monitoring coal bed methane production

8/14/2019 Monitoring Coal Bed Methane Production

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Monitoring Coal Bed Methane Production: A Case Study from

the Powder River Basin, Wyoming, United States of America.

Olusoga Martins AkintundeDepartment of Geophysics

Stanford University

Abstract

The growing significance of the Powder River Basins Coal Bed Methane (CBM) to

United States domestic energy supplies has heightened interest in its exploration and

exploitation. Systematic and continued development of this resource would require

adequate characterization of the CBM reservoir, better reservoir management practices

and monitoring strategies that would ensure optimum and/or efficient gas production in

an environmentally sound manner. The primary objective of this study is to monitor time-

variant changes in the velocity of seismic waves associated with dewatering during

methane production. Dewatering causes a decrease in pore pressure resulting in an

increase in gas saturation. Gas lowers the velocity of seismic waves whereas a decrease

in pore pressure increases the velocity. Knowledge of the spatial distribution of gas

between wells might be helpful in identifying both the source of produced water and thespatial efficiency of the dewatering process.

This study is based on three cross-well seismic surveys executed at a test site in the

Powder River Basin. The surveys were run between a pair of monitoring wells that

straddle a completed CBM production well. The monitoring wells were provided for

these surveys with the help of CononcoPhillips, Bureau of Land Management (BLM),

and the Western Resources Project (WRP). The first survey was completed in December

2002 before dewatering began and thus provided the baseline image or tomogram. Thesecond and third surveys were run in July 2003 and June 2004 respectively. Changes in

the P-wave travel times can be seen in unprocessed seismograms though tomography was

used to image the velocity changes between wells from the traveltime differences.

From the baseline tomogram, I have delineated four major lithologic units that are typical

of the Upper Cretaceous sediments in the Powder River Basin. And from the time-lapse

surveys, I estimate that the spatial distribution of the velocity changes approximates 6%

above the coal as well as inside the coal layer. This difference can be attributed primarily

to changes in pore-fluid saturation occasioned by the dewatering process. The observed

changes demonstrate feasibility to monitor the CBM production process and that the

changes are consistent with theoretically derived coal-physics models and observations

from production data.


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system is due to pressure gradients between the fracture system and production wells.

The majority of gas in a coal gas reservoir diffuses through the primary storage system,desorbs at the interface between the primary and secondary systems, and then flows

through the secondary systems to wells (Mavor et al. 2002).

Figure 2: Principal mechanisms for primary storage of methane within the coals cleats and micropores

Located in the Central part of the PRB of Wyoming and Montana (figure 3), the Big

George is currently one of the worlds most established coal fields. Along with theWyodak, the Big George is a CBM producer in the PRB.

Figure 3: Map of geologic age of coal of United States showing the location of the Powder River Basin

Free as in the micro ores

Adsorbed as

Free gas in the cleats

Methane dissolved in water

Cleats

Pore s ace filled with water


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PRB accounts for 800 billion tons of coal and is estimated to produce 25 trillion cubic

feet of gas - about 20% of the CBM being produced in the United States. The PRB coalsare biogenic, permeable, shallow and low-rank coals. They are characterized by higher

moisture content and lower carbon content when compared with high-rank bituminous

coals. The common practice for primary CBM recovery at the PRB is to depressurize the

coal, usually by pumping water from CBM production wells installed in coal beds for

several months. The wells are completed by installing well casing to the top of the coal

bed, reaming the coal, and then leaving the coal bed open to the hole. Water is then

removed from the well by installing a submersible pump in the open coal bed; pumping

reduces hydrostatic pressure in the coal that allows the methane to be released. The gas is

transported to the surface in the space between the tubing attached to the submersible

pump and the well casing and the produced water is discharged to the surface via the

tubing (figure 4).

At the initial stage of dewatering (figure 5), gas production is negligible, but increases

and stabilizes as dewatering progresses over an appreciable period. Richardson and

Lawton (2002) showed that the dewatering process affects the acoustic and elastic

properties of the CBM reservoir, causing appreciable acoustic impedance discontinuity

within the coal-bearing layer and the surrounding strata. These changes in turn affect the

amplitude and travel times of reflected and transmitted waves, paving the way for cross-

well seismic mapping of changes caused by the dewatering process. Time-lapse seismic

monitoring depends on production-related changes in the acoustic velocity and density of

the reservoir rocks. Most potential monitoring applications rely on saturation changes that

dominantly affect the P-wave velocity of the reservoir. This is because the bulk modulusof the rock is related to both the bulk modulus of the rock matrix and the bulk modulus of

the reservoir fluids.

