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Abstract

Reservoir pore characteristics are an important part of shale gas reservoir capacity evaluation. Compared with Marine shale, the study of coal measure shale is relatively backward in China. In order to deeply analyze the reservoir properties of coal measure shale gas and give priority to favorable reservoirs, the pore structure characteristics of shale in coal measures from the Shanxi Formation of Lower Permian in Huainan Coalfield of China were investigated in this study. The pore size distribution, specific surface area (SSA), and pore volume (PV) were obtained. The BET surface area and PVs are inconsistent with the variability in total organic content (TOC) and composition of the samples. There are unobvious negative correlations between PV, SSA, and TOC for the samples in this study. However, the contribution of clay minerals to PV and SSA seems to be positive, especially for the mixed-layer minerals of illite and smectite.

Keywords

Pore structure shale in coal-bearing strata Shanxi formation influence factor

Introduction

Coal-bearing shale gas is one of the three types of coal-based gas (Ge et al., 2020). Shales in coal-bearing strata are often interbedded with coal seams and tight sandstone, and often have geological characteristics such as small thickness, poor continuity, large variation in gas content, and low porosity (Gao et al., 2021). Hence, the pore structure characteristics of these shales have a significant impact on gas enrichment and development conditions (Hou et al., 2023a, 2023b; Xu et al., 2019). The pore structure of shale is more complex than that of traditional sandstone and carbonate reservoirs (Ibad and Padmanabhan, 2022; Liang et al., 2019; Xiao et al., 2019). Pore types in shales or mudstones often include intraparticle organic matter pores, intrapartical mineral pores, intergranular mineral pores, and microfractures (Loucks et al., 2012). The image analysis method based on scanning electron microscopy (SEM) can directly observe the pore morphology of shale (Ma et al., 2019; Zhu et al., 2021). CO₂ adsorption method, low-temperature N₂ adsorption method, and Mercury injection capillary pressure (MICP) method can quantitatively characterize the overall characteristics of shale pores and reduce the interference of human factors (Chandra et al., 2020; Liu et al., 2019; Ma et al., 2019).

Previous studies have shown that the pore structure of coal measure shale is affected by many factors. In China, studies on coal measure shales of Taiyuan Formation in Huainan coalfield, Shanxi Formation, and Taiyuan Formation in Qinshui Basin have found that organic matter can promote the development of nanoscale pores (Chen et al., 2020; Ge et al., 2020; Pan et al., 2017), significantly promoted the pore development of <50 nm, followed by the content of clay minerals, while quartz inhibited the pore development (Yan et al., 2017; Yuan et al., 2021). However, Zhang et al. (2017) studied the pore structure of coal measure shales of Yan’an Formation and Taiyuan Formation in the northeastern margin of the Ordos Basin and found that organic matter and clay content had a poor correlation with total surface area and porosity, and believed that single factor had no significant influence on pore structure (Zhang et al., 2017). Ding et al. (2021) studied the shale of Taiyuan Formation in Linxing area, Ordos Basin, and found that there was no significant linear correlation between organic matter, quartz content, clay mineral content, and total pore volume (PV) with specific surface area (SSA; Ding et al., 2021). It should be noted that the increase of clay minerals may exacerbate pore collapse, leading to a decrease in PV and SSA (Pan et al., 2017). According to the above studies, there are some differences in the pore structure and influencing factors of coal measure shales in different regions. So far, the study of shale pore mainly focuses on marine shale, while the study of pore structure in coal-measured shale is still relatively insufficient and needs to be further carried out.

The Huainan coalfield is located in northern Anhui Province, China (Liu et al., 2014). It covers an area of 1572 km² and is 70 km long from east to west and 25 km long from south to north (Figure 1). The coal-bearing strata in Huainan Coalfield were deposited in a typical marine-continental transitional environment of the Permian, with an average thickness of approximately 1000 m. There are many mines in Huainan coalfield, which provides an ideal research area for studying shale properties. The Permian strata include the Shanxi Formation (the thickness is 52–88 m; with an average of 70 m; Figure 1), the Lower Shihezi Formation (the thickness is 35–169 m; with an average of 131 m), the Upper Shihezi Formation (the thickness is 221–646 m; with an average of 548 m), and the Sunjiagou Formation (the thickness is 184–281 m; with an average of 227 m; Table 1). The organic matter (present-day total organic content [TOC]) in Shanxi Formation is richer than other Formations according to our previous studies (Yu et al., 2020). Therefore, this paper conducts research on organic matter characteristics, mineral composition, and pore characteristics of shale samples taken from boreholes, aiming to provide a scientific basis for reservoir evaluation and optimization of coal measure shale gas.

