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Article

Characterisation of the Full Pore Size Distribution of and Factors Influencing Deep Coal Reservoirs: A Case Study of the Benxi Formation in the Daning–Jixian Block at the Southeastern Margin of the Ordos Basin

by
Xiaoming Chen
1,
Tao Wang
2,3,*,
Song Wu
4,*,
Ze Deng
3,*,
Julu Li
1,
Zhicheng Ren
1,
Daojun Huang
5,
Wentian Fan
6 and
Gengen Zhu
7
1
School of Resource and Environment, Yangtze University, Wuhan 430100, China
2
Institute of Unconventional Oil and Gas Development, Chongqing University of Science and Technology, Chongqing 401331, China
3
PetroChina Research Institute of Petroleum Exploration & Development, Beijing 100083, China
4
Guizhou Wujiang Energy Investment Co., Ltd., Guiyang 550081, China
5
Research Institute of Exploration and Development, Changqing Oilfield Company of PetroChina, Xi’an 710021, China
6
College of Geosciences, China University of Petroleum (Beijing), Beijing 102249, China
7
School of Geosciences, China University of Petroleum (East China), Qingdao 266000, China
*
Authors to whom correspondence should be addressed.
Processes 2024, 12(11), 2364; https://doi.org/10.3390/pr12112364
Submission received: 14 September 2024 / Revised: 21 October 2024 / Accepted: 23 October 2024 / Published: 28 October 2024
(This article belongs to the Special Issue Theoretical Progress in the Exploration and Development of Deep Coal-Measure Gas)
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Abstract

:
The complex geological environment in deep layers results in differences in the pore and fracture structures and states of coalbed methane (CBM) occurrences between deep and shallow coal reservoirs. The coexistence of multiphase gases endows deep CBM with both “conventional” and “unconventional” geological attributes. Based on systematically collected coal samples from the Benxi Formation in the Daning–Jixian area of the Ordos Basin, high-pressure mercury intrusion (HPMI), low-temperature N2 adsorption (LTN2A), and low-pressure CO2 adsorption (LPCO2A) experiments were conducted to characterise the pore structures across the full pore size distribution of the Benxi Formation coals. The aim of this research is to gain an in-depth understanding of the pore size distribution of full-size pores and to explore the factors influencing their pore structure and control over the gas content in coal reservoirs. The results indicate that the pore size distribution of the coal samples from the Benxi Formation in the study area is unimodal and that nanopores are present. The pore sizes are relatively small, with an average total pore volume (PV) of 0.073 cm3/g and an average total specific surface area (SSA) of 227.87 m2/g. Among these, micropores account for 92.26% of the total PV and 99.57% of the total SSA, making micropores the primary contributors to the gas storage space in the Benxi Formation coals. Mesopores and macropores contribute relatively little to the PV and SSA, which is unfavourable for CBM permeability. The development of pores in the Benxi Formation coals in the study area is influenced by the coal maturity, vitrinite content, and ash yield. Generally, the PV increases when the coal’s rank increases; an increase in the vitrinite content promotes the development of micropores, whereas a relatively high ash yield leads to decreases in the PV and SSA. The influence of the SSAs of coal pores on the gas content is reflected mainly by its effect on the adsorbed gas content. Since adsorbed gas molecules exist mainly in coal pores in the adsorbed state, the SSAs of coal pores strongly affect the storage capacity of coal for adsorbed gas.