Historically, the first quantitative data set on time-lapse reservoir monitoring emanated

from rock physics measurements at Stanford University in the mid 1980s. The results of

the study show that laboratory measurements on heavy-oil saturated core samples showed

large decreases in seismic rock velocity when the viscous oil was heated (Nur et al. 1984;

Wang and Nur, 1986, and Nur, 1989). Other notable time-lapse reservoir monitoring

studies in the last two decades are: steam injection by Pulin et al. 1987, Lumley 1995a, b

and Jenkins et al. 1997. Others include fireflood EOR process by Greaves and Fulp 1987,

a North Sea gas cap expansion project by Johnstad et al. 1995 and West Texas CO2injection project by Harris et al 1995. It is, however, pertinent to note that recent and

current effort on time-lapse seismic reservoir monitoring has focused mostly on

hydrocarbon reservoirs (Tura and Lumley, 1998, 1999, Landro et al. 2003, Landro 2001

and Harris et al. 1995). This underscores the need to apply the same tool to the CBM

reservoir process. Continued and systematic development of the CBM resource for

optimum gas production would require understanding the effectiveness of the efficiency

of the dewatering process and knowledge of the spatial distribution of gas between wells

In this study, cross-well seismic surveys were designed to image production-induced

changes in P-wave velocity associated with primary CBM production at a site in the

Powder River Basin (PRB). The pre- and post- CBM production cross-well surveys were


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executed in December, 2002, August, 2003 and June 2004 respectively in the vicinity of

the Big George formation.

Figure 4: Schematic diagram of Coal Bed Methane production at the Powder River Basin

Figure 5: Conceptual CBM Production at the PRB (modified from Ayers, 2002)

Geologic Background

Geologically, the PRB is an intermontane foreland arch basin formed during the

Laramide Orogeny. It is in southeast Montana and northeast Wyoming, characterized by

rocks of late Cretaceous to early Tertiary age (figure 3). The basin is flanked by the


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Bighorn Mountains on the west, the Casper Arch-Laramie Range on the southwest and

south, the Hartville Uplift on the southeast, the Black Hills on the east, and the Miles CityArch on the northeast, all formed during the Laramide Orogeny (Flores 2004). Also, it is

a thick sequence of sedimentary rock formed in a large down warp within the

Precambrian Basement. Sediments range from Paleozoic at the bottom through Mesozoic

to Tertiary at the top. The Basins started evolving about 60 million years ago, when it

was just a flat, sea-level plain, mudding from a retreating sea that had covered Wyoming

for 40,000,000 years. Over the next 14 million years, the Basin rose to its present 3000ft

with the Big Horns Mountains and the Black Hills rising thousands of feet higher.

Following were episodes of geological processes favoring sediment accumulation into

pure peat. Further effects of temperature, erosion and deposition transformed the peat into

some of the thickest, low ash coals in the world. Presently, the PRB has fully matured

geologically as an energy source and a potential factor in global climate and energychange (Harris et al 2004, Akintunde et al 2004) .Details about the geology is contained

in Larson1989, Debruin et al. 2000, Randall 1991, Rice et al. 2000 and Flores R.M. 2004).

Methodology

This study attempts to address two fundamental issues that are germane to the problem of

seismically monitoring the CBM recovery process. They are:

(i) Map production-induced changes in P-wave velocity caused by dewatering

(ii) Interpret velocity changes in terms of gas saturation and pressure changes.

To actualize the above-stated goals: I did coal physics feasibility analysis, processed

three crosswell field seismic data sets, and carried out time-lapse image analysis and

interpretation. These methods of study and their results are discussed as follows.

