Core flooding experiments of CO2 enhanced coalbed methane recovery

International Journal of Coal Geology 131 (2014) 113–125

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Core flooding experiments of CO2 enhanced coalbed methane recovery R. Sander, L.D. Connell ⁎, Z. Pan, M. Camilleri, D. Heryanto, N. Lupton CSIRO Earth Science and Resource Engineering, Clayton, Victoria, Australia

a r t i c l e

i n f o

Article history: Received 1 April 2014 Received in revised form 6 June 2014 Accepted 6 June 2014 Available online 14 June 2014 Keywords: CO2 storage Enhanced coalbed methane Core flooding experiments Reservoir simulation

a b s t r a c t This paper presents the results of CO2 enhanced coal bed methane (ECBM) core floods on intact coal core from the Bowen Basin and the Hunter Valley, Australia, at pore pressures of 4 MPa and 10 MPa. The core floods involved flooding with CO2 to displace methane from the core and then reversing the flood by injecting methane to displace the CO2 from the previous flood. An important parameter for ECBM is the displacement or sweep efficiency which was estimated directly from the mass balance over the core flood. Displacement efficiencies obtained through CO2 injection were excellent with more than 99% of the CH4 recovered during the core floods. The reverse experiments in which CH4 was injected to displace CO2 were notably less effective with an average of 95% displacement obtained for the Bowen Basin core sample and only 71% displacement obtained for the Hunter Valley core sample by the end of the experiment. History matching was performed with the reservoir simulator SIMED II which used a hydrostatic permeability model, the extended Langmuir model, and a bi-disperse diffusion model. In general, good history matches were obtained between simulated and observed flow rates, mass balances, and breakthrough times demonstrating that the model could accurately represent the ECBM process. It was found that the triple porosity gas diffusion model provided an improved agreement to observations over the unipore model. Connell–Lu–Pan's hydrostatic permeability model was used in the history matching which differentiates between bulk and pore sorption strain. During the CO2 flooding experiments a change in permeability was observed as CO2 displaced CH4 in the core. As the stress conditions were constant, this was the result of the sorption strain impacting on the porosity and thus permeability. However, for the reverse core flood in which CH4 was injected to displace CO2, no permeability changes were observed, implying that pore and bulk strain were the same and thus cancelled out. © 2014 Elsevier B.V. All rights reserved.

1. Introduction Coal is usually considered to be a naturally fractured, dual porosity material with the fracture, or cleat, system providing the means for the Darcy flow of gas and water. The coal matrix forms a microporosity that provides the large surface area where gas is stored through adsorption. Gas must migrate out of this matrix microporosity in order to be produced. It has been observed that coal adsorbs roughly twice as much CO2 as methane (CH4) (Gentzis, 2000; Gunter et al., 1997; Puri and Yee, 1990), though sorption ratios as high as 8:1 have been observed for some low rank coals (Stanton et al., 2001). During primary recovery gas is released from the coal by lowering the reservoir pressure which is usually initially achieved through producing the formation water. This primary recovery of gas is limited to the degree to which the reservoir pressure can be drawn-down, a function of operational parameters such as well spacing and reservoir properties such as permeability. As the reservoir pressure is drawn-down the gas production rate drops and eventually the well operational cost exceeds the value of the ⁎ Corresponding author. Tel.: +Tel. +61 3 9545 8352; fax: +61 3 9545 8380. E-mail address: [email protected] (L.D. Connell).

http://dx.doi.org/10.1016/j.coal.2014.06.007 0166-5162/© 2014 Elsevier B.V. All rights reserved.

produced gas and further production becomes uneconomic. In most cases, considerable volumes of gas remain in place at the end of primary recovery. Enhanced recovery involves injecting a gas to displace the reservoir methane and offers the potential for higher rates of recovery rates compared with primary recovery. The gas injected during enhanced recovery acts to displace the reservoir methane from the coal cleat system, creating a partial pressure gradient between the cleats and the methane adsorbed within the coal matrix leading to diffusive exchange with the injectant gas and desorption of the methane. Continued injection acts to sweep the displaced methane towards production wells and also supports reservoir pressure, sustaining production rates. Since recovery of the reservoir methane is determined by the methane partial pressure rather than the reservoir pressure, the ultimate recovery can be significantly higher. Injectant gases commonly considered for enhanced coalbed methane recovery (ECBM) are nitrogen (N2), CO2, and flue gas (N2/CO2 mixture) with CO2 injection receiving considerable attention due to its potential as a greenhouse gas mitigation strategy (Mavor et al., 2004; Pagnier et al., 2005; Reeves and Oudinot, 2005; Sams et al., 2005; Wolf et al., 2000). Core flooding experiments on intact coal samples provide an opportunity to observe the processes involved in enhanced gas

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drainage (Fulton et al., 1980; Reznik et al., 1984). In core flooding experiments the coal core sample is initially saturated with methane to reservoir pressure to represent a virgin reservoir state. To replicate enhanced recovery, the displacement gas is then injected into one end of the core and the gas outflow rate and composition monitored through time. These data can then be used to calculate a mass balance for gas within the coal sample, providing direct observations of the diffusional exchange and ultimate displacement or sweep efficiency during ECBM. Mazumder and Wolf (2008) performed core floods comparing the performance of CO2 and flue gas (N2/CO2 mixture). Shimada et al. (2005) injected CO2, N2, and their mixtures to investigate displacement efficiencies and evaluate the performance of the extended Langmuir model. Jessen et al. (2007, 2008) present a series of coal core flooding experiments using N 2 , CO 2 and mixtures of CO2 and N2 on reconstituted crushed coal to investigate adsorption models. Connell et al. (2011) present a series of ECBM core flooding experiments where N2 and an artificial flue gas mixture (90%N2:10%CO2) were used to displace adsorbed methane from coal. The results from these core flooding experiments were used in history matching to test the reservoir simulation of these processes. It was found that while good matches were obtained for the binary gas displacement experiments, those involving mixtures of three gases could not be closely matched by the simulations. It was concluded that this could be a result of inaccuracies in the extended Langmuir model for more complex mixtures but that this could be satisfactory in predictions of gas flow for binary mixtures. In addition, the Busch et al. (2004) triple porosity model was found to lead to a closer representation of the gas flow process. Zhou et al. (2013) present N 2 and CO 2 ECBM core flooding experiments which were used in history matching with an established reservoir simulator used for coal bed methane. In their experiments the observed permeability was approximately constant during the N2 displacement flood and decreased considerably during the CO2 flood. It was found that the extended Langmuir model provided a closer match with the observations of gas composition for the N2 flood than the CO2 flood. In the simulations the permeability variation with swelling/shrinkage was described by a Sawyer et al. (1990) based model. This current paper extends the work of Connell et al. (2011) to CO 2 ECBM, applying the same methodology of core flooding combined with history matching using a reservoir simulator, but with CO2 displacement as the focus. CO2 is a high adsorbing gas for coal and represents a significantly different displacement process compared with the low adsorbing nitrogen dominated enhanced recovery experiments presented in Connell et al. (2011). The objective in the current work is to test reservoir simulation of CO2 ECBM to investigate how representative existing modelling approaches are and, if necessary, identify areas for improvement. To further that aim and constrain the use of history matching, many properties are measured independently of the core flooding experiments. The current work extends that of Zhou et al. (2013) by investigating the models used to represent diffusive exchange between matrix and cleat, using a permeability model specifically developed for hydrostatic conditions and conducting reverse core floods and also repeat core floods to verify observed behaviours. The core flooding experiments were conducted using two core samples; one from the Bowen Basin and the second from the Hunter Valley, Australia. Two core samples were chosen to investigate a range of behaviour and these locations are areas of active interest for coal bed methane production. Of particular interest in the history matching of these core floods is the accuracy of the model descriptions for the following processes; ∙ the diffusional exchange of gas between matrix and cleat, ∙ the extended Langmuir model for multi-component gas adsorption,

∙ the permeability behaviour in response to gas adsorption, ∙ and the displacement efficiency of CO2 for adsorbed CH4.