Figure 1.

Location of the study area in the Huainan coalfield of China, and Shanxi Formation of Lower Permian stratigraphy and depositional environment.

Table 1.

Summary table of the Permian strata in Huainan coalfield.

System	Series	Group	Age	Thickness (m)
Permian	Upper	Sunjiagou	P₃sj	184–281/227
	Upper	Upper Shihezi	P₃ss	221–636/548
	Middle-Upper	Lower Shihezi	P_2–3xs	35–169/131
	Lower	Shanxi	P₁s	52–88/70

Materials and methods

Shale samples

Fresh shale samples were collected from six coal geological exploration boreholes in the Huainan Coalfield, most of which were located in the Deep Panji area (Figure 1). The mudstone section can be divided into four parts vertically, including the marine facies mudstone section about 8 m thick at the bottom of the Shanxi Formation, the mudstone section between the No. 1 and No. 3 coal seam, the sandy mudstone section on the No. 1 coal seam and the mudstone at the top of the Shanxi Formation. The samples were collected in these layers with a piece of sample every 10 m. All shale samples taken from the boreholes were from the Shanxi Formation of Lower Permian with a burial depth of 1000–1550 m. Table 2 shows the results of the basic properties of all shale samples including number of samples, formation age and group, depth, TOC, and kerogen type. An experimental scheme including mineralogical, geochemical, and petrophysical features is shown in Table 3.

Table 2.

Material composition of the samples collected from the Huainan coalfield.

Sample	Age	Group	Depth(m)	TOC (wt. %)	Kerogen type^a	Thermal maturity R₀ (%)
H-1	P_1S	Shanxi Fm.	1236.1	3.25	III	0.91
H-2	P_1S	Shanxi Fm.	1447.7	1.38	II₂	0.92
H-3	P_1S	Shanxi Fm.	1409.6	8.87	II₁	0.92
H-4	P_1S	Shanxi Fm.	1513.9	2.27	II₂	0.87
H-5	P_1S	Shanxi Fm.	1163.5	0.11	II₂	Nd^b
H-6	P_1S	Shanxi Fm.	1056.3	0.37	II₂	0.98
H-7	P_1S	Shanxi Fm.	1396.4	2.48	II₂	0.92

Kerogen types are divided by quartering method (TI) and TI = [100 × a + 50 × b + (−75) × c + (−100) × d]/100. a-sapropelic content, b-exinite content, c-vitrinite content, d-inertinite content. TI ≥ 80, I kerogen type; 80>TI ≥ 40, II₁ kerogen type; 40>TI ≥ 0, II₂ kerogen type; TI<0, III kerogen type.

nd: no measured point.

Table 3.

Experimental programs used in the study.

Test projects	Laboratory
Mineralogy: X-ray diffraction	Petroleum Geology Research and Laboratory Center, RIPED, PetroChina
Organic geochemistry (TOC and Kerogen type)
Mercury (Hg) intrusion tests	Key Laboratory of Coalbed Methane Resources and Reservoir Formation Process, the Ministry of Education, China University of Mining and Technology
Low pressure N₂ physisorption
High resolution FIB/SEM	Instruments’ Center for Physical Science, University of Science & Technology of China

High-pressure mercury intrusion experiment

The mercury intrusion method is a common method for the analysis of pores in porous media. The experimental error mainly comes from the space betweencoal particles under low pressure and coal compression and pore damage caused by high mercury injection pressure (Liu et al., 2018; Mavhengere et al., 2015). it is found that the effect of coal compressibility on the invasion of mercury volume can not be ignored using fractal theory to analyze the mercury invasion data (Liu et al., 2018). Despite the destructive nature of MICP, as samples may undergo elastic deformation or even irreversible destruction, it remains a widely accepted standard measurement with good PV and pore size distribution (PSD) in the macro- and mesopore ranges. Micromeritics Autopore IV^TM porosimeter was used to determine the PSD (3–100,000 nm) and porosity for all samples at pressures up to 60,000 psia (414 MPa), which equates to a pore diameter of 3.0 nm. Samples were dried under vacuum at 80°C for more than 24 h before testing. The pore throat radius can be calculated from the inlet pressure at the beginning of the sudden pressure drop, assuming that the pore is cylindrical and obtained by Washburn equation. Previous researchers have outlined detailed steps for MICP measurement (Vavra et al., 1992).

Low-pressure N₂ adsorption experiment

The N₂ adsorption experiments were performed on TriStar II 3020 apparatus at 77 K. After grinding, the sample with a particle size of 60 mesh (250 μm) was dehydrated for 12 h at 353 K. The relative pressure of adsorption-desorption was 0.01∼0.995. The multi-point Brunauer–Emmett–Teller (BET) method was used to calculate the SSA (Brunauer et al., 1938). The Barrett, Johner, and Halenda (BJH) method was used to calculate pore size and PV distributions for pore sizes ranging from 1.7 to 100 nm (Barrett, 1951). Based on N₂ adsorption data, the micropore PV and SSA (including the macropores and mesopores) can be estimated by the T-plot method (de Boer et al., 1966). Therefore, the surface area of micropores can be calculated by SSA without external area, while the detailed process and method of T-plot method have been clearly expounded in previous studies (Tian et al., 2015).

FE-SEM observation and permeability

High-resolution field emission scanning electron microscopy (FE-SEM) produced by FEI Sirion200 was used to observe pore types and microstructure of samples. Secondary electron imaging provided information on pore size, type, microstructure, and other information of shale. The Pulse attenuation permeability meter (PDP-200) was used to analyze the permeability of the sample. Nitrogen was used for measured permeability. The PDP-200 software can calculate the permeability of the sample from the pressure decay data (Han et al., 2020). The accuracy of the pressure sensor is ±0.1% of the full scale, and the working pressure can reach 6.89 MPa.

Petrology and organic geochemistry

For correlation analysis of pore structures, the mineral composition was measured by X-ray diffraction (XRD) at 24°C and 35% humidity with D/MAX-2500, TTR diffractometer. The relative mineral percentage was estimated according to SY/T 5983-1994 and SY/T 5163-1995, the standards of China's oil and gas industry. The TOC data and Kerogen type were measured by LECO CS-200 analyzer and Axiophot biological microscope following the GB/T 19145-2003 and SY/T 5125-1996, respectively.

Results

Organic matter and mineral composition

Mineral composition and organic matter in shale are closely related to pore structure (Yang et al., 2017). Table 4 shows the XRD analysis data of the test samples. The mineral compositions of these shale samples are mainly quartz and clay minerals. The average content of quartz is 32.7%, ranging from 29.2% to 36.7%. The average content of clay minerals is 60.3%, ranging from 50.6% to 69.7%. In addition, the content of mixed illite/smectite ranges from 9% to 66%, with an average of 45.57%. The content of kaolinite is 23%∼77%, with an average content of 40.43%. Siderite was widely detected, and its concentration varied significantly from 0 to 7.0%, with an average of 3.8%. Five shale samples contained pyrite, with the highest concentration reaching 4.9%.

Table 4.

Mineral compositions (wt. %) of all samples from the Huainan coal field (Nd is no data).

Sample	Quartz	Albite	Dolomite	Pyrite	Siderite	Illite/smectite	Illite	Kaolinite	Chlorite
H-1	29.2	3.6	nd	2.0	7.0	50	7	34	9
H-2	36.3	5.5	3.6	1.6	2.4	44	8	41	7
H-3	30.9	nd	nd	1.5	3.1	42	9	41	8
H-4	31.0	nd	nd	2.4	7.0	55	8	31	6
H-5	34.2	nd	nd	nd	0.6	53	4	36	7
H-6	30.3	nd	nd	nd	nd	9	5	77	9
H-7	36.7	1.8	nd	4.9	2.6	66	5	23	6

The average TOC is 2.68%, which varies from 0.37% to 8.87% (Table 2), indicating that shale organic matter content in Shanxi Formation is relatively rich, but it is heterogeneous.

Mercury intrusion porosimetry

The capillary pressure invasion curves of the seven samples are shown in Figure 2(a). The curve morphology of seven shale samples is different to a certain extent. The invasion curve reached quasi-saturation, the plateau near the maximum injection pressure of 60,000 psi (Figure 2(a)). This suggests that increased pressure could allow mercury to enter most of the pores in shale. Thus, the maximum amount of mercury injected corresponds to the connected pores of the sample. It can be seen from Table 4 that the porosity of all samples ranges from about 1.2 to 4.3%, with an average of 2.36%. Almost all samples required very high-pressure injection to perform the pore network within the coal, indicating poor connectivity and/or difficult-to-access pores. The relationship between pore size and incremental mercury volume shows that only a small amount of mercury is injected before 100 psi, corresponding to a pore width of about 1.8 μm (Figure 2(b)). When the mercury pressure drops to 20 psi, some mercury remains in the pore network, and it is difficult to escape from the pores, which indicates that there are few macropores and mespores in coal measure shale, and the connectivity is poor.

Figure 2.

MICP injection curves (a) and incremental pore volume (b) plots for shale samples.

In the samples tested, a cumulative volume of 0.007 cm³/g for a pressure of 10,000 psi (69 MPa) corresponds to the mesopore region with an average pore radius of about 17 nm. The samples H-2 and H-3 have low Hg porosity and intrusion volume, with low permeability for 0.000468 × 10⁻³ and 0.000324 × 10⁻³μm², respectively (Table 5), while the sample H-4 is 4.29% with triple as much. The samples H-1 and H-5 show low porosity and intrusion volume. Unfortunately, permeability measurements of these two samples were not conducted in this paper.

Table 5.

Volume and porosity of the shale samples determined by mercury intrusion and permeability.

Sample ID	Total intrusion volume (10⁻²ml/g)	Porosity (%)	Permeability (×10⁻⁷μm²)
H-1	0.59	1.28	ND
H-2	0.80	1.38	4.68
H-3	0.60	1.25	3.24
H-4	2.03	4.29	9.01
H-5	1.03	2.20	ND
H-6	1.10	2.39	2.54
H-7	1.77	3.71	3.12

The mercury incremental invasion plot (Figure 2(b)) shows that PVs are significant in the meso- and macro-pore range. The H-4 sample has the largest PV, while the H-3 sample has the smallest. The incremental intrusion plot shows that all samples have broad peaks ranging from 2 to 6 nm, with an additional peak greater than 10,000 nm, which may be related to the stress-relaxation rupture of the samples (Clarkson et al., 2013). Figure 3 shows that organic-rich shale has a large pore coverage, but pores smaller than 30 nm dominate. As can be seen from Figure 3, the pores of most samples have similar PSD characteristics: (1) the proportion of PV with pore size less than 30 nm in most samples is about 50–80% except for H-3 sample in Figure 3(a); (2) as the pore size increases from 30 nm to 50 μm, the PV increment decreases from 0.0003 to 0.0001 cm³/g until zero; (3) the percentage of pore surface area under 30 nm calculated by cumulative pore area is extremely high is extremely high (over 95%) for all samples; and (4) the surface area of pores have the same trend with the incremental PV of these samples.

Figure 3.

Mercury intrusion method to obtain the total PV, volume proportion (a), SSA and SSA proportion (b) less than 30 nm of samples.

N₂ adsorption–desorption isotherm

The pore structure and adsorption mechanism can be interpreted from N₂ sorption–desorption isotherm of shale. (Kuila et al., 2012). All the samples, except H-3, have strong hysteresis loop effects (Figure 4). The sorption and desorption isotherms of these shale samples are similar to those of Types IV and H3, respectively, which means the slit-shaped pores are predominant (Clarkson et al., 2012; Sing, 1985). For all samples, when the relative pressure is around 0.45, there is an obvious hysteresis loop. The “forced closure” of the desorption branch at p/p₀≈0.45 is the result of poor stability of the hemispherical meniscus in the process of capillary evaporation in pores with a critical (Groen et al., 2003; Kuila and Prasad, 2013). This “forced closure” on the N₂ desorption isotherm of these test shales also verifies the possibility of nanopores smaller than 4 nm in coal measure shales.

Figure 4.

Isothermal adsorption–desorption curves of Shanxi shale samples. P₀ is the saturated vapor pressure and P is the gas equilibrium pressure.

At higher pressures, all isotherms show hysteresis loops, but no plateau in a narrow range of values, indicating that all samples contain mesopores and macropores (Sing, 1985; Tian et al., 2015). The large adsorption capacity at low relative pressure(p/p0 < 0.01) implies the moderate occurrence of nanopores with pore size less than 2 nm (Kuila and Prasad, 2013). When the relative pressure is less than 0.01, the high adsorption capacity of sample H-5 and H-4 and the low adsorption capacity of the sample H-3 indicate that the sample H-5 and H-4 includes a large number of micropores, while the sample H-3 has a negligible micropores, corresponding their surface area of the micropores (S_mic) and micropore volume (V_mic) in Tables 6 and 7.

Table 6.

Parameters of SSA obtained from N₂ adsorption isotherms.

Sample	SSA (m²/g)^a	S_mic (m²/g)^b	S_ext (m²/g)^c	SSA contributed by micropores (%)
H-1	9.48	1.17	8.30	12.39
H-2	8.04	0.73	7.31	9.08
H-3	5.82	0.16	5.66	2.69
H-4	12.81	0.61	12.20	4.74
H-5	14.45	2.37	12.08	16.41
H-6	8.68	0.22	8.46	2.56
H-7	10.07	0	10.07	0.00

SSA, calculated by the BET method.

S_mic, the SSA of the micropores.

S_ext is the SSA of all pores, excluding micropores.

Table 7.

Parameters of total PV from N₂ adsorption isotherms.

Sample	Total PV (10⁻²cm³/g)^a	V_mic (10⁻⁴cm³/g)^b	Average pore size (nm)^c	Total PV contributed by micropores (%)
H-1	2.54	4.92	10.93	1.94
H-2	2.22	2.90	11.17	1.30
H-3	2.39	0.30	16.49	0.13
H-4	3.40	1.58	10.76	0.46
H-5	3.14	10.27	8.98	3.27
H-6	2.39	0.39	11.22	0.16
H-7	3.14	0	12.34	0.00

Total PV, estimated to be the volume of liquid N₂ at a relative pressure of 0.99 with the BJH method.

V_mic, the micropore volume.

Average pore size = 4*total PV/SSA.

SSA and PV

SSA is an important adsorption site for gas on the surface of solid particles. Organic kerogen and clay minerals usually contain a large amount of SSA, which may provide an important adsorption site for shale gas (Chalmers et al., 2012; Chalmers and Bustin, 2007). However, the surface area of organic kerogen and clay minerals may vary greatly depending on the geological background, regional distribution, and chemical composition of shales (Cao et al., 2015).

Tables 6 and 7 show the relevant information of SSA, PV, and pore size of Shanxi Formation shale samples studied. It can be seen from Table 6 that the average SSA of N₂ adsorption data calculated by BET method is 9.91 m²/g, ranging from 5.82 to 14.45 m²/g. Using N₂ adsorption data, the mean values of surface area (S_ext) and microporous surface area (S_mic) calculated by T-plot method were 9.15 and 0.75 m²/g, respectively, with the ranges changing from 5.66 to 12.20 m²/g and from 0 to 2.37 m²/g, respectively. It can be seen from Table 7 that the average total PV is 0.027 cm³/g, with a range of 0.022∼0.034 cm³/g. The average pore size is 11.698 nm, ranging from 8.980 to 16.492 nm. Using N₂ adsorption data, the T-plot method was used to estimate the micropore volume. The mean value was 0.0003 cm³/g, and the variation range was 0∼0.0010 cm³/g. In addition, we also noticed that the surface area and volume of micropores in the H-7 shale sample were small or even negligible from Tables 6 and 7, indicating that there are few micropores with pore size less than 2 nm in the sample. However, the contribution of micropores to SSA is more significant than that of the PV from Tables 6 and 7.

Pore size distribution

The BJH model is considered as a method with wide application and applicability (Wang et al., 2015). PSD can be represented by cumulative, incremental, and differential PV distribution curves (Qiu et al., 2021). From these distribution curves, PSD, PSD peak, and dominant pore size can be further analyzed. Hence, the BJH method was used to calculate PSD based on the data from N₂ adsorption and desorption experiment in the study. Taking shale sample H-7 as an example, Figure 5 shows the PSD obtained from N₂ isotherm adsorption and desorption branches. It can be seen that the changes of dV/dr and dV/dlogr plots are similar, but the PSDs obtained by adsorption and desorption branches are significantly different. The dV(d) curve of the adsorption branch has a peak at about 1–2 nm, showing a single peak curve (Figure 5(a)), but the dV(Log(d)) curve of the adsorption branch shows a double peak at 1–2 and 25 nm, respectively. In addition, the PSD of the adsorption curve lacks a peak at the pore size of about 4 nm, while the PSD of the desorption curve has a strong peak at about 4 nm.

Figure 5.

Comparison of the PJH–PSD obtained from the data of N₂ adsorption and desorption curve for sample H-7 to show the peak at about 4 nm of the desorption curve.

The dV/dlogr curves of PV distribution appear to be bimodal for shale samples obtained by the BJH method (Figure 6), with major peaks around 2–4 and 30–50 nm. All samples have a notable decrease of dV/dr plots less than 3 nm. In addition, samples H-4 has the highest PV, and Samples H-2, H-3 have lower PV and lower PSD peak than all of the other samples (Table 7). From Figure 6, we observe that shale samples H-2 and H-3 have less SSA than other samples. Similar results of total intrusion volume for the samples H-4, H-2, and H-3 obtained by MICP method have also been displayed in Table 4.

Figure 6.

PSD obtained from N₂ adsorption curve by BJH method.

In addition, the cumulative volume map of the study sample obtained is shown in Figure 7. The PSD of all samples is multimodal, mainly distributed between 20 and 40 nm, and the other distribution is around 3 nm.

Figure 7.

PSD defined by incremental pore volume for all samples from the Shanxi Formation, Huainan Coal Field.

Discussion

Comparison of HG intrusion and N₂ adsorption

Due to differences of pore characteristics acquisition methods, it is difficult to have a specific method to reproduce the pore characteristics of shale independently and comprehensively. Both the mercury intrusion method and the nitrogen physical adsorption method cover only a certain range of pore size. Therefore, mercury intrusion data and physical adsorption data were attempted to compare data from this study, such as H-4 sample (Figure 8). The modes detected for pores at 1–2 nm and around 20–30 nm are not observed in Hg intrusion experiments. Generally, BJH and MICP PSD do not agree at all for the sample shown. This may be due to the fact that high-pressure mercury disrupts the original pore structure of the coal and compresses the matrix, leading to an underestimation of pore diameter (Kuila et al., 2012). Another possibility is that the pore geometry deviates from a slit-shaped geometry, causing some of the observed differences in the range of pore sizes. Another possibility, as suggested by some scholars (Clarkson et al., 2013).

Figure 8.

Comparison of PSD defined by Hg intrusion and N₂ adsorption data for H-4 sample. Thick arrows represent the corresponding y-axis values of each curve (N₂ adsorption curve is read from the left axis and Hg intrusion curve is read from the right axis).

Pore structure parameters characteristics

There is a positive relationship between PV and SSA (R² = 0.6704) in Shanxi Formation shale samples (Figure 9(a)). The SSA increased with the increase of the PV, which is consistent with previous results (Iqbal et al., 2021; Xu et al., 2020). However, the PV and SSA have insignificant negative correlation with the average pore size (Figure 9(b) and (c)), indicating that PV and SSA may increase with the decreasing of pore size, that is, the smaller the average pore size, the larger the PV and SSA will be. It is believed that the pores in clay minerals are dominated by micropores, which are smaller than the average pore size (Han et al., 2016; Ross and Bustin, 2009). As indicated in Table 4, the average clay mineral content of these samples exceeds 60%, which can provide abundant SSA and PV of micropores.

Figure 9.

Relationship between SSA and total PV (a), and relationships between average pore size and total PV (b), SSA (c).

Compared with the world-famous Marine shales (Lohr et al., 2020), the studied shales have relatively small SSA but similar total PV (Table 8). The SSA of shales in the study area is much lower than that of most Marine shales (except for the Chang-7 shale, Marcellue shale, and Woodford shale samples). The PV of shale investigated are higher than Marcellue shale, Woodford shale, and shale samples in Chang-7 of Yanchang formation and Qiongzhusi formation, but lower than Barnett shale, Haynesville shale, Longmaxi shale, and Niutitang shale. This obvious difference may be closely related to the material characteristics, sedimentary enrichment process, and formation environment of shale. Marine shale is rich in organic pores with high contribution rate of organic matter. However, the contribution rate of clay mineral pores in continental or transitional shale is higher than that in Marine shale. Compared with Marine shales, the shale samples of coal measured strata in this study are deposited in the marine-continental transitional environment. The shale samples have more clay minerals, mainly rich in kaolinite and mixed-layer minerals of illite and smectite, and a low degree of thermal evolution of organic matter (see Table 2 for thermal maturity). They are mainly intra- and intergranular pores and a few organic matter pores (Yang et al., 2013).

Table 8.

Total PV and SSA for the typical marine shales and coal measured shales from Shanxi formation in the study area.

Basin or region	Age	Shale	Specific surface area (m²/g)^a	Total pore volume (10⁻³cm³/g)	Thermal maturity	Source
Fort Worth Basin	Mississippian	Barnett	13.75	38.64^b	1.45%	Clarkson et al. (2013)
East Texas	Late Jurassic	Haynesville	12.84	48.81^b	2.0%
Appalachian Basin	Devonian	Marcellus	2.3	15.59^b	1.2%
Anadarko Basin	Upper Devonian Lower Mississippian	Woodford	2.43–7.48 (4.955)^a	2.373–32.54^b (17.46)	1.4%
Sichuan Basin	Lower Silurian	Longmaxi Fm.	16.92–57.32 (31.97)	28.8–76.2^c (38)	1.9%–2.8%	Tian et al. (2015); Ji et al. (2020); Zhao et al. (2019)
		Qiongzhusi Fm.	13.18–21.59 (18.74)	19.2–24.1^c (22.1)	2.91%–3.86%	Tian et al. (2015); Ji et al. (2020); Zhao et al. (2019)
		Niutitang Fm.	9.4–33.9 (20.209)	15.4–51.1^c (33.43)	3.1%–4.0%	Yang et al. (2014); Wang et al. (2020)
Ordos Basin	Upper Triassic	Chang-7 of Yanchang Fm.	0.38–3.03 (1.5413)	1.215–7.495 (3.804)	0.5%–1.2%	Liu et al. (2015)
Huainan Coalfield	Lower Permian	Shanxi Fm.	5.82–14.45 (9.91)	22.246–34.035^c (27.475)	0.81%–1.02%	This study

Data in brackets are the average values of the corresponding parameters.

SSA and total PV are obtained using BET method and BJH method respectively.

Total PV is calculated to be the volume of liquid N₂ at a relative pressure of 0.98.

Relationships between the TOC and the SSA and PV

Figure 10 shows that there is unobvious correlation between total PV, SSA, and TOC of the shale samples. This result is contrary to the classical relationship of the previous conclusion (Ge et al., 2020), but similar to the results of other studies (Kuila et al., 2012; Zhu et al., 2014). Ju et al. (2018) found that the PV decreases with the increase of TOC of coal measure shales in Huainan coalfield and Longyan coalfield, and believed that the ductile deformation mechanism is the main factor leading to the evolution of the micropore structure (Ju et al., 2018). Yu et al. (2020) showed that TOC had a weak positive correlation with SSA and micropore volume, and a weak negative correlation with macropore volume (Yu et al., 2020), which indicated the complex relationship between the TOC of shale and PV and SSA.

Figure 10.

Relationships between SSA and TOC (a), quartz contents (b), clay minerals contents (c).

In fact, the occurrence pattern of organic matter in shale can be an important reason for this inconsistent relationship. Organic matter in shales includes non-porous organic matter, which is predominantly immature kerogen, thermally altered kerogen consisting of porous or “spongy” organic matter, and organic matter classified as “pendular” by Walls and Diaz (2012) (technically called bitumen) (Walls et al., 2012).

Although the abundance of organic matter in shale samples was high or moderate (Table 2), their SSA and PV were very low as in sample H-3 (Tables 6 and 7). This revealed potentially poorly developed organic nanopores in these shales. Therefore, the organic matter in these shale samples may be a non-porous form that lacks obvious contribution to the PV and SSA. Similar conclusions have been found for the Jurassic young shale in the Western Canadian sedimentary basin, where TOC is not significantly correlated with SSA (Ross and Bustin, 2009). In addition, In the Barnett, Woodford, Haynesville, Marcellus, and New Albany shales, no obvious correlation was found between PV, micropore volume, and TOC (Mastalerz et al., 2013). In fact, kerogen is highly heterogeneous. One kerogen particle is porous, while the dependent one may be non-porous (Curtis et al., 2012; Loucks et al., 2012). Using the rock thin section and FIB/SEM method, we found a few organic sporophytes and stripped organic matter from some samples in Figure 11(a) and (b). This suggests that organic nanopores may not be developed uniformly in the same sample. Therefore, shales from different sedimentary environments have different chemical compositions and thermal evolution degrees of organic matter, which have an important impact on pore structure characteristics.

Figure 11.

Displayed microscopic organic matter and intergranular pore of shale samples of Shanxi Formation (a) sporophore, d = 0.55 mm, plane-polarized light; (b) stripped organic matter; (c) mixed I/S with intergranular pore.

Relationships between the minerals and the SSA with PV

Figures 10 and 12 also show relationships between PV, SSA, and mineral composition of shale samples. There is no obvious correlation between PV and quartz content (Figure 10(b)) or clay mineral content (Figure 10(c)). In general, clay minerals can provide considerable SSA and PV for shale gas adsorption (Ma et al., 2020). As shown in Figure 12(a) and (b), there is no obvious or weak correlation between the clay mineral content of shale samples and PV, SSA. This may imply that the total amount of clay minerals has no significant effect on pore development if only considering the homogeneity. If the H-6 sample is removed, the other samples show some correlation. However, there are many types of clay minerals, and different clay mineral contents have different contributions to SSA and PV. The SSA of different clay minerals from small to large is illite, chlorite, kaolinite, I-S mixed layer, and montmorillonite (Ji et al., 2012). Comparison of clay minerals of Shanxi Formation shale samples in the study area is given in Table 4. These shales contain a large mixed of I/S, but a small number of kaolinite and a few of chlorite and illite. A large number of I/S mixed layers positively contributed to the SSA and PV of the samples due to the presence of fracture-like pores (Figure 11(c)). Similar results were obtained in previous studies (Kuila et al., 2012), which indicated that the I/S mixed layers, but not organic content, controlled the small-scale pore characteristics of the Niobrara Formation shale.

Figure 12.

Relationships between mixed-layer minerals of illite and smectite and total PV (a), SSA (b).

The above conclusion indicates that the factors affecting the pore characteristics of transitional shale tend to be more complex, and further attention should be paid to the characteristics of organic matter and mineral composition, such as TOC, mineral morphology and heterogeneity, and the content of organic components.

Conclusions

The average content of organic matter in shales in Shanxi is 2.68%, ranging from 0.37% to 8.87%, mainly composed of clay minerals and quartz.

As determined by the MICP method, all samples were dominated by pores with pore size below 30 nm and a very high percentage of SSA (above 95%). Information on pore size can be obtained by incremental Hg intrusion, which partly explains the difference with the results obtained by adsorption.

PSD, SSA, and PV with pore sizes ranging from 2 to 200 nm were obtained by N₂ adsorption analysis. The PSD defined by BJH method and PV increment were mainly bimodal and multimodal. The average SSA was 9.91 m²/g, ranging from 5.82 to 14.45 m²/g, and the average PV was 0.027 cm³/g, ranging from 0.022 to 0.034 cm³/g. The SSA and PV of micropores are 0–2.3712 m²/g and 0–0.0010 cm³/g, respectively.

There are unobvious correlation between total PV, SSA, and TOC of the shale samples. However, the content of the I/S mixed layer may contribute more to SSA and PV. Future studies should pay more attention to the characteristics of organic matter and the mineral composition itself.

Footnotes

Author contributions

Writing–original draft preparation was done by Y. J. and X.H.;conceptualization was done by X.H.;validation was done by D. H.,L.H.,and X.S.;resources by D. H.;formal analysis was carried out by F.H and D. H;writing–review and editing was done by X.H and Z.K.

ORCID iDs

Hongjie Xu

Huihu Liu

Data availability statement

The data used to support the findings of this study are included within the article.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research,authorship,and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research,authorship,and/or publication of this article: This research was funded by Institute of Energy,Hefei Comprehensive National Science Center,grant number 21KZS218,Anhui Provincial Natural Science Foundation,grant number 2108085MD134 and 2008085MD121,National Natural Science Foundation of China,grant number 42277483 and 42202200,the Public welfare geological work project of Anhui Province (Grant No. 2023-g-1-21,2021-g-2-14),Natural Science Research Project of Anhui Educational Committee,grant number KJ2020A0317,and Natural Science Foundation of Hubei Province,grant number 2020CFB2201.

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Pore structure characteristics of shale in coal-bearing strata: In the case of the Shanxi formation of lower Permian in Huainan coalfield in China

Abstract

Keywords

Introduction

Materials and methods

Shale samples

High-pressure mercury intrusion experiment

Low-pressure N2 adsorption experiment

FE-SEM observation and permeability

Petrology and organic geochemistry

Results

Organic matter and mineral composition

Mercury intrusion porosimetry

N2 adsorption–desorption isotherm

SSA and PV

Pore size distribution

Discussion

Comparison of HG intrusion and N2 adsorption

Pore structure parameters characteristics

Relationships between the TOC and the SSA and PV

Relationships between the minerals and the SSA with PV

Conclusions

Footnotes

Author contributions

ORCID iDs

Data availability statement

Declaration of conflicting interests

Funding

References

Low-pressure N₂ adsorption experiment

N₂ adsorption–desorption isotherm

Comparison of HG intrusion and N₂ adsorption