Modeling of CBM production, CO2 injection, and tracer movement at a field CO2 sequestration site

International Journal of Coal Geology 96-97 (2012) 120–136

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Modeling of CBM production, CO2 injection, and tracer movement at a field CO2 sequestration site Hema J. Siriwardane a, b,⁎, Benjamin D. Bowes a, Grant S. Bromhal b, Raj K. Gondle a, Arthur W. Wells c, Brian R. Strazisar c a b c

Department of Civil & Environmental Engineering, West Virginia University, Morgantown, WV 26506-6103, USA National Energy Technology Laboratory, US Department of Energy, P.O. Box 880, Morgantown, WV 26507-0880, USA National Energy Technology Laboratory, US Department of Energy, P.O. Box 10940, Pittsburgh, PA 15236, USA

a r t i c l e

i n f o

Article history: Received 6 September 2011 Received in revised form 24 February 2012 Accepted 24 February 2012 Available online 24 March 2012 Keywords: Carbon sequestration Reservoir modeling Tracer injection Tracer modeling

a b s t r a c t Sequestration of carbon dioxide in unmineable coal seams is a potential technology mainly because of the potential for simultaneous enhanced coalbed methane production (ECBM). Several pilot tests have been performed around the globe leading to mixed results. Numerous modeling efforts have been carried out successfully to model methane production and carbon dioxide (CO2) injection. Sensitivity analyses and history matching along with several optimization tools were used to estimate reservoir properties and to investigate reservoir performance. Geological and geophysical techniques have also been used to characterize field sequestration sites and to inspect reservoir heterogeneity. The fate and movement of injected CO2 can be determined by using several monitoring techniques. Monitoring of perfluorocarbon (PFC) tracers is one of these monitoring technologies. As a part of this monitoring technique, a small fraction of a traceable fluid is added to the injection wellhead along with the CO2 stream at different times to monitor the timing and location of the breakthrough in nearby monitoring wells or offset production wells. A reservoir modeling study was performed to simulate a pilot sequestration site located in the San Juan coal basin of northern New Mexico. Several unknown reservoir properties at the field site were estimated by modeling the coal seam as a dual porosity formation and by history matching the methane production and CO2 injection. In addition to reservoir modeling of methane production and CO2 injection, tracer injection was modeled. Tracers serve as a surrogate for determining potential leakage of CO2. The tracer was modeled as a non-reactive gas and was injected into the reservoir as a mixture along with CO2. Geologic and geometric details of the field site, numerical modeling details of methane production, CO2 injection, and tracer injection are presented in this paper. Moreover, the numerical predictions of the tracer arrival times were compared with the measured field data. Results show that tracer modeling is useful in investigating movement of injected CO2 into the coal seam at the field site. Also, such new modeling techniques can be utilized to determine potential leakage pathways, and to investigate reservoir anisotropy and heterogeneity. © 2012 Elsevier B.V. All rights reserved.

1. Introduction Since carbon dioxide (CO2) is considered to be the single largest contributor to greenhouse gas emissions, many efforts have been undertaken to safely sequester CO2 in deep geologic formations (U.S.D.O.E., 2010). One such potential option is to sequester carbon dioxide into deep, unmineable coal seams (Beecy and Kuuskraa, 2001; U.S.D.O.E., 2010; White et al., 2005). This paper focuses on enhanced coalbed methane production from and sequestration of carbon dioxide in a deep unmineable coal seam. Issues related to enhanced coalbed methane recovery by injecting CO2 have been addressed in the past ⁎ Corresponding author at: Department of Civil & Environmental Engineering, West Virginia University, Morgantown, WV 26506-6103, USA. Tel.: + 1 304 293 9946; fax: + 1 304 293 7109. E-mail address: [email protected] (H.J. Siriwardane). 0166-5162/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.coal.2012.02.009

(Bromhal et al., 2005; Palmer and Mansoori, 1998; Pan and Connell, 2007; Sams et al., 2003; Shi and Durucan, 2004; Siriwardane et al., 2006; Smith et al., 2004). Some of the major issues related to ECBM/ CO2 storage considered in this paper include coal shrinkage/swelling, stress-dependent permeability and reservoir heterogeneity (Clarkson et al., 2008; Gierhart et al., 2007; Karacan, 2007; Mazumder et al., 2006; Mitra and Harpalani, 2007; Pan and Connell, 2007; Rogers, 1994; Shi and Durucan, 2010; Siriwardane et al., 2009). Several research studies, including site characterization and monitoring activities, were undertaken to demonstrate the potential of CO2 sequestration in unmineable coal seams at different pilot field sites (Fujioka et al., 2010; Koperna et al., 2009; Oudinot et al., 2008; Oudinot et al., 2011; Reeves et al., 2003; U.S.D.O.E., 2010; Wageningen and Mass, 2007; Wageningen et al., 2009; Winschel et al., 2010). In recent years, surface monitoring technologies such as tiltmeters and traceable fluids have been used to track the movement of injected fluids

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Nomenclature a Variable porosity factor Ar Anisotropic ratio of the reservoir = kf/kb C sh Shrinkage constant (ton/SCF) C sw Swelling constant (ton/SCF) E Modulus of Elasticity (or Young's Modulus) (psi) G Shear modulus (psi) K Bulk modulus (psi) k Permeability of the material (mD) k0 Reference state permeability (mD) kb Permeability in the butt cleat direction (mD) kf Permeability in the face cleat direction (mD) p Pore pressure (psi) qi Gas production rate of well “i” (SCF/day) qmax Maximum gas production rate of well “i” (SCF/day) Qmeasured Measured cumulative gas production (SCF) qmeasured Measured gas production rate (SCF/day) (Qmeasured)max Maximum measured gas production (SCF) (qmeasured)max Maximum measured gas production rate (SCF/day) Qsimulated Simulated cumulative gas production (SCF) qsimulated Simulated gas production rate (SCF/day) Ri Relative production ratio for gas production rate for well “i” RMIN Minimum relative production ratio among all producers Va Adsorbed volume of the gas that causes swelling of the coal matrix (SCF/ton) Vd Desorbed volume of the gas that causes shrinkage of the coal matrix (SCF/ton) α Poroelastic constant εvsw Volumetric swelling strain εvsh Volumetric shrinkage strain εij Strain tensor ν Poisson's ratio σij Stress tensor ϕ Cleat porosity (%) ϕ0 Reference state cleat porosity (%) ϕMAX Specified maximum porosity (%) ϕMIN Specified minimum porosity (%) ψwell Normalized error for gas production rate of individual production well ψcumulative Normalized error for cumulative gas production