1. Introduction

Coalbed methane (CBM) in moderately to shallowly buried strata in China is characterised by low single-well production rates and a poor development efficiency. Except for a few blocks, such as the Panzhuang block in the Qinshui Basin and the Baode block in the Ordos Basin, the production rates generally remain at 50% to 60% or lower [1,2]. Among these rates, the proportion of low-yield, low-efficiency wells with production rates less than 500 m3/d in the eastern margin of the Ordos Basin is large (56%), making profit development challenging [3]. China’s CBM resources at depths greater than 2000 m are estimated to be approximately 40 × 1012 m3, which is roughly equivalent to conventional natural gas resources. CBM’s exploration and development have broad prospects, but systematic research and large-scale exploration and development have yet to be carried out [4,5,6]. Since 2019, the PetroChina Coalbed Methane Company has conducted systematic research and achieved technological breakthroughs in the Ordos Basin, which is the most resource-rich area for deep CBM in China. The company has achieved breakthroughs when exploring for deep CBM at burial depths greater than 2000 m in the eastern margin of the basin, specifically in the Danning-Jixian block. In the Jishen 6–7 Ping 01 well, upon production, an industrial gas flow of 10 × 104 m3/d was achieved [7,8,9]. In 2021, a geological reserve of 112.2 billion cubic metres of CBM was reported in the Danning-Jixian CBM field. This field has become a large-scale, high-abundance CBM reservoir, with coal seams buried deeper than 2000 m and reserves exceeding 100 billion cubic metres, marking a significant breakthrough in the exploration and development of CBM reserves deeper than 1500 m in China [10,11]. Deep CBM exhibits favourable reservoir conditions, such as a large storage volume, intact coal structure, high gas content, abundant free gas, and weak hydrodynamic conditions. Owing to its high gas saturation, reservoir pressure, and critical desorption pressure, deep CBM typically shows the characteristics of “rapid gas appearance and high initial production” [12,13,14,15]. However, deep coal seams are situated in a “triply stacked” stratigraphic environment that is characterised by high levels of tectonic stress, high formation temperatures, and high reservoir pressures. Additionally, the high degree of the thermal evolution of coal and the effects of coupled comprehensive geological conditions significantly impact the permeability, porosity, fracturing structure, and gas-forming phase of coal [16]. The compositional characteristics of the gas-forming phases in deep coal seams enhance the gas storage capacity and increase the driving energy for CBM production, which is a crucial geological reason for the “high production immediately upon production” of deep CBM wells [17,18,19,20]. This property has significantly challenged the industry’s long-standing understanding of the unconventional properties of CBM and has highlighted the need for in-depth explorations of the conventional geological properties of deep CBM.
Currently, the technical methods used for characterising the pore and fracture structures of coal reservoirs can be divided into image analysis, fluid intrusion, and radiation detection methods [18,19,20,21,22,23]. In image analysis methods, techniques such as scanning electron microscopy (SEM), atomic force microscopy (AFM), and CT imaging can be used to directly observe the pore structure, morphology, and connectivity of coal samples and analyse the mechanisms of pore and fracture formation. However, the obtained pore distribution data often lack statistical representativeness, making quantitative characterisations difficult [18]. Regarding fluid intrusion, commonly used techniques include high-pressure mercury intrusion (HPMI), low-temperature N2 adsorption (LTN2A), and low-pressure CO2 adsorption (LPCO2A) experiments. HPMI is often used to characterise the pore structures of larger pores with average pore diameters greater than 50 nm. LTN2A is typically employed to characterise the pore structure features of micropores ranging from 1.5 to 50 nm. LPCO2A primarily measures the characteristics of the distribution of micropores with diameters less than 2 nm [19,20]. Compared with LTN2A experiments, LPCO2A methods differ significantly in calculating the specific surface area (SSA) and pore volume (PV), but in both methods, pore sizes are measured with continuity. Fluid intrusion methods provide precise physical characterisations at the nanometre scale but also have limitations. For example, in HPMI experiments, high pressures and the corrosive nature of mercury can damage coal samples and alter the characteristics of the distribution of internal pores [22,23]. In radiation detection methods, the working mechanism uses instruments that emit high-energy electromagnetic field signals and feedback signals are then received through receiving instruments to analyse and predict the three-dimensional structural characteristics of coal fractures to determine physical parameters such as the pore size distribution, porosity, and permeability. Compared with the more researched fluid invasion methods used by earlier researchers, the radiation detection method has advantages such as the nondestructive testing of samples [21,22,23,24]. Domestic and foreign scholars have conducted a large amount of research on the nanopores of coal and rock [24,25,26,27,28,29]. Hainbuchner et al. (2004) and Ju et al. (2005) studied the microdeformation process, pore distribution, and surface characteristics of coal micropores in metamorphic environments using methods such as small-angle neutron scattering and small-angle X-ray scattering [24,25]. Fan et al. (2013) studied the micropore structure of coal rock under different coal reservoir conditions using experimental methods such as low-temperature nitrogen adsorption [26]. Jiang et al. (2015) analysed the influence of pore characteristics in coal reservoirs on multicoalbed methane extraction [27]. Gao et al. (2017) discussed the relationships between the fractal geometric parameters and the porosity and permeability of coal reservoirs [28]. Lu et al. (2017) used sedimentology, the microscopic composition of coal, and stratigraphy to construct a new depositional model to reveal the microscopic characteristics of coal [29]. Wei et al. (2019) reported that the Shihexi Formation coal in the Pianji Deep Area, Huainan Coalfield has abundant mesopores and macropores, with micropores (<2 nm) providing most of the specific surface area, whereas the pore volume is mainly composed of mesopores and macropores [10].
The eastern margin of the Ordos Basin is an important area for CBM development in China. However, the geological conditions that are required for CBM, especially in coal reservoirs, are relatively complex. Early CBM exploration and development focused primarily on strata at medium-to-shallow depths (<1000 m) [9,10]. Deep coal seams exhibit significant differences from medium-to-shallow coal seams in terms of their reservoir properties and pore characteristics [30,31,32]. Notably, there are clear differences in the pore size distributions between shallow and deep coal samples. Shallow coal samples have well-developed pores of various sizes, whereas deep coal samples predominantly feature small pores (with pore diameters ranging from 10 to 100 nm) [33,34,35,36]. Systematic research on fractures, matrix properties, pore characteristics, and their formation mechanisms in deep coal reservoirs is lacking, which restricts the understanding of the characteristics of deep CBM in the eastern margin of the Ordos Basin and the optimization of reservoir modification processes. In this work, coal rock samples from the Benxi Formation in the Danning-Jixian block are investigated. By comparing the effectiveness of different testing methods for determining coal rock porosities, in this study, LPCO2A, LTN2A, and HPMI experiments are utilized to obtain the full pore size distribution characteristics of coal samples from the Benxi Formation, in which the pores range in size from nanometres to micrometres. Additionally, in this study, measurements of the vitrinite reflectance (Ro,max) and maceral composition are included, and a proximate analysis of the samples is conducted. The effects of coal maturity, vitrinite content, and ash yield on pore development at different scales are studied. In this research, the aim is to explore the pore structure characteristics of the Benxi Formation in the Danning-Jixian block and their impact on the gas contents, with the goal of providing insights and guidance for CBM exploration and development at the southeastern margin of the Ordos Basin.