Coal Physics Feasibility Analysis (Numerical Modeling)

Prior to the field experiments, I carried out a modeling study to predict changes in P-

wave velocity in dry coal when saturated with fluids (water and methane). I extracted P-

wave velocities for dry coal and their pressure dependence from laboratory measurementson bituminous, Permian coal sample contained in Yu et al. 1993. The sample has a

porosity of 2.9% and density of 1.35g/cc. I modeled the data using Gassmann equation

(Gassmann, 1951), appropriate fluid properties and coal physics relationships for P-wave

velocity, effective fluid modulus and density of a fluid-saturated rock (Mavko et al 1998).

Gassmann theory is a physical expression that relates the fluid-saturated moduli to the

known moduli and fluid properties of a reservoir process undergoing changes in pore

fluid. It permits estimation of velocity of fluid-saturated rocks from dry rocks and vice

versa. Also, it is equivalent to the low-frequency limit of Biots theory (mavko et al

1998). Additionally; the Gassmann theory is strictly valid for low (quasistatic)

frequencies, implying that the calculated fluid-saturated velocities are of low frequency.

We used dry data because the most plausible way for applying ultrasonic core data to


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field conditions is to use velocities measured on (nearly) dry cores and then employ

Gassmann for the fluids substitution. This Gassmanns analysis assumes free gas,homogeneous saturations and does not account for gas adsorption and/or desorption.

Figure 6 shows the results of the fluid substitution based on the famous Gassmanns

equation. The green curve represents the dry data set extracted from Yu et al 1993. It

shows that P-wave velocity (Vp) in dry coal increases as differential pressure (confining

pressure minus pore pressure) increases. The influence of increasing differential pressure

Figure 6: Gassmann-predicted changes in P-wave velocity in fluid-saturated coal

is to close the thin cracks and penny-shaped pores and to make better contact between

particles in the rock. With addition of 100% water (H2O) as depicted by the blue curve,the Gassmann-derived P-wave velocity increases considerably. Coal when fully saturated

with water tends to exhibit a large increase in P-wave velocity perhaps due its large bulk

modulus (2.25 GPa) and zero shear modulus. It is pertinent to remark that the Gasmann-

derived curve for the 100% water saturated coal compares favorably with the laboratory

measured P-wave velocity data for the water saturated coal, represented by the black

curve (Yu et al. 1993). The observed variations especially at lower differential pressure

might be due to dispersion effects and some numerical uncertainties.

Addition of methane (CH4) into the water-coal mixture decreases the P-wave velocity(the red curve) due to the high compressibility of methane and density effect.

Quantitatively, the magnitude of the changes in P-wave velocity due to CH4 floodingwould depend partly on the degree of water/methane saturations and partly on pore


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pressure. For shallow reservoir conditions (lower differential pressures) typical of the

Powder River Basin, the Gassmann predicted changes in P-wave velocity range from 5%to 15% depending on pore pressure and the degree of methane/water saturations.

Furthermore, the observed changes in P-wave velocity are largest at low differential

pressure. As pointed out by Wang et al 1989, seismic velocities are more sensitive to

pore-fluid saturations at low differential pressures. To quantitatively and qualitatively

relate changes in P-wave velocity to changes in saturation and pore pressure, I extended

the Gassmann coal physics analysis to a sensitivity study. Figure 7 relates the computed

fluid density (RHOF), fluid bulk modulus (Kf), density of the saturated fluid (RHOBsat)

and saturated bulk modulus (Ksat) to water saturation (Sw) for the dry Vp when saturated

with CH4 and H2O. We used these parameters to estimate the saturated velocities (Vpsat)

at an assumed reference pressure of 5Mpa as shown in figure 7. Subsequently, we

calculated changes in Vp (Vp) as functions of changes in pressure (P) and watersaturation (Sw) at an assumed reference pressure of 5Mpa and water saturation of 0.5,

and plotted the results as shown in figure 8. The estimated Vp due to CH4 and H2O

saturations show sensitivity to both P and Sw (figure 8). The observed reduction in

Vp can be attributed to the high compressibility of methane.

While the Gassmann-predicted changes in P-wave velocity of the examined coal from Yu

et al 1993 provide necessary proof of concept and validation for the results of field

experiment, it might not be strictly valid for the Powder River Basins Big George coal.

Powder River Basin coals are shallow, of lower-rank and sub-bituminous. Coal

depending on its rank and geology exhibits significant variations in porosity and structure.