Oudinot et al., 2008; Oudinot et al., 2011; Shi and Durucan, 2010). The research work presented in this paper is focused on the numerical modeling of coalbed methane recovery, CO2 and tracer injection, and tracer movement at the same field site. The modeling approach presented in this paper is different from the previous reservoir modeling approaches (Koperna et al., 2009; Oudinot et al., 2008; Oudinot et al., 2011; Shi and Durucan, 2010; Siriwardane et al., 2009) as highlighted below. • An extensive reservoir area consisting of 63 wells and a 30-year production history was considered based on available site characterization and field data. • One of the unique aspects of the present paper is the use of tracer data in addition to production and injection data in determining the permeability anisotropy factor. In this study, tracer injection data was incorporated into the modeling work. This approach is the first in a modeling study of CO2 sequestration in a coal seam. • The numerical results on the breakthrough of the tracers were compared with field measurements. • As a part of modeling effort, the influence of reservoir, geologic and geomechanical properties on the reservoir performance was investigated. These parameters include reservoir permeability, anisotropy, shrinkage/swelling, Young's modulus and Poisson Ratio. 2. Methodology A generalized three dimensional swelling and shrinkage model was developed and has been implemented in an existing coalbed methane reservoir simulator, PSU-COALCOMP, which has been used in several previous studies (Bromhal et al., 2005; Gorucu et al., 2005; Manik and Ertekin, 1998; Siriwardane et al., 2006). The modified PSU-COALCOMP model is based on constitutive equations that account for the coupled fluid pressure deformation behavior of a porous medium that undergoes swelling and shrinkage. Coal swelling will cause a reduction of permeability, which in turn may reduce injection volumes during large-scale sequestration operations. The coal matrix swells when it adsorbs CO2 causing a reduction in permeability, while the coal matrix shrinks when CH4 gas is released from the matrix causing an increase in permeability. The net swelling and shrinkage strains are computed on the basis of the amount of CO2 adsorbed and the amount of CH4 desorbed (Siriwardane et al., 2006). In the modified PSU-COALCOMP, the swelling and shrinkage strains of the coal matrix are expressed as given below. The swelling strain can be expressed as: sw

dεv ¼ C in underground geologic formations (Koperna et al., 2009; Oudinot et al., 2008; Oudinot et al., 2011; Siriwardane and Gondle, 2011; Wells et al., 2010; Winschel et al., 2010). However, field monitoring studies are somewhat expensive and time consuming. Therefore, research efforts to incorporate limited field measurements with reservoir modeling techniques can be useful to predict reservoir performance and to investigate subsurface fluid movements. In the past, several modeling studies were performed to investigate aspects related to enhanced coalbed methane production and CO2 injection (Bromhal et al., 2005; Connell and Detournay, 2009; Koperna et al., 2009; Oudinot et al., 2008; Ozdemir, 2009; Pekot and Reeves, 2003; Sams et al., 2003; Shi and Durucan, 2004; Shi et al., 2008; Sinayuc and Gumrah, 2009; Siriwardane et al., 2009; Wageningen et al., 2009). The modeling study reported in this paper is based on a field demonstration site (i.e., the Pump Canyon sequestration site) located in the San Juan coal basin of northern New Mexico; the field project is a part of the U.S.D.O.E.'s Southwest Regional Carbon Sequestration Partnership (Koperna et al., 2009; Oudinot et al., 2008; Oudinot et al., 2011). Results from a few modeling studies at this project site have been reported in the recent literature (Koperna et al., 2009;

121

sw

dV a

ð1Þ

where εvsw C sw Va

volumetric swelling strain, swelling constant (ton/SCF), and adsorbed volume of the gas that causes swelling of the coal matrix (SCF/ton).

The shrinkage strain can be expressed as: sh

sh

dεv ¼ C dV d

ð2Þ

where εvsh C sh Vd

volumetric shrinkage strain, shrinkage constant (ton/SCF), desorbed volume of the gas that causes shrinkage of the coal matrix (SCF/ton).

Eqs. (1) and (2) can be used to compute volumetric strains during swelling or shrinkage that can occur during the process of sorption

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and desorption. The amount of swelling and shrinkage depends on the sorbed or desorbed volumes of each gas into the coal matrix. The shrinkage and swelling constants have different values for different gases. The volumes of sorption and desorption can be expressed as functions of gas pressures. The stress–strain relationships for a coal should include the effects of pore pressure as well as swelling and shrinkage. More details on the mathematical formulation of the swelling and shrinkage model incorporated into the modified PSU-COALCOMP can be found elsewhere (Siriwardane et al., 2006; Siriwardane et al., 2009). The constitutive equations for the coal matrix in the incremental form can be written as:   2G dσ ij ¼ 2G dεij þ K− dεkk δij þ α dpδij −C SW f 1′ ðpÞ dpK δij 3 SH þ C f 2′ ðpÞ dpKδij

(a) General location Idaho

Wyoming Nebraska

Colorado Utah Pump Canyon Sequestration Site

Arizona

New Mexico

Texas

ð3Þ

where C sh C sw σij εij p G K α

(b) Sections, township, and range

shrinkage constant (ton/SCF) swelling constant (ton/SCF) stress tensor strain tensor pore pressure (psi) shear modulus (psi) bulk modulus (psi) poroelastic constant

14 23

Because the advective flow of fluids in coal seams is within fractures, the cubic law of parallel plate flow (Palmer and Mansoori, 1998) is often used to model flow, as shown below:  k ¼ k0

ϕ ϕ0

3

ð4Þ

In this study k0 is the reference state permeability (mD) and ϕ0 is the reference state porosity (mD). This model was implemented into an existing reservoir simulator, PSU-COALCOMP. According to the workflow in this computer code, the adsorbed volume of different gas components is computed first. These volumes are then used to determine swelling and shrinkage volumetric strains as shown in Eqs. (1) and (2). The change in the cleat porosity can be calculated on the basis of volumetric strains of the coal matrix. The updated porosity is then used to compute the new permeability as shown in Eq. (4). 3. Site description This paper involves a modeling study at the Pump Canyon pilot sequestration site located in northern New Mexico (Koperna et al., 2009; Oudinot et al., 2008; Oudinot et al., 2011). The Pump Canyon project sits in a high permeability (as high as 550 mD) region of the San Juan basin (Koperna et al., 2009). Pump Canyon is located just south of the Allison Unit, the first project in the world to inject CO2 into a coalbed methane reservoir for enhanced coalbed methane (ECBM) production (Koperna et al., 2009; Oudinot et al., 2008; Oudinot et al., 2011; Reeves et al., 2003). The location of the Pump Canyon site is shown in Fig. 1. Geologic details of the study region can be found elsewhere (Fassett, 2011; Koperna et al., 2009; Oudinot et al., 2008; Oudinot et al., 2011; Weber et al., 2010; Wilson et al., 2010; Wilson et al., 2012). Potential leakage pathways are discussed in a recent publication (Wilson et al., 2009; Wilson et al., 2012). The area of the site selected for this study consists of 61 production wells and 2 pressure observation wells (POW). The selected area consists of high permeability in the reservoir (Koperna et al., 2009; Oudinot et al., 2008). A new injection well was completed by July 2008

31 N

9W

8W

13

18

24

26

25

35

36

19 30 31

30 N 2

1

11

12

14

6 7

13

18

9W

8W

16

15

14

20

21

22

23

29

28

27

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33

34

35

4

3

2

17

32

5 8 17

9 16

10 15

31 N 30 N

11 14

Injection Source Note: Data source on maps and well locations (GOTECH, 2010; U.S.G.S., 2010).