2. Geological Setting

The Danning-Jixian block is located at the southern end of the Jinxian fault–fold zone at the eastern margin of the Ordos Basin, southeast of the Yishan slope zone. The block borders the Shilou West block to the north and the Yanchuan South block to the south. The block exhibits distinct east-west structural zoning, which, from east to west, is divided into the eastern slope zone, Taoyuan anticline zone, central steep slope zone, and western buffer zone. The faults are underdeveloped, the structure is not highly pronounced, and the dip angles of the strata are generally less than 2° [8,9,37]. The deep No. 8 coal seam in the study area is located at the base of the Taiyuan Formation, with burial depths ranging primarily between 2000 and 2400 m (Figure 1). It is currently the main target for deep CBM exploration and development in the region [9,37].
The deep No. 8 coal seam in the study area is widely distributed, with thicknesses ranging from three to twelve m and an average thickness of approximately six m (Figure 1). The central part of the seam features one to two layers of interbedded mudstone. The roof of the coal seam consists of thick limestone from the Taiyuan Formation, whereas the floor is composed of thick mudstone. During the late Carboniferous to early Permian, the sedimentary environment in the study area evolved from a barrier coast to a delta [9,36]. During the late Carboniferous, the subsidence of the North China Sea was minimal. In the study area, marine transgression occurred from east to west, and as a result, the barrier coastal facies of the Benxi Formation developed. During one stage of sedimentation of the Benxi Formation, the sea level gradually decreased, and the depositional water body progressively shallowed, resulting in highstand system tract sedimentation overall. The Taiyuan Formation consists mainly of carbonate platform facies and lagoonal subfacies. At the beginning of sedimentation, the regional sea level continued to drop, balancing the rate of growth in the accommodation space with the sediment supply, which connected lagoonal peatlands with tidal flat peatlands and formed the large-scale, continuous, and thick No. 8 coal seam. The thick coal region is predominantly distributed in the northeastern part of the study area and extends in a band-like manner (Figure 1) [37].

3. Sample Collection and Methods

3.1. Samples

In this study, 12 samples were collected from the Benxi Formation coal seam in the Danning-Jixian block at eastern margin of the Ordos Basin. A composite columnar diagram of the sampled strata is shown in Figure 1. Initially, the samples were examined for vitrinite reflectance, maceral composition, and proximates. The full pore size distribution of the coal samples was characterised via LPCO2A, LTN2A, and HPMI experiments.

