Ection 1Characterization of the Stress Sensitivity of Pores for Different Rank Coals By

Equation Chapter 1 Section 1Characterization of the stress

sensitivity of pores for different rank coals by nuclear

magnetic resonance

Song Lia,b, Dazhen Tanga, Zhejun Panb*, Hao Xu a, Weiqiang Huangc

a The Coalbed Methane Reservoir Laboratory of National Engineering Center, School of Energy

Resources, China University of Geosciences, Beijing 100083, PR China

b CSIRO Earth Science and Resource Engineering, Clayton, Victoria 3168, Australia c The Basic Experiment Center of China Coalbed Methane National Engineering Research Center Co.,

*Corresponding author email: [email protected] (Z. Pan)

Phone number: +61 3 9545 8394

Fax: +61 3 9545 8380

Abstract:

Nuclear magnetic resonance (NMR) experiments of stress sensitivity on the pore and fracture systems of coal samples with different ranks were performed. Pore compressibility was calculated based on the NMR results and the relationship between pore compressibility and effective stress were discussed. A mathematical model for pore compressibility was developed to describe the experimental data. The

experimental results showed different characteristics of NMR T2 distributions, which were in good accordance to the diverse pore and fracture structures for the different rank coals. Medium and high rank coals have more developed adsorption space, and the main peak of T2 spectra locate at the low T2 value section; while for the low rank coals, all the pores and fractures are well developed, with peaks corresponding to them are all obvious. Furthermore, the pore spaces showed different stress sensitivity for different rank coals. For low rank coals, seepage space changes dramatically as the confining pressure changes, and seepage space is the main controlling factor of stress sensitivity. As the metamorphism degree increasing, adsorption space becoming dominant in the pore and fracture structure of coal rocks. Thus adsorption space in high and medium rank coals decrease significantly with the increase of the confining pressure, and stress sensitivity is controlled by their adsorption space, as suggested by the experimental results. The pore compressibility of coal rock decreases with confining pressure increasing and the experimental data can be accurately described by the developed stress dependant pore compressibility model.

Keywords: coal reservoir; nuclear magnetic resonance; pore and fracture structure; stress sensitivity; pore compressibility

1 Introduction

Stress sensitivity of coal’s pore volume, or the pore volume change with respect to the effective stress change, is an important property in the process of primary and enhanced coalbed methane (CBM) recovery. In the virgin coal reservoirs, all stresses are at the state of equilibrium. However, during the CBM development process, the coal reservoir pressure decreases gradually with the discharge of water and CBM, leading to the increase of the effective stress on the coal reservoir. This will lead to the original pores and fractures to be partially closed under stress, resulting in the decrease of the porosity and permeability of the coal reservoir (Tao et al, 2012). Since the coal seams are well known for their dual porosity characteristic containing pores and fractures and coal is soft, the stress sensitivity of coal reservoir is much stronger than that of conventional reservoirs (Harpalani, 1984; Gilman and Beckie, 2000; Su et al., 2001; Gu and Chalaturnyk, 2010; Meng et al., 2011). Thus, coal reservoir permeability changes significantly during CBM processes and has a big impact on the

CBM production behavior.

Coal pores could be classified into micropores (less than 10 nm in diameter), transition pores (10-100 nm in diameter), mesopores (100-1000 nm in diameter), and macropores (greater than 1000 nm in diameter) by previous researchers (Gamson et al., 1998; Xu et al., 2005; Liu et al., 2009). Micropores and transition pores (also named together as adsorption space) are the main place for methane storage via adsorption. Mesopores, macropores and fractures (also named together as seepage space) mainly provide conduits for fluid flow (Yao et al., 2009; Li et al., 2012).

Therefore, the pores and fractures in coal reservoirs directly affect the CBM extraction effect, and it is also an important parameter for the evaluation of CBM production. During CBM process, the evolution of the original pore and fracture is under the control of the factors including in-situ stress, reservoir pressure, and the swelling and shrinking effects during the adsorption and desorption of the CBM

(Harpalani and Schraufnagel, 1990; Pan and Connell, 2011). A lot of efforts have been made to describe the stress sensitivity on porosity and permeability and the progress of the work in this area can be found in a detailed review on coal permeability models by Pan and Connell (2012). Coal permeability models are often established based on the coal cleat porosity and in-situ effective stress changes (eg. Sommerton et al.,

1975; Palmer and Mansoori, 1998; McKee et al., 1988; Shi and Durucan, 2004).

