Marroquin Hart (2004)
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AUTHORS
Ivan Dimitri Marroqun Earth andPlanetary Sciences, McGill University, 3450University, Montreal, Quebec, Canada H3A 2A7
Ivan Dimitri Marroqun is a Ph.D. student ofgeophysics at McGill University, Canada. Hereceived his B.Sc. combined degree in physicsand geology from the Universite de Montrealin 1994 and his M.Sc. degree in geophysicsfrom Ecole Polytechnique in 1998. His currentresearch includes characterization of conglom-erate and coalbed reservoirs by 3-D seismic-based means.
Bruce S. Hart Earth and PlanetarySciences, McGill University, 3450 University,Montreal, Quebec, Canada H3A 2A7;[email protected]
Bruce Hart holds a Ph.D from the Universityof Western Ontario. He held positions with theGeological Survey of Canada, Penn State, andthe New Mexico Bureau of Mines and MineralResources prior to joining McGill Universityin 2000. His research interests focus on theintegration of 3-D seismic data with other datatypes for reservoir characterization programs.He has been an associate editor of the AAPGBulletin since 2000.
ACKNOWLEDGEMENTS
Principal funding for this project was providedby a Department of Energyfunded investiga-tion of optimizing infill drilling in naturallyfractured tight-gas reservoirs (contract numberDE-FC26-98FT40486; Larry Teufel, principal in-vestigator). Portions of this project were fundedby a Natural Science and Engineering ResearchCouncil award (238411-01) to Hart. We thankWilliams Energy (in particular, Ralph Hawks) andTom Engler (New Mexico Tech) for supplyingdata and insights into Rosa field. Software usedin this study was graciously provided by Land-mark Graphics Corporation (GeoGraphix) andHampson-Russell. We thank both companiesfor their ongoing support of our research andformer AAPG editor John Lorenz and two anon-ymous reviewers for their helpful commentsthat helped improve the focus of this paper.
Seismic attribute-basedcharacterization of coalbedmethane reservoirs: An examplefrom the Fruitland Formation,San Juan basin, New MexicoIvan Dimitri Marroqun and Bruce S. Hart
ABSTRACT
The Fruitland Formation of the San Juan basin is the largest pro-
ducer of coalbed methane in the world. Production patterns vary
from one well to another throughout the basin, reflecting factors
such as coal thickness and fracture and cleat density. In this study,
we integrated conventional P-wave three-dimensional (3-D) seis-
mic and well data to investigate geological controls on production
from a thick, continuous coal seam in the lower part of the Fruitland
Formation. Our objective was to show the potential of using 3-D
seismic data to predict coal thickness, as well as the distribution and
orientation of subtle structures that may be associated with en-
hanced permeability zones. To do this, we first derived a seismic
attribute-based model that predicts coal thickness. We then used
curvature attributes derived from seismic horizons to detect subtle
structural features that might be associated with zones of enhanced
permeability. Production data show that the best producing wells
are associated with seismically definable structural features and thick
coal. Although other factors (e.g., completion practices and coal type)
affect coalbed methane production, our results suggest that conven-
tional 3-D seismic data, integrated with wire-line logs and produc-
tion data, are useful for characterizing coalbed methane reservoirs.
INTRODUCTION
The San Juan basin is the worlds largest producer of coalbed meth-
ane. Most coalbed methane in this basin is produced from coal
seams of the Upper Cretaceous Fruitland Formation. Production
began in 1951 with the Ignacio Blanco-Fruitland gas field at Ignacio,
AAPG Bulletin, v. 88, no. 11 (November 2004), pp. 16031621 1603
Copyright #2004. The American Association of Petroleum Geologists. All rights reserved.
Manuscript received August 25, 2003; provisional acceptance December 31, 2003; revised manuscriptreceived May 3, 2004; final acceptance May 19, 2004.
DOI:10.1306/05190403088
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Colorado. Today, Fruitland coalbed methane wells have
a cumulative production of 9 tcf (0.25 1012 m3),an annual production of 0.9 tcf (0.025 1012 m3),and contain in-situ gas resources estimated at 55 tcf
(1.6 1012 m3; Dugan, 2002).The production patterns in the Fruitland vary on
basinwide and local scales, reflecting differences in geo-
logic settings and engineering factors (e.g., completion).
Geologic factors affecting the capacity of coal seams to
store and produce coalbed methane include coal com-
position and rank, gas composition, water production,
coal thickness, and degree of fracturing (e.g., Kaiser
and Ayers, 1994). An understanding of the controls
acting on these seams can be used to guide exploration
and development activities. Despite the increasing im-
portance of coal seams as sources of methane gas, little
seismic data have been acquired to aid in characterizing
coalbed methane reservoirs (Shuck et al., 1996). Shuck
et al. (1996) and Ramos and Davis (1997) used seismic
data to help characterize a coalbed methane reservoir
in the Fruitland Formation at Cedar Hill field in the
San Juan basin (Figure 1). These studies sought to de-
termine fracture location, direction, and density. Shuck
et al. used multicomponent seismic data, whereas Ramos
and Davis used both amplitude variation with offset
(AVO) and modeling analysis. Selected aspects of these
studies are summarized in the Discussion.
This paper aims to show how two of the primary
geologic controls on coalbed methane production, coal
thickness and subtle structures that may be associated
with enhanced permeability zones, may be predicted
from conventional (P-wave) three-dimensional (3-D)
seismic data. We use 3-D seismic attributes to generate
a model that predicts the thickness of the coal seam and
then use curvature analysis to delineate subtle struc-
tural features. We note that the best production trends
are associated with thick coal and seismically detectable
structural lineaments. This methodology should have
application in other coalbed methane plays. Issues that
arose during the course of our attribute analyses shed
insights on how to address certain technical aspects of
these studies.