3.2. Experimental Methods

The bulk coal samples were crushed and screened, and the following sample types were used for various tests: block samples for HPMI; 60–80-mesh samples for gas adsorption, vitrinite reflectance, and maceral composition analyses; and 200-mesh samples for proximate analysis. Vitrinite reflectance, maceral composition, and proximate analyses were conducted according to national standards (GB/T 6948-2008 and GB/T212-2008) [38,39]. Gas adsorption measurements were performed via an Autosorb iQ-MP automated gas adsorption analyser following national standards (GB/T 21650.2-2008 and GB/T 21650.3-2011) [40,41]. The Brunauer–Emmett–Teller (BET) and nonlocal density functional theory (NLDFT) models were used to analyse and calculate the measured data [42,43,44]. Specifically, NLDFT models were applied to the LPCO2A data to analyse the pore distribution information between 0.3 and 1.5 nm, whereas NLDFT models were used to analyse the pore distribution information between 1.06 and 78 nm for the LTN2A data. HPMI was conducted following the (GB/T 21650.1-2008) national standard [45] using the PoreMaster mercury intrusion apparatus from Quantachrome Instruments (Atlanta, USA). The highest test pressure was 413 MPa, and the pore sizes were calculated via the Washburn equation [46]. The range for measuring pore sizes spans from 3.6 nm to 960 μm. The CH4 adsorption isothermal experiment was conducted with a gravimetric Rig 3 isothermal adsorption instrument following the GB/T 19560-2008 standard [47]. The samples were crushed and sieved to a size of 60–80 mesh, after which moisture-equilibrium treatment was performed for at least seven days. The experimental temperature was 70 °C, and the maximum pressure reached approximately 25 MPa. The experimental results were fitted via the Langmuir equation; thus, the Langmuir volume (VL) and Langmuir pressure (PL) were obtained. The specific expression is as follows:
V = V L × P P L + P
where V represents the methane gas adsorption capacity, cm3/g; P represents the methane gas pressure, MPa; V L represents the Langmuir volume, cm3/g; and P L represents the Langmuir pressure, MPa.

4. Results

4.1. Basic Properties of the Coal Samples

Studies show that Ro rises with an increase in the level of coalification. In the study area, the Ro,max values for coal samples from the Benxi Formation vary between 1.82% and 3.19% (Table 1), which classifies them as high-rank coals. The maceral composition is predominantly vitrinite (44.06% to 86.00%), followed by inertinite (10.00% to 41.34%). The amounts of liptinite and mineral components are relatively low, with the mineral components consisting mainly of clay minerals, silica, carbonates, and iron sulfides. The moisture content (Mad) values of the Benxi Formation coal range from 0.70% to 2.13%, with an average of 1.22%. The ash yield (Ad) values ranged from 5.50% to 33.87% (with a mean of 15.71%). The volatile matter yield (Vdaf) values range from 6.29% to 12.92% (with a mean of 8.62%), indicating that the coal has a relatively low volatile matter content [48]. The fixed carbon (FCad) contents range from 57.48% to 87.45% (with a mean of 76.24%), indicating an exceptionally high fixed carbon content in the coal (Table 1). In summary, the coal from the Benxi Formation in the study area is characterised by very low moisture (M), low ash (A), very low volatile matter (V), and high fixed carbon (FC) contents.