Although some researchers have carried out stress sensitivity experiments on coals and discussed the stress sensitivity of coal reservoirs under different conditions (Chen et al., 2008; Zheng et al.,2012), there is still lack of experimental results on pore compressibility, which is a key parameter for the coal permeability models.

Furthermore, the sensitivity of pore and fracture with respect to stress conditions for different coal ranks is still not well studied.

In this work, we performed coal pore volume measurements under different confining pressures using the nuclear magnetic resonance (NMR) for different rank

Chinese coals. Then we discussed the relationship between effective stress and different pore and fracture systems and their controlling factors for different rank coals. We also calculated pore compressibility with respect to the stress and proposed a stress based pore compressibility model and validated it with the experimental data obtained in this work.

2 Experimental

2.1 Sample collection

Coal samples with different rank were collected from main coal-bearing basins in China. Two of them were collected from the Yingjiahao coal mine (sample name:

L-1 and L-2) in the Western Ordos Basin, with vitrinite reflectance of 0.43% and

0.44%, respectively. Another two were collected from Luzhongde coal mine (M-1) and Yushe coal mine (M-2) in the Western Guizhou Basin, with vitrinite reflectance of

0.71% and 1.89%, respectively. The other two samples were collected from

Fenghuangshan coal mine (H-1 and H-2) in the Southern Qinshui Basin, with vitrinite reflectance of 3.15% and 3.21%, respectively. The coal samples were divided into three groups according to their coal ranks: low rank coals in the Western Ordos Basin, medium rank coals in the Western Guizhou Basin, and high rank coals in the Southern

Qinshui Basin. The maceral composition and proximate analysis of the samples are given in Table 1.

2.2 NMR principles and theory

Since the 1980s, the NMR measurement has become an indispensable tool for characterizing properties of reservoir rocks such as sandstones and carbonates. In the magnetic field, the number of hydrogen atoms present within the fluid in a porous

rock can be detected by a means of transversal relaxation time (T2), and thus the physical properties of the rock can be analyzed (Hodgkins and Howard, 1999). The

NMR could be a powerful tool for pore size or structure analysis, because it does not suffer from many of the inherent disadvantages of mercury porosimetry or nitrogen condensation such as the necessity of a pore shape assumption, measuring only the smallest constriction in a pore, sample compression (porosimetry), or limited pore size range (condensation) (Kevin et al., 1987). This method was recently employed on the petrophysical analysis for coal samples (Yao et al., 2010a). The NMR measurement of coal is in the condition of a low external magnetic field. There are two reasons: first, coal is a weak magnetic substance, when in the low magnetic field condition, a few paramagnetic minerals within the coals would not influence the measured results; second, in the low resonance frequency, the magnetic information of solid state proton (13C and 1H) can be shielded, thus they would not influence the measured results either (Yao et al., 2010b).

When a low and uniform magnetic field and short pulse spacing are used, the

NMR T2 distribution examined is mainly attributed to surface relaxation, which occurs at the interface between water and coal. Surface relaxation is a function of the surface to volume ratio of pores (Kenyon, 1992):

where T2 is the transverse relaxation time resulting from surface interactions, is a constant representing the transverse relaxation strength, and S/V is the surface to volume ratio that relates to the pore size.

Since smaller pores have higher S/V values, Eq. shows that 1H in smaller pores

relaxes faster than in larger pores (Kenyon, 1997). Hence, the T2 distribution can reveal the pore size distribution: the smallest pores have the shortest relaxation time and the largest pores have the longest relaxation time. Thus, pore and fracture at

various sizes can be distinguished using NMR T2 distributions. For coals, T2 less than

10 ms corresponds to micropores (<10nm) and transition pores (10 to 100 nm)

(adsorption space); T2 greater than 10 ms corresponds to mesopores (100nm to 1m), macropores and fractures (>1m) (Li et al., 2012). The peak area reflects the pore

volume of certain pores, and the continuity between T2 spectrum peaks represents the connectivity among pores and fractures of the coals (Li et al., 2012).

