university of oklahoma graduate college production...
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UNIVERSITY OF OKLAHOMA
GRADUATE COLLEGE
PRODUCTION CORRELATION TO 3D SEISMIC ATTRIBUTES IN THE
BARNETT SHALE, TEXAS
A THESIS
SUBMITTED TO THE GRADUATE FACULTY
in partial fulfillment of the requirements for the
Degree of
MASTER OF SCIENCE
By
MELIA REBECA DA SILVA RODRIGUEZ
Norman, Oklahoma
2013
PRODUCTION CORRELATION TO 3D SEISMIC ATTRIBUTES IN THE
BARNETT SHALE, TX
A THESIS APPROVED FOR THE
CONOCOPHILLIPS SCHOOL OF GEOLOGY AND GEOPHYSICS
BY
______________________________
Dr. Kurt J. Marfurt, Chair
______________________________
Dr. Jamie P. Rich
______________________________
Dr. Vikram Jayaram
© Copyright by MELIA REBECA DA SILVA RODRIGUEZ 2013
All Rights Reserved.
To my family and my dear friends whose constant love, support, and inspiration made
me happier in every step of this journey.
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Acknowledgements
Thanks to Devon Energy Corporation for providing the data for this project. The
seismic re-processing was done in ProMAX®, the prestack seismic inversion was
performed in Hampson and Russell®, attribute analysis and SOF application were done
in AASPI®, Petrel® was used for seismic interpretation, and Transform® was used for
performing multivariate linear and non-linear regression. I would like to thank all of
these software suppliers for providing academic licenses to the university.
Thanks to Dr. Kurt Marfurt for his constant guidance and support, not only on this
thesis, but through the past two years. Thanks to Dr. Jamie Rich and Dr. Vikram
Jayaram for their insights, recommendations, understanding, and help. Thanks to the
AASPI consortium and to the ConocoPhillips School for funding my Master’s at OU. I
am so grateful for the opportunity I had by being part of the CPSGG family. Thanks to
all the professors, at the school, especially to: Dr. Marfurt, Dr. Pigott, Dr. Slatt, Dr.
Kwiatkowski, Dr. Elmore, Dr. Madden, and Dr. Mitra, with whom I took classes, and
from whom I learned a lot. Thanks to the entire staff: Donna, Teresa, Nancy, Jocelyn,
Adrianne, Davon, for being so patience, helpful, and considerate every day. Thanks to:
Mark Aisengberg, Tim, Atish, Roderick, Summit, Alfredo, Luis, Oswaldo, Bradley,
Shiguang, Tengfei, Bo, and to all the people who helped me, directly or indirectly, in
the last couple of years.
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Table of Contents
Acknowledgements ......................................................................................................... iv
List of Tables .................................................................................................................. vii
List of Figures ................................................................................................................ viii
Abstract ........................................................................................................................... xx
Chapter 1: Introduction ..................................................................................................... 1
Chapter 2: Geologic Background ..................................................................................... 5
Fort Worth Basin Regional Setting ............................................................................ 5
Barnett Shale Lithology, Stratigraphy, and Mineralogy ............................................ 8
Chapter 3: Data Conditioning ......................................................................................... 13
Introduction .............................................................................................................. 13
Available data ........................................................................................................... 13
Refined velocity analysis and NMO ......................................................................... 20
Chapter 4: Prestack Seismic Inversion ........................................................................... 30
Introduction .............................................................................................................. 30
Seismic to well tie .................................................................................................... 31
Wavelet extraction .................................................................................................... 34
Prestack inversion analysis ....................................................................................... 37
Lambda-Rho and Mu-Rho computation ................................................................... 47
Chapter 5: Production correlation to 3D seismic attributes ............................................ 50
Introduction .............................................................................................................. 50
Barnett Shale Gas Resource Potential and Production ............................................. 51
Brittleness prediction from Lambda-rho and Mu-rho .............................................. 55
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Multivariate statistical analysis ................................................................................ 64
First 90 days production estimation from seismic attributes volumes ..................... 78
3D seismic attribute analysis .................................................................................... 81
Chapter 6: Conclusions ................................................................................................... 92
References ...................................................................................................................... 94
Appendix A .................................................................................................................... 98
Clay mineralogy identification through XRD in core samples from the Lower
Barnett Shale, Wise County, TX .................................................................. 98
Appendix B ................................................................................................................... 105
Information about horizontal sections within Survey A ......................................... 105
Appendix C ................................................................................................................... 107
Seismic amplitudes extracted from each horizontal section within survey A ........ 107
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List of Tables
Table 1. Typical mineral composition of the Barnett Shale (after Bruner and Smosna,
2011). .............................................................................................................................. 12
Table 2. Acquisition parameters for survey A ............................................................... 13
Table 3. Processing history of survey A ........................................................................ 15
Table 4. Summary of seismic to well tie for wells used to perform seismic inversion . 34
Table 5. Parameters for zero phase statistical wavelet extraction ................................. 34
Table 6. Basic reservoir characteristics of the Barnett Shale productive areas
(Montgomery et al., 2005; Jarvie et al., 2007; Bruner and Smosna, 2011). ................... 54
Table 7. Correlation coefficients for each set of variables considered for brittleness
index prediction from well A measurements. ................................................................. 65
Table 8. Variance solution table from linear regression of λρ and µρ. .......................... 66
Table 9. Sensitivity solution table from non-linear regression of λρ and µρ ................. 66
Table 10. Sensitivity of production prediction to; brittleness index, λρ, coherence, shape
index, curvedness, most positive curvature k1 and µρ. ................................................... 87
Table 11. Lower Barnett samples analyzed through XRD ............................................ 98
Table 12. Clay minerals identified in sample LB_7568 ................................................ 99
Table 13. Clay minerals identified in sample LB_7557 .............................................. 100
Table 14. Horizontal sections generated for each well inside survey A. ..................... 106
Table 15. Seismic amplitudes corresponding to λρ, µρ, and brittleness index extracted
along the horizontal sections inside survey A. ............................................................. 108
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List of Figures
Figure 1. Paleographic reconstruction of the southern mid-continent from Blakey
(2005) suggesting that the FWB occupied a narrow inland seaway, bordered by an
island-arc chain on the east and by a broad carbonate platform on the west during the
late Mississippian (325Ma) (modified from Loucks and Ruppel, 2007). ....................... 6
Figure 2. Present location and aerial extent of the FWB. The boundaries of the FWB,
are the Bend arch on the west, the Llano uplift on the south, the Red River and
Muenster arches on the north, and the Pennsylvanian Ouachita overthrust on the east
(Modified from Pollastro et al., 2007). ............................................................................. 7
Figure 3. Extension of the Barnett Shale, highlighting the extension of the Fort Worth
Basin in green, the location of Wise County in orange, and the outline of seismic survey
A in yellow (modified from Chesapeake Energy Corporation, 2013). ............................ 9
Figure 4. Simplified stratigraphic column of the Fort Worth Basin in Wise County
Stratigraphically, the Barnett Shale lies between two prominent limestone units
(modified from Montgomery et al., 2005). In my survey, the Barnett lies directly on the
Viola Limestone. .............................................................................................................. 9
Figure 5. Depositional profile and processes of the Barnett Shale (Loucks and Ruppel,
2007). Most deposition in the FWB occurred under euxinic conditions, except from
short episodes when hyperpycnal flow transported oxygenated waters into the basin. A
sea level curve by Ross and Ross (1987) indicates that deposition began during a second
order highstand below the storm wave base, with several third order fluctuations by the
end of Barnett deposition (Slatt et al., 2009). ................................................................. 10
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Figure 6. (a) Outline of Survey A including the fold map resulting from 3D acquisition.
Survey boundaries are highlighted in black. 198 inlines increase from East to West. 219
crosslines increase from South to North. Higher fold values refer to a larger number of
traces per CDP, providing better seismic imaging. (b) Frequency spectrum of the
seismic data. The spectrum between 20 Hz and 100 Hz is the result of deconvolution
and time variant spectral whitening. ............................................................................... 14
Figure 7. Illustration of maximum recovery angle for an offset of 14,000 ft. Angles
equal or greater than 45⁰ allow inversion for densities in addition to P- and S-
impedances. .................................................................................................................... 16
Figure 8. Representative CMP gather along line AA’ after prestack Kirchhoff time
migration using one azimuth and 60, irregularly wide, offset bins. The red P-wave log
corresponds to well JH43. The location of the CMP gather is denoted by the red dot
along line AA’ on the map view. Arrows indicate the top of the Lower Barnett Shale, at
about t=1.25 s (7,000 ft), and the top of the Basement at about 1.7 s (12,000 ft).
Frequency loss and tuning effects cause reflector loss on offsets greater than 9,000 ft
(30 degrees) between t=1.1 s-1.7 s. ................................................................................ 17
Figure 9. Generalized processing workflow for prestack time-migrated gathers from
survey A. ......................................................................................................................... 18
Figure 10. (a) Representative reverse NMO gather from Survey A, highlighting the top
of the Lower Barnett Shale around t=1.3s (7,000ft). (b) Schematic diagram showing the
top of the Lower Barnett Shale after migration (red line) and following reverse NMO
application (blue line). The dashed line indicates the desired horizontal reflector at the
reservoir level. ∆t represents the move out corresponding to the migration velocity field
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used, while ∆t’ represents the move out that should be applied to flatten the target
reflector. Note that ∆t>∆t’, which indicates the seismic data were overcorrected, by
applying a migration velocity that was slower than needed. The location of the gather is
denoted by the red dot along line AA’ on the map view. ............................................... 19
Figure 11. A representative velocity analysis semblance panel and CMP supergather,
before mute, corresponding to well JH43. Location is denoted by the red dot in along
the AA’ line on the map view. Events can be resolved fairly well, however high
velocity interbed multiples generate “bullseyes” right below the Lower Barnett event,
which complicates the velocity interpretation for this particular horizon. These
multiples are indicated by the magenta circle at t=1.3 s. Horizon oriented velocity picks
prevented large variations of velocity values in the shallower and deeper areas. .......... 21
Figure 12. Previous migration velocity cube through line AA’ co-rendered with seismic
amplitude. Velocities range between 9,000 ft/s and 15,000 ft/s. For the target area (t=1.2
s-1.4 s ) RMS velocities fall below 13,000 ft/s. For the basement (t=1.7 s) the picked
velocity is between 13,500 ft/s-14,500 ft/s ..................................................................... 22
Figure 13. New RMS velocity field through line AA’ co-rendered with the re-migrated
data. Velocities range between 9,000 ft/s and 15,000 ft/s. For the target area (t=1.2 s-1.4
s) RMS velocities are about 13,000 ft/s-13,500 ft/s. For the basement (t=1.7 s) the new
picked velocity is approximately 15,000 ft/s. ................................................................. 23
Figure 14. Prestack time-migrated gathers after reverse NMO, new velocity picking,
and NMO correction with 30% stretch mute shown (a) before and (b) after prestack
structure-oriented filtering, including (c) the rejected noise after the filter application.
The location of the gathers corresponds to well JH43, and is denoted by the red dot
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along the AA’ line on the map view. The effects of offset-dependent frequency loss and
migration stretch are seen for offsets greater than 12,000 ft (40 degrees) in the
conditioned gathers, while in the gathers migrated with the original velocity field
frequencies and amplitudes are only preserved for offsets nearer than 9,000 ft (30
degrees) at the reservoir level (1.2 s-1.4 s). Moreover, the reflectors on the conditioned
gathers look flatter throughout, with the exception of the deeper area right above and
below basement level (t=1.7 s). Prestack SOF improves the imaging and coherence of
the reflectors through the entire seismic record. ............................................................ 25
Figure 15. Line AA’ trough the resulting stacked volume after reverse NMO, NMO,
and mute application (Figure 11). Structure-oriented filtering needed to be applied to
remove low frequency noise (ground roll) and improve the imaging of reflectors below
700 ms. Note the improvement in vertical resolution at the top of the Upper and Lower
Barnett Shales, as well as in the area indicated by the pink arrows. .............................. 26
Figure 16. Line AA’ through the resulting stacked volume after reverse NMO, NMO,
mute, and SOF application. The amplitude of the seismic data is highly increased after
SOF. Imaging of the reflectors in the target area (1.2 s-1.4 s) is improved trough
structure-oriented filtering. ............................................................................................. 28
Figure 17. Line AA’ through the stack resulting from the difference between the
seismic volumes generated before and after SOF, shown in Figures 15 and 16,
respectively. Low frequency data were removed from the target reflectors, between 1.2
s -1.4s through SOF. ....................................................................................................... 29
Figure 18. Well head location for the eight wells used for simultaneous prestack
seismic inversion. Wells are colored by normalized first 90 days production. .............. 31
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Figure 19. Linear regression of S-wave sonic logs from P-wave sonic and density
values for wells JH9, JH42, JH43. The tracks on the left show the original S-wave log
in red, and the calculated log in blue. ............................................................................. 32
Figure 20. Seismic-well tie for JH43. Well location is denoted by the red dot on the
map. The P-wave sonic, S-wave sonic, and density logs are shown on the left tracks.
The synthetic trace calculated from the prestack gathers (SYN) is shown in blue, while
the extracted seismic trace is shown in red. The prestack time-migrated gathers are
shown in the furthest right column. The correlation between the synthetic and the
seismic trace is about 80% for a time window between 1100 ms and 1400 ms. ............ 33
Figure 21. Angle gathers from 0⁰-42⁰ generated from the input prestack time-migrated
data. The red log P-wave logs denote the location of (a) well JH9 and (b) well JH43,
previously shown in Figures 8, 10, 11, 14, and 20. ........................................................ 35
Figure 22. Amplitude spectrum in (a) time domain and (b) frequency domain of the
statistically extracted wavelets for 0°-14° (blue), 14°–28° (red), and 28°–42° (yellow).
Phase is similar in the three wavelets because all previous conditioning processes
applied were phase-neutral. Amplitudes are also very similar in all angle-stack ranges,
with the exception of a slight decrease in high frequencies content at the farther angles,
(28°–42°). This effect should be fixed by applying non-stretch NMO (MPNMO)
(Zhang, 2013). The peak frequency of the farther wavelet falls right below 40 Hz, while
the dominant frequency value for the near and middle wavelets is 50 Hz. .................... 36
Figure 23. Vertical slice AA’ through the low frequency model used for ZP inversion.
Model was generated from six different horizons pics and from eight wells. The
colored P-wave log corresponds to well JH43. .............................................................. 38
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Figure 24. Vertical slice AA’ through the low frequency model used for ZS inversion.
Model was generated from six different horizons pics and from eight wells. The
colored S-wave log corresponds to well JH43. .............................................................. 39
Figure 25. Vertical slice AA’ through the low frequency model used for density
inversion. Model was generated from six different horizons pics and from eight wells.
