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Pore structure and fractal characterization of main coal-bearing synclines in western Guizhou, China
Journal of Natural Gas Science and Engineering 63 (2019) 58–69
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Pore structure and fractal characterization of main coal-bearing synclines in western Guizhou, China
T
Pengfei Rena,b,c, Hao Xua,b,c,∗, Dazhen Tanga,b,c, Yukui Lid, Zhenlong Chene, Changhua Sund, Fulin Zhangf, Shida Chena,b,c, Fudong Xina,b,c, Likun Caoa,b,c a
School of Energy Resources, China University of Geosciences (Beijing), Beijing, 100083, PR China Coal Reservoir Laboratory of National Engineering Research Center of CBM Development & Utilization, China University of Geosciences, Beijing, 100083, PR China c Key Laboratory of Marine Reservoir Evolution and Hydrocarbon Enrichment Mechanism, Ministry of Education, Beijing, 100083, PR China d Guizhou Natural Gas Energy Investment Co. Ltd, Guiyang, 550081, PR China e Petroleum Exploration & Production Research Institute, Sinopec East China Oil & Gas Company, Nanjing, 210011, China f The 10th Oil Production Plant, Daging Oilfield of CNPC, Daqing, 163513, PR China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Pore structure Fractal dimension Medium-high rank coals Main coal-bearing syncline in western Guizhou
The coal pore structure is closely related to the accumulation and migration of coalbed methane (CBM). This study collected 12 coal samples in western Guizhou and used low-temperature carbon dioxide adsorption/ desorption (LT-CO2GA), low-temperature nitrogen adsorption/desorption (LT-N2GA) and nuclear magnetic resonance (NMR) experimental methods to systematically discuss the pore structure and fractal characterization of main coal-bearing syncline. The study shows that coal rank is an important reason for the difference of 6 synclines coal reservoirs. Bide syncline and Panguan syncline have similar pore structure and fractal characteristics. As the coal rank increases, the DR–SSA, DA–TPV, D2, DNMR and VL increase linearly, whereas the BET–SSA and BJH –TPV decrease gradually. Moreover, the connectivity between pores decreases with increasing coal rank. A positive correlation is observed between coal rank, BET–SSA, and BJH –TPV and D2, whereas a negative correlation is seen between moisture, ash yield and D2. DNMR is positively correlated with coal rank, mineral content and ash yield, but negatively correlated with volatile matter content. Although the north synclines have good gas content and a good development potential, poor connectivity and high heterogeneity must be valued during the development process.
1. Introduction Coal is an organic rock composed of matrix pores and fractures, which serve as the reservoirs and migration channels for coalbed methane (Close, 1993; Shi and Durcan, 2003). Based on the pore and fracture parameters of coal reservoirs, multiple pore and fracture classifications have been established, so as to describe pores and fractures (Hodot, 1966; Sing, 2004; Cai et al., 2013). The Hodot classification system (Hodot, 1966) is widely applied, including micropores (< 10 nm), transition pores (10–100 nm), mesopores (100–1000 nm) and macropores (> 1000 nm). For a more accurate description, many scholars define pores < 2 nm as supermicropores (Zhao et al., 2016; Chen et al., 2017). The pores < 100 nm are defined as absorption pores, which control gas adsorption and desorption, but the pore size greater than 100 nm of pores are primarily considered seepage pores (Shi and Durancan, 2005; Yao et al., 2009; Li et al., 2014; Zhao et al., 2016).
∗
Coal reservoir description and evaluation, especially the fine characterization and geological significance of pore fractures in coal reservoirs, is always one of popular research fields in CBM. The experimental methods for studying pore characteristics include (1)microscopy methods, 2D scanning electron microscopy (SEM) and transmission electron microscopy (TEM); (2) three-dimensional (3D) structure reconstruction by X-ray computed topography (CT), focused ion beam scanning electron microscopy (FIB-SEM), and atomic force microscopy (AFM); (3)LT-CO2GA, LT-N2GA, high-pressure mercury intrusion porosimetry (MIP) and NMR (Li et al., 2014, 2017; Zhao et al., 2016; Bustin et al., 2008; Nelson, 2009; Yao et al., 2010; Clarkson et al., 2011, 2013; Chalmers et al., 2012; Xu et al., 2012, 2018; Liu et al., 2015; Fu et al., 2017). Because of different detectable scales of different experimental methods, the evaluation of pore distribution and pore structure characteristics needs to be combined with different experimental techniques. For example, the LT-N2GA method has been proven to be an
Corresponding author. School of Energy Resources, China University of Geosciences (Beijing), Beijing, 100083, PR China. E-mail address: [email protected] (H. Xu).
https://doi.org/10.1016/j.jngse.2019.01.010 Received 26 October 2018; Received in revised form 9 January 2019; Accepted 14 January 2019 Available online 19 January 2019 1875-5100/ © 2019 Elsevier B.V. All rights reserved.
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samples are from coal mines at depths of 200–400 m.
effective means of detecting micropores ranging from 1.7 to 300 nm, while LT-CO2GA can detect only micropores < 2 nm (Chen et al., 2017; Zhao et al., 2016). NMR is widely used to identify pores, fractures and the pore size distribution (PSD) (Yao et al., 2010). Generally, the pore structure of coal reservoirs is characterized by studying PSD, volume, specific surface area (SSA), pore type and connectivity, which directly affects the gas enrichment ability and migration characteristics of CBM (Chen et al., 2017; Zhao et al., 2016; Li et al., 2017). Coal rank has a greater impact on the pore structure of coal reservoirs (Zhao et al., 2016; Li et al., 2017). With the increase in coal rank, the porosity, micropore volume and surface area of adsorption pores showed a ‘U’ shape trend (the lowest value of bituminous coal stage) (Zhao et al., 2016; Zhao et al., 2010; Bustin and Clarkson, 1998; Gürdal, G., 2001), while the adsorption capacity is gradually increased (Chen et al., 2017; Zhao et al.). Fractal theory has been widely used in the analysis of pore structure of porous media (Mahamud and Novo, 2008; Othman et al., 2010; Xiao et al., 2017a). In recent years, many scholars have discovered the effects of pore structure parameters such as porosity, pore area, pore size, water saturation and tortuosity fractal dimension on relative permeability in porous media (Xiao et al., 2017b, 2018; Liang et al., 2018). Multifractal theory has been widely applied to the pore structure distribution of coal and shale (S. Li et al., 2015a,b; Song et al., 2018; Liu et al., 2018). Song et al. explored that shear- and ductile deformed coals had more complex multifractal structure and higher heterogeneity (Song et al., 2018). Liu et al. showed that high organic matter shale has better microporous connectivity and lower micropores heterogeneity (Liu et al., 2018). Fractal dimension evaluations based on LT-N2GA (FHH model))and NMR have been widely used to describe the fractal characterization of coal (Yao et al., 2008; Zhang et al., 2014; Zhou et al., 2016). Yao et al. examined the fractal dimension of the pore surface of coal and demonstrated that higher rank coals have higher heterogeneity (Yao et al., 2008). Therefore, revealing the pore structure characteristics and fractal characterization of coal reservoirs is of great significance to explore and develop CBM. The CBM resources of the Upper Permian coal seam in Guizhou Province amounts to 3.06 × 1012m3 (Qin and Gao, 2012; Ren et al., 2018). However, the development of coalbed methane in this area is slow due to the undefined accumulation mode of coalbed methane and complex terrain conditions. The coal rank in western Guizhou shows an increasing trend from southwest to northeast (Qin and Gao, 2012; Ren et al., 2018). At present, many scholars (Li et al., 2012, 2014; 2015a,b; Chen et al., 2017) have conducted extensive research on the pore structure of coal reservoirs in the PanGuan syncline and the Zhina coalfield, but few studies have been conducted on the main syncline in northwestern Guizhou. However, the highest daily gas production of CBM wells in northwest Guizhou is also close to 1000 m3 in recent years (Ren et al., 2018). In this paper, based on the coal samples from the 6 main CBM development synclines in western Guizhou, the pore structure and fractal characterization of 6 synclines coal reservoirs are evaluated through LT-CO2GA, LT-N2GA and NMR with the goal of providing theoretical support and engineering guidance for CBM exploration and development in western Guizhou, especially in northwestern Guizhou.