Fig. 1. Location of the Pump Canyon Sequestration site.

at the middle of the study area (Koperna et al., 2009; Oudinot et al., 2008). The injection well (EPNG COM A 1) is located in Section 32 of Township 31 N and Range 8 W (Latitude: 36.85° and Longitude: −107.69°) as shown in Fig. 1 (GOTECH, 2010; Koperna et al., 2009; Oudinot et al., 2008). It has been reported that coals in New Mexico, Colorado and Wyoming have large CO2 storage potentials (U.S.D.O.E., 2010). Pump Canyon, a sequestration site managed by Southwest Partnership was selected as a demonstration area based on the storage capacity, geologic data, gas production history, nearby CO2 resources and in-place infrastructure that can be utilized for large scale projects (Oudinot et al., 2008). (a) General location (b) Sections, township, and range Note: Data source on maps and well locations (GOTECH, 2010; U.S.G.S., 2010). In the pilot study, carbon dioxide was injected into the Fruitland coal formation. This formation is located at a depth of approximately 3000 ft (914 m). The total thickness of the coal seam is approximately 60 ft (18 m) spread out over three separate seams over a distance of

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175 feet gross interval (Koperna et al., 2009; Oudinot et al., 2008). Cleat orientation was measured at a nearby well NBU #403 (approximately 7 miles due east of the field site), and was found to be N 35° E. (Koperna et al., 2009; Oudinot et al., 2008). More details on the geologic column, isopachs and structure maps of the site can be found elsewhere (Koperna et al., 2009; Oudinot et al., 2008). Previous modeling studies related to the Pump Canyon sequestration site include a geostatistical and reservoir modeling approach to history-match field production and field injection data (Koperna et al., 2009; Oudinot et al., 2008; Oudinot et al., 2011). Some unknown properties were also determined by an optimization process (Koperna et al., 2009; Oudinot et al., 2008; Oudinot et al., 2011). Similar studies at different pilot sites can also be found in the literature (Calderon et al., 2010; Pekot and Reeves, 2003; Sams et al., 2003; Shi and Durucan, 2004; Siriwardane et al., 2009). In the current paper, an extensive reservoir area consisting of 63 wells and a 30-year production history was considered. The influence of coal swelling and shrinkage was considered, and tracers were used to supplement the modeling results for the first time in coal seams. 4. Reservoir geometry The Pump Canyon coalbed methane reservoir contains several wells scattered across the study area. Fig. 2 shows the locations of production

9W 8W

31 N

31 N

30 N

30 N

123

wells, the injection well, and other references wells in the area. Fig. 2 also shows the selected reservoir area and its orientation in the face cleat direction. In view of the large area of the reservoir and the 30-year production history, it was impractical to consider a refined model for the full area due to limitations in computational resources. Therefore, reservoir modeling of the study area was performed by considering two different grid block configurations in order to history match the measured gas production and measured CO2 injection. The history matching over the large area was done by using a coarse grid (i.e. grid 1) as shown in Fig. 3. A smaller reservoir area containing 9 producer wells and an injection well was used to investigate the pressure response during CO2 injection. Fig. 4 shows the boundaries of the smaller reservoir area in relation to the larger grid area. A refined grid was selected for this area and is shown in Fig. 5. The results obtained from the larger model were mapped onto the smaller grid for consistency. These reservoir simulation models were oriented with respect to the face cleat direction as described in the previous section. Since the study area used a large number of production wells to extract methane from the coal seam, unknown reservoir properties were determined by history matching the gas production from these producer wells. The grid block configuration (scaled) shown in Fig. 3 was used primarily for this purpose. The grid block configuration shows a refined grid covering the region near the injection well and the adjacent production wells. The reservoir model consists of 40 grid blocks in X-direction and 40 grid blocks in Y-direction with a total of 1600 grid blocks. The reservoir dimensions of these grid blocks are presented in Table 1. As shown in Fig. 3, the notation of ‘1’ indicates active grid blocks with no wells and ‘0’ indicates inactive grid blocks illustrating the no flow boundary. The well names were modified with a new numbering system as required by the input for the reservoir simulator. The grid blocks with a well are identified as ‘n’, where “n” represents the well identity number. More details on the locations and production history of these wells can be found elsewhere (GOTECH, 2010). The purpose of the coarse grid block configuration used in Fig. 3 was to determine the unknown reservoir properties for a significant area by history matching the gas production data. Fig. 5 shows the refined grid block configuration and finite difference grid used for injection modeling The refined grid block configuration consists of 63 × 63 grid blocks in X- and Y-directions, respectively. The indices of wells included in the larger and smaller grid areas are shown in Tables A1 and A2 in the Appendix A, respectively. Coalbed methane production in the Pump Canyon reservoir began in the early 1980s (GOTECH, 2010). The number of production wells has been increasing as shown in Fig. 6. There is no history of CO2 injections at this site prior to 2008. Fig. 6 shows bubble maps of production rates for producing wells over different time periods. The bubble (circle) sizes in these figures are proportionate to the production rates in these wells. Coalbed methane production was active in the study area during the period of CO2 injection (Koperna et al., 2009; Oudinot et al., 2008; Oudinot et al., 2011). Bottomhole pressure data from various wells in the study area suggest an initial reservoir pressure near 1700 psi (11.72 MPa), and after years of gas production, reservoir pressures have depleted to as low as 200 psi (1.37 MPa) in some instances (Koperna et al., 2009). It has been reported that the porosity in the coal formation is less than 2% (Oudinot et al., 2008). There was no reliable water production data at this site. 5. Modeling of coalbed methane production

Reference Wells

5.1. Discretization of the study area

Production Wells Injection Well

9W 8W Note: 1 feet = 0.3048 m

Fig. 2. Selection and orientation of reservoir geometry.

A model was constructed by discretizing the study area into a finite difference grid. The grid was oriented along the rotated axes as shown in Fig. 2 to align with the principle cleat direction so as to simplify the permeability model. Fig. 4 shows the rotated grid axes with respect to north. As described earlier, the grid has a refined

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Note: 1 feet = 0.3048 m

Fig. 3. Details of the reservoir model.

central region for increased accuracy around the location of the injection well. The coarse grid covers an area of over 18 square miles centered around the injection well. A no flow boundary was placed around this region. The three coal seams in the injection target area were idealized as one coal seam with a total thickness equal to the sum of the three individual seams. The depth of the equivalent coal seam was taken as 3012 ft. The constructed finite difference grids are shown in Figs. 3 and 5. 5.2. Input parameters Porosity in the reservoir is unknown and was estimated. Estimates for porosity are based on an index of production rates. It was assumed that producers with higher production rates lie in areas with higher porosity. Porosity for each well block was estimated and the results were interpolated to include the entire study area. Porosity values

were assumed to range from a minimum of 1% to a maximum of 2% based on a few reported porosity values at the project site (Koperna et al., 2009; Oudinot et al., 2008; Oudinot et al., 2011). However, lower values of porosity for Fruitland coal have also been reported based on analytical modeling studies in the literature (Clarkson et al., 2008). The correlation between cleat permeability and cleat porosity is not well established. However, the cubic power law relationship between permeability and porosity is widely used in the dynamic permeability modeling (Oudinot et al., 2008; Palmer and Mansoori, 1998; Shi and Durucan, 2004; Shi and Durucan, 2010; Siriwardane et al., 2009). The reservoir permeability depends on the pore fluid pressure and the swelling/shrinkage of the coal matrix. Without any shrinkage of the coal matrix, the permeability of the reservoir decreases with a decrease in reservoir pressure due to compressibility. On the other hand, as the coal matrix shrinks with gas production, the