The colored density log corresponds to well JH43. ....................................................... 40
Figure 26. Inversion analysis for P-impedance, S-Impedance, and density, with
comparison of the original seismic and the inverted synthetic using three different zero
phase wavelets for near, middle, and far offsets for wells (a) JH9 and (b) JH43. .......... 41
Figure 27. Maps of total RMS error for (a) the synthetic inverted trace extracted along
the top of the Lower Barnett Shale, (b) P-impedance (ZP), (c) S-impedance (ZS), and (d)
density (ρ). The dashed line indicates the position of section AA’. Well heads location
are colored by RMS error values. The highest RMS error corresponds to ZS
computation. The error tends to be higher in the wells where S-wave logs were
predicted through linear regression of P-wave. .............................................................. 42
Figure 28. Crossplots of original seismic P-impedance from logs against inverted
impedance. All the plotted values are within the Marble Falls-Viola interval. The data
are colored by time (ms), where the transition from cold to warm colors represent an
increase in arrival times (depths). ................................................................................... 43
Figure 29. Vertical slice AA’ through the ZP volume. Warm colors indicate areas with
lower impedance values, in this case associated with the presence of shale. The colored
P-wave log corresponds to well JH43. ........................................................................... 44
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Figure 30. Vertical slice AA’the through ZS volume. Warm colors indicate areas with
lower impedance values, in this case associated with the presence of shale. The colored
S-wave log corresponds to well JH43. ........................................................................... 45
Figure 31. Vertical slice AA’ through density. Warm colors indicate areas with lower
density values. The colored density log corresponds to well JH43. ............................... 46
Figure 32. Lambda-rho volume computed from seismic inversion results. Warm colors
indicate lower lambda-rho values, associated with the presence of shales. ................... 48
Figure 33. Mu-rho volume computed from seismic inversion results. Warm colors
indicate lower mu-rho values, associated with the presence of shales. .......................... 49
Figure 34. Geographic extent of the Barnett Shale (Pollastro et al., 2007). The play is
defined by the geographic extent of the shale to the east and north and a minimum
thickness of 100 ft to the west (Bruner and Smosna, 2011), as well as by vitrinite
reflectance values of more than 1.1% (Jarvie et al., 2007). ............................................ 52
Figure 35. Productive areas in the Barnett Shale (Pollastro et al., 2007). The continuous
gas assessment unit, highlighted in magenta, represents the core area for production. . 53
Figure 36. Gamma Ray, quartz mineral, calcite mineral, clay mineral, and brittleness
index logs from well A. Brittleness index values were calculated using Jarvie et al.
(2007) equation. Higher brittleness index values are associated with an increase in
quartz content. Black lines highlight the formation tops. ............................................... 58
Figure 37. P- and S-sonic, density, λρ, µρ, and brittleness index logs corresponding to
well A. λρ and µρ logs were computed from P-sonic, S-sonic, and density. Brittleness
index decreases with higher values of µρ. ...................................................................... 59
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Figure 38. VP/VS, Poisson’s ratio (ν), Eρ, ZP, ZS, and brittleness index logs
corresponding to well A. These parameters were computed from P-sonic, S-sonic, and
density. Brittleness index tend to decrease with higher values of ZP, ZS, Eρ, Poisson’s
ratio, and VP/VS. .............................................................................................................. 60
Figure 39. Crossplots of: Eρ vs. VP/VS; Eρ vs. Poisson’s ratio (ν); λρ vs. µρ; and ZP vs.
ZS measured in well A. The data are colored by brittleness index. Brittleness tends to
increase with decreasing values of each parameters. λρ and µρ are the two variables that
best correlate to brittleness index. From Table 7 it can be noticed that the ranking of the
remaining variables which best predict brittleness is as follows: ZP-ZS; Eρ-υ, and Eρ-
VP/VS. .............................................................................................................................. 61
Figure 40. Illustration of outlier analysis for the λρ distribution corresponding to well A
measures. Three λρ outliers were found in the right tail of the distribution using a 5
point smoother and a threshold of 0.4% (α=0.002). All the values that fall outside the
threshold, and that fall under the smoothened PDF are considered outliers. All the
rejected λρ values were higher than 120 GPa*g/cm3. .................................................... 63
Figure 41. One dimensional crossplots of (a) λρ vs. Brittleness index and (b) µρ vs.
Brittleness index corresponding to values computed for well A. Brittleness index is
higher at lower values of µρ and λρ. Points are colored by true vertical depth. ............. 67
Figure 42. Predicted brittleness through (a) linear regression and (b) non-linear
regression methods using two input variables: λρ and µρ. Non-linear regression result
shows a better correlation between predicted and original brittleness. .......................... 68
Figure 43. N-fold and leave-out cross validation plots for (a) predicted brittleness index
through linear regression and (b) predicted brittleness index through non-linear
xvi
regression. The absolute error calculate from both cross validation methods decreases
considerably when non-linear regression is utilized to model brittleness index from λρ
and µρ values, particularly for more than 10 iterations. ................................................. 69
Figure 44. Stratal slices through the λρ volume from the top of the Lower Barnett Shale
to the top of the Viola limestone. Cold colors represent higher λρ values. .................... 71
Figure 45. Stratal slices through the µρ volume from the top of the Lower Barnett Shale
to the top of the Viola limestone. Cold colors represent higher µρ values. ................... 72
Figure 46. Stratal slices through the brittleness volume from the top of the Lower
Barnett Shale to the top of the Viola limestone. Cold colors represent more brittle rock,
and therefore, higher amount of quartz minerals. ........................................................... 73
Figure 47. Hypothetic representation of uniform sampling along a horizontal section. A
single value from the seismic volume of interest is generated within the section using
the most common value of the distribution. ................................................................... 74
Figure 48. Crossplots of λρ and µρ vs. brittleness index extracted from horizontal well
sections. The correlation between µρ and brittleness is similar to the one found on well
A. However, the correlation between λρ and brittleness is not conclusive for the
seismic-computed values. Data are colored by true vertical depth. ............................... 76
Figure 49. Brittleness index computed from λρ and µρ attributes vs. brittleness index
from the predicted seismic volume. The non-linear approximation is similar to the one
obtained from well A, presented on Figure 35. This validates the non-linear regression
method as a way to accurately predict brittleness from λρ and µρ, both from well logs
and seismic measurements. ............................................................................................ 77
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Figure 50. Schmidt-inspired diagram showing azimuthal direction and first 90 days
production values for all the horizontal wells available within survey A. The angle
represents the azimuth of the horizontal well and the production values become higher
toward the edges of the diagram. Black dots representthe azimuthal position of the
wells, being their diameter proportional to their production. Wellbores in the survey
map are colored according to first 90 days production values. Most wells are drilled in
the NW-SE direction, perpendicular to the Mineral Wells fault and the main direction
for induced fractures. ..................................................................................................... 79
Figure 51. Crossplots of (a) first 90 days production vs. horizontal length before
normalizing production values and (b) normalized first 90 days production vs.
brittleness index for the wells located inside seismic survey A. Normalized production
increases with horizontal length and brittleness index. .................................................. 80
Figure 52. Crossplots of (a) first 90 days production vs. shape index and (b) first 90
days production vs. curvedness. Production values tend to increase for not strongly
deformed bowl-shaped features. The attribute values correspond to the mode of the
distribution within each horizontal well section. ............................................................ 83
Figure 53. Predicted production using curvedness and shape index as the input
attributes vs. scaled first 90 days production. The correlation coefficient between the
predicted and the original production values is lower than 0.5 when only two attributes
are used in the regression. Therefore brittleness index, λρ, µρ, curvature and coherence
were also used as input attributes for the prediction. Brittleness index, generated by
regression of λρ and µρ is the attribute that better correlates to the scaled first 90 days
production. ...................................................................................................................... 84
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Figure 54. Crossplots of (a) first 90 days production vs. most positive curvature and (b)
first 90 days production vs. coherence. Production values are higher when the most
positive curvature is negative (a bowl) and the features are more coherent. This
interpretation is consistent with shape index and curvedness relationships presented on
Figure 45. ........................................................................................................................ 85
Figure 55. Crossplots of (a) first 90 days production vs. λρ and (b) first 90 days
production vs.µρ. The relationship between production and elastic parameters is clearly
non-linear. ....................................................................................................................... 86
Figure 56. N-fold and leave-out cross validation plots for predicted production through
non-linear regression. The absolute error calculate from both cross validation methods
is lowest when using 5 folds or iterations. ..................................................................... 87
Figure 57. Predicted vs. original first 90 days production calculated through non-linear
regression using: λρ, µρ, brittleness index, most positive curvature k1, coherence, shape
index, and curvedness as the input variables for analysis. Predicted production values
match accurately the normalized values available from the horizontal wells. ............... 89
Figure 58. Stratal slices between 380 ft below the top of the Lower Barnett Shale and
the top of the Viola limestone. From top left to lower right mages correspond to: shape
index co-rendered with curvedness, curvature co-rendered with coherence, brittleness
index, and predicted normalized first 90 days production. Production trend in the target
area seems to be mainly influenced by shape index and brittleness index. .................... 90
Figure 59. Cross-section through the computed production volume. Well bores are
colored according to their first 90 days production values. Wells JH8, JH16, JH27, and
JH42 were not considered in the non-linear regression analysis, however the closely
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match the values of predicted first 90 days production within the target area. The red
line in the map indicates the location of the cross-section ............................................. 91
Figure 60. Location of well B, relative to survey A and well A ................................. 102
Figure 61. (a) Ethylene Glycol pattern for sample LB_7568. The intensity of all the
peaks, except for the quartz peak found at 3.34Å (27°), is relatively low. This is an
indirect indicator of low clay content in the sample, (b) Comparison of Air-dried,
Ethylene Glycol, and Heat Treated patterns. The location of the illite and quartz peaks
do not vary from one profile to another, but the intensity of peaks is significantly lower
in the heat treated pattern. The dashed line represents the scaled removed background.
...................................................................................................................................... 103
Figure 62. (a) Smoothened Ethylene Glycol pattern for sample LB_7557. Quartz
intensity is again considerably higher than the intensity of the clay minerals. (b)
Comparison of Air-dried, Ethylene Glycol, and Heat Treated patterns.The location of
the illite and quartz peaks do not vary from one profile to another, however the intensity
of peaks is significantly different in the heat treated pattern. The removed background is
illustrated by the dashed line. ....................................................................................... 104
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Abstract
The Barnett Shale is the most prolific gas play in Texas. However, production
forecast in this unconventional reservoir has represented a puzzle for decades.
Production from the Barnett Shale is not only a function of the geology and the
reservoir quality, but it is also significantly affected by completion quality in horizontal
and vertical wells. The success of these completion techniques is related to the length
and number of perforation intervals, the horizontal length of the wells, and the number
and extent of hydraulic fracture procedures. Hydraulic fracturing techniques and
horizontal drilling are routinely applied to enhance production in the Barnett. The Holy
Grail in unconventional reservoirs is to apply affective stimulation techniques to
increase production. Since geological features play an important role in the rock’s
response to completion techniques, they also need to be taken into account.
Production from the Barnett has proven to be poorer in areas near faults and
structural flexures. Natural fractures, which are more common near fault zones, are
mostly or completely healed with carbonate cements in the Barnett Shale so they have
little or nothing to do with gas production. However, it is thought that some these
healed fractures inhibit the growth of induced fractures, reducing the effectiveness of
stimulation techniques.
Estimating brittleness, which is defined as the rock’s ability to accommodate
strain before failure, is a key feature for effective reservoir stimulation in the Barnett. In
the Barnett Shale brittleness is controlled by the mineral content. Areas with higher
quartz content are more brittle (weaker), and hence, more amenable to hydraulic
fracturing.
xxi
Geometric attributes such as: curvature, shape index, curvedness, and coherence
are typically used to identify strongly deformed zones that may be associated with
higher natural fracture density. Attributes derived from prestack seismic inversion such
as P-impedance, S-impedance, λρ, µρ, and Poisson’s ratio help to predict fluid,
lithology, and geochemical properties. These attributes are visually correlated with peak
production of wells. This relationship is, however, complex and clearly non-linear.
Attempting to model production from seismic attributes using linear regression methods
yields high errors and non-accurate results. Multivariate non-linear regression re-
organizes multiple input seismic attributes to model production in a reliable manner in
areas where only 3D surface seismic data are available.
In this thesis prestack-time migrated gathers from a 3D seismic survey located in
the Fort Worth Basin were conditioned to improve the seismic resolution and, thus, the
accuracy of the prestack seismic inversion resulting in P-impedance, S-impedance, λρ,
and µρ volumes. A brittleness index volume was calculated from the prestack elastic
parameters λρ and µρ volumes, which in turn were calibrated to mineralogy and elastic
logs measured in a nearby cored well. Brittleness index, 3D geometric seismic
attributes, and elastic parameters derived from prestack inversion, were then correlated
to normalized production in the Lower Barnett Shale using a non-linear regression
algorithm. Finally, an estimated production volume was generated from the input
attributes, and calibrated with well log data which were not used in the analysis.
Applying this workflow I found that the quantitative correlation of production to
curvature and prestack inversion attributes requires a non-linear algorithm. The
resulting production volume matches both the data used for the prediction, and the one
xxii
used for validation. More brittle areas in the Lower Barnett Shale are related to higher
production zones. Calcite-rich areas are less brittle, making the rock stronger and less
easily fractured. In this analysis higher productive areas in the Lower Barnett Shale are
controlled by the presence of brittle bowl shaped features with low curvature, which
have not previously undergone strong deformation.
1
Chapter 1: Introduction
More than 22% of all the gas produced in the United States comes from
unconventional reservoir (Energy Information Administration, 2012). Thus,
unconventional reservoirs have become the focus of activity in several basins across the
country. “Mudrock” resource plays, where the source rock also acts as the reservoir, are
characterized by low porosity and low primary permeability (Boyer, 2013). Thus,
commercial production depends on natural fracturing and stimulation techniques
(Montgomery et al., 2005).
Although the late Mississippian, organic-rich, petroliferous, black Barnett Shale
was recognized as a probable source rock for oil and gas in north-central Texas long
ago, it became a target in the 1980s, when Mitchell Energy and Development Corp.
pursued the Barnett as a possible producer of hydrocarbons. Initial recoveries from the
unconventional reservoir were uneconomic. In the mid-1990s improved geologic and
engineering analysis, combined with more effective completion techniques, resulted in
rapid development of the Barnett Shale (Montgomery et al., 2005).
Gas production from the Barnett Shale relies mainly on hydraulic fracture
stimulation and horizontal drilling techniques (Gale et al., 2006). Hydraulic fracturing
stimulation treatment success depends on mineralogy and natural, perhaps cemented,
fractures that form zones of weakness (Prakashrao, 2008; Jarvie et al., 2007). For these
reasons I hypothesize that, a relationship exists between Barnett production and seismic
attributes such as: brittleness, λρ, µρ, most positive and most negative curvatures,
curvedness, and shape index, which are associated to fracture behavior.