GEOLOGICAL FRAMEWORK
The San Juan basin lies in the east-central part of the
Colorado Plateau in northwestern New Mexico and
southwestern Colorado. This basin is roughly circular
and covers an area of about 6700 mi2 (17,353 km2;
Laubach and Tremain, 1994). During the Late Cre-
taceous, shoreline progradation (to the northeast) led
to deposition of the northwest-southeaststriking
Pictured Cliffs Sandstone (Ayers et al., 1994). Inter-
mittent transgressive-regressive shifts of the shoreline
resulted in the interfingering of the upper tongues of
the Pictured Cliffs Sandstone and continental facies of
the overlying Fruitland Formation. The Fruitland For-
mation is the primary coal-bearing formation in the San
Juan basin.
The Laramide orogeny began during the Late Cre-
taceous and continued into the Eocene. Various as-
pects of this tectonic event have been discussed by
Hamilton (1987) and Lorenz and Cooper (2003). The
San Juan basin is structurally delimited by the Hogback
monocline to the northwest, Archuleta anticlinorium
to the northeast, Nacimiento uplift to the east, Defi-
ance uplift to the west, and the Zuni uplift to the south.
Coalification of the Fruitland began during the Lar-
amide orogeny, and this tectonic activity may have in-
fluenced the development of cleats (the term applied to
fractures in coals), minor folds, and faults in this unit.
Based on the variability of cleat strike in Fruitland coals,
Tremian et al. (1994) noted the existence of two prin-
cipal face-cleat domains, northwest-southeast strike
in the north of the basin (domain 2), and northeast-
southwest strike in the south (domain 1), separated by
an east-trending transition zone (Figure 1). They stated
that permeability in the face-cleat direction should
be greater than in the butt-cleat direction because of
greater connectedness in the former direction. Laubach
and Tremain (1994) suggested that fracture swarms,
which are associated with increasing fracture and cleat
connectivity and intensity, should be considered as ex-
ploration targets in coalbed methane reservoirs.
DATABASE
Our database consisted of a time-migrated 3-D seismic
data volume and wire-line logs from 59 wells (Figure 1)
within and around the survey area (27 of which were
in the seismic area). Records of gas production from
36 wells in the seismic area were used to analyze spatial
trends in coalbed methane production. Unfortunately,
we did not have wire-line logs for all of these wells. The
3-D seismic grid covers an area of about 17 mi2 (45 km2)
with a bin size of 150 150 ft (45 45 m). The seismicdata were acquired with a northeast-southwest ori-
entation and then processed to have crosslines oriented
1604 Seismic Attribute-Based Characterization of Coalbed Methane Reservoirs
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north-south and inlines running east-west. Seismic
data quality is variable but is uniformly poor around
the margins of our data set.
Various combinations of gamma-ray, caliper, den-
sity, and resistivity logs were available for wells; sonic
logs were not available for any of the wells in our study
area. As such, we generated pseudosonic logs from den-
sity logs using the Gardner equation (Gardner et al.,
1974). This equation provided unreasonably low ve-
locities values for coal because coals fall off the main
trend used to derive the equation. Accordingly, we
assumed a coal velocity of 8695 ft/s (2650 m/s) based
on handbooks published by geophysical logging com-
panies. Sonic logs were used for modeling purposes
Figure 1. Location map showing cleat strike domains of the Fruitland Formation coal seams in the San Juan basin and majorstructural elements bounding the basin (modified from Tremain et al., 1994). The study area is in a boundary region between aregion (domain 1) dominated by northeast-southweststriking face cleats and a region (domain 2) dominated by northwest-southeaststriking face cleats. The inset at the upper right shows the 3-D seismic area with well locations. Line AA0 is the location ofthe log cross section shown in Figure 2.
Marroqun and Hart 1605
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and to make well ties. No checkshot surveys or other
sources of velocity information were available. Image
logs, oriented core, or other independent means of de-
tecting fracture orientation were not available to us.
RESULTS
Geologic Analysis
We identified coal seams and established the vertical
and lateral extents of the Pictured Cliffs Sandstone and
Fruitland Formation using gamma-ray and density logs.
Figure 2 shows a sample log cross section through the
Fruitland Formation in the seismic area. The top of
the Pictured Cliffs Sandstone was identified by high
gamma-ray and low-density log values. This hot shale
signature is thought to be a marine flooding surface.
Upper Pictured Cliffs tongues have a blocky well-log
response and are considered to be stratigraphically lo-
cated between the Picture Cliffs Sandstone and lower-
most Fruitland coal seam (e.g., Ayers et al., 1994).
These strata were found to have an average thickness of
135 ft (41 m). Coal is identified on logs by a combi-
nation of low-density and low gamma-ray values. Fol-
lowing Ayers et al. (1994), we identified coal using
density and gamma-ray cutoff values 2.0 g/cm3 and
75jAPI, respectively. Two coal-bearing intervals weredelineated: a thick, continuous coal seam in the lower
part of the Fruitland Formation (with an average thick-
ness of 26 ft [8 m]) and an upper succession of thin,
laterally less continuous coal seams interbedded with
clastic strata. The thick accumulation of the lower Fruit-
land coal seam is believed to have been deposited
during periods of shoreline stillstands (cf. Ayers et al.,
1994) and produces most of the coalbed methane in
the study area. The overlying succession of thin, dis-
continuous coal seams is interpreted to have been
deposited in unstable flood-plain settings related to the
renewed progradation of the shoreline. An irregular
low thickness value (2.89 ft [0.88 m]) was observed for
the lower continuous seam in one well at the extreme
west margin of the seismic area. We were not able to
determine the cause of this anomalous value (e.g., fault-
ing); the well is located in an area of poor seismic data
quality, and so it was disregarded from further analyses.
