combinations in the rows. The highlighted rows are runs that displayed possible geologic information that aligned with the low amplitude lenses.

58 4.5.1 Run #15

Figure 4.25: Run 15 generated from the RAI, instantaneous frequency, sweetness, envelope, and the chaos attributes.

Run #15 was generated by inputting the RAI, instantaneous frequency, sweetness, signal envelope, and chaos (Figure 4.25). This run has the most attributes and the highest correlation with the low amplitude lenses. The attributes utilized for this run delineate the low amplitude lenses and supports the conclu- sion that there is sandstone inside this area. When the attributes are combined, the results of the neural network suggests the material within the low amplitude lenses is similar across the study area. The thickness of the picked lenses probably does not have an effect on this attribute since the southeast corner has the same similarity as the other picked lenses.

59 4.5.2 Run #6

Figure 4.26: Run 6 generated from the RAI, instantaneous frequency, impedance, and envelope attributes.

Run #6 was generated using RAI, envelope, and instantaneous frequency. This run shows differences in similarity within the picked lenses which could relate to geologic complexities (Figure 4.26). This geologic difference can be analyzed by examining the 13-10A well (dark blue) and the 13-10 well (pink) where there is detailed well log and core information. Well 13-10A ( 36 ft.) is slightly thicker( 28 ft) than at the 13-10 well (Figure 4.15). Well logs and possibly the core studies support that 13-10A has cleaner sandstone when compared to the 13-10 which has more mudstone within the reservoir interval (Gallagher, 2014; Dylan Rose-Coss, personal communication, 2014). Run #6 could be detecting the differences in lithology, thickness, or a combination of both. The southeast corner also

60 shows up as pink in run #6. This could also be due to a combination of lithology and thickness.

4.5.3 Run#16

Run #16 combines RAI, instantaneous frequency, sweetness, envelope, chaos, and variance (Figure 4.27). This run shows that there is only one major similarity on this surface (pink). However, the small areas of blue similarities occur near the edges and outside of the low amplitude lenses and could relate to lithology within the lenses and outside.

Figure 4.27: Run 16 generated from the RAI, instantaneous frequency, sweetness, envelope, chaos, and variance attributes.

61 4.5.4 Run#4

Figure 4.28: Run 4 generated from the RAI, impedance and instantaneous frequency attributes.

Run #4 combined RAI and instantaneous frequency (Figure 4.28). This combination shows a curved feature in dark blue within the middle of the low amplitude lenses that does align with proposed ﬂow directions. This feature appears similar to a meandering channel. The neural network found similar dark blue features that run perpendicular to the low amplitude lenses on the eastern edge of the survey which are more difﬁcult to interpret. So even though this run does not line up with the low amplitude lenses, it could still be displaying relevant geologic information.

62 CHAPTER 5

DISCUSSION

The 3D VSP interpretation of the Morrow B appears to corroborate the incised valley theory described by Gallagher (2014), Sonnenburg et al. (1990), Krystinik and Blakeney (1990), Wheeler et al. (1990), Al-Shaieb et al (1995), and Puckette and Al-Shaieb (2008). The 3D VSP is covering a small portion of the incised valley, but still displays some possible channel-like features. The low amplitude sand lenses appear channel like in their geometry and align with the overall east paleoflow direction (Figure 1.2(b); Figure 2.2; Figure 4.10; Puckette and Al-Shaieb, 2008). The seismic attributes also support that the 3D VSP survey could be imaging a portion of a channel system. Gallagher (2014) describes the Morrow B as being fluvial deposited with a coarse sandstone lithology. From this small view of the field, it is difficult to tell if the channels are more braided or meandering. By comparing the core and the interpreted seismic, one can start to understand the geological depositional environment. If one looks at a modern braided river system analog the geometries are very similar (Figure 5.1). The interpreted channels could also be a small section of a meandering channel which can look braided.The Morrow B in this area might have been deposited at a transition zone between the steeply dipping braided river system and meandering depositional environment (Gallagher, 2014).

63 Figure 5.1: A modern braided river system from the Congo (Zaire) River on top compared to the interpreted channel outline as shown from the channel isochron. Geometric similarities can be seen between the two environments.

Some individual channels can be interpreted in the 3D VSP data. The direction, thickness, and geometry of the channels has changed over deposition. The channel features are anywhere in size from 500 to 1,000 ft. across and 20-35 ft. thick. Since the seismic resolution is not higher, only a broad scale interpretation can be presented. The data could be showing two different times of paleoflow and channel geometry. There appears to be channel-like features oriented approximately west-east while another set could be going northwest-southeast (Figure 5.2). The paleoflow data does suggest that there was multiple directions of flow during the Morrow B deposition (Brown, 2014). The channel features running west-east appear to be thicker then the channels running northwest- southeast (Figure 5.2). The channels running east-west could have been longer lived than the channels running northwest-southeast based solely on thickness. The feature running northwest to southeast (delineated by a white oval on Figure 5.2) could also be interpreted as running west-east as well. This would suggest that most of the channel flow was in the west-east direction. Brown (2014) found that towards the end of the Morrow deposition the flow was mainly towards the east. The 3D VSP might be imaging the mostly eastward oriented channels since other channels have been overprinted.