2
The relationship between seismic attributes and geological facies is often
complex, sometimes inconsistent, and may involve non-linear features (Brouwer et al.,
2011). The relationship with attributes with production is even more challenging.
Methods that combine two or more primary attributes can be used to generate a more
complete and unique isolation of a target feature in the seismic data. Multi non-linear
regression allows re-organization of multiple input attributes and achieves a high
quality extraction of a target feature or rock property from the seismic data. Non-linear
analysis is usually applied when the expression of the feature in the seismic data is
highly variable or weak, or when two or more attributes are needed to adequately image
the target.
Several studies show the utility of geostatistical techniques in solving
classification problems in this particular formation. Verma et al. (2012) generated
volumetric estimates of TOC in the Barnett Shale using gamma ray as a proxy where
the gama ray volume was computed from seismic attributes and well control.
Combining supervised neural network analysis with gamma ray logs (serving as ground
truth), they generated a gamma ray volume from P-impedance, S-impedance, spectral
components, relative impedance, sweetness, quadrature, and coherence 3D volumes as
input. In the lower Barnett, high gamma ray values indicated TOC rich, more ductile
layers, while relatively low gamma ray values are indicative of TOC poor, but more
brittle layers. The generated volume closely matches not only the gamma ray values
from the wells that were used in the study, but also those that were not included in the
neural network training to validate the process. Roy et al. (2012) used unsupervised
classification techniques involving principal component analysis (PCA) and self-
3
organizing maps (SOM) to cluster the multiattribute behavior into “petrotypes”.
Volumetric estimates of petrotypes were then calibrated a posteriori with production.
Thompson (2010) used microseismic data and estimated ultimate recovery
(EUR) in the Barnett shale, and found that most microseismic events and greater EUR
occurred in “bowl-shaped” areas. Perez Altamar (2013) built a brittleness index
teamplate from elastic moduli and mineralogy logs from a fully cored well. Then, used
this template to generate brittleness volumes from surface seismic prestack inversion.
He correlated his results to production, and to the occurrence of microseismic events.
The objective of this thesis is to obtain a relationship between the first 90 days
production and seismic attributes that are directly or indirectly related to completion
using an extensively drilled area of the Barnett Shale illuminated by a modern 3D
survey. The existence of such a relationship would provide a means to predict
production from surface seismic data in never, or less developed areas.
I start with an overview of the regional and local geology in chapter 2. Then, I
present the data conditioning process, which includes new velocity analysis and
prestack structure-oriented filtering application in Chapter 3. As simultaneous inversion
results highly depend on seismic acquisition and processing parameters, in chapter 4, I
perform prestack seismic inversion on the conditioned data. In chapter 5, I predict a
brittleness index seismic volume using λρ and µρ well log data from a fully cored well
located 5 miles away from the seismic survey. I calibrate my results with elastic
parameters derived from prestack simultaneous inversion, thereby providing means to
estimate fracture behavior where only seismic data are available. Finally, I use a non-
linear regression algorithm to estimate the first 90 days production from the predicted
4
brittleness volume, as well as λρ, µρ, curvature, shape index, curvedness, and coherence
seismic attributes. The ultimate product is a volume of estimated first 90 days
production that allows identification of productive trends and reveals the most
prospective areas for exploration, drilling, and efficient hydraulic fracturing in the
Barnett Shale.
5
Chapter 2: Geologic Background
Fort Worth Basin Regional Setting
The Fort Worth Basin (FWB) is a wedge-shaped, elongated, northward deepening
depression that covers approximately 54,000 mi2 (140,000 km
2) in north-central Texas.
It is a foreland basin that was situated on the southern leading edge of Laurussia
(Loucks and Ruppel, 2007 after Gutschick and Sandberg, 1983) and, therefore, it is
associated with the late Paleozoic Ouachita orogeny (Montgomery et al., 2005), a major
event of thrust-fold deformation generated by convergence of Laurussia and Gondwana
(Figure 1).
The FWB preserved fill consists roughly of 12,000 ft (3,660m) of Ordovician-
Mississippian carbonates and shales, Pennsylvanian clastics and carbonates, and thin
Cretaceous strata which is only present in the eastern portion of the basin (Montgomery
et al., 2005). According to Bruner and Smosna (2011), today the basin is a shallow,
asymmetric feature with a north-south structural axis that runs parallel to the Ouachita
thrust Front (Figure 2).
6
Figure 1. Paleographic reconstruction of the southern mid-continent from Blakey
(2005) suggesting that the FWB occupied a narrow inland seaway, bordered by an
island-arc chain on the east and by a broad carbonate platform on the west during the
late Mississippian (325Ma) (modified from Loucks and Ruppel, 2007).
The structural setting of the basin is dominated by major and minor faulting, local
folding, fracturing, and karst-related features (Montgomery et al., 2005). The Mineral
Wells-Newark East (MW-NE) fault system (Figure 2) is a particularly important
basement feature that influenced thermal and depositional history of the Barnett Shale,
as well as hydrocarbon migration in the northern area of the FWB (Pollastro, 2003). The
natural fractures have limited vertical extent, generally less than 32 in long (Lancaster et
al., 1993), and run parallel to the Muenster Arch axis in the northern portion of the
basin.
7
Figure 2. Present location and aerial extent of the FWB. The boundaries of the FWB,
are the Bend arch on the west, the Llano uplift on the south, the Red River and
Muenster arches on the north, and the Pennsylvanian Ouachita overthrust on the east
(Modified from Pollastro et al., 2007).
8
Barnett Shale Lithology, Stratigraphy, and Mineralogy
The Mississipian Barnett Shale, one of the largest unconventional reservoirs in the
United States, extends over an area of 28,000 mi2 (72,520 km
2) across the FWB and
adjoining Bend arch in north-central Texas (Figure 3). The Barnett Shale is present in
38 counties in Texas, but production is mainly restricted to Denton, Tarrant, Johnson,
and Wise Counties in the northeastern portion of the FWB, where the shale is relatively
thick (Montgomery et al., 2005). This project focuses on a seismic survey located in
Wise County, Texas (Figure 3).
The lithology and stratigraphy of the Mississipian Barnett Shale are variable
within the FWB. The Middle to Upper Ordovician Viola Formation and the Lower
Ordovician Ellenburger group uncomformably underlie the Barnett Shale, while the
Pennsylvanian Marble Falls Formation overlies Barnett strata within the FWB. In the
area of study, the Forestburg Limestone divides the Barnett in lower and upper
members (Bruner and Smosna, 2011). Figure 4 shows a simplified stratigraphic column
of the Pre-Cambrian-Pennsylvanian interval in Wise County, TX.
The thickest Barnett section encompasses about 1,000 ft (300m) in the
northeastern portion of the FWB, near the Muenster Arch, coincidently where the basin
is deepest. The thickness of the Forestburg Limestone also increases to the northeast,
accounting for more accommodation space (Loucks and Ruppel, 2007). Over the
Muenster Arch Barnett strata are eroded.
9
Figure 3. Extension of the Barnett Shale, highlighting the extension of the Fort Worth
Basin in green, the location of Wise County in orange, and the outline of seismic survey
A in yellow (modified from Chesapeake Energy Corporation, 2013).
Figure 4. Simplified stratigraphic column of the Fort Worth Basin in Wise County
Stratigraphically, the Barnett Shale lies between two prominent limestone units
(modified from Montgomery et al., 2005). In my survey, the Barnett lies directly on the
Viola Limestone.
10
Lancaster et al. (1993) initially considered the Barnett to be a normal marine shelf
deposit because of the presence of fossils and interbedded limestones. Nonetheless, the
depositional model discussed by Loucks and Ruppel (2007) suggests that the Barnett
was deposited over a 25 m.y. time span (average accumulation rate of 14mm/yr) in a
deeper water foreland basin with depths of 400 ft-700 ft, and poorly connected to the
open ocean, accounting for the anoxic conditions that characterize Barnett strata (Figure
5).
Figure 5. Depositional profile and processes of the Barnett Shale (Loucks and Ruppel,
2007). Most deposition in the FWB occurred under euxinic conditions, except from
short episodes when hyperpycnal flow transported oxygenated waters into the basin. A
sea level curve by Ross and Ross (1987) indicates that deposition began during a second
order highstand below the storm wave base, with several third order fluctuations by the
end of Barnett deposition (Slatt et al., 2009).
Several authors described Barnett lithology as black, organic rich, petroliferous,
fossileferous, and siliceous shale, including black, finely crystalline, petroliferous, and
fossiliferous limestone, and minor dolomite (Lancaster et al., 1993; Loucks and Ruppel,
2007). Based on X-Ray analyses, Loucks and Ruppel (2007) identified three major
lithofacies in the Barnett:
11
Laminated siliceous mudstone, predominant in the Upper and Lower Barnett
intervals and dominated by silt sized peloids and fragmented skeletal material.
Laminated argillaceous lime mudstone (marl), characteristic of the Forestburg
Limestone, where calcite and dolomite are the main constituents, and
Skeletal, argillaceous lime packstone, widely spread in the Lower Barnett and
locally present in the upper member, where thin beds of compacted shells, debris,
and skeletal particles are present.
In general, the two dominant sediment sources during Barnett deposition in the
FWB were the Caballos Arkansas island chain to the south and the Chappel carbonate
shelf to the west (Montgomery et al., 2005). The laminated facies were likely formed
from suspension settling. Sediments include siliceous (extrabasinal) and carbonate
(intrabasinal) clay to very fine silt size particles transported by mud plumes from the
shallow water shelf and peloids formed by clay size material flocculation within the
water column (Loucks and Ruppel, 2007). Although an alternative mechanism of
transport from the shelf is turbidity currents, the Barnett lithofacies do not show the
dictinctive Bouma turbidity series. The fine grain size and the lack of divisions in the
turbidite sequence suggest that deposition occurred at great distances from the sediment
source. Loucks and Ruppel (2007) affirm that: “By the time turbidity currents reached
the Wise County area, most of the coarser grained material had dropped out of the
flow, leaving only fine-grained sediment to be deposited in these deeper and more distal
parts of the basin”. The skeletal debris flow transported from the northern slope is
interpreted to form the lime packstone facies. The extremely thin (less than 1 in (2.4
cm)) debris flow events resulted from probably as much as 90% compaction of their
12
original thickness (Loucks and Ruppel, 2007). To the northeast, where the shale is
thicker, the Barnett members contain a significant amount of interbedded limestone
deposited by a series of debris flows, which volume decreases to the south and west
(Montgomery et al., 2005). The lower member can be locally divided in five different
shale units separated by 10-30 ft thick limestones, while the upper member is thinner
and undifferentiated (Bruner and Smosna, 2011).
Phosphatic material present in the Barnett was incorporated through upwelling,
either on the slope or within the basin, forming hardgrounds that represent depositional
hiatuses with episodes of fine-grained allochthonous sediment deposition in the deeper
water basin (Loucks and Ruppel, 2007). Generally the formation is rich in silica (35-
50%) and relatively poor in clay minerals (10-50%) (Table 1). Organic content is
higher in clay rich intervals, more common in the Lower Barnett. The primary
producing facies of the Barnett correspond to the silica rich intervals, which are more
brittle and hence amenable to hydraulic fracturing (Montgomery et al., 2005).
Mineral: Percentage (%):
Quartz 35-50
Clays, primarily illite and minor smectite 10-50
Calcite, dolomite, siderite 0-30
Feldspar 7
Pyrite 5
Phosphate, gypsum Trace
Table 1. Typical mineral composition of the Barnett Shale (after Bruner and Smosna,
2011).
13
Chapter 3: Data Conditioning
Introduction
Data conditioning is a fundamental step prior to seismic interpretation and
reservoir characterization. In particular, an accurate estimation of rock properties during
prestack inversion can be only accomplished with properly conditioned seismic data.
Undesirable effects that are commonly removed or reduced during the gather
conditioning process prior to seismic inversion may include random noise, NMO
stretch, and non-horizontal reflections (Singleton, 2009). In this Chapter, I present the
data conditioning workflow performed on time migrated prestack gathers through a
fourth round of velocity analysis improvement, mimicking the processing flows
implemented by Dowdell (2013).
Available data
Devon Energy Corporation provided the 3D unmigrated, wide azimuth, prestack
data from a seismic survey located in Wise County, North Central Texas (Figure 6a).
The frequency content of the seismic data ranges between 20 Hz and 110 Hz (Figure
6b). The acquisition parameters for survey A, such as date, sampling interval, record
length, and CMP bin spacing are summarized in Table 2.
Survey location Wise County, Texas
Size 14.32 mi2
Date 02/15/2006
Sample rate 2 ms
Record length 4.0 s
CDP bin size 110 ft*110 ft
Inline direction West-East
Crossline direction South-North
Total number of inlines 198
Total number of crosslines 219
Table 2. Acquisition parameters for survey A
14
Figure 6. (a) Outline of Survey A including the fold map resulting from 3D acquisition.
Survey boundaries are highlighted in black. 198 inlines increase from East to West. 219
crosslines increase from South to North. Higher fold values refer to a larger number of
traces per CDP, providing better seismic imaging. (b) Frequency spectrum of the
seismic data. The spectrum between 20 Hz and 100 Hz is the result of deconvolution
and time variant spectral whitening.
15
The prestack gathers were time pre-processed by a contractor company according
to the sequence shown on Table 3.
Processing sequence done by GMG/AXIS
Geometry assignment and QC
Apply refraction statics (datum: 800 ft, replacement velocity: 12,000 ft/s)
Edit traces, spike, and noise reduction
Gain and full trace equalization
Velocity analysis (1.0 mi spacing)
Gausss-Seidel residual statics
AXI Deconvolution (1000 ms windows, 50% overlap)
Time variant spectral whitening (7-14-105-130 Hz; 7filters)
Statistically robust gain (1000 ms window)
Velocity analysis (0.10 mi spacing)
Gauss-Seidel residual statics
Azimuthal velocity analysis (every 3rd
inline and crossline CDP)
Gauss-Seidel Residual Statics
-40 degree phase rotation to match wells
Table 3. Processing history of survey A
In 2012, the full azimuth data were migrated as part of the processing using a
prestack Kirchhoff time-migration. These efforts were conducted at The University of
Oklahoma as part of the AASPI consortium development. Only one azimuthal direction,
from 0 to 360 degrees, and 60 offset bins were considered for this migration procedure.
The maximum offset selected was 14,000 ft, which results in angles of about 45⁰ at the
target depth (7,000 ft) as illustrated in Figure 7.
16
Figure 7. Illustration of maximum recovery angle for an offset of 14,000 ft. Angles
equal or greater than 45⁰ allow inversion for densities in addition to P- and S-
impedances.