2. Methodology 2.1. Experimental design The core flooding experiments were performed in a tri-axial cell that allowed core samples to be tested at reservoir pressures and temperatures with confining pressures representative of the average stress under hydrostatic conditions. A detailed description of the core flooding rig is presented in Connell et al. (2011); a schematic is provided in Fig. 1. The confining pressure of the cell and the gas flow into the core sample are controlled through pumps (Teledyne ISCO 500D syringe pumps). The outflow pressure is controlled using a backpressure control device with outflow gas then at atmospheric pressure passing through a flow meter (Ritter Apparatebau MilliGascounter) and then to a gas chromatograph (Shimadzu GC/ MS QP2010). The experiment is housed in a temperature controlled cabinet to enable tests to be performed at reservoir temperature. The radial and axial deformation is recorded using strain gauges and the volumetric deformation is measured through changes in the confining pump volume. After installation in the rig the core sample is vacuumed to remove residual gas. The coal sample is then pressurised with methane at the test pressure and the volume of CH4 adsorbed until equilibration is measured. The displacement experiment involves flowing CO2 into one end of the CH4 saturated sample while maintaining a constant outflow pressure. This core flood continues until steady state conditions are achieved; that is with inflow and outflow gas compositions and rates are equal. The core floods where CO2 displaces CH4 will be called the forward flood in this paper. At the end of the forward flood, the core sample is then flooded with CH4 to displace the adsorbed CO2; this experiment will be referred to as the reverse flood. Both coal samples were sourced from coal mines; one was sourced from the northern Bowen Basin, Queensland, Australia, with the other from the Hunter Valley, New South Wales, Australia. The following core floods were performed; - Bowen Basin core sample at 35 °C ○ 4 MPa CO2 displacing CH4 ○ 4 MPa CH4 displacing CO2 ○ 10 MPa CO2 displacing CH4 ○ 10 MPa CH4 displacing CO2 - Hunter Valley core sample at 36 °C ○ ○ ○ ○

4 MPa CO2 displacing CH4 4 MPa CH4 displacing CO2 4 MPa CO2 displacing CH4 4 MPa CH4 displacing CO2

The confining pressure for these experiments was 1 MPa more than the pore pressure.

2.2. Coal core sample characterisation A range of gas related and geomechanical properties of the core samples were determined prior to the core flooding experiments in a separate tri-axial rig following the procedure described in Pan et al. (2010a,b); these are summarised below in Table 1. The experimental sorption data and the corresponding best fit Langmuir sorption isotherms for CH4 and CO2 are presented in Figs. 2 and 3 for the Hunter Valley and the Bowen Basin coal respectively.

R. Sander et al. / International Journal of Coal Geology 131 (2014) 113–125

115

Core sample with viton & lead foil membranes

Confining pressure control pump Pressure vessel Confining fluid N2

Back pressure controller

CH4

Inflow pumps

Flow meter GC/MS for gas composition analysis

Fig. 1. Experimental set-up of the tri-axial cell for the core flooding experiments from Connell et al. (2011).

2.3. Reservoir simulator used in the history matching

model differentiates between bulk and pore sorption strains due to gas desorption and is written as:

2.3.1. Model description

n h

io M S S k ¼ k0 exp −3 C pc pc −pp þ ε p −ε b

2.3.1.1. Permeability behaviour. History matching of the core flood results was performed using a modified version of SIMED II (Stevenson, 1997). SIMED II is a dual-porosity, multi-component, compositional reservoir simulation programme specifically designed to describe the behaviour of gas flow in coal seam methane reservoirs. An important process during CO2-ECBM is the permeability behaviour in response to coal swelling due to the displacement of methane with the higher adsorbing CO2. In field trials significant declines in permeability have been observed (Fujioka et al., 2010; Reeves et al., 2003; Van Bergen et al., 2009). An important aspect to this permeability behaviour is the strain behaviour that occurs under reservoir conditions where strain in the horizontal plane is resisted by the surrounding geology and coal swelling is taken up, to a large extent, by the coal porosity leading to the observed permeability decline. However, the tri-axial experiments in this work were under hydrostatic conditions with a uniform confining pressure that was held constant and with the sample able to undergo tri-axial strain. As with Connell et al. (2011), the hydrostatic permeability model of Connell et al. (2010) was used in SIMED II to represent these experimental conditions. The

Table 1 Properties of the Bowen Basin and Hunter Valley coal samples. Property

Bowen Basin Coal Core

Hunter Valley Coal Core

Coal density ρ Pore compressibility cf Max strain εCH4 Max strain εCO2 εR in Eq. (4) Porosity ϕ Young's Modulus Ε Poisson's ratio ν Biot coefficient β Bulk modulus K Isotherm data Langmuir volume VLCH4 Langmuir pressure PLCH4 Langmuir volume VLCO2 Langmuir pressure PLCO2

1280 kg/m3 0.00005 1/kPa 0.0125 0.017 4.15 × 10−4 t/m3 0.015 2.2 GPa 0.32 0.9 2 GPa

1357 kg/m3 0.000084 1/kPa 0.013 0.023 4.87 × 10−4 t/m3 0.015 2.2 GPa 0.28

3

28.5 m /t 2529 kPa 43.36 m3/t 1289 kPa

1.7 GPa 29.79 m3/t 2562 kPa 42.84 m3/t 1252 kPa

ð1Þ

in which k is the permeability, k0 is the permeability at reference pore and confining pressure, CM pc is the compressibility, pc is the change in confining pressure, pp is the change in pore pressure, εSp is the change in pore sorption strain and εSb is the change in bulk sorption strain. Connell et al. (2010) proposed that both pore and matrix sorption strain are functions of bulk sorption strain and can be related by the constant γ, so that S

S

εp ¼ γεb :

ð2Þ

Substituting Eq. (2) into Eq. (1) yields n h
 io M S k ¼ k0 exp −3 C pc pc −pp þ ðγ−1Þεb :

ð3Þ

Eq. (3) is used in the numerical model to simulate the permeability behaviour in the tri-axial cell with γ determined through history matching of the laboratory data. Under hydrostatic conditions, if the pore and bulk sorption strain are equal (γ = 1), permeability would not be affected by sorption strain. Sorption strain is a function of the coal's adsorbed gas content as illustrated in Fig. 4 which presents the volumetric swelling with gas adsorption for methane and CO2 (after the compressive effects of pore and confining pressures have been deducted) for the Hunter Valley core sample. The sorption strain with respect to gas content follows the same regression for both gases; however as CO2 gas contents are much higher the sorption strain is greater. With this approach the sorption strain with compressive effects deducted was described with the following model, S

εb ¼ εR G

ð4Þ

where G is the total gas content and εR the slope of the linear regression of the sorption strain observations with respect to gas content. During the core flooding the gas content of the core sample changes over time as gas displacement occurs; as CO2 displaces CH4 there is a net swelling but when the flood is reversed and CH4 displaces CO2 there is shrinkage.