64 Figure 5.2: Channel interpretation based on thickness and channel orientation from the low amplitude lenses isochron map. The possible shorter lived channels running northwest-southwest are pointed out by the orange arrows. The possible longer lived channels running west-east are pointed out by the yellow arrows. The white oval delineates an area where the feature can be interpreted as either oriented northwest-southeast or west-east.

Seismic resolution plays a large role in interpretation. If the relative acoustic impedance is not high enough, some stratigraphic features will not be visible. The areas in which the apparent channels are resolvable, the relative acoustic impedance must be higher. They do show up better along strike with wells 13- 10A and 14-1 where the geophone arrays were deployed. No channels can be resolved on the edges of the survey due to edge effects or low resolution. If the low amplitude lenses are displaying geologic data, then it does appear to be channels. Also, the small size of the survey may not show all reﬂection terminations that can aid in stratigraphic interpretation (Sarzelejo and Hart, 2006). Well logs reveal that there is Morrow B sandstone covering the entire area of the 3D VSP. There are a few possibilities to why the low amplitude lenses

65 are visible. The first reason could be that the Morrow B in the channel area is thicker and has a higher acoustic impedance compared to the areas around it. The channel thicknesses do not vary significantly based on the low amplitude lens isochron (Figure 4.9). The well logs do support that the Morrow B is thicker and has a higher contrast between the sandstone and mudstone as compared to wells outside the low amplitude lenses. Well 13-10 is an exception and could be due to internal stratigraphic changes such as a small point bar. Well log comparisons can be ambiguous since they are different vintages and qualities of logs (Sarzelejo and Hart, 2006). The seismic attributes discussed in this study aid in interpreting the channel locations and lithology. The channel outline picked from the original data approximately line up with features seen in the seismic attribute calculations. The seismic attributes give valuable information about lithology and how it changes within the Morrow B area. There are also considerations to take when utilizing seismic attributes for seismic interpretation. Seismic attributes can be greatly influenced by acquisition parameters and initial processing (Moro et al., 2013). Overall, there appears to be more sandstone lithology within the picked channel areas as opposed to outside.The neural network supported the channel theory by finding similarities within different attributes that also overlapped with the picked channels.

66 CHAPTER 6

CONCLUSIONS AND FUTURE WORK

6.1 Conclusions

The 3D VSP interpretation and attribute analysis can be interpreted as a channel system supporting the incised valley model of deposition. The resolved channel system is oriented in the approximate eastward paleoflow direction and has the correct dip as hypothesized by geologists working on the Morrow B. The seismic attribute study also delineates the proposed channel outlines and could support variations in thickness of sandstone lithologic units within that area. The attributes that best aid in interpreting the channel outline are the signal envelope, sweetness, and the chaos attribute. Over nine of 17 of the neural network runs also show clear delineation of the channel like features by finding similarities within the reservoir, and this is an indication that the attributes are discerning lithologic characteristics. One consequence if the channel area does have a cleaner sandstone compared to areas outside of the channel is that a preferential pathway for CO2 to flow through the reservoir may exist. The potential 70 ft. fault or stratigraphic feature resolved in the 3D VSP seismic volume could also affect CO2 flow in the area. The channel like features should have an effect on CO2 flow within the reservoir. The two injection wells in the area, 13-10A and 14-1 are placed on the edges of the channel feature. CO2 should be flowing in an east-west or southeast- northwest direction within the channel feature, which provides a high porosity flow path. It is possible that production will be better in production wells that also lie within the channel rather then outside or in an adjacent channel feature such as in wells 13-12, 13-2, 13-6, and 13-14. The possible fault feature discussed in section 4.2 lies directly northwest of injector 14-1. The CO2 movement could be impacted differently depending on if the fault is a barrier or a conduit to flow. If the fault is a barrier, the CO2 should flow in the opposite direction of the fault towards the southeast where there is a lack of wells due to a road. Modeling of CO2 flow in this area should take these interpretations into account. Observations of CO2 break through and sweep efficiency from production wells within the 3D VSP volume might also be able to support this theory.

67 6.2 Suggestions for Future Work

(1)Compare well logs to core data in a more in depth fashion in order to understand the geology of the channel feature. Also find production data for wells within and outside the channels to see if there is a difference. (2)Analyze how the 3D VSP data set fits into the full field 3D seismic data set. The channel features within the 3D VSP might fit into a larger channel system seen in the 3D seismic. (3)Use a trained neural network in order to constrain a better understanding of the lithology from the seismic attributes. (4)Repeat this same process and compare the results for the repeated 3D VSP data for the same area in order to see if there is CO2 is using the possible interpreted channel for a fluid pathway.

68 REFERENCES

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69 Hart, B.S., 2008. Channel detection in 3-D seismic data using sweetness: AAPG Bulletin, v. 92. no. 6. Hass, J., 2003, What is amplitude?: An Acoustics Primer. http://www. indiana.edu/~emusic/acoustics/amplitude.htm. Henry, M.E. and T.C. Hester, 1995, Anadarko Basin Provence (058): USGS National Assessment of Oil and Gas Resources. IPCC, 2005, IPCC Special Report on Carbon Dioxide Capture and Storage: Cambridge University Press. Krystinik, L.F. and B.A. Blakeney,1990, Sedimentology of the upper Mor- row Formation in eastern Colorado and western Kansas, in Sonnenberg, S. A., et al., eds., Morrow sandstones of southeast Colorado and adjacent areas: Rocky Mountain Association of Geologists, Denver, Colorado: p. 37-50. McKay, R. H. and J. T. Noah, 1996, Integrated perspective of the depositional environment and reservoir geometry, characterization, and performance of the Upper Morrow Buckhaults Sandstone in the Farnsworth Unit, Ochiltree County, Texas: Oklahoma Geological Survey Circular, no. 98, p. 101-114. Moro, Y.D., A. Fernandez, S. Verma, and K. Marfurt, 2013, 3-D surface seismic attribute and prestack impedance inversion characterization of the Red Fork formation, Oklahoma, USA: Search and Discovery Article 41167, Adapted from oral presentation at AAPG Annual Convention and Exhibition, Pittsburgh, Pennsylvania, May 19-22, 2013. Muller, K.W and W.L. Soroka, 2010, 3D VSP technology now a standard high-resolution reservoir imaging technique: Part 1, acquisition and processing: The Leading Edge. Special Section: Borehole Geophysics. Munson, T. W., 1988, Depositional, diagenetic, and the production history of the Upper Morrow Buckhaults sandstone, Farnsworth Field, Ochiltree County, Texas: M.S. thesis, West Texas State University. NETL and DOE, 2010, Carbon Dioxide Enhanced Oil Recovery. Untapped Domestic Energy Supply and Long Term Carbon Storage Solution. Parker, R.L., 1956, Farnsworth Morrow Oil Field: The Panhandle Geonews. v. 4, no. 1. Pasala, S.M., C.B. Forster, S.J. Lim, and M.D. Deo, 2003, Simulating the impact of faults on CO2 sequestration and enhanced oil recovery in sandstone aquifers: Society of Petroleum Engineers, 84186. Perry, W.J., 1989, Tectonic Evolution of the Anadarko Basin Region, Okla- homa: USGS Survey Bulletin, 1866-A. Pruess, K., 2004, Numerical simulation of CO2 leakage from a geologic disposal reservoir including transitions from super to subcritical conditions and boiling of liquid CO2: Society of Petroleum Engineers Journal, p. 237-248. Puckette, J. and Z. Al-Shaieb, 2008, Sequence stratigraphy, lithofacies, and reservoir quality, Upper Morrow sandstones, northwestern shelf, Anadarko Basin: Oklahoma Geological Survey Circular, v. 111.

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71 APPENDIX A

TRAIN ESTIMATION MODEL RUNS

Figure A.1: Run 1 generated from the RAI, impedance, chaos attributes.

72 Figure A.2: Run 2 generated from the RAI, chaos, and signal envelope attributes.

73 Figure A.3: Run 3 generated from the RAI and signal envelope attributes.

74 Figure A.4: Run 4 generated from the RAI and instantaneous frequency attributes.

75 Figure A.5: Run 5 generated from envelope and instantaneous frequency attributes.

76 Figure A.6: Run 6 generated from the RAI, signal envelope, and instantaneous frequency attributes.

77 Figure A.7: Run 7 generated from the RAI, instantaneous frequency, signal envelope, and chaos attributes.

78 Figure A.8: Run 8 generated from the sweetness and chaos attributes.

79 Figure A.9: Run 9 generated from the RAI and sweetness attributes.

80 Figure A.10: Run 10 generated from the sweetness and signal envelope attributes.

81 Figure A.11: Run 11 generated from the instantaneous frequency and signal envelope attributes.

82 Figure A.12: Run 12 generated from the RAI, instantaneous frequency, sweetness, and signal envelope attributes.

83 Figure A.13: Run 13 generated from the variance and sweetness attributes.

84 Figure A.14: Run 14 generated from the RAI, variance, and sweetness attributes.

85 Figure A.15: Run 15 generated from the RAI, instantaneous frequency, sweetness, signal envelope, and chaos attributes.

86 Figure A.16: Run 16 generated from the RAI, instantaneous frequency, sweetness, signal envelope, chaos, and variance attributes.

87 Figure A.17: Run 17 generated from the RAI, instantaneous frequency, sweetness, signal envelope, chaos, and RMS.

88 Geologic characterization of the Morrow B reservoir in Farnsworth Unit, TX using 3D VSP seismic, seismic attributes, and well logs.

Paige Czoski

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