The migrated data possess a good signal-to-noise ratio, but exhibit the
characteristic offset-dependent frequency loss as a result of Normal Move-Out (NMO)
stretch. During NMO and common offset migration the values of each sample are
shifted by an amount determined by the velocity at zero offset time (t0), resulting in
significant data stretch (an increase of the wavelength) for larger offsets (Singleton,
2009). In addition, the reflectors in the common reflection point (CRP) gather are not
horizontal and exhibit residual normal move-out (RMO), even after three iterations of
velocity analysis. Figure 8 show the effects of NMO stretch and residual velocity errors
for offsets greater than 9,000 ft on gathers from survey A. The proposed processing
objective is to re-pick velocities on the prestack migrated gathers that flatten the
reflectors and reduce the effects of NMO stretch at larger offsets within the target area
17
using a NMO algorithm developed by Zhang et al. (2013). Figure 9 shows the
generalized processing workflow.
Figure 8. Representative CMP gather along line AA’ after prestack Kirchhoff time
migration using one azimuth and 60, irregularly wide, offset bins. The red P-wave log
corresponds to well JH43. The location of the CMP gather is denoted by the red dot
along line AA’ on the map view. Arrows indicate the top of the Lower Barnett Shale, at
about t=1.25 s (7,000 ft), and the top of the Basement at about 1.7 s (12,000 ft).
Frequency loss and tuning effects cause reflector loss on offsets greater than 9,000 ft
(30 degrees) between t=1.1 s-1.7 s.
18
Figure 9. Generalized processing workflow for prestack time-migrated gathers
from survey A.
Inaccuracies in velocity picking cause the far offsets to be over- or under-
corrected, decreasing the bandwidth of, or introducing artifacts into the migrated image.
Such errors negatively affect subsequent seismic interpretation, including 3D attribute
and prestack inversion analysis. A method to flatten the reflectors consists of re-
estimating the RMS velocity field after removing the previously applied migration
velocity by reversing the normal move-out (RNMO) from prestack time migrated
gathers. Figure 10 shows the prestack gathers shown in Figure 8 after reverse NMO.
19
Figure 10. (a) Representative reverse NMO gather from Survey A, highlighting the top
of the Lower Barnett Shale around t=1.3s (7,000ft). (b) Schematic diagram showing the
top of the Lower Barnett Shale after migration (red line) and following reverse NMO
application (blue line). The dashed line indicates the desired horizontal reflector at the
reservoir level. ∆t represents the move out corresponding to the migration velocity field
used, while ∆t’ represents the move out that should be applied to flatten the target
reflector. Note that ∆t>∆t’, which indicates the seismic data were overcorrected, by
applying a migration velocity that was slower than needed. The location of the gather is
denoted by the red dot along line AA’ on the map view.
20
Refined velocity analysis and NMO
The previously applied velocities were slower than they should be, as depicted in
Figure 10b. Thus, RMS velocity analysis was performed over the reverse NMO gathers
to construct a new set of prestack time migrated gathers that are consistent with the data
available. A supergather was created to increase the accuracy of the velocity analysis,
by combining (summing) 9 CDP’s (3 inlines * 3 crosslines). The velocity picks were
made over a semblance panel every 20 inlines and 20 crosslines (Figure 11).
The velocities were also interpolated every inline and crossline to obtain a pick
for every CMP, smooth every 3 inlines and crosslines to match the supergather
dimensions, and then then resampled to 2.0 ms to match the seismic sampling interval.
The result from velocity analysis is a smooth RMS field that provides higher velocity
values that are consistent with the seismic data provided. Figures 12 and 13 show the
previous and new velocity fields along line AA’.
Even when velocity analysis is carefully done, the effects of NMO stretching are
particularly hard to solve on horizontal reflections with low velocities. To reduce the
stretch on the stacking process, the part of the data with more severe stretching needs to
be muted (Singleton, 2009). A stretch mute percentage of 30% was applied to the
prestack gathers during the NMO process. Additional mutes were manually picked over
NMO corrected gathers using a 20 by 20 CRP grid, and interpolated through the entire
seismic volume in order to remove the low frequency content in the larger offsets.
21
Figure 11. A representative velocity analysis semblance panel and CMP supergather,
before mute, corresponding to well JH43. Location is denoted by the red dot in along
the AA’ line on the map view. Events can be resolved fairly well, however high
velocity interbed multiples generate “bullseyes” right below the Lower Barnett event,
which complicates the velocity interpretation for this particular horizon. These
multiples are indicated by the magenta circle at t=1.3 s. Horizon oriented velocity picks
prevented large variations of velocity values in the shallower and deeper areas.
22
Fig
ure
12. P
revio
us
mig
rati
on v
eloci
ty c
ube
thro
ugh l
ine
AA
’ co
-ren
der
ed w
ith s
eism
ic a
mpli
tude.
Vel
oci
ties
ran
ge
bet
wee
n
9,0
00 f
t/s
and 1
5,0
00 f
t/s.
For
the
targ
et a
rea
(t=
1.2
s-1
.4 s
) R
MS
vel
oci
ties
fal
l bel
ow
13,0
00 f
t/s.
For
the
bas
emen
t (t
=1.7
s)
the
pic
ked
vel
oci
ty i
s bet
wee
n 1
3,5
00 f
t/s-
14,5
00 f
t/s
23
Fig
ure
13. N
ew R
MS
vel
oci
ty f
ield
thro
ug
h l
ine
AA
’ co
-ren
der
ed w
ith t
he
re-m
igra
ted d
ata.
Vel
oci
ties
ran
ge
bet
wee
n 9
,000
ft/
s
and 1
5,0
00 f
t/s.
For
the
targ
et a
rea
(t=
1.2
s-1
.4 s
) R
MS
vel
oci
ties
are
about
13,0
00
ft/
s-13,5
00
ft/
s. F
or
the
bas
emen
t (t
=1.7
s)
the
new
pic
ked
vel
oci
ty i
s ap
pro
xim
atel
y 1
5,0
00 f
t/s.
24
To improve the signal-to-noise ratio of the prestack gathers I applied structure-
oriented filtering (SOF) to each common offset-azimuth volume independently. This
process uses the inline and crossline dip derived from the stacked volume to retain the
coherent signal and reject the events that are inconsistent with the computed dip. Figure
14 shows a prestack gather before and after SOF, as well as the rejected noise. Reflector
imaging is improved in the area of interest (t=1.2 s-1.4 s) after SOF application. The
resulting prestack time migrated gathers with the new interpreted velocities, mutes, and
structure oriented filtering, were used to perform simultaneous impedance inversion
(see Chapter 4).
The prestack data were also stacked for conventional interpretation. Stack is an
excellent noise suppressor. Figure 15 shows the stack of the flattened gathers, prior to
SOF application. The resulting volume after picking new velocities, muting, and
applying SOF is shown on Figure16. The difference between the stacks before and after
SOF is shown on Figure 17. The SOF, mainly performed to condition the prestack
gathers for subsequent seismic inversion, improves the lateral resolution and continuity
of the reflectors in the stacked section, especially in the target area, between t=1.2 s-1.4
s. The final stacked volume shown in Figure 16 was used to interpret the top of the
Marble Falls, Upper Barnett Shale, Forestburg, Lower Barnett Shale, and Viola
limestone horizons based on well to seismic tie. A suite of 3D seismic attributes was
also generated from the stacked data using the AASPI software. These attributes were
then correlated with production data from survey A, within the Lower Barnett Shale
target zone (see Chapter 5).
25
Figure 14. Prestack time-migrated gathers after reverse NMO, new velocity picking,
and NMO correction with 30% stretch mute shown (a) before and (b) after prestack
structure-oriented filtering, including (c) the rejected noise after the filter application.
The location of the gathers corresponds to well JH43, and is denoted by the red dot
along the AA’ line on the map view. The effects of offset-dependent frequency loss and
migration stretch are seen for offsets greater than 12,000 ft (40 degrees) in the
conditioned gathers, while in the gathers migrated with the original velocity field
frequencies and amplitudes are only preserved for offsets nearer than 9,000 ft (30
degrees) at the reservoir level (1.2 s-1.4 s). Moreover, the reflectors on the conditioned
gathers look flatter throughout, with the exception of the deeper area right above and
below basement level (t=1.7 s). Prestack SOF improves the imaging and coherence of
the reflectors through the entire seismic record.
26
Fig
ure
15. L
ine
AA
’ tr
ough t
he
resu
ltin
g s
tack
ed v
olu
me
afte
r re
ver
se N
MO
, N
MO
, an
d m
ute
appli
cati
on (
Fig
ure
11).
Str
uct
ure
-ori
ente
d f
ilte
ring nee
ded
to b
e ap
pli
ed t
o r
emove
low
fre
quen
cy n
ois
e (g
round r
oll
) an
d i
mpro
ve
the
imag
ing o
f
refl
ecto
rs b
elow
700 m
s.
Note
the
impro
vem
ent
in v
erti
cal
reso
luti
on a
t th
e to
p o
f th
e U
pper
and L
ow
er B
arn
ett
Shal
es, as
wel
l as
in t
he
area
indic
ated
by t
he
pin
k a
rro
ws.
27
28
Fig
ure
16. L
ine
AA
’ th
rough t
he
resu
ltin
g s
tack
ed v
olu
me
afte
r re
ver
se N
MO
, N
MO
, m
ute
, an
d S
OF
appli
cati
on. T
he
ampli
tude
of
the
seis
mic
dat
a is
hig
hly
incr
ease
d a
fter
SO
F. Im
agin
g o
f th
e re
flec
tors
in t
he
targ
et a
rea
(1.2
s-1
.4 s
) is
impro
ved
tro
ugh s
truct
ure
-ori
ente
d f
ilte
ring.
29
Fig
ure
17. L
ine
AA
’ th
rough t
he
stac
k r
esult
ing f
rom
the
dif
fere
nce
bet
wee
n t
he
seis
mic
volu
mes
gen
erat
ed b
efo
re a
nd a
fter
SO
F, sh
ow
n i
n F
igure
s 15 a
nd 1
6, re
spec
tivel
y.
Low
fre
qu
ency
dat
a w
ere
rem
oved
fro
m t
he
targ
et r
efle
ctors
, b
etw
een 1
.2 s
-
1.4
s th
rough S
OF
.
30
Chapter 4: Prestack Seismic Inversion
Introduction
Acoustic impedance (Z) is the product of density and P-wave velocity (ρVP),
while the shear impedance is the product of density and S-wave velocity (ρ VS), both of
which represent intrinsic rock properties that are directly correlated to a particular
lithologic unit. The goal of prestack inversion is to obtain reliable estimates of ZP, ZS,
and density from seismic amplitudes in order to predict fluid, lithology, and/or
geochemical properties. The simultaneous inversion algorithm for the estimation of P-
impedance, S-impedance, and density is based on three assumptions (Hampson et al.,
2006):
1. The linear approximation for reflectivity coefficients is valid,
2. The Aki-Richards equations accurately describe PP and PS reflections as a
function of angle, and
3. The logarithm of P-impedance is linearly related to both S-impedance and
density through equations:
( ) ( ) ( ) (1)
( ) ( ) ( ) (2)
In this chapter, I give an overview of the simultaneous prestack inversion process
performed over the reprocessed time-migrated gathers from survey A in order to obtain
P- and S-impedances, density, P-wave and S-wave velocities, lambda-rho (λρ), mu-rho
(λρ), and VP/ VS ratio volumes within the Barnett Shale target zone.
31
Seismic to well tie
Figure 18 shows the location of the wells used for prestack simultaneous
inversion of seismic survey A. Before performing the inversion, three wells: JH9, JH42,
and JH43, within the survey bounds, were selected. These wells had S-wave sonic logs
that were used to predict S-wave logs for five more wells. This process was
accomplished through linear regression of P-wave sonic values (Figure 19).
Eight wells were tied to the prestack seismic data and were used to construct the
background models used for prestack simultaneous inversion. The seismic-well tie for
well JH43 is illustrated in Figure 20. The correlation between synthetic and seismic
within the respective time windows for all the wells considered for inversion is
summarized in Table 4.
Figure 18. Well head location for the eight wells used for simultaneous prestack
seismic inversion. Wells are colored by normalized first 90 days production.
32
Fig
ure
19. L
inea
r re
gre
ssio
n o
f S
-wav
e so
nic
log
s fr
om
P-w
ave
sonic
and d
ensi
ty v
alues
for
wel
ls J
H9,
JH42, JH
43. T
he
trac
ks
on t
he
left
show
the
ori
gin
al S
-wav
e lo
g i
n r
ed,
and
the
calc
ula
ted l
og i
n b
lue.
33
Fig
ure
20. S
eism
ic-w
ell
tie
for
JH43
. W
ell
loca
tion i
s den
ote
d b
y t
he
red d
ot
on t
he
map
. T
he
P-w
ave
sonic
, S
-wav
e so
nic
, an
d
den
sity
logs
are
show
n o
n t
he
left
tra
cks.
Th
e sy
nth
etic
tra
ce c
alcu
late
d f
rom
the
pre
stac
k g
ather
s (S
YN
) is
show
n i
n b
lue,
whil
e th
e
extr
acte
d s
eism
ic t
race
is
show
n i
n r
ed. T
he
pre
stac
k t
ime-
mig
rate
d g
ather
s ar
e sh
ow
n i
n t
he
furt
hes
t ri
ght
colu
mn. T
he
corr
elat
ion
bet
wee
n t
he
synth
etic
an
d t
he
seis
mic
tra
ce i
s ab
out
80%
for
a ti
me
win
do
w b
etw
een 1
100
ms
and 1
400
ms.
34
Table 4. Summary of seismic to well tie for wells used to perform seismic inversion
Wavelet extraction
The prestack time-migrated data were converted to angle gathers ranging from 0°
to 45° (Figure 21). The wavelets were statistically extracted for three angle stacks (0–
14°, 14–28°, and 28–42°) from the conditioned data. The parameters used for wavelet
extraction are included in Table 5. The major difference between the prestack migrated
gathers before and after data conditioning through a fourth iteration of velocity analysis
was in the far offsets (angles) due to NMO stretch removal. Therefore, the extracted far-
angle wavelet should present similar amplitude and frequency content to the wavelets
extracted for the near and middle angles (Figure 22).
Zero phase statistical wavelet extraction parameters
Top 100 ms above Marble Falls surface
Bottom 100 ms below Viola limestone surface
Wavelet length 120 ms
Taper length 25 ms
Sample rate 2 ms
Phase rotation 0 degress
Angles 0-14;14-28;28-42
Table 5. Parameters for zero phase statistical wavelet extraction
Well Name Correlation coefficient Time window
TB1 0.841 1150 ms-1310 ms
JH42 0.839 1150 ms-1345 ms
JH43 0.829 1200 ms-1365 ms
JV1 0.792 1150 ms-1350 ms
BJ11 0.791 1150 ms-1354 ms
JH9 0.781 1200 ms-1350 ms
JV2 0.754 1150 ms -1370 ms
SG1 0.629 1150 ms -1364 ms
35
Figure 21. Angle gathers from 0⁰-42⁰ generated from the input prestack time-migrated
data. The red log P-wave logs denote the location of (a) well JH9 and (b) well JH43,
previously shown in Figures 8, 10, 11, 14, and 20.
36
Figure 22. Amplitude spectrum in (a) time domain and (b) frequency domain of the
statistically extracted wavelets for 0°-14° (blue), 14°–28° (red), and 28°–42° (yellow).
Phase is similar in the three wavelets because all previous conditioning processes
applied were phase-neutral. Amplitudes are also very similar in all angle-stack ranges,
with the exception of a slight decrease in high frequencies content at the farther angles,
(28°–42°). This effect should be fixed by applying non-stretch NMO (MPNMO)
(Zhang, 2013). The peak frequency of the farther wavelet falls right below 40 Hz, while
the dominant frequency value for the near and middle wavelets is 50 Hz.
37
Prestack inversion analysis
The angle gathers were inverted to P-impedance and S-impedance using a model-
based simultaneous inversion algorithm. In this algorithm the input low-frequency
model, obtained from the seismic data and the well logs, is continually modified until a
stable solution for the inversion is reached (Singleton, 2009). The input P-impedance,
S-impedance, and density models were built from eight well logs and six interpreted
horizons, as shown in Figures 23 to 25.
For quality control purposes the error between the original parameters from the
logs with respect to the modeled and inverted data needs to be evaluated prior to
performing the inversion process. Figure 26 illustrates the accuracy of the inversion for
wells JH9 and JH43. The correlation coefficient for the wells considered for the seismic
inversion ranges between 0.81-0.85 and the total error is 0.5. The error calculation
window begins at 100 ms above the top of the Marble Falls horizon down to 1,500 ms.
In all the wells the highest error between the log and the inverted results is found on the
S-impedance computation, even when the model accurately approximates the log
parameter. The S-impedance inverted results seem to be higher than they should be in
the Upper and Lower Barnett Shale intervals, while they are lower than the log response
through the Upper Barnett Limestone and Forestburg intervals. In this sense,
lithological discrimination using S-impedance could be more challenging than
discrimination done from P-impedance and density, which seem to better match the
different lithologies on the wellbores considered in this study. Figure 27 shows maps of
the total RMS error for the synthetic, ZP, ZS, and density.
38
F
igu
re 2
3. V
erti
cal
slic
e A
A’
thro
ugh t
he
low
fre
quen
cy m
odel
use
d f
or
ZP i
nver
sion.
Model
was
gen
erat
ed f
rom
six
dif
fere
nt
hori
zons
pic
s an
d f
rom
eig
ht
wel
ls. T
he
colo
red P
-wav
e lo
g c
orr
esponds
to w
ell
JH43.
39
Fig
ure
24. V
erti
cal
slic
e A
A’
thro
ugh t
he
low
fre
quen
cy m
odel
use
d f
or
ZS i
nver
sion
. M
odel
was
gen
erat
ed f
rom
six
dif
fere
nt
hori
zons
pic
s an
d f
rom
eig
ht
wel
ls. T
he
colo
red S
-wav
e lo
g c
orr
esponds
to w
ell
JH43.
40
Fig
ure
25. V
erti
cal
slic
e A
A’
thro
ugh t
he
low
fre
quen
cy m
odel
use
d f
or
den
sity
inver
sion.
Model
was
gen
erat
ed f
rom
six
dif
fere
nt
hori
zons
pic
s an
d f
rom
eig
ht
wel
ls. T
he
colo
red d
ensi
ty l
og c
orr
esponds
to w
ell
JH43.
41
Figure 26. Inversion analysis for P-impedance, S-Impedance, and density, with
comparison of the original seismic and the inverted synthetic using three different zero
phase wavelets for near, middle, and far offsets for wells (a) JH9 and (b) JH43.
(a)
(b)
42
Figure 27. Maps of total RMS error for (a) the synthetic inverted trace extracted along
the top of the Lower Barnett Shale, (b) P-impedance (ZP), (c) S-impedance (ZS), and (d)
density (ρ). The dashed line indicates the position of section AA’. Well heads location
are colored by RMS error values. The highest RMS error corresponds to ZS
computation. The error tends to be higher in the wells where S-wave logs were
predicted through linear regression of P-wave.
(a) (b)
(c) (d)
43
A linear relationship between the original P-impedance from well data and the
inverted result was computed. This was done in order to adjust the inversion to better
match the well information prior to generating the inverted volumes. The relationship
between original and inverted impedances, along with a total correlation well map is
shown on Figure 28.
Simultaneous prestack inversion uses the low-frequency models to generate
volumes of P-impedance, S-impedance, and density, which are shown in Figures 29, 30,
and 31, respectively.
Figure 28. Crossplots of original seismic P-impedance from logs against inverted
impedance. All the plotted values are within the Marble Falls-Viola interval. The data
are colored by time (ms), where the transition from cold to warm colors represent an
increase in arrival times (depths).
44
Fig
ure
29. V
erti
cal
slic
e A
A’
thro
ugh t
he
ZP
volu
me.
War
m c
olo
rs i
ndic
ate
area
s w
ith
low
er i
mped
ance
val
ues
, in
this
cas
e
asso
ciat
ed w
ith t
he
pre
sence
of
shal
e. T
he
colo
red
P-w
ave
log c
orr
esponds
to w
ell
JH43.
45
Fig
ure
30
. V
erti
cal
slic
e A
A’t
he
thro
ugh Z
S v
olu
me.
War
m c
olo
rs i
ndic
ate
area
s w
ith
low
er i
mped
ance
val
ues
, in
this
cas
e
asso
ciat
ed w
ith t
he
pre
sence
of
shal
e. T
he
colo
red
S-w
ave
log c
orr
esponds
to w
ell
JH43.
46
F
igu
re 3
1. V
erti
cal
slic
e A
A’
thro
ugh d
ensi
ty. W
arm
colo
rs i
ndic
ate
area
s w
ith l
ow
er d
ensi
ty v
alues
. T
he
colo
red d
ensi
ty l
og
corr
esponds
to w
ell
JH43.
47
Lambda-Rho and Mu-Rho computation
The Lamé parameters λ and μ, which characterize the stress-strain relationship in
linear, isotropic elastic media, are related to VP, VS, and density through the following
equations:
√
, and (3)
√
, (4)
where the Lamé parameter λ relates uniaxial and lateral strain to uniaxial stress, and
the “shear modulus”, μ, relates shear stress to shear strain. λ is sensitive to pore fluids,
while µ is not affected by their presence as shear waves are only sensitive to the matrix
of the rock since shear waves do not propagate through fluids or gasses.
Once the seismic inversion was completed, λρ and μρ volumes were computed
using:
( ) ( )
(5)
( )
(6)
In carbonates and mudrocks λρ and µρ parameters are directly linked to lithology
and geomechanical behavior (Goodway, 1997). The VP/VS ratio, the Poisson’s ratio (υ)
and λ/μ, are also related:
(7)
Figures 28 and 29 show the λρ, and μρ derived from seismic inversion results.
Crossplots obtained from seismic inversion help to delineate different lithologic
units within the zone of interest.
48
[Begin the text of your thesis on this page.]
Fig
ure
32. L
ambda-
rho v
olu
me
com
pute
d f
rom
sei
smic
inver
sion r
esult
s. W
arm
colo
rs i
ndic
ate
low
er l
ambda-r
ho v
alues
,
asso
ciat
ed w
ith t
he
pre
sence
of
shal
es.
49
Fig
ure
33. M
u-r
ho v
olu
me
com
pute
d f
rom
sei
smic
inver
sion r
esult
s. W
arm
colo
rs i
ndic
ate
low
er m
u-r
ho v
alues
, as
soci
ated
wit
h
the
pre
sen
ce o
f sh
ales
.
50
Chapter 5: Production correlation to 3D seismic attributes
Introduction
Development of the Barnett Shale play depends on a combination of geological,
geochemical, geophysical data, and engineering process. Geological and geophysical
analysis identifies geohazars, as well as brittle and ductile zones in the reservoir.
Geochemical data discriminate TOC-rich from TOC-poor areas of the reservoir. Giving
these measurements, engineering processes, such as hydraulic fracturing and multi-
stage horizontal drilling result in successful completion (Jarvie et al., 2007). The
correlation of completion and production to geophysical measurements remains a major
challenge. According to Jarvie et al. (2007), mineralogy is highly correlated to the best
Barnett wells. Quartz, clay, and carbonate volumes change laterally and vertically
within the shale, as seen in Table 1. These changes result in variable fracture gradients,
defined as the pressure increase per unit of depth (Jarvie et al., 2007). The zones with
45% quartz and less than 27% clay have higher production (Bowker et al., 2003),
suggesting that brittleness of the shale is key to the stimulation, providing the creation
of a fracture network and the linkage between the wellbore and the micro-reservoirs
(Jarvie et al., 2007).
In this chapter, I present a background on the Barnett Shale gas production and
potential, and I show the results of non-linear correlation between first 90 days
production and brittleness-predicted from λρ and µρ values, curvature, and shape index
attributes.
51
Barnett Shale Gas Resource Potential and Production
The thermally mature and hydrocarbon bearing Barnett Shale extends over an area
of 28,000 mi2 (72,520 km
2) along the FWB (Pollastro, 2003). The entire Barnett play, a
continuous, multi-TCF gas accumulation is present over a 7,000 mi2 (18,100 km
2)
section of the FWB. The U.S. Geological Survey (2003) resource assessment of the
Bend arch–Fort Worth Basin province estimates a total undiscovered shale gas resource
of 26.2 TCF for the entire Barnett Shale play (Figure 34).
The core area (Figure 35) covers roughly 1,800 mi2 (4,700 km
2) close to the
Texas/Oklahoma border (Jarvie et al., 2007; Bruner and Smosna, 2011). Production in
this portion of the basin is particularly good due to a combination of several factors:
Total organic carbon (TOC) in clay rich intervals averages 3.5% or higher.
Vitrinite reflectance (RO) equals or exceeds 1.3%,
Shale thickness is greater than 350 ft (107 m), and
The Viola-Simpson and Marble Falls formations, which represent underlying and
overlying fracture barriers, are present (Jarvie et al., 2007; Bruner and Smosna,
2011).
The dense Viola-Simpson and Marble Falls limestones generally have a higher
fracture threshold than the shale, acting as a fracture barrier and inhibiting water
encroachment from adjacent stratigraphic units like the deeper Ellenburger Group
(Jarvie et al., 2007). In the areas where the lower fracture barrier is not present,
horizontal drilling and severe stimulation can enhance production (Jarvie et al., 2007).
52
Figure 34. Geographic extent of the Barnett Shale (Pollastro et al., 2007). The play is
defined by the geographic extent of the shale to the east and north and a minimum
thickness of 100 ft to the west (Bruner and Smosna, 2011), as well as by vitrinite
reflectance values of more than 1.1% (Jarvie et al., 2007).
53
Figure 35. Productive areas in the Barnett Shale (Pollastro et al., 2007). The continuous
gas assessment unit, highlighted in magenta, represents the core area for production.
54
By 2010, at least 15,675 wells were producing, or have produced, gas from the
Barnett. Daily production reaches more than 5.5 million cubic feet of natural gas, which
represents more than 8.5% of the total natural gas produced in the United States (Powell
Shale Digest; Texas Railroad Comission, 2012).
Gas is produced from both vertical and horizontal wells, with the latter ones being
the best producers. Nonetheless, both vertical and horizontal wells show similar overall
production patterns of a rapid initial decline, followed by progressive flattening over
time (Jarvie et al., 2007). By 2007, the mean Estimated Ultimate Reserves for vertical
wells were in the order of 1.0-2.5 BCF, while reserves for horizontal wells ranged
widely, averaging about 2.5 BCF (Jarvie et al., 2007).
Studies show that productive, organic-rich areas have porosity averaging 6%,
permeability less than 0.01 mD, with pore throat radii smaller than 100 nm (Bowker,
2003) and mean water saturation of 25% (Table 6).
Reservoir Characteristics
Porosity 5-8%
Permeability 0.1-10 nD
Water saturation 20-30%
Gas saturation 70-80%
Formation pressure 3,000-4,000 psi
Pressure gradient 0.46-0.52 psi/ft
Storage capacity 450-720 MCF/ac-ft
Maturity >1.41% Ro in high maturity areas
Type of kerogen Type II with a minor admixture of type
III
TOC 5-7%, averaging 6.41%
Drilling depth 6,500-8,500 ft
Average thickness 350 ft, although it varies from 50 ft
to1,000 ft
Table 6. Basic reservoir characteristics of the Barnett Shale productive areas
(Montgomery et al., 2005; Jarvie et al., 2007; Bruner and Smosna, 2011).
55
Completion procedures in the Barnett depend on the presence of fracture barriers.
In areas where these barriers are absent, or where their ability to contain fracture growth
is questionable, production casing is cemented in place. In the areas where the barriers
are present the production string is usually not cemented. In both scenarios, however,
multistage stimulation is performed (Jarvie et al., 2007).
Although the Barnett gas play covers a large area of the Fort Worth Basin,
production in Wise County is constrained by geological and geochemical factors such
as the presence of porous, water-wet zones in the Viola-Simpson interval to the
northwest, the erosional pinch out of the Viola-Simpson formation, which places the
Lower Barnett directly on top of the karsted, potentially water-bearing carbonates from
the Ellenburger Group, and the greater drilling depths and cost to the eastern portion of
the basin (Jarvie et al., 2007).
Brittleness prediction from Lambda-rho and Mu-rho
Rock deformation can be ductile or brittle. If a rock absorbs a high amount energy
before fracturing it is consider ductile. Ductile materials deform plastically, thereby
accommodating significant strain before they fracture. From production patterns and
core analysis it is suggested that previously existing natural fractures can negatively
affect well performance in the Barnett. Such fractures tend to be severely mineralized
with calcite, forming barriers to fluid flow and making the rock more ductile
(Montgomery et al., 2005; Jarvie et al., 2007; Bowker, 2007). Detailed studies on core
samples recovered from Wise County indicate that most of the natural fractures are
wholly or partially sealed with calcite (Montgomery et al., 2005). Brittle rocks, on the
other hand, are unable to accommodate significant strain before failure, thus producing
56
microfactures that can remain open by proppant injected during hydraulic fracturing.
These microfractures can enhance productivity by storing large amounts of free gas,
especially in organic-rich facies (Bowker, 2003; Bruner and Smosna, 2011). The
excellent original organic content of the Barnett and its kerogen type, combined with its
porosity and brittle mineralogical composition, impact the generation and sorption of
large amounts of gas in the Barnett Shale (Jarvie et al., 2007).
Brittleness can be defined as the measurement of stored energy before fracture. In
the Barnett Shale brittleness is controlled by the quartz content, while ductility mainly
relates to the presence of clay minerals and total organic content (Jarvie et al., 2007).
Brittleness is not only related to mineralogy, but also with elastic parameters (Grieser
and Bray, 2007) and with the grade of stratification (Slatt and Abousleiman, 2011).
Jarvie et al. (2007) and Wang and Gale (2009) described the brittleness index (BI) as a
relative measurement that defines ductile and brittle zones based on mineralogical
content of the rock. Jarvie’s et al. (2007) equation takes into account the amount of
quartz, calcite, and clay minerals. Wang and Gale (2009) introduce dolomite and total
organic carbon content into the equation. In this thesis I utilize Perez Altamar’s (2013)
studies on a survey that overlaps Survey A, using a brittleness index based on Jarvie’s et
al. (2007) equation:
ClayCalciteQuartz
QuartzBI
. (9)
Brittleness index was calculated from elemental capture spectroscopy (ECS) log
data on well A, located 5 miles to the north east of survey A. Figure 36 illustrates the
gamma ray, percent quartz, percent calcite, percent clay, and brittleness index logs from
well A. In areas where the quartz content is higher, the brittleness index value increases
57
as well. In areas where the clay mineral content is higher, the brittleness value
decreases. There is an interval in the uppermost part of the Lower Barnett where the
clay content reaches 70%, decreasing the brittleness index value. Below this interval,
the brittleness index is fairly constant and presents the highest values within the whole
section.
Conventional log analysis for reservoir identification and characterization is not
applicable to the Barnett Shale (Montgomery et al., 2005). For example, brittleness
index calculations based on ECS logs is somehow limited, as this mineralogy log tool
does not recognize different mineral forms, each of which may have different strength
that can affect the geomechanical properties of the rock. Therefore, basic reservoir
characteristics of the productive interval of the Barnett Shale are highly dependent on
core analyses (Montgomery et al., 2005). For this study four different core samples
from the Barnett Shale, corresponding to well B were available for geochemical
analysis. X-Ray diffraction (XRD) was performed to quantify the amount of clay
minerals. The results are presented in Appendix A and compared to the mineral content
derived from ECS logs corresponding to well A.
Eρ, λρ, µρ, VP/VS, ZP, ZS, and Poisson’s ratio (ν) values were computed from P-
sonic, S-sonic, and density logs for well A (Figures 37 and 38). Crossplots of Eρ vs.
VP/VS, Eρ vs. ν, λρ vs. µρ, and ZP vs. ZS are illustrated on Figure 39.
Using commercial software, linear and non-linear regression analyses were
performed to correlate brittleness index to Eρ-VP/VS; Eρ- ν, λρ- µρ, and ZP-ZS values
measured on well A, thereby forming a template.
58
Fig
ure
36. G
amm
a R
ay,
quar
tz m
iner
al, ca
lcit
e m
iner
al, cl
ay m
iner
al, an
d b
ritt
lenes
s in
dex
logs
from
wel
l A
. B
ritt
lenes
s
index
val
ues
wer
e ca
lcula
ted u
sing J
arvie
et
al. (2
007)
equat
ion.
Hig
her
bri
ttle
nes
s in
dex
val
ues
are
ass
oci
ated
wit
h a
n
incr
ease
in q
uar
tz c
onte
nt.
Bla
ck l
ines
hig
hli
ght
the
form
atio
n t
ops.
59
Fig
ure
37
. P
- an
d S
-sonic
, den
sity
, λρ
, µ
ρ, an
d b
ritt
lenes
s in
dex
logs
corr
espondin
g t
o w
ell
A.
λρ a
nd µ
ρ l
ogs
wer
e
com
pute
d f
rom
P-s
onic
, S
-sonic
, an
d d
ensi
ty. B
ritt
lenes
s in
dex
dec
reas
es w
ith
hig
her
val
ues
of
µρ.
60
Fig
ure
38. V
P/V
S, P
ois
son’s
rat
io (
ν),
Eρ, Z
P, Z
S, an
d b
ritt
lenes
s in
dex
logs
corr
espondin
g t
o w
ell
A.
Thes
e par
amet
ers
wer
e
com
pute
d f
rom
P-s
onic
, S
-sonic
, an
d d
ensi
ty. B
ritt
lenes
s in
dex
ten
d t
o d
ecre
ase
wit
h h
igher
val
ues
of
ZP, Z
S, E
ρ, P
ois
son’s
rati
o, an
d V
P/V
S.
61
Figure 39. Crossplots of: Eρ vs. VP/VS; Eρ vs. Poisson’s ratio (ν); λρ vs. µρ; and ZP vs.
ZS measured in well A. The data are colored by brittleness index. Brittleness tends to
increase with decreasing values of each parameters. λρ and µρ are the two variables that
best correlate to brittleness index. From Table 7 it can be noticed that the ranking of the
remaining variables which best predict brittleness is as follows: ZP-ZS; Eρ-υ, and Eρ-
VP/VS.
(a) (b)
(c) (d)
62
Outlier analysis was conducted prior to the regression. The parameters for this
analysis are: a smoothened continuous distribution function to that represents the data,
and a threshold that defines the limits of the distribution. The software uses the fast
Gauss transform (FGT) to model the probability density function (PDF) of the data. The
FGT can be considered as the convolution of a Gaussian filter with the series of ordered
sample values. For the Gaussian function, a zero mean and a standard deviation, which
are inherent to the histogram of the data and its smoothened PDF, are used. The
smoother length can be adjusted between 0.5 and 5.0 standard deviations. The PDF
follows the individual distribution bins more closely when the smoother length is
smaller. With higher smoothing values the PDF trails off sooner, being more likely to
consider a bin far out on the tail as an outlier. Outliers are expected to be in the tails of
the distribution (Transform® user manual, 2013). The threshold parameter (alpha)
defines the width of the left and right tails. It considers all values smaller than the first
value with a predicted probability of alpha to define the left tail, and all the values
larger than the last predicted probability alpha to define the right tail, as illustrated in
Figure 40 (Transform® user manual, 2013).
Using a smoother length of 5, and a degree of rejection of 0.4%, only three
outliers were found. All of them were present in the λρ distribution, and corresponded to
values greater than 120 GPa*g/cm3. These outliers were rejected from the rest of the
analysis.
63
Figure 40. Illustration of outlier analysis for the λρ distribution corresponding to well A
measures. Three λρ outliers were found in the right tail of the distribution using a 5
point smoother and a threshold of 0.4% (α=0.002). All the values that fall outside the
threshold, and that fall under the smoothened PDF are considered outliers. All the
rejected λρ values were higher than 120 GPa*g/cm3.
64
Multivariate statistical analysis
The simultaneous statistical analysis of multiple variables is known as
multivariate statistical analysis. This method can be used to model a “response”
attribute from a set of input attributes. The goal of multivariate analysis is to understand
how variables related to what we try to predict. The correlation measures how well the
input variables predict the response. Variables can be related through high or low
standard correlation coefficient values. High correlation coefficient values usually
indicate a linear relationship between variables, while low correlation coefficients
between the variables can represent non-linear relations (Transform® user manual,
2013). Using commercial software I used two types of multivariate statistical analysis:
linear and non-linear regression.
Linear regression assumes that the response variable is a linear combination of the
predictor variables. The algorithm uses principal component analysis (PCA) to rotate
and translate the input data to a new coordinate system, so that the axes of the new
system are orthogonal and related to the maximum variance direction. The first axis
represents the greatest variance by any projection of the data. Least-squares regression
is performed in this new coordinate system, making it easy to evaluate how many
components are needed to explain the variability (Transform® user manual, 2013).
Non-linear regression, on the other hand, assumes that the response variable is a
transformation of the sum of transformed predictors. Transformations are independent
for each variable. The algorithm looks for the optimal transformation between linear,
monotonic, higher order, and periodic functions. This method typically reduces errors
seen on the linear regression (Transform® user manual, 2013).
65
After performing outliers analysis, brittleness index was predicted through linear
and non-linear regression of Eρ-VP/VS; Eρ- ν, λρ- µρ, and ZP-ZS values measured on well
A (Figure 39). The best solution was achieved through non-linear regression of λρ and
µρ because these parameters were the most useful to discriminate high and low
brittleness index zones. Table 7 summarizes the correlation coefficients of the linear
and non-linear regressions using Eρ-VP/VS; Eρ-ν, ZP-ZS, and λρ- µρ.
Variables Regression method Correlation coefficient R2
Eρ and VP/VS Non-linear 0.788 0.621
Eρ and ν Non-linear 0.809 0.660
ZP and ZS Non-linear 0.823 0.671
λρ and µρ Non-linear 0.839 0.675
Table 7. Correlation coefficients for each set of variables considered for brittleness
index prediction from well A measurements.
The crossplots of λρ and µρ versus brittleness index are shown on Figure 41.
Higher values of λρ and µρ are associated with a decrease in brittleness. The points
corresponding to the Lower Barnett Shale interval (depths greater than 7,990 ft) are
associated with higher brittleness index values. The magnitude of the correlation
coefficients is approximately 0.70, which could indicate a non-linear relationship
between the variables.
In order to compare the results, the graphical solution of brittleness prediction
through linear and non-linear regression of λρ and µρ are illustrated on Figure 42.
Variance values indicate the influence of any component on the total solution
from linear regression analysis. The table below shows the variance for each component
after linear regression of λρ and µρ:
66
Component Variance
PCA1 0.897
PCA2 0.103
Table 8. Variance solution table from linear regression of λρ and µρ.
PCA1 has approximately 90% of the data, while PCA2 only has 10%, this values
are not insignificant, which means that both components should be retained in the
regression solution.
From the non-linear regression analysis, sensitivity is a useful tool to indicate the
contribution of a particular variable on the final prediction, as illustrated in Table 9:
Variable Sensitivity
λρ 0.31
µρ 0.06
Table 9. Sensitivity solution table from non-linear regression of λρ and µρ
N-fold (“k-fold”) and leave-out cross validations are two ways to check the results
from the predicted response. N-fold cross validation randomly breaks the data in N
groups. N-1 folds are then used to predict the remaining one, which is haphazardly
selected. Leave-out cross validation means that all samples but one are the training set
to predict the one left-out randomly in each iteration. For this analysis, several iterations
of N-fold and leave-out cross validations were performed. The plots resulting from such
iterations for both, the linear and the non-linear regressions, are shown in Figure 43.
Non-linear regression yields better correlation when predicting brittleness from λρ
and µρ values. This is probably related to several factors that are not considered when
computing brittleness from ECS logs, such as:
The plastic and elastic regime of the rock,
The origin and habit of the mineral components of the rock,
67
Temperature,
Texture, and
Type of saturation fluid.
Figure 41. One dimensional crossplots of (a) λρ vs. Brittleness index and (b) µρ vs.
Brittleness index corresponding to values computed for well A. Brittleness index is
higher at lower values of µρ and λρ. Points are colored by true vertical depth.
68
Figure 42. Predicted brittleness through (a) linear regression and (b) non-linear
regression methods using two input variables: λρ and µρ. Non-linear regression result
shows a better correlation between predicted and original brittleness.
69
Figure 43. N-fold and leave-out cross validation plots for (a) predicted brittleness index
through linear regression and (b) predicted brittleness index through non-linear
regression. The absolute error calculate from both cross validation methods decreases
considerably when non-linear regression is utilized to model brittleness index from λρ
and µρ values, particularly for more than 10 iterations.
70
In order to test the brittleness prediction, I generated a brittleness volume using
the equation obtained from non-linear multivariable regression analysis. The values of
λρ and µρ were derived from the prestack simultaneous inversion of the high quality
long offset seismic data.
Four different stratal slices of seismic-computed λρ, µρ, and brittleness, extracted
from the top the Lower Barnett Shale to the top of the Viola, are shown in Figures 44-
46. Low values of λρ and µρ are associated with more brittle areas in the Lower Barnett,
while the reservoir becomes more ductile as the value of µρ increases. This latter
parameter seems to control the value of brittleness index in the interval of interest. The
brittleness index is higher in the lowermost portion of the Lower Barnett Shale, which is
consistent with the brittleness values calculated for well A. This suggests a lower
amount of clay minerals in this interval, which is validated by the X-Ray diffraction
(XRD) results presented on Appendix A.
The low quartz and high calcite mineral content in the Viola formation are
responsible for the low brittleness index values in this interval. This ductile behavior is
validated by the performance of the Viola Limestone as a fracture barrier, as seen on
micreoseismic data.
71
Fig
ure
44. S
trat
al s
lice
s th
rough t
he
λρ v
olu
me
fro
m t
he
top o
f th
e L
ow
er B
arnet
t S
hal
e to
the
top o
f th
e V
iola
lim
esto
ne.
Cold
colo
rs r
epre
sent
hig
her
λρ v
alues
.
72
Fig
ure
45. S
trat
al s
lice
s th
rough t
he
µρ v
olu
me
from
the
top o
f th
e L
ow
er B
arnet
t S
hal
e to
the
top o
f th
e V
iola
lim
esto
ne.
Cold
colo
rs r
epre
sent
hig
her
µρ v
alues
.
73
Fig
ure
46. S
trat
al s
lice
s th
rough t
he
bri
ttle
nes
s volu
me
from
the
top o
f th
e L
ow
er B
arn
ett
Shal
e to
the
top o
f th
e
Vio
la l
imes
tone.
Cold
colo
rs r
epre
sent
more
bri
ttle
rock
, an
d t
her
efore
, hig
her
am
ount
of
quar
tz m
iner
als.
74
The λρ, µρ, and brittleness index were extracted along 44 horizontal wells located
inside survey A in order to validate the non-linear relationship computed from well A
and shown in Figure 42. Nine of those wells were not considered in the analysis, for
posterior validation. The horizontal sections were considered to be those for which the
inclination of the well path was equal to or greater than 80⁰. A single value of λρ, µρ,
and brittleness index, corresponding to the mode of each distribution, was computed for
each of the horizontal wells inside the survey. To obtain such value uniformly spaced
samples were located along each well horizontal section using a sampling interval of
0.62 ft, after converting the volumes from time to depth. Once the sampling is done, a
cylinder with a supplied radius is placed at each sample location inside the volume.
Only samples that fall inside the cylinder and the seismic volume are taken into
account. A set of seismic samples is then created inside each cylinder, as illustrated on
Figure 47.
Figure 47. Hypothetic representation of uniform sampling along a horizontal section. A
single value from the seismic volume of interest is generated within the section using
the most common value of the distribution.
75
Finally, to determine a single value of the seismic attribute, I computed the mode,
or most frequent value, within the sampling set. The top depth, base depth, horizontal
length, and azimuth for each horizontal section generated inside survey A are
summarized in Appendix B.
Figure 48 shows the crossplots of λρ and µρ derived from seismic inversion
versus the predicted brittleness index value derived from each well horizontal section.
The trend of the relationship between brittleness and the elastic parameters computed
after seismic inversion is consistent with the trends obtained from well A. Nonetheless,
in this case, µρ presents a much higher correlation with brittleness than λρ. This is a
result of estimating the brittleness volume from the patterns presented on well A. From
the well logs we can see that variations on the brittleness index are clearly associated
with and controlled by variations on µρ.
To corroborate the results, I generated a crossplot of the computed brittleness
from λρ and µρ seismic attributes versus the brittleness index extracted for each
horizontal section. Predicted brittleness index values were again calculated through
non-linear regression. The crossplot is shown on Figure 49. The relationship is very
similar to the one obtained from well log measurements (Figure 41), which validates the
use of non-linear regression as a method to predict brittleness from λρ and µρ seismic
attributes within survey A.
After obtaining a reliable brittleness volume I correlated the first 90 days
production to geometrical and simultaneous inversion attributes.
76
Figure 48. Crossplots of λρ and µρ vs. brittleness index extracted from horizontal well
sections. The correlation between µρ and brittleness is similar to the one found on well
A. However, the correlation between λρ and brittleness is not conclusive for the
seismic-computed values. Data are colored by true vertical depth.
77
Figure 49. Brittleness index computed from λρ and µρ attributes vs. brittleness index
from the predicted seismic volume. The non-linear approximation is similar to the one
obtained from well A, presented on Figure 35. This validates the non-linear regression
method as a way to accurately predict brittleness from λρ and µρ, both from well logs
and seismic measurements.
78
First 90 days production estimation from seismic attributes volumes
Production in the Barnett Shale is enhanced by hydraulic fracturing, which is
considered to be more effective in brittle rocks. The process is performed by pumping a
mixture of water, sand and other additives under high pressure into natural gas bearing
shale more than a mile and a half below the surface. This allows natural gas and oil to
flow back into the well bore and be brought back to the surface (Fort Worth Chamber of
Commerce). Hydraulically induced fractures in the Fort Worth Basin strike between 45°
and 80° from North, and dip approximately 81°NW (Bruner and Smosna, 2011). Figure
50 shows the azimuthal direction and the first 90 days production values of 44
horizontal wells inside survey A. The production values are normalized from 0 to 10,
with 10 representing the highest production in the field. In the study area the wells are
drilled perpendicular to maximum horizontal stress direction in order to drain several
parallel sections of the reservoir. Such wells do not produce economically unless
stimulated.
Generally, production increases with increasing horizontal length of the well, as
illustrated in Figure 51. Taking this into account, first 90 days production values from
horizontal wells were scaled by the horizontal length of each well.
The brittleness index extracted from the horizontal well intervals was then
correlated to the scaled first 90 days production data, as shown in Figure 51.
79
Fig
ure
50. S
chm
idt-
insp
ired
dia
gra
m s
ho
win
g a
zim
uth
al d
irec
tion a
nd f
irst
90 d
ays
pro
duct
ion v
alu
es f
or
all
the
hori
zonta
l w
ells
avai
lable
wit
hin
surv
ey A
. T
he
angle
rep
rese
nts
the
azim
uth
of
the
hori
zonta
l w
ell
and t
he
pro
duct
ion v
alues
bec
om
e hig
her
tow
ard t
he
edg
es o
f th
e dia
gra
m. B
lack
dots
rep
rese
ntt
he
azim
uth
al p
osi
tion o
f
the
wel
ls, bei
ng t
hei
r dia
met
er p
roport
ional
to t
hei
r pro
duct
ion. W
ellb
ore
s in
the
surv
ey m
ap a
re c
olo
red
acco
rdin
g t
o f
irst
90 d
ays
pro
duct
ion v
alues
. M
ost
wel
ls a
re d
rill
ed i
n t
he
NW
-SE
dir
ecti
on, per
pen
dic
ula
r to
the
Min
eral
Wel
ls f
ault
and t
he
mai
n d
irec
tion f
or
ind
uce
d fr
actu
res.
80
Figure 51. Crossplots of (a) first 90 days production vs. horizontal length before
normalizing production values and (b) normalized first 90 days production vs.
brittleness index for the wells located inside seismic survey A. Normalized production
increases with horizontal length and brittleness index.
81
3D seismic attribute analysis
Curvature and curvature related attributes can be used to predict fracture
intensities, due to the relationship that exists between deformation and the stresses that a
bed undergoes while folding or buckling. White et al. (2012) interpreted natural
fractures along a horizontal borehole image log along the Mississippi Lime. They
calculated fracture density along the well bore and then correlated the measurements
with seismic attributes. They observed a correlation between high fracture density
interpreted from the horizontal image logs and 3D structural curvature.
Curvature is the measure of a surface deviation from a plane, and it describes how
bent a curve is at a particular point P. For a particular point on a curve, the curvature is
defined as the rate of change of angle dΩ with respect to the arc length dS:
where R is the radius of the circle tangent to the point P which makes greatest possible
contact with the curve.
In 3D any surface can be defined by two orthogonal principal curvatures: k1 and
k2. There, k1 is the principal most positive curvature and (when positive) describes
ridge-like features, whereas k2 is the principal most negative curvature and (when
negative) describes valley-like features. Both of them can have positive and negative
values, but k1 is always greater than or equal to k2 (k1≥ k2).
The combination of most positive and negative curvature defines the local shape.
The shape indices describe the local morphology of a surface, independently of its
scale. The shape index (SI) can be derived from the equation proposed by Koenderink
and van Doorn (1992):
82
(
)
Shape index ranges between -1 and 1. With -1.0 corresponding to bowls, -0.5 to
valleys, 0.0 to saddles, 0.5 to ridges, and 1.0 to domes.
The structural shape is usually associated to a particular play. However, this
attribute does not measure the magnitude of the total deformation. The shape index
needs to be modulated by the curvedness (C). This attribute describes the magnitude of
the surface curvature, in order to differentiate strongly deformed features from early
planar ones. Curvedness is defined as (Koenderink and van Doorn, 1992):
√
Coherence measures the similarity of neighboring waveforms. This attribute is
useful in edge-detection and structural interpretation because when the vertical and
lateral variation in the seismic data is low the traces are more similar and the coherence
values are higher. In this project I used a Sobel-filter similarity algorithm described by
Luo et al. (2003) for coherence computations. This algorithm uses a generalized Hilbert
transform, and aids the interpretation of structures that fall below the tuning thickness.
The normalized first 90 days production was first correlated to shape index and
curvedness, as shown in Figure 52. The relationship shows that production tend to
increase in areas that approximates bowl shapes and do not present strong deformations.
Production prediction through non-linear regression of these two attributes is shown on
Figure 53. Curvature and coherence were then correlated to production, as illustrated by
the crossplots on Figure 54. The relationships reflect that production is higher when the
curvature of the surface is lower, and when the geological features are more coherent.
83
Figure 52. Crossplots of (a) first 90 days production vs. shape index and (b) first 90
days production vs. curvedness. Production values tend to increase for not strongly
deformed bowl-shaped features. The attribute values correspond to the mode of the
distribution within each horizontal well section.
84
Figure 53. Predicted production using curvedness and shape index as the input
attributes vs. scaled first 90 days production. The correlation coefficient between the
predicted and the original production values is lower than 0.5 when only two attributes
are used in the regression. Therefore brittleness index, λρ, µρ, curvature and coherence
were also used as input attributes for the prediction. Brittleness index, generated by
regression of λρ and µρ is the attribute that better correlates to the scaled first 90 days
production.
85
Figure 54. Crossplots of (a) first 90 days production vs. most positive curvature and (b)
first 90 days production vs. coherence. Production values are higher when the most
positive curvature is negative (a bowl) and the features are more coherent. This
interpretation is consistent with shape index and curvedness relationships presented on
Figure 45.
86
Figure 55. Crossplots of (a) first 90 days production vs. λρ and (b) first 90 days
production vs.µρ. The relationship between production and elastic parameters is clearly
non-linear.
87
Finally, normalized production was predicted through non-linear regression using:
brittleness index, λρ, µρ, most positive curvature k1, coherence, shape index, and
curvedness as the input seismic attributes. Table 10 shows the sensitivity of the solution
to each of the mentioned variables:
Variable Sensitivity
Brittleness index 0.05
Lambda-rho 0.04
Coherence 0.04
Shape index 0.03
Curvedness 0.02
Most positive curvature 0.02
Mu-rho 0.01
Table 10. Sensitivity of production prediction to; brittleness index, λρ, coherence, shape
index, curvedness, most positive curvature k1 and µρ.
Figure 56 shows the n-fold and leave-out cross validation plots obtained through
non-linear regression.
Figure 56. N-fold and leave-out cross validation plots for predicted production through
non-linear regression. The absolute error calculate from both cross validation methods
is lowest when using 5 folds or iterations.
88
Figure 57 shows the relationship between original and predicted first 90 days
production using 35 horizontal wells. The correlation coefficient between the prediction
and the original values is 0.91, which means that the non-linear regression method can
be used to estimate first 90 days production from the input dataset.
A volume of predicted production was generated from prior correlation with
seismic attributes. A stratal slice between the intra-Lower Barnett horizon (380 ft below
top of the member) and the Viola limestone was computed through each seismic
attribute volume. Figure 58 shows the calculated slices corresponding to shape index
co-rendered with curvedness, curvature co-rendered with coherence, brittleness index,
and predicted first 90 days production. Production trends in the target area seem to be
mainly controlled by the shape index and brittleness. More brittle areas to the east are
associated with larger production values, while the ductile regions in the southern and
northern portion of the survey resulted to be less productive. At the same time
production is compartmentalized by broad bowl-shaped areas with lower curvedness.
Figure 59 shows a cross-section through the computed production volume
including horizontal and vertical sections from nine different wells. Four of those nine
wells were part of the set of wells that were not considered in the non-linear regression
analysis, for results validation. The well bores are colored according to their normalized
production value. The predicted production matches closely the normalized first 90 days
production for the wells displayed in the image and also for the rest of the wells across
seismic survey A. Therefore, the studies confirm that there is an effective non-linear
relationship between production and fracture behavior, which can be described from
brittleness index, curvature, and curvature-related attributes.
89
Figure 57. Predicted vs. original first 90 days production calculated through non-linear
regression using: λρ, µρ, brittleness index, most positive curvature k1, coherence, shape
index, and curvedness as the input variables for analysis. Predicted production values
match accurately the normalized values available from the horizontal wells.
90
Fig
ure
58. S
trat
al s
lice
s bet
wee
n 3
80
ft
bel
ow
the
top o
f th
e L
ow
er B
arnet
t S
hal
e an
d t
he
top o
f th
e V
iola
lim
esto
ne.
Fro
m t
op l
eft
to l
ow
er r
ight
mag
es c
orr
espond t
o:
shap
e in
dex
co
-ren
der
ed w
ith c
urv
edn
ess,
curv
ature
co
-ren
der
ed w
ith c
oher
ence
, bri
ttle
nes
s in
dex
, an
d p
redic
ted n
orm
aliz
ed f
irst
90 d
ays
pro
du
ctio
n.
Pro
duct
ion t
rend i
n t
he
targ
et a
rea
seem
s to
be
mai
nly
infl
uen
ced b
y s
hap
e in
dex
and b
ritt
lenes
s in
dex
.
91
Fig
ure
59. C
ross
-sec
tion
thro
ugh t
he
com
pute
d p
roduct
ion v
olu
me.
Wel
l bore
s ar
e co
lore
d a
ccord
ing t
o t
hei
r fi
rst
90 d
ays
pro
duct
ion
val
ues
. W
ells
JH
8, JH
16,
JH27, an
d J
H42 w
ere
no
t co
nsi
der
ed i
n t
he
non
-lin
ear
regre
ssio
n a
nal
ysi
s, h
ow
ever
the
close
ly m
atch
the
val
ues
of
pre
dic
ted f
irst
90 d
ays
pro
du
ctio
n w
ithin
the
targ
et a
rea.
Th
e re
d l
ine
in t
he
map
indic
ates
the
loca
tion o
f th
e cr
oss
-sec
tion
92
Chapter 6: Conclusions
In my analysis I observe that adequate seismic data reprocessing provides better
quality data suitable to perform long-offset simultaneous prestack inversion of ZP, ZS,
and density. The random noise, NMO stretch, and non-horizontal reflections are
reduced through accurate velocity re-picking after prestack time migration and reverse
NMO. Prestack structure-oriented filtering improves lateral and vertical resolution in
the target area by balancing the amplitude content along the seismic record. The
combination of Kirchhoff prestack time migration, velocity analysis, NMO, and
prestack structure-oriented filtering results in better imaged long offsets gathers. These
gathers can be used for simultaneous prestack inversion. A clean seismic volume with
improved frequencies and resolution, suitable for seismic interpretation, is obtained as a
product.
Simultaneous prestack inversion provides estimates of ZP, ZS, and density, which
can be used to discriminate lithologies within the Upper and Lower Barnett Shale, as
well as limestones fracture barriers. By first computing a brittleness index template
from elastic parameters measured in a cored well, I generate a brittleness index volume
based on non-linear regression of λρ and µρ values computed from seismic inversion
results.
Brittleness index in the Barnett Shale is dominated by quartz, while ductility is
dominated by clay content and calcite. More brittle areas in the Lower Barnett Shale
facilitate the implementation and extent of hydraulic fracturing, and correlate to higher
productive zones.
93
Clay model experiments by White et al. (2013) show that fracture intensity has a
non-linear relationship with curvature. Specifically, no fractures are initiated during
elastic deformation at low values of curvature. Few additional fractures are initiated
beyond the “saturation” point, represented by high values of curvature.
In Wise County, the diagenetic overprint of calcite-filled fractures make the rock
stronger, and less easily fractured. Using microseismic data in a neighboring survey,
Thompson (2010) and Perez Altamar (2013) showed that most events occurred in
broad, bowl-shaped areas. For these reasons, quantitative correlation of production to
curvature attributes requires a non-linear algorithm. Such non-linear algorithms also
help estimate brittleness index from λρ and µρ.
In this analysis higher productive areas in the Lower Barnett Shale are constrained
by brittle bowl shaped features with low curvature, which have not undergone strong
deformation.
94
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98
Appendix A
Clay mineralogy identification through XRD in core samples from the Lower
Barnett Shale, Wise County, TX
In this project two samples from a core of the Newark East Field were
qualitatively analyzed through X-Ray Diffraction (XRD) to recognize distinctive clay
phases that could be associated with fracture behavior in the Lower Barnett Shale
member. The specimens were taken at 7,557 ft and 7,568 ft in well B. Figure 53
illustrates the location of the core, relative to the location of survey A.