We hand drew the isopach map of the thickness of
the lower coal seam in and around the 3-D seismic
survey area (Figure 3). Our intent was only to generate
a map that shows the principal trends in the data. We
note that the coal is thicker in the southeastern part of
the seismic area and shows a northwest-southeast trend
that is approximately parallel to the inferred Pictured
Cliffs shoreline orientation. The coal seam generally
thins toward the north part of the seismic area.
Seismic Character of Coal Seams and Related Rocks
We generated seismic models by convolving a Butter-
worth zero-phase wavelet of bandwidth 51555
65 Hz (picked to approximate frequency characteristics
Figure 2. Log cross section through the Fruitland Formation. The logs, from left to right, are the gamma ray (GR) and bulk density(RHOB). Note the presence of the thick coal seam overlain by a succession of thin coal seams and the general thickening trend of thisseam to the southeast (right). See Figure 1 for location.
1606 Seismic Attribute-Based Characterization of Coalbed Methane Reservoirs
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of our 3-D seismic data; see below) with bulk density
and our pseudosonic logs from wells in the seismic area.
An example of a seismic model is shown in Figure 4.
From this example, we note that the top of the Fruit-
land Formation corresponds to a zero crossing, although
for ease of picking, we interpreted the Fruitland horizon
as a moderate-amplitude continuous peak in the 3-D
seismic data. A moderate-amplitude trough observed
in the seismic model, between the Fruitland and the
top of the thick coal seam, represents the succession
of thin coal seams. We interpreted this horizon in the
3-D seismic data as a continuous and moderate- to
weak-amplitude trough. This reflection is caused by
the interference effects from the individual reflections
Figure 3. Hand-drawn isopachmap of the lower Fruitland coalseam with 5-ft (1.5-m) contourintervals showing general thick-ness trends. Symbols show datacontrol points. The coal seamis thickest in the southeasternpart of the study area.
Figure 4. Seismic model showing thepredicted response of the Fruitland coalseams and related strata. Line generatedusing wells shown in Figure 2. Seismichorizons: FRLD = top of Fruitland Formation,TopTC = top thick coal seam, BottomTC =bottom of thick coal seam, MainPC = topof the Pictured Cliffs Sandstone.
Marroqun and Hart 1607
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associated with the succession of thin coal seams.
Interference results from short-path multiples, such
that their overlapping waveforms superpose and
outweigh the amplitude of the initial primary reflec-
tion (Gochioco, 1991, 1992). The top of the thick coal
seam, on both seismic model and 3-D seismic data, is a
robust, continuous, high-amplitude trough. The bottom
of the thick coal seam, on the seismic model, corre-
sponds to an intermediate position on the waveform
that is close to the reflection peak, and therefore, we
interpreted this horizon on the 3-D seismic data as the
underlying robust, high-amplitude peak. Moreover,
the bottom of this coal defines the top of the underlying
upper Pictured Cliffs tongues. The top of the Pictured
Cliffs Sandstone, in the seismic model, corresponds to a
trough of weaker amplitude. The corresponding hori-
zon on the 3-D seismic data is a fairly low-amplitude
trough of poor quality. The interpreted horizons in the
3-D seismic data are shown in Figure 5 on an arbitrary
northeast-southwest seismic line trough the survey area.
A statistical wavelet was extracted from the 3-D
seismic data to determine the frequency content of the
data in a time window that covers the horizons of
interest. We observed a predominant peak frequency
( fp) of 40 Hz. By assuming an interval velocity (V ) of8695 ft/s (2650 m/s ) for coal seams (see above), we
calculate the dominant wavelength (l) to be
l V=fp 8695 ft=s = 40 Hz 217 ft 66 m 1
yielding a tuning thickness (l/4) of 54 ft (16 m). Thisvalue is at least twice the average thickness of the lower
Fruitland coal seam in our study area. Therefore, we
conclude that this seam is a seismic thin bed, for which
amplitude variations have been used to estimate the
true coalbed thickness (Gochioco, 1991; Brown, 1999).
SEISMIC ATTRIBUTE ANALYSIS
Seismic attribute studies are useful for integrating
wire-line log and 3-D seismic data to make predictions
of physical properties (e.g., porosity and thickness)
throughout a 3-D seismic survey area (e.g., Schultz
et al., 1994; Hampson et al., 2001). We used a window-
based approach (Chen and Sidney, 1997) to derive a
statistical relationship between coal thickness and seis-
mic attributes (Taner et al., 1979; Brown, 1999; Chen
and Sidney, 1997). The time window for seismic at-
tribute extraction was defined by the picks for the
top and base of the thick coal seam. The thickness of
this seam in each well was determined from the bulk
density log. The multiattribute analysis was based on
27 geophysical well logs in the 3-D seismic survey and
35 amplitude, frequency, and phase attributes. This
list was further increased by applying the following
nonlinear transforms to each attribute: natural log, ex-
ponential, square, inverse, and square root.
Figure 5. Arbitrary seismic transectshowing the interpreted seismic horizons.Transect corresponds to seismic modelshown in Figure 4. Seismic horizons:FRLD = top of Fruitland Formation,TopTC = top thick coal seam, BottomTC =bottom of thick coal seam, MainPC =top of Pictured Cliffs Sandstone.