2.3 Experimental work

To characterize the stress sensitivity of different rank coals, cylindrical cores of about 25mm in diameter and 30mm in length were prepared for NMR test under different confining pressures. Firstly, coal samples were dried at 70°C for 48 hours in a vacuum oven. Then they were saturated in distilled water for another 48 hours with vacuum. At last, NMR measurements under different confining pressures (0MPa,

2MPa, 4MPa, 6MPa, 8MPa, 10MPa) were performed. Assuming effective stress coefficient is unity, effective stress equals the difference between the confining pressure and the pore pressure. The value of effective stress by increasing the confining pressure is the same as that by decreasing pore pressure. Therefore, measurement of stress sensitivity can be done through increasing the confining pressure in the laboratory instead of reducing pore pressure. NMR measurements were performed using a MiniMR-MG instrument with a resonance frequency of

23MHz. The main NMR measurement parameters include the echo spacing of 0.2ms, the waiting time of 1.5s, the echo numbers of 5000, the scanning numbers of 64, and

the environment temperature of 32°C. After the measurements, T2 distributions were computed by multi-exponential inversion of the echo data with 64 preset decay times logarithmically spaced from 0.01 ms to 10000 ms. Coals under different confining

pressure were tested to obtain a series of T2 distributions, which can be used to study the pore and fracture changes under different effective stress.

3 Results 3.1 T2 distributions of different rank coals

Fig.1 shows that different rank coals have diverse characteristics of NMR T2

distributions, but the same rank coals have similar NMR T2 distributions for the six

samples studied in this work. For low rank coals, T2 distributions show continuous trimodal characteristics with distinct peaks, indicating that all the pores and fractures are well developed with good size distribution. The average peak area for the two low rank coals corresponding to adsorption space is up to 51% of total area, which is

2.03×104 ms, and the average peak area for the two samples corresponding to seepage space is 1.93×104 ms, accounts for 49% of the total area.

As also shown in Fig.1, T2 distributions of medium rank coals also show

trimodal characteristics, with the main peak at the low T2 value section and the two

sub-peaks at the high T2 value section. Additionally, the area of the main peak is much greater than that of the sub-peak, and the main peak and sub-peaks are separated from each other, revealing poor size distribution between adsorption space and seepage space. For the medium rank coals, the average peak area for the two samples corresponding to adsorption space is 1.36×104 ms, up to 81% of the total area; and the average peak area for the two samples corresponding to seepage space is 0.33×104 ms, only accounting for 19% of the total area.

T2 distributions of the high rank coals show the characteristic of two independent

peaks, as shown in Fig.1. The main peak is located at the low T2 value section, and the

sub-peak is located at the high T2 value section. Additionally, the area of the main peak is much greater than that of the sub-peak, which implies that in high rank coals, adsorption space is well developed and seepage space is undeveloped. The two detached peaks indicate that the connectivity between pores and fractures is poor. For high rank coals, the average peak area for the two samples corresponding to adsorption space is 2.82×104 ms, up to 92% of the total area; and the average peak area for the two samples corresponding to seepage space is 0.23×104 ms, only accounting for 8% of the total area.

From the above results, it can be seen that the volume of adsorption space in the high rank coals is the highest followed by the low and medium rank coals; while the volume of seepage space is the highest in the low rank coals, and those in medium or high rank coals are much lower and only differ slightly. This may be because that low rank coals have low compaction and contraction degrees and loose structures. Thus as a result, seepage space is more developed. With the burial depth increasing, the compaction degree of coals continuously grows, resulting in the sharp reduction of seepage space in medium rank coals. Furthermore, with the increase of the metamorphic degree, almost all the oxygen functional groups and side chains in the high rank coals have phased out and the arrangement of the aromatic rings became more ordered. Thus, adsorption space is best developed in high rank coals.

3.2 Stress sensitivity of different rank coals

T2 spectrum peak values show a decreased trend with confining pressure increase. In order to more directly describe the impact of confining pressure on pore

and fracture structures, dimensionless area of T2 spectral peak is defined as the ratio

of Si (the area of T2 spectrum peak at different confining pressure) and S0 (the area of T2 spectrum peak with no confining pressure).

For low rank coal samples, with confining pressure increasing, peak values corresponding to seepage space decrease significantly, and peak values corresponding to adsorption space decline slightly (Fig. 2a, 2b). All pores and fractures are developed in low rank coals, and when confining pressure increases, both adsorption and seepage spaces are compressed, resulting in a decrease of the peak values. To better examine the ratio of decrease in peak areas due to confining pressure on the

adsorption and seepage spaces, the dimensionless areas of T2 spectral peak of

adsorption space and seepage space (Si/S0) with respect to pressure are plotted in Fig.