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125

Table 1 Reservoir properties of history matching grid. Reservoir property

Input

Reservoir grid (I, J, K) Individual grid block size, I-direction Individual grid block size, J-direction Reservoir depth Average reservoir thickness Cleat permeability (mD) Cleat porosity (%) Permeability anisotropy Coal density Poisson's ratio Elastic modulus Palmer and Mansoori exponent CH4 swelling/shrinkage constant CO2 swelling/shrinkage constant Initial reservoir temperature Initial reservoir pressure Initial water saturation Water viscosity Water density Gas composition, % (CH4, CO2) Sorption volume constant for CH4 (weighted average) Sorption pressure constant for CH4 (weighted average) Sorption volume constant for CO2 (weighted average) Sorption pressure constant for CO2 (weighted average) Initial gas composition — CH4, CO2 (%, %) Minimum bottomhole pressure Coal desorption time

40, 40, 1 10 × 765 ft (233 m), 20 × 365 (111 m), 10 × 765 ft (233 m) 10 × 765 ft (233 m), 20 × 365 (111 m), 10 × 765 ft (233 m) 3012 ft (918.0 m) 60 ft (18.2 m) Fig. 8 (based on the production index) Fig. 7 (based on the permeability and ‘a’ factor) 1.5%–2.0% 99.88 pcf (1600 kg/m3) 0.32 521,000 psi (3592 MPa) 3 3.0 × 10− 5 ton/SCF (0.961 kg/m3) 1.2 × 10− 5 ton/SCF (0.384 kg/m3) 126 °F (52 °C) 1700 psi (11.72 MPa) 1 0.7 cp 62.4 pcf (1000 kg/m3) (100, 0) 490 SCF/ton (1.53 × 10− 2 m3/kg) 548 psi (3.77 MPa) 909 SCF/ton (2.84 × 10− 2 m3/kg) 329 psi (2.26 MPa) (90, 10) 15 psi (0.1 MPa) 1 day

Fig. 4. Location of the refined grid relative to the history matching grid.

reservoir permeability increases. The combined influence of these two processes determines the reservoir dynamic permeability as discussed by several researchers (Palmer, 2009; Shi and Durucan, 2010). The magnitude of the predicted permeability change depends on the reservoir properties such as compressibility, shrinkage constants, and the model used. Porosity of the block containing well “i” was determined as follows: ϕi ¼

ϕMAX −ϕMIN RMAX −RMIN

   Ri −RMIN þ ϕMIN

ð5Þ

where Ri RMIN ϕMAX ϕMIN Qi Qmax

The relative production ratio, defined as the production rate i of well “i” divided by the maximum production rate = QQmax Minimum relative production ratio among all producers Specified minimum porosity Specified maximum porosity Production rate of well “i” (SCF/day) Maximum production rate of well “i” (SCF/day)

Face cleat permeability of the reservoir was estimated based on the porosity values determined in the previous step. The relationship shown below was used to estimate reservoir permeability (Schwerer and Pavone, 1984):  n ϕ ¼ a kf

ð6Þ

where

Fig. 5. Refined grid block configuration for modeling CO2 injection.

ϕ kf

Porosity Intrinsic permeability in the face cleat direction, mD (Fig. 2)

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Fig. 6. Bubble map showing the gas production rates at different times.

a n

Variable porosity factor 0.33 for coal

The value of the factor “a” was determined by an iterative process until the production history and injection data compared well with model predictions. Fig. 7 shows the porosity map used in the modeling study. Fig. 8 shows the permeability map created by using an “a” factor of 0.0024 in Eq. (6). The porosity and permeability values shown in Figs. 7 and 8 were used as input parameters in the reservoir model. Historical data on the bottomhole pressures shows an initial reservoir pressure of 1700 psi (11.72 MPa). Moreover, the three individual coal seams in the study area were idealized as one larger coal seam with composite properties. A uniform top depth of 3012 ft (918 m) was used on the basis of published data (Oudinot et al., 2008). Initial water saturation of the reservoir was unknown. Limited available information on water saturation was not considered reliable based

on the published literature (Koperna et al., 2009; Oudinot et al., 2008; Oudinot et al., 2011). However, it is typical for undisturbed coal formations to be completely saturated or near full saturation prior to any production. Hence, an initial water saturation of 95% was assumed for simulation purposes. This assumed value compares well with initial water saturation levels reported in other studies (Oudinot et al., 2008). Water production data was unreliable, and therefore was not used in the history matching process. More details on the Langmuir parameters and formation thickness of individual coal seams can be found elsewhere (Koperna et al., 2009; Oudinot et al., 2008). The combined thickness of all coal seams is 60 ft (18.2 m), which was used as the thickness of the reservoir layer in this study. The sorption time constant used in this study was assumed to be 1 day as reported in a previous study (Oudinot et al., 2008). Tests were performed with other sorption constants, but sorption constant of 1 day was determined to be best for the history matching of the reservoir. Production data was obtained from available sources

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127

Fig. 7. Initial porosity map.

(GOTECH, 2010) and was incorporated into the reservoir model. Production rates were also used in the estimation of porosity and permeability values as described earlier. In addition, production data shows activation times and any shut in periods for production wells.

The geomechanical properties were used from published literature (Levine, 1996; Siriwardane et al., 2009). Table 1 shows a summary of input parameters including estimates of geomechanical parameters used in the study.

Fig. 8. Initial face cleat permeability.

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5.3. History matching results

Table 2 Normalized error.