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45

Gas content, m3/t

40

CO2

35 30 25

CH4

20 15 10 5 0 0

1

2

3

4

5

6

7

8

9

10 11 12

Pore Pressure (MPa) Fig. 2. Adsorption measurements and the best fit Langmuir isotherms for CH4 and CO2 for the Hunter Valley coal core. The unfilled symbols are adsorption measurements while the filled symbols are for desorption.

2.3.1.2. Coal as a triple porosity system. Flow behaviour in coal is generally modelled assuming a dual porosity system in which gas diffuses between the macro-porous cleats and the micro-porous matrix within which the gas is adsorbed. This can be described by the Warren–Root model (Warren and Root, 1963) which is based on a pseudo-steady state assumption for diffusion within the micro-porosity and is applied to each of the individual gas species in a multi-component simulation. It can be written as dC i C Si −C i ¼ dt τi

ð5Þ

in which C i is the average gas content of component i in the matrix block, CSi is the adsorbed gas content of component i at its cleat partial pressure, and τi is the sorption time constant of component i, which is defined as τi ¼

1 σDiM

ð6Þ

σ is the shape factor combining shape and size of the coal matrix blocks and DiM is the diffusion coefficient of component i.

45

Gas content, m3/t

40

CO2

35 30 25 20

CH4

15 10 5 0 0

1

2

3

4

5

6

7

8

9

10 11 12

Pore Pressure (MPa) Fig. 3. Adsorption measurements and the best fit Langmuir isotherms for CH4 and CO2 for the Bowen Basin coal. The unfilled symbols are adsorption measurements while the filled symbols are for desorption.

Eq. (5) neglects multi-component diffusion and is thus only an approximation (Lu and Connell, 2007b). In addition Warren–Root does not distinguish between the diffusional process and adsorption kinetics and it is assumed that diffusion dominates over adsorption kinetics in determining the transfer rate. So in using this model to match experimental measurements these two transient effects are combined into the sorption time. Several researchers have found that Warren–Root is not an accurate description of the observed matrixcleat gas transfer behaviour and have proposed other models, usually based on more complex pore structures (Clarkson and Bustin, 1999; Cui et al., 2004; Pan et al., 2010a,b; Shi and Durucan, 2003; Smith and Williams, 1984). The bidisperse model is one such model where it is assumed that there are two distinct micro-porosities (as opposed to the one uniform micro-porosity with Warren–Root) each being characterised by different rates of diffusion. Busch et al. (2004) presented a very simple model based on this concept that provided an improved agreement, when compared with Warren–Root, with the observed diffusion. In Busch et al. (2004) the transfer between the micro-porosity and cleat system was described by two separate sorption times, in effect dividing the micro-porosity into two regions and the coal therefore having a triple porosity. Connell et al. (2011) used this approach to model the diffusional exchange in a series of enhanced coal bed methane core floods and obtained a significantly better agreement with the observations than that using Warren–Root. This triple porosity model is a simple extension of the Warren–Root model and can be written as,

dC 1i ϕ1 C Si −C 1i dC 2i ϕ2 C Si −C 2i ¼ ¼ and : dt τ 1i dt τ2i

ð7Þ

The total quantity of gas adsorbed is the same but is partitioned between the two micro-porosities whose fractions are denoted as ϕ1 and ϕ2.

2.3.2. Model grid The dimensions of each core sample are summarised in Table 2. The model grid used to represent the cores follows the approach presented by Connell et al. (2011) where the cylindrical core sample is represented by a rectangular cuboid (see Fig. 5). With this rectangular approximation there is no spatial discretisation at right angles to the flow path and model grid is 1 block wide as depicted in Fig. 5. The justification for this approach is that there are no lateral flow gradients within the core as flow occurs along the longitudinal axis and the core has uniform properties; therefore spatial discretisation would serve no mathematical purpose. This also means that the circular cross-section of the core sample can be approximated by a square as long as the flow areas of the core sample are honoured in the model. Table 2 presents the lateral surface area of each core sample and the resulting model grid dimensions. Dead space within the sample cell and the tubing network of the experimental equipment is also accounted for in the model as this plays an important role in the observed gas storage and flow behaviour. At test conditions of 10.01 MPa and 34.72 °C the void volume was determined as 44.35 ml for the Bowen Basin core sample. The void volume for the Hunter Valley core sample was 56.19 ml. The coal core model is described by 1 × 1 × 90 grid blocks. The first 20 grid blocks on either side are used to represent the dead space in the laboratory equipment; the central 50 grid blocks describe the coal core. A schematic of the model grid is presented in Fig. 5. For the model of the Bowen Basin core at the upstream (injection) end of the model the void space is 14.35 ml, on the downstream (production) side it is 30 ml. For the Hunter Valley core the void space at the upstream end is 18.73 ml and at the downstream end 37.46 ml.

R. Sander et al. / International Journal of Coal Geology 131 (2014) 113–125

117

2

1.8

N2 (up) N2 (down)

1.6

CH4 (up) CH4 (down)

1.4

CO2 (up)

Volumetric swelling (%)

CO2 (down) 1.2

1 0.8

0.6 0.4 0.2 0 0

5

10

15

20

25

30

35

40

Gas Content (m3/ton)

Fig. 4. Volumetric swelling as a function of gas content measured for CH4 and CO2 on the Hunter Valley core sample.

3. History matching of the ECBM core flooding experiments 3.1. Introduction The observations from the core flooding experiments were used to history match SIMED II simulations. The observed gas injection rate and the downstream pressure were used as the controlling parameters for the simulation while the simulated outflow rate and upstream pressure were history matched to the equivalent observations. In the history matching the sorption times (τ1i and τ2i) for each gas species and the gas content fractions (ϕ1 and ϕ2) were adjusted to obtain the optimal agreement between the simulated and observed outflow rates for each gas species. The upstream pressure was matched by tuning core permeability and γ which determines the change in permeability during the core floods as a result of sorption strain (see Eqs. (2) and (3)). For each core flood the mass balances were calculated from the cumulative flow and composition measurements. The initial mass balances were determined from the volume of gas within the coal core at the end of adsorption equilibration. The outflow of methane during the core flood was estimated from the total outflow gas rate multiplied by the methane composition. The cumulative outflow was deducted from the initial methane gas content to determine that left in place at the end of the core flood. For CO2 the quantity within the core was determined from the difference between cumulative CO2 inflow and outflow at the end of the core flood. During the reversed flood where methane was used to displace CO2 the cumulative CO2 outflow was deducted from that within the coal at the start of the core flood and the methane mass balance from the difference between cumulative inflow and outflow. All the gas volumes presented n this paper have been corrected from measurement pressure and

Table 2 Dimensions of the Bowen Basin and the Hunter Valley coal cores. Core dimensions/properties

Bowen Basin Coal core

Hunter Valley Coal core

Volume Length Diameter Core area (circle) Width of equivalent area (square: x = y) for model Dead space in experimental equipment/model Mass Bulk density