Figure 7: Plots of computed fluid properties versus Sw for Vpdry at reference pressure of 5Mpa


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Figure 8: Estimated Vp as functions ofP and Sw due to CH4 and H2O saturations (upper left and righthand plots show P-wave velocity in the dry rock versus differential pressure and P-wave velocity in the

saturated rock as a function of water saturation. The reference pressure and saturation values are shown.

Crosswell Seismic Data Acquisition

Two observation wells, spanning 150ft and straddling a production well (CP 23-35), were

used for the crosswell surveys. The source well is labeled in Figure 9 by 23-35BG, while

the receiver well is labeled 23-35W. The baseline or reference survey was acquired

shortly after the production well was completed and before dewatering began in

December, 2002, while, the first monitoring survey was run in July, 2003. Each of thesurveys covered about 900ft to the total depth of the wells at about 1400ft. Shots were

fired from a down-hole piezoelectric source every 1.25 ft in the source well. The seismic

waves propagated between wells were picked up by an array of hydrophones positioned

inside the receiver well. Crosswell seismic profile is generally recorded either in form of

common source or common receiver fans. In the common receiver mode of recording,

the receiver is positioned at depth and the source is moved in the other borehole to create

the fan. The receiver is then repositioned and the source scan repeated. A typical common

receiver profile (CRP) from the CBM data is shown in figure 10, illustrating direct P-

wave and tube waves. In the common source profile (CSP), the source is positioned at

depth and the receiver is moved in the other observation well to create the fan. The

source is then repositioned and the receiver scan repeated. As shown in figure 11, theCSP equally permits identification of direct P-wave. In both common source and


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common receiver sorts; the direct arrival travel path increases hyperbolically with

increasing source to receiver offset, and as such the direct wave traveltime has hyperbolicmoveout. For large offsets, the direct and reflected events asymptote a straight line that

has moveout of the mediums velocity (figure 12).

Figure 9: Crosswell Tomography Survey Geometry

Figure 10: Data plot (about 100 traces) in common receiver sort showing direct P-waves and tube waves.

Direct P-waves

Tube

Waves


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Figure 11: Data plot (about 120 traces) in common source gather showing the direct arrival travel path as itincreases hyperbolically with increasing source to receiver offset. Tube waves are also present.

Figure 12: Data plot (about 90 traces) in a common source profile showing weak, far-offset direct P-waves

obscured by tube waves. The tube waves are recognized by their high amplitude, low propagation speed.

TubeWaves

1/V


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The crosswell seismic profile can also be viewed in common mid-point (CMP) and

common vertical offset (CVO) for ease of analysis and processing. In the CVOarrangement, the direct arrival travel path is constant and therefore its moveout is zero. In

the CMP sort, direct arrivals have twice the hyperbolic moveout. The [CMP, CVO]

domain offers an advantage for both picking traveltimes and wavefoeld separation. In

picking, the CVO direct arrivals are maximally flat, while in CMP direct arrivals have

twice the moveout. The [CSP, CRP] and [CMP, CVO] comprise the two major domains

or four major data sets for analysis and processing crosswell seismic profiles. In each of

the three surveys that we used for this study, the data were acquired by first lowering the

source and detectors to their deepest positions. The detector array was kept stationary as

the source was raised and fired in motion every 1.25 feet with a sweep spectrum of 200-

2000 Hz. After the source reached its shallowest depth, it was again lowered as the

detector array is raised to its next position. Acquisition continued in this fashion until acomplete survey of 182 source positions and 116 detector positions were completed. The

survey took approximately 24 hours to complete.

Data Processing

A zero-offset gather from the data, shown in figure 13, provides useful stratigraphic

information. The coal has a lower seismic velocity (~7500 ft/sec) and is easily delineated