Reservoir modeling was performed on the basis of input parameters shown in Table 1. One of the unknown input parameters is the anisotropic ratio (Ar), which is defined as: k Ar ¼ f kb

ð7Þ

where anisotropic ratio permeability in the face cleat direction (mD) permeability in the butt cleat direction (mD)

Ar kf kb

To determine a suitable value for the anisotropic ratio (Ar), a parametric study was performed to investigate the influence of Ar on the gas production. The influence of anisotropic ratio on the cumulative gas production is shown in Fig. 9 for different values of anisotropic ratio in the range of 1.0 to 3.0. The comparison between measurements and computed values of cumulative gas production corresponding to anisotropic ratio of 1.5 and 2.0 can be considered as good. A statistical analysis was performed on cumulative gas production and gas production at selected individual wells to determine the best value of anisotropic ratio (Ar). In the statistical analyses, the normalized error, the square of the normalized error, and the coefficient of correlation between the computed and measured values of gas production were considered. The normalized error (ψwell) for an individual well was defined as: q −q ψwell ¼ ∑ measured simulated qmeasured

ð8Þ

max

where ψwell

normalized error for gas production rate of individual production well qmeasured measured gas production rate at any period of time (SCF/day) qsimulated simulated gas production rate at any period of time (SCF/day) (qmeasured)max Maximum measured gas production rate (SCF/day)

The normalized error (ψcumulative) for cumulative gas production was defined as: Q −Q ψcumulative ¼ ∑ measured simulated Q measured

ð9Þ

max

4.5E+08 Ar = 2.0

MEASURED

Cumulative Gas Production (MCF)

4.0E+08

Ar = 1.5

ISOTROPIC ANISOTROPY = 1.5

3.5E+08

Ar = 1.0

ANISOTROPY = 2.0 ANISOTROPY = 2.5

3.0E+08

ANISOTROPY = 3.0

ψwell

Well # 3 Well # 4 Well # 5 Well # 10 Well # 21 Well # 29 Well # 32 Well # 42 Well # 46 Cumulative

Ar = 1

Ar = 1.5

Ar = 2

Ar = 2.5

Ar = 3

− 22.16 − 5.48 − 2.40 − 17.42 − 12.02 − 24.64 12.97 − 4.41 − 19.16 − 24.17

− 18.42 − 22.38 − 2.26 − 36.51 − 6.19 − 18.63 8.43 − 19.06 − 14.64 − 7.90

− 14.85 − 38.21 − 1.18 − 54.52 − 1.94 − 14.04 4.40 − 33.15 − 10.71 4.49

− 11.24 − 52.72 0.02 − 72.52 1.38 − 9.98 0.97 − 46.91 − 7.31 14.44

− 8.23 − 65.13 1.49 − 89.35 4.41 − 6.59 − 2.45 − 59.83 − 4.15 22.75

where ψcumulative normalized error for cumulative gas production Qmeasured measured cumulative gas production at any period of time (SCF) Qsimulated simulated cumulative gas production at any period of time (SCF) (Qmeasured)max Maximum measured gas production (SCF) Tables 2, 3 and 4 show a summary of the normalized error, the square of the normalized error, and the coefficient of correlation between the computed and measured values of individual gas production rates, respectively. Table 5 shows a summary of the normalized error, the square of the normalized error, and the coefficient of correlation between the computed and measured values of cumulative gas production. As can be seen from these tables, the best values of the anisotropic ratio based on the normalized error and the square of the normalized error seem to be 1.5 and 2.0. A comparison of measured and computed data on gas production at a few individual wells is shown in Fig. 10, corresponding to an Ar value of 1.5 and 2.0. Simulated production rates compare well with measured production rates at a majority of the wells. It is noted that computed results are slightly different from measurements in some of the wells. This is expected given the large area and the number of wells considered in this study. These differences could be a result of localized heterogeneities in the coal seam near those wells, loss of resolution due to the size of grid blocks used in the model, and unknown geologic variability. However, the measured cumulative production data from all the production wells (i.e., 61 production wells) compare well with modeling results as shown in Fig. 11 for anisotropic ratios of 1.5 and 2.0. Therefore, these values (Ar = 1.5 and 2.0) were selected for the reservoir analysis during CO2 and tracer injections. As shown in a later section of this paper, the tracer modeling efforts also show that the anisotropic ratio of 1.5 or 2.0 for this reservoir is a suitable value. Based on a previous study performed over a smaller area of the reservoir, an anisotropic ratio value of 1.8 has been reported in the literature

Ar = 2.5

Ar = 3.0

Table 3 Square of the normalized error.

2.5E+08 2.0E+08

(ψwell)2

1.5E+08 1.0E+08 5.0E+07 0.0E+00 Dec-88 Sep-91 Jun-94 Mar-97

Dec-99 Sep-02 May-05 Feb-08 Nov-10

Date Note: 1 MCF = 0.02678 x 103 N-m3

Fig. 9. Influence of anisotropic ratio on cumulative gas production.

Well # 3 Well # 4 Well # 5 Well # 10 Well # 21 Well # 29 Well # 32 Well # 42 Well # 46 Cumulative

Ar = 1

Ar = 1.5

Ar = 2

Ar = 2.5

Ar = 3

4.43 5.94 3.68 11.11 8.06 8.90 3.29 11.74 6.17 2.80

7.40 16.61 3.30 38.02 9.40 7.86 1.73 21.56 6.77 0.71

10.75 36.34 4.86 79.74 10.60 8.42 0.98 38.52 7.87 0.86

13.27 62.63 6.60 137.16 11.59 9.20 0.80 62.18 8.82 2.02

15.13 91.07 8.11 204.90 12.41 9.96 1.05 90.76 9.57 3.71

H.J. Siriwardane et al. / International Journal of Coal Geology 96-97 (2012) 120–136

R2 Ar = 1

Ar = 1.5

Ar = 2

Ar = 2.5

Ar = 3

0.90 0.14 0.71 0.09 0.62 0.66 0.44 0.27 0.74 0.9990

0.73 0.13 0.72 0.09 0.61 0.56 0.44 0.27 0.69 0.9906

0.55 0.13 0.59 0.09 0.64 0.45 0.44 0.26 0.67 0.9753

0.42 0.13 0.44 0.09 0.68 0.37 0.44 0.26 0.67 0.9595

0.32 0.13 0.30 0.10 0.72 0.30 0.43 0.26 0.68 0.9451

Gas Production Rate (MCF/day)

7000

Table 4 Coefficient of correlation.

Well # 3 Well # 4 Well # 5 Well # 10 Well # 21 Well # 29 Well # 32 Well # 42 Well # 46 Cumulative

129

FC STATE COM 1

6000

Measured

5000

Computed (Ar = 1.5) Computed (Ar = 2.0)

4000 3000 Ar = 1.5 Ar = 2.0

2000 1000 0 Dec-88 Sep-91 Jun-94 Mar-97

Dec-99 Sep-02 May-05 Feb-08 Nov-10

Date

6. Modeling of CO2 injection

800 700 600

EPNG COM A 300S Measured Computed (Ar = 1.5) Computed (Ar = 2.0) Ar = 1.5

500

Ar = 2.0

400 300 200 100 0 Dec-88 Sep-91 Jun-94 Mar-97

Dec-99 Sep-02 May-05 Feb-08 Nov-10

Date 5000 4500

Gas Production Rate (MCF/day)

(Koperna et al., 2009; Oudinot et al., 2008). Fig. 12 shows a comparison of measured and computed reservoir pressure variation with time. This comparison of reservoir pressure can also be considered as good. Moreover, the comparisons of measured and computed production history suggest that the estimated unknown reservoir properties are acceptable for the study area. The influence of shrinkage constant on the variation of pressuredependent permeability (i.e., dynamic permeability) corresponding to a grid block near the center of the reservoir is shown in Fig. 13. The trend of this permeability variation is similar to those reported in the literature for San Juan fairway region (Palmer, 2009; Shi and Durucan, 2010). It has been reported that the dynamic permeability changes by a factor of 4 to 320 depending on location, initial permeability and initial porosity (Shi and Durucan, 2010). The model presented by Shi and Durucan (2010) considers the sorption of a single component gas such as methane. The model used in the present study is based on the Ideal Adsorbed Solution (IAS) that predicts adsorption of gas mixtures based on isotherms for individual components (Manik and Ertekin, 1998). The trend of dynamic permeability variation computed in the present study is similar to those reported in the literature (Liu et al., 2011; Palmer, 2009; Shi and Durucan, 2010), despite the differences in these models and site-specific properties. The shrinkage constant used in this study is in the same range as reported in a previous study at a nearby field site (Siriwardane et al., 2009).