0.000329 m3 0.114 m 0.0606 m 0.002884 m 0.05370 m

0.000366 m3 0.1263 m 0.06085 m 0.002908 m 0.05393 m

0.00004435 m3

0.00005619 m3

0.4207 kg 1280 kg/m3

0.4966 kg 1357 kg/m3

temperature to SPE standard conditions of 15 °C and 100 kPa. Fig. 6 presents a summary of the gas contents determined from the core flood mass balances compared with the adsorption isotherms from Figs. 2 and 3. In the simulations, the initial gas content was calculated using the adsorption isotherm for the gas species with the pore pressure for the core flood. The accuracy of this initial gas content reflects the accuracy of the adsorption isotherm which was determined from a separate measurement programme. 3.2. Forward CO2 floods The observed and the history matched gas species flow rates from the core floods where CO2 displaced methane are presented in Fig. 7 for the Bowen Basin sample and Fig. 8 for the Hunter Valley sample. The core floods on the Hunter Valley coal core were performed at 4 MPa whereas the core flooding experiments on the Bowen Basin coal core were carried out at 4 MPa and 10 MPa. The history matched best fit matrix-fracture transport properties used to obtain these results are summarised in Table 3. There is close agreement between the history matched reservoir simulations and the equivalent observations (see Figs. 7 and 8) demonstrating the accuracy of the models used in SIMED II for ECBM. This good agreement is over the entire range of the experiments including the early time flow behaviour. The upstream pressures are also well matched, except for the core flood at 10 MPa with the Bowen Basin sample, for which a satisfactory history match could not be obtained (Fig. 8 — Experiment 2); this will be discussed below. The good history match of observed and numerical results is reflected in the close agreement of the mass balances presented in Table 4. The mass balances for the Hunter Valley coal core are a particularly good match; the initial measured CH4 content is 17.6 m3/t and 17.1 m3/t for the first and the second core flood respectively; compared to a simulated CH4 content of 18.4 m3/t. The final measured CH4 content is 0.1 m3/t and the simulated CH4 content is 0 m3/t. The measured CO2 content at the end of the core flood is 33.3 m3/t which compares well to the model prediction of 32.8 m3/t and 32.6 m3/t for the first and the second core flood respectively. For the Bowen Basin coal core there is close agreement between the measured and the simulated mass balances (see Table 4). The initial measured CH4 content at 4 MPa is 18.8 m3/t and the simulated CH4 content is 17.7 m3/t. At the end of the experiment the gas contents are 0.2 m3/t CH4 and 30.7 m3/t CO2 which compare well with the model results of 0 m3/t and 32.6 m3/t respectively. At 10 MPa the initial

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R. Sander et al. / International Journal of Coal Geology 131 (2014) 113–125

Inflow

volume. Therefore, in comparison to the same experiment performed at 4 MPa, breakthrough was accelerated.

Grid blocks

Tubing void volume

Coal

Tubing void volume

Core sample Outflow Model grid

Fig. 5. Schematic of the model grid used to represent the coal cores in the simulations (from Connell et al., 2011).

measured gas content is 24.1 m3/t CH4 while that simulated is 22.8 m3/t. At the end of the experiment the gas content is 0 m3/t CH4 and 35.5 m3/t CO2 while the simulated are 0.6 m3/t CH4 and 37.6 m3/t CO2. The results indicate that the CH4 Langmuir isotherm used in the simulation of the Bowen Basin core flooding experiments slightly underestimates the coal's adsorption capacity. In contrast, the CO2 Langmuir isotherm slightly overestimates the observed CO2 content. The time to CO2 breakthrough was accurately simulated with breakthrough occurring after less than a day of injection for all four core floods. For the core floods performed at 4 MPa the injection rate started at approximately 0.009 m3/day and decreased to a constant injection rate of approximately 0.0055 m3/day. For the 10 MPa core flood performed on the Bowen Basin core sample the injection rate was notably higher, starting at approximately 0.025 m3/day and decreasing to a constant rate of approximately 0.013 m3/day. The higher injection rate is due to the high pore pressure and the associated larger injectant

40

Gas content, m3/t

35 30 25 20 15 CH4 Hunter Valley

10

CO2 Hunter Valley

5

CO2 Bowen Basin

CH4 Bowen Basin

0 0

1

2

3

4 5 6 7 8 9 Pore Pressure (MPa)

10 11 12

Fig. 6. Gas content measurements estimated from the core flooding mass balances compared with the independently measured isotherms that were used in the numerical simulations for the Hunter Valley (solid line) and the Bowen Basin cores (dotted line). The non-filled symbols are the gas content from the Bowen Basin core floods, the filled symbols are Hunter Valley core foods.

3.3. Reverse CH4 floods The history matches for the CH4 reverse core flood experiments are presented in Figs. 9 and 11 and the mass balance comparisons are presented in Table 4. For the Hunter Valley core flooding experiments there is excellent agreement between measured and simulation results for the initial CO2 content (33.3 m3/t measured and 32.9 and 32.8 m3/t simulated for the first and the second core flood). However, there are significant differences in the final gas content between observed and simulated core floods. For the first core flood at 4 MPa the CO2 outflow rate is notably overestimated in the simulation; the final measured CO2 gas content is 11.1 m3/t whereas the simulation yields 6.5 m3/t. It should be noted that at the end of these reverse core floods there is a mixed gas state within the core whereas for the forward core floods where CO2 displaced CH4 virtually all the CH4 had been flushed from the core and it was essentially pure CO2. The significance of this is that the gas contents at the end of the forward floods are calculated in the simulations by the pure component isotherms while for the reverse floods the simulated gas content is calculated using the mixed gas extended Langmuir model. The agreement between the simulated and observed CO2 outflow rates is not as close as that obtained for CH4 (see Fig. 9 (Experiment 1)). This is also reflected in the gas contents, where the simulated final CH4 gas content is in very close agreement with that observed (14.8 m3/t observed compared to 14.3 m3/t simulated). For the second core flood performed on the same coal core under identical pressure and temperature conditions, there are significant differences between the observed and the simulated CH4 contents (19.3 m3/t observed and 14.1 m3/t simulated), whereas the difference between the final CO2 contents is less than for Expriment 2 (8.2 m3/t observed and 6.9 m3/t simulated). For Fig. 9 Experiment 2, a good match is obtained for the CO2 flow rate, but the CH4 flow rate is notably overestimated by the model. While Experiments 1 and 2 used very similar conditions the observed final CH4 gas contents were significantly different (14.8 m3/t for Experiment 1 and 19.3 m3/t CH4 for Experiment 2). While Experiment 1 was over a longer duration, the cumulative CH4 injected for the two experiments is almost the same as demonstrated in Fig. 10. The final CH4 content should also be similar which is the case for the simulated results (14.3 m3/t and 14.1 m3/t for CH4 and 6.5 m3/t and 6.9 m3/t for CO2 for experiment 1 and 2 respectively). A possible source of error is from the measured flow rates. As the flow rates are very low, inaccuracies associated with the flow meter become proportionally more significant. Furthermore, the flow rates have to be converted from experimental to standard conditions by applying equations of state. Inaccuracies in the conversion process affect the mass balance and can be cumulative over the experiment, thus causing discrepancies between numerical and observed results. Only at steady state, when the outflow rate equals the inflow rate, can consistency checks be performed that allow potential recalibration of the flow meter to correct for these various potential artefacts. As steady state flow was not achieved for the CH4 floods on the Hunter Valley coal core, recalibration of the flow meter was not possible and potential inaccuracies could not be eliminated. For the Bowen Basin core sample the experimental uncertainty relating to non-steady state flow was eliminated. As demonstrated in Fig. 11, for this set of experiments steady state was achieved and inflow was equal to outflow. Good matches between the observed and the predicted outflow rates and upstream pressures were obtained. The mass balance confirms the good agreement, but also highlights that the numerical model underestimates the final gas content for both CH4 and CO2 for the core floods at 4 MPa (see). This is based on the sorption isotherm used in the model. The initial experimental CO2