4.2. HPMI Experiment

The curves for mercury intrusion and extrusion in coal effectively illustrate the characteristics of its pore structure. Different curve types represent different pore structures. The mercury intrusion and extrusion curves of the coal samples in the study area resemble a double-arc shape [48], with both the intrusion and extrusion curves exhibiting concave arc shapes (Figure 2). Based on the mercury intrusion and extrusion curves, the volume of intruding mercury changes only slightly when the intrusion pressure is below 30 MPa, and the amount of mercury intrusion changes very gradually. This stage of mercury intrusion is related to certain macropores and microcracks [48]. When the mercury intrusion pressure exceeds 30 MPa, the amount of mercury intrusion increases significantly, resulting in a rapid increase. This stage of mercury intrusion is associated with mesopores and some macropores. Especially at the maximum intrusion pressure of 413 MPa, the amount of mercury intrusion continues to increase, indicating the possible presence of pores in the coal samples with diameters less than 3 nm, which can be characterised via gas adsorption methods. Based on the degree of closure of the intrusion and extrusion curves, most of the coal samples from the Benxi Formation in the study area have relatively small pores, with some samples showing nearly overlapping intrusion and extrusion curves (Figure 2), suggesting that the pores in the Benxi Formation coal samples in the study area are mostly open or have well-developed microfractures.
The PV and SSA values for the coal samples from the Benxi Formation in the study area, along with the pore size distribution curves, are shown in Figure 3 and Figure 4. The trends within different pore size segments exhibit distinct differences. For pore sizes greater than 30 nm, the increases in the PVs and SSAs are relatively minor, indicating fewer macropores. When the pore sizes are below 30 nm, there are notable transformations in the PVs and SSAs, with multiple peaks observed. This indicates a sharp increase in the quantity of mesopores. Nanoscale pores are developed primarily within the coal samples from the study area [49,50]. The HPMI results are summarized in Table 2, in which the pore structure parameters (PV and SSA) and pore size distribution curves in the range of 3 nm to 10 µm are calculated on the basis of the Washburn equation via HPMI data (Table 2) [46]. The PVs of the Benxi Formation coal samples in the study area range from 0.018 to 0.059 cm3/g (with a mean of 0.035 cm3/g). The SSAs of the pores in the coal samples of the Benxi Formation range from 10.09 to 32.37 m2/g (with a mean of 18.67 m2/g). Among them, the coal sample from well DJ57 has a larger PV and SSA, and the coal sample from well P20 has a smaller PV and SSA, which means that the coal sample has a lower degree of metamorphism and more developed macropores.

4.3. LTN2A Experiment

Owing to the limitations of the measurement range, HPMI cannot be used to characterise the mesopore structure of coal samples accurately. Therefore, in this study, experiments involving LTN2A are employed to assess the characteristics of mesopore development within the coal samples. The low-temperature N2 adsorption-desorption curves for the Benxi Formation coal samples from the Danning-Jixian block are shown in Figure 5. According to the International Union of Pure and Applied Chemistry (IUPAC) classification [14], in the study area, the coal samples are predominantly classed as Type IV. When the relative pressure p/p0 < 0.1, the adsorption curve increases sharply, indicating strong adsorption of N2 by these coal samples. Initially, as the pressure increases, the adsorption curve increases slowly, suggesting that the process in the sample transitions from monolayer adsorption to multilayer adsorption. When p/p0 approaches one, capillary condensation effects cause the curve to rise sharply. The sample does not reach adsorption saturation or equilibrium, indicating the presence of larger pores in the coal samples. For the desorption curve, when p/p0 = 0.5, the curve lies above the adsorption branch. Additionally, when p/p0 ≈ 0.5, the curve rapidly decreases, and after this point, the adsorption and desorption curves nearly coincide.
The characteristics of the hysteresis loop can offer valuable insights into the pore structure [51,52,53]. The adsorption aggregation theory posits that varying relative pressures within the same pore can result in the emergence of a hysteresis loop between the branches of adsorption and desorption. This is primarily caused by the complex combination of different pore geometries [19]. According to the IUPAC classification of hysteresis loops [14], the hysteresis loops of the coal samples in the study area are primarily Type H3. When the relative pressure is low (p/p0 < 0.5), the adsorption and desorption curves essentially overlap, indicating that the pore types are mainly cylindrical, conical, and slit-shaped. When p/p0 is high, there is a slight hysteresis loop, suggesting the presence of open pores.
Based on the BET method and the NLDFT model, the pore structure parameters and pore size distribution curves of the coal samples within the pore size range of 1.06 to 77.7 nm were obtained from the LTN2A data (Table 2) [42,43]. In the study area, the BET surface areas of the coal samples from the Benxi Formation range from 0.29 to 22.10 m2/g (with a mean of 1.03 m2/g). The DFT PV values range from 0.001 to 0.004 cm3/g (with a mean of 0.003 cm3/g). The average pore diameters range from 1.22 to 7.032 nm (with a mean of 5.373 nm). Among these samples, the coal sample from the DJ57 well has a larger PV and SSA and a smaller pore diameter than the other coal samples. This indicates that the coal sample has a lower degree of metamorphism, with a greater development of mesopores, resulting in the highest SSA.
Figure 6 shows that the range of PVs for the coal samples in the study area exhibits a distinct multipeak feature with respect to the pore diameters, with the main peak regions primarily distributed in the pore diameter ranges of 1.06–1.5 nm and 5–10 nm, with the highest peak located in the 5–10 nm pore diameter range. When the pore diameters exceed 10 nm, the range of PVs is small. Figure 7 presents the range of pore SSAs of the coal samples in the study area as a function of the pore diameter. Like the distribution of PVs, some coal samples also present distinct multipeak features in the distribution of pore SSAs, with the main peak regions occurring primarily in the 1.06–1.5 nm and 5–10 nm pore diameter ranges. The coal sample from the G11 well exhibited a unimodal distribution, with overall pore diameters ranging between 5 nm and 10 nm and a main peak at approximately 6 nm.