(11501235) by its larger travel time relative to the surrounding zones. Strong tube

waves are present in the data, though not easily seen in this gather. The same zero-offset

gathers (though rotated 1800) are shown for all the three data sets in figure 14 to illustratewhat I refer to as zero-offset repeatability test. This involves geophysical examination of

the differences between the surveys on a horizon basis. Intuitively; we expect geologic

events outside the reservoir level to be static (time-invariant), while seismic events within

the coal reservoir are expected to be dynamic (time-variant). I performed this zero-offset

repeatability analysis by putting red skeletal markers on horizons that are both within and

outside the reservoir level in survey 1 as shown in figure 14. We then projected same

onto surveys 2 and 3 respectively. As displayed in the resultant images, there is a

traveltime delay (a positive traveltime shift) within the reservoir level in survey 2 relative

to survey 1. And in survey 3, there is a negative traveltime shift within the reservoir level

relative to survey 2. The scenario is such that there is an increase in traveltime changes

between surveys 1 and 2 within the coal reservoir. In survey 3, the observed increase intraveltime in survey 2 has disappeared and tends to approximate the traveltime observed

in survey 1 within the reservoir. Though these production-induced changes are small,

they are real and statistically significant for time-lapse processing and interpretation. It is

interesting to note that observed differences in horizons outside the reservoir are quite

negligible, showing that events outside the reservoir are repeatable.

I pre-processed the data to suppress the tube waves and conditioned the direct arrivals

for travel time picking of the P-wave first arrivals (figure 15). More than 21,000 seismic

traces were picked and processed with travel time tomography to produce the velocity

image between wells. The traveltime picking was systematically done and same part of

the first arriving wavelet was picked for different offsets to minimize picking errors and


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Figure 14: A display of zero-offset sections for the three data sets showing evidence of production-inducedchanges within the coal reservoir and repeatability of events outside the reservoir.

Figure 15: Data plot of a common receiver gather showing typical direct P-waves picks (in green)


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Figure 16: Maps of P-waves traveltime picks (in msec) for the three surveys. The presumably coal zone iseasily delineated through rays traveling from the soirce locations to the receiver locations.


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Figure 17 (A to C): Maps of traveltime picks difference for the time-lapse surveys.


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Theoretically, an observed data vector t (observed traveltimes) is a discreetly sampled

function of some specific model parameter vector S (the slowness or the reciprocal of thevelocity). And for a ray lm in a medium where the slowness is S(x,y), the traveltime along

the ray is traditionally given as

tm = S x,y( )dlm m =1, ..., n,lm (1)

Where dlm is the incremental distance along the raypath lm. In general the raypath and

traveltimes depend on the slowness distribution. In geophysics unlike medical imaging,

straight rays are rarely found and as a result, the problem in equation (1) becomes highly

Non-linear since the unknown S(x,y) is also implicitly present in the path (Nolet, 1987;

Peterson et al 1985). If the medium is perturbed to SI(x,y) = S(x,y) + S (x,y), the new

traveltime calculated along the new raypath lI is

( ) 1111

, dlyxStl= (2)

Using Femats principle, it can be shown (Aki and Richards, 1980) that the difference in

traveltimes between the two media is

( )dlyxStl= , (3)

where t= t1 t. The nonlinear problem is then solved as a sequence of linearized stepsthat seeks to minimize the difference between real and calculated traveltimes.

Prior to the 2-D processing, I considered 3-D deviations of the wells by applying

necessary deviation corrections that allow for a projection of the 3-D deviations (in a N-E

coordinate) on to a 2-D plane (figures 18 and 19). This is necessary to ensure realistic

velocity estimates from the 2-D inversion. The well deviations can cause artificial

anisotropy that can impact on the fidelity of the 2-D tomography result for reliable

geologic interpretation of velocity model. For this study, the picked traveltimes were

imported into TOMOXpro where 2D tomography was performed. The algorithm

performs a non-linear traveltime inversion and reconstructs velocity models for anysurvey geometry. Through an iterative process, it can obtain a reliable velocity model

(Zhang and Toksoz, 1998). A 1-D velocity model was used to start the 2-D inversion.

This 1-D initial model was constructed from zero-offset data (Figure 13). The resulting 1-

D starting model for the inversion is shown in Figure 20. This approach was used in

processing the baseline survey. For the crosshole tomography of the second and third

surveys, I used the output of the 2-D inversion for the baseline survey as the starting

model with a view to minimizing time-lapse noise and subsequently used the same

inversion algorithm to get their respective tomograms. The resulting 2-D tomogram for

the baseline survey is shown in figure 21, where we plotted the gamma ray logs for the

observation wells alongside to validate qualitative geologic interpretation in conjunction

with the baseline tomogram.