Gas Production Rate (MCF/day)

900

EPNG COM A 300

4000

Measured

3500

Computed (Ar = 1.5) Computed (Ar = 2.0)

3000 2500 Ar = 1.5

2000

Ar = 2.0

1500 1000 500 0 Dec-88 Sep-91 Jun-94 Mar-97

6.1. Overview of field injection

Dec-99 Sep-02 May-05 Feb-08 Nov-10

Date Note: 1 MCF/day = 0.02678 x 103 N-m3/day

Injection of carbon dioxide at the Pump Canyon sequestration site began on July 23, 2008 and continued through August 27, 2009. During this time, a total of about 18,000 tons (16,329 metric tons) of carbon dioxide was injected into the reservoir. Injection pressures and rates were determined on site and are shown in Fig. 14. Injection pressures were not constant due to periodic shutdowns; however, a bottomhole pressure of 1100 psi (7.58 MPa) was maintained for most of the injection period. (Koperna et al., 2009).

Table 5 Statistics for cumulative gas production. Ar

R2

ψcumulative

(ψcumulative)2

1.0 1.5 2.0 2.5 3.0

0.999 0.9906 0.9753 0.9595 0.9451

− 24.17 − 7.90 4.49 14.44 22.75

2.80 0.71 0.86 2.02 3.71

Fig. 10. History matching of selected gas production wells at the field site.

6.2. Simulating carbon dioxide injection Reservoir properties were extracted from the larger historymatched model and interpolated to fit the refined grid. Properties extracted from the larger model include porosity, permeability, reservoir pressure, water saturation, and carbon dioxide mole fraction in the reservoir. The reservoir porosity, permeability, reservoir pressure, and water saturation at the beginning of injection are shown in Figs. 15, 16, 17 and 18, respectively. Measured daily carbon dioxide injection rates (Koperna et al., 2009; Oudinot et al., 2008) were used as input for the simulator and corresponding injection pressures were simulated. The measured daily injection rates were used as input in the reservoir simulator in order to account for the dynamic permeability response of the coalbed reservoir. Influence of swelling constant on injection rates was investigated by varying its value, and it was found that the swelling constant of 1.2 × 10 − 5 ton/SCF (0.384 kg/m 3) provided a good match with filed injection data. This

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4.0E+08

100

Computed (Ar = 1.5)

3.5E+08

sh

C Csh

Ar = 2.0

Measured

Csh= 3E-04ton/SCF

= 3E-05 ton/SCF

C sh = 1E-04 ton/SCF Csh sh

Ar = 1.5

C Csh

Computed (Ar = 2.0)

sh

C Csh

Csh= 2E-04ton/SCF

= 2E-04 ton/SCF = 3E-04 ton/SCF

10

3.0E+08

Csh= 1E-04ton/SCF

k/k0

Cumulative Gas Production (MCF)

4.5E+08

2.5E+08

Csh= 3E-05ton/SCF

2.0E+08 1 1.5E+08 1.0E+08 5.0E+07

0.1 0

0.0E+00 Dec-88 Sep-91 Jun-94 Mar-97

200

400

600

800

1,000

1,200

1,400

1,600

1,800

Reservoir Pressure (psi)

Dec-99 Sep-02 May-05 Feb-08 Nov-10

Note: 1 psi = 0.00689 MPa 3 1 ton/SCF = 32036.9253 kg/m

Date Note: 1 MCF = 0.02678 x 103 N-m3

Fig. 11. History matching of gas production wells at the field site.

Fig. 13. Influence of shrinkage constant on the variation of dynamic permeability.

swelling constant is in the same range as the shrinkage constant used in this modeling study.

• Three coal seams found in the Fruitland coal formation (the upper coal, the middle coal and the lower coal seams) were combined as one layer with weighted average properties in this study. However,

6.3. Injection modeling results

(a) field injection rate

• Potential near-wellbore damage due to injection. • Rapid drop in coal permeability around the injection well occurs at the onset of injection. This is likely due to a large degree of swelling in the micropore space of the coal matrix. The swelling model used in the study may not be able to account for rapid changes in permeability.

2500

2000

Injection Rate (SCF/day)

The CO2 injection rate shown in Fig. 14(a) was used as the input constraint (input boundary condition) in the model to compute bottomhole pressures in the model. Alternatively, the bottomhole pressure (Fig. 14(b)) could have been used as a boundary condition to compute injection rates. The use of injection rate as an input constraint was chosen due to anticipated changes in permeability caused by swelling. A comparison of computed injection pressure with reported measurements is shown in Fig. 19. This comparison is considered as acceptable. Results of injection modeling suggest that swelling and shrinkage of the coal matrix could have a profound effect on the permeability of the reservoir. In the later stages of the injection period, the simulated injection pressure is under-estimated and could be due to several factors, for example:

Injection Rate (SCF/day)

1500

1000

500

0 Jun-08 Jul-08 Sep-08 Oct-08 Dec-08 Feb-09 Mar-09 May-09 Jul-09 Aug-09 Oct-09

Date

(b) field injection pressure 1400 Pressure (psia)

1,800

1200

Bottomhole Pressure (psi)

SIMULATED CURVE

1,400 1,200 1,000 800 600 400

Injection Pressure (psia)

SIMULATED POINTS

1,600

1000

800

600

400

200

200 0 08/11/87 05/07/90 01/31/93 10/28/95 07/24/98 04/19/01 01/14/04 10/10/06

Date Note: 1 psi = 0.00689 MPa

Fig. 12. Variation in reservoir pressure throughout production history.

0 Jun-08 Jul-08 Sep-08 Oct-08 Dec-08 Feb-09 Mar-09 May-09 Jul-09 Aug-09 Oct-09

Date Note: 1 psi = 0.00689 MPa 3 1 SCF/day = 0.02678 N-m /day

Fig. 14. Field injection data (Koperna et al., 2009; Oudinot et al., 2008).

H.J. Siriwardane et al. / International Journal of Coal Geology 96-97 (2012) 120–136

131

Fig. 15. Porosity map for refined grid.

the swelling and sorption properties of individual layers may be different, causing significantly different permeability response. However, there was no field or lab data available on the variability of properties among seams.

Also, a contour map of simulated reservoir pressure from January 20, 2009, approximately 8 months after injection began, is shown in Fig. 20. One view clearly shows the pressure contours, while the second includes the positions of nearby wells and shows that high

Fig. 16. Face cleat permeability for refined grid.