1608 Seismic Attribute-Based Characterization of Coalbed Methane Reservoirs
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The choice of attributes to use in our regression
expression was made using statistical methods de-
scribed by Hampson et al. (2001). The attributes were
first analyzed to determine which single attribute cor-
related the most strongly with coal thickness (both
values defined at well locations). This attribute was then
paired with all the remaining attributes to determine
which combination of attributes (e.g., two, three, or
more attributes) best predicted coal thickness. As more
attributes are added into the regression model, the pre-
diction error will continuously decrease and conse-
quently improve the fit to the data. This could lead to
overfitting of the data, however, so cross-validation
was used to determine the optimum number of attri-
butes to keep (Hampson et al., 2001). This method con-
sists of dividing the data into two subsets (e.g., the
training data and validation data). Although the train-
ing data set is used to train the model, the validation
data set is used to estimate the validation error. The
validation error is a measure of the quality of the fit to
the validation data set. Therefore, the point at which
adding new attributes starts to increase the validation
error determines the optimum number of attributes to
use in the analysis (Hampson et al., 2001).
Figure 6 shows the total validation error and pre-
diction error against the number of attributes used in
the stepwise linear regression. We note that the valida-
tion error curve begins to increase after the third attri-
bute, defining this as the optimum number of attributes
to be used. The analysis yielded the following linear
relationship:
where
X1 is maximum absolute amplitude; X2 is integratedtrace; and X3 is total energy;
with a correlation coefficient of R2 = 0.87 and averageerror of 3.2%. Figure 7 shows the crossplot of the pre-
dicted thickness vs. the actual thickness values.
The attributes composing the regression expres-
sion are defined as follows:
The maximum absolute amplitude is the maximumvalue in the analysis window interval. Theoretically,
below tuning thickness (e.g., the range of measured
coalbed thicknesses in the present study) amplitude
is directly proportional to bed thickness because of
the effects of destructive interference from reflec-
tions at the top and base of the bed. This trend is
observed in our data (Figure 8a). This attribute is
the single best predictor of bed thickness, with a
correlation coefficient of 0.84. The integrated trace is essentially a band-limited
(recursive) inversion, with low impedance being
represented by negative numbers. The value we used
is the average value of the integrated trace in the
analysis window. For a thinning wedge of constant
acoustic impedance, the inversion result will best
estimate the impedance of thick beds, but will not be
able to reproduce the impedance of thinner beds,
such as the coal bed studied in this paper. Thick coals
in our data are associated with strong negative values
of integrated trace, and thin coals have weaker
negative values (Figure 8b). As such, there is a strong
negative correlation between integrated trace and
thickness. Strangely, equation 2 suggests an inverse,
Figure 6. Validation errorand prediction error plottedagainst the number of attri-butes used in the stepwiselinear regression. The validationerror indicates that a combina-tion of three attributes bestmodels predicted coal thickness(cf. Hampson et al., 2001).
Thickness 3:922 2:005e 8X1 3:183e9 1=X26:812e18 1=X3 2
Marroqun and Hart 1609
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not a negative, correlation between integrated trace
and coal thickness. The total energy is the sum of all squared trace am-
plitudes in the window interval. Like the maximum
absolute amplitude, there is a strong positive cor-
relation between this attribute and coal thickness
(Figure 8c). As was the case for the integrated trace
attribute, the inverse relationship between total en-
ergy and bed thickness implied by equation 2 seems
counterintuitive.
We suggest that equation 2 integrates the three
attributes in a way that captures subtle variations in
waveform shape. Examination of that equation sug-
gests that the inverse transform attributes (integrated
trace and total energy) only have a significant influence
on the result when the linear attribute (maximum ab-
solute amplitude) is small. They fine-tune the correla-
tion at low-coal thickness values. This is consistent with
the only slight improvement in correlation coefficient
that was obtained using all three attributes (0.87) vs.
the maximum absolute amplitude (0.84). The details of
this interaction are investigated below through forward
modeling.
We obtained some negative values, because our
model (equation 2) is statistical in nature, and these
values were set to zero in our map. These values, as well
as some exceptionally high thickness values close to the
margins of the seismic area, are thought to be caused by
poor input data quality, although it is also possible that
the model performs poorly when extrapolating beyond
the range of the input data. We thus tested the per-
formance of the model by searching for hidden extra-
polation points (Montgomery and Peck, 1992). This
procedure consists of defining the smallest possible
ellipsoid enclosing the original data points. If a given
data point lies outside of the boundary region, the pre-
diction involves an extrapolation point. For this test, we
used the diagonal elements hii of the hat matrix H =X(X0X)1X0, where the matrix X is made up of theattribute values at well locations (Montgomery and
Peck, 1992). The largest value of hii (e.g., hmax) definesthe external boundary of the ellipsoid. Any point that
satisfies
h00 x0 X0X1 xh00 hmax 3
is considered to be an interpolation point, and x is a
vector of attribute values at any location in the seis-
mic area. The largest value of hii, 0.934, is much largerthan the second next larger value, 0.249, and therefore
could be an outlier (Montgomery and Peck, 1992).
However, this observation corresponds to the lowest
measured coal thickness (e.g., 11.85 ft [3.6 m]), and we
demonstrate the importance of this point to the re-
gression analysis in the paragraph below. We therefore
decided to use this value as hmax. Table 1 shows per-formance test results for six points across the seismic
area (locations shown in Figure 9). We note that the
three first h00 values exceed hmax, and consequentlyare extrapolation points. These results confirm our pre-
vious observations concerning the poor data quality on
the margins of the seismic area. The rest of the h00values lie in the boundary and therefore represent in-
terpolation points.