4.4. LPCO2A Experiment

Owing to the high energy and rapid equilibration of CO2 molecules at 273 K, CO2 can enter smaller pores, particularly those exhibiting diameters smaller than two nm, which provide a significant portion of the storage space for the absorbed gas. As shown in Figure 8, the LPCO2A isothermal adsorption curves of the Benxi Formation coal samples in the study area are highly similar to the Type I isotherms in the IUPAC classification [14]. When the relative pressure reaches its maximum value, the CO2 adsorption isotherm does not plateau. This is primarily because the Benxi Formation coal samples in the study area generally contain many micropores, which may have similar pore size distribution characteristics. At relative pressures (p/p0) below 0.01, the rapid adsorption of CO2 molecules on the micropore surfaces leads to a swift elevation in the adsorption capacity. As the relative pressure continues to increase, the rates of increase in adsorption and total adsorption gradually decrease; this indicates that the micropores in the coal samples are filled with CO2 molecules, and when the test pressure approaches atmospheric pressure, the adsorption capacity stabilizes, corresponding to the saturation adsorption volume being equal to the filling volume of the micropores. The adsorption capacities of different coal samples vary due to their different maturities. For example, the CO2 adsorption capacity of the coal sample from the P20 well is significantly greater than that of the samples from the DJ57, DJ63, and G11 wells, suggesting that the number and degree of development of micropores in the coal samples in the study area may be related to the degree of metamorphism.
In the NLDFT model, the pore size distribution curves and pore structure parameters of the coal samples in the pore size range of 0.3 to 1.5 nm were obtained from LPCO2A data (Table 2) [42,43]. The DFT SSA values range from 143.64 to 275.76 m2/g (with a mean of 226.94 m2/g), which are significantly greater than the results from the LTN2A analysis. This suggests that the coal samples possess a larger fraction of micropores. The DFT PV values derived from the CO2 adsorption isotherms range from 0.042 to 0.081 cm3/g (with a mean of 0.067 cm3/g). The average pore diameters range from 0.501 to 0.524 nm (with a mean of 0.513 nm). Based on the micropore pore size distributions obtained from CO2 isothermal adsorption measurements (Figure 9 and Figure 10), the micropores in the samples generally exhibit distributions with multiple peaks, with peaks at approximately 0.3–0.4 nm, 0.4–0.7 nm, and 0.7–0.9 nm; this suggests a broad range of micropore distributions in the coal samples, with pore diameters of 0.36 nm, 0.52 nm, and 0.82 nm corresponding to the main peaks.

4.5. CH4 Adsorption Isotherms

Isothermal adsorption experiments involving high-pressure methane were performed on coal samples from the Benxi Formation in the study area, utilizing the gravimetric method. The methane adsorption amounts at different pressure points were fitted and analysed via the Langmuir equation, resulting in methane isothermal adsorption curves and Langmuir parameters (VL and PL). The experimental results are shown in Figure 11 and Table 3. The VL values (equilibrium water-based parameters) of the Benxi Formation coal samples in the study area range from 18.44 to 32.28 cm3/g (with a mean of 25.72 cm3/g). The PL values range from 2.66 to 3.79 MPa (with a mean of 3.22 MPa). The average VL values of the coal samples from wells P20 and DJ57 are 28.46 cm3/g and 23.22 cm3/g, respectively. With increasing Ro,max values, the adsorption capacities of the Benxi Formation coal samples increase. These findings indicate that the Benxi Formation coal samples in the study area have an elevated VL, which is favourable for methane adsorption, and a low PL, which is unfavourable for methane storage. Coal has many adsorption sites on the internal surfaces of its pores and has a strong adsorption affinity for CH4 molecules [28,54,55,56,57]. Even at low gas pressures (<15 MPa), the methane adsorption capacity of coal increases rapidly, resulting in a steep isothermal adsorption curve. As adsorption progresses, the number of remaining adsorption sites in coal decreases continuously, leading to a relatively slow increase in methane adsorption. At higher pressures (>15 MPa), the isothermal adsorption curve increases more gradually.