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Note: 1 psi = 0.00689 MPa

Fig. 17. Reservoir pressure map for refined grid.

pressures due to injection are largely a local phenomenon around the point of injection. Clearly there are high reservoir pressure levels in the area immediately around the injection well. However, reservoir pressures drop back to levels comparable with the outer reaches of

the model in a horizontal distance of about 200 ft (61 m) on any side of the well. Swelling of the coal matrix appears to be localized around the injection well, as evident from pressure distribution around the injection well (Fig. 20).

Fig. 18. Water saturation for refined grid.

H.J. Siriwardane et al. / International Journal of Coal Geology 96-97 (2012) 120–136 1,800

300 Measured

1,600

Computed (Ar=1.5)

1,200 1,000

Ar= 2.0

800 Ar= 1.5

600

FC STATE COM 1

250

Computed (Ar=2.0)

1,400

EPNG COM A 300

Tracer Signal (fl/L)

Injection Pressure (psia)

133

200

FC STATE COM 1

150

100 EPNG COM A 300

400 50 200 0

0 Jun-08 Jul-08 Sep-08 Oct-08 Dec-08 Feb-09 Mar-09 May-09 Jul-09 Aug-09

Sep-08 Oct-08 Dec-08 Feb-09 Mar-09 May-09 Jul-09 Aug-09 Oct-09 Dec-09 Jan-10 Mar-10

Date

Date

Note: 1 psi = 0.00689 MPa

Fig. 21. Measured tracer signal data.

Fig. 19. Comparison of computed injection pressure with measurements.

8. Tracer modeling 8.1. Introduction to tracer injection Traceable fluids can be injected with carbon dioxide to help monitor reservoir response during injection. Perfluorocarbon (PFC) tracers were used at the Pump Canyon site to monitor fluid movement through the reservoir system. PFC tracers are an ideal choice for tracking carbon dioxide movement during sequestration operations. PFC tracers are completely soluble in carbon dioxide, which allows for a smooth and uniform mixture for injection. In addition, there are no toxins or radioactive materials in these tracers making them environmentally friendly. These tracers are much more detectable than most other types, partly due to their extremely low background levels and are stable at reservoir temperature and pressure. This allows for a smaller volume of tracer to be injected with the carbon dioxide. Previous studies have shown that even with tracer injection levels at 0.01% of the CO2 mass, tracer detection can be up to four orders of magnitude more sensitive than direct detection of CO2 (Wells et al., 2010; White et al., 2010). Two separate, 3 week-long, 20 liter sequential additions of perfluorocarbon (PFC) tracers to CO2 were made at the wellhead shortly after the start of CO2 injection. In the passive monitoring mode, soil-gas or atmosphere was sampled by exposing 3 inch (7.62 cm) long glass tubes (Gerstel tubes) containing a sorbent to collect the tracer. The tubes were exposed (for periods of approximately 2 months) and collected as a set, which was returned to the laboratory for GC/negative ion MS analysis at the femtoliter/liter level in the soil-gas or atmosphere. Monitoring took place over a grid to detect potential leakage of tracer into the soil or atmosphere. For comparison to modeling results reported here, only three monitoring locations were relevant. These three locations are distinct from all other samplers in that their purpose was not to detect leakage to the near-surface. These 3 samplers were mounted 3 in. (7.5 cm) away from vents to CO2 sensors that sample a split stream of production gas for monitoring CO2 concentrations at the 3 off-set wells. Tracer detection at these locations was used as an indication of breakthrough of injected gas to the offset wells at depth. 8.2. Field tracer data

Note: 1 psi = 0.00689 MPa 1 feet = 0.3048 m Fig. 20. Simulated reservoir pressure on January 20, 2009.

Two tracers were dissolved in carbon dioxide and injected into the reservoir at the Pump Canyon site. The first tracer consisted of 90% PMCH and 10% PTCH, and was injected from September 18, 2008 through October 8, 2008. The second tracer consisted of 100% PDCH and was injected from October 18, 2008 through November 12, 2008. The weight of tracer mixture (90% PMCH) injected during the first tracer injection run (starting September 18, 2008) was 68.34 lb (31 kg). The ratio of tracers in the mixture was by weight. The weight

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FIELD

1.00 Normalized Production Rate

Normalized Production Rate

1.00

0.80

0.60

Injection Rate = 1%

0.40

Injection Rate = 0.1% Injection Rate = 0.01%

0.20

Injection Rate = 0.001%

0.00 Nov-08

Jan-09

Mar-09

Apr-09

Jun-09

Jul-09

Sep-09

Computed (Ar=1.5) Computed (Ar=2.0)

0.80 Ar=1.5

0.60 Ar=2.0

0.40

0.20

EPNG COM A 300

0.00 Sep-08

Date

Dec-08

Mar-09

Jul-09

Oct-09

Jan-10

May-10

Date

Fig. 22. Influence of injection volume on tracer production rate. Fig. 24. Tracer modeling comparison for EPNG Com A 300.

of tracer (PTCH) injected during the second tracer injection run (starting October 9, 2008) was 86 lb (39 kg). The ratio of tracer to CO2 injected during tracer injection was regulated at 0.0022 lb (1 g) of tracer (or tracer mixture) for every 88.18 lb (40,000 g) of CO2. It is assumed that all of the added PFC tracer was carried with the CO2 into the reservoir. The small volume of tracer released to the atmosphere during tracer injection would be many orders of magnitude less than that added to the CO2. The volume of each tracer injected into the reservoir is 0.7 ft 3 (20 L), a very small portion of the total CO2 injection volume, so the gases should be well-mixed in the injection well. Tracer signal at production wells was measured approximately every two months after injection of the tracers. While a two month time window limits the precision, the results accurately portray the timing of tracer arrival at production wells. Measured tracer signal data is shown in Fig. 21. Tracer signals were seen only at the two production wells closest to the injection well. These two wells are approximately the same distance away from the injection well with EPNG Com A 300 being to the southwest and FC State Com 1 to the east. Tracers reach the east offset well about 90 days after injection. The southwest offset well saw tracer production after about 240 days. Tracer results suggest significant reservoir anisotropy, which has also been reported in the literature (Koperna et al., 2009). 8.3. Modeling tracer injection For simulation purposes, only one tracer was introduced into the reservoir. For the modeling purpose, these tracers were considered as a fluid very similar to CO2, but as a separate non-reactive gas that

Normalized Production Rate

1.20

1.00

FC STATE COM 1

9. Summary and conclusions

FIELD

0.80

Computed (Ar=1.5) Computed (Ar=2.0)