2.2. Experimental methods Samples were first cut into columns (2.5 cm (d) × 3 cm (h)) for NMR testing, and the remaining samples were used for other tests. The mean vitrinite reflectance measurements and microscopic analyses (500 points) were then performed using a Leitz MPV-3 photometer microscope following ISO7404.5–1994 (1994) and ISO 7404.3–1994 (1994). Proximate analysis, including the analysis of ash yield, moisture content, volatile matter and fixed carbon content, was conducted on all 12 samples following ISO 1171–1997 (1997), ISO 562–1998 (1998), and ISO 11722–1999 (1999). All samples were pulverized to 60–80 mesh for the further analysis of LT-N2GA (77.15 K) and LT-CO2GA (273.15 K) by using a Micromeritics ASAP2020 specific surface. Prior to the tests, the coal samples (1–2 g), which were prepared for adsorption analysis, was dried for 48 h and degassed under high vacuum at 393.15 K for approximately 12 h. Due to the different adsorption theories, different models are available for different pore sizes. For example, Dubinin's pore volume filling theory is often used to describe supermicropores, while BET models and BJH models are often used for micropores and transition pores (2–100 nm). According to the specifications of the standard SY/T 6490–2007, NMR measurements were performed by using a Reccore-04 NMR analyzer, a Supra 22 k centrifuge and a DJV220A electronic scale following the same experimental procedure as before (Yao et al., 2010). 2.3. Fractal dimension as evaluated with LT-N2GA and NMR Coal pores, which have uneven structures and irregular surface geometries, are formed during a complex coalification process, which makes it difficult to characterize them in detail (Li et al., 2017; Zhang et al., 2014). To solve this problem, evaluating fractal dimension by LTN2GA and NMR is widely used to describe the SSA, roughness, volume and complexity of adsorption pores. The FHH method is widely used to describe the irregular geometric and structural properties of a porous solid, including coal and shale (Yao et al., 2008; Zhang et al., 2014; Fu et al., 2017; Li et al., 2018). Fractal dimension (D), as an important parameter in fractal theory, is a quantitative representation and basic parameter of fractals that can characterize the complexity and irregularity of a surface (Yao et al., 2008). The range of fractal dimension is 2 ≤ D ≤ 3. When D approaches 2, the solid surface is almost completely smooth. And as D gets closer to 3, the surface is more complicated (Othman et al., 2010). The formula is as follows (Yao et al., 2008; Li et al., 2018):
V P ln ⎛ ⎞ = constant + A ⎡ln ⎛ln ⎛ 0 ⎞ ⎞ ⎤ ⎥ ⎢ ⎝ V0 ⎠ ⎣ ⎝ ⎝ P ⎠⎠⎦ ⎜
⎟
(1)
where P is the equilibrium pressure, MPa; P0 is the saturation pressure of the gas; V is the volume of N2 adsorbed at each pressure, ml/g; and V0 is the volume adsorbed in a monolayer. A is a key parameter for acquiring the LT-N2GA fractal dimension (D = 3 + A), and D1 and D2 represent the roughness of SSA and the complexity of volume of adsorption pores with P/P0 of 0–0.5 and 0.5–1, respectively (Yao et al., 2008; Li et al., 2018). Zhang and Weller explain the basic theory of NMR fractal geometry and its relationship with the pore structure of sandstone (Zhang and Weller, 2014). Based on the characteristics of coal pores, pervious scholars have established the fractal dimension equation for coal as evaluated using NMR (Fu et al., 2017; Tao et al., 2018). The formula is as follows (Zhou et al., 2016):
2. Experimental work 2.1. Sample preparation The study area is located in western Guizhou Province (Fig. 1). Currently, the main stratum under development is Longtan Formation, which has a large number of coal seams. The sedimentary environment of Longtan Formation is mainly tidal flats-lagoons and deltas. From southwest to northeast, the coal rank increases gradually (Qin and Gao, 2012; Ren et al., 2018). In this paper, fresh samples from 12 major coal seams were collected from 6 main CBM synclines in the study area. All
lg(W) = (3 − D)lg(T2) + (D−3)lg(T2 max)
(2)
where W is the percent of cumulative pore volume with a transverse 59
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Fig. 1. Location map of the study area and stratigraphic columns of coal-bearing strata.
between 0.95% and 2.57%. The vitrinite content is within 49.28%–71.12%, the inertinite content is within 4.38%–17.69% and the mineral content is within 14.42%–40.5%. The proximate analyses indicate that the moisture (Mad) content ranges from 0.49% to 2.14%; the ash yield (Aad) ranges from 7.13% to 33.58%; the volatile component (Vad) ranges from 0.35% to 18.69%; and the fixed carbon content ranges from 63.44% to 81.85%. In general, the vitrinite reflectance gradually increases from south to north, and the northern syncline has a higher mineral content and a lower volatile content.
relaxation time less than T2 in the total volume of pores, %; D is the pore fractal dimension; T2 is the transverse relaxation time, ms; and T2max is the transverse relaxation time corresponding to the maximum pore size, ms. 3. Results and discussion 3.1. Proximate and maceral analyses From Table 1, it showed that the vitrinite reflectance (Ro,
ran
%) is 60
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Table 1 Coal composition and proximate analysis of different coal Samples. Sample number
Sample Sample Sample Sample Sample Sample Sample Sample Sample Sample Sample Sample a
1 2 3 4 5 6 7 8 9 10 11 12
Syncline
Panguan Panguan Panguan Panguan Panguan Panguan Panguan Bide Yizhitang Huangnitang Yumo Changgang
Ro,
ran
0.95 1.05 1.08 1.12 1.14 1.26 1.75 1.65 2.24 2.39 2.43 2.57
(%)
Coal composition (%)
Proximate analysis (%)
Vitrinite
Inertinite
Liptinite
Mineral
a
57.93 70.84 55.60 59.19 61.45 49.28 55.66 68.03 71.12 54.50 54.36 54.94
12.68 6.61 13.80 17.69 13.17 14.22 12.14 12.67 4.38 5.00 9.44 8.06
3.61 8.13 6.31 1.53 / / / / / / / /
25.78 14.42 24.29 21.59 25.38 36.50 32.2 19.30 24.50 40.50 36.20 37.00
1.01 1.28 1.04 0.96 0.54 0.53 0.49 0.71 1.50 1.70 1.49 2.14
Mad
Aad
Vad
Fcad
11.21 7.49 11.68 8.77 11.10 7.13 18.25 15.35 19.21 33.58 15.76 20.64
18.69 17.79 13.74 14.96 13.31 11.54 2.47 2.81 0.35 1.28 0.90 0.96
69.09 73.44 73.54 75.31 75.05 80.80 78.79 81.13 78.94 63.44 81.85 76.26
Mad = moisture content; Aad = ash yield; Vad = volatile content; Fcad = fixed carbon content.
3.2. PoreStructure distributions as evaluated with LT-CO2GA, LT-N2GA and NMR
Table 2 The SSA and TPV calculated by LT-CO2GA and LT-N2GA of 12 coal samples. Sample number
3.2.1. Pore structure characterization of supermicropores by LT-CO2GA Micropores are an important region for CBM adsorption, and the contribution of supermicropores can't be ignored for CH4 adsorption (Bustin et al., 2008). The CO2 adsorption isotherms (Fig. 2) are increasing slowly. The pore structure parameters of supermicropores as evaluated by LT-CO2GA are shown in Table 2, which shows that the DRSSA is within 94.5853–243.5830 m2/g and the DA–TPV is within 0.04161–0.08528 ml/g. The DR–SSA and DA–TPV are positively correlated. The DR–SSA and DA–TPV of Bide syncline are similar to that in Panguan syncline, but they are smaller than that of northern syncline. From Fig. 3, the supermicropore size distribution curves are bimodal with pore sizes ranging from 0.42 to 0.68 nm and 0.82–0.9 nm. The peaks of the Bide syncline and the Panguan syncline are generally lower than the peaks of the north other syncline. It can be found from Fig. 2 that the DR–SSA and DA–TPV are positively correlated with the coal
Sample Sample Sample Sample Sample Sample Sample Sample Sample Sample Sample Sample a
Syncline
1 2 3 4 5 6 7 8 9 10 11 12
Panguan Panguan Panguan Panguan Panguan Panguan Panguan Bide Yizhitang Huangnitang Yumo Changgang
Ro, ran (%)
0.95 1.05 1.08 1.12 1.14 1.26 1.65 1.75 2.24 2.39 2.43 2.57
CO2 adsorption
N2 adsorption
DR-SSAa
DA-TPV
BJH-TPV
BET-SSA
94.5853 116.0050 101.6152 126.3315 120.7459 133.6757 157.1320 150.0911 228.1755 243.5830 236.8681 230.3140
0.04854 0.04502 0.04161 0.04956 0.04524 0.06457 0.04709 0.04673 0.06615 0.07395 0.06934 0.08528
0.004047 0.003223 0.005855 0.00313 0.002874 0.00483 0.001447 0.002331 0.001094 0.001363 0.000905 0.000889
0.9333 0.9827 1.3958 0.8207 0.9431 1.2554 0.3186 0.7646 0.4166 0.7621 0.2694 0.4373
SSA = specific surface area (m2/g), TPV = total pore volume (ml/g).
Fig. 2. The CO2 adsorption curves and the relationship between DR-SSA, DA-TPV and vitrinite reflectance. 61
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Fig. 3. Pore size distribution as evaluated by LT-CO2GA.
Fig. 4. Adsorption and desorption curves of LT-N2GA for all samples.
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Fig. 5. The relationship between BET-SSA/BJH-TPV and vitrinite reflectance.
Fig. 6. Pore size distributions as evaluated by LT-N2GA.
Fig. 7. NMR T2 distributions of fully saturated-water coal samples.
correlation between BET-SSA and BJH-TPV (Fig. 5). The Panguan syncline has a higher BET-SSA and BJH-TPV. With the increase in coal rank, the BET-SSA and BJH-TPV decrease gradually (Fig. 5). From Fig. 6, it showed that the pore size distribution curve appear to be multimodal. The peak value of pore volume at 10–100 nm is higher than that at 2–10 nm, which indicates that the pores at 10–100 nm have the greatest contributions to the BJH-TPV. With the increase in coal rank, the pore volume of the different pore sizes decreases, especially for pores with pore size ranging from 10 to 100 nm. The reason is that with an increase in coal burial depth, the geothermal temperature and pressure increase, which causes the pore space to be compressed, and the pore space were blocked by bitumen at the catgenic stage accompanied by complex physical and chemical changes (Radlinski et al., 2004; Gürdal and Yalçın, 2001; Laxminarayana and Crosdale, 1999; Levine, 1993).
rank, which indicates that the coal rank is the main reason for the influence of the supermicropores development in each syncline. 3.2.2. Pore structure characterization of absorption pores as evaluated by LT-N2GA From Fig. 4, it showed that the adsorption curve increased slowly in the initial period of adsorption, while the curve increased sharply and became nearly vertical when the pressure was close to 1.0. According to the porous material adsorption/condensation theory (Krooss et al., 2002), pore shape types can be shown by the change characteristics of LT-N2GA adsorption/desorption curves. The adsorption/desorption curves of all samples belongs to H4 (Fig. 5) following the classification by IUPAC (Thommes et al., 2011). The adsorption branch of H4 is a composite of Types I and II of isotherm. When the p/p0 is low, the more pronounced uptake is associated with the filling of micropores, indicating that the pores of this type contain narrow pores. From Table 2, it showed that the BET-SSA is 0.2694–1.3958 m2/g, and the BJH-TPV is 0.000889–0.005855 ml/g. There is a good positive
3.2.3. Pore structure distributions as evaluated by NMR The pore structure distribution of coals can be determined by 63
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Fig. 8. The fractal characterization of 12 coal samples as evaluated by LT-N2GA.
indicate that the micropores/transition pore volume increases with the increase in coal rank. The reason is that the volumes of mesopores and macropores/microcracks decrease faster, while the volumes of micropores and transition pores decrease slowly, as a result the proportion of micropores increases continuously.
analyzing the hydrogen transverse relaxation time (the proton T2 spectrum) of water in saturated water coal cores by using low-field NMR (Grunewald and Knight, 2009). In general, The T2 peaks corresponding to micropores/transition pores, mesopores and macropores/ microcracks are 0.5–2.5 ms, 20–50 ms and > 100 ms, respectively (Yao et al., 2010). In addition, the continuity of the T2 spectrum represents the connectivity between pores. The Bide syncline and the Panguan syncline have two peaks, but the other North synclines have a single peak with a higher value (Fig. 7). The Bide syncline and the Panguan syncline have better pore connectivity than the other North synclines. The bimodal distribution clearly and gradually transformed into a single peak with increasing coal rank, which indicates the contents of mesopores and macropores/microcracks gradually decrease with the increase in coal rank. Moreover, the connectivity between pores decreases with the increase in coal rank. In other words, with the increase in coal rank, the content of micropores/transition pores gradually increases, which are not inconsistent with the reduction of the TPV in micropores/transition pores by using LT-N2GA. This result shows only that the percentage of micropores/transition pore volume in the total pore volume increases with the increase in coal rank, but it does not
3.3. Fractal characterization from LT-N2GA and NMR 3.3.1. Fraction dimensions of adsorbed pores The FHH fractal results are shown in Fig. 8, which show that there is a demarcation point at ln (ln (p0/p)) = −0.37, corresponding to p0/ p = 0.5. The fractal curve has obvious segmentation, indicating that the fractal characteristics of the two intervals are different, which can be found in various early research work (Yao et al., 2008; Zhang et al., 2014; Fu et al., 2017). The fractal dimensions D1 (p0/p < 0.5) and D2 (p0/p > 0.5) are calculated using formula D = 3 + A, and all values are between 2 and 3, which accords with the range of fractal dimensions (Yao et al., 2008). The D1 values are relatively low, ranging from 2.1231 to 2.5165 (avg. 2.3373), and the D2 values are from 2.5471 to 2.7571 (avg. 2.654). The D2 values of Bide syncline and the Panguan 64
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Fig. 9. The relationship between D1/D2 and vitrine reflectance.
2008). D1 may indicate the fractal dimension of the pore surface, so D1 has an unclear relationship to changes in ash yield.
syncline are lower than other northern synclines. A negative correlation between D1 and D2 is observed in Fig. 9, which can be found in the results of previous studies (Yao et al., 2008; Fu et al., 2017). Previous’ studies have shown that D1 and D2 may represent the fractal dimension of the coal pore surface and the fractal dimension of coal the pore structure, respectively (Yao et al., 2008; Fu et al., 2017). When D1 gets higher, the surface becomes rougher, providing more adsorption sites and, as a consequence, stronger methane adsorption capacity (Yao et al., 2008). On the contrary, higher D2 leads to the more complicated pore structure of the coal, resulting in a stronger capillary condensation effect and reducing the methane adsorption capacity of coal (Yao et al., 2008). Therefore, from the aspect of adsorption capacity, D1 and D2 are negatively correlated. To systematically discuss the factors influencing D1 and D2, the relationships between coal ranks, pore structure, coal composition and D1 and D2 were studied. Fig. 9 shows that D1 has no clear correlation with vitrinite reflectance, which indicates that coal rank has a more complicated influence on D1. In contrast, D2 has a positive correlation with vitrinite reflectance, indicating that with increasing coal rank, the pore structure of coal is more complicated. From Fig. 10, it can be found that D1 has no significant correlation with BET-SSA, BJH-TPV, moisture content and ash yield. D2 is negatively correlated with BET-SSA and BJH-TPV and is positively correlated with moisture content and ash yield. In the process of coalification, comprehensive chemical and physical alterations occur in organic material. The compression of the pore space and the blockage of the bitumen in the process of coalification result in a reduction of the pore volume and increased pore structure heterogeneity, which causes an increase in the fractal dimension D2. BET-SSA has a good positive correlation with BJH-TPV. Therefore, as the BET-SSA increases, D2 decreases. Yao et al. (2008) showed that the fractal dimension D2 may reflect surface behavior at the liquid/vapor interface. D2 has a positively correlated with moisture content, which is because water molecules may cause vibration of adsorbate molecules on the adsorbent surface (Khalili et al., 2000), resulting in an increase in D2. The coal ash can fill pores, which causes more heterogeneous pore structure, resulting in greater D2 (Yao et al.,
3.3.2. Fraction dimensions of seepage pore To calculate the fractal dimension of the seepage pores, the NMR data with T > 2.5 ms are selected. Fig. 11 show that the fractal dimensions (DNMR) of the seepage pores are generally higher, ranging from 2.83 to 2.99 (avg. 2.93). The DNMR values of Bide syncline and the Panguan syncline are lower than other northern synclines. As coal rank increases, DNMR gradually increase (Fig. 12). The coal rank increasing, the volatile yield decrease. Therefore, there is a negative correlation between volatiles and DNMR. With increasing the degree of coalification, physical compaction increases, which causes the pore fracture spaces to be compressed and deformed. Moreover, the blocking of the pores and fracture space by bitumen in the catagenic stage will further cause an increase in DNMR. With the increase in mineral content, DNMR gradually increases. Ash is the combustion product of the minerals in coal, so ash has the same effect on DNMR as minerals. The minerals in coal can fill pores and fracture spaces, which results in a greater DNMR. 3.4. Discussion In general, the Bide syncline and Panguan syncline have a lower DR–SSA and DA–TPV, higher BET–SSA and BJH–TPV, better pore connectivity and lower D2, DNMR and Langmuir volume (VL) than the northern other synclines (Fig. 13). The main reason for the phenomenon is coal rank. From Fig. 14, the VL has a good positive correlation with DR-SSA and DA-TPV, but a negative correlation with BET-SSA and BJH-TPV, indicating that the supermicropores provide more adsorption sites. Therefore, higher DR-SSA coal has a higher gas adsorption capacity, which is consistent with previous studies (Zhao et al., 2016). A trend of 'U′ is presented between D2 and VL. Higher D2 leads to a more complex pore structure of the coal and a stronger capillary condensation effect, resulting in a lower methane adsorption capacity of the coal (Yao et al., 2008). However, when D2 is about 2.75, the reason for the increase in the VL is that these samples have higher coal ranks, resulting 65
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Fig. 10. The relationship between D1/D2 and BET-SSA, BJH-TPV, moisture content and ash yield.
4. Conclusions
in higher DR-SSA and higher VL. The maximum gas adsorption capacity determines the storage capacity of the gas and also reflects the production potential of natural gas. From Fig. 13, it can be seen that the gas content of each syncline is basically similar, indicating that the north synclines have a good development potential. However, poor connectivity and high heterogeneity are bound to have an impact on the development of the northern syncline, which must be valued during the development process.
(1) Bide syncline and Panguan syncline have similar pore structure characteristics of coal reservoirs, which are different from that in other northern synclines. Bide syncline and Panguan syncline have a lower DR–SSA and DA –TPV, higher BET–SSA and BJH–TPV and better pore connectivity. Coal rank is an important reason for the difference of 6 synclines coal reservoirs. As the coal rank increases, the DR–SSA and the DA–TPV increase linearly, whereas the BET–SSA and BJH–TPV decrease gradually. Moreover, the 66
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Fig. 11. The relationship between log (w) and log (T2).
Fig. 12. The relationship between DNMR and vitrine reflectance, volatile content, mineral and ash yield.
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Fig. 13. Isothermal adsorption curves and gas content of each syncline.
Fig. 14. The relationship between Langmuir volume and pore structure parameters.
Acknowledgments
connectivity between pores decreases with increasing coal rank. (2) Bide syncline and Panguan syncline have lower D2 and DNMR. The D1 values are relatively low, ranging from 2.12 to 2.52 (avg. 2.34), and the D2 values range from 2.5471 to 2.7571 (avg. 2.654). D1 has a negative correlation with D2. The DNMR of the seepage pores is generally higher, ranging from 2.83 to 2.99 (avg. 2.93). A positive correlation is observed between coal rank, BET–SSA and BJH–TPV and D2 whereas a negative correlation exists between moisture and ash yield and D2. A positive correlation between coal rank, mineral content and ash yield and DNMR is observed, whereas a negative correlation is observed between volatile yield and DNMR. The complex physical and chemical changes in the process of coalification are the main reasons for the differences in the pore structure characterization and fractal characterization of main coal-bearing synclines. The north synclines have similar gas content to the Panguan synclines and Bide synclines, indicating that it has a good prospects for development, but low connectivity and high heterogeneity must be considered in the development process.
This work was supported by the National Natural Science Foundation of China (U1703126, 41530314) and the Key Project of the National Science & Technology (2016ZX05044-001). Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.jngse.2019.01.010. References Bustin, R.M., Bustin, A.M.M., Cui, X., Ross, D., et al., 2008. Impact of shale properties on pore structure and storage characteristics. In: SPE Paper 119892 Presented at the Society of Petroleum Engineers Shale Gas Production Conference in Fort Worth, Texas; November 16–18. Bustin, R.M., Clarkson, C.R., 1998. Geological controls on coalbed methane reservoir capacity and gas content. Int. J. Coal Geol. 38 (1–2), 3–26. Cai, Y.D., Liu, D.M., Pan, Z.J., et al., 2013. Pore structure and its impact on CH4, adsorption capacity and flow capability of bituminous and subbituminous coals from Northeast China. Fuel 103, 258–268. Chalmers, G.R., Bustin, R.M., Power, I.M., 2012. Characterization of gas shale pore
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Pore structure and fractal characterization of main coal-bearing synclines in western Guizhou, China
T
Pengfei Rena,b,c, Hao Xua,b,c,∗, Dazhen Tanga,b,c, Yukui Lid, Zhenlong Chene, Changhua Sund, Fulin Zhangf, Shida Chena,b,c, Fudong Xina,b,c, Likun Caoa,b,c a
School of Energy Resources, China University of Geosciences (Beijing), Beijing, 100083, PR China Coal Reservoir Laboratory of National Engineering Research Center of CBM Development & Utilization, China University of Geosciences, Beijing, 100083, PR China c Key Laboratory of Marine Reservoir Evolution and Hydrocarbon Enrichment Mechanism, Ministry of Education, Beijing, 100083, PR China d Guizhou Natural Gas Energy Investment Co. Ltd, Guiyang, 550081, PR China e Petroleum Exploration & Production Research Institute, Sinopec East China Oil & Gas Company, Nanjing, 210011, China f The 10th Oil Production Plant, Daging Oilfield of CNPC, Daqing, 163513, PR China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Pore structure Fractal dimension Medium-high rank coals Main coal-bearing syncline in western Guizhou
The coal pore structure is closely related to the accumulation and migration of coalbed methane (CBM). This study collected 12 coal samples in western Guizhou and used low-temperature carbon dioxide adsorption/ desorption (LT-CO2GA), low-temperature nitrogen adsorption/desorption (LT-N2GA) and nuclear magnetic resonance (NMR) experimental methods to systematically discuss the pore structure and fractal characterization of main coal-bearing syncline. The study shows that coal rank is an important reason for the difference of 6 synclines coal reservoirs. Bide syncline and Panguan syncline have similar pore structure and fractal characteristics. As the coal rank increases, the DR–SSA, DA–TPV, D2, DNMR and VL increase linearly, whereas the BET–SSA and BJH –TPV decrease gradually. Moreover, the connectivity between pores decreases with increasing coal rank. A positive correlation is observed between coal rank, BET–SSA, and BJH –TPV and D2, whereas a negative correlation is seen between moisture, ash yield and D2. DNMR is positively correlated with coal rank, mineral content and ash yield, but negatively correlated with volatile matter content. Although the north synclines have good gas content and a good development potential, poor connectivity and high heterogeneity must be valued during the development process.
1. Introduction Coal is an organic rock composed of matrix pores and fractures, which serve as the reservoirs and migration channels for coalbed methane (Close, 1993; Shi and Durcan, 2003). Based on the pore and fracture parameters of coal reservoirs, multiple pore and fracture classifications have been established, so as to describe pores and fractures (Hodot, 1966; Sing, 2004; Cai et al., 2013). The Hodot classification system (Hodot, 1966) is widely applied, including micropores (< 10 nm), transition pores (10–100 nm), mesopores (100–1000 nm) and macropores (> 1000 nm). For a more accurate description, many scholars define pores < 2 nm as supermicropores (Zhao et al., 2016; Chen et al., 2017). The pores < 100 nm are defined as absorption pores, which control gas adsorption and desorption, but the pore size greater than 100 nm of pores are primarily considered seepage pores (Shi and Durancan, 2005; Yao et al., 2009; Li et al., 2014; Zhao et al., 2016).
∗
Coal reservoir description and evaluation, especially the fine characterization and geological significance of pore fractures in coal reservoirs, is always one of popular research fields in CBM. The experimental methods for studying pore characteristics include (1)microscopy methods, 2D scanning electron microscopy (SEM) and transmission electron microscopy (TEM); (2) three-dimensional (3D) structure reconstruction by X-ray computed topography (CT), focused ion beam scanning electron microscopy (FIB-SEM), and atomic force microscopy (AFM); (3)LT-CO2GA, LT-N2GA, high-pressure mercury intrusion porosimetry (MIP) and NMR (Li et al., 2014, 2017; Zhao et al., 2016; Bustin et al., 2008; Nelson, 2009; Yao et al., 2010; Clarkson et al., 2011, 2013; Chalmers et al., 2012; Xu et al., 2012, 2018; Liu et al., 2015; Fu et al., 2017). Because of different detectable scales of different experimental methods, the evaluation of pore distribution and pore structure characteristics needs to be combined with different experimental techniques. For example, the LT-N2GA method has been proven to be an
Corresponding author. School of Energy Resources, China University of Geosciences (Beijing), Beijing, 100083, PR China. E-mail address: [email protected] (H. Xu).
https://doi.org/10.1016/j.jngse.2019.01.010 Received 26 October 2018; Received in revised form 9 January 2019; Accepted 14 January 2019 Available online 19 January 2019 1875-5100/ © 2019 Elsevier B.V. All rights reserved.
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samples are from coal mines at depths of 200–400 m.
effective means of detecting micropores ranging from 1.7 to 300 nm, while LT-CO2GA can detect only micropores < 2 nm (Chen et al., 2017; Zhao et al., 2016). NMR is widely used to identify pores, fractures and the pore size distribution (PSD) (Yao et al., 2010). Generally, the pore structure of coal reservoirs is characterized by studying PSD, volume, specific surface area (SSA), pore type and connectivity, which directly affects the gas enrichment ability and migration characteristics of CBM (Chen et al., 2017; Zhao et al., 2016; Li et al., 2017). Coal rank has a greater impact on the pore structure of coal reservoirs (Zhao et al., 2016; Li et al., 2017). With the increase in coal rank, the porosity, micropore volume and surface area of adsorption pores showed a ‘U’ shape trend (the lowest value of bituminous coal stage) (Zhao et al., 2016; Zhao et al., 2010; Bustin and Clarkson, 1998; Gürdal, G., 2001), while the adsorption capacity is gradually increased (Chen et al., 2017; Zhao et al.). Fractal theory has been widely used in the analysis of pore structure of porous media (Mahamud and Novo, 2008; Othman et al., 2010; Xiao et al., 2017a). In recent years, many scholars have discovered the effects of pore structure parameters such as porosity, pore area, pore size, water saturation and tortuosity fractal dimension on relative permeability in porous media (Xiao et al., 2017b, 2018; Liang et al., 2018). Multifractal theory has been widely applied to the pore structure distribution of coal and shale (S. Li et al., 2015a,b; Song et al., 2018; Liu et al., 2018). Song et al. explored that shear- and ductile deformed coals had more complex multifractal structure and higher heterogeneity (Song et al., 2018). Liu et al. showed that high organic matter shale has better microporous connectivity and lower micropores heterogeneity (Liu et al., 2018). Fractal dimension evaluations based on LT-N2GA (FHH model))and NMR have been widely used to describe the fractal characterization of coal (Yao et al., 2008; Zhang et al., 2014; Zhou et al., 2016). Yao et al. examined the fractal dimension of the pore surface of coal and demonstrated that higher rank coals have higher heterogeneity (Yao et al., 2008). Therefore, revealing the pore structure characteristics and fractal characterization of coal reservoirs is of great significance to explore and develop CBM. The CBM resources of the Upper Permian coal seam in Guizhou Province amounts to 3.06 × 1012m3 (Qin and Gao, 2012; Ren et al., 2018). However, the development of coalbed methane in this area is slow due to the undefined accumulation mode of coalbed methane and complex terrain conditions. The coal rank in western Guizhou shows an increasing trend from southwest to northeast (Qin and Gao, 2012; Ren et al., 2018). At present, many scholars (Li et al., 2012, 2014; 2015a,b; Chen et al., 2017) have conducted extensive research on the pore structure of coal reservoirs in the PanGuan syncline and the Zhina coalfield, but few studies have been conducted on the main syncline in northwestern Guizhou. However, the highest daily gas production of CBM wells in northwest Guizhou is also close to 1000 m3 in recent years (Ren et al., 2018). In this paper, based on the coal samples from the 6 main CBM development synclines in western Guizhou, the pore structure and fractal characterization of 6 synclines coal reservoirs are evaluated through LT-CO2GA, LT-N2GA and NMR with the goal of providing theoretical support and engineering guidance for CBM exploration and development in western Guizhou, especially in northwestern Guizhou.
2.2. Experimental methods Samples were first cut into columns (2.5 cm (d) × 3 cm (h)) for NMR testing, and the remaining samples were used for other tests. The mean vitrinite reflectance measurements and microscopic analyses (500 points) were then performed using a Leitz MPV-3 photometer microscope following ISO7404.5–1994 (1994) and ISO 7404.3–1994 (1994). Proximate analysis, including the analysis of ash yield, moisture content, volatile matter and fixed carbon content, was conducted on all 12 samples following ISO 1171–1997 (1997), ISO 562–1998 (1998), and ISO 11722–1999 (1999). All samples were pulverized to 60–80 mesh for the further analysis of LT-N2GA (77.15 K) and LT-CO2GA (273.15 K) by using a Micromeritics ASAP2020 specific surface. Prior to the tests, the coal samples (1–2 g), which were prepared for adsorption analysis, was dried for 48 h and degassed under high vacuum at 393.15 K for approximately 12 h. Due to the different adsorption theories, different models are available for different pore sizes. For example, Dubinin's pore volume filling theory is often used to describe supermicropores, while BET models and BJH models are often used for micropores and transition pores (2–100 nm). According to the specifications of the standard SY/T 6490–2007, NMR measurements were performed by using a Reccore-04 NMR analyzer, a Supra 22 k centrifuge and a DJV220A electronic scale following the same experimental procedure as before (Yao et al., 2010). 2.3. Fractal dimension as evaluated with LT-N2GA and NMR Coal pores, which have uneven structures and irregular surface geometries, are formed during a complex coalification process, which makes it difficult to characterize them in detail (Li et al., 2017; Zhang et al., 2014). To solve this problem, evaluating fractal dimension by LTN2GA and NMR is widely used to describe the SSA, roughness, volume and complexity of adsorption pores. The FHH method is widely used to describe the irregular geometric and structural properties of a porous solid, including coal and shale (Yao et al., 2008; Zhang et al., 2014; Fu et al., 2017; Li et al., 2018). Fractal dimension (D), as an important parameter in fractal theory, is a quantitative representation and basic parameter of fractals that can characterize the complexity and irregularity of a surface (Yao et al., 2008). The range of fractal dimension is 2 ≤ D ≤ 3. When D approaches 2, the solid surface is almost completely smooth. And as D gets closer to 3, the surface is more complicated (Othman et al., 2010). The formula is as follows (Yao et al., 2008; Li et al., 2018):
V P ln ⎛ ⎞ = constant + A ⎡ln ⎛ln ⎛ 0 ⎞ ⎞ ⎤ ⎥ ⎢ ⎝ V0 ⎠ ⎣ ⎝ ⎝ P ⎠⎠⎦ ⎜
⎟
(1)
where P is the equilibrium pressure, MPa; P0 is the saturation pressure of the gas; V is the volume of N2 adsorbed at each pressure, ml/g; and V0 is the volume adsorbed in a monolayer. A is a key parameter for acquiring the LT-N2GA fractal dimension (D = 3 + A), and D1 and D2 represent the roughness of SSA and the complexity of volume of adsorption pores with P/P0 of 0–0.5 and 0.5–1, respectively (Yao et al., 2008; Li et al., 2018). Zhang and Weller explain the basic theory of NMR fractal geometry and its relationship with the pore structure of sandstone (Zhang and Weller, 2014). Based on the characteristics of coal pores, pervious scholars have established the fractal dimension equation for coal as evaluated using NMR (Fu et al., 2017; Tao et al., 2018). The formula is as follows (Zhou et al., 2016):
2. Experimental work 2.1. Sample preparation The study area is located in western Guizhou Province (Fig. 1). Currently, the main stratum under development is Longtan Formation, which has a large number of coal seams. The sedimentary environment of Longtan Formation is mainly tidal flats-lagoons and deltas. From southwest to northeast, the coal rank increases gradually (Qin and Gao, 2012; Ren et al., 2018). In this paper, fresh samples from 12 major coal seams were collected from 6 main CBM synclines in the study area. All
lg(W) = (3 − D)lg(T2) + (D−3)lg(T2 max)
(2)
where W is the percent of cumulative pore volume with a transverse 59
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Fig. 1. Location map of the study area and stratigraphic columns of coal-bearing strata.
between 0.95% and 2.57%. The vitrinite content is within 49.28%–71.12%, the inertinite content is within 4.38%–17.69% and the mineral content is within 14.42%–40.5%. The proximate analyses indicate that the moisture (Mad) content ranges from 0.49% to 2.14%; the ash yield (Aad) ranges from 7.13% to 33.58%; the volatile component (Vad) ranges from 0.35% to 18.69%; and the fixed carbon content ranges from 63.44% to 81.85%. In general, the vitrinite reflectance gradually increases from south to north, and the northern syncline has a higher mineral content and a lower volatile content.
relaxation time less than T2 in the total volume of pores, %; D is the pore fractal dimension; T2 is the transverse relaxation time, ms; and T2max is the transverse relaxation time corresponding to the maximum pore size, ms. 3. Results and discussion 3.1. Proximate and maceral analyses From Table 1, it showed that the vitrinite reflectance (Ro,
ran
%) is 60
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Table 1 Coal composition and proximate analysis of different coal Samples. Sample number
Sample Sample Sample Sample Sample Sample Sample Sample Sample Sample Sample Sample a
1 2 3 4 5 6 7 8 9 10 11 12
Syncline
Panguan Panguan Panguan Panguan Panguan Panguan Panguan Bide Yizhitang Huangnitang Yumo Changgang
Ro,
ran
0.95 1.05 1.08 1.12 1.14 1.26 1.75 1.65 2.24 2.39 2.43 2.57
(%)
Coal composition (%)
Proximate analysis (%)
Vitrinite
Inertinite
Liptinite
Mineral
a
57.93 70.84 55.60 59.19 61.45 49.28 55.66 68.03 71.12 54.50 54.36 54.94
12.68 6.61 13.80 17.69 13.17 14.22 12.14 12.67 4.38 5.00 9.44 8.06
3.61 8.13 6.31 1.53 / / / / / / / /
25.78 14.42 24.29 21.59 25.38 36.50 32.2 19.30 24.50 40.50 36.20 37.00
1.01 1.28 1.04 0.96 0.54 0.53 0.49 0.71 1.50 1.70 1.49 2.14
Mad
Aad
Vad
Fcad
11.21 7.49 11.68 8.77 11.10 7.13 18.25 15.35 19.21 33.58 15.76 20.64
18.69 17.79 13.74 14.96 13.31 11.54 2.47 2.81 0.35 1.28 0.90 0.96
69.09 73.44 73.54 75.31 75.05 80.80 78.79 81.13 78.94 63.44 81.85 76.26
Mad = moisture content; Aad = ash yield; Vad = volatile content; Fcad = fixed carbon content.
3.2. PoreStructure distributions as evaluated with LT-CO2GA, LT-N2GA and NMR
Table 2 The SSA and TPV calculated by LT-CO2GA and LT-N2GA of 12 coal samples. Sample number
3.2.1. Pore structure characterization of supermicropores by LT-CO2GA Micropores are an important region for CBM adsorption, and the contribution of supermicropores can't be ignored for CH4 adsorption (Bustin et al., 2008). The CO2 adsorption isotherms (Fig. 2) are increasing slowly. The pore structure parameters of supermicropores as evaluated by LT-CO2GA are shown in Table 2, which shows that the DRSSA is within 94.5853–243.5830 m2/g and the DA–TPV is within 0.04161–0.08528 ml/g. The DR–SSA and DA–TPV are positively correlated. The DR–SSA and DA–TPV of Bide syncline are similar to that in Panguan syncline, but they are smaller than that of northern syncline. From Fig. 3, the supermicropore size distribution curves are bimodal with pore sizes ranging from 0.42 to 0.68 nm and 0.82–0.9 nm. The peaks of the Bide syncline and the Panguan syncline are generally lower than the peaks of the north other syncline. It can be found from Fig. 2 that the DR–SSA and DA–TPV are positively correlated with the coal
Sample Sample Sample Sample Sample Sample Sample Sample Sample Sample Sample Sample a
Syncline
1 2 3 4 5 6 7 8 9 10 11 12
Panguan Panguan Panguan Panguan Panguan Panguan Panguan Bide Yizhitang Huangnitang Yumo Changgang
Ro, ran (%)
0.95 1.05 1.08 1.12 1.14 1.26 1.65 1.75 2.24 2.39 2.43 2.57
CO2 adsorption
N2 adsorption
DR-SSAa
DA-TPV
BJH-TPV
BET-SSA
94.5853 116.0050 101.6152 126.3315 120.7459 133.6757 157.1320 150.0911 228.1755 243.5830 236.8681 230.3140
0.04854 0.04502 0.04161 0.04956 0.04524 0.06457 0.04709 0.04673 0.06615 0.07395 0.06934 0.08528
0.004047 0.003223 0.005855 0.00313 0.002874 0.00483 0.001447 0.002331 0.001094 0.001363 0.000905 0.000889
0.9333 0.9827 1.3958 0.8207 0.9431 1.2554 0.3186 0.7646 0.4166 0.7621 0.2694 0.4373
SSA = specific surface area (m2/g), TPV = total pore volume (ml/g).
Fig. 2. The CO2 adsorption curves and the relationship between DR-SSA, DA-TPV and vitrinite reflectance. 61
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Fig. 3. Pore size distribution as evaluated by LT-CO2GA.
Fig. 4. Adsorption and desorption curves of LT-N2GA for all samples.
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Fig. 5. The relationship between BET-SSA/BJH-TPV and vitrinite reflectance.
Fig. 6. Pore size distributions as evaluated by LT-N2GA.
Fig. 7. NMR T2 distributions of fully saturated-water coal samples.
correlation between BET-SSA and BJH-TPV (Fig. 5). The Panguan syncline has a higher BET-SSA and BJH-TPV. With the increase in coal rank, the BET-SSA and BJH-TPV decrease gradually (Fig. 5). From Fig. 6, it showed that the pore size distribution curve appear to be multimodal. The peak value of pore volume at 10–100 nm is higher than that at 2–10 nm, which indicates that the pores at 10–100 nm have the greatest contributions to the BJH-TPV. With the increase in coal rank, the pore volume of the different pore sizes decreases, especially for pores with pore size ranging from 10 to 100 nm. The reason is that with an increase in coal burial depth, the geothermal temperature and pressure increase, which causes the pore space to be compressed, and the pore space were blocked by bitumen at the catgenic stage accompanied by complex physical and chemical changes (Radlinski et al., 2004; Gürdal and Yalçın, 2001; Laxminarayana and Crosdale, 1999; Levine, 1993).
rank, which indicates that the coal rank is the main reason for the influence of the supermicropores development in each syncline. 3.2.2. Pore structure characterization of absorption pores as evaluated by LT-N2GA From Fig. 4, it showed that the adsorption curve increased slowly in the initial period of adsorption, while the curve increased sharply and became nearly vertical when the pressure was close to 1.0. According to the porous material adsorption/condensation theory (Krooss et al., 2002), pore shape types can be shown by the change characteristics of LT-N2GA adsorption/desorption curves. The adsorption/desorption curves of all samples belongs to H4 (Fig. 5) following the classification by IUPAC (Thommes et al., 2011). The adsorption branch of H4 is a composite of Types I and II of isotherm. When the p/p0 is low, the more pronounced uptake is associated with the filling of micropores, indicating that the pores of this type contain narrow pores. From Table 2, it showed that the BET-SSA is 0.2694–1.3958 m2/g, and the BJH-TPV is 0.000889–0.005855 ml/g. There is a good positive
3.2.3. Pore structure distributions as evaluated by NMR The pore structure distribution of coals can be determined by 63
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Fig. 8. The fractal characterization of 12 coal samples as evaluated by LT-N2GA.
indicate that the micropores/transition pore volume increases with the increase in coal rank. The reason is that the volumes of mesopores and macropores/microcracks decrease faster, while the volumes of micropores and transition pores decrease slowly, as a result the proportion of micropores increases continuously.
analyzing the hydrogen transverse relaxation time (the proton T2 spectrum) of water in saturated water coal cores by using low-field NMR (Grunewald and Knight, 2009). In general, The T2 peaks corresponding to micropores/transition pores, mesopores and macropores/ microcracks are 0.5–2.5 ms, 20–50 ms and > 100 ms, respectively (Yao et al., 2010). In addition, the continuity of the T2 spectrum represents the connectivity between pores. The Bide syncline and the Panguan syncline have two peaks, but the other North synclines have a single peak with a higher value (Fig. 7). The Bide syncline and the Panguan syncline have better pore connectivity than the other North synclines. The bimodal distribution clearly and gradually transformed into a single peak with increasing coal rank, which indicates the contents of mesopores and macropores/microcracks gradually decrease with the increase in coal rank. Moreover, the connectivity between pores decreases with the increase in coal rank. In other words, with the increase in coal rank, the content of micropores/transition pores gradually increases, which are not inconsistent with the reduction of the TPV in micropores/transition pores by using LT-N2GA. This result shows only that the percentage of micropores/transition pore volume in the total pore volume increases with the increase in coal rank, but it does not
3.3. Fractal characterization from LT-N2GA and NMR 3.3.1. Fraction dimensions of adsorbed pores The FHH fractal results are shown in Fig. 8, which show that there is a demarcation point at ln (ln (p0/p)) = −0.37, corresponding to p0/ p = 0.5. The fractal curve has obvious segmentation, indicating that the fractal characteristics of the two intervals are different, which can be found in various early research work (Yao et al., 2008; Zhang et al., 2014; Fu et al., 2017). The fractal dimensions D1 (p0/p < 0.5) and D2 (p0/p > 0.5) are calculated using formula D = 3 + A, and all values are between 2 and 3, which accords with the range of fractal dimensions (Yao et al., 2008). The D1 values are relatively low, ranging from 2.1231 to 2.5165 (avg. 2.3373), and the D2 values are from 2.5471 to 2.7571 (avg. 2.654). The D2 values of Bide syncline and the Panguan 64
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Fig. 9. The relationship between D1/D2 and vitrine reflectance.
2008). D1 may indicate the fractal dimension of the pore surface, so D1 has an unclear relationship to changes in ash yield.
syncline are lower than other northern synclines. A negative correlation between D1 and D2 is observed in Fig. 9, which can be found in the results of previous studies (Yao et al., 2008; Fu et al., 2017). Previous’ studies have shown that D1 and D2 may represent the fractal dimension of the coal pore surface and the fractal dimension of coal the pore structure, respectively (Yao et al., 2008; Fu et al., 2017). When D1 gets higher, the surface becomes rougher, providing more adsorption sites and, as a consequence, stronger methane adsorption capacity (Yao et al., 2008). On the contrary, higher D2 leads to the more complicated pore structure of the coal, resulting in a stronger capillary condensation effect and reducing the methane adsorption capacity of coal (Yao et al., 2008). Therefore, from the aspect of adsorption capacity, D1 and D2 are negatively correlated. To systematically discuss the factors influencing D1 and D2, the relationships between coal ranks, pore structure, coal composition and D1 and D2 were studied. Fig. 9 shows that D1 has no clear correlation with vitrinite reflectance, which indicates that coal rank has a more complicated influence on D1. In contrast, D2 has a positive correlation with vitrinite reflectance, indicating that with increasing coal rank, the pore structure of coal is more complicated. From Fig. 10, it can be found that D1 has no significant correlation with BET-SSA, BJH-TPV, moisture content and ash yield. D2 is negatively correlated with BET-SSA and BJH-TPV and is positively correlated with moisture content and ash yield. In the process of coalification, comprehensive chemical and physical alterations occur in organic material. The compression of the pore space and the blockage of the bitumen in the process of coalification result in a reduction of the pore volume and increased pore structure heterogeneity, which causes an increase in the fractal dimension D2. BET-SSA has a good positive correlation with BJH-TPV. Therefore, as the BET-SSA increases, D2 decreases. Yao et al. (2008) showed that the fractal dimension D2 may reflect surface behavior at the liquid/vapor interface. D2 has a positively correlated with moisture content, which is because water molecules may cause vibration of adsorbate molecules on the adsorbent surface (Khalili et al., 2000), resulting in an increase in D2. The coal ash can fill pores, which causes more heterogeneous pore structure, resulting in greater D2 (Yao et al.,
3.3.2. Fraction dimensions of seepage pore To calculate the fractal dimension of the seepage pores, the NMR data with T > 2.5 ms are selected. Fig. 11 show that the fractal dimensions (DNMR) of the seepage pores are generally higher, ranging from 2.83 to 2.99 (avg. 2.93). The DNMR values of Bide syncline and the Panguan syncline are lower than other northern synclines. As coal rank increases, DNMR gradually increase (Fig. 12). The coal rank increasing, the volatile yield decrease. Therefore, there is a negative correlation between volatiles and DNMR. With increasing the degree of coalification, physical compaction increases, which causes the pore fracture spaces to be compressed and deformed. Moreover, the blocking of the pores and fracture space by bitumen in the catagenic stage will further cause an increase in DNMR. With the increase in mineral content, DNMR gradually increases. Ash is the combustion product of the minerals in coal, so ash has the same effect on DNMR as minerals. The minerals in coal can fill pores and fracture spaces, which results in a greater DNMR. 3.4. Discussion In general, the Bide syncline and Panguan syncline have a lower DR–SSA and DA–TPV, higher BET–SSA and BJH–TPV, better pore connectivity and lower D2, DNMR and Langmuir volume (VL) than the northern other synclines (Fig. 13). The main reason for the phenomenon is coal rank. From Fig. 14, the VL has a good positive correlation with DR-SSA and DA-TPV, but a negative correlation with BET-SSA and BJH-TPV, indicating that the supermicropores provide more adsorption sites. Therefore, higher DR-SSA coal has a higher gas adsorption capacity, which is consistent with previous studies (Zhao et al., 2016). A trend of 'U′ is presented between D2 and VL. Higher D2 leads to a more complex pore structure of the coal and a stronger capillary condensation effect, resulting in a lower methane adsorption capacity of the coal (Yao et al., 2008). However, when D2 is about 2.75, the reason for the increase in the VL is that these samples have higher coal ranks, resulting 65
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Fig. 10. The relationship between D1/D2 and BET-SSA, BJH-TPV, moisture content and ash yield.
4. Conclusions
in higher DR-SSA and higher VL. The maximum gas adsorption capacity determines the storage capacity of the gas and also reflects the production potential of natural gas. From Fig. 13, it can be seen that the gas content of each syncline is basically similar, indicating that the north synclines have a good development potential. However, poor connectivity and high heterogeneity are bound to have an impact on the development of the northern syncline, which must be valued during the development process.
(1) Bide syncline and Panguan syncline have similar pore structure characteristics of coal reservoirs, which are different from that in other northern synclines. Bide syncline and Panguan syncline have a lower DR–SSA and DA –TPV, higher BET–SSA and BJH–TPV and better pore connectivity. Coal rank is an important reason for the difference of 6 synclines coal reservoirs. As the coal rank increases, the DR–SSA and the DA–TPV increase linearly, whereas the BET–SSA and BJH–TPV decrease gradually. Moreover, the 66
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Fig. 11. The relationship between log (w) and log (T2).
Fig. 12. The relationship between DNMR and vitrine reflectance, volatile content, mineral and ash yield.
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Fig. 13. Isothermal adsorption curves and gas content of each syncline.
Fig. 14. The relationship between Langmuir volume and pore structure parameters.
Acknowledgments
connectivity between pores decreases with increasing coal rank. (2) Bide syncline and Panguan syncline have lower D2 and DNMR. The D1 values are relatively low, ranging from 2.12 to 2.52 (avg. 2.34), and the D2 values range from 2.5471 to 2.7571 (avg. 2.654). D1 has a negative correlation with D2. The DNMR of the seepage pores is generally higher, ranging from 2.83 to 2.99 (avg. 2.93). A positive correlation is observed between coal rank, BET–SSA and BJH–TPV and D2 whereas a negative correlation exists between moisture and ash yield and D2. A positive correlation between coal rank, mineral content and ash yield and DNMR is observed, whereas a negative correlation is observed between volatile yield and DNMR. The complex physical and chemical changes in the process of coalification are the main reasons for the differences in the pore structure characterization and fractal characterization of main coal-bearing synclines. The north synclines have similar gas content to the Panguan synclines and Bide synclines, indicating that it has a good prospects for development, but low connectivity and high heterogeneity must be considered in the development process.
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