5. Discussion

5.1. Full-Scale Pore Structure Characterisation

On the basis of the combined characterisation of the full-scale pore structure features of the Benxi Formation coal samples from the Danning-Jixian block via HPMI, LTN2A, and LPCO2A experiments, different testing methods and calculation principles led to varying degrees of reliability for different pore size ranges, with the overlapping pore size ranges determined via measurements [20,58,59]. In this study, the best measurement ranges for each technique were selected for an overlay analysis (Figure 12): the pores in the 0.3–1.5 nm pore size range were characterised via LPCO2A data, those in the 1.5–50 nm pore size range were characterised via LTN2A data, and those in the >50 nm pore size range were characterised via HPMI data. This approach provides a comprehensive pore size distribution profile of the Benxi Formation coal (Figure 13 and Figure 14).
According to the results of the combined characterisation of the PV distribution with the pore diameters for the Benxi Formation coal samples from the study area (Figure 13), the PV distribution type is predominantly unimodal with micropore dominance. The pore size distribution shows the micropores are unimodal and are primarily concentrated within the span of 0.3 to 1.5 nm. According to Table 4 and Figure 15a, the overall PVs of the Benxi Formation coal samples in the study area range from 0.046 to 0.092 cm3/g, with an average of 0.073 cm3/g. Micropores contribute most to the PV, followed by mesopores and macropores, which contribute less. Micropores primarily occupy a pore size range of 0.3 to 0.7 nm, with micropore PVs ranging from 0.042 to 0.081 cm3/g (with an average of 0.068 cm3/g), accounting for 88.94% to 96.71% of the total PV (with an average of 92.46%). Mesopores and macropores follow, with mesopores in the 5 to 10 nm pore size range and with volumes ranging from 0.001 to 0.004 cm3/g (with an average of 0.002 cm3/g), which constitute 1.23% to 7.53% of the total PV (with an average of 3.48%). Macropores, which are primarily composed of pores larger than one μm, have volumes ranging from 0.001 to 0.009 cm3/g (with an average of 0.003 cm3/g), accounting for 1.16% to 9.83% of the total PV (with an average of 4.06%).
According to the results of the combined characterisation of the distribution of pore SSAs according to pore diameters for the Benxi Formation coal samples in the study area (Figure 14), the distribution of pore SSAs is predominantly unimodal and the micropores are primarily concentrated within the span of 0.3 to 1.5 nm. According to Table 5 and Figure 15b, the total pore SSAs of the Benxi coal samples in the study area range from 145.02 to 276.20 m2/g, with an average of 227.87 m2/g. Micropores contribute the most to the SSA, with minimal contributions from mesopores and macropores. Specifically, the micropore SSAs range from 143.66 to 275.76 m2/g (with an average of 226.95 m2/g), accounting for 99.07% to 99.84% of the total pore SSA (with an average of 99.57%). The mesopore SSAs range from 0.38 to 1.34 m2/g (with an average of 0.86 m2/g), whereas the macropore SSAs range from 0.01 to 0.15 m2/g (with an average of 0.06 m2/g), with mesopores and macropores contributing to less than 1% of the total pore SSA. Based on this analysis, it is concluded that micropores make the largest contributions to the PV and SSA in the study area. This finding indicates that the primary sites for methane adsorption and storage in coal are numerous high-surface-area micropores, which also serve as the initial pathways for gas migration and diffusion after desorption [17,60]. Therefore, further investigation into their development and structural characteristics is essential. The combined characterisation of the nanopore structures in coal reservoirs via LTN2A, LPCO2A, and HPMI has special significance for micropore analyses.

5.2. Factors Influencing Pore Structure Development

Researchers have studied the factors affecting the pore structure of coal and rock [17,20,31,61,62,63,64], and most of them maintain that coalification level, maceral composition, and tectonic deformation are the primary elements that influence the PV and SSA. In addition, the combined effects of temperature and stress play a pivotal role. With increasing temperature, the aromatization of coal gradually increases, and the side chains are gradually reduced and shortened, which promotes the development of pores in coal [17,54].