0.60

Ar= 2.0 Ar= 1.5

0.40

0.20

0.00 Sep-08

was assumed to behave conservatively. Since the amount of tracer used is relatively small in actual field conditions, a percentage of tracer along with CO2 was injected into the reservoir as a mixture. The tracer was modeled as a gas with a very small sorption constant. The volume of injected tracer is negligible compared to the volume of CO2 injected at any time during the injection period. The tracer injection period was assumed to be continuous from September 18, 2008 through November 12, 2008. Fig. 22 shows the influence of the amount of injected tracer on the transport of tracers. As can been seen from Fig. 22, all these tracer volumes produce identical relative tracer production rates at production wells that receive tracer signals. The volume of injected tracer has no influence on the relative concentration of that tracer arriving at a production well. As in the field, simulations show that tracer arrivals were seen only at the two production wells closest to the injection well. A relative comparison of simulated tracer production and field measured tracer data for the two closest production wells, FC State Com 1 and EPNG Com A 300, is shown in Figs. 23 and 24, respectively. Fig. 23 shows that modeling results compare reasonably well with measured breakthrough time. Modeling and field data both suggest that reservoir anisotropy exists. Different anisotropic ratios ranging from 1 to 3 were considered by slightly adjusting the Langmuir volume constants to fit tracer data obtained from the field. Results show anisotropic ratio of 1.5 and 2.0 as reasonable values for history matching of tracer injection, CO2 injection and ECBM production. Reservoir anisotropy alone, however, does not explain the difference in tracer arrival times measured in the field seen in Fig. 24. By simply changing permeability anisotropy or sorption coefficients, it was not possible to match the field data. Thus, significant reservoir heterogeneity and local structural complexities (Wilson et al., 2012) may exist in addition to reservoir anisotropy.

Dec-08

Mar-09

Jul-09

Oct-09

Jan-10

Date Fig. 23. Tracer modeling comparison for FC State Com 1.

May-10

The Pump Canyon demonstration site was selected to assess the suitability of deep, unmineable coals seams for sequestration of carbon dioxide. An injection well was drilled in July 2008 and injection of carbon dioxide began shortly thereafter. About 18,000 tons (16,329 metric tons) of carbon dioxide was injected into the formation over a fifteen month period. A tracer was injected during a portion of the injection period and tracer production at nearby production wells was monitored. History matching of the Pump Canyon sequestration site was performed and unknown reservoir and geomechanical properties were estimated. Results based on these input parameters were compared with historical production data. These comparisons are considered to be good as shown earlier in this paper. Injection of carbon dioxide into the reservoir system was simulated, based on the input parameters

H.J. Siriwardane et al. / International Journal of Coal Geology 96-97 (2012) 120–136

determined from history matching. Injection of tracer into the coal seam was also modeled and measured tracer signal data was compared with simulated results. These comparisons can be considered as good. The reservoir anisotropic ratio that gives the best comparison between computed results and measurement of tracer signals appear to be between 1.5 and 2.0. Anisotropic ratio (Ar) of 1.8 was reported in the literature (Koperna et al., 2009; Oudinot et al., 2008), but this anisotropic factor was determined based on the optimization of production data and injection data. One of the unique aspects of the present paper is the use of tracer data in addition to production and injection data in determining the anisotropic factor. Swelling of the coal matrix appears to be localized around the injection well, as evident from pressure distribution around the injection well. Permeability in the region directly around the injection well drops rapidly when injection begins. This reduced permeability creates a localized region of high pressure around the point of injection. In a relatively small distance the reservoir pressures drop quickly back to a level more comparable to the pressure of the depleted reservoir before injection began. This suggests that the well stimulation methods such as hydraulic fracturing could reduce the negative effects of swelling on injectivity provided they could be performed without causing caprock instability. Tracer data, both simulated and measured, indicates a significant level of reservoir anisotropy. In addition to anisotropy, simulations suggest that some reservoir heterogeneity may also exist. The reservoir properties that are shown in Table 1 were determined on the basis of history matching of production, CO2 injection, and tracer migration. These properties seem to reproduce all of the measured data available at the site. Successful field tests (Koperna et al., 2009; Oudinot et al., 2008) at the Pump Canyon site show the potential option for permanent geologic storage of carbon dioxide in deep unmineable coal seams at the Pump Canyon reservoir. In the future, the history-matched model presented in this paper can be used to realistically investigate the effect of CO2–coal interactions on a significantly large area of the San Juan Basin and assess the potential for carbon storage. Acknowledgments This technical effort was performed in support of the National Energy Technology Laboratory's ongoing research in CO2 Sequestration under the RES contract DE-FE0004000. The authors would like to thank Ryan Frost at Conoco Philips for providing us with useful field data, and to Anne Oudinot, Genevieve Young, Brian McPherson, Grigg Reid and others in the Southwest Regional Partnership for their assistance in obtaining necessary data. Also, we are greatly indebted to Turgay Ertekin for the use of the reservoir simulator PSU-COALCOMP, to which the equations for shrinkage, swelling, and their effects on permeability were added, and which was used for the reservoir simulations in this paper. Authors would also like to thank Thomas Wilson for providing useful comments to this paper. Appendix A

Table A1 Index of wells included in the larger grid (GOTECH, 2010). Index of wells in larger grid Well #

Well name

Well #

Well name

1 2 3 4 5 6 7

DAWSON GAS COM 1 DAWSON GAS COM 1S EPNG COM A 300 EPNG COM A 300S FC STATE COM 1 FC STATE COM 3 FC STATE COM 3A

33 34 35 36 37 38 39

HOWELL D 353 HOWELL D 353S HOWELL G COM 300 HOWELL J 300S KERNAGHAN B 1A KERNAGHAN B 5 KERNAGHAN B 6

135

Table A1 (continued) Index of wells in larger grid Well #

Well name

Well #

Well name

8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32

FC STATE COM 4 FC STATE COM 4A FLETCHER 2 FLORANCE 103 FLORANCE H 3 FLORANCE H 37R HALE 350 HALE 350S HALE 351 HALE 351S HALE 352S HALE 353 HALE 353S HOWELL A 300 HOWELL A 301 HOWELL A 301S HOWELL A 302 HOWELL A 303 HOWELL C COM 300 HOWELL D 350 HOWELL D 350S HOWELL D 351 HOWELL D 351S HOWELL D 352 HOWELL D 352S

40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63

KERNAGHAN B 7 KERNAGHAN B 8 KERNAGHAN B 8S MOORE 6E MOORE A 3A MOORE A 8 MOORE B 3 MOORE GAS COM 1 PRITCHARD A 10 PRITCHARD A 1A PRITCHARD A 2A QUINN 339 QUINN 339R QUINN 339S QUINN 340S QUINN 341S QUINN 342 QUINN 342S SJ-32-8-UNIT 234 SEYMOUR 723S WOODRIVER COM 300 WOODRIVER COM 300S HOWELL C COM POW 300R QUINN POW 2

Table A2 Index of production and injection wells present in the smaller grid (GOTECH, 2010). Index of wells in smaller grid Well #

Well name

3 4 5 6 7 8 9 10 11 13

EPNG COM A 300 EPNG COM A 300S FC STATE COM 1 HOWELL D 352S HOWELL A 300 MOORE B 3 KERNAGHAN B 8S FLETCHER 2 HOWELL D 351 INJECTION WELL

Selection and orientation of reservoir geometry for reservoir modeling study.

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