geologic characterization of the morrow b reservoir in...

Geologic characterization of the Morrow B reservoir inFarnsworth Unit, TX using 3D VSP seismic, seismic attributes,

and well logs.

Paige Czoski

Submitted in Partial Fulfillmentof the Requirements for the Degree of

Master of Science in Geophysics

New Mexico Institute of Mining and TechnologySocorro, New Mexico

December, 2014

ABSTRACT

Farnsworth Field is located in Ochiltree, Texas and has been selected for aCarbon Capture Utilization and Storage (CCUS) project that is being supportedby the Department of Energy, the Southwest Regional Partnership on CarbonSequestration, and Chaparral Energy Co. LLC. One million tonnes of 100% CO2produced from the Arkalon Ethanol Plant in Liberal KS and the Agrium FertilizerPlant in Borger TX will be injected into the Morrow B formation and monitoredusing seismic methods (Grigg and McPherson, 2012). Previous geologic char-acterization hypothesizes that the Morrow B Formation was an incised valleydepositional environment. This study focuses on the 3D Vertical Seismic Profile(VSP) survey that overlaps two injection wells covering an area of approximately1 by 2 miles. The purpose of this study is to geologically characterize the 3D VSPusing seismic attributes and well logs. The Morrow B was auto picked in the 3DVSP data using gamma ray logs to locate the formation in depth. Low amplitudelens features that resemble channels were manually picked within the MorrowB. Seismic attributes aided in the geologic characterization by providing litho-logic and stratigraphic interpretations. The attributes discussed in this study arecurvature, instantaneous frequency, signal envelope, sweetness, relative acousticimpedance, chaos, root mean square amplitude, and variance. An unsupervisedneural network was utilized to compare the seismic attributes to find similaritiesthat might relate to geology. The possible channel interpretation can influenceCO2 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 SouthwestPartnership 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 throughoutthe years. I would also like to thank Bob Will (Schlumberger) for helping me learnto use Petrel and aiding me in my interpretation. I also want to thank my fellowgraduate students who are researching the Farnsworth Unit, especially DylanRose-Coss who aided in the well log interpretation, Ashley Hutton who helpedme input the seismic data and interpret faults, Sara Gallagher, Evan Gragg, andWilliam Ampomah.

I want to thank all my friends and family who helped me through myeducation. I want to thank my parents Dana and Richard Czoski who supportedme through my years of education and encouraged a love for geology by takingme to museums and other interesting geologic areas all over the world. BrookeCzoski for always being there for me as my best friend and sister. I would also liketo thank my fellow graduate students and Logan Roberts for emotional supportthrough my graduate school experience.

This thesis was typeset with LATEX1 by the author.

1The 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 andTechnology thesis format was written for the Tech Computer Center by John W. Shipman.

CONTENTS

LIST OF FIGURES v

1. INTRODUCTION 11.1 Research Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Farnsworth Unit History . . . . . . . . . . . . . . . . . . . . . . . . . 61.3 Carbon Capture Utilization and Storage (CCUS) or Enhanced Oil

Recovery (EOR) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2. GEOLOGIC SETTING 92.1 Regional Stratigraphic Framework . . . . . . . . . . . . . . . . . . . 92.2 Anadarko Basin Tectonic Evolution . . . . . . . . . . . . . . . . . . . 102.3 Depositional Environment . . . . . . . . . . . . . . . . . . . . . . . . 122.4 Paleoflow Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162.5 Controls on Reservoir Quality and Heterogeneity . . . . . . . . . . 17

3. METHODOLOGY 193.1 Seismic Survey Overview: Imaging goals . . . . . . . . . . . . . . . 193.2 3D VSP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213.3 Crosswell Tomography . . . . . . . . . . . . . . . . . . . . . . . . . . 233.4 Well Log Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233.5 Data Analysis Software . . . . . . . . . . . . . . . . . . . . . . . . . . 233.6 Seismic Attributes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

3.6.1 Curvature Attributes . . . . . . . . . . . . . . . . . . . . . . . 243.6.2 Variance Attribute . . . . . . . . . . . . . . . . . . . . . . . . 253.6.3 The Hilbert Transform . . . . . . . . . . . . . . . . . . . . . . 253.6.4 Instantaneous Frequency . . . . . . . . . . . . . . . . . . . . 273.6.5 Signal Envelope . . . . . . . . . . . . . . . . . . . . . . . . . . 273.6.6 Sweetness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283.6.7 Relative Acoustic Impedance (RAI) . . . . . . . . . . . . . . 283.6.8 Root Mean Square (RMS) Amplitude . . . . . . . . . . . . . 28

3.7 Neural Network Comparison of Seismic Attributes . . . . . . . . . 28

4. RESULTS 304.1 Morrow B Sandstone Delineation from 3D VSP Data . . . . . . . . . 30

4.1.1 Low Amplitude Lenses . . . . . . . . . . . . . . . . . . . . . 344.2 Stratigraphic and Structural Features Visible Within the Morrow B 414.3 Well Log Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 444.4 Seismic Attributes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

4.4.1 Curvature Attributes . . . . . . . . . . . . . . . . . . . . . . . 484.4.2 Chaos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 494.4.3 Variance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 504.4.4 Instantaneous Frequency . . . . . . . . . . . . . . . . . . . . 514.4.5 Signal Envelope . . . . . . . . . . . . . . . . . . . . . . . . . . 524.4.6 Sweetness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 534.4.7 Relative Acoustic Impedance . . . . . . . . . . . . . . . . . . 554.4.8 Root Mean Squared Amplitude . . . . . . . . . . . . . . . . . 56

4.5 Petrel’s Train Estimation Modeling . . . . . . . . . . . . . . . . . . . 574.5.1 Run #15 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 594.5.2 Run #6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 604.5.3 Run#16 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 614.5.4 Run#4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

5. DISCUSSION 63

6. CONCLUSIONS AND FUTURE WORK 676.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 676.2 Suggestions for Future Work . . . . . . . . . . . . . . . . . . . . . . . 68

REFERENCES 69

A. TRAIN ESTIMATION MODEL RUNS 72

LIST OF FIGURES

1.1 Farnsworth Unit (pink square) located in northern Texas. Pur-ple lines represent state borders and green lines represent countyboundaries. The field area is delineated by the blue outline in theblown up figure to the left. Well 13-10A is displayed by the greentriangle. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.2 Paleogeography of the Morrow. Light green represents shales andmudstones, dark green represents fluvial systems, and blue repre-sents seas. (Gallagher, 2014; Swanson, 1979). . . . . . . . . . . . . . 3

1.3 (a) Structure map for the top of the Morrow B. The field dips to-wards the south east and is deeper within the middle of the field.(b) The Morrow B isopach generated from gamma ray well logs forthe entire field (blue outline in Figure 1.1). The thicker sandstoneruns through the middle of field. These observations support thehypothesis 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 andwater into a reservoir. The super critical fluid pushing previouslyunproduced oil out of pore spaces, and pushing it towards a pro-duction well. (NETL and DOE, 2010). . . . . . . . . . . . . . . . . . 8

2.1 Stratigraphic chart showing the stratigraphic framework of the LowerAtokan-aged and Upper Morrowan strata in the FWU. Wirelinelog 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 figuredisplays 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 changeswith 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

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 oferosion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.7 The top surface in depth of the Morrow B with wells 13-10A and13-14 where the FMI images were taken. The rosette diagramsshowing the azimuth of flow direction determined from the FMIimages taken in the Morrow B formation. The paleoflow directionis mostly to the east, northeast, and southeast. (Courtesy of DylanRose Coss, 2014; Brown, 2014) . . . . . . . . . . . . . . . . . . . . . 17

3.1 (a) FWU map of seismic surveys. The injector wells are trianglesand the producers are circles. This study focuses on the orangesquare on the north west corner of the field. (b) A zoomed in areasquare in (a). (b)shows the location of VSP (red oval) and crosswelltomography (yellow dashed lines) used for this study (Modifiedfrom Grigg and McPherson, 2012). . . . . . . . . . . . . . . . . . . . 20

3.2 A model of the 3D VSP survey as designed by WesternGeco withthe 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 thesurface represent the vibroseis shot points and the red lines repre-sent ray paths traveling down to where the geophones are placedat 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 producingwell 13-16, the green dot is injection well 13-10A, the blue dot isproducing 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 col-lection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

3.4 Equation displaying how curvature attributes are derived (Chopraand Marfurt, 2007). . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

3.5 The complex trace z(t) calculated from the real trace x(t) and theimaginary trace y(t) generated from the Hilbert Transform (Hardage,2010). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

3.6 How instantaneous seismic attributes are calculated from the com-plex seismic trace z(t) (Hardage, 2010). . . . . . . . . . . . . . . . . . 27

4.1 A display on how the Morrow B top and base were picked on the3D VSP utilizing the 13-10A GR log. . . . . . . . . . . . . . . . . . . 31

4.2 The Morrow B Top two way time surface in amplitude generatedfrom the auto picked horizon. The color bar is in amplitude withyellow being high amplitude and blues being low amplitude. Thecontour interval is 2.5 ms. . . . . . . . . . . . . . . . . . . . . . . . . 32

4.3 The Morrow B Base two way time surface in amplitude generatedfrom the auto picked horizon. The color bar is in amplitude withyellow being high amplitude and blues being low amplitude. Thecontour interval is 2 ms. . . . . . . . . . . . . . . . . . . . . . . . . . 33

4.4 An isochron showing the thickness in the Morrow B based on thepicked horizons in two way time. Orange is thinner and red isthicker. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

4.5 A cross section through the VSP survey as shown in the box onthe upper right. The yellow horizon is showing that the seismicflattened. The blue horizon is the top of the Morrow B. These lowamplitude lenses were manually picked throughout the entire 3DVSP volume. The green boxes highlight the areas of low amplitudethat could be interpreted as channels. . . . . . . . . . . . . . . . . . 36

4.6 A cross section through the VSP survey as shown in the box on theupper right. The pink and black lines display how the top and baseof the low amplitude lenses were manually picked throughout theentire 3D VSP volume. . . . . . . . . . . . . . . . . . . . . . . . . . . 37

4.7 A 3D view of the top of the low amplitude lens shown in two waytime (ms) with an inline and cross line for scale. . . . . . . . . . . . 38

4.8 A map showing the low amplitude lenses with the wells in thearea. The outline of the 3D VSP data is in black, the outline ofthe Morrow B top surface from the 3D VSP is in pink, the channeloutline is in red with the channel filled in with elevation time. Thesoutheast features are deeper. . . . . . . . . . . . . . . . . . . . . . . 39

4.9 An isochron map showing the thickness of the low amplitude lensesin two way time. The pink outline is the outline of the Morrow Btop as picked from the 3D VSP. The black line delineates the outlineof the low amplitude lenses that were hand picked. This outlineappears 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 lensesin two way time. The pink outline is the outline of the Morrow Btop as picked from the 3D VSP. The paleoflow directions for wells13-10A and 13-14 as delineated by Brown (2014) as overlain on thisfigure. The paleo flow directions support the idea that these lowamplitude lenses are overall striking towards the east. . . . . . . . . 41

4.11 The surface of the Morrow B Base (a). The box in the south eastrepresents the feature of interest while the lines crossing it repre-sent the inline sections shown in (b), (c), and (d). The white box inthe 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 moveswest, the surfaces come together and form a slope. . . . . . . . . . . 43

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 lightblue dashed line indicates the fault. The Morrow B top is shown ingreen. (Ashley Hutton, personal communication, 2014). . . . . . . 44

4.13 A well log section cross cutting the low amplitude lenses as shownby the black arrow in the map in upper left corner. The thickness ofthe Morrow B increases within the channel and decreases outside.The contrast between the sandstone and mudstone also increaseswithin the low amplitude lens. . . . . . . . . . . . . . . . . . . . . . 45

4.14 The map in the lower left corner shows the cross section acrossthe low amplitude lenses and GR logs for 13-12, 13-10A, 13-5, and13-14. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

4.15 A cross section cutting through the middle of the low amplitudelenses. Wells 13-10A, 13-14, and 13-2 have thicker and cleanersandstone. Well 13-10 is not as thick and has more mudstone butstill lies within the low amplitude lens. . . . . . . . . . . . . . . . . . 47

4.16 The Curvature attribute for the Morrow B base. The yellow linedelineates the low amplitude lenses. . . . . . . . . . . . . . . . . . . 48

4.17 The Chaos attribute for the Morrow B base. The yellow line delin-eates the low amplitude lenses. . . . . . . . . . . . . . . . . . . . . . 49

4.18 The Variance attribute for the Morrow B base. The yellow line de-lineates the low amplitude lenses. . . . . . . . . . . . . . . . . . . . . 50

4.19 The Instantaneous Frequency attribute for the Morrow B base. Theyellow line delineates the low amplitude lenses. . . . . . . . . . . . 51

4.20 The Signal Envelope attribute for the Morrow B base. The yellowline delineates the low amplitude lenses. . . . . . . . . . . . . . . . . 52

4.21 The Sweetness attribute for the Morrow B base. The yellow linedelineates 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 yel-low line delineates the low amplitude lenses. . . . . . . . . . . . . . 56

4.24 The attributes are listed at on the top and the different combina-tions in the rows. The highlighted rows are runs that displayedpossible geologic information that aligned with the low amplitudelenses. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

4.25 Run 15 generated from the RAI, instantaneous frequency, sweet-ness, 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, sweet-ness, envelope, chaos, and variance attributes. . . . . . . . . . . . . 61

4.28 Run 4 generated from the RAI, impedance and instantaneous fre-quency attributes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

5.1 A modern braided river system from the Congo (Zaire) River ontop compared to the interpreted channel outline as shown fromthe channel isochron. Geometric similarities can be seen betweenthe two environments. . . . . . . . . . . . . . . . . . . . . . . . . . . 64

5.2 Channel interpretation based on thickness and channel orientationfrom the low amplitude lenses isochron map. The possible shorterlived channels running northwest-southwest are pointed out bythe orange arrows. The possible longer lived channels runningwest-east are pointed out by the yellow arrows. The white ovaldelineates an area where the feature can be interpreted as eitheroriented northwest-southeast or west-east. . . . . . . . . . . . . . . 65

A.1 Run 1 generated from the RAI, impedance, chaos attributes. . . . . 72A.2 Run 2 generated from the RAI, chaos, and signal envelope attributes.

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73A.3 Run 3 generated from the RAI and signal envelope attributes. . . . 74A.4 Run 4 generated from the RAI and instantaneous frequency at-

tributes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75A.5 Run 5 generated from envelope and instantaneous frequency at-

tributes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76A.6 Run 6 generated from the RAI, signal envelope, and instantaneous

frequency attributes. . . . . . . . . . . . . . . . . . . . . . . . . . . . 77A.7 Run 7 generated from the RAI, instantaneous frequency, signal en-

velope, and chaos attributes. . . . . . . . . . . . . . . . . . . . . . . 78A.8 Run 8 generated from the sweetness and chaos attributes. . . . . . 79A.9 Run 9 generated from the RAI and sweetness attributes. . . . . . . 80A.10 Run 10 generated from the sweetness and signal envelope attributes.

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81A.11 Run 11 generated from the instantaneous frequency and signal en-

velope attributes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82A.12 Run 12 generated from the RAI, instantaneous frequency, sweet-

ness, and signal envelope attributes. . . . . . . . . . . . . . . . . . . 83A.13 Run 13 generated from the variance and sweetness attributes. . . . 84A.14 Run 14 generated from the RAI, variance, and sweetness attributes. 85A.15 Run 15 generated from the RAI, instantaneous frequency, sweet-

ness, signal envelope, and chaos attributes. . . . . . . . . . . . . . . 86A.16 Run 16 generated from the RAI, instantaneous frequency, sweet-

ness, signal envelope, chaos, and variance attributes. . . . . . . . . 87

A.17 Run 17 generated from the RAI, instantaneous frequency, sweet-ness, signal envelope, chaos, and RMS. . . . . . . . . . . . . . . . . 88

This thesis is accepted on behalf of the faculty of the Institute by the followingcommittee:

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 ChaparralEnergy Co. LLC, is a commercial scale Carbon Utilization and Storage (CCUS)project supported by both the Department of Energy (DOE) and the SouthwestRegional Partnership on Carbon Sequestration (Figure 1.1). The primary goalincludes 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). Seismicmonitoring over time will aid in understanding the CO2 migration within thereservoir. 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 commercialsequestration at active CCUS projects (Grigg and McPherson, 2012).This studyfocuses on geologic characterization of the Morrow B reservoir utilizing the 3DVertical Seismic Profile (VSP) study.

Figure 1.1: Farnsworth Unit (pink square) located in northern Texas. Purple linesrepresent state borders and green lines represent county boundaries. The fieldarea is delineated by the blue outline in the blown up figure to the left. Well13-10A is displayed by the green triangle.

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

Recent thesis research by Sara Gallagher (2014) concluded that the depo-sitional environment for the Morrow B is an incised valley system based on coredata. Figure 1.2 displays the paleogeography and location of FWU. Previouswork in the area supports Gallagher’s conclusions of an upper Morrow fluvialsystem (Gallagher, 2014; Sonnenburg et al., 1990; Krystinik and Blakeney, 1990;Wheeler et al. 1990; Al-Shaieb et al., 1995). The Morrow B isopach and formationtop map (Figure 1.3), could be interpreted as an incised valley based on geome-tries. Channel features might be imaged by interpreting the 3D VSP data usingseismic attributes. Understanding the channel geometries and the distributionof lithologies could lead to estimations of where the CO2 will accumulate andmigrate within the reservoir.

Reservoir heterogeneity, reservoir and seal structural geometry, stratigraphicfeatures, 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 todescribe the reservoir adequately for simulation (Ebanks Jr., 1987). The 3D VSPsurvey 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

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 reser-voir (Gibson-Poole et al., 2009). Stratigraphic heterogeneities such as intrafor-mation silts and shales, could reduce the effective vertical permeability and gen-erate a more complicated fluid pathway (Gibson-Poole et al., 2009). A positivefor CO2 storage security is that stratigraphic discontinuities create localized trapsreducing the reliance on the top seal (Gibson-Poole et al., 2009). Most of thesestratigraphic heterogeneities will be below the level of resolution for the 3D VSP.

Fractures/faults can also have a strong and unpredictable effect on EORproduction (Ebanks Jr., 1987). Faulting can lead to complicated CO2 flow pathsand 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 pro-duction efficiency (Pasala et al., 2003). Faulting within the reservoir should beresolvable in the 3D VSP survey.

Figure 1.3: (a) Structure map for the top of the Morrow B. The field dips towardsthe south east and is deeper within the middle of the field. (b) The Morrow Bisopach generated from gamma ray well logs for the entire field (blue outline inFigure 1.1). The thicker sandstone runs through the middle of field. These obser-vations 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 charac-terization of the Farnsworth Unit in Ochiltree County, Texas. ”Seismic Attributesare all the information obtained from seismic data, either by direct measurementsor by logical or experience-based reasoning” (Taner, 2001). The main objective forattributes is to give detailed and accurate structural, stratigraphic, and lithologicdata to the interpreter after performing mathematical calculations on the data

(Taner, 2001). A detailed geologic model for the field was generated using seis-mic attributes from a baseline 3D Vertical Seismic Profile (VSP) and correlatedwith well log data.

1.2 Farnsworth Unit History

Farnsworth Unit (FWU) produces from the upper Morrow and is the largestMorrowan 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 forthe CCUS project. The sandstone ranges in thickness from 0 to 54 feet withinin the field with the average being 29 feet (Munson, 1988). FWU was originallyexploited as a gas discovery by the J.M. Huber Corporation in the Pazoureck for-mation in May 1952 (Parker, R.L., 1956). The first well was drilled to 8,096 ft. andproduced 1,219,664 thousand cubic feet of natural gas (MCF) and 10,643 barrelsof distillate cumulative until June 1956 (Parker, R.L., 1956). Several other wellswere drilled in the surrounding area from 1952-1955 (Parker, R.L., 1956). On July24, 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 inthe same field produced at 332 barrels per day (Parker, R.L., 1956). Past operatorsin 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 theimplementation of water flooding, however, after its implementation the westernside produced more (Munson, 1988). Peak production during water flooding wasreached in 1972 with 7,967 barrels of oil per day (BOPD) produced (McKay andNoah, 1996).

Currently, Chaparral Energy Co. LLC is utilizing CO2 flooding in theFarnsworth Unit using one hundred percent anthropogenic CO2 captured fromthe Arkalon Ethanol Plant in Liberal, Kansas and the Agrium Fertilizer Plant inBorger, Texas (Grigg and McPherson, 2012). Up to 25 injection wells will be uti-lized for CO2 flooding of the field. CO2 injection rates of up to 0.2 million tonnesper 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 offour producers. Once the fluid is produced, the oil, water, and CO2 is separatedabove ground and the resulting produced CO2 is reinjected to supplement newCO2. In order to improve the efficiency of the recovery process, Farnsworth Unitis implementing water alternating gas (WAG) cycles to deter the lower viscosityCO2 from moving ahead of the displaced oil as it migrates to producing wellswhich improves reservoir sweep efficiency(NETL and DOE, 2010).

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 fossilfuels from power plants, factories, and both commercial and residential build-ings. Carbon Capture and Storage (CCS) could be applied to large point sourcesof 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 asupercritical fluid and transported by pipeline to the field where it is injected intoa geologic reservoir such as depleted oil and gas reservoirs, saline aquifers, andunmineable coal seams (IPCC, 2005). Fortunately, tools developed by the oil andgas industry such as pipeline construction, compression, well drilling technol-ogy, injection technology, and monitoring methods can be adapted for geologicstorage 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 withinan older oil field. Primary production often yields 5-40% of the original oil inplace (IPCC, 2005). Water flooding will produce an additional 10-20% and CO2flooding can recover 7-23% of the original oil in place (IPCC, 2005). PermanentCO2 storage can be accomplished while increasing oil production. Between 33%and 50% of injected CO2 remains permanently in the reservoir while the balanceis collected with the oil, separated, and then re-injected (IPCC, 2005). Over longproject 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 oneanother (NETL and DOE, 2010). When they encounter each other, the physicalforces holding the two phases apart disappears, resulting in reduced viscosityand swelling of the miscible fluid, which results in the removal of residual oilfrom the pore spaces (NETL and DOE, 2010). The crude oil and CO2 mixture mi-grates towards the producing well due to the pressure differential between theinjector and producing well. Figure 1.4 represents this process schematically. It isimportant to geologically characterize the reservoir to understand the potentialfluid pathways for the CO2 oil mixture. For example, Morrow B sand channelgeometries affect where the highest production will be and where future injectorand producing wells should be drilled.

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

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 mudstoneintervals (Munson, 1988). Upper Morrow reservoirs in Oklahoma, the Texas Pan-handle, southeastern Colorado, and western Kansas have produced greater than320 million barrels of oil and 3.5 trillion cubic ft. of gas from reservoirs less than6,000 ft. deep (Puckette and Shaieb, 2008). However, drilling depths can reach upto 21,500 ft. in the deeper part of the basin (Ball et al., 1991). Although the trapsin the Morrow are mainly stratigraphic, there are also some structural traps (Ballet al., 1991). The Morrow likely entered the oil window during the Permian timewhen migration was occurring (Ball et al., 1991). Average porosity and perme-ability for the upper Morrow sandstones are 13.4% and 50.6 millidarcies (Pucketteand Shaieb, 2008). The depositional environment for the Morrow was dependenton sea level fluctuations.

Figure 2.1: Stratigraphic chart showing the stratigraphic framework of the LowerAtokan-aged and Upper Morrowan strata in the FWU. Wireline log is from well32-2. (Gallagher, 2014; Modified from Munson(1988) and Puckette et al. (2008) byDylan Rose-Coss and Sara Gallagher).

2.2 Anadarko Basin Tectonic Evolution

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

through the Permian (Ball et al., 1991; Perry, 1989). The Wichita orogeny resultedfrom the collision of the North American and South American plates in the earlyPennsylvanian period (Roscoe and Alder, 1983; Gallagher, 2014). The possiblesediment source areas that could account for the upper Morrowan rocks are theCimarron arch and Keyes Dome to the west and northwest, the Sierra GrandeUplift to the west, the Central Kansas Uplift to the northeast, the Bravo Domeof the west-southwest, Dalhart Basin to the west, and the Plainview Basin andNemaha 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 dis-plays possible structures that could be sediment source areas (Gallagher, 2014;Modified from Sonnenburg et al., 1990 in DeVries, 2005).

2.3 Depositional Environment

Munson (1988) concluded that the Morrow B deposition in the FWU wascontrolled by a fluvial-deltaic system. His conclusions were based on sand-bodygeometry, petrographic log signatures, and grain size analysis methods that arenow obsolete (Ehrlich, 1983; Gallagher, 2014). Based on more modern techniquesand data collected from the FWU and Morrow B reservoirs in the region, geolo-gists concluded that the deposits are from incised valleys that covered the areaduring 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 toother Morrowan deposits in southeastern Colorado, Southwestern Kansas, andthe Oklahoma Panhandle (Wheeler et al., 1990; Puckette et al., 2008; Gallagher,2014).

The deposition of Morrowan rocks was controlled by sea level fluctuationsin the Pennsylvanian time (Sonnenburg et al., 1990; Krystinik and Blakeney, 1990;Wheeler et al. 1990; Al-Shaieb et al., 1995; Gallagher, 2014). The Upper Morrowandeposits 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 transportwas 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 valleyswere filled with fluvial sediments (Figure 2.4; Gallagher, 2014). As the seas rose,the valleys become flooded and estuarine and floodplain sediments were de-posited. Subsequently these deposits were covered with near shore to offshoremarine muds (Figure 2.4; Wheeler et al., 1990; Gallagher, 2014).

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

Figure 2.4: Model of incised valley depositional system and how it changes withsea level. LST = low stand surface of erosion. TSE = Transgressive surface oferosion (Gallagher, 2014; Puckette et al., 2008).

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 in-cised valley model of deposition that also fit a basin-wide sequence stratigraphicmodel (Figure 2.6; Gallagher, 2014). The conglomerate facies were deposited dur-ing a sea level lowstand (Gallagher, 2014). The Morrow B sandstone was de-posited by fluvial processes during transgression (Gallagher, 2014). The marineor marine-influenced mudstones above and below represent highstand systems(Gallagher, 2014).

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

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

2.4 Paleoflow Data

Based on structure and isopach maps, multiple channel-like features canbe 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 con-sistent 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 theMorrow sandstone thickness in the field and is also consistent with the flow andchannel direction. Generally, the thickest sections of Morrow lie within the mid-dle of the field. Ambiguities that do not support an incised valley model withinthe structure map and isopach map are probably due to large faults in the eastand south west sides of the field that have been mapped by Ashley Hutton (per-sonal communication, 2014). Brown (2014) from Schlumberger’s PetrotechnicalServices 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 FMIlog measures real-time mircrosensitivity images and dip data in the formationof interest (Brown, 2014). Brown (2014) found that the paleoflow directions are

roughly east with some components of flow to the northeast and southeast (Fig-ure 2.7). Brown (2014) analyzed how paleoflow changed throughout the MorrowB formation in well 13-10A. Starting from the deepest (earliest) flow was eastnortheast to east southeast, east southeast, northeast, east east southeast, andflowing 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 13-14where the FMI images were taken. The rosette diagrams showing the azimuth offlow direction determined from the FMI images taken in the Morrow B formation.The paleoflow direction is mostly to the east, northeast, and southeast. (Courtesyof 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 waterflood then the eastern side (Munson, 1988). The western side also has higher aver-age (mean and median) permeability than the reservoir in eastern side of the field(Munson, 1988; Gallagher, 2014). Gallagher (2014) determined that the reservoirquality in FWU does not appear to be controlled by depositional processes. Theeastern side is paleogeographically downstream (Figure 1.3 (a)) and may repre-sent a transition from braided to meandering processes (Gallagher, 2014). How-ever, Gallagher (2014) points out that there is no decrease in grain size, sorting,

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

CHAPTER 3

METHODOLOGY

3.1 Seismic Survey Overview: Imaging goals

Objectives for acquired seismic data is to improve the geologic under-standing and to directly monitor the CO2 plume movement in the reservoir overtime. 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 sim-ulation models and understanding the geology and to model CO2 movement inthe reservoir. Figure 3.1(a) shows an overview of the layout of the seismic surveysat Farnsworth Unit. Triangles represent the injection wells and circles representproduction wells. There are two 3D VSP and cross well surveys on the east andwest side of the field. The two 3D VSP surveys and crosswell tomography arecentered on, or occur on, transects that include injection wells to image CO2 flowthrough the reservoir. The UniQ Survey imaged the entire 40 square miles ofthe field. The data used for this study focuses on the west side 3D VSP baselinewhich was collected in February of 2014 (Figure 3.1 (b); Figure 3.3).

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

3.2 3D VSP

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

Figure 3.2: A model of the 3D VSP survey as designed by WesternGeco with thegeophone 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 incidenceangle). The yellow dots on the surface represent the vibroseis shot points and thered lines represent ray paths traveling down to where the geophones are placedat approximately 3500 ft .

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

The baseline 3D VSP data was collected in February of 2014 with addi-tional surveys planned for the future. WesternGeco designed the survey to focuson and get high resolution data for Morrow B. Figure 3.2 is the survey model de-signed by WesternGeco to best image the reservoir. The grey surface shown onthe bottom is the Morrow B top surface with the estimated seismic image that willbe collected. The survey overlaps two injection wells, the 13-10A and 14-1, andwas designed to image the Morrow B (Figure 3.1; Figure 3.3). Both the 13-10Aand the 14-1 wells had 40 tri-component geophones at depths of approximately3,500 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 fre-quency of 80-180 Hz, recorded over the area (Figure 3.3). WesternGeco completedthe basic data processing for New Mexico Tech in May of 2014.

3.3 Crosswell Tomography

Crosswell tomography is a technique in which a signal is produced fromwithin a well and it is received by other geophones in different wells. The geo-phone array stays stationary as the source moves upwards. The geophone islater 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 thereservoir without the influence of near surface anhydrite layers and weatheredzones (Daley et al., 2008). Crosswell tomography is being conducted in order totime lapse image the CO2 moving between injector and producers.

The baseline crosswell acquisition also occurred in February 2014 with ad-ditional surveys planned. The survey runs from wells 13-6, 13-10A, 13-14, and14-1 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 inMay of 2014. This study interprets the 2D crosswell tomography lines and com-pares 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 seismicwith the lithology. Gamma Ray (GR) logs were utilized for this study. The welllog data was collected at different times with different operators so there can besome 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 theseismic attributes.

3.6 Seismic Attributes

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

the magnitude of the trace envelope is related to acoustic impedance contrast, in-stantaneous and average velocities relate to rock properties, and frequencies canrelate to bed thickness. (Taner, 2001). Physical attributes subdivided into instan-taneous and wavelet attributes (Subrahmanyam and Rao, 2008). Instantaneousattributes are computed sample by sample and indicate instantaneous variationsof various parameters (Subrahmanyam and Rao, 2008; Taner, 2001). Wavelet at-tributes 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 de-positional characteristics (Taner, 2001).

Twenty-four attributes were generated but only seven were analyzed moredeeply since they aligned best with the low amplitude lenses. The attributeswere generated for the entire 3D VSP volume and then applied to the MorrowB top and base surfaces. The attributes that were most useful are instantaneousfrequency, signal envelope, sweetness, relative acoustic impedance, chaos, rootmean square amplitude, and variance.

3.6.1 Curvature Attributes

This attribute is useful in identifying faults and channel like features. Thisattribute calculation is a black box operator in Petrel so the actual mathematicalprocess 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 circlethat 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 aninterpreted horizon using least-squares or some other approximation method(Equation seen in Figure A1; Chopra and Marfurt, 2007). This equation yields thecoefficients from which other curvature attributes can be calculated includingminimum and maximum curvatures, principal curvatures, most-positive, most-negative, dip curvature, strike curvature, curvedness, and shape index (Chopraand Marfurt, 2007). If the horizon is picked on a noisy surface then this attributecan lead to incorrect curvature interpretations (Chopra and Marfurt, 2007). TheMorrow B surface could have been too noisy to have generated accurate curva-ture attributes.

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

3.6.2 Variance Attribute

This attribute is useful in for edge detection and discontinuities. This at-tribute calculation is a black box operator in Petrel so the actual mathematicalprocess done on the data is unknown. The attribute was generated with a shortwindow 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 HilbertTransform must be introduced. Taner and Sheriff (1977) introduced using theHilbert transform to calculate instantaneous amplitude, phase, and frequency.The Hilbert transform is used to calculate the seismic amplitude, phase, and fre-quency instantaneously and is used to calculate attributes in most seismic inter-pretation software (Hardage, 2010). The complex trace z(t) is comprised of thereal seismic trace x(t) and an imaginary seismic trace y(t) (Hardage, 2010). Theimaginary trace y(t) is calculated using the Hilbert Transform. n other words,the Hilbert transform applies a 90 degrees phase shift to every sinusoidal com-ponent of a signal. The real trace x(t) and the imaginary trace y(t) calculatedusing 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 dimensionalspace (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 thatextends 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 calculationsare utilized for the discussed attributes in this study.

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

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

3.6.4 Instantaneous Frequency

The instantaneous frequency is the time derivative of the instantaneousphase and is measured in hertz (Figure A2; Subrahmanyam and Rao, 2008). Theinstantaneous phase is generated by taking the arctangent of the imaginary tracey(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, andchanges in deposition ( Subrahmanyam and Rao, 2008). The signal envelope isgenerated by taking the square root of the real trace x(t) plus the imaginary tracey(t) (Subrahmanyam and Rao, 2008).

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 sweet-ness is the amplitude divided by the square root of hertz but is best though of arelative 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 actualmathematical process done on the data is unknown. Petrel is probably calculatingthe running sum of the trace and a low cut filter is applied (Subrahmanyam andRao, 2008). The calculated attribute is the integration of the complex trace z(t) andapproximates 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 sothe actual mathematical process done on the data is unknown. ”RMS value of awaveform represents a squaring of the amplitude of each point of a waveformand 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 thatwould be to complex for conventional statistical methods (Strecker et al., 2002).Neural networks are based on the mammalian brain’s ability to take large inputsof data and unknowns and then classify the data and find patterns (Strecker etal., 2002). In this study, an unsupervised neural network is free to search, rec-ognize, and classify structural patterns spanning the entire 3D VSP seismic dataset (Strecker et al., 2002). The unsupervised neural network compares multipleattributes in order to find similarities between them based on three classes.

In Petrel, the train estimation model was used for the unsupervised neuralnetwork seismic attribute comparison. The train estimation model will find sim-ilarities within multiple input data. The train estimation model took selected in-puted attributes and characterized them into three different classes. Three classeswere chosen based on the estimated lithology of the area: mudstone, shale, andsandstone. The three classes did not have any physical constraints pertaining tolithology applied in order to classify them so no quantitative information can beascertained. The unsupervised network is showing similarities in the combina-tions of attributes based on three classes not constrained by physical parameters.

Hypothetically, the similarities in the data do correspond to changes in lithologyand bed thickness.

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 fromtops picked in the 13-10A well. The top and base horizons were then picked intwo way time (TWT) since seismic attributes are generated in time domain. Thetop of the Morrow B was picked at a zero crossing with the base picked at thetrough (Figure 4.1; Figure 4.2; FIgure 4.3). The Morrow B could have been pickedon the next zero crossing, however no amplitude interpretations can be made ona 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 andbase surfaces throughout the entire 3D VSP volume. The auto picker horizon wasmanually checked for accuracy afterwards. The Morrow B base surface appearsto show differences in amplitude which could indicate differences in lithology(Figure 4.3). An isochron map displays how thickness varies in the field (Figure4.4). The edge of the survey are thinner then the interior portions. The northwestside of the survey does not vary much in thickness while the southeast side of thesurvey is thicker.

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

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

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

Figure 4.4: An isochron showing the thickness in the Morrow B based on thepicked 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 forma-tion and are apparent on the Morrow B base surface (Figure 4.3). These loweramplitude packages resemble lenses in some areas (Figure 4.5). The lenses of-ten separate, come together, and separate again which possibly make them con-formable with channel features interpreted by geologic studies. These darkerareas were manually picked at the top and the base (Figure 4.6). The lenses weredelineated by lower amplitude then the material to the sides of it (Figure 4.5). Noexact amplitude constraints were used to pick the lenses since it was manuallypicked. This process was repeated throughout the entire 3D VSP volume usingboth inlines and crosslines (Figure 4.7). Figure 4.8 shows the outline of the hori-

zon shown in Figure 4.7 in red and the lenses themselves in two way time withnearby wells also plotted.

An isochron map was generated from the top and the base of the low am-plitude lenses (Figure 4.9). The black outline of the low amplitude lenses is shownon this figure that appears on Figure 4.8 and future attribute and neural networkfigures. The red areas are the thickest with the thinnest areas being pink. Thechannels are approximately 30-35 feet in the deepest areas and 20-25 feet on theedges of the low amplitude lenses. The low amplitude lenses are overall thickerwithin the middle of the axis and thin as they go outwards. The thicker areascould be interpreted as long-term channels while the some of the features thatare thinner (light blue) could be short-lived channels that were abandoned overtime. The paleoflow data from well 13-10A and 14-1 is also overlain on Figure4.10. The low amplitude lens orientation strikes approximately east or southeastwhich 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; Pucketteet al., 2008). It is possible that there are more channels but they are not resolvabledue to being too thin or distorted by edge effects near the boundaries of the 3DVSP survey. The areas with thicker lenses run approximately east and some ofthe thinner lens areas could be interpreted as going more southeast. It could beinterpreted that channels were flowing in this area towards the east over longerperiods of time as compared to the thinner channels.

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

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

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

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 surfacefrom the 3D VSP is in pink, the channel outline is in red with the channel filled inwith elevation time. The southeast features are deeper.

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

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

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 fault-ing or a stratigraphic features. The main area delineated by the box in the southeast corner (Figure 4.11 (a)) has an offset of approximately 70 ft. One explanationfor the feature could be that the seismic is displaying different lenses of similarlithology that were deposited at different times. As one steps through inlinesheaded west (Figure 4.11 (b)(c)(d)), the two layers slowly start to come together

until they form a slope. The other possibility is that this could be a small low an-gle thrust fault. The collision of the North American and South American-Africanplates started in the early Middle Pennsylvanian with an orientation of roughlynortheast to southwest (Sonnenburg et al., 1990). Compressional stresses fromthe continental collision were transported deep into the continent (Walper, 1977).A low angle thrust fault orientations in the field also strike northeast to south-west, which aligns with the tectonic activity in the Morrowan (Sonnenburg et al.,1990; Evan Gragg, personal communication, 2014). The low angle fault couldhave occurred either syndepositional or shortly after deposition of the MorrowB. There is another similar feature in the north west corner of the VSP. The north-west 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 trackingvolume for the 3D VSP to enhance fault-like features. An ant tracking volume ismade by generating a variance attribute for the entire volume and then runningthe ant tracking volume algorithm that finds similarities in variance. There is isa fault-like feature at the location of the southeast box (Figure 4.11(a)) and Petrelautomatically 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 believesthat a fault of this size should be resolvable (personal communication, 2014). Thefeature does not appear very fault-like when looking at it in relation to the seis-mic data (4.12, (b)). It is difficult to interpret one large, continuous fault from theseismic cross section (Figure 4.12(a)). The ant tracking volume could be delin-eating a series of smaller faults that occurred at different times or a combinationof both faults and stratigraphic features. Another possibility is that this featurecould be an artifact from migration processing of the data. This feature does occurvery close to well 14-1 where the geophone array was deployed. Data process-ing seems unlikely as a reason because a similar feature is not observed near the13-10a which also had a geophone array.

Figure 4.11: The surface of the Morrow B Base (a). The box in the south eastrepresents the feature of interest while the lines crossing it represent the inlinesections shown in (b), (c), and (d). The white box in the upper north west corneris a another similar feature. In (b), (c), and (d) the Morrow B top is delineatedwith the green line. Figure (b) shows the largest offset which is about 70 ft. Asone moves west, the surfaces come together and form a slope.

Figure 4.12: (a) Two way time 3D VSP ant tracking volume generated by AshleyHutton with the box showing the fault like feature. (b) The light blue dashedline 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 betweenthe inside and outside of the low amplitude lenses. Gamma ray (GR) and spon-taneous potential (SP) logs were analyzed for lithology and thickness. A sectioncrossing the low amplitude lenses are in Figure 4.13. The GR well logs displaywhere the reservoir sand thickens within the low amplitude lens (well 13-14) andthins outside the lens (wells 13-5 and 14-3). The contrast between the sand andthe mudstone also increases within the low amplitude lenses. A cross sectiondisplaying well logs within and outside the low amplitude lenses illustrate dif-ferences in both thickness and lithology (Figure 4.14). Wells 13-10A and 13-14 arewithin the low amplitude lenses and display thick course sandstone beds in the

GR log. Well 13-10A has a thickness of 36 ft. and well 13-14 has a thickness of29 ft. Wells 13-12 and 13-15 fall outside the low amplitude lenses and still havesandstone, but they are not as thick. Well 13-12 has a thickness of 25 ft. and 13-5has a thickness of 20 ft. The contrast between the sand bed and the surroundingmudstone differs between the logs within the lens and outside. The GR contrastis greater between the mudstone and sandstone within the lens. The low contrastbetween the reservoir and the mudstone outside the channels could be the reasonwhy they are not evident in the seismic data.

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

Figure 4.14: The map in the lower left corner shows the cross section across thelow 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 andother internal stratigraphic differences. An example of this is from wells 13-10Aand 13-10. Both wells are within the low amplitude lenses but have differentGR log characteristics (Figure 4.15). Well 13-10A has a thick ( 36 ft.) and cleanMorrow B sandstone compared to 13-10 where it is thinner ( 28 ft.) and appearsto have more mudstone.

Figure 4.15: A cross section cutting through the middle of the low amplitudelenses. 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 amplitudelens.

4.4 Seismic Attributes

4.4.1 Curvature Attributes

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

Curvature can be mathematically described as the second-order deriva-tive of the curve (Chopra and Marfurt, 2007). Thus a large curvature will meanthat the curve is bent more and a straight line will be equal to zero (Chopra andMarfurt, 2007). Although curvature attributes can be useful in delineating chan-nels, however, they did not prove to be useful in this data set (Figure 4.16). Themost positive and negative curvature, the gaussian curvature (product of mini-mum and maximum curvature), and strike curvature (curvature extract along adirection perpendicular to the dip) were calculated and proved not be helpful in

delineating the channel boundaries (Chopra and Marfurt, 2007). Curvature at-tributes are very sensitive to noise and edge effects, which could be a factor inthis 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 delin-eates the low amplitude lenses.

The chaos attribute measures the lack of organization in the dip and az-imuth estimation. Chaos is similar to variance but has a black box procedurewithin Petrel so that the actual mathematical calculation is hidden. Chaos isscaled 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

chaotic areas grey/white follow the paleo current and approximately align withthe low amplitude lenses. Other less chaotic areas on the surface could be dis-playing 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 linedelineates 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

such as channels (Figure 4.18). The variance attribute highlights the proposedfault or stratigraphic feature discussed earlier as well as the outline of the chan-nel areas in red and yellow. The highlighted areas on the edges of the surfacemight 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. Theyellow line delineates the low amplitude lenses.

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

sand/shale ratio indicator, hydrocarbon indicator, and a fracture zone indica-tor (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 thinshale bedding while lower frequencies indicate more massive bedding of sand-stone lithologies (Taner, 2001). The Morrow B base best delineates areas of thickersandstones and thinner shales. The lower frequency areas line up approximatelywith the outline of the low amplitude lenses meaning that there could be moremassively bedded sandstones.

4.4.5 Signal Envelope

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

The signal envelope (reflection strength) is the envelope of the seismic sig-nal, representing the instantaneous energy of the signal that is proportional tothe reflection coefficient (Subrahmanyam et al., 2008). The signal envelope at-tribute can aid in interpreting the following characteristics: Acoustic impedancecontrast (reflectivity), bright spots due to possible gas accumulation, sequenceboundaries, thin-bed tuning, major changes in depositional environment, andthe spatial correlation to porosity and lithology (Taner, 2001). Figure 4.20 dis-plays the Morrow B base signal envelope attribute. The higher amplitudes couldcorrespond 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 linedelineates the low amplitude lenses.

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

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 thicknessvariation indicator (Suarez et al., 2008). Impedance variations within the chan-nels can be used to delimit facies change (Suarez et al., 2008). The impedanceamplitude 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 Suarezet al., (2008), the study focuses on channels in the Red Fork formation in theAnadarko Basin. Higher amplitudes within their study area correspond to chan-nel facies since it enhances impedance contrast boundaries making lithology changesmore evident (Suarez et al., 2008). There are higher amplitudes within the north

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 channelsbut 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 yellowline delineates the low amplitude lenses.

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

of the channels (Figure 4.23). There are areas of high amplitude which mightcorrespond 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 combinedusing the train estimation model. The highlighted rows in Figure 4.24 are thecombinations that generated the most useful geologic information and alignedwith the low amplitude lenses. The dark blue, light blue, and pink colors in theoutput surfaces (Figures 4.25-4.28) correspond to the three classes as describedin section 3.7. These classes hypothetically correspond to differences in lithologyand/or bed thickness. The attribute combinations that were considered not use-ful for geologic interpretation were those predominantly characterized as usuallyhaving 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 wellcould 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 con-sistent feature that appears in most of the attributes is in the southeast edge of thesurvey. This could be due to the increase in thickness as seen in the isochron mapor could be a similarity in lithology. Run #15, run #6, run #15, and run #4 will beanalyzed in detail.

Figure 4.24: The attributes are listed at on the top and the different combina-tions in the rows. The highlighted rows are runs that displayed possible geologicinformation that aligned with the low amplitude lenses.

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 at-tributes and the highest correlation with the low amplitude lenses. The attributesutilized 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 amplitudelenses is similar across the study area. The thickness of the picked lenses prob-ably does not have an effect on this attribute since the southeast corner has thesame similarity as the other picked lenses.

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 couldrelate to geologic complexities (Figure 4.26). This geologic difference can be ana-lyzed by examining the 13-10A well (dark blue) and the 13-10 well (pink) wherethere is detailed well log and core information. Well 13-10A ( 36 ft.) is slightlythicker( 28 ft) than at the 13-10 well (Figure 4.15). Well logs and possibly the corestudies support that 13-10A has cleaner sandstone when compared to the 13-10which has more mudstone within the reservoir interval (Gallagher, 2014; DylanRose-Coss, personal communication, 2014). Run #6 could be detecting the differ-ences in lithology, thickness, or a combination of both. The southeast corner also

shows up as pink in run #6. This could also be due to a combination of lithologyand 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 similarityon this surface (pink). However, the small areas of blue similarities occur nearthe edges and outside of the low amplitude lenses and could relate to lithologywithin the lenses and outside.

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

4.5.4 Run#4

Figure 4.28: Run 4 generated from the RAI, impedance and instantaneous fre-quency attributes.

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

CHAPTER 5

DISCUSSION

The 3D VSP interpretation of the Morrow B appears to corroborate theincised valley theory described by Gallagher (2014), Sonnenburg et al. (1990),Krystinik and Blakeney (1990), Wheeler et al. (1990), Al-Shaieb et al (1995), andPuckette and Al-Shaieb (2008). The 3D VSP is covering a small portion of theincised valley, but still displays some possible channel-like features. The lowamplitude sand lenses appear channel like in their geometry and align with theoverall east paleoflow direction (Figure 1.2(b); Figure 2.2; Figure 4.10; Pucketteand Al-Shaieb, 2008). The seismic attributes also support that the 3D VSP surveycould be imaging a portion of a channel system.

Gallagher (2014) describes the Morrow B as being fluvial deposited with acoarse sandstone lithology. From this small view of the field, it is difficult to tellif the channels are more braided or meandering. By comparing the core and theinterpreted seismic, one can start to understand the geological depositional envi-ronment. If one looks at a modern braided river system analog the geometries arevery similar (Figure 5.1). The interpreted channels could also be a small section ofa meandering channel which can look braided.The Morrow B in this area mighthave been deposited at a transition zone between the steeply dipping braidedriver system and meandering depositional environment (Gallagher, 2014).

Figure 5.1: A modern braided river system from the Congo (Zaire) River on topcompared 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 di-rection, 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 20-35ft. thick. Since the seismic resolution is not higher, only a broad scale interpre-tation can be presented. The data could be showing two different times of pale-oflow and channel geometry. There appears to be channel-like features orientedapproximately west-east while another set could be going northwest-southeast(Figure 5.2). The paleoflow data does suggest that there was multiple directionsof flow during the Morrow B deposition (Brown, 2014). The channel featuresrunning west-east appear to be thicker then the channels running northwest-southeast (Figure 5.2). The channels running east-west could have been longerlived than the channels running northwest-southeast based solely on thickness.The feature running northwest to southeast (delineated by a white oval on Figure5.2) could also be interpreted as running west-east as well. This would suggestthat most of the channel flow was in the west-east direction. Brown (2014) foundthat towards the end of the Morrow deposition the flow was mainly towards theeast. The 3D VSP might be imaging the mostly eastward oriented channels sinceother channels have been overprinted.

Figure 5.2: Channel interpretation based on thickness and channel orientationfrom the low amplitude lenses isochron map. The possible shorter lived channelsrunning northwest-southwest are pointed out by the orange arrows. The possiblelonger 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 eitheroriented northwest-southeast or west-east.

Seismic resolution plays a large role in interpretation. If the relative acous-tic impedance is not high enough, some stratigraphic features will not be visible.The areas in which the apparent channels are resolvable, the relative acousticimpedance 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 beresolved on the edges of the survey due to edge effects or low resolution. If thelow amplitude lenses are displaying geologic data, then it does appear to be chan-nels. Also, the small size of the survey may not show all reflection terminationsthat can aid in stratigraphic interpretation (Sarzelejo and Hart, 2006).

Well logs reveal that there is Morrow B sandstone covering the entire areaof the 3D VSP. There are a few possibilities to why the low amplitude lenses

are visible. The first reason could be that the Morrow B in the channel area isthicker and has a higher acoustic impedance compared to the areas around it. Thechannel thicknesses do not vary significantly based on the low amplitude lensisochron (Figure 4.9). The well logs do support that the Morrow B is thicker andhas a higher contrast between the sandstone and mudstone as compared to wellsoutside the low amplitude lenses. Well 13-10 is an exception and could be dueto internal stratigraphic changes such as a small point bar. Well log comparisonscan be ambiguous since they are different vintages and qualities of logs (Sarzelejoand Hart, 2006).

The seismic attributes discussed in this study aid in interpreting the chan-nel locations and lithology. The channel outline picked from the original data ap-proximately line up with features seen in the seismic attribute calculations. Theseismic attributes give valuable information about lithology and how it changeswithin the Morrow B area. There are also considerations to take when utiliz-ing seismic attributes for seismic interpretation. Seismic attributes can be greatlyinfluenced by acquisition parameters and initial processing (Moro et al., 2013).Overall, there appears to be more sandstone lithology within the picked channelareas as opposed to outside.The neural network supported the channel theoryby finding similarities within different attributes that also overlapped with thepicked channels.

CHAPTER 6

CONCLUSIONS AND FUTURE WORK

6.1 Conclusions

The 3D VSP interpretation and attribute analysis can be interpreted as achannel system supporting the incised valley model of deposition. The resolvedchannel system is oriented in the approximate eastward paleoflow direction andhas the correct dip as hypothesized by geologists working on the Morrow B. Theseismic attribute study also delineates the proposed channel outlines and couldsupport variations in thickness of sandstone lithologic units within that area. Theattributes 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 runsalso show clear delineation of the channel like features by finding similaritieswithin the reservoir, and this is an indication that the attributes are discerninglithologic characteristics. One consequence if the channel area does have a cleanersandstone compared to areas outside of the channel is that a preferential pathwayfor CO2 to flow through the reservoir may exist. The potential 70 ft. fault orstratigraphic feature resolved in the 3D VSP seismic volume could also affectCO2 flow in the area.

The channel like features should have an effect on CO2 flow within thereservoir. The two injection wells in the area, 13-10A and 14-1 are placed on theedges of the channel feature. CO2 should be flowing in an east-west or southeast-northwest direction within the channel feature, which provides a high porosityflow path. It is possible that production will be better in production wells thatalso lie within the channel rather then outside or in an adjacent channel featuresuch as in wells 13-12, 13-2, 13-6, and 13-14. The possible fault feature discussedin section 4.2 lies directly northwest of injector 14-1. The CO2 movement couldbe 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 faulttowards the southeast where there is a lack of wells due to a road. Modeling ofCO2 flow in this area should take these interpretations into account. Observationsof CO2 break through and sweep efficiency from production wells within the 3DVSP volume might also be able to support this theory.

6.2 Suggestions for Future Work

(1)Compare well logs to core data in a more in depth fashion in order tounderstand the geology of the channel feature. Also find production data forwells 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 dataset. The channel features within the 3D VSP might fit into a larger channel systemseen in the 3D seismic.

(3)Use a trained neural network in order to constrain a better understand-ing of the lithology from the seismic attributes.

(4)Repeat this same process and compare the results for the repeated 3DVSP data for the same area in order to see if there is CO2 is using the possibleinterpreted channel for a fluid pathway.

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APPENDIX A

TRAIN ESTIMATION MODEL RUNS

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

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

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

Figure A.4: Run 4 generated from the RAI and instantaneous frequency at-tributes.

Figure A.5: Run 5 generated from envelope and instantaneous frequency at-tributes.

Figure A.6: Run 6 generated from the RAI, signal envelope, and instantaneousfrequency attributes.

Figure A.7: Run 7 generated from the RAI, instantaneous frequency, signal en-velope, and chaos attributes.

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

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

Figure A.10: Run 10 generated from the sweetness and signal envelope at-tributes.

Figure A.11: Run 11 generated from the instantaneous frequency and signalenvelope attributes.

Figure A.12: Run 12 generated from the RAI, instantaneous frequency, sweet-ness, and signal envelope attributes.

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

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

Figure A.15: Run 15 generated from the RAI, instantaneous frequency, sweet-ness, signal envelope, and chaos attributes.

Figure A.16: Run 16 generated from the RAI, instantaneous frequency, sweet-ness, signal envelope, chaos, and variance attributes.

Figure A.17: Run 17 generated from the RAI, instantaneous frequency, sweet-ness, signal envelope, chaos, and RMS.

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

Paige Czoski

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