2c and 2d for low rank samples L-1 and L-2, respectively. When the confining

pressure is increased to 10MPa, the Si/S0 of adsorption space is 0.96-0.97 for the two coals, meaning that the adsorption space is 96-97% of its initial volume and the

reduction ratio is only 3-4%. While the Si/S0 of seepage space is 0.60 and 0.67 for the two coals, indicating a reduction ratio of 40 to 33%. In low rank coals, the reduction rate of seepage space is higher than that of adsorption space, indicating a higher stress sensitivity of seepage space.

Compared with low rank coal samples, medium rank coal samples are denser

with higher compaction and metamorphic degrees. The changes of T2 spectrum peak values with respect to confining pressure are plotted in Fig. 3a and 3b for samples M-

1 and M-2, respectively. The Si/S0 ratios for Sample M-1 (Ro = 0.71%) have similar trend to the two low rank coal samples, as its variation degree of adsorption space is

less than that of seepage space. When the confining pressure is 10MPa, the Si/S0 of adsorption space is 0.96, indicating a 4% reduction; while the Si/S0 of seepage space

is 0.88, meaning a 12% decrease (Fig 3c). However, sample M-2 (Ro = 1.89%) is on the contrary to sample M-1 on the reduction degree between adsorption space with

18% decrease and seepage space with 12% decrease (Fig 3d). Stress sensitivity of adsorption space is greater than that of seepage space for M-2. Part of the reason may be that in the metamorphic process of coal rocks, adsorption space is gradually increased, and seepage space is progressively decreased. Thus the domination of coal rocks' stress sensitivity is translated from seepage space to adsorption space.

In high rank coals, with confining pressure increasing, changes of T2 spectrum peak values with respect to confining pressure are plotted in Fig. 4a and 4b for

samples H-1 and H-2, respectively. The Si/S0 of adsorption space and seepage space reduce with respect to confining pressure, with the descent rate of adsorption space higher than that of seepage space, demonstrating that adsorption space has higher stress sensitivity than seepage space for the two high rank coals (Fig. 4c, 4d). When

the confining pressure is 10MPa, the Si/S0 of adsorption space is 0.85 and 0.92 for the

two coals, indicating an 15 and 8% reduction. However, the Si/S0 of seepage space is

0.97 and 0.99, representing a reduction of only 3 and 1%. Stress sensitivity is in opposite directions between high rank and low rank coals: low rank coals have the greatest stress sensitivity in seepage space, while high rank coals have the greatest stress sensitivity in adsorption space.

The exponential decay curves in the Figs 2 to 4 to describe the Si/S0 change with respect to confining pressure change are visual help only. Detailed modeling of pore volume change with respect to confining pressure via pore compressibility is described in the next section.

4 Discussions

4.1 Pore compressibility calculation using NMR results

To better understand the above results between pore space change with respective to stress, pore compressibilities based on the NMR results will need to be calculated. Pore compressibility of coals, relating changes in the porosity with respect to pore pressure, is one of the important parameters for characterizing coal reservoir permeability change during CBM/ECBM processes. For coals, pore compressibility, also called cleat compressibility, is defined as (Seidle et al., 1992):

1 骣f C = 琪 f f 琪 f f桫 P p Pc

Where Cf is pore compressibility, f is cleat porosity, Pp is pore pressure and Pc is confining pressure. This is a key parameter for coal permeability models such as the

Shi and Durucan model (2004) described as:

n Ee s- s = -(P - P ) + V 01-n 0 3( 1 - n )

-3C f (s - s 0 ) k= k0 e

where s is the effective horizontal stress, s 0 is the effective horizontal stress at the

initial reservoir pressure, P is reservoir pore pressure, P0 is the initial reservoir pore

pressure, E is Young’s modulus, v is Poisson’s ratio, eV is the volumetric swelling/shrinkage strain, k is permeability and k0 is the initial reservoir permeability

(Shi and Durucan, 2004).

As the T2 distributions amplitude is proportional to the hydrogen content in pores, which represents the pore volume of the coal at water-saturated condition.

Thus, the compressibility can be calculated using the dimensionless area of T2 spectral

peak (Si/S0) :

1 骣V- V 骣S/ S - 1 C = -p p,0 = - 0 pc 琪琪 Vp,0桫 P c- P c ,0 桫 P c Pp Pp

Where Cpc is the averaged pore volume compressibility with respect to

confining pressure change at constant pore pressure, VP is the pore volume at

confining pressure Pc, VP,0 is the pore volume at confining pressure Pc,0, which is zero,

S/S0 is the dimensionless area of T2 spectral peak at confining pressure Pc.

In the calculation of compressibility using Eq. , it should be noted that (1) the compressibility calculated is the averaged compressibility with confining pressure

change from 0 to Pc, which is 2, 4, 6, 8, 10 MPa; (2) the calculated compressibility

using Eq (4) is different to Cf using Eq (x), the relation between them is Cf=Cpc-Cbc

(Pan et al., 2010). Since Cbc, the bulk compressibility with respect to confining

pressure change at constant pore pressure, is often two magnitude smaller than Cpc,

Cpc can be regarded as very close to Cf. (3) both the seepage space and adsorption space are evaluated for compressibility using Eq (4), the compressibility for the seepage space can be regarded as close to the cleat compressibility, which is a key parameter for coal reservoir permeability change, while the compressibility for the adsorption space is can be related to the compressibility of the coal matrix when the porosity for the adsorption space is known.

The compressibility of seepage space in low rank coal samples is relatively high, far greater than that of adsorption space. The average compressibility of seepage space is 0.0547 MPa-1, while the average compressibility of adsorption space is only

0.0069 MPa-1. With the increase of confining pressure, the compressibility of seepage space in low rank coals is gradually reduced, while the compressibility of adsorption space is almost constant (Fig. 5). When the confining pressure increases, seepage space deformation occurs first and its volume changes significantly, therefore, the compressibility of seepage space in low-rank coals is relatively high. The compressible seepage space reduces as the confining pressure continuously increases, and thus the compressibility of seepage space gradually reduces. However, adsorption space is difficult to be compressed mainly due to its pore structure. Moreover, some seepage space might change into adsorption space under stress. Therefore reduction of adsorption space is difficult thus the compressibility of adsorption space is lower than that of the seepage space. The overall pore compressibility including both seepage and adsorption space of low rank coal with an average value of 0.0236 MPa-1.

For medium rank coals, the compressibility of both adsorption space and seepage

space show a decreasing trend as the confining pressure increasing. Sample M-1 (Ro =

0.71%) is similar to the low rank coal as the compressibility of seepage space is

higher than that of adsorption space; while for sample M-2 (Ro = 1.89%), the compressibility of seepage space is lower than that of adsorption space (Fig. 6). The average pore compressibility of medium rank coal samples is 0.0145 MPa-1, lower than that of the low rank coals. Furthermore, the pore compressibility is almost the same as the compressibility of adsorption space, indicating that the pore compressibility is dominated by adsorption space. This is possibly because that in the metamorphic process of coal rocks, adsorption space gradually increased while seepage space slightly reduced. Compared with low-rank coals, medium rank coals have higher compaction and metamorphic degrees, and lower stress sensitivity. And the main controlling factor of its stress sensitivity is adsorption space.

For high rank coal samples, the compressibility of adsorption space is relatively higher than that of the seepage space. The average compressibility of adsorption space is 0.0167 MPa-1, while that of seepage space is 0.0071 MPa-1. The average pore compressibility of high rank coals is 0.0160 MPa-1, and it is controlled by adsorption space, which is similar to medium rank coals. As the confining pressure increasing, the compressibility of adsorption space for high rank coal decreases, while the compressibility of seepage space is almost constant (Fig. 7). With the highest degree of compaction and metamorphism, high rank coals have the most developed adsorption space, but least developed seepage space. Consequently, the compressibility of adsorption space reduces significantly, while the compressibility of seepage space is essentially the same for the two high rank coals.

4.2 Pore compressibility modeling

In practical applications, pore compressibility of coal rock was regarded as a constant but now more broadly accepted as a variable (Pan and Connell, 2012). Experiments have shown that pore compressibility of coals is dependent on effective stress (e.g., Durucan and Edwards, 1986; Zheng et al., 2012). However, modeling of cleat compressibility is rare and the only model is an empirical exponential equation proposed by McKee et al. (1988). In this work, we try to develop a cleat compressibility model to describe the cleat compressibility results from NMR experiments.

To derive the compressibility with respect to stress change, we first assume

that pore volume VP can be divided into compressible pore volume VC and

incompressible pore volume VIC (Liu and Rutqvist, 2010):

VP=VC+VIC

Therefore, is given by:

1dVP 1dVC V C骣- 1 dV C CP = - = - = 琪 VP ds V C+ V IC d s V C + V IC桫 V C d s

Assume the compressibility of the compressible pore space is constant k:

1 dV -C = k VC ds

Thus, is given by:

-k (s - s0 ) VC= V C0 e

Where 0 is initial stress, is the volume of the compressible pore under initial stress condition. Thus Eq. can be rewritten to: e-k (s - s 0 ) C= k P -k (s - s 0 ) e+ VIC/ V C0

To simply Eq. when the stress change is small, can be approximated by:

VC0 VC = 1+k (s - s 0 )

Substituting Eq. (5) and (7) into Eq. , it yields:

kVC0 CP = VC0+ V IC + kV IC (s - s 0 )

Defining VC0/VIC=a and (VIC+VC0-VICk0)/(VICk)=b, CP can be rewritten as:

a C = P s + b

Since the experimental results are average pore compressibility, the average

pore compressibility ( ) using Eq. can be calculated as:

1 s a CP = ds s 0 s- s0 s + b

Pore compressibility is variable, average pore compressibility also is variable:

a骣s + b CP = ln 琪 s- s0桫 s 0 + b

Fig.8 shows the modeling results for the overall pore compressibility for all the six coal samples. It can be seen from the figure that the model can accurately describe the experimental data. This suggests that Eq. could be useful model to be combined with the permeability model such as the Shi and Durucan model described in Eqs. and to describe the coal reservoir permeability change.

5 Conclusions

We have presented a methodology to characterize the stress sensitivity of coal’s pores in this work using NMR. In this work, coal pores were characterized with coal samples under different confining pressures to obtain the relationship between pore volume change and stress. We also developed method to calculate the cleat compressibility based on the NMR results and developed a stress dependent cleat compressibility model.

From the NMR experimental results, different characteristics of NMR T2 distributions were observed for coals with different ranks. The results showed that low rank coals have well developed seepage space and high stress sensitivity, and the stress sensitivity is mainly controlled by seepage space. This is in good accordance to the low metamorphism degree and loose structure for low rank coals. For high and medium rank coals, seepage space decreases while adsorption space becoming dominant in the pore and fracture structure. Moreover, in high and medium rank coals, stress sensitivity is mainly controlled by adsorption space. This is also in good accordance to the higher metamorphism and compaction degrees of coals as the burial depth increases. The results also shown there are significant differences between pore compressibilities for different rank coals.

Pore compressibility shows strong stress dependence. Pore compressibility of low rank coals is relatively high, and is controlled by both adsorption space and seepage space. However, high rank coals and medium rank coals have low pore compressibility and are controlled by adsorption space. A stress dependent pore compressibility model was proposed and it agreed very well with the compressibility data obtained from the NMR experiments.

Acknowledgements

This work was financially supported by the National Natural Science Foundation of

China (41272175), the National Basic Research Program of China (973)

(902009CB219600), and the Key Project of the National Science & Technology

(2011ZX05034). This work was also partly supported by the Chinese Scholarship

Council.

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Districts Sample Ro Proximate analysis (%) Coal maceral composition (%) No. (%) M,ad A,d V,daf FC,ad V I E M Western L-1 0.43 14.8 2.7 28.0 54.5 64.2 30.4 5.1 0.3 Ordos Basin L-2 0.44 15.6 2.7 29.3 52.4 21.2 75.6 2.7 0.5 Western M-1 0.71 0.3 9.2 27.8 62.7 60.5 33.1 5.6 0.8 Guizhou Basin M-2 1.89 0.2 16.9 16.9 66.0 70.3 24.3 - 5.4 Southern H-1 3.15 2.5 16.26 6.52 74.72 86.0 9.5 - 4.5 Qinshui Basin H-2 3.21 2.16 7.58 7.08 83.18 90.8 5.5 - 3.7 Fig.1 T2 distributions of different rank coals with no confining pressure

Fig.8 Coal sample experimental results of pore compressibility from 2 to 10MPa