R. Sander et al. / International Journal of Coal Geology 131 (2014) 113–125

119

0.01 0.01

0.009

Obs CH4

0.008

Obs CO2

Sim CH4

0.009

0.007

3

Gas rate, m /d

3

Gas rate, m /d

0.008

0.006 0.005 Sim CH4

0.004

Obs CH4

0.003

Sim CO2

0.002

Obs CO2

CO2 in

0.006 0.005 0.004 0.003 0.002

CO2 in

0.001

0.001

0

0 0

1

2

3

4 5 Time, days

6

7

8

0

1

2

3

4 5 Time, days

6

7

8

9

4,005

4,115

Sim Pup Obs Pup

4,110

4,000

Pdown

Pressure, kPa

Pressure, kPa

Sim CO2

0.007

4,105

3,995

Sim Pup Obs Pup Pdown

3,990

4,100

4,095

3,985

0

1

2

3

4

5

6

7

0

8

1

2

3

4

5

6

7

8

9

Time, days

Time, days

Fig. 7. Observed (Obs) and simulated (Sim) outflow rates with the CO2 injection rate (top figures); upstream (Pup) and downstream pressure (Pdown) (lower figures) for the Hunter Valley coal core at 4 MPa: Experiment 1 (left figures), Experiment 2 (right figures).

content compares as 33.8 m3/t to 33.0 m3/t calculated by the model for the experiment at 4 MPa. The experimental gas content at the end is 20.8 m3/t CH4 and 2.2 m3/t CO2 which compares to the model results

of 17.2 m3/t CH4 and 0.8 m3/t CO2. At 10 MPa we determined an initial experimental CO2 content of 35.1 m3/t, whereas the model yields 38.5 m3/t. The final experimental CH4 content is 23.7 m3/t and the CO2 4,140

0.01 Sim CH4 Obs CH4

0.009

4,135

Sim CO2 Obs CO2

0.007

Pressure, kPa

Gas rate, m 3/d

0.008

CO2 in

0.006 0.005 0.004 0.003

4,130

4,125

Sim Pup Obs Pup Pdown

0.002 0.001 0

4,120

0

1

2

3 Time, days

4

5

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2

3 Time, days

4

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6

10,120

0.025 Sim CH4 Obs CH4 Sim CO2 Obs CO2 CO2 in

Sim Pup Obs Pup Pdown

10,115

3

Pressure, kPa

0.02

Gas rate, m /d

1

0.015 0.01 0.005 0

10,110

10,105

10,100

0

1

2

3 Time, days

4

5

6

0

1

2

3

4

Time, days

Fig. 8. Observed (Obs) and simulated (Sim) outflow rates with the CO2 injection rate (left figures) and upstream pressures (Pup) downstream pressure (Pdown) (right figures) for the Bowen Basin coal core at 4 MPa (top figures) and 10 MPa (bottom figures).

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Table 3 Best fit reservoir properties describing matrix-fracture transport for the CO2 and the CH4 core flooding experiments (the sorption time constant is in days). Coal Sample

Pressure Mpa

Hunter Valley Hunter Valley Bowen Basin Bowen Basin Hunter Valley Hunter Valley Bowen Basin Bowen Basin

4 4 4 10 4 4 4 10

Gas in coal

Injectant

CH4 CH4 CH4 CH4 CO2 CO2 CO2 CO2

Triple porosity

CO2 CO2 CO2 CO2 CH4 CH4 CH4 CH4

content 0.9 m3/t. The model predicts a CH4 content of 21.8 m3/t and a CO2 content of 2.1 m3/t. This is in agreement with the findings from the CO2 flooding experiments described above — the isotherm used in the numerical modelling of the core floods slightly underestimates the sorptive capacity of the coal towards CH4. Similarly, the CO2 isotherm slightly overestimates the CO2 sorption capacity of the coal, except for the CH4 core flood at 4 MPa for which the initial experimental CO2 content was recorded as 33.8 m3 /t whereas the numerical model predicted a CO2 content of 33.0 m3/t. However, this is potentially an experimental error as the final CO2 content for the corresponding experiment (the first CO2 flood at 4 MPa on the Bowen Basin core sample) is only 30.7 m3/t rather than 33.8 m3/t. The time to CH4 breakthrough during the core floods was also accurately represented by the model except for the second experiment on the Hunter Valley coal core at 4 MPa for which the CH4 flow rate was overestimated by the model.

Sorption time constant CO2

Sorption time constant CH4

ϕ1

ϕ2

τ1

τ2

τ1

τ2

0.70 0.70 0.30 0.40 0.50 0.50 0.30 0.30

0.30 0.30 0.70 0.60 0.50 0.50 0.70 0.70

0.80 0.80 0.25 0.50 0.30 0.30 1.00 0.30

0.30 0.30 0.30 0.17 2.50 2.50 0.25 0.25

0.80 0.80 0.50 0.10 2.50 2.50 3.00 3.00

0.35 0.35 0.29 0.29 0.10 0.10 0.25 0.50

the time the experiment was stopped, so that the ultimate displacement efficiency could not be determined but must be higher than the preliminary 71%. The lower displacement efficiencies are in agreement with the observation made by Connell et al. (2011) who also indicated that CH4 was an ineffective injectant; an average displacement of 84% was achieved when CH4 was injected to displace N2 present in the coal. In comparison, for the reverse experiment (N2 displacing CH4) an average displacement of 99% was determined.

3.5. Matrix-fracture transport Fig. 13 compares the optimal simulated outflow rates obtained with the dual porosity and the triple porosity models to the observed outflow rate for the first CO2 core flood on the Hunter Valley core sample at 4 MPa. These results demonstrate that the triple porosity model provides a closer match to the observed outflow rate than the dual porosity model. The properties describing matrix-fracture transport (i.e. the sorption time constants defined by the shape factor and the diffusion coefficient) determine the flow behaviour until steady state is achieved, at which point there is no further flow between the matrix and fracture systems. Thus, at steady state is no difference between the flow rates calculated using the dual and the triple porosity models. This is indicated in Fig. 13 in which the dual porosity flow rate approaches the triple porosity flow rate as the experiment approaches steady state. For the CO2 floods on the Hunter Valley coal core the best match was obtained using the triple porosity model (Eq. (7)) for a matrix partitioned into two regions of micro-porosity — one contributing 70% of the gas stored and the other contributing 30%. The larger region was described by a sorption time constant of 0.8 days (for both CH4 and CO2), while the sorption time constants for the smaller region were 0.3 days for CH4 and 0.35 days for CO2 (see Table 3). The gas transfer rate is a function of the gas content difference between cleat and matrix and is inversely proportional to the sorption time; smaller values lead to a higher potential rate. For the triple porosity model represented by Eq. (7), since the transfer rate for each gas storage

3.4. Displacement efficiency The simulations involve a displacement efficiency of 100% which means that if the simulation of the core flooding experiment is continued for a sufficiently long time, the gas initially in the coal will be completely displaced. The actual displacement efficiency of the experiments is likely to be lower as coal is a heterogeneous rock and depending on the flow paths of the injected gas, some of the initial gas may remain trapped in the core. However, for the CO2 floods the CH4 displacement was greater 99% for all four experiments, independent of the coal sample or the pore pressure. This highlights the effectiveness of CO 2 as an injectant. The results are comparable to those obtained with nitrogen (N 2) core floods described in Connell et al. (2011). Analysis of the displacement efficiency for the experiments in which CH4 displaces CO2 in the coal shows that CH4 is less effective as an injectant than CO 2 or N2 . For the Bowen Basin coal core the average displacement was approximately 95%, while for the Hunter Valley coal core it was only 71%. However, neither of the two Hunter Valley CH4 core flooding experiments had achieved steady state by

Table 4 Mass balances for the experimental and the simulated CO2\CH4 core flooding experiments (Lab refers to the observed and SIM the simulated). Coal Sample

Pressure, MPa

Gas in coal

Injectant

Lab Gc,init, m3/t

Lab Gc,end, m3/t

Lab sweep efficiency, %

SIM Gc,init, m3/t

SIM Gc,end, m3/t

SIM sweep efficiency, %

CH4

CO2

CH4

CO2

CH4

CO2

CH4

CO2

CH4

CO2

CH4

CO2

Hunter Valley Hunter Valley Bowen Basin Bowen Basin Hunter Valley Hunter Valley Bowen Basin Bowen Basin

4 4 4 10 4 4 4 10

CH4 CH4 CH4 CH4 CO2 CO2 CO2 CO2

CO2 CO2 CO2 CO2 CH4 CH4 CH4 CH4

17.6 17.1 18.8 24.1 0.0 0.0 0.0 0.0

0.0 0.0 0.0 0.0 33.3 33.3 33.8 35.1

0.1 0.1 0.2 0.0 14.8 19.3 20.8 23.7

33.3 33.3 30.7 35.5 11.1 8.2 2.2 0.9

99.3 99.7 99.2 100.0 – – – –

– – – – 66.5 75.5 93.5 97.5

18.4 18.4 17.7 22.8 0.0 0.0 0.0 0.0

0.0 0.0 0.0 0.0 32.9 32.8 33.0 38.5

0.0 0.0 0.0 0.6 14.3 14.1 17.2 21.8

32.8 32.6 32.6 37.6 6.5 6.9 0.8 2.1

99.8 100.0 100.0 97.4 – – – –

– – – – 80.2 79.0 97.7 94.7

R. Sander et al. / International Journal of Coal Geology 131 (2014) 113–125 4,015

0.007 Obs CH4 Sim CH4 CH4 in

0.006

Obs CO2 Sim CO2

Sim Pup Obs Pup Pdown

4,010

0.005 0.004

Pressure, kPa

Equipment failure

3

Gas rate, m /d

121

0.003 0.002

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3 4 Time, days

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3 4 Time, days

6

7

4,010

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Sim Pup Obs Pup Pdown

Obs CH4

0.01

Sim CH4

4,005

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Pressure, kPa

Sim CO2

0.008

CH4 in

3

Gas rate, m /d

5

0.006 0.004

4,000

3,995 0.002

3,990

0 0

1

2

3

4

5

6

Time, days

0

1

2

3 Time, days

4

5

6

Fig. 9. The history match for the Hunter Valley reverse core flood at 4 MPa; Observed (Obs) and simulated (Sim) outflow rates and the CH4 injection rate (CH4 in. (left figures) and upstream pressures (Pup) and downstream pressure (Pdown) (right figures) for Experiment 1 (top figures) and Experiment 2 (bottom figures).

fraction is driven by the same concentration gradient the sorption time will determine the differences in the rate of gas transfer. For the experiment presented in Fig. 11 the larger gas storage fraction (70%) has a slower rate of transfer (τCH4 and τCO2 = 0.8 days) than the small fraction (30% and τCH4 = 0.3 days and τ CO2 = 0.35 days). As demonstrated in Table 3 for the two CO2 core floods performed on the Hunter Valley coal sample the matrix-fracture transport properties were the same, providing confidence in the history property values. Using the Warren–Root dual porosity model (Eq. (5)) a sorption time constant of 0.8 days for both CH4 and CO2 was used to yield the match presented in Fig. 11. The higher sorption time constant of 0.3

Cumulative CH4 injected, m3

30 25 20 15 10 Experiment 1

5

Experiment 2

0 0

1

2

3

4

5

6

7

Time, days Fig. 10. Cumulative CH4 injected for the experiments performed on the Hunter Valley core sample at 4 MPa.

and 0.35 days for CH4 and CO2 respectively resulted in significant differences between observed and simulated gas rates and thus the results are not presented here. These properties had to be adjusted to improve the agreement between simulations and observations. For the CO2 core flood performed on the Bowen Basin sample at 4 MPa, porosity fractions of ϕ1 = 0.3 and ϕ2 = 0.7 were used to model the experimental data. The sorption time constant τ1 for the smaller region 1 was 0.25 days for CH4 and 0.5 days for CO2. For the larger region 2, τ2 was estimated as 0.3 days for CH4 and 0.29 days for CO2. This is summarised in Table 3. For the same experiment at a pore pressure of 10 MPa the core flood was best approximated with triple porosity fractions of ϕ1 = 0.4 and ϕ2 = 0.6, which is less than that found for the porosity fractions from the history match of the 4 MPa core floods. The sorption time constants also had to be adjusted; for region 1 τ1 is 0.5 days for CH4 and 0.17 days for CO2, for region 2 τ2 is 0.1 days for CH4 and 0.29 days for CO2 (see Table 3). The differences in the triple porosity fractions and the sorption time constants from one experiment to the next are a result of the history matches being approximations rather than exact representations of the core flooding experiments. The better the agreement between laboratory observations and predicted flow rates, the greater is the confidence in the properties determined through history matching, particularly when they can be reproduced in another experiment such as for the CO2 core floods on the Hunter Valley coal. For the reverse core flooding experiments on the Hunter Valley core sample at 4 MPa (see Fig. 9) it was difficult to obtain a close match for both CO2 and CH4 outflow rates; for Experiment 1 there was close agreement for CH4 outflow but less with CO2. For Experiment 2 CO2 outflow was in good agreement but the differences for CH4 outflow were significant. The history match of the flow rates was done by tuning the matrix-fracture transport properties, that is the desorption times and porosity fractions, and while the optimal properties were the same for the two core floods the behaviour of the differences between

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0.008

4,140 Obs CH4 Obs CO2 Sim CH4 Sim CO2 CH4 in

0.006

Sim Pup Obs Pup Pdown

4,135 Pressure, kPa

Gas rate, m 3/d

0.007

0.005 0.004 0.003

4,130

4,125

0.002 0.001 0

4,120

0

2

4

6

8

10

12

14

0

2

4

6

8

10

0.02

14

10,150

0.018 0.016

Obs Pup Sim Pup

10,145

Pdown

0.014 Pressure, kPa

Gas rate, m 3/d

12

Time, days

Time, days

0.012 0.01 Obs CH4

0.008

Sim CH4

0.006

Obs CO2 Sim CO2

0.004

CH4 in

10,140 10,135 10,130

0.002

10,125

0 0

1

2

3

4

5

6

7

8

9

0

1

2

3

4

5

6

7

8

9

Time, days

Time, days

Fig. 11. The observed (Obs) and simulated (Sim) outflow rates and the CH4 injection rate (CH4 in. (left), and upstream, (Pup) and downstream (Pdown), pressures (right) for the Bowen Basin core flood at 4 MPa (top) and 10 MPa (bottom).

simulated and measured flow rates was not consistent. However, it was evident that for both CH4 and CO2 the sorption time constant had to increase significantly to approximate the shape of the outflow rates for at least one of the two porosity regions of the coal matrix (see Table 3). The measured flow rates and the corresponding simulation results for the CH4 reverse flooding experiments on the Bowen Basin coal core were presented in Fig. 11. The optimal triple porosity fractions were ϕ1 = 0.3 and ϕ2 = 0.7, which are the same for both experiments (performed at pore pressures of 4 MPa and 10 MPa, see Table 3). At 4 MPa the region 1 sorption time constants, τ1, were 1 day and 3 days for CH4 and CO2 respectively. For region 2 τ2 for CH4 was determined as 0.25 days and for CO2 as 0.5 days. For the experiment performed at 10 MPa τ1 for CH4 was 0.3 days and 3 days for CO2. τ2 was estimated as 0.25 days and 0.5 days for CH4 and CO2 respectively. Analogous to the observation made for the CH4 flooding experiments on the Hunter Valley core sample, when CH4 displaces CO2 for both CH4 and CO2 the sorption time constant of one porosity region is noticeably larger than of the other. Only for the sorption time constants describing CH4 adsorption at 10 MPa this is not the case and the CH4 sorption time constants for both matrix porosity regions are comparable (τ1 = 0.3 days and τ2 = 0.25 days). Analysis of the best fit sorption time constants indicates a trend that for the experiments in which CH4 displaces CO2 (CH4 floods) the average sorption time constants are noticeably larger than for the CO2 floods. This result is consistent with those reported by Connell et al. (2011) where it was found that a comparatively larger sorption time constant is necessary to match core flooding experiments during which the gas that is the less effective displacing agent (CH4) is injected to displace the more effective displacing agent (N2). Larger sorption time constants imply that sorption processes are slower and it will take longer to achieve steady state. This is in agreement with the observations made during the core flooding experiments in this study; the CH4 floods took noticeably longer to equilibrate than the CO2 floods for the same coal core and pressure — if steady state was achieved at all.

3.6. Permeability behaviour Changes in coal permeability during gas recovery and gas injection processes are a result of changes in effective stress (the difference between confining and pore pressure) and sorption strain as demonstrated in Eq. (3). In the tri-axial cell the coal core is under hydrostatic pressure conditions with the confining pressure controlled to maintain a constant difference with the pore pressure therefore, from Eq. (3), any changes in permeability are a result of sorption strain. Sorption strain is a function of the adsorbed gas content as illustrated in Fig. 4. The CH4 and the CO2 Langmuir isotherms presented in Figs. 2 and 3 for the Hunter Valley and the Bowen Basin coal core respectively demonstrate that at the same pressure and temperature the coal adsorbs more CO2 than CH4. Therefore, during the core flooding experiments changes in permeability as a result of sorption strain are expected if Eq. (3) is an accurate representation. The sorption strain should increase during CO2 core floods (CH4 desorbs, CO2 adsorbs) and decrease during CH4 core floods (CO2 desorbs, CH4 adsorbs). However, when γ = 1 bulk and pore strain is the same and they cancel each other out in Eq. (3) and permeability does not change with gas content and sorption strain. The upstream pressure is history matched by tuning the core's absolute permeability and the constant γ, if necessary. We found that the best agreement between simulated and observed upstream pressures for the CO2 core flooding experiments was obtained with γ not equal to 1. The value of γ and the associated change in core permeability for each core flood are summarised in Table 5. A comparison of the upstream pressure obtained with γ = 10 and γ = 1 for the first CO2 core flood performed on the Hunter Valley coal core is presented in Fig. 12 which highlights that the history match is significantly improved by the larger γ. For the simulations to match the upstream pressure behaviour as the CH4 is displaced by the higher adsorbing CO2, the permeability has to increase, even though the coal swells due to the increase in gas content. This is contrary to what is observed in the reservoir, where uniaxial strain means that coal swelling has a significant

Difference between observed and simulated rates, kPa

R. Sander et al. / International Journal of Coal Geology 131 (2014) 113–125

4,105 4,104

Pressure, kPa

4,103 4,102 4,101 4,100

gamma = 10

4,099

gamma = 1 Pup

0.6 gamma = 10

0.5

gamma = 1

0.4 0.3 0.2 0.1 0.0 0

4,098

123

1

2

3

4

5

6

7

8

-0.1

0

1

2

3

4

5

6

7

8

Time, days

Time, days Fig. 12. A comparison between observed and simulated upstream pressures (right) for the first CO2 core flooding experiment performed on the Hunter Valley coal core and the difference between the pressures (left).

impact on coal porosity, reducing permeability. Under the hydrostatic conditions used in these tests the sample is free to swell under constant confining pressure. This bulk swelling must act to open up the porosity and therefore increase the permeability. For the CO2 core flood performed on the Bowen Basin core at 10 MPa we were not able to obtain a close approximation of the upstream pressure behaviour irrespective of the value selected for γ (see Fig. 8). Hence, the upstream pressure is presented for γ = 1, meaning the predicted permeability remains constant throughout the experiment. To history match the behaviour of the upstream pressure during the CH4 core flooding experiments a γ of 1 was used for all but one experiment (Hunter Valley coal core, 4 MPa, experiment 2 — see Fig. 9). The model predictions were very good matches of the experimental upstream pressures without any need for tuning of the pressure through γ as demonstrated in Figs. 9 and 11. This implies that for these cases permeability remained constant during the core floods and in the simulations pore and bulk strains were the same. This is in spite of the significant differences in initial and final total gas contents observed for the experimental core floods which range from ΔGc = 5.6 m3/t (Hunter Valley core, core flood 2) up to ΔGc = 10.8 m 3/t (Bowen Basin core at 4 MPa) as indicated in Table 4. The experiment that exhibited the smallest difference between initial and final gas content (Δ = 5.6 m 3 /t) required a γ greater than 1 to match laboratory observations. As a result, the permeability in the simulations decreased as the gas content decreased during these core floods. This is contrary to the usually observed behaviour within the reservoir of a permeability increase with a decrease in total adsorbed gas content, however it is consistent with the CO2 floods above where permeability increased as CO2 displaced CH4. This behaviour is the result of the hydrostatic confining conditions, where increases in bulk strain have been found to open up the cleat system and while decreases in bulk strain have, in general, no impact, as the pore and bulk strains are changing in proportion.

Table 5 Best fit reservoir properties used to model upstream pressures. Coal Sample

Pressure MPa

Gas in coal

Injectant

kinitial mD

kend mD

γ -

Hunter Valley Hunter Valley Bowen Basin Bowen Basin Hunter Valley Hunter Valley Bowen Basin Bowen Basin

4 4 4 10 4 4 4 10

CH4 CH4 CH4 CH4 CO2 CO2 CO2 CO2

CO2 CO2 CO2 CO2 CH4 CH4 CH4 CH4

0.75 0.32 0.90 0.50 0.75 0.75 1.35 0.90

0.93 0.36 1.27 0.50 0.75 0.41 1.35 0.90

10 5 20 1 1 35 1 1

However, the experimental CH4 core floods performed on the Hunter Valley coal core were not consistent and we were not able to match those core floods adequately (see Section 3.3). This may have affected the numerical representation of the core permeability and could explain the non-constant permeability behaviour predicted for the second core flooding experiment on the Hunter Valley coal core. The results presented in Table 5 show that for the same core sample permeability can vary from experiment to experiment. For the Bowen Basin core sample the variations can be attributed to the different pressure conditions at which these experiments were performed (4 and 10 MPa). As gas sorption capacity is a function of pressure, at higher pore pressures the sample contains more gas which results in higher sorption strain and lower core permeability. For the Hunter Valley core sample, for which all experiments were performed at a pressure of 4 MPa, the permeability from the simulations was constant at 0.75 mD with the second CO2 core flooding experiment being the only exception for which the initial permeability was determined as 0.32 mD. However coal permeability can be complex; in addition to sensitivities to the difference between pore and confining pressures and sorption strain, it can be affected by the history of stress and as coal is a soft material can be easily altered through damage. 3.7. Adsorption model The good agreement in mass balances, flow rates, and breakthrough times between the experimental and the numerically modelled CO2 core floods indicates that the extended Langmuir model is sufficient to describe binary gas flow in coal. This is in agreement with findings presented by Clarkson and Bustin (2000), Jessen et al. (2007), and Connell et al. (2011). For the two CH4 core flooding experiments performed on the Hunter Valley coal core discrepancies between experimental results and model predictions were observed. However, this is most likely caused by the experiment not achieving steady state flow which meant recalibration of the flow-meter was not possible (see Section 3.3) rather than inadequacy of the sorption model. The numerical model delivered comparable results for both core floods whereas the experimental results exhibited significant variations. The applicability of the sorption model is also demonstrated by the results obtained for the CH4 core flooding experiments performed on the Bowen Basin coal core for which experimental and predicted results were in good agreement. 4. Conclusions The experiments presented here have provided direct observations of the process of CO2 enhanced coal bed methane and the opportunity to test model descriptions used in reservoir simulation. A range of

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0.0006

0.007 Difference between observed and 3 simulated rates, m /d

Error triple porosity

0.006 CH 4 rate, m 3 /d

CH4 out

0.005

Dual porosity Triple porosity

0.004 0.003 0.002 0.001 0

0.0004

Error dual porosity

0.0002 0 0

2

4

6

8

-0.0002 -0.0004 -0.0006

0

2

4 Time, days

6

8

Time, days

0.0012 0.007

0.001

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0.003

Dual porosity Triple porosity

0.002 0.001

Error triple porosity Error dual porosity

3

0.005

0.0008 simulated rates, m /d

Difference between observed and

CO 2 rate, m 3 /d

0.006

0.0006 0.0004 0.0002 0 -0.0002

0

2

4

6

8

-0.0004

0 0

2

4 Time, days

6

Time, days

8

Fig. 13. Comparison of CH4 and CO2 outflow rates for the first CO2 Hunter Valley core flood at 4 MPa using the dual porosity and the triple porosity models. The outflow rates are presented on the left (top: CH4, bottom: CO2) and the difference between observed and simulated outflow rates is presented on the right (CH4 top, CO2 bottom).

properties were measured independently to the core flood experiments with history matching only used for properties that were unique to the floods. These unique properties related to mixed gas diffusion, permeability and the effect of sorption strain on the hydrostatic permeability. It was found that the reservoir simulator was able to accurately match observations of flow rates, mass balances, and breakthrough times for the experiments where CO2 displaced CH4 from the cores. With the reverse core floods the agreement, while not as close, was still good for most core floods. In one core flood, the differences were significant but this set of observations was not consistent between the repeated experiments, suggesting that there may have been some systematic error in the measured flow rates. While the coal samples used in the experiments came from different basins the adsorption isotherms were very similar. However there were differences in the permeabilities and the sorption time constants. It would useful for any future experimental work to consider a broader range of adsorption behaviours and coal ranks to assess how broadly these conclusions apply. The general good agreement with the observations and constrained use of history matching builds confidence in the predictive use of reservoir simulators for CO2-ECBM. It can be concluded that many of the standard assumptions used in existing simulators can provide representative predictions such as the extended Langmuir model, at least for binary gases. This conclusion is consistent with the core flooding results of Connell et al. (2011) where binary mixtures were accurately represented with the extended Langmuir model but ternary gas mixtures were subject to greater error. This compares with Zhou et al. (2013) who found that extended Langmuir worked well for N2 ECBM but was less accurate for CO2 ECBM. For matrix and cleat diffusive exchange in the simulations, it was found that a simple triple porosity model, based on Busch et al.'s

(2004) approach, was more representative than the Warren and Root unipore model (Warren and Root, 1963). Some caution should be exercised in the physical interpretation of this finding as the triple porosity model introduces three additional properties over the single property Warren–Root model. It is not surprising that the greater degrees of freedom in the triple porosity model lead to a better representation of the observations. One interpretation is that the triple porosity model's better fit implies that the coal's micro-porosity is not homogeneous, but that two regions of micro-porosity exist, characterised by different gas sorption time constants. However, it could also relate to inaccuracies from the simplifications introduced to derive the Warren–Root model. References Busch, A., Gensterblum, Y., Krooss, B.M., Littke, R., 2004. Methane and carbon dioxide adsorption–diffusion experiments on coal; upscaling and modelling. Int. J. Coal Geol. 60, 151–168. Clarkson, C.R., Bustin, R.M., 1999. The effect of pore structure and gas pressure upon the transport properties of coal: a laboratory and modelling study. 2. Adsorption rate modelling. Fuel 78, 1345–1362. Clarkson, C.R., Bustin, R.M., 2000. Binary gas adsorption/desorption isotherms: effect of moisture and coal composition upon carbon dioxide selectivity over methane. Int. J. Coal Geol. 42, 241–271. Connell, L.D., Lu, M., Pan, Z., 2010. An analytical coal permeability model for tri-axial strain and stress conditions. Int. J. Coal Geol. 84 (2), 103–114. Connell, L.D., Sander, R., Pan, Z., Camilleri, M., Heryanto, D., 2011. History matching of enhanced coal bed methane laboratory core flood tests. Int. J. Coal Geol. 87, 128–138. Cui, X., Bustin, R., Dipple, G., 2004. Selective transport of CO2, CH4 and N2 in coals: insight from modelling of experimental gas adsorption data. Fuel 83 (3), 293–303. Fujioka, M., Yamaguchi, S., Nako, M., 2010. CO2-ECBM field tests in the Ishikari Coal Basin of Japan. Int. J. Coal Geol. 82, 287–298. Fulton, P.F., Parente, C.A., Rogers, B.A., Shah, N., Reznik, A.A., 1980. A laboratory investigation of enhanced recovery of methane from coal by carbon dioxide injection. Presented at the SPE/DOE Symposium on Unceonventional Gas Recovery, Pittsburgh, Penn.

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