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Figure 18: Schematic showing three dimensional deviations of the source (in red) and receiver (in blue)

wells. Their orientations or azimuths are in a North-East geographic coordinate system.

Figure 19: Schematic of the survey coverage and geometry of the two wells after deviation corrections

(thickly colored sections of the red and blue lines illustrate survey coverage within the subsurface).


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Figure 20: 1-D velocity model (blue line) from zero-offset data. The red dots show thepicked zero offset points used to create the 1-D starting model for the inversion.

Figure 21: Baseline tomogram with gamma ray logs (insets are tomogram-derivedvelocity logs).


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A cross-plot of the baseline velocity data with the gamma ray data for each of the

observation wells further shows consistency in geologic interpretation in which with thelow-velocity and low-radioactive coal is easily identified (figure 22). The baseline and

repeat tomograms exhibit the same geologic characteristics (figure 23) and provide

images of the vertical and lateral changes in velocity. To permit quantitative estimation of

changes in P-wave velocity due to CBM production, I computed the differences between

baseline and repeat tomograms and obtained the results shown in figure 24.

Figure 22: Cross-plots of baseline velocity and gamma ray in the two wells showing subsurface

heterogeneity & spatial distribution of velocity within the coal layer and other subsurface strata

Data Interpretation

Four distinct geologic units can be identified from the baseline tomogram. They include

shaly-sand, sandy-shale, coal, and sandstone. Of interest to us is the low-velocity,

biogenic and low-rank Big George coal zone at a depth of around 1150ft to1240ft. The

coal aquifer is confined above by the low permeability sandy-shale. Qualitative

interpretation of the gamma ray logs plotted alongside the baseline tomogram (Figure 21)

shows geological features similar to the ones observed on the baseline and repeat

tomograms (figure 23). This good correlation corroborates the quality of the tomographicinversion process. The difference tomograms are shown in figure 24. The 1st

difference


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tomogram (Vp1) was computed from the derived tomograms for the baseline survey of

December 2002 and 1

st

repeat survey of July 2003 (after about 8 months of dewatering).The 2nd

difference tomogram (Vp2) was computed from the derived tomograms for the

baseline survey of July 2002 and second repeat survey of June 2004 (after about 19

months of continuous dewatering).

Figure 23: Baseline and Repeat Tomograms (from left: survey 1, survey 2 and survey 3).

Also, I calculated the 3

rd

difference tomogram (

Vp3) from the repeat tomograms of the2nd survey (July 2003) and 3rd survey (June 2004). The 1st difference tomogram (Vp1)

shows a reduction in Vp of about 4% to 5% within the coal zone, perhaps due to partial

gas saturation and/or methane desorption. Both the 2nd and 3rd tomograms (Vp2 and

Vp3) also show quantifiable reductions in Vp (between 2% to 5%) in some portions of

the coal zone due to gas saturation. However, continued depressurization and subsequent

increase in differential pressure (confining pressure/overburden pressure minus pore

pressure) coupled with gas saturation might have influenced the observed changes in P-

wave velocity of about 0.5% to 6% in other portions of the 2 nd and 3rd difference

tomograms. These changes are not uniform due to the heterogeneous nature of the

confined coal aquifer. Interestingly, the observed changes in pore pressure (figure 25) are

so small to have had any considerable effect on the P-wave velocity in the saturated coal.As shown in figure 25, the pore pressure changes by 77 Psi (0.5Mpa) between the 1st and


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2nd

surveys, and by 7 psi (0.09Mpa) between the 2nd

and 3rd

surveys. These changes are

small and appear not to have much effect on changes in P-wave velocity.

Figure 24: Measured difference tomograms from the time-lapse surveys. From left is the 1st difference

tomogram, Vp1 (survey2 minus survey 1); 2nd difference tomogram, Vp2 (survey 3 minus survey 1) and

3rd difference tomogram, Vp3 (survey 3 minus survey 2). Arrow shows the location of the production well.

On the other hand, the measured gas pressure data in figure 26 shows that gas production

increases between the 1st and 2nd surveys, justifying the observed reduction in P-wave

velocity as observed on the 1st difference tomogram. And between the 2nd and 3rd surveys,

gas production reduces, corroborating the observed increase in P-wave velocity observed

on the 2nd and 3rd difference tomograms. This shows that the changes in P-wave velocityin the saturated coal depend primarily on gas saturation occasioned by the dewatering

process. With continued dewatering, pore pressure reduces causing the closing of

presumably layer cavities or air-filled cracks (Terry 1959) in the coal. This allows the

signals to travel more efficiently through the coal with greater velocity and less

attenuation, more so with the observed slight reduction in gas production between the 2nd

and 3rd surveys.

There are also changes both above and below the coal zone. These changes which are

approximately 6% might be due to pressure-related anomalies and fluid drainage and /or

migration through hydraulically active fractures (Colmenares and Zoback, 2003) to

surrounding porous and permeable beds. Also, the observed changes above the coal zone

might be due to water coming from the overlying, low-permeability sandy-shale due to


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depressurization effects. There is no gas in this caprock, suggesting that the observed

increases in P-wave velocity above the reservoir level are due to water saturation.

Figure 25: Water Head Pressure as a function of time

The scenario is such that besides the gas that is migrating from the reservoir to the

surface through the production well, water is also flowing in through the overlying sandy-

shale. This phenomenon tends to undermine the spatial efficiency of the dewatering

process. Because the changes are so small, there is also some possibility for inversion

artifacts in the difference tomograms. Besides, residual differences in the repeat time-

lapse surveys that are independent of changes in subsurface geology could cause time-

lapse noise and impact the desired repeatability of the time-lapse surveys. These

differences can arise from geometry variations in the repeat surveys, hydrophone

positioning and recording fidelity differences. The observed time-lapse changes agreereasonably well with predictions based on Gassmanns equation (figures 27 and 28),

underscoring the effectiveness and applicability of high resolution crosswell seismic for

monitoring.

In creating the model shown in figures 27 and 28, I remodeled the laboratory data from

Yu et al. (1993) to match the prevailing geo-reservoir conditions at the PRB and the

observed baseline coal velocities. Prior to dewatering, the P-wave velocity in the water-


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saturated coal seam (average baseline velocity) was around 7500ft/s and about 8% to

15% higher than the velocity in the dry coal. At the initial stage of depressurization, theP-wave velocity increases due to a reduction in pore pressure and an increase in

differential pressure. As differential pressure goes up, the number of cracks and the thin

gaps contributing to attenuation decreases, leading to a rise in Vp. Also; with methane

desoption, the coal matrix shrinks and the velocity drops due to partial gas saturation and

more so that methane has high compressibility (low bulk modulus). This initial gas

Figure28: P-wave velocitys behavior in saturated coal as a function of differential pressure.

drainage at a pore pressure change of about 77Psi or 0.5Mpa produces a negative velocity

difference of about 6% (i.e. a velocity of about 7050ft/s) when compared with the

baseline velocity (of 7500ft/s) as shown in figures 27, 28 and 32.. However, with

continued dewatering and depending on the degree of pore-fluid saturation (gas), the

velocity tends to increase again (about 7850ft/s) with a change in pore pressure of around

7Psi or 0.09Mpa. Consequently, the velocity differences between the fully saturated

water condition and the subsequent stages of gas-saturated state might show a positive

increase following several months of dewatering or depressurization (figures 21, 23 and

24). And this observation is true for our case in that a reduction in gas production level in

survey 3 as shown in figure 26 impacted on the P-wave velocity. This provides a physical,

corroborative evidence for the observed time-lapse changes in the 2nd and 3rd difference

tomograms.


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Conclusions

I have imaged and quantitatively characterized production-induced changes in P-wave

velocity at the Powder River Basins Big George Coal using time-lapse crosswell seismic

surveys. The results show that the spatial distribution of the P-wave velocity changes

approximates 6% both within and outside the coal layer. This difference is due to a

combination of gas saturation and changes in pore pressure. Besides, we observed that

the magnitudes of the P-wave velocity changes depend largely on the degree of changes

in gas production. Though these changes are small and non-uniform due to the

heterogeneous nature of the coal aquifer, they are statistically significant for time-lapse

monitoring. The observations further suggest that produced water comes from the

overlying sandy shale and that pressure drawdown in the coal is not spatially efficient.

References

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