Composite amplitude is commonly used to esti-
mate bed thickness below tuning (e.g., Gochiocco, 1991;
Brown, 1999), although this attribute was not selected
for our analysis using stepwise linear regression anal-
ysis. Composite amplitude is the sum of the absolute
amplitudes of reflections identified at the top and base
of the reservoir (Brown, 1999). We measured the Spear-
mans rank correlation coefficient (r s) between variousamplitude attributes and coal thickness to determine
how composite amplitude ranked in comparison to
other attributes we had generated. The Spearmans
rank correlation coefficient is a measure of the mono-
tonic association between two variables (Lehmann and
DAbrera, 1975). Unlike the correlation coefficient,
Figure 7. Crossplot of the predicted thickness vs. the actualthickness values. Note the tight fit and the presence of a singlelow thickness value.
1610 Seismic Attribute-Based Characterization of Coalbed Methane Reservoirs
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Figure 8. Crossplots of the maximum absolute amplitude (a, d), integrated trace (b, e), and total energy (c, f ) against coal seamthickness or wedge model thickness. Attributes extracted from the seismic data are in the left column, and attributes extracted fromthe wedge model are in the right column. Note the similarity in trends between model results and data.
Marroqun and Hart 1611
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Spearmans rank correlation coefficient works on ranked
data and measures both linear and nonlinear relation-
ships between variables. Table 2 shows that maximum
absolute amplitude and total energy attributes have a
high-ranking correlation with coal thickness (e.g., r s =0.74 and 0.72, respectively). However, the integrated
trace has a moderate correlation of 0.62. We also notethat composite amplitude has a strong correlation with
coal thickness (0.68). According to Hampson et al.
(2001), the stepwise linear regression methodology
will not select attributes that provide essentially the
same information to the predicted model. As such, the
composite amplitude attribute must provide the same
information provided by the maximum absolute am-
plitude and/or total energy, both of which scored higher
in Table 2.
We undertook the modeling of various bed thick-
ness and lithology profiles (i.e., do the beds above and
below the coal have the same acoustic impedance?) in
an attempt to determine under what circumstances
composite amplitude might provide a better result than
maximum absolute amplitude. Our results indicated
that different combinations of bed thickness and lithol-
ogy profiles determine which of the two attributes cor-
relates best to bed thickness. However, the results are
complex, in ways that we do not yet fully understand.
We generated a simple wedge model to examine
the physical basis of the observed empirical relation-
ships between attributes and coal thickness (Figure 10).
This model was generated using the Butterworth zero-
phase wavelet employed in our original seismic mod-
els (Figure 5). From the wedge model results, we ex-
tracted the same attributes identified in equation 2,
Table 1. Hidden Extrapolation Test Results Showing thePerformance of the Regression Model to Predict Coal Thickness
Data Point h 00 Category
1 132.780 hidden extrapolation
2 2.660 hidden extrapolation
3 223.825 hidden extrapolation
4 0.254 interpolation
5 0.697 interpolation
6 0.053 interpolation
Figure 9. Attribute-basedthickness map of the lowerFruitland coal seam. The thickestcoal is predicted for the south-eastern region of the survey area,in accordance with the welldata (Figures 2, 3). Northwest-southeaststriking thicknesstrends, delineated by yellow,red, and green colors, are evi-dent in the southeast part ofthe survey area. The numberedpoints (16) show the locationof data points used to test theperformance of the regressionmodel to predict coal thickness(Table 2). Points 13 are identifiedas extrapolation points causedby bad data quality around thesurvey margin. Our porosity pre-diction is not expected to be validin these areas. Points 46 areidentified as interpolation pointsthat are within the expected rangeof attribute variation, so the po-rosity values are considered tobe valid in these areas.
1612 Seismic Attribute-Based Characterization of Coalbed Methane Reservoirs
-
then crossplotted them against coal thickness. The
results (Figure 8d, f) show the same trends as those
observed in the data (Figure 8a, c), leading us to con-
clude that they are truly responding to variations in coal
seam thickness (i.e., the observed correlations are not
spurious; cf. Kalkomey, 1997). On a 3-D crossplot of
attributes against coal thickness (Figure 11a), the low-
est coal thickness point appears to be an outlier that
may be associated with bad data. However, an identical
plot of the model results (Figure 11b) shows a trend
that neatly reproduces the trend observed in the data.
This observation leads us to conclude that this was not a
bad data point, therefore justifying its inclusion in the
extrapolation point analyses described previously. Our
previous efforts with seismic modeling to validate
attribute studies (e.g., Hart and Balch, 2000; Hart and
Chen, in press; Tebo and Hart, in press) have likewise
provided important information.
Our attribute-based map of coal seam thickness
(Figure 9) suggests that coal is generally thicker in the
southeast portion of the seismic area, and thickness
patterns show a northwest-southeast trend. This belt of
thicker coal coincides with the local depocenter observed
from the log-based isopach map (Figure 3). We believe
that the thickness trends are geologically plausible be-
cause they are consistent with depositional models and
regional mapping of northwest-southeaststriking
belts of thicker coal associated with paleoshoreline po-
sitions (e.g., Ayers et al., 1994). Furthermore, the area
of thicker coal in the southeast part of the study area
overlies a similar relative thickening at the level of the
underlying Dakota Formation (Hart and Chen, in press),
suggesting that this may have been a localized area of
enhanced subsidence during the Upper Cretaceous.