5.2.1. Coalification Effects on the Pore Structure

The degree of coalification is an essential factor in determining the characteristics of pore development within coal. As shown in Figure 16, there is a positive association between Ro,max and both the total PV and total SSA (Figure 16a). Specifically, as Ro,max increases, the PVs and SSAs in the coal samples also increase correspondingly. This phenomenon indicates that the degree of metamorphism has a significant effect on the development of pores in the coal seams of the study area. Further observations reveal that the influence of metamorphism on pore development varies with pore size. The micropore PVs and SSAs are strongly positively correlated with Ro,max (Figure 16b). In contrast, the mesopore PVs and SSAs exhibit somewhat weaker negative correlations with Ro,max (Figure 16c), suggesting that mesopore development is less affected by the degree of metamorphism. This is because mesopore formation is influenced primarily by the initial conditions and reaction processes, with the degree of metamorphism having a relatively minor effect on these factors. The macropore PVs are weakly positively correlated with Ro,max (Figure 16d), indicating that the degree of metamorphism has some impact on the development of macropores but does not have a dominant influence. Macropore development is primarily controlled by other factors [20,65]. As the degree of metamorphism increases, the organic matter content in pores reaches its maximum, while its content in micropores also increases. Owing to mechanical compaction, the pore diameters of organic matter pores decrease. In the high-rank coal stage, with further increases in metamorphism, the chemical structure of the coal molecules becomes significantly more aromatic under the predominant influence of temperature, and directional alignment occurs, leading to the formation of a series of micropores and mesopores. Moreover, compaction and mechanical damage result in a continuous reduction in the number of macropores. This results in high-rank coal being predominantly microporous, with an extremely low macropore content, and the pore types are mainly secondary gas pores and large molecular structure pores, with underdeveloped primary pores.

5.2.2. Maceral Composition and Pore Structure

The coal samples from the Benxi Formation in the study area primarily contain organic maceral components, including vitrinite and inertinite. Therefore, the vitrinite content has a crucial effect on the characteristics of pore development in coal samples [20].
In the study area, the coal samples from the Benxi Formation display a high vitrinite content, ranging from 44.06% to 86.00%, with an average of 69.92% (Table 1). As a result, vitrinite is identified as the predominant maceral component. This section focuses on discussing the impact of vitrinite on the pore structure parameters of coal samples. As shown in Figure 17, the vitrinite content is significantly positively correlated with both the total PV and total SSA (Figure 17a); it also shows a clear positive correlation with the micropore PVs and SSAs (Figure 17b). However, it has a certain negative correlation with the mesopore PVs and SSAs (Figure 17c) and has no significant relationship with the macropore PVs and SSAs (Figure 17d). The vitrinite content and inertinite content tend to increase and decrease, respectively, with opposite impacts on pore development between the two groups (Figure 18). These findings indicate that vitrinite notably contributes to the micropore PVs and SSAs of the coal samples. As the vitrinite content increases, the mesopore PVs and SSAs in the coal samples tend to decrease. This is primarily because during coalification, many organic matter pore cavities are destroyed, leading to the formation of organic gas pores. We suggest that, as a result of this study, the microscopic coal maceral components determine the characteristics of the pore size distribution and types of pore development in coal [30,66,67]. Vitrinite is the primary carrier of primary pores, metamorphic pores, and microscopic endogenous fissures, followed by liptinite, which mainly develops into plant tissue pores. In contrast, the inertinite group exhibits minimal pore development. Consequently, the PV generally increases with increasing structural vitrinite content and decreases with increasing inertinite and mineral contents [17,67]. The differences in pores and fissures among microscopic coal maceral components are related to the drainage and gas expulsion processes that occur during coalification. During coalification, vitrinite experiences significant drainage, leading to a higher fluid pressure within its developed pores and a greater tendency for fissure formation. In contrast, inertinite and liptinite undergo less drainage and are less likely to develop fissures. Consequently, vitrinite-rich coal bands exhibit dense fissure development, whereas dark coals have fewer fissures. It is generally believed that a greater hydrocarbon generation potential is more favourable for the development of metamorphic pores. Vitrinite, which has the highest content in coal, combined with its high thermoplasticity and brittleness, generates a relatively large amount of gