AMPLITUDE INVERSION OF FAST AND SLOW CONVERTED WAVES FOR FRACTURE CHARACTERIZATION OF THE MONTNEY FORMATION IN POUCE COUPE FIELD, ALBERTA, CANADA

Transcription

1 AMPLITUDE INVERSION OF FAST AND SLOW CONVERTED WAVES FOR FRACTURE CHARACTERIZATION OF THE MONTNEY FORMATION IN POUCE COUPE FIELD, ALBERTA, CANADA by Tyler L. MacFarlane

2 c Copyright by Tyler L. MacFarlane, 2014 All Rights Reserved

3 A thesis submitted to the Faculty and the Board of Trustees of the Colorado School of Mines in partial fulfillment of the requirements for the degree of Master of Science (Geophysics). Golden, Colorado Date Signed: Tyler L. MacFarlane Signed: Dr. Thomas L. Davis Thesis Advisor Golden, Colorado Date Signed: Dr. Terence K. Young Professor and Head Department of Geophysics ii

4 ABSTRACT The Montney Formation of western Canada is one of the largest economically viable gas resource plays in North America with reserves of 449TCF. As an unconventional tight gas play, the well development costs are high due to the hydraulic stimulations necessary for economic success. The Pouce Coupe research project is a multidisciplinary collaboration between the Reservoir Characterization Project (RCP) and Talisman Energy Inc. with the objective of understanding the reservoir to enable the optimization of well placement and completion design. The work in this thesis focuses on identifying the natural fractures in the reservoir that act as the delivery systems for hydrocarbon flow to the wellbore. Characterization of the Montney Formation at Pouce Coupe is based on time-lapse multicomponent seismic surveys that were acquired before and after the hydraulic stimulation of two horizontal wells. Since shear-wave velocities and amplitudes of the PS-waves are known to be sensitive to near-vertical fractures, I utilize isotropic simultaneous seismic inversions on azimuthally-sectored PS 1 and PS 2 data sets to obtain measurements of the fast and slow shear-velocities. Specifically, I analyze two orthogonal azimuths that are parallel and perpendicular to the strike of the dominant fracture system in the field. These volumes are used to approximate the shear-wave splitting parameter (γ (s ) ) that is closely related to crack density. Since crack density has a significant impact on defining the percolation zone, the work presented in this thesis provides information that can be utilized to reduce uncertainty in the reservoirs fracture model. Isotropic AVO inversion of azimuthally limited PS-waves demonstrates sufficient sensitivity to detect contrast between the anisotropic elastic properties of the reservoir and is capable of identifying regions with high crack density. This is supported by integration with spinner production logs, hydraulic stimulation history of the field, and microseismic. Results also show significant fracture network heterogeneity that is not typically accounted for in iii

5 engineering-driven development despite a strong link to production. The main value of this work lies in the integration of fracture characterization with preceding RCP theses that defined the geomechanical model and composition of the reservoir at Pouce Coupe. Geophysical attributes that relate to the composition and natural fractures enable a more complete understanding of the reservoir and indicate that a successful well is dependent on both the hydrocarbon storage capacity of the matrix and a large permeable network of natural fractures. iv

6 TABLE OF CONTENTS ABSTRACT iii LIST OF FIGURES viii LIST OF TABLES xiv LIST OF ABBREVIATIONS xv ACKNOWLEDGMENTS xvi DEDICATION xvii CHAPTER 1 INTRODUCTION Montney Geology Stratigraphy Reservoir Characteristics Regional Tectonics Pouce Coupe data set Field Development Time-Lapse Multicomponent Seismic Microseismic Data Previous Pouce Coupe Research Research Objective CHAPTER 2 SEISMIC MODEL OF FRACTURES Natural Fracture Background Induced/Natural Fracture Interaction v

7 2.3 Fracture Compliance Methodology Numeric Modeling Pouce Coupe Seismic Implications CHAPTER 3 MULTICOMPONENT SEISMIC DATA PROCESSING Fixed Receiver Rotation Bin Size and COV Interpolation Azimuthal Sectoring Processing Conclusions CHAPTER 4 CONVERTED WAVE SEISMIC INVERSION Inversion Theory Constrained Sparse Spike Inversion Available Data and PS Seismic Interpretation Low Frequency Model PS 1 Inversion Data Conditioning Well Tie and Wavelet Extraction Inversion Parameters Inversion Results and Quality Control PS 2 Inversion Data Conditioning Well Tie and Wavelet Extraction PS 2 Inversion Results and Quality Control Inversion Conclusions vi

8 CHAPTER 5 FRACTURE NETWORK INTERPRETATION AND INTEGRATION Shear Velocity Fracture Characterization Interpretation Limitations Fracture Map Fracture/Production Correlation Rock Composition Analysis Microseismic Comparison to SWS Analysis CHAPTER 6 CONCLUSIONS AND RECOMMENDATIONS Recommendations REFERENCES CITED vii

9 LIST OF FIGURES Figure 1.1 Map of the Peace River embayment that bounds the Montney Formation. 4 Figure 1.2 Paleogeographic map of North America during the Early Triassic (245Ma). 5 Figure 1.3 Montney type log and stratigraphic chart of the Triassic Figure 1.4 Depositional model of the Montney Formation Figure 1.5 East-West modeled stratigraphic section of the Montney Formation Figure 1.6 Map of maximum horizontal stress derived from well bore breakouts. Pouce Coupe field is highlighted by a star Figure 1.7 Pouce Coupe Field layout and timeline of operations Figure 1.8 Pouce Coupe Average Daily Gas Production for Montney wells Figure 1.9 Source and Receiver layout of the Pouce Coupe seismic survey Figure 1.10 Figure 1.11 Offset/Azimuth distribution in individual bins of the Pouce Coupe Seismic Microseismic events: a) from the 02/07-07 stimulation recorded by the toolstring, b) from the 02/07-07 stimulation recorded by the toolstring c) from the 02/02-07 well recorded by the toolstring d) from the 02/02-07 well recorded by the toolstring Figure 1.12 Downhole microseismic acquisition geometry Figure 1.13 Model of SWS Figure 1.14 Baseline SWS magnitude and PS 1 polarization orientation Figure 1.15 Monitor 1 SWS magnitude and PS 1 polarization orientation Figure 1.16 Monitor 3 SWS magnitude and PS 1 polarization orientation Figure 1.17 Inverted λρ and µρ cross section viii

10 Figure 1.18 Inverted P-Impedance cross section through well 02/02-07 with geobodies of best clusters Figure 2.1 Schematic of an orthorhombic model Figure 2.2 Figure 2.3 Figure 2.4 Mohr-Coulomb graphical representation of stress fields producing tensional and shear rock failures Mohr-Coulomb graphical representation of fracture failure produced by increasing pore pressure Simulated hydraulic fracture in the presence of perforations that act as permeable conduits and glass plates that represent cemented natural fractures Figure 2.5 Schematic model of a medium with multiple sets of parallel fractures Figure 2.6 Workflow for modeling the seismic response of fractures Figure 2.7 Figure 2.8 Figure 3.1 Figure 3.2 PS reflection coefficients of the four fracture models associated with the Montney C/D interface along azimuths aligned with the x 1 and x 2 axes. 38 Comparison between anisotropic and isotropic PS reflection coefficient equations used to represent fracture model Previous processing of the Pouce Coupe data set. PS 1 is displayed on the left, and PS 2 is on the right New AVO compliant processing of the Pouce Coupe data set. PS 1 is displayed on the left, and PS 2 is on the right Figure 3.3 Schematic of field components H1/H2, and rotated PS 1 /PS 2 coordinates. 46 Figure 3.4 PS 1 orientation as determined by SWS analysis within a time window of ms Figure 3.5 Overburden shear-wave splitting for three analysis windows Figure 3.6 Common offset vector diagram and its relationship to offset and azimuth. 49 Figure 3.7 Old and New AVO compliant PS1 pre-migration stacks Figure 3.8 PS 1 and PS 2 limited azimuth stacks sorted in 10 azimuth sectors from 0 to ix

11 Figure 3.9 Limited azimuth ranges allowed in PS 1 and PS 2 volumes Figure 4.1 Deterministic and stochastic inversion workflow overview Figure 4.2 Representation of the convolutional model Figure 4.3 Jason s workflow for the simultaneous CSSI Figure 4.4 Pouce Coupe basemap with the location of wells and 02/07/07 displayed Figure 4.5 Cross-section of the PS 1 seismic with inlayed seismograms from wells and 02/ Horizon interpretation is also displayed Figure 4.6 PS 1 time structure map of the a) Triassic b) Montney E c) Belloy Figure 4.7 Bandwidth of the Pouce Coupe seismic with the lowpass filter applied to the models shown in red and the bandpass filter applied to the inversion result in cyan Figure 4.8 Inverse distance interpolation technique Figure 4.9 Figure 4.10 Figure 4.11 Cross section of the shear velocity model with a) 30Hz high cut filter applied b) 9Hz high cut filter applied Cross section of the compressional velocity model with a) 30Hz high cut filter applied b) 9Hz high cut filter applied Cross section of the density model with a) 30Hz high cut filter applied b) 9Hz high cut filter applied Figure 4.12 PS 1 substack Figure 4.13 PS 1 substack Figure 4.14 PS 1 substack Figure 4.15 PS 1 substack Figure 4.16 All PS 1 substacks overlaid to QC data alignment. The red box highlights a misaligned region, and the blue box highlights a area of high data quality Figure 4.17 Basic workflow for wavelet estimation and well correlation x

12 Figure 4.18 Well 02/07-07 well tie with the full PS 1 substack Figure 4.19 Well 02/07-07 well tie with the PS 1 substack Figure 4.20 Well 02/07-07 well tie with the PS 1 substack Figure 4.21 Well 02/07-07 well tie with the PS 1 substack Figure 4.22 Well 02/07-07 well tie with the PS 1 substack Figure 4.23 Well well tie with the full PS 1 substack Figure 4.24 Well well tie with the PS 1 substack Figure 4.25 Well well tie with the PS 1 substack. The overall correlation is Figure 4.26 Well well tie with the PS 1 substack Figure 4.27 Well well tie with the PS 1 substack Figure 4.28 Angle dependent wavelets for each of the four PS 1 substacks Figure 4.29 Final multi-well angle dependent wavelets for each of the four PS 1 substacks Figure 4.30 Cross section of the inverted V (1) S (0-30Hz) Figure 4.31 Cross section of the bandpass filtered inverted V (1) S (9-30Hz) Figure 4.32 Cross section of a) low frequency model of V S (0-9Hz) b) inverted V (1) S (0-9Hz) Figure 4.33 Pseudo V (1) S logs extracted from the PS 1 inversion in comparison to the V S log from well 02/ Figure 4.34 Seismic residuals associated with the four PS 1 substacks Figure 4.35 PS 2 substack Figure 4.36 PS 2 substack Figure 4.37 PS 2 substack xi

13 Figure 4.38 PS 2 substack Figure 4.39 All PS 2 substacks overlaid to QC data alignment Figure 4.40 Histogram of the RMS(PS 1 )/RMS(PS 2 ) extraction Figure 4.41 Angle dependent wavelets for each of the PS 2 substacks in comparison with the PS 1 multi-well wavelet Figure 4.42 Well 02/07-07 well tie with the PS 2 substack Figure 4.43 Well 02/07-07 well tie with the PS 2 substack Figure 4.44 Well 02/07-07 well tie with the PS 2 substack Figure 4.45 Well 02/07-07 well tie with the PS 2 substack Figure 4.46 Cross-section of the PS 2 inverted V (2) S (0 30Hz) Figure 4.47 Cross-section of the PS 2 bandpass filtered inverted V (2) S (9 30Hz) Figure 4.48 Pseudo V (2) S logs extracted from the PS 2 inversion Figure 4.49 Cross section of a) low frequency model of V S (0-9Hz) b) PS 2 inverted (0-9Hz) V (2) S Figure 4.50 Seismic residuals associated with the four PS 2 substacks Figure 4.51 Comparison of pseudo V (2) S logs extracted from PS 2 inversions that use the original and scaled LFM s Figure 5.1 Subset of the Pouce Coupe seismic survey used for interpretation Figure 5.2 Cross section of γ (s ) along the well bore of 02/ Figure 5.3 Cross section of γ (s ) along the well bore of 02/ Figure 5.4 γ (s ) time slice at 2108 ms through the Montney D subunit Figure 5.5 γ (s ) time slice at 2156 ms through the Montney C subunit Figure 5.6 Incoherency map generated from the PP seismic along the Montney D horizon xii

14 Figure 5.7 Figure 5.8 γ (s ) % time slice maps through a) the Montney D b) Montney C. Stage by stage gas flows obtained from a spinner log are presented as a percentage of the total flow Oblique view of the well layout and frac stages highlighting the proximity of wells 00/07-07 and 02/ Figure 5.9 Average daily water production of well 00/ Figure 5.10 Figure 5.11 Figure 5.12 γ (s ) time slice at 2140ms through the Montney C subunit highlighting the large fracture signature surrounding the three stimulation stages of well 00/ Cross section of P impedance (derived from PP seismic) through well 02/02-07 including geobodies of clusters 1 and Cross section of P impedance (derived from PP seismic) through wells 00/07-07 and 02/07-07 including geobodies of clusters 1 and Figure 5.13 γ (s ) timeslices integrated with microseismic Figure 5.14 Comparison of the shear-wave splitting and azimuthal velocity inversion techniques for fracture characterization xiii

15 LIST OF TABLES Table 1.1 General reservoir properties of the Montney Formation within Pouce Coupe. 8 Table 1.2 Hydraulic stimulation parameters Table 1.3 Stage-by-stage production data from spinner logs as percent of total flow volume Table 1.4 Pouce Coupe seismic survey acquisition parameters Table 2.1 Properties of unfractured Montney C/D reservoir units Table 2.2 Tsvankin parameters of the Montney C associated with the four fracture models Table 2.3 Shear-wave splitting coefficients for the four Montney C fracture models.. 39 Table 4.1 Misfit functions and their influence on the Inversion Table 4.2 PS 1 constrained sparse spike inversion parameters xiv

16 LIST OF ABBREVIATIONS Ultra Sonic Borehole Image UBI Amplitude Variation with Offset AVO Amplitude Variation with Angle AVA Shear-Wave Splitting SWS Fast Converted Wave Mode PS 1 Slow Converted Wave Mode PS 2 Limited Azimuth Stack LAS Common Offset Vector COV Pre-Stack Time Migration PSTM Horizontal Transverse Isotropy HTI Normalized Root Mean Squared Constrained Sparse Spike Inversion NRMS CSSI Low Frequency Model LFM National Energy Board NEB Gamma Ray GR xv

17 ACKNOWLEDGMENTS First and foremost, I would like to thank my advisor Dr. Tom Davis for his guidance, friendship, and life changing opportunities he has provided. You are a one of a kind professor who I am privileged to have shared the same penalty box with. I also need to express gratitude to the rest of my committee: Dr. Ilya Tsvankin, Dr. Jeff Grossman, and Dr. Robert Benson for their wisdom and support. A special thanks needs to go to Talisman Energy for acquiring the unique Pouce Coupe data set and graciously providing it to the RCP. The last 2 years at the Colorado School of Mines have proved to be an spectacular experience that stems from the amazing people that I have been fortunate to surround myself with. As a whole you are all the most talented and friendly group of people I could have expected to encounter. I truly hope we are able to stay in touch no matter where this worldly group disperses to after graduation. I am especially grateful to the students who preceded my time on the Pouce Coupe project. Collaborations between Jared Atkinson, Heather Davey, Chris Steinhoff, Matthew Lee, and Claudia Duenas have always pushed me and broadened my understanding of reservoir characterization. I have also received advice from numerous people throughout my program. Most notably is David D amico at Talisman Energy who has been leading Talisman s interaction with the RCP. I also worked closely with Tom Bratton who is a spectacular mentor and scientist. Peter Mesdag, Brad Bacon, Walt Lynn, Michael O Brien have also provided significant guidance throughout my time at Mines. Lastly, I need to deeply thank my wife Jackie for her love, support, and putting up with a long distance relationship immediately after our wedding. You are my world, and I can not wait to be by your side again. xvi

18 To my family and friends that make life great. xvii

19 CHAPTER 1 INTRODUCTION Research presented in this thesis is part of a joint effort between the Reservoir Characterization Project (RCP) at the Colorado School of Mines and Talisman Energy Inc. The Pouce Coupe project began in 2008 with the objective of improving the economics of tight gas development of the Montney Formation by understanding how hydraulic stimulations interact with the reservoir. This multidisciplinary project focuses on defining reservoir components critical to a wells success, and utilizes time-lapse multicomponent seismic to demonstrate integrated technology that can be used to optimize future well locations. The onset of horizontal drilling and multistage hydraulic fracturing led to the economic development of unconventional resources in the early 2000s. Production from unconventionals has risen sharply and now contributes 23.1% of the total gas produced in the United States (Zhongmin and Krupnick (2013)). While significant technological advancements have enabled rapid unconventional growth, there is still substantial uncertainty in predicting reservoir sweet spots. A consequence of this lack of geologic/geophysical information is engineering-driven field development, which typically assumes uniform/isotropic reservoir properties in heterogeneous/anisotropic fields. This results in lost profits due to misplaced wells and unsuccessful stimulations. In 2010, $30 billion was spent on hydraulic fracturing within the US, and approximately 25% of all completions failed to meet performance expectations (Machnizh (2013)). Clear examples of both a successful and unsatisfactory stimulation will be exhibited within Pouce Coupe. Previous research on this project includes the development of a geomechanical model which determined several criteria for a successful well, including the presence of natural fractures (Davey (2012)). Natural fractures can increase a reservoir s permeability by orders 1

20 of magnitude and strongly impact how a hydraulic stimulation interacts with the reservoir (Olson et al. (2012)). Results in this thesis aim to improve upon previous Pouce Coupe fracture characterizations performed by Atkinson (2010) and Steinhoff (2013). Vertical resolution of fractures is expected to improve from the reservoir scale ( 300m) to subunit scale ( 40m) due to a shift from previous travel time based measurements of seismic anisotropy to pre-stack amplitude measurements. While many interrelated reservoir properties are responsible for the success of an unconventional well, this thesis focuses on modeling the anisotropic nature of fractures, processing converted-wave seismic data for the purpose of azimuthal amplitude analysis, and providing an interpretational tool for fracture characterization. This fracture characterization methodology utilizes two separate constrained sparse spike inversions to predict azimuthally dependent shear velocities in the study area s vertical symmetry planes that are parallel and perpendicular to the expected dominant fracture set. It is important to note that the inversion utilizes an isotropic PS-wave AVO-inversion algorithm, which is currently the state of the art for commercial availability. Therefore, many anisotropic effects are not taken into account, which limits the interpretation to qualitative observations. Input into these inversions include azimuthally sectored pre-stack converted wave seismic representative of fast and slow wave modes. Fast wave mode is acquired from source-receiver (S/R) azimuths parallel to the dominant fracture set, and the slow wave mode represents S/R azimuths perpendicular to the fracture set. The elastic properties of a fractured rock are shown to be azimuthally dependent, and therefore converted waves in the fast and slow mode will exhibit different seismic amplitudes for the same spatial location. The inversions utilize Amplitude Variation with Offset (AVO) to determine shear impedances that can be related back to the fracture model. Fractional difference between the fast and slow shear velocities is defined as the shear wave splitting parameter (γ) and provides a predictive and quantitative measurement of reservoir anisotropy due to fractures and the prevailing stress field. Integration of multicomponent 2

21 seismic with microseismic, image logs and cluster analysis enables calibration of the seismic response and a more complete interpretation of the reservoir. 1.1 Montney Geology One of the largest economically viable gas resource plays in North America is the Montney Formation located in Northern Alberta and British Columbia. In 2013, marketable reserves from the Montney were estimated by Canada s National Energy Board (NEB) at 449tcf gas, 14.5 billion bbl of natural gas liquids (NGL), and million bbl of oil (British Columbia Oil and Gas Commission, 2013). Despite this play frequently being called new, hydrocarbon exploration of the Montney began with the exploitation of conventional oil from sandstones on the eastern margin in the 1950s (ERCB, 2012). A resurgence of this formation began in 2005 with the onset of horizontal drilling and multistage hydraulic fracturing that produced economic drivers that opened up the majority of the Montney that is rich in gas and condensate. Current drilling operations and this research project are focused on the unconventional tight siltstones and shales that make up the greatest volume of rock within the Montney Stratigraphy The Montney Formation is a Triassic age unconventional reservoir confined to the Peace River Embayment (Figure 1.1). This sub-basin developed during early Carboniferous and Permian when subsidence occurred as a broad downwarp with a large central half-graben (Edwards et al. (1994)). This produced a marine to marginal-marine continental shelf with water depths increasing toward the west (Figure 1.2). Montney deposition began after a major transgression eroded Carboniferous/Permian strata and subsequent regression that transported multi-cycled sediment from the craton in the east and north (Edwards et al. (1994)). Erosion has led to unconformable boundaries above and below Montney that can be easily distinguished in well logs and seismic. The underlying Belloy Formation is a Permian age carbonate and the overlying Doig Phosphate is an organic-rich shale with a 3

22 high gamma ray (GR) response. Figure 1.1: Map of the Peace River embayment that bounds the Montney Formation. Various Montney oil and gas fields are shown in green and red respectively. Pouce Coupe Field highlighted by a black star (Zonneveld et al. (2011)). Six Montney subunits (A-F) associated with lower order transgression/regression cycles are frequently observed (Figure 1.3). Units A-C are commonly called the Lower Montney, and units D-F are linked to the upper Montney. The lower Montney typically consists of shoreface facies and coarse siltstones deposited more proximal to the sediment source. Within the confines of this Pouce Coupe study area, Montney C was classified by Derder (2012) as a finely laminated siltstone, and production data has proven it to be the best producing subunit. Montney D is also frequently targeted for exploitation, and expected to have been deposited in a more distal environment. 4

23 Figure 1.2: Paleogeographic map of North America during the Early Triassic (245Ma) which is representative of the time during Montney deposition. A black box highlights the approximate bounds of the formation and a red star shows the Pouce Coupe fields paleolocation (Blakey (2011)). Figure 1.4 shows the general depositional model of the Montney as a continental ramp with progressive parasequences off-lapping to the west (Zonneveld et al. (2011)). This model demonstrates the complexities associated with the Montney, and how reservoir properties can change dramatically depending on where deposition occurred in the model. Deep water successions in the west can include turbidite channels and fan complexes, while deltaic or estuary influenced upper shoreface facies exist to the east. Montney Formation within Pouce Coupe is expected to have been deposited on the proximal slope where turbidite potential exists (Derder (2012)) Reservoir Characteristics As a self-sourcing petroleum system, the Montney Formation acts as the source, reservoir, and seal largely due to the formations high organic content, and low porosity/permeability. When burial depth is sufficient to thermally mature the type II-III kerogen within the reser- 5

24 Figure 1.3: Montney type log and stratigraphic chart of the Triassic. (Davey (2012)). Figure 1.4: Depositional model of the Montney Formation. Environments range from shoreface in the East, to congenital slope and distal marine in the West (Davey (2012)). 6

25 voir, the organic content undergoes a volume expansion as hydrocarbons are generated. This volume expansion combine with very low matrix permeability leads to three desirable traits within the Montney: 1) reservoir overpressure, 2) naturally occurring fractures, and 3) low water saturations (Meissner (1978)). All of these characteristics make the Montney and other unconventional plays like it exceptional hydrocarbon targets. While the Montney is frequently referred to as shale, the most accurate description of the reservoir rock at Pouce Coupe is an organic-rich argillaceous siltstone interbedded with shale (Davey, 2012). Siltstones dominate the central-west, and shales become more prominent in the far west as the depositional environment becomes more distal (Figure 1.5). Reservoir rocks within this research project are clastic-rich, composed of >60% quartz and feldspar, and contain lower concentrations of clay minerals and dolomite (Derder (2012)). Figure 1.5: East-West modeled stratigraphic section of the Montney with approximate location of Pouce Coupe highlighted by a star (Davey (2012)). Holding true to the meaning of a tight gas reservoir, Montney permeability ranges from 0.02 to 0.05 Ka (air) in md, and porositys between 5-10% (ERCB, 2012). Since these parameters are too tight for fluid flow, fractures (natural or induced) are necessary for 7

26 commercial production of hydrocarbons. Samples with natural fractures are observed to increase permeability by orders of magnitude and range from 1.7 to 24 Ka in md (ERCB, 2012). High values of total organic carbon (TOC) within the Montney range from 1-5% and are one of the key reasons for the formation s success as an unconventional reservoir. Due to the monocline slope of the Peace River Embayment, Montney reservoir depths gradually increase from outcrop in the east to over 4000m. Depth to the reservoir top within Pouce Coupe Field ranges from m. This overburden is sufficient for thermal maturity of the type II/III kerogen in the gas generation window. Formation thickness follows a similar east-west trend that ranges from 0 to 300m, with an average of 250m for unconventional assets. A summary of relevant reservoir properties for Pouce Coupe Field is shown in Table 1.1. Table 1.1: General Reservoir Properties of the Montney Formation Within Pouce Coupe (Steinhoff (2013)) Property Approximate Range Permeability (md) 20 Porosity(%) 7-9 TOC (%) Thermal Maturity (%R 0 ) 1-2 Thickness (m) Burial Depth (m) Regional Tectonics Tectonic activity was much more pronounced in the Paleozoic prior to deposition of the Montney. This tectonic activity caused variations in structure and accommodation space that explain local thickness variations and location of turbidity flows. Some of these faults were reactivated in the Triassic and thus could have an impact in generating fractures within the reservoir. However, the magnitude of such events was diminished and varied spatially with the strongest stresses acting in the western foothills (Edwards et al. (1994)). The Laramide Orogeny extending from the Late Cretaceous into the Tertiary was the major 8

27 tectonic event responsible for the current compressional stress state observed throughout the field where strike-slip and reverse failures are expected. Within Pouce Coupe Field, the stress field indicates that vertical strike-slip failure will be the most common due to the overburden stress (S v ) having a lower magnitude than the maximum horizontal stress (S H ), but less then the minimum horizontal stress (S h ) (S H >S v >S h ). It is important to note that a large differential in horizontal stress will tend to produce linear fractures that propagate in the direction of S H. According to the World Stress Map and field data observations, the maximum horizontal stress orientation is N40 E (Figure 1.6). Figure 1.6: Map of maximum horizontal stress orientation derived from well bore breakouts. Pouce Coupe field is highlighted by a star (Heidbach et al. (2008)). 9

28 1.2 Pouce Coupe data set Hydraulic stimulations are necessary for unconventional tight gas development to be economic, and is often the single largest expense associated with bringing a well online. The Pouce Coupe Field is data rich and based around a time-lapse multicomponent seismic survey acquired by Talisman Energy in 2008 to monitor and evaluate hydraulic stimulations of two horizontal wells Field Development Wells of specific interest are the 02/02-07 well drilled into the Montney C, and the 02/07-07 well drilled into Montney D. After well stimulation, both wells were allowed to flow just long enough to recover the treatment balls at the surface before being shut in to maintain maximum reservoir pressure. This ensures that stimulated fractures remain propped open due to the artificially high pore pressure. Figure 1.7 displays the field layout and timeline of field operations, which took place in rapid succession. Over an 11 day interval, three timelapse seismic surveys were acquired before and after each of the two hydraulic stimulations. It is also important to note that well 00/07-07 produces from the Montney C directly below well 02/07-07 and hence does not show up clearly on the basemap. This third important well was drilled and hydraulically stimulated 10 months prior to acquisition of the baseline seismic survey. This rich data set also includes: 1. Several well logging suites (two wells have shear sonic) 2. Downhole and surface microseismic 3. Spinner production data 4. Ultrasonic Borehole Image (UBI) 5. Vertical Seismic Profile (VSP) 10

29 Figure 1.7: Pouce Coupe Field layout and timeline of operations. Wells highlighted in red contain sonic logs, wells shown in orange enclose downhole microseismic tool strings, and well was logged with a UBI. Modified from Atkinson (2010). 11

30 The stimulations in each well being monitored were comprised of 5 stages using an open hole packer system. Amount of proppant, proppant size, and H 2 O load were identical in both the 02/02-07 and 02/07-07 wells. The primary difference between these two completions is the interval between stage spacing. Well 02/02-07 has stages spaced 200m, and the stages in well 02/07-07 are separated by 250m. Consistent wellbore and completion design enable the significant production differences observed in these wells to be attributed to heterogeneity in the reservoir. These heterogeneities need to be predictively mapped for the full potential of a unconventional reservoirs to be realized. Table 1.2 summarizes hydraulic stimulation parameters for all three Montney producing wells. Well Stimulation Date (mm/dd/yy) Table 1.2: Hydraulic Stimulation Parameters Fluid Type # of Stages Amt of Proppant Proppant Size H 2 O Load (m 3 ) Closure Pressure (MPa) 02/ /12/08 Clear Frac 5 100T 20/ / /17/08 Clear Frac 5 100T 20/ / /10/08 Clear Frac 3 100T 20/ N/A Cumulative flow volume and spinner log production data demonstrate large differences in the production profiles between both Pouce Coupe study wells. Figure 1.8 shows average daily production per month for the three Montney producing wells within Pouce Coupe Field from initial production to November The most notable observation is that well 02/02-07 produced 84% more gas than well 02/07-07 in the first two months of production despite the wells being separated by 500m. This indicates significant differences in rock quality and level of hydraulic fracturing success. Well 00/07-07 produces from the Montney C with a comparable production profile to well 02/02-07 despite the application of a less intensive stimulation. The success of individual hydraulic fracture stages is assessed by spinner production logs acquired for wells 02/02-07 and 02/07-07 on Jan 13, 2009 and Jan 15, 2009 respectively (Table 1.3). Well 02/02-07 shows consistent production from all stages, demonstrating a successful completion and resultant high volume of net production. In contrast, 43% of 12

31 Figure 1.8: Pouce Coupe Average Daily Gas Production for Montney wells production from well 02/07-07 comes from one stage. Operational challenges prevented the spinner tool from reaching the toe of well 02/07-07, leading to commingled production measurements from stages 1 and 2. Table 1.3: Stage by Stage Production Data From Spinner Log as Percent of Total Flow Volume Stage 02/02-07 (%) 02/07-07 (%) (commingled) (commingled) Time-Lapse Multicomponent Seismic Time-lapse multicomponent seismic acquisition was performed by CGG Veritas using a permanent three component geophone array buried 3.5m below the surface, and dynamite 13

32 sources buried 5.5m. This permanent recording system enabled the fast timeline necessary to monitor the immediate seismic response of hydraulic stimulation prior to production. High data quality resulted, and ensured high time lapse repeatability. Use of three-component geophones (2 horizontal and 1 vertical axis), enable compressional (PP) and converted (PS) wave modes to be recorded. PS converted wave seismic refers to a down going P wave generated by a conventional source at the surface, and an up going Sv wave that is generated by P wave incidence upon a reflection interface. This process is called mode conversion. The 3000m by 1600m survey was acquired using megabin geometry with parallel source and receiver lines (Figure 1.9). This acquisition technique provides the advantage of rapidly building fold along the survey edges, uniform wave field sampling, and was cost effective (Goodway and Ragan (1996)). Both source and receiver lines were spaced 200m apart, and a typical patch includes 9 receiver lines, each with 31 stations. Table 1.4 summarizes acquisition parameters. Figure 1.9: Source and Receiver layout of the Pouce Coupe seismic survey One consequence of this source and receiver line spacing is a large natural bin size of 100m x 50m. The rectangular geometry also creates an azimuthally biased range of offsets 14

33 Table 1.4: Pouce Coupe Seismic Survey Acquisition Parameters Survey geometry Megabin Source Line Interval 200/400m Source Interval 100m Number of Sources 1241 Charge Size: Baseline = 0.5kg Monitor 1 = 0.2 kg Monitor 2 = 0.2 kg Receiver Line Interval 200m Receiver Interval 200m Number of Receivers 162 Geophones CT-Single, 144 OYO Geospace 3C Nails, 61 DSU 3C Number of Recording Channels 340 (144 permanently buried) Recording Instrument Sercel 408 XL, 2ms sample Rate displayed by Figure For this study, the consequences of this azimuth bias are limited because the processing workflow requires the data to be sectored in orientations parallel and perpendicular to the maximum horizontal stress (N40E), which happens to lie along the survey s diagonal. Thus, far offsets are available in both necessary azimuths. Baseline survey was acquired on December 8-10, Two subsequent monitor surveys were performed immediately after wells 02/02-07 and 02/07-07 were stimulated. For the purpose of this study, only the baseline seismic survey was used Microseismic Data Microseismic data were acquired from surface and downhole arrays to monitor the interaction of two 5 stage hydraulic stimulations within the reservoir. This thesis only integrates microseisms recorded by downhole sensors in the and wells to interpret fracture geometry (length, height, and orientation) (Figure 1.11). Failure mechanisms derived from amplitude ratio analysis by Lee (2014) are also incorporated in the final interpretation of the fracture network in Chapter 5. Failure mechanisms derived from microseism amplitude ratio analysis have the benefit of being on similar investigative scales as surface seismic, which enables an effective integration of these complimentary fracture characterization measurements. 15

34 Figure 1.10: Offset/Azimuth distribution in individual bins of the Pouce Coupe Seismic showing a significant offset bias dependent on azimuth. Well contains a tool string of 10 3C geophones. The tool string in well encloses 50 3C geophones spaced by 15m (Figure 1.12). Rapid attenuation of low magnitude microseisms combined with an increasing observation bias associated with increasing sourcereceiver distance introduce data quality limitations. Within the Pouce Coupe data set, these sources of error are mitigated through the use of a multi-well array. Geophone locations near the heel and the toe of production wells provide reasonable coverage of all hydraulic stimulation stages in the 02/02-07 and 02/07-07 wells. 1.3 Previous Pouce Coupe Research RCP research on the Pouce Coupe data set began with Atkinson (2010) and was progressed by Davey (2012), Steinhoff (2013), Lee (2014), and Dueñas (2014). Atkinson (2010) demonstrated that the stress conditions of the reservoir are greatly altered in a hydraulic stimulation through reservoir modeling of fluid flow and stress regime analysis. However, the time-lapse compressional seismic response was determined to be too small for observation due to the low permeability of the reservoir which prevented fluid from flow from the 16

35 Figure 1.11: Microseismic events: a) from the 02/07-07 stimulation recorded by the toolstring, b) from the 02/07-07 stimulation recorded by the toolstring c) from the 02/02-07 well recorded by the toolstring d) from the 02/02-07 well recorded by the toolstring (Lee (2014)). 17

36 Figure 1.12: Downhole microseismic acquisition geometry (Lee (2014)). induced fractures into the background rock. This initiated the use of converted wave seismic and shear wave splitting (SWS) analysis to monitor the hydraulic stimulation. SWS analysis is a measurement of seismic anisotropy based on time delays associated with fast and slow shear wave propagation through anisotropic media. As a reflected shear wave passes through a preferentially aligned fractured media, it splits into a fast (PS 1 ) and slow (PS 2 ) mode with orthogonal sets of particle propagation (Figure 1.13) (Rüger (2001)). At vertical incidence, the PS 1 -wave will have particle motion in the direction of the fracture strike, and the PS 2 -wave will be polarized orthogonally. The arrival time delays between the fast and slow wave modes are associated with fracture density (equation 1.1). The PS 1 orientation, which can be used as a proxy for fracture strike is also determined by SWS analysis. Grossman et al. (2013) provides a detailed a detailed overview of the processing sequence that developed the SWS analysis maps in the Pouce Coupe data set. SW S = t P S2 t P S1 t P S2 (1.1) 18

37 Figure 1.13: Model of SWS. As a shear wave passes through uniform fractured media it splits in to the fast (S 1 ) and slow (S 2 ) modes with perpendicular polarizations (Hardage et al. (2011)) 19

38 Steinhoff (2013) advanced the SWS analysis performed by Atkinson (2010) utilizing a detailed seismic reprocessing effort by Sensor Geophysical to increase the NRMS repeatability of the time lapse surveys and thereby preserve time delays within the reservoir. A significant improvement in data quality resulted, and enabled a reasonable correlation between observed time delays at the base of the reservoir and production data. Key improvements in the processing workflow included; 1. Careful overburden layer stripping 2. Improved alignment of the two horizontal components of the 3C geophone with fast and slow shear wave particle polarizations 3. Detailed velocity analysis in a simultaneous time lapse processing workflow SWS analysis on baseline survey was used to characterize natural fractures within the reservoir prior to stimulation (Figure 1.14). The dominant fracture orientation was observed to shift from the expected maximum horizontal stress orientation of N40 E in the east part of the survey, towards an orthogonal direction in the west. Within the reservoir interval, maximum magnitude of SWS was 3% with most of the survey having values close to 0%. Monitor 1 was acquired immediately after well 02/02-07 was stimulated and used to determine the induced hydraulic fracturing effect. SWS analysis detected 3 areas within the survey with increased time delays when compared to the baseline survey (Figure 1.15). SWS magnitude reaches 8% near stage 3 and 4 of well 02/02-07 with the build up constrained to the south part of the wellbore indicating a potential asymmetric frac. Monitor 2 was acquired to study the hydraulic fracturing effect of well 02/07-07 and produced an interesting result. SWS magnitude did not have a significant increase surrounding the 02/07-07 well that was stimulated, however the anomaly surrounding well 02/02-07 expanded (Figure 1.16). A wellbore scale geomechanical characterization of the Montney Fm. performed by Davey (2012) provided a model that defined reservoir characteristics prone to successful hydraulic stimulations. Davey developed the modified rock quality index (RQI) to define best parts 20

39 Figure 1.14: Baseline SWS magnitude (color) and PS 1 polarization orientation (needle). Weak splitting anomaly are observed (Steinhoff (2013)). Figure 1.15: Monitor 1 SWS magnitude (color) and PS 1 polarization orientation (needle) (Steinhoff (2013)). 21

40 Figure 1.16: Monitor 3 SWS magnitude (color) and PS 1 polarization orientation (needle). After two hydraulic stimulating, the splitting anomalies appear to have dispersed throughout the survey (Steinhoff (2013)). of the reservoir in an effort to understand how composition and rock fabric relate to stress anisotropy, fracturing, and reservoir properties. Principal factors determined to affect reservoir quality of the Montney Fm. included natural fractures, orientation and magnitude of the stress field, and rock brittleness (Davey (2012)). Hydraulic fracture propagation was also observed to be most effective in homogeneous zones, and not necessarily the regions with the most brittle rock. Heterogeneity in the rock was observed to be a detriment to fracture growth and the best locations occurred where homogeneous and heterogeneous intervals intersect (Davey (2012)). Dueñas (2014) produced a seismic-based characterization of rock fabric and rock composition through the combination of cluster analysis and elastic property inversion of compressional wave seismic. Several well logging suites were analyzed to define six multidimensional clusters. Two clusters associated with the best producing intervals were isolated when cross plotted in λρ vs µρ space. This provided a crucial link between wellbore scale investigations and seismic which can now be used to predictively identify desirable rock fabric properties defined by Davey s RQI. A constrained sparse spike inversion was performed on the baseline PP seismic data set to produce λρ and µρ volumes (Figure 1.17). Geobodies defined by λρ, µρ polygons mapped homogeneous zones of brittle rock throughout the survey and 22

41 demonstrated a methodology that could be used to predictively identify optimal rock facies (Figure 1.18). Figure 1.17: Inverted λρ and µρ cross sections used to perform compositional analysis (Dueñas (2014)). Detailed work with the downhole microseismic data set was performed by Lee (2014). Amplitude ratio analysis of microseisms provided a relatively accurate method of determining composite focal mechanism solutions for clouds of microseismic data. The data set demonstrated that the Montney reservoir is dominated by strike-slip failures during hydraulic stimulation, and concluded that the success of a hydraulic stimulation is largely dependent on naturally occurring weak planes such as fractures 23

42 Figure 1.18: Inverted P-Impedance cross section through well 02/02-07 with geobodies of best clusters displayed in blue (Dueñas (2014)). 1.4 Research Objective This thesis expands the multidisciplinary work already performed in Pouce Coupe by enhancing the vertical resolution of fracture characterization through the use of amplitude differences in the PS 1 and PS 2 seismic volumes. The importance of fractures is well defined by the modified RQI and microseismic where fracture propagation has been interpreted to occur along weak planes such as fracture within the reservoir. This previous research produced a need for a robust and detailed fracture mapping methodology. Inversion of the converted wave data will be the primary tool utilized in my research to derive anisotropic elastic properties of the reservoir can be related to crack density. This type of work is based on amplitude variation-with-offset (AVO), which has the advantage of extracting information from the Montney subunit reflectors. Vertical resolution is the most significant limitation of previous traveltime based interpretations that look at the cumulative fracture effect of the entire 300m reservoir. Operators typically target individual subunits, which are expected to have different fracture characteristics due to their heterogeneous nature. This fracture 24

43 characterization workflow will be integrated with microseismic, production data, composition analysis, and knowledge of the fields development history to verify its accuracy. Mapping fractures on the subunit scale enables integration with seismic-based compositional analysis to identify the best reservoir targets, as defined by geomechanical analysis. 25

44 CHAPTER 2 SEISMIC MODEL OF FRACTURES The focus of this chapter is to provide background theory on how natural fractures form, their influence on hydraulic stimulations, and develop an anisotropic elastic model of fractured rock that can be used to evaluate seismic based observations within Pouce Coupe. Effective elastic models will be generated to represent a range of geologically realistic fracture sets we expect to see in Pouce Coupe. To accomplish this, we will look into the compliance tensor s ability to represent different sources of seismic anisotropy to model the effective elastic properties of orthorhombic media that contain up to two fracture sets (Figure 2.1). As previously discussed, a dominant fracture set parallel to σ H is well documented in Pouce Coupe, while a secondary orthogonal set is hypothesized. Figure 2.1: Schematic of an orthorhombic model that combines vertical cracks and horizontal layering. Vertical symmetry planes are determined by the crack orientation (Rüger (2001)). Since the inversions performed in the following chapters will utilize amplitude variation with angle (AVA) trends in the seismic, it is important to understand how the effective elastic properties associated with various fracture models impact the seismic response. Reflection 26

45 coefficient modeling provides the link between the elastic properties of individual media, and the amplitudes observed in seismic. This anisotropic modeling will demonstrate how the presence of vertical fractures causes well known azimuthal anisotropy that provides the basis for fracture characterization. Due to limitations of Pouce Coupe s seismic survey design, AVA analysis is restricted to two azimuths that align with the vertical symmetry planes. Therefore, modeling will also focus on these specific cases. This chapter will conclude with a section on seismic implications that will discuss the challenges, pitfalls, and sources of error associated with the fracture characterization technique performed in this thesis. The largest source of error is due to differences between the anisotropic response of the earth that is recorded by seismic, and the isotropic AVO equations utilized in the inversion. To address this issue, isotropic and anisotropic reflection coefficient modeling will be compared. 2.1 Natural Fracture Background The occurrence of natural fractures is closely associated with mature over-pressured source rock reservoirs like the Montney due to basic failure theory of porous brittle-elastic rocks (Meissner (1978)). Controls on the stress conditions that cause a rock to break are defined by Mohr s stress circle, and the failure envelope that is related to a rocks tensile (σ) and shear (τ) strength (Figure 2.2). On a Mohr diagram, rock failure occurs when the stress circles become tangent to the failure envelope. Rock failures of particular value are tensile fractures, which occurs when the point of tangency is on the negative side of the origin. Elevated pore pressures caused by the thermal maturity of kerogen or fluid injection have the benefit of shifting the stress circles toward tensile failure by reducing the effective stress (Figure 2.3). Equation 2.1 defines effective stress as a function of externally applied total stress (σ) and pore pressure (PP). σ (eff) = σ P P (2.1) 27

46 Figure 2.2: Mohr-Coulomb graphical representation of stress fields producing tensional and shear rock failures (Meissner (1978)). Figure 2.3: Mohr-Coulomb graphical representation of fracture failure produced by increasing pore pressure (Meissner (1978)). 28

47 Stratigraphic architecture and composition of sedimentary layers also have an impact on controlling the fracture density within a unit (Zahm and Hennings (2009)). Observations in many reservoirs have shown that fracture spacing is proportional to the thickness of the bed (Davey (2012)). However, extent of this stratigraphic control on fractures decreases with an increase in tectonic activity. Since Pouce Coupe is located on the fringe of the Alberta foothills where a high differential in horizontal stress was caused by the Larimide orogeny, we expect fractures to be largely controlled by the regional stress field (Wikel (2011)). 2.2 Induced/Natural Fracture Interaction In the early development of hydraulic stimulations, treatments were designed using idealized models that assume an homogeneous and isotropic earth. Accordingly, well spacing, stage spacing, lateral length, and injected volumes of fluid/proppant were applied uniformly to a wide range of reservoir conditions. The density, orientation, and connectivity of natural fractures is known to be heterogeneous throughout fields and it is becoming increasingly apparent that the interaction between natural and induced fractures is complex (Olson et al. (2012)) (Chuprakov et al. (2011)). Therefore, proper characterization of the natural fracture network and understanding their role during a hydraulic stimulation is crucial for optimal completion design. The end result of a proper fracture characterization is use in a geomechanical model to predict future fluid flow patterns, and whether new tensile fractures will be created in stimulation. Geomechanic analysis of induced fracture interaction with natural fractures demonstrates how discontinuities can change the path of fluid flow associated with hydraulic fractures (Chuprakov et al. (2011)) (Olson et al. (2012)) (Davey (2012)) (Figure 2.4). Natural fractures often act as planes of weakness in a reservoir, but can also act as barriers to induced fractures. The extent to which fractures act as a conduit or barrier frequently depends on the degree of diagenetic cementation/mineralization, prevailing stress conditions, and the orientations of both natural fractures and incident hydraulic fracture (Chuprakov et al. (2011))(Davey (2012)). 29

48 Figure 2.4: Simulated hydraulic fracture in the presence of perforations that act as permeable conduits and glass plates that represent cemented natural fractures (Olson et al. (2012)). 2.3 Fracture Compliance Methodology The fracture-characterization model used in this thesis is based on effective media theory to derive the overall impact of fractures on the elastic properties, even though fracture size is small compared to a typical seismic wavelength (Tsvankin and Grechka (2011)). This theory assumes that the displacement field for a long seismic wavelength is nearly constant inside a representative volume element (RVE) that may contain numerous small-scale heterogeneities such as fractures (Tsvankin and Grechka (2011)). This enables the exact compliance tensor (s) that is heterogeneous on the micro scale, to be represented as an effective stiffness tensor (s e ) that has the same average elastic properties of the original medium on the scale of seismic wavelength (Tsvankin and Grechka (2011)). Equation 2.2 demonstrates the relationship of the effective compliance tensor to stress (τ ) and strain (ɛ) by Hooke s law. Since fractures can be represented as sources of extra strain in relation to the background rock, the effective compliance tensor is equivalent to the sum of the background rock compliance (s b ) and the fracture compliance ( s) (equation 30

49 2.3)(Tsvankin and Grechka (2011)). ɛ ij = s e ijkl τ kl (2.2) s e = s b + s (2.3) While many different fracture theories can be used for modeling fracture compliance, we will focus on the scalar crack model (Tsvankin and Grechka (2011)). This technique has the benefit of simplicity since s is defined by three scalars (α 1, α 2, α 3 ) that represent the elastic properties of three orthogonal fracture sets (equation 2.4) (Schoenberg and Sayers (1995)). Equation 2.4 represents the crack-density tensor, where α 1 corresponds to a set of vertical fractures normal to the x 1 axis, α 2 to a set of vertical fractures normal to the x 2 axis, and α 3 to a set of horizontal fractures normal to the x 3 axis which is defined to be vertical. Figure 2.5 show a schematic of how this method utilizes the α s to effectively model a medium with orthogonal sets of fractures by assuming each fracture set to be independent from the others. s = α α α α 2 + α α 1 + α α 1 + α 2 (2.4) Application of this modeling technique to Pouce Coupe Field will utilize VTI symmetry of the background rock due to the horizontally laminated nature of Montney and two α coefficients that represent vertical fracture sets. Within Pouce Coupe, stress conditions, image logs, and microseismic give no indication that horizontal fractures are present. Therefore α 3 can be set to zero. Equations presented below will utilize both the compliance tensor (s) and the stiffness tensor (c), which have a simple inverse relationship (2.5). The stiffness tensor proves to be useful due to well known equations in linear elastic theory that relate the anisotropic velocity field to the individual stiffnesses. The stiffness tensor of the VTI background medium is 31

50 Figure 2.5: Schematic model of a medium with multiple sets of parallel fractures (A) as the sum of independent parallel fracture sets (B and C) (Schoenberg and Sayers (1995)). 32

51 defined by equation 2.6, with equations 2.7 through 2.12 showing how each element in the tensor is related to density (ρ), vertical velocities, and anisotropic coefficients. Note that V P 0 and V S0 represent the vertical P and S wave velocities respectively, and the Thomsen parameters (γ, ɛ, and δ) characterize the strength of velocity anisotropy. γ is close to the fractional difference between horizontal and vertical velocity of SH-waves. ɛ is the fractional difference between the horizontal and vertical compressional velocity. δ is a function of several stiffness tensor coefficients that describes P and S wave anisotropy at oblique incidence angles (Thomsen (1986)). S = C 1 (2.5) [S (b) ] 1 = C (b) = C 11 C 12 C C 12 C 11 C C 13 C 13 C C C C 66 (2.6) C 11(b) = (1 + 2ɛ)ρV 2 P 0 (2.7) C 33(b) = ρv 2 P 0 (2.8) C 55(b) = ρv 2 S0 (2.9) C 66(b) = (2γ + 1)ρV 2 S0 (2.10) C 13(b) = C Numeric Modeling C 12(b) = C 11 2C 66 (2.11) C 2 55 (2δ + 2)(C 33 C 55 ) (2δ + 1)(C 2 33) (2.12) The objective of numeric modeling is to obtain the azimuthal PS reflection coefficients of the Montney D/Montney C interface for various geologically realistic fracture models. This will provide insight into the seismic response we expect to observe in the Pouce Coupe 33

52 seismic, and illustrate the project s feasibility despite notable pitfalls. Figure 2.6 summarizes the numeric modeling workflow which is based on the concept of scalar fractures and effective compliance presented in the preceding section. Figure 2.6: Workflow for modeling the seismic response of fractures. The first step in the workflow is to populate the stiffness tensor of the unfractured background rock (s b ) using well logs, estimates of the Thomsen parameters and equations 2.7 through Since sonic pulses recorded by logging tools follow the fastest path from the source to the receiver, and fractures have the effect of lowering wavefield velocities, sonic logs are representative of the unfractured background rock (Asquith and Krygowski (2004)). To 34

53 obtain the slow shear velocity the full wave-field of a dipole sonic log needs to be processed, and this was not performed in Pouce Coupe. Since well is the only well in the field with density, shear sonic, and compressional sonic logs through the entire reservoir, it will be used to obtain average reservoir properties of the Montney C and D subunits. Thomsen parameters can be obtained through a combination of P-wave NMO velocity analysis, core analysis and modern logging tools such as Schlumberger s sonic scanner. Note that velocity anisotropy measurements from core and logs are not at the seismic scale. Unfortunately, Pouce Coupe does not have the necessary data available to determine the Thomsen parameters, so values are estimated from literature where the Ft. Union siltstone will be used as a proxy for the Montney (γ=0.040, ɛ=0.045, δ=-0.045) (Thomsen (1986)). Thomsen parameters found in literature demonstrate a significant amount of variability between siltstone/shale formations, and the values used in this thesis may underestimate the anisotropy associated with the background rock. It is therefore instructive that further information be obtained to ensure accurate measurements of the VTI parameters are used in the modeling to reduce uncertainty. Table 2.1 summarizes the unfractured reservoir properties of Montney C and D used to generate the background compliances. Table 2.1: Properties of the unfractured Montney C/D subunits obtained from well logs and literature Property Montney D Montney C ρ (g/cc) V S0 (m/s) V P 0 (m/s) ɛ γ δ Four fracture models are generated using various combinations of α to demonstrate a range of geologic scenarios that can be encountered within the Montney. These α-values cannot be obtained from data available within Pouce Coupe, so they are estimated to generate reasonable shear velocity anisotropy of up to 10%. To keep the model simple to 35

54 understand, fractures will only be simulated in the Montney C half-space. Model 1: No fractures (α 1 = α 2 = 0) Model 2: One parallel set of fractures with low crack density (α 1 = 0.03, α 2 = 0.0) Model 3: One parallel set of fractures with high crack density (α 1 = 0.07, α 2 = 0.0) Model 4: Two orthogonal fracture sets of equal crack density (α 1 = α 2 = 0.07) At this point, we have sufficient input to calculate the effective compliance tensors utilizing equation 2.3. Since the background compliance has VTI symmetry, the addition of at least one vertical fracture set generates an effective medium with orthorhombic symmetry. To condition these effective compliance tensors for the reflection coefficient modeling software, the Tsvankin parameters must be calculated. We previously used the widely accepted notation developed by Thomsen (1986) to represent the VTI elastic properties of the Montney background (V P 0, V S0, γ, ɛ, and δ). Tsvankin (1997) extended this notation to orthorhombic symmetry by defining two vertical velocities (V P 0 and V S0 ) and seven dimensionless anisotropy parameters (γ (1), ɛ (1), δ (1), γ (2), ɛ (2), δ (2), and δ (3) ). The superscripts (1) and (2) refers to the x 1 and x 2 axes, which defines the normal direction of the [x 2, x 3 ] and [x 1, x 3 ] symmetry planes respectively. The definition of these parameters is presented below in terms of the stiffnesses c ij and density (equations ). C 33 V P 0 = ρ C 55 V S0 = ρ (2.13) (2.14) ɛ (1) = c 22 c 33 2c 33 (2.15) δ (1) = (c 23 + c 44 ) 2 (c 33 c 44 ) 2 2c 33 (c 33 c 44 ) (2.16) γ (1) = c 66 c 55 2c 55 (2.17) 36

55 ɛ (2) = c 11 c 33 2c 33 (2.18) δ (2) = (c 13 + c 55 ) 2 (c 33 c 55 ) 2 2c 33 (c 33 c 55 ) (2.19) γ (2) = c 66 c 44 2c 44 (2.20) δ (3) = (c 12 + c 66 ) 2 (c 11 c 66 ) 2 2c 11 (c 11 c 66 ) (2.21) Table 2.2 represents the Tsvankin parameters calculated for the four Montney C fracture models. Note that models 1 and 4 are effectively VTI since γ (1) = γ (2) = γ, ɛ (1) = ɛ (2) = ɛ, δ (1) = δ (2) = δ, and γ (3) =0. Table 2.2: Tsvankin Parameters for the Four Montney C Fracture Models Parameter Model 1 Model 2 Model 3 Model 4 V P 0 (m/s) V S0 (m/s) γ (1) ) ɛ (1) δ (1) γ (2) ɛ (2) δ (2) δ (3) The final and most important step in the modeling workflow is to calculate the PS reflection coefficients for each fracture model in azimuths parallel to the x 1 and x 2 axes. This will demonstrate that seismic amplitudes are highly sensitive to azimuthal velocity variations associated with vertical fracture systems (Xu and Tsvankin (2006)). In models where fractures are present, these axes coincide with the two orthogonal symmetry planes of the system. Reflection coefficients provide insight into understanding the amplitude signatures in reflected wave seismic in the presence of fractures. Modeling was accomplished using code developed by Petr Jìkek in his PhD thesis. Jìlek (2001) extended the work of Rüger (1996) to develop azimuthally-dependent linearized approximations of PS wave reflection coefficients 37

56 for arbitrarily anisotropic media. Figure 2.7 presents the reflection coefficients calculated at the interface between the Montney D halfspace with VTI symmetry, and the various representations of the Montney C halfspace that exhibits VTI or orthorhombic symmetry depending on the assumed fracture model. Each model is uniquely colored, and uses circles and squares to represent reflection coefficients associated with S/R azimuths in the x 1 and x 2 directions respectively. Figure 2.7: PS reflection coefficients of the four fracture models associated with the Montney C/D interface along azimuths aligned with the x 1 and x 2 axes. Note that the reflection coefficients from Model 3 (x1)=model 4 (x1)=model 4 (x2) are nearly overlain. Two key observations can be made from Figure 2.7 that provide insight into how the presence of fractures will impact the seismic response: 1: There is no azimuthal variation in the reflection coefficients of models 1 and 4, even though both models exhibit different AVA behaviour. Since the fracture character- 38

57 ization methodology applied in this thesis requires azimuthal variation in the AVA signature, the inversion performed in chapter 4 will not be able to identify orthogonal fractures sets. 2: Models 2 and 3 demonstrate that media with one dominant fracture set display greater amounts of azimuthal variation of PS-wave reflection coefficients with increasing values of crack density. This concept is well established in literature and provides the basis for the fracture characterization presented in chapters 4 and 5. Another way to quantify these modeled results is through use of the shear wave splitting coefficient γ (S), which can be defined in terms of the Tsvankin parameters (γ (1) and γ (2) ) or the fractional difference between the fast (V S1 ) and slow (V S2 ) vertical shear wave velocities (equation 2.22) (Tsvankin (2012)). This parameter has several benefits that include being directly related to previous Pouce Coupe SWS analysis where up to 8% SWS was observed (Steinhoff (2013)). γ (S) is also noted by Bakulin et al. (2000) to be close to the crack density and therefore provides an excellent way to quantify the fractures we plan to characterize. Table 2.3 summarizes the γ (S) results for the four fractures models. γ (S) = γ(1) γ (2) 1 + 2γ (2) V S1 V S2 V S2 (2.22) Table 2.3: Shear-Wave Splitting Coefficients for the Four Montney C Fracture Models Fracture Model γ (S) Model 1 0 Model 2 4.7% Model 3 8.8% Model Pouce Coupe Seismic Implications As previously discussed, the fracture characterization technique applied in this thesis is based on performing isotropic inversions on two converted wave data sets that are azimuthally 39

58 limited to the two vertical symmetry planes associated with a fracture network with one dominant orientation. This inversion will generate two azimuthally dependent shear velocity volumes whose differences are expected to be related to the fracture network. It is therefore necessary to demonstrate how the anisotropic AVA response of each seismic volume relates to the isotropic AVA equations used in the inversion. This will help us understand the final results and the associated error/uncertainty. Without going into detail about the inversion algorithm that will be presented in chapter 4, isotropic reflection coefficient equations are utilized in the inversion to determine elastic properties that provide the best match to the input AVA response. Since isotropic equations are unable to precisely match the anisotropic AVA character over the entire incident angle range, the inversion will generate isotropic properties (V p, V s, and ρ) that provide the closest match. Therefore, the isotropic equations must be capable of producing AVA trends that are reasonably close to the anisotropic reflection coefficients calculated in both the [x 1, x 3 ] and [x 2, x 3 ] symmetry planes for the fracture signatures defined in the previous section to be identified. Figure 2.8 compares the anisotropic reflection coefficients for fracture model 3 (red) with isotropic modeling that only uses the average log values from well as input (cyan). We can see that this isotropic equation does not provide a good match to the anisotropic character in either symmetry plane. However, when we scale the shear velocity in the isotropic equation by 0.97 (blue) and 1.02 (green), we obtain a very reasonable match to the anisotropic AVA character in both symmetry planes up to an incidence angle of 30. This indicates that the isotropic equations are capable of reasonably approximating the initial trend of the anisotropic reflection coefficients with sufficient accuracy. The deviation of the isotropic coefficient after 35 degrees is partially related to increasing errors in the approximations used to calculate the reflection coefficients (Jìlek (2001)). It is very important to note that the elastic properties in the isotropic models used to fit the anisotropic data do not correspond to any parameter previously used to represent an 40

59 Figure 2.8: Comparison between anisotropic and isotropic PS reflection coefficient equations used to represent fracture model 3. 41

60 isotropic, VTI, or orthorhombic medium. They are simply elastic properties that provide the best fit of an isotropic equation to anisotropic response. To avoid confusion, these parameters will be defined as V (1) P, V(1) S, and ρ(1) to define isotropic elastic properties derived in the [x 2, x 3 ] symmetry plane (parallel to the fractures) and V (2) P, V(2) S elastic properties derived in the [x 1, x 3 ] plane (orthogonal to the fractures)., and ρ(2) to define isotropic Modeling results also indicate that the use of isotropic equations will tend to underestimate the difference in the inverted fast and slow shear velocities due to the anisotropic reflection coefficient responses of both symmetry planes converging at large angles and the inability of the isotropic equations to match this behavior. Table 2.3 demonstrated that model 3 exhibited 8.8% SWS. If we use the percentage shear velocity difference between V (1) S and V(2) S as a proxy for the SWS parameter, we can roughly compare how sensitive the application of isotropic equations in the two symmetry planes is to azimuthal anisotropy. Since there is only a 5.1% difference in shear velocity, we can see that the isotropic equations provide a lower resolution measurement of the fracture density, but can still help identify large fracture anomalies. 42

61 CHAPTER 3 MULTICOMPONENT SEISMIC DATA PROCESSING A need to reprocess the Pouce Coupe multicomponent seismic data set was demonstrated when interpretational focus was shifted from traveltime based techniques such as SWS towards amplitude sensitive methodologies including AVO inversion. The primary objective of previous processing efforts was to preserve time delays associated with the difference between fast and slow shear wave modes. SWS analysis performed by Steinhoff (2013) was enabled by this last effort, which resulted in a significant improvement of data quality, and a high NRMS repeatability necessary for time lapse work. A consequence of this previous processing effort was the alteration of relative amplitudes by non AVO friendly processes, and the PS 2 wave field was not captured above the Montney. This is apparent simply by looking at the large amplitude and seismic character variations within the fast and slow converted wave data set (Figure 3.1). This level of amplitude variation would severely inhibit any AVO work. This chapter will summarize basic PS converted wave processing theory related to key steps performed by Sensor Geophysical to produce AVO compliant PS 1 and PS 2 converted wave seismic volumes. Multicomponent seismic processing is a complex subject that has significant deviations from workflows used on conventional compressional seismic. The complexity is due to several unique problems related to: binning traces, polarity reversals, receiver misalignment, statics, non hyperbolic move out, lack of S/R reciprocity, high noise levels, shear wave splitting, and the vector alignment of horizontal geophones with SV wave particle displacement. Readers are referred to Hardage et al. (2011) for a more thorough description of a processing workflow. Significant advancements in multicomponent processing have been made in recent years to address these challenges and produce high quality data sets. Figure 3.2 shows the end result of the latest Pouce Couple processing effort. Important differences between the old and new processing workflows include: 43

62 1. Fixed receiver rotation 2. No layer stripping 3. Use of natural 100x50m bin size 4. Less aggressive trace interpolation 5. Azimuthal sectoring Figure 3.1: Previous processing of the Pouce Coupe data set. PS 1 is displayed on the left, and PS 2 is on the right 3.1 Fixed Receiver Rotation When PS converted wave data are acquired, the particle displacement is close to the horizontal plane and is thus dominantly recorded on the horizontal components of the geophone (H1 and H2). These coordinates are arbitrarily assigned prior to acquisition, and are not typically aligned with the polarizations of the fast (S 1 ) or slow (S 2 ) shear waves. This causes 44

63 Figure 3.2: New AVO compliant processing of the Pouce Coupe data set. PS 1 is displayed on the left, and PS 2 is on the right the recorded amplitudes of both wave modes to be mixed in variable proportions between H1 and H2. A mathematical process called an Alford rotation effectively rotates the receivers so they align with the particle displacement of the PS 1 and PS 2 wave modes. Receiver rotation allows the PS 1 and PS 2 wave modes to be recorded on individual components (Figure 3.3). Leakage is the term used when part of the PS 1 wave field is recorded on the PS 2 component, and vice versa. This mixing of wave field energy distorts the true amplitudes of the seismic and is important to minimize. The near surface is typically highly anisotropic and therefore has a large effect on the polarization of shear waves. SWS analysis on the first clear reflection event enables the orientation of PS 1 /PS 2 polarizations to be determined shortly before the PS waves reach the receiver (Figure 3.4). Based on information from shear wave splitting analysis of seismic horizons between ms, it was observed that the PS 1 orientation agreed with the regional maximum horizontal stress direction of N40 E within the area encompassing the two 45

64 Figure 3.3: Schematic of field components H1/H2, and rotated PS 1 /PS 2 coordinates study wells. The PS 1 orientation does shift to a more N-S orientation in the western part of the survey, but this coincides with a lower data quality area that is away from the core study area, and will be excluded from interpretation. Due to the lateral consistency of the fast orientation in the near surface, the orientation of PS 1 was selected to be a fixed N40 E. Figure 3.4: PS 1 orientation as determined by SWS analysis within a time window of ms (Steinhoff (2013)). Use of a fixed receiver coordinate to define the fast and slow shear directions has benefits and risks associated with it. One key benefit of this process is that it reduces the need to perform layer stripping when the fast/slow orientations do not change vertically from the 46

65 surface to the reservoir. Layer stripping is a process to remove time delays associated with azimuthal anisotropy in the overburden to ensure the seismic character of the reservoir is not contaminated by overburden effects (Gaiser (Gaiser)). Within Pouce Coupe, image logs and microseismic data can be used as a proxy for the PS 1 orientation at the reservoir level because they measure fracture strike, which typically aligns with the polarization azimuth of the fast shear-wave. Results from both these tools image fractures that tend to strike in the general orientation of N40 E, which aligns with the PS 1 polarization azimuth observed by SWS in the shallow layer. SWS analysis from two intermediate zones in the overburden is also used justify foregoing layer stripping (Figure 3.5). Weak SWS time delays of up to 3ms are observed within these intermediate layers, and a large portion of the survey surrounding the study wells have time delays under the sample rate of 2ms. Even though weak splitting effects leads to unstable picking of the PS 1 orientation, the dominant direction is clearly N40 E. SWS analysis performed by Steinhoff (2013) required these intermediate zones to be layer stripped, due to the interpretational sensitivity to time delays. In comparison to amplitude analysis, time shifts under one sample rate will have a negligible impact on stacked seismic amplitudes. The inherent risk of performing a fixed receiver orientation is to cause distortions in areas where the PS 1 polarization azimuth is laterally variable. Misalignment between the true PS 1 orientation and the fixed receiver azimuth will distort seismic amplitudes by allowing shear energy to leak across the PS 1 and PS 2 domains. It is important to note that intermediate processing steps must include shear-wave splitting analysis to determine the legitimacy of fixing the receiver coordinates, even though layer stripping was not applied to the data. 3.2 Bin Size and COV Interpolation The bin size in the previous processing was selected to be 50m x 50m and utilized Common Offset Vector (COV) interpolation regularize the data. The natural bin size of 100m x 50m was determined to be optimal for current AVO analysis because it minimized the number of COV interpolated traces that can smear the fracture signature. Common offset vectors 47

66 Figure 3.5: Overburden shear wave splitting for three analysis windows. The colour scale represents the magnitude of time delays between PS 1 and PS 2 events and the needle represents the orientation of the PS 1 polarization. The PS 1 polarization is consistently orientated at N40 E within the area of interest surrounding the two study wells. 48

67 are simply the sorting of data typically characterized by azimuth and offset into the inlineoffset and cross-line offset domain (Figure 3.6). This new parameterization produced high folds, improved the balance of lateral amplitudes, and reduced migration effects. Figure 3.7 demonstrates the improvement between old and new pre-migration stacks after time variant AVO scalars have been applied. Note the improved lateral consistency of Montney subunit reflectors. Figure 3.6: Common offset vector diagram and its relationship to offset and azimuth (Sensor Geophysical (2013)). Data regularization is often a necessary part of the processing workflow on data sets acquired with sparse megabin acquisition geometry (Goodway and Ragan (1996)). Since the fracture length is expected to be only 250m, traces interpolation needed to be kept to a minimum while ensuring a sufficient distribution of azimuths and offsets in the data set (Atkinson (2010)). This process only utilized traces from the immediate CDP neighbourhood to fill gaps in the COV domain. 3.3 Azimuthal Sectoring The last critical step in the processing sequence was to azimuthally limit the PS 1 and PS 2 data sets so they only contain traces within a narrow range of each volume s respective fixed receiver orientation. Reflection amplitudes dim and approach zero as a traces S/R 49

68 Figure 3.7: Old and New AVO compliant PS1 pre-migration stacks. The blue line at the top shows each volumes fold. Amplitudes appear to be more stable across the section with the new workflow (Sensor Geophysical (2013)). 50

69 azimuths become perpendicular to the fixed receiver orientation. This dimming effect is easily observed when the data are sorted into Limited Azimuth Stacks (LAS) (Figure 3.8). LAS also verify the success of the fixed received coordinate system because near zero seismic energy is observed on either data set from S/R azimuths perpendicular to each wave modes assigned receiver azimuth. For both PS 1 and PS 2 volumes, azimuths were limited to +/-40 of each volumes respective receiver orientation. This range was selected to balance amplitude preservation and ensuring that each converted wave volume contained sufficient fold to produce stable substacks necessary for the simultaneous inversion performed in the next chapter. Narrower ranges are theoretically more ideal, however the sparsity of the Pouce Coupe data set dictates a +/-40 range ( Figure 3.9). Figure 3.8: PS 1 and PS 2 limited azimuth stacks sorted in 10 azimuth sectors from 0 to 360. Red line represents the azimuth of the PS 1 receiver and the blue line represents the PS 2 receiver azimuth (Sensor Geophysical (2013)). 51

70 Figure 3.9: Limited azimuth ranges allowed in PS 1 and PS 2 volumes. 3.4 Processing Conclusions The latest processing of the Pouce Coupe converted wave seismic by Sensor Geophysical produced two volumes that represent seismic wave fields propagating along the symmetry planes of an anisotropic medium. This was principally achieved through the use of fixed PS 1 /PS 2 receiver orientations, and azimuthal sectoring. Integration of information obtained from microseismic, image logs, and most importantly previous SWS analysis performed by Steinhoff (2013) enabled the use of a simplified and effective workflow to preserve amplitudes. Another key aspect of this workflow is the minimization of trace interpolation, which is important to prevent the smearing of the subtle seismic signature of fractures. The natural bins are so large in Pouce Coupe that interpolation exclusive to only the four adjacent bins means that interpolated traces can effectively come from a 300m x 150m region. 52

71 CHAPTER 4 CONVERTED WAVE SEISMIC INVERSION The primary objective of this chapter is to apply amplitude inversion to provide a crucial link between seismic reflection amplitudes, and the azimuthally variant fracture model developed in chapter 2. Seismic inversion is a complicated and non-unique process that has the goal of transforming reflected wave amplitudes into elastic properties of the reservoir. This enables geophysicists to work in the domain of physical rock properties that are easier to interpret than seismic amplitudes, with improved vertical resolution (Pendrel (2001)). To summarize how inversion ties in with the previous chapters and the thesis objective, it should be noted that chapter 2 analyzes the anisotropic and isotropic reflection coefficient response to various effective models of fractured media. The key outcome of this modeling was the observation that isotropic equations were able to approximate the anisotropic AVA response in the two vertical symmetry planes over a limited range of incident angles. The accuracy of this technique appears to be sufficient to preserve the azimuthal seismic amplitude response, and estimate the difference between V (1) S and V (2) S characterization at Pouce Coupe. to enable fracture The converted-wave processing sequence presented in Chapter 3 produced the PS 1 and PS 2 seismic data sets whose amplitudes are sensitive to the fast and slow shear wave velocities respectively. By performing an isotropic seismic inversion on the PS 1 and PS 2 volumes, we are estimating the effective values of V (1) S and V(2) S for the entire survey which can be used to indicate regions of high crack density. The inversion algorithm applied to the Pouce Coupe data set is a deterministic simultaneous constrained sparse spike inversion. Details of the inversion theory, workflow, QC s, and results will be presented in this chapter. 53

72 4.1 Inversion Theory Many different inversion algorithms exist and can be broadly categorized into trace based deterministic, or model based stochastic (Figure 4.1). The difference between them is largely attributed to how the algorithms utilize modelled data obtained from extrapolated well logs. Each algorithm can be further subdivided based on whether the data input is post stack, or pre stack. Pre stack inversions are commonly called simultaneous inversions because three different properties are solved at the same time (V P, V S, and ρ). Figure 4.1: Deterministic and stochastic inversion workflow overview (Dueñas (2014)). The fundamental objective of any seismic inversion is to determine a set of geologically realistic elastic properties that satisfy the convolutional model (Figure 4.2) (Russell (1988)). Equation 4.1 represents the simplest form of the convolutional model and describes seismic (S(t)) as the convolution of the earths reflectivity (R(t)) and a wavelet (W(t)). Noise, attenuation, and other complexities of seismic are typically neglected in inversion or assumed to have been sufficiently mitigated in the processing workflow. The inverse process that begins with seismic therefore requires a priori of information about the wavelet and an 54

73 understanding of how elastic properties relate to reflectivity. Figure 4.2: Representation of the convolutional model. S(t) = R(t) W (t) + N(t) (4.1) One of the largest benefits of a successful inversion is an improvement in vertical resolution that results from deconvolving the wavelet from seismic. The wavelet tends to smear a series of reflection coefficients together and introduces tuning effects that reduce the effectiveness of amplitude interpretation on raw seismic (Russell (1988)). The wavelet is known to be both time and spatially variant with a potentially complex shape due to side lobe energy. Since inversion assumes an invariant wavelet, it is frequently necessary to limit the extent of data input. The wavelet extraction process within Jason software uses a recursive model based technique to estimate the amplitude and phase spectrums of the seismic. Significant research has been performed by Zoeppritz (1919), Aki and Richards (1980), Rüger (2001), and Jìlek (2001) to derive equations for the reflection coefficients as a function of the earths elastic properties. These equations provide a crucial link between the reflectivity series and the elastic properties we desire. There is an inherent assumption with the inversion algorithm that the isotropic Aki and Richards reflectivity equations are adequate to effectively model Pouce Coupe s converted wave seismic in the two symmetry planes of a fractured medium (equation 4.2). The feasibility of this assumption and the associated 55

74 consequences of this are discussed in chapter 2. where, R P SV = 2 cos i 1 α 1 (ab + cd cos i 2 α 2 cos j 2 β 2 )pα 1 /(β 1 D) (4.2) a = ρ 2 (1 2β 2 2p 2 ) ρ 1 (1 2β 2 1p 2 ) (4.3) b = ρ 2 (1 2β 2 2p 2 ) + 2ρ 1 β 2 1p 2 (4.4) c = ρ 1 (1 2β 2 1p 2 ) + 2ρ 2 β 2 2p 2 (4.5) d = 2(ρ 2 β 2 2 ρ 1 β 2 1) (4.6) Constrained Sparse Spike Inversion E = b cos i 1 α 1 + c cos i 2 α 2 (4.7) F = b cos i 1 α 1 + c cos i 2 α 2 (4.8) G = a d cos i 1 α 1 cos j 2 β 2 (4.9) H = a d cos i 2 α 2 cos j 1 β 1 (4.10) This section above has introduced the broad fundamentals of the inversion process. We will now discuss some of the technical details associated with Jason s CSSI workflow that is applied in this thesis (Figure 4.3). Input into the inversion includes; multiple partially stacked seismic volumes with limited ranges of incidence angles, well logs, and seismic horizons. The software utilizes this input combined with the basic reflection coefficient and convolutional theory presented above, with a series of constraints to generate elastic property models at each CDP. The elastic models must be some combination of V P, V S and ρ to satisfy the required inputs of the reflectivity equations. According to Fugro Jason (2013) the process can be divided into three steps: 1. Estimate elastic parameter contrast to create synthetics that match the input seismic. 56

75 2. Elastic parameter contrasts are integrated to create elastic parameter volumes. 3. Optimize the elastic parameters by modifying the low frequency trend and applying constraints. Figure 4.3: Jason s workflow for the simultaneous CSSI (Duenas, 2014) The heart of these constraints is the objective function which the inversion algorithm attempts to minimize (Equation 4.11). The equation is quite complicated and each part of it will be briefly discussed below. On that note, the equation serves a simple purpose to keep the inversion simple, match the seismic, match the well logs, and ensure the results is geologically realistic (Pendrel (2001)). It is necessary to keep the inversion simple due to the non-unique nature of inversion and the assumption that fewest reflection coefficients needed to match the data are the best representation of the earth. Hence the sparse spike name of the inversion. F = (F Seismic + F Contrast + F T rend + F Spatial + F SV D + F T ime ) (4.11) Individual terms in equation 4.11 are called misfit functions that are calculated per trace over the entire inversion time gate. These misfits are interrelated so changing one term 57

76 tends to effect the others. The summation is over the entire trace gate being inverted, and is evaluated after every iteration. Table 4.1 summarizes each of the misfit function. The preceding explanation of the objective function is a summary of Fugro Jason (2013) and readers are referred to this source for a detailed explanation of the mathematics. Table 4.1: Misfit Functions and Their Influence on the Inversion Misfit Function F Seismic F Contrast F T rend F Spatial F SV D F T ime Inversion Effect Controls the seismic residuals Controls elastic parameter variance Used to stabilize low frequencies relative to the trend Controls the smoothness of the output Stabilizes the inversion Constrains the alignment of stacks 4.2 Available Data and PS Seismic Interpretation The objective of this section is to introduce the important wells and the basic seismic interpretation required to perform the inversion. Since we are working with PS data, wells require a shear sonic, compressional sonic and density log to effectively model the seismic, extract a wavelet, and generate a low frequency model(lfm). Within Pouce Coupe, wells and 02/07-07 meet these requirements (Figure 4.4). It is also important to note that only well extends below the base of the Montney and well 02/07-07 terminates in the Montney D. This extent of well bore coverage will play a role in building a the LFM that will be discussed in section 4.3 since the intent of this project is to characterize the entire Montney Formation. Figure 4.5 shows the quality of the well ties and the important horizons that will be used to generate the low frequency model. You can see that the quality of the well tie is excellent, and several horizons were consistent throughout the field. Displayed horizons include: Triassic, Doig, Montney F, Montney E, Montney D, Montney C, and Belloy. Figure 4.6 shows time structure maps for the Triassic, Montney E, and Belloy, which were the most reliable picks in the survey and are used to generate the LFM. 58

77 Figure 4.4: Pouce Coupe basemap with the location of wells and 02/07/07 displayed. Figure 4.5: Cross-section of the PS 1 seismic with inlayed seismograms from wells and 02/ Horizon interpretation is also displayed. 59

78 Figure 4.6: PS 1 time structure map of the a) Triassic b) Montney E c) Belloy. 60

79 4.3 Low Frequency Model The low frequency model is an integral part of the inversion that enhances the qualitative and quantitative capabilities of the generated elastic models (Kumar and Negi (2012)). The need for low frequencies stems from limitations of the seismic bandwidth of the data we are trying to invert. There are no low frequencies in the seismic and these are critical to obtain the absolute values of the elastic properties we seek. The amplitude spectrum of the Pouce Coupe converted wave seismic shows a narrow seismic band of approximately 9-30Hz (Figure 4.7). Figure 4.7: Bandwidth of the Pouce Coupe seismic with the lowpass filter applied to the models shown in red and the bandpass filter applied to the inversion result in cyan. Input into the LFM typically comes from well logs which have a white amplitude spectrum, and horizons that enable geologically realistic extrapolation. Depending on the amount of well control, several interpolation methods can be used including: inverse distance, locally weighted, and triangulation. Due to the limited amount of wells with shear velocity logs available in Pouce Coupe, both wells and 07/07-07 were used and interpolated using 61

80 the inverse distance technique (Figure 4.8). The inverse distance method simply decreases each wells weight in the model with increasing distance toward other wells. Models generated in this section are optimized for the PS 1 inversion due to lack of a slow mode shear sonic log. A scaled version of the shear velocity model will be used in the PS 2 inversion. Since Jason software only uses the LFM to set the absolute values of the elastic parameters, this simply has the effect of scaling the entire volume of inverted results by a constant. Figure 4.8: Inverse distance interpolation technique (modified from Fugro Jason (2013)). The initial model contains the full-bandwidth of high and low frequencies contained in the the well logs. When the model is used in the inversion, a merge filter is applied so only the low frequencies from the model are available to constrain the inversion. From Figure 4.7 the merge cutoff frequency for the LFM was determined to be 9Hz. Figure 4.9 to Figure 4.11 show the modeled shear velocity, compressional velocity, and density with high cut filters of 30Hz, and 9Hz applied. These filters represent the high and low frequency limits of Pouce Coupe PS seismic amplitude spectrum. 4.4 PS 1 Inversion Inversion of the PS 1 data set generated in chapter 3 will be presented in this section. The objective of this inversion is to produce a seismically derived V (1) S volume. The compressional velocity and density volumes generated from inversion of converted wave seismic 62

81 Figure 4.9: Cross section of the shear velocity model with a) 30Hz high cut filter applied b) 9Hz high cut filter applied. 63

82 Figure 4.10: Cross section of the compressional velocity model with a) 30Hz high cut filter applied b) 9Hz high cut filter applied. 64

83 Figure 4.11: Cross section of the density model with a) 30Hz high cut filter applied b) 9Hz high cut filter applied. 65

84 are poorly constrained and generally produce unstable results. Therefore we will focus on the inverted shear velocities only Data Conditioning As previously discussed, the input into the seismic inversion requires a minimum of 3 partial stacks with limited incidence angles to stabilize the inversion. Four sub stacks were generated with angle ranges: 10-25, 18-33, 26-41, and (Figure 4.12 to Figure 4.15). Since pre stack seismic is recorded as a function of offset, and AVO equations are a function of angle, an offset to incident angle conversion had to be performed. A ray parameter approach utilized P and S velocity models to calculate the incidence angle at each time sample. 15 angle ranges were selected for each substack to help overcome high noise levels associated with low fold. Each stack exhibited stable seismic character throughout the angle ranges. The success of an inversion is dependent on the time alignment of the substacks. To ensure the highest level of data alignment, small static adjustments are calculated and applied to each substack to improve each traces cross-correlation. Misalignment is commonly caused by inaccurate migration velocities or statics applied in the processing workflow (Yilmaz (2001)). The Pouce Coupe seismic displayed minimal levels of misalignment in the PS 1 seismic with time shifts not exceeding 6ms and a mean shift of +/- 2ms. After the volumes have been optimally aligned with static adjustments, we can identify any problem area by overlying all substacks (Figure 4.16). We expect each CDP to have all traces closely aligned for a with only slight amplitude differences. Figure 4.16 shows us that we have excellent alignment in the core part of our survey surrounding wells 02/07-07 and 02/02-07, and misalignment in the Western part of the survey by well Due to the lower quality of seismic in this region, it will be excluded from interpretation. 66

85 Figure 4.12: PS 1 substack Figure 4.13: PS 1 substack

86 Figure 4.14: PS 1 substack Figure 4.15: PS 1 substack

87 Figure 4.16: All PS 1 substacks overlaid to QC data alignment. The red box highlights a misaligned region, and the blue box highlights a area of high data quality Well Tie and Wavelet Extraction Well correlation to the seismic and wavelet extraction is an iterative process that has the most significant impact on the inversion results. The wavelet is a function of the seismic source, attenuation, and the processing sequence applied to the data. Its importance to the inversion algorithm is easily see if we review the convolutional model presented in section 4.1. Due to the limited availability of shear sonic logs, we will focus on wells and 02/07-07 for the wavelet extraction. The well tie and wavelet workflow is an iterative process that involves multiple updates to the well logs through bulk shifts and stretch/squeeze operations, and updating the wavelet extraction parameters (Figure 4.17). Wavelets were extracted using deterministic techniques that utilize well reflectivity to estimate amplitude and phase together. Once an optimal well tie was obtained on the full angle stack, angle dependent wavelets were extracted for each of the substacks. Figure 4.18 to Figure 4.27 show the final well ties for both wells with all substacks. The cross-correlation for each well is averaged over the entire wavelet extraction window of ms. Seismograms from well 02/07-07 had a significantly 69

88 better correlations compared to well This is likely due to the lower quality of data surrounding the well, as observed in the previous data conditioning section. Figure 4.17: Basic workflow for wavelet estimation and well correlation Fugro Jason (2013)). Figure 4.18: Well 02/07-07 well tie with the full PS 1 substack. The overall correlation is Extracted wavelets from both wells and all substacks exhibited consistent amplitude and phase spectrums which indicates an overall high quality of the Pouce Coupe seismic data (Figure 4.28). Since wavelets extracted from both wells exhibited similar characteristics, a multi-well wavelet was generated to ensure slight lateral wavelet variations in the seismic are accounted for. Multi-well wavelet extraction optimizes the well ties for all wells 70

89 Figure 4.19: Well 02/07-07 well tie with the PS 1 substack. The overall correlation is Figure 4.20: Well 02/07-07 well tie with the PS 1 substack. The overall correlation is

90 Figure 4.21: Well 02/07-07 well tie with the PS 1 substack. The overall correlation is Figure 4.22: Well 02/07-07 well tie with the PS 1 substack. The overall correlation is

91 Figure 4.23: Well well tie with the full PS 1 substack. The overall correlation is Figure 4.24: Well well tie with the PS 1 substack. The overall correlation is

92 Figure 4.25: Well well tie with the PS 1 substack. The overall correlation is Figure 4.26: Well well tie with the PS 1 substack. The overall correlation is

93 Figure 4.27: Well well tie with the PS 1 substack. The overall correlation is simultaneously. Figure 4.29 shows the final AVA wavelets used in the inversion Inversion Parameters This chapter began with an introduction to inversion theory and presented the objective function which is the weighted sum of several distinct misfit functions. To minimize the objective function, sensitivity analysis was performed on the scalar weights associated with each term. Parameters used in this inversion are displayed in Table 4.2. Weights are evaluated based on their ability to improve: 1. The signal to noise ratio for each stack (derived from the synthetic data and the residuals for each stack) 2. Well log correlation 3. Well log normalized standard deviation 4. Reflection coefficient sparseness Parameters for the merge filter have the most significant impact on the inversion results because it determines the spectrum overlap between seismic and the low frequency model. 75

94 Figure 4.28: Angle dependent wavelets for each of the four PS 1 substacks. The blue and red curves represents the wavelet extracted from well and 02/07-07 respectively. Figure 4.29: Final multi-well angle dependent wavelets for each of the four PS 1 substacks (Red=10-25, Green=18-33, Purple=26-41, Cyan=34-49 ). 76

95 Based on the amplitude spectrum presented in Figure 4.7 and sensitivity analysis, the low frequency cutoff was chosen to be 9Hz with 4Hz overlap in the filter. Table 4.2: PS 1 Constrained Sparse Spike Inversion Parameters Misfit Function Inversion Effect Contrast misfit multiplier 1 Contrast misfit P-velocity uncertainty Contrast misfit S-velocity uncertainty Contrast misfit density uncertainty Seismic misfit multiplier 1 Seismic misfit S/N ratio substack Seismic misfit S/N ratio substack Seismic misfit S/N ratio substack Seismic misfit S/N ratio substack Seismic misfit power 1.7 Wavelet scale factor Wavelet scale factor Wavelet scale factor Wavelet scale factor Merge cutoff frequency (Hz) Inversion Results and Quality Control Results from inversion of the PS 1 data set produced a fairly high quality result given the low fold and narrow bandwidth of the PS data used as input. Several quality control (QC) checks were performed to evaluate the inversions success and potential problems. Tests include: 1. Compare full-bandwidth and band limited elastic properties from inversion with logs in section view 2. QC impact of low frequency trend 3. QC magnitude of seismic-synthetic residuals 4. QC of pseudo logs extracted from inverted volumes Figure 4.30 and Figure 4.31 show cross-sections of the full-bandwidth and band-limited inverted V (1) S volumes through well 02/ The full-bandwidth volume represents input 77

96 from both the LFM and the seismic, while the band limited component only comes from the seismic data. Both volumes produce a good match with the well logs, and the band limited volume exhibits a higher level of contrast. To observe the impact of the low frequency model, the same low pass filter applied to the elastic model generated in section 4.3 is applied to the full-bandwidth inversion. The results are similar to those in the LFM due to the limited extent of the seismic bandwidth below 9Hz (Figure 4.32). This is a positive results because it implies that the low frequency component of the inversion is obtained from the model, and not the seismic which does not contain reliable data at this frequency range. Figure 4.30: Cross section of the inverted V (1) S (0-30Hz). A detailed comparison between log and inversion derived V (1) S is obtained by extracting a pseudo log at the well location. Figure 4.33 shows the full-bandwidth and band limited V (1) S pseudo logs on the same scale as shear velocity logs from well Note the high correlation between these logs with a small differences at the base of the well. Modeling demonstrated that the deviation between isotropic and anisotropic reflection coefficients is less significant in the symmetry plane parallel to the fracture set. Therefore we should expect to see a close match between the well log and the inverted V (1) S. The last QC presented for the PS 1 inversion is analysis of the seismic residuals. The residual volume is generated by subtracting the synthetic data generated by the inversion 78

97 Figure 4.31: Cross section of the bandpass filtered inverted V (1) S (9-30Hz). Figure 4.32: Cross section of a) low frequency model of V S (0-9Hz) b) inverted V (1) S (0-9Hz). 79

98 Figure 4.33: Pseudo V (1) S logs extracted from the PS 1 inversion in comparison to the V S log from well 02/ from the original input seismic. This QC is useful for identifying regions where the algorithm was not capable of replicating the seismic substacks given the resultant inverted elastic properties, extracted wavelet, and reflection coefficient equations. Low residuals are ideal because it means that the elastic model generated by the inversion is capable of reproducing the observed seismic. This alleviates the concern that the isotropic modeling equation would not be able to adequately reproduce the anisotropic seismic response. Figure 4.34 shows the residuals associated with each of the four substacks. The low residual amplitudes observed here are a strong indicator of a successful inversion. 4.5 PS 2 Inversion Inversion of the PS 2 volumes follow a similar workflow as the PS 1 data set. Extra steps are necessary to normalize the amplitudes, and to account for time shifts relative to the PS 1 volume. To preserve the relative changes between inversion results of the PS 1 and PS 2 80

99 Figure 4.34: Seismic residuals (red) overlying the four PS 1 substacks (black) along the wellbore of 02/ The low amplitudes of the residuals indicate a successful inversion. data sets, many critical components of the inversion need to be quality controlled to ensure they are consistent with the PS 1 inversion. This reduces the risk that observed differences between PS 1 and PS 2 inverted elastic properties will be due to the algorithm and not the data. One key difference in data input is that the low frequency shear velocity model has been scaled by 0.95 to account for the general velocity reduction in the slow shear azimuth. It would be ideal to use dipole shear sonic log processed for fast and slow modes in the PS 1 and PS 2 inversion respectively, but this data are not available. Since the PS 2 volume was processed to include only PS waves traveling perpendicular to the maximum horizontal stress and expected fracture orientation, there is an increased source of error associated with applying the isotropic equations. Reflection coefficient analysis by Rüger (2001), and Jìlek (2001) have demonstrated that the reflectivity series in a symmetry axis perpendicular to a system of fractures adds a significant anisotropic component to the AVO gradient that is not present on an orthogonal symmetry plane that is parallel to the fracture set. This challenge was initially observed in chapter 2 and leads us to expect to less stable inversion result in comparison to the PS 1 inversion. 81

100 4.5.1 Data Conditioning Since the data set was not layer stripped, time delays of approximately 10ms exist between the Montney reflectors in the PS 1 and PS 2 data sets. Layer stripping results from the previous processing flow indicate that these delays are not uniform throughout the survey and need to be adjusted per CDP. To accomplish this, volume alignment was performed on the PS 1 /PS 2 full stacks to calculate spatially variant time shifts. These time shifts were then applied to each of the 4 substacks. Since the same statics are applied to each CDP for each of the 4 substacks, there is no risk of induced substack misalignment. Once the PS 2 data set was effectively converted to the PS 1 time domain, another pass of data alignment was applied to reduce misalignment between the individual substacks (Figure 4.35 to Figure 4.38). Figure 4.39 shows the end result of the alignment. The same western region that exhibited misalignment in the PS 1 data set also shows misalignment on the PS 2 seismic. There is also a slight increase in overall misalignment in the PS 2 data set in comparison to the PS 1. The final step in the data conditioning sequence was to regularize the PS 2 amplitudes to the PS 1 volume. This was performed using a ratio obtained from two rms amplitude extractions over the strong reflection events of the Triassic from both the PS 1 and PS 2 substacks. The histogram of the RMS(PS 1 )/RMS(PS 2 ) attribute indicated that a mean scalar of 0.9 needs to be applied to the PS 2 substacks for the amplitudes between these volumes to be regularized (Figure 4.40) Well Tie and Wavelet Extraction Since the PS 2 seismic has been time aligned to the PS 1 data set, the previously generated well time-depth relationships from the original PS 1 inversion do not require additional adjustments. However it is necessary to ensure that the PS 2 wavelet is similar to that extracted from the PS 1 volume. We do not expect a significant change in the wavelets phase or amplitude content due to similarity in source signature, receiver coupling, and propagation effects throughout the field. There is likely a difference in attenuation, but this was mitigated 82

101 Figure 4.35: PS 2 substack Figure 4.36: PS 2 substack

102 Figure 4.37: PS 2 substack Figure 4.38: PS 2 substack

103 Figure 4.39: All PS 2 substacks overlaid to QC data alignment. The red and blue boxes identify regions with high and low misalignment respectively. The same trend is observed in the PS 1 substack Figure 4.40: Histogram of the RMS(PS 1 )/RMS(PS 2 ) extraction. The blue line represents the scalar mode of

104 through the data regularization performed in the previous step. AVA wavelets used in the PS 2 inversion were extracted from well 02/07-07 only. A very poor seismogram/seismic correlation at well prevented an effective wavelet extraction at this location. Figure 4.41 shows the angle dependent wavelets for each of the PS 2 substacks in comparison with the PS 1 multi well wavelet used in the preceding inversion. The strong correlation between these two wavelets indicates: 1. Consistent processing workflows used to generate PS 1 and PS 2 volumes 2. Effectiveness of the PS 2 to PS 1 time conversion 3. Effectiveness of the PS 2 amplitude regularization Figure 4.41: Angle dependent wavelets for each of the PS 2 substacks (blue) in comparison with the PS 1 multi-well wavelet (red). The wavelets exhibit near identical phase and amplitude spectrums between the PS 1 and PS 2 data sets. 86

105 The 02/07-07 angle dependent seismograms exhibited an good match with the PS 2 substacks with correlations ranging from (Figure 4.42 through Figure 4.45). It is important to note that these correlations are approximately 0.10 lower then the same well ties with the PS 1 substacks. This is likely due to a combination of lower S/N in the PS 2, increased misalignment, and additional inaccuracies associated with using the isotropic reflection coefficient equations to model seismic in the symmetry axis plane perpendicular to the fracture set. Figure 4.42: Well 02/07-07 well tie with the PS 2 substack. The overall correlation is PS 2 Inversion Results and Quality Control Sensitivity analysis on the PS 2 data set demonstrated that the inversion parameters in Table 4.2 are also appropriate for the PS 2 inversion. This consistency demonstrated by the wavelet and inversion parameters builds confidence in the robustness of both the seismic data processing workflow and the inversion algorithm. Results and quality control checks will be presented in the same format as the PS 1 inversion. Figure 4.46 and Figure 4.47 show cross-sections of the full-bandwidth and band limited inverted V (2) S volume through well 02/ These results demonstrate two interesting observations. The first is that the quality of the full-bandwidth inversion is lower then the 87

106 Figure 4.43: Well 02/07-07 well tie with the PS 2 substack. The overall correlation is Figure 4.44: Well 02/07-07 well tie with the PS 2 substack. The overall correlation is

107 Figure 4.45: Well 02/07-07 well tie with the PS 2 substack. The overall correlation is volume generated from the PS 1 data. The PS 2 result has less lateral consistency throughout the survey and generally appears to have less vertical contrast then the PS 1 inversion. Pseudo logs allow us to compare the well logs with the seismic in more detail. Despite the crosssection view having less continuity, the full-bandwidth inverted data shows a close match to the log, with the difference between them being under 3% (Figure 4.48). Figure 4.46: Cross-section of the PS 2 inverted V (2) S (0 30Hz). 89

108 Figure 4.47: Cross-section of the PS 2 bandpass filtered inverted V (2) (9 30Hz). S Figure 4.48: Pseudo V (2) S logs extracted from the PS 2 inversion. 90

109 Figure 4.49 shows a comparison of the modeled and full-bandwidth V (2) S volumes with a 9 Hz high cut filter applied. These two volumes are very similar as they should be due to a lack of seismic frequencies in this bandwidth. It is also noteworthy to mention that the fullbandwidth results have similar character to the LFM which indicates that the impedance contrasts calculated within the seismic bandwidth are relatively small. This is partially expected because Montney subunit reflectors in the PS 2 seismic substacks have smaller amplitudes when compared to the PS 1 volumes. The final QC is the synthetic seismic residuals which exhibit relatively low amplitudes compared to the input seismic (Figure 4.34). The residual amplitudes are only slightly larger then the results from the PS 1 inversion which demonstrates that the seismic response was effectively modeled. 4.6 Inversion Conclusions This chapter presented the inversion of the PS 1 and PS 2 seismic data sets to obtain V (1) S and V (2) S volumes representative of fast and slow azimuths respectively. Both inversions are evaluated to be successful after a number of quality control checks demonstrated that the substack seismic modeled from the inverted elastic properties provided a reasonable match to the input seismic. While we know the isotropic equations are less accurate then anisotropic equations, we are capable of obtaining useful information from the amplitudes of converted wave seismic in either symmetry plane. Results appear geologically realistic, and match the character of available well logs. This work enables fracture characterization to be performed in the following chapter Inversion of the PS 2 data set proved to be more challenging due to the input seismic exhibiting lower S/N, increased sub stack misalignment, and lack of a slow shear velocity log for the LFM. It is necessary to discuss how the scaled LFM used in the PS 2 inversion impacts the results of the interpretation of the data to make sense. As previously mentioned, the LFM is only used to set the absolute value of inverted properties to a realistic range. By scaling the shear velocity LFM used in the PS 1 inversion by 0.95, we are effectively scaling the inverted V (2) S by Figure 4.51 demonstrates the how the inversion utilizes the 91

110 Figure 4.49: Cross section of a) low frequency model of V S (0-9Hz) b) PS 2 inverted V (2) S (0-9Hz). 92

111 Figure 4.50: Seismic residuals (red) overlying the four PS 2 substacks (black) along the wellbore of 02/ The low amplitudes of the residuals indicate a successful inversion. modeled data by comparing PS 2 inversions ran with the original and scaled shear velocity LFM. Results show no difference in the inverted property character despite the application of a different LFM. Since we do not have a slow shear velocity log to accurately set the trend, results from this inversion are relative and need to be interpreted with the background trend in mind. The largest drawback to using a scaled version of the PS 1 model is that it makes the assumption that velocity anisotropy is consistent throughout the entire Montney, which is not geologically realistic. The influence of this assumption on the inversion results is expected to be mitigated by the 9Hz high cut filter that is applied to the model data prior to use in the algorithm, which will not preserve thin vertical contrasts. 93

112 Figure 4.51: Comparison of pseudo V (2) S logs extracted from PS 2 inversions that used the original and scaled LFM s. Demonstrates how the different LFM effectively scales the inverted elastic properties 94

113 CHAPTER 5 FRACTURE NETWORK INTERPRETATION AND INTEGRATION This chapter focuses on the interpretation of the V (1) S and V (2) S volumes inverted in the previous chapter combined with the integration of several preceding Pouce Coupe theses. As stated in the introduction, the objective of the Pouce Coupe project is to improve the economics of tight gas development in the Montney by understanding how hydraulic stimulations interact with the reservoir. Characterizing the natural fractures is an important piece of the puzzle, however this is a very complex multi-disciplinary problem that is a function of numerous interrelated reservoir properties. Fracture mapping using the percent difference between inverted shear velocities will be presented first to demonstrate how fracture intensity is predicted to vary throughout the reservoir. This will be followed by integration with compositional analysis, microseismic, and production data to understand how natural fractures interact with the reservoir and stimulation. Going beyond the fracture map generated in this chapter, data integration will demonstrate that: 1. The percent difference in inverted V (1) S and V (2) S volumes is an effective fracture indicator. 2. Hydraulically induced fractures travel along pre-existing weak fracture planes 3. Large regions with high crack densities are associated with improved production 4. Future well development in the Montney can be predictively optimized using multicomponent seismic 5.1 Shear Velocity Fracture Characterization To characterize the fracture network we will utilize a modified version of the shear wave splitting coefficient presented in section 2.4 that will use V (1) S and V (2) S in the place of V S1 95

114 and V S2, respectively. This modified splitting coefficient will be defined by γ (s ) (Equation 5.1). Since the background rock is horizontally layered, it exhibits VTI symmetry with no azimuthal anisotropy. Therefore, the key properties that will influence the observed difference between the symmetry-plane S-wave velocities include a system of preferentially oriented fractures and the stress field, which are closely related. γ (s ) = V (1) S V (2) S V (2) S (5.1) Interpretation Limitations Before the interpretation of the γ (s ) volume is presented, several limitations previously presented need to be reviewed. First and foremost, the shear impedance volumes can only be interpreted qualitatively due to the inverted V (1) S and V (2) S volumes not being a direct physical property of the reservoir. The second constraint on the interpretation is due to the fixed receiver orientation chosen in the processing workflow. This restricts analysis to regions that satisfy the assumption that the symmetry planes are orthogonal to each other and that one of them is oriented at N40 E. SWS analysis by Steinhoff (2013) enabled analysis to be restricted to an area of high confidence near the two study wells (Figure 5.1). The area to the immediate west of well 02/02-07 is questionable for this type of analysis due to an observed shift in shear-wave polarization azimuth at the reservoir level. The last important limitation of the γ (s ) map is the inability to identify regions with two equal sets of perpendicular fractures. This is due to a lack of difference between symmetry plane S-wave amplitudes. To account for the -5% scaling of the V (2) S by the LFM γ (s ) will need to be interpreted as deviations from this scale factor to ensure the interpretation is driven by the seismic, and not the LFM. To address this scaling issue, color bars of the γ (s ) data will reflect the -5% shift of the shear velocity LFM relative to the PS 1 inversion. 96

115 Figure 5.1: Subset of the Pouce Coupe seismic survey focused on the two study wells used for interpretation Fracture Map This section will introduce the γ (s ) fracture identification volume to indicate the general fracture trends surrounding the study wells. Areas highlighted in red indicate large inverted shear-velocity differentials above the 5% scaling we know to be caused by the low frequency models. Based on the fracture model developed in chapter 2, red regions are interpreted to have relatively high crack densities. Since the shear wave splitting parameter is approximately equal to the physical fracture density of the rock, there is an opportunity for similar workflows in the future that utilize anisotropic equations to quantitatively estimate fracture density for input into discrete fracture network models. This would provide a useful link between geophysics and reservoir engineering since the percolation zone and flow rates are dependent Figure 5.2 and Figure 5.3 show two cross sections of the γ (s ) volume through wells 02/02-07 and 02/07-07 respectively. Well 02/02-07 penetrates the Montney C subunit and 97

116 exhibits high gas production. This well path shows a large portion of the lateral penetrating fractured zones and indicates that fractures are associated with improved production. In contrast, well 02/07-07 penetrates the Montney D and is a poor producer. This well path intersects relatively few regions that exhibited significant azimuthal anisotropy, with the exception of the wells toe. Previous work by Steinhoff (2013) and Lee (2014) have speculated the potential presence of a sub seismic scale wrench fault that intersects well 02/07-07 near the toe (Figure 5.6). Such a fault would be expected to have an increased fracture density in the surrounding region which could induce the velocity anisotropy observed. Figure 5.2: Cross section of γ (s ) along the well bore of 02/ Within the Montney, operators observe a general trend that the Montney C is a better producer then the Montney D. γ (s ) time slices through these reservoir targets provide evidence to help explain this trend by demonstrating a strong relationship between laterally extensive regions with high γ (s ) and high production. Figure 5.4 is a time slice through the Montney D and indicates relatively few fractured zones, especially surrounding the 02/07-07 well. When the Montney C unit is investigated in Figure 5.5, we can see a significant increase in the lateral extent of fracturing which improves the probability of Montney C wells intersecting fractured regions. These results suggests that natural fractures have a close link and positive correlation to production that will be elaborated on in the following section 98

117 Figure 5.3: Cross section of γ (s ) along the well bore of 02/ with the integration of detailed production data. While these time slice maps are effective for demonstrating the general trend associated with the C and D subunits, they also exhibit notable heterogeneity. There are regions in the Montney D with higher crack densities that represent missed opportunity, and areas in the Montney C with low crack density which represent an increase in economic risk. This demonstrates the need to predictively identify natural fractures prior to drilling to ensure well placement is optimized. 5.2 Fracture/Production Correlation Integration of the γ (s ) volume with cumulative flow and stage by stage spinner production data initially presented in section allows for a detailed correlation. The spinner logs are especially valuable because it enables the reservoir quality to be evaluated along the well bore (Figure 5.7). It is observed that stages with higher production have a strong correlation with large γ (s ) values. To gain more insight into the fracture maps link to production, a few anomalies will be discussed in detail. Stage 3 of well 02/07-07 proves to be of the most significant interest since this single stage accounts for 43% of the total wells production. While there is a fracture anomaly at 99

118 Figure 5.4: γ (s ) time slice at 2108ms through the Montney D subunit. Figure 5.5: γ (s ) time slice at 2156ms through the Montney C subunit. 100

119 Figure 5.6: Incoherency map generated from the PP seismic along the Montney D horizon. The linear trend near the toe of well 02/07-07 is interpreted to be a potential fault (Lee (2014)) 101

120 Figure 5.7: γ (s ) % time slice maps through a) the Montney D b) Montney C. Stage by stage gas flows obtained from a spinner log are presented as a percentage of the total flow. 102

121 this stage location, it is not believed to be the direct cause for the high production. It is interpreted that this stage represents communication with well 00/07-07 that was drilled into the Montney C directly below well 02/07-07 and stimulated in February The vertical separation between the lateral sections of these wells ranges from 60-80m (Figure 5.8). The primary evidence for this interpretation is a spike in the monthly water production for well 00/07-07 during the exact month well 02/07-07 was stimulated (Figure 5.9). We can also see that stage 3 of well 02/07-07 and stage 1 from 00/07-07 are vertically overlain, which increases the probability of a cross-well permeability link forming after two these stimulations occurred in such close proximity. Since the the high γ (s ) anomaly at this stage is not observed to be in direct contact with the Montney C, it is believed that the cross well communication occurred after well 02/07-07 was stimulated. Future time lapse analysis on the 2nd monitor survey acquired after the 02/07-07 stimulation would greatly aid in the interpretation of this anomaly since it is known that communication existed prior to this monitor being acquired. Figure 5.8: Oblique view of the well layout and frac stages highlighting the proximity of wells 00/07-07 and 02/ Another important observation is that one of the largest continuous high γ (s ) anomalies surrounds well 00/07-07 in the Montney C (Figure 5.10). This is the only well that was hydraulically stimulated prior to the acquisition of the baseline seismic survey, and therefore 103

122 Figure 5.9: Average daily water production of well 00/ The dramatic increase of water production in December 2008 corresponds to the hydraulic stimulation of well 02/07-07 indicating cross well communication. one of the only areas we can reasonably expect a high crack density. This prominent anomaly surrounding a known fractured zone is one of the strongest pieces of evidence supporting the value of the fracture characterization technique presented in this thesis. It demonstrates that despite known inaccuracies of the method, valuable information about a reservoirs fracture network can be remotely obtained. Stimulation stages 2 to 4 of well 02/02-07 demonstrate regions that do not show as strong of a correlation between γ (s ) and production. Interestingly, all three stages are positioned on the fringe of γ (s ) highs and exhibit significant variability in stage-by-stage production values that range from 13-25% of the total well flow. It is likely that these stages have limited and variable connectivity to nearby fractured zones that is difficult to observe with the lateral resolution limits of the Pouce Coupe seismic. The Fresnel Zone at the reservoir level is calculated to be 630m, which indicates that only large features will be detectable. Since each seismic bin is 100mx50m, sharp contrast in rock properties are also likely to be smeared. To put the 50mx100m bin into context, even when we exclude the fact that the 104

123 Figure 5.10: γ (s ) time slice at 2140ms through the Montney C subunit highlighting the large fracture signature surrounding the three stimulation stages of well 00/

124 COV interpolation mixes traces from the neighbouring CDPs, the area of 3 NHL hockey rinks can easily fit into one bin. This is a very large area for heterogeneous reservoir to be represented by a single amplitude. Therefore, regions that exhibit sharp property contrasts need to be interpreted with the low lateral resolution of the Pouce Coupe seismic in mind. 5.3 Rock Composition Analysis The true value of being able to identify fractures is observed when the γ (s ) map is integrated with compositional analysis performed by Dueñas (2014). For a well to be successful, the reservoir must contain sufficient storage capacity and a delivery system that enables fluid flow from a large volume of rock into the wellbore. One without the other will be demonstrated to be insufficient for successful well production. Figure 5.11 and Figure 5.12 show geobodies associated with the best rock composition along the paths of wells 00/07-07, 02/02-07 and 02/ The best producing wells (00/07-07 and 02/02-07) are observed to penetrate thick layers of the geobody and have large crack densities surrounding the wellbore. The most interesting result is obtained from well 02/07-07, which is not in drilled into the best quality rock and has shows a significant γ (s ) high at the toe of the well near stages 1 and 2. Excluding stage 3 where we are confident of cross-well flow, the commingled production from stage 1 and 2 is the best producing interval. Knowing that well 02/07-07 produced 84% less gas then well 02/02-07, and that 43% of its flow is due to cross-well production, the laterally extensive fracture anomaly at stages 1 and 2 is not representative of a high producing region. This is likely due to the lack of storage capacity as defined by the compositional analysis 5.4 Microseismic One of the key observations from the microseismic analysis performed by Lee (2014) was the interpretation that microseismic events result from failure along natural fractures or other weak planes. Primary evidence for this conclusion was based on the similarity of derived microseismic focal mechanisms and the orientation of fractures observed in the FMI 106

125 Figure 5.11: Cross section of P impedance (derived from PP seismic) through well 02/ Geobodies of clusters 1 and 2 are shown in blue (modified from Dueñas (2014)). Figure 5.12: Cross section of P impedance (derived from PP seismic) through wells 00/07-07 and 02/ Geobodies of clusters 1 and 2 are shown in blue (modified from Dueñas (2014)). 107

126 log of an adjacent well. Since this observation is based on data that is typically accepted to be higher resolution than surface multicomponent seismic, it provides an excellent test of the reliability of the γ (s ) volume as a predictor of weak planes. If the γ (s ) map is an accurate indicator of fractures, we expect microseismic events to focus on regions with high shear-wave velocity anisotropy. Figure 5.13 shows the γ (s ) time slices through wells 02/02-07 and 02/07-07 with and without microseismic events. The excellent correlation between regions predicted to have high crack density and microseismic events is a strong indicator that the azimuthal inversion of PS seismic is a valuable tool for predicting fractures in the reservoir. Only microseismic events from stage 2 of well 02/07-07 fail to align with any significant γ (s ) anomaly. It is possible that there is an isolated open fracture at this location that is below the resolution limits of the data. Upon closer inspection of the stage 2 microseismic events, we can see two distinct clouds on either side of the perforation with the majority of events falling at the toe of the well where there is a strong fracture anomaly. It likely that the two event clouds were caused by a failed plug between stages 1 and Comparison to SWS Analysis The original effort to map the fracture network in Pouce Coupe utilized traveltime-based SWS analysis. Comparison of the baseline SWS results with the limited azimuth AVO inversion presented in this thesis demonstrate the complementary nature of both methodologies and superior resolution of amplitude-based fracture characterization. The clear advantage of SWS analysis is that it enables the azimuth of the fast shear-wave polarization to be determined, which can be used as a proxy for fracture orientation. This provides valuable information that can be used to position wells in fields with laterally variant stress-fields. The amplitude-based technique presented in this thesis is not able to obtain this information. In fact, results from the SWS analysis can be seen as an important requirement prior to performing amplitude analysis because it also acts as a quality control check to determine if the assumptions applied during the data processing chapter of this thesis are accurate. 108

127 Figure 5.13: a-b) γ (s ) timeslice through well 02/02-07 with and without microseismic events c-d) γ (s ) timeslice through well 02/07-07 with and without microseismic events. Note the strong correlation between microseismic events and regions with high shear velocity anisotropy. 109

128 When the techniques are evaluated based on vertical resolution, sensitivity to fractures, and overall workflow robustness, there is a clear advantage associated with the amplitude based determination of γ (s ) for fracture characterization. Steinhoff (2013) demonstrated the effectiveness of SWS to monitor hydraulic stimulations using the highly repeatable time lapse seismic, but could not utilize the baseline survey to predict the best reservoir zones. Therefore, the most valuable application of SWS analysis in Pouce Coupe has been demonstrated to be reservoir monitoring, while amplitude analysis has demonstrated viability as an exploration tool. Figure 5.14 shows a comparison of the baseline SWS analysis and γ (s ) time slices through the Montney C and D. Several notable differences include: 1. There is significantly more vertical and lateral detail in the γ (s ) volume. 2. SWS analysis has the vertical resolution of hundred s of meters. All time delays are cumulative between the nearest layer stripped region (2000 ms) and the reservoir base at 2300 ms. Since the amplitude-based technique utilizes seismic inversion, tuning effects are reduced and the resolution is increased to Montney sub-unit level (10 s of meters) (Delbecq et al. (2013)). 3. SWS demonstrated low sensitivity to known fractures caused by the successful hydraulic stimulation of well 00/ The baseline survey shows a weak SWS anomaly but a large γ (s ) anomaly near the stimulated well bore. 4. Both workflows are very sensitive to data quality, seismic conditioning and careful analysis. The primary challenge with kinematic shear-wave splitting analysis is that the technique frequently relies on sub-sample rate time shifts, which are only detectable on highly repeatable time lapse seismic (Johnston (2013)). The primary challenge associated with the amplitude analysis method performed in this thesis is noise in the seismic amplitudes and inaccuracies associated with the application of isotropic equations in the inversion. 110

129 Figure 5.14: Comparison of the shear-wave splitting (left) and azimuthal velocity inversion (right) techniques for fracture characterization. The azimuthal inversion shows improved vertical resolution and sensitivity to fractures. 111