Information about the core samples, which were provided by Professor Roger
Slatt at the University of Oklahoma, is presented in Table 7:
Table 11. Lower Barnett samples analyzed through XRD
Under the advisory of Professor Andy Madden, the core samples were analyzed in
a Rigaku Ultima-IV X-ray diffractometer, located at the Devon Nanolab at the
University of Oklahoma. Bragg-Brentano and Parallel Bean configurations were used
for the air-dried measurements. Diffraction patterns were scanned from 2° to 70° 2θ
(1°≥ θ ≥35°), with a step size of 0.02° and a counting time of 3s. After XRD analysis of
the dry specimens, the samples were placed in a desiccator containing ethylene glycol at
60°C overnight and analyzed in Bragg Brentano from 2° to 32° 2θ. This configuration
was chosen after evaluating the noise in the diffraction patterns from the air-dried
samples. All the specimens were heated in a muffle furnace at 550°C for one hour, and
analyzed in the diffractometer under the same conditions as the glycolated samples.
Sample name Well Name Depth (ft) Weight (g)
LB_7568 Well B 7,568.0 5.279
LB_7557 WellB 7,557.2 4.665
99
PDXL software was used to generate an initial list of peak positions and to
identify the mineral groups from the glycolated patterns. Due to the high noise content
in sample LB_7557 the data were smoothened through an Extended Gaussian algorithm
and the all the “potential” symmetrical peaks were identified. The results obtained after
the analysis are described below:
Sample LB_7568: The diffraction pattern for the glycolated sample (EG) with
the interpretation of the major clay phases is present on Figure 50 and compared to the
air-dried (AD) and heat-treated (HT) patterns in Figure 54. After comparing the AD,
EG and HT diffractograms for LB_7568, the following clay minerals were identified:
illite-smectite, illite, quartz, and feldspar. The peak with a d-spacing of 4.49Å could
correspond to a small amount of illite in a plane different than (0 0 l), given by the high
quartz content in the sample. The identified peaks, along with the corresponding clay
minerals are shown in Table 8.
Table 12. Clay minerals identified in sample LB_7568
2θ d-spacing Height
(cps) Int. I (cps)
Asymmetry
factor Clay mineral
8.69 10.16 7.69022 3.99105 1.02 Illite-Smectite
8.84 9.99 29.047 15.4379 1.02 Illite
17.1 5.18 7.93759 5.62922 1.03 Illite-Smectite
17.5 5.04 23.1774 17.2071 1.03 Illite
19.7 4.49 27.8113 23.0331 1.01 Illite hkl
20.8 4.26 116.334 44.6272 1.13 Quartz
21.9 4.05 27.7758 5.31689 1.15 Anorthite
26.4 3.36 28.9582 10.5424 1.11 Illite
26.6 3.34 758.724 202.784 1.20 Quartz
27.6 3.20 6.6805 3.62446 1.06 Anorthite
27.9 3.18 27.2037 17.789 1.10 Anorthite
29.8 2.98 33.4679 16.1776 1.06 Orthoclase
100
Sample LB_7557:The diffractogram for the EG specimen is shown on Figure 55.
The identified clay phases for sample LB_7557 are: smectite, mixed-layer illite-
smectite, illite, quartz, and feldspar (Table 9). These minerals are consistent with the
results presented by Rowe et al. (2008), who identified pyrite peaks in the Lower
member of the Barnett shale.
Table 13. Clay minerals identified in sample LB_7557
The mineralogy of the samples from well B made it impossible to prepare an
organic mount with the filter peel method. This is an indication of a relatively little
amount of clay in the specimens, which is consistent with the analyses of Loucks and
Ruppel (2007) and Waters et al. (2011), who set an average clay volume in the Barnett
2θ d-spacing Int. I (cps) Int. W (deg) Asymmetry
factor Clay mineral
4.99 17.61 2.93776 3.79951 1.20 Smectite
8.29 10.61 0.905666 0.438905 1.03 Illite-Smectite
8.84 9.99 10.014 7.96112 1.16 Illite
10.6 8.27 4.82401 2.98619 1.00 Smectite
15.5 5.70 2.90516 2.17894 1.16 Smectite
17.3 5.15 2.71867 1.45104 1.04 Illite-Smectite
17.6 5.01 2.17472 1.38465 1.00 Illite
19.6 4.50 3.4247 2.27122 1.04 Illite hkl
20.8 4.26 8.76614 5.40483 1.01 Quartz
21.0 4.22 3.32382 1.20681 1.11 Microcline
21.8 4.05 2.54825 1.34116 1.05 Anorthite
23.4 3.79 6.05727 3.08986 1.01 Microcline
26.1 3.41 5.23126 1.00027 1.05 Smectite
26.4 3.36 26.9271 10.3443 1.18 Illite
26.5 3.34 44.2974 17.1135 1.06 Quartz
26.7 3.33 21.8223 7.4252 0.99 Anorthite
26.9 3.29 5.18741 1.46747 1.19 Anorthite
27.5 3.24 0.326683 0.149056 1.05 Microcline
27.9 3.18 6.76475 4.51826 1.03 Anorthite
29.1 3.06 5.00264 5.52073 1.04 An67
29.6 3.02 1.44167 0.545081 1.04 Microcline
30.1 2.96 1.59666 0.461822 1.07 Microcline
101
between 25%-29%. In this study it is obvious from the diffraction patterns of the
glycolated specimens that it was easier to identify smectite, illite, illite-smectite, quartz,
and feldspar phases for sample LB_7568. Nonetheless, no attempt was done to quantify
the clay minerals. From the analyzed samples there seems to be an increase in smectite
content in the upper interval of the Lower Barnett, which is associated with a more
ductile behavior of the rock. The presence of this swelling clay could complicate
drilling, as the hydrated clay can act as a barrier for fracture propagation. The ECS logs
mineralogy from well A also identifies an interval with higher amount of clay minerals
in the upper part of the Lower Barnett Shale.
The diffraction pattern for the lower Barnett samples: LB_7557 and LB_7568 are
consistent with those presented by Rowe et al. (2008). The same major peaks identified
by these authors have been estimated from the diffractogram obtained in this study.
According to Kale (2009), who studied Barnett mineralogy through FTIR in the
Newark East Field, illite is the dominant clay in the Lower Barnett Shale, while quartz
and feldspar are fairly constant through the entire interval. In the samples studied in this
project quartz and feldspar are also consistently present in the samples, and illite is the
most common clay. In the deepest Barnett interval, the lack of smectite, and the
stronger presence of quartz are associated with more brittle behavior of the rock, as
observed in well log data from well A.
102
Figure 60. Location of well B, relative to survey A and well A
103
Figure 61. (a) Ethylene Glycol pattern for sample LB_7568. The intensity of all the
peaks, except for the quartz peak found at 3.34Å (27°), is relatively low. This is an
indirect indicator of low clay content in the sample, (b) Comparison of Air-dried,
Ethylene Glycol, and Heat Treated patterns. The location of the illite and quartz peaks
do not vary from one profile to another, but the intensity of peaks is significantly lower
in the heat treated pattern. The dashed line represents the scaled removed background.
104
Figure 62. (a) Smoothened Ethylene Glycol pattern for sample LB_7557. Quartz
intensity is again considerably higher than the intensity of the clay minerals. (b)
Comparison of Air-dried, Ethylene Glycol, and Heat Treated patterns.The location of
the illite and quartz peaks do not vary from one profile to another, however the intensity
of peaks is significantly different in the heat treated pattern. The removed background is
illustrated by the dashed line.
105
Appendix B
Information about horizontal sections within Survey A
Table 10 presents the top depth, base depth, horizontal length, and azimuth for
each horizontal section generated inside survey A. The highlighted cells correspond to
the wells that were not included in the non-linear regression analysis.
Well Name Top MD (ft) Base MD (ft) Horizontal
Length (ft) Azimuth (⁰)
JH1 8311.926 9549.919 1237.99 140.13
JH2 8267.767 10051.75 1783.98 151.98
JH3 8240.896 10724.87 2483.97 129.8
JH4 8262.896 9949.881 1686.99 139.59
JH5 8294.903 10829.85 2534.95 146.34
JH6 8280.873 10321.84 2040.97 170.26
JH7 8199.889 10870.85 2670.96 112.34
JH8 8292.898 10899.88 2606.98 129.91
JH9 8418.894 11174.87 2755.98 117.3
JH10 7768.856 11325.79 3556.94 153.67
JH11 7867.878 10609.84 2741.96 160.07
JH12 7824.848 10859.78 3034.93 117.58
JH13 8645.817 10449.79 1803.97 130.72
JH14 8387.141 10511.11 2123.97 130.92
JH15 8187.884 10730.85 2542.97 121.34
JH16 8227.909 10515.84 2287.93 126.25
JH17 8272.873 9920.853 1647.98 132.74
JH18 8242.797 10178.74 1935.94 154.46
JH19 8220.862 11181.84 2960.98 1.26
JH20 8241.899 11432.83 3190.94 176.31
JH21 8514.881 11042.79 2527.90 139.59
JH22 8698.861 12360.82 3661.96 122.19
JH23 8101.885 10263.86 2161.97 158.18
JH24 7725.838 10874.79 3148.95 157.19
JH25 7969.851 10679.8 2709.95 158.43
JH26 7983.905 11759.8 3775.9 110.69
JH27 8290.813 12223.72 3932.9 150.86
JH28 8300.885 10635.77 2334.88 169.13
JH29 8459.879 12161.77 3701.9 122.2
JH30 8546.867 11964.81 3417.94 124.98
JH31 7944.855 10461.76 2516.91 107.57
JH32 8329.86 9789.826 1459.97 137.43
JH33 8332.794 10964.63 2631.84 146.48
106
JH34 8242.803 10222.75 1979.94 146.89
JH35 7846.859 10419.83 2572.97 114.4
JH36 8299.839 12473.7 4173.86 151.79
JH37 8258.846 13819.78 5560.93 133.45
JH38 8415.777 13691.65 5275.87 135.75
JH39 7936.855 11738.77 3801.91 110.94
JH40 7995.829 12597.75 4601.92 109.82
JH41 8148.837 11804.73 3655.89 114.72
JH42 8267.767 10051.75 1783.98 151.98
JH43 8112.885 9914.866 1801.98 146.25
JH44 8213.621 10235.52 2021.90 145.11
Table 14. Horizontal sections generated for each well inside survey A.
107
Appendix C
Seismic amplitudes extracted from each horizontal section within survey A
Table 11 presents the amplitude of λρ, µρ, and brittleness index extracted along
the horizontal sections for each of the wells inside survey A. The highlighted cells
correspond to the wells that were not included in the non-linear regression analysis.
Well Name Amplitude λρ
(Gpa*g/cm3)
Amplitude µρ
(Gpa*g/cm3)
Amplitude
Brittleness Index
JH1 125.26 83.1 2.76
JH2 65.022 54.07 3.54
JH3 80.68 94.1 1.94
JH4 88.31 56.76 3.37
JH5 170.06 54.46 3.7
JH6 131.54 76.7 3.55
JH7 125.26 83.1 2.76
JH8 50.94 66.55 3.2
JH9 132.54 82.42 3.79
JH10 156.45 54.75 4.15
JH11 111.94 73.88 3.3
JH12 129.9 38.75 4.32
JH13 125.45 79.76 3.19
JH14 64.25 58.97 3.67
JH15 130.19 179.39 2.12
JH16 48.3 80.89 2.73
JH17 24.94 43.84 4.17
JH18 92.86 56.91 3.29
JH19 138.7 59.81 3.24
JH20 177.8 46.49 3.98
JH21 49.78 48.04 4.04
JH23 45.12 51.01 3.84
JH24 65.02 54.07 3.55
JH25 86.68 53.79 3.6
JH26 97.53 56.53 3.3
JH27 69.09 75.27 2.32
JH28 138.7 59.81 3.24
JH30 113.33 64.3 2.52
JH31 100.01 87.5 3.38
JH32 97.53 56.53 3.3
108
JH33 43.21 41.06 4.28
JH34 42.12 38.57 4.33
JH35 88.31 56.76 3.37
JH36 132.16 66.15 3.15
JH37 119.81 107.53 3.48
JH38 45.61 40.99 4.32
JH39 108.78 64.05 3.08
JH40 53.02 45.28 4.15
JH41 116.52 174.6 1.52
JH42 48.3 80.89 2.73
JH43 104.28 65.01 3.02
JH44 132.16 66.15 3.15
Table 15. Seismic amplitudes corresponding to λρ, µρ, and brittleness index extracted
along the horizontal sections inside survey A.
Table 12 presents the amplitude of curvature and curvature-related attributes
extracted along the horizontal sections for each of the wells inside survey A. The
highlighted cells correspond to the wells that were not included in the non-linear
regression analysis.
Well name Shape Index Coherence Curvedness k1
JH1 0.69 0.91 1.78E-04 2.58E-04
JH2 -0.2 0.9 1.06E-04 6.88E-05
JH3 -0.54 0.97 1.36E-04 1.28E-04
JH4 0.67 0.99 7.93E-05 8.11E-05
JH5 0.1 0.98 7.28E-05 4.34E-05
JH6 0.22 0.99 1.01E-04 1.21E-04
JH7 -0.59 0.98 9.61E-05 -1.56E-05
JH8 0.58 0.99 6.99E-05 7.08E-05
JH9 0.34 0.99 8.73E-05 7.63E-05
JH10 -0.09 0.89 1.04E-04 1.32E-04
JH11 -0.14 0.3 1.26E-04 1.05E-04
JH12 0.2 0.98 1.03E-04 1.27E-04
JH15 0.64 0.99 1.03E-04 1.54E-04
JH16 0.13 0.98 7.26E-05 9.71E-06
JH17 0.55 0.98 9.96E-05 4.40E-05
JH18 0.21 0.99 7.49E-05 3.47E-05
JH19 -0.09 0.98 9.94E-05 5.51E-05
JH20 0.5 0.98 7.53E-05 1.96E-05
109
JH23 -0.61 0.87 8.82E-05 2.63E-04
JH24 -0.61 0.94 8.75E-05 3.19E-06
JH25 -0.77 0.95 5.55E-05 -2.09E-05
JH26 -0.33 0.56 6.44E-05 1.59E-05
JH27 -0.4 0.99 8.51E-05 3.83E-05
JH28 -0.26 0.99 7.71E-05 3.29E-05
JH31 -0.16 0.96 8.10E-05 1.28E-04
JH33 -0.51 0.99 1.01E-04 -5.91E-06
JH34 -0.26 0.99 1.07E-04 4.38E-05
JH35 0.46 0.99 1.35E-04 6.31E-05
JH36 -0.56 0.98 1.19E-04 2.20E-05
JH39 -0.12 0.45 1.28E-04 1.29E-04
JH40 -0.63 0.54 1.57E-04 -5.99E-05
JH41 0.37 0.44 1.16E-04 1.81E-04
JH42 -0.75 0.97 9.25E-05 -7.08E-05
JH43 0.74 0.98 1.20E-04 3.60E-05
JH44 -0.29 0.99 9.63E-05 1.21E-04
Table 12. Seismic amplitudes corresponding to Shape index, coherence, curvedness,
and k1 extracted along the horizontal sections inside survey A.