Table 2. Spearmans Rank Correlation Coefficient betweenSeismic Amplitude Attributes and Coal Thickness
Amplitude Attribute
Spearmans Rank
Correlation Coefficient
Maximum absolute amplitude 0.74
Total energy 0.72
Variance in amplitude 0.72
Kurtosis amplitude 0.70
Average energy 0.69
Root mean square amplitude 0.69
Composite amplitude 0.68
Average reflection strength 0.66
Average absolute amplitude 0.63
Total absolute amplitude 0.62
Integrated trace 0.62Trace length 0.58
Amplitude envelope 0.58
Total amplitude 0.52Reflection strength slope 0.49Integrate absolute amplitude 0.48
Mean amplitude 0.47Amplitude 0.41Skew amplitude 0.23
Figure 10. Wedgemodel and correspondingsynthetic traces. Attributesextracted from this modelwere used to investigatethe relation between attri-butes composing the re-gression model and coalthickness (Figure 8d, f; 11b).
Marroqun and Hart 1613
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Curvature Attribute Analysis
Important opening-mode fractures contribute to
permeability pathways for gas in coalbed methane
reservoirs. Strata that have been faulted or folded may
also influence gas production (Pashin, 1998). Kaiser
and Ayers (1994) pointed out that minor folds and
faults in the San Juan basin enhance fracture and cleat
permeability in the Fruitland.
Horizon attributes, such as various types of cur-
vature, have been successfully used to detect subtle
faults in reservoirs (e.g., Steen et al., 1998, Roberts,
2001). Hart et al. (2002) showed that horizon at-
tributes might also be used to detect highly productive
fracture swarms in tight-gas reservoirs. Accordingly,
we sought to investigate whether curvature maps could
be used to predict the location and orientation of subtle
structural features too small to be detected on vertical
seismic transects, time slices, or horizon slices. We de-
rived 11 curvature attributes (Roberts, 2001) and com-
puted these attributes from the top thick coal seismic
horizon using aperture values of one, three, five, and
seven bins. The aperture value defines the size of a 3 3grid of points used in the computation of curvature
attributes (Stewart and Wynn, 2000; Roberts, 2001).
With an aperture of one bin, our maps were excessively
noisy. As larger aperture values were used, the resulting
curvature horizon shrank inward from the margins
of the survey and curvature lineaments identified only
regional trends. We thus defined the best results in
terms of delineation and distribution of curvature lin-
eaments. We found that the maximum-, strike-, and
dip-curvature attributes defined below (all derived using
an aperture of three bins) provided the most geolog-
ically reasonable results. A depth-converted structure
map, generated by integrating log picks and seismic
horizons (methodology described by Hart, 2000), and
shaded relief maps of the top thick coal seismic hori-
zon helped us to identify and interpret other struc-
tural features.
Figure 12 shows the depth-converted structure
map of the top of the thick coal (TopTC seismic pick).
The map shows that the study area is crossed by a
northwest-southeaststriking structural low that ap-
proximately coincides with the trend of the thick coal
inferred from both log-based and attribute-based thick-
ness maps (Figures 4, 9, respectively). Shaded relief
maps of this surface (Figure 13a, b) show subtle struc-
tural features that are not readily apparent on the
depth-converted structure map, including a variety of
Figure 11. Three-dimen-sional crossplots of coalthickness vs. the maximumabsolute amplitude anda combination of inversetransform attributes (i.e.,integrated trace and totalenergy). (a) Points showingattributes from seismic dataand well-based coal thick-ness. (b) Line showing con-tinuous variation in attri-butes and thickness alongthe wedge model (Figure 10).Note the similarity in trendsbetween the two results. Asingle low-coal thicknessdata point appears to be anoutlier, but its presence ispredicted by the model re-sults. The incorporation ofthe integrated trace andtotal energy attributes intoour regression analysis(equation 3) helps themodel to fit this point.
1614 Seismic Attribute-Based Characterization of Coalbed Methane Reservoirs
-
northwest-southeast and northeast-southwesttrending
structures. The poor data quality along the western mar-
gin of the survey is obvious from this display.
Maximum curvature shows the locations where
the horizon forms anticline, syncline, or flat-dipping
surfaces as positive, negative, and zero curvature val-
ues, respectively (Roberts, 2001). Lineaments of high
positive maximum curvature values are well defined
across the seismic area (Figure 13c) and generally cor-
respond to features mapped from the shaded relief
maps (Figure 13a, b). Strike curvature describes the
shape of the surface into areas of valley shapes (nega-
tive curvature values) and ridge shapes (positive cur-
vature values) (Roberts, 2001). Curvature lineaments
on the strike curvature map (Figure 13d) display are less
pronounced. However, these lineaments mostly corre-
spond to structures delineated on maximum curvature
display (Figure 13c). Dip curvature shows relief var-
iations in the surface and is a measure of the rate of
change of dip in the maximum dip direction, i.e., at
right angles to the strike curvature (Roberts, 2001).
The dip curvature display (Figure 13e) shows curvature
lineaments that are bigger and more continuous in
comparison to lineaments mapped on other displays.
According to Roberts (2001), this attribute will tend to
exaggerate local relief along the surface.
The lineaments mapped from these displays
(Figure 13a, e) indicate the presence of low-amplitude
structural features. A seismic transect that shows the
expression of typical curvature-defined features is shown
in Figure 14. Lineament azimuths were analyzed to de-
termine if a consistent trend existed between mapped
lineaments and cleat strike from the San Juan basin
(Figures 1, 15a). The analysis was performed on binary
images (e.g., black lines on white background) with
processing image software (methodology described by
Hart et al., 2002). The software extracted the orien-
tation of each lineament, and the lineations so defined
were plotted as rose diagrams. Mapped lineaments from
shaded relief maps (Figure 13a, b) show major trends that
strike toward 45 and 145j (Figure 15c, d, respectively).These lineaments are subparallel to major face-cleat
strikes identified in outcrop (Tremain et al., 1994) and
probably reflect real structural features. In contrast,
lineaments observed on maximum-, strike-, and dip-
curvature maps (Figure 13c, e) show multimodal pat-
terns (Figure 15e, g, respectively). To identify clusters
in lineament trends, we combined all mapped linea-
ments into a single rose diagram (Figure 15h). Although
this composite rose diagram shows considerable scatter,
major trends centered at 45 and 145j are concordantwith face-cleat strikes as measured in outcrop.
Figure 12. Depth-convertedstructure map (elevation abovesea level in feet) of the top ofthe thick coal seam. Note thenorthwest-southeast structurallow access cutting the seismicarea and contours, indicatingpoor data quality around thewestern and northern marginsof the survey area.
Marroqun and Hart 1615
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1616 Seismic Attribute-Based Characterization of Coalbed Methane Reservoirs
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Acquisition artifacts could conceivably influence
the orientation of structures derived from our horizon
attribute analysis. The orientation of acquisition arti-
facts was inferred from amplitude striping on the TopTC
amplitude horizon. Amplitude lineaments show a strong
preferred trend of 45j (Figure 15b), the acquisitionorientation. Although we identified some lineaments
that strike in this direction, other northeast-southwest
trends are different and may be differentiated from
acquisition artifacts because of their orientation (cf.
Hart et al., 2002). Analysis of outcrop and core cleat
orientation (Tremain et al., 1994) (Figure 1) and pro-
duction trends (Kaiser and Ayers, 1994) from the
Fruitland suggest that both northwest-southeast and
northeast-southweststriking structural trends could
be present in our study area. We sought to further re-
duce the problem of discerning real structure features
from noise by focusing on lineaments that were iden-
tified via several approaches. Lineaments identified
using only one approach were considered to be possibly
the result of map generation (e.g., gridding) or pro-
cessing artifacts (Hesthammer and Fossen, 1997).
We created a map combining our coal thickness
prediction with curvature analysis results and a bubble
plot of cumulative gas production normalized by the
number of years of production to evaluate stratigraphic
and structural controls on coalbed methane production
(Figure 16). Stratigraphic factors controlled the devel-
opment of the lower Fruitland seam and, indirectly, the
distribution of coalbed methane resources. As previ-
ously discussed, the predicted coal thickness trends are
geologically realistic. The combination of stratigraph-
ic and structural data suggests that the relative de-
crease in coal thickness at the north-central part of
the seismic area is probably caused by paleotopo-
graphic variations on which peat swamps developed;
Figure 13. Horizon attributes derived from seismic horizon corresponding to the top of the thick coal, with their interpretedlineaments. (a) Shaded relief map with illumination from the northwest (subtle structural features show up as reddish lineaments).(b) Shaded relief map with illumination from the northeast (subtle structural features show up as reddish lineaments). (c) Maximumcurvature map. Positive curvature (concave down) lineaments are shown in red and yellow, and negative curvature (concave up)lineaments are shown in dark blue and purple. (d) Strike curvature map. Positive curvature (concave down) lineaments are shown in redand yellow, and negative curvature (concave up) lineaments are shown in dark blue and purple. (e) Dip curvature map. Positivecurvature (concave down) lineaments are shown in yellow and orange, and negative curvature (concave up) lineaments are in greenand blue. Also shown is the location of seismic transect BB0 shown in Figure 14.
Figure 14. Seismic transect BB0 throughFruitland Formation at Rosa field show-ing the expression in cross section ofcurvature-defined lineaments (arrows)at the thick coal level that might be re-lated to minor structural features. Linelocation shown in Figure 13ae.
Marroqun and Hart 1617
-
areas of topographic highs may have produced thin
coal accumulations. Despite the lack of well control in
this area, the depth-converted structure map (Figure 12)
shows a subtle structural high in this area that sup-
ports this interpretation.
Structural features mapped through horizon at-
tribute analysis also appear to influence coalbed meth-
ane production. The best producing wells (e.g., wells
producing gas exceeding 80 mmcf/yr [2.3 106 m3/yr])are concentrated in areas of thicker coal and in the
vicinity of structural lineaments, especially northwest-
southeaststriking features. Another area of above-
average gas production is near the west-central margin
of the 3-D survey area, but the poor quality of seismic
data in the margins makes the interpretation uncer-
tain. Four wells that are found in a patch of thicker
coal and in the proximity of lineaments (outlined by
yellow dashed line in Figure 16) have lower coalbed
methane production. These wells have the longest
records of production in Rosa field (e.g., 19882001),
so engineering factors, including improvements in com-
pletion technologies with time, may be at least partially
responsible for the lack of correlation to thickness and
structural lineaments. As a general rule, most wells with
lower normalized cumulative production (e.g., gas pro-
duction lower than 30 mmcf/yr [0.8 106 m3/yr]) aredrilled in areas of lower coal accumulation that are away
from structural lineaments. We conclude that the in-
tegration of attribute-based coal thickness, structural
trends, and production data yields a consistent inter-
pretation that explains much of the variability in coal-
bed methane production.
Figure 15. Rose diagrams derived frommapped lineaments: (a) principal cleatstrike measured in outcrop by Tremainet al. (1994) (Figure 1); (b) amplitudehorizon, showing strong northeast-south-west trend related to acquisition foot-print; (c) shaded relief display with illu-mination source position northwest (Fig-ure 13a); (d) shaded relief display withillumination source position northeast(Figure 13b); (e) maximum curvaturedisplay (Figure 13c); (f) strike curvaturedisplay (Figure 13d); (g) dip curvaturedisplay (Figure 13e); and (h) the combi-nation of all lineaments derived fromhorizon attributes. Two dominant trendsare apparent in (ha) northwest-south-east set (apparently corresponding todomain 2 orientations) and a bimodalnortheast-southwest trend. The latterprobably contains trends associated withacquisition artifacts (see b) and realstructural trends. Hart et al. (2002)describe a methodology for distinguish-ing acquisition artifacts from real struc-tural trends that have similar orientations.
1618 Seismic Attribute-Based Characterization of Coalbed Methane Reservoirs
-
DISCUSSION
Several studies (e.g., Schultz et al.,1994; Hirsche et al.,
1997; Kalkomey, 1997; Pennington, 1997; Hart, 1999,
Hart and Chen, in press; Hampson et al., 2001) have
examined the methodology and concepts to be used
when conducting a seismic attribute study (i.e., our
attribute-based prediction of coal thickness), and a full
review is beyond the scope of this paper. In this study,
we used seismic modeling to identify seismic horizons
because we lacked velocity logs with which to make
well ties. The seismic models allowed us to identify and
carefully map the horizons of interest. The empirical
relationship we derived between attributes and phys-
ical properties had a high degree of statistical signif-
icance, and we successfully identified hidden extrap-
olation points in areas of poor seismic data quality.
The coal thickness map was geologically reasonable,
and the results, when integrated with curvature anal-
ysis, helped to explain patterns in coalbed methane pro-
duction in the study area.
Our horizon curvature attribute analyses identified
two dominant structural trends, one striking northwest-
southeast and the other northeast-southwest. These ori-
entations are consistent with basin-scale mapping of
face-cleat orientations in outcrop (Tremain et al., 1994)
and production trends from the Fruitland (Kaiser and
Ayers, 1994). Multicomponent 3-D seismic data (in-
cluding a multicomponent 3-D vertical seismic profile)
and AVO analysis from the Cedar Hill field (approx-
imately 50 km [30 mi] west of our study area) also
indicated the presence of northwest-southeast and
northeast-southwest structural trends (Shuck et al.,
1996; Ramos and Davis, 1997). The highest coalbed
methane production from that area is associated with
northwest-southeasttrending structures or from areas
of enhanced fracture density where different structures
intersect. These results are in general agreement with
the results presented in this paper; however, we em-
phasize that our study was conducted with conven-
tional P-wave 3-D seismic data instead of the more
expensive approaches used at Cedar Hill field.
Our simple seismic modeling efforts neglected
elastic attenuation effects in coal seams but were able to
reproduce complex attribute behaviors (e.g., Figure 11).
Hughes and Kennett (1983) used seismic models to
Figure 16. Bubble plot ofcumulative production normal-ized by number of years ofproduction and curvature lin-eaments superimposed overpredicted thickness map. Lin-eaments mapped from shadedrelief lineaments are in violet,maximum-curvature lineamentsare in black, strike-curvaturelineaments are in light blue,and dip-curvature lineamentsare in yellow. Except for thewells circled by the yellowdashed line, the best produc-tion seems to be associatedwith wells located where thecoal seam is thickest (red andyellow) and are associatedwith structural lineaments.
Marroqun and Hart 1619
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study attenuation in coal and concluded that loss of
seismic energy is small because coal seams are thin in
comparison to the predominant wavelength. We con-
clude that simple acoustic models are useful for un-
derstanding coal-bearing rocks, at least for coals that
are seismic thin beds.
CONCLUSIONS
The controls on coalbed methane production are many
and include a variety of geologic, hydrodynamic, and
engineering factors. In this paper, we have shown the
potential application of using 3-D seismic and curva-
ture attributes to predict coalbed thickness and the
location of enhanced permeability zones in a coal seam
from the lower part of the Fruitland Formation in our
study area. We draw the following general conclusions:
1. Seismic attribute studies may be used to predict coal
thickness. In our case, a combination of three at-
tributes (maximum absolute amplitude, integrated
trace, and total energy) yielded a multivariate linear
regression expression that has a 0.87 correlation co-
efficient with the input data. The map generated
from this analysis has geologically reasonable coal
thickness trends. Coal thickness variations that strike
northwest-southeast are related to the paleoshore-
line orientation. An area of thicker coal accumula-
tion in the southeast part of our study area appears to
be related to a local depocenter that was present
throughout at least some of the Late Cretaceous.
2. Horizon attributes derived from seismic mapping of
the top of the coal, including shaded relief and var-
ious measures of curvature, identified geologically
reasonable structural features in the study area. We
needed to vary the aperture of our curvature calcu-
lations to generate these maps. Structures that strike
northwest-southeast and northeast-southwest cor-
respond to the orientation of face cleats measured
in outcrop.
3. Integration of our attribute-derived thickness map,
our map of seismically defined subtle structures, and
coalbed methane production data yield a picture that
is consistent with known or inferred geological con-
trols on coalbed methane production. The best pro-
duction is associated with thick coal deposits that are
close to seismically defined structures.
4. Seismic modeling is a useful technique for validating
seismic attribute studies. Seismic modeling helped
us to confirm that the attributes selected by the step-
wise linear regression methodology were indeed
physically related to coal thickness, thus minimiz-
ing the possibility of basing our results on spurious
correlations. This same seismic modeling helped us
to identify that an apparent outlying data point
should be kept for the analysis and was not caused
by bad data.
5. Our results were derived from mediocre-quality
conventional (P-wave) 3-D seismic data. Similar re-
sults should be obtainable for coalbed methane res-
ervoirs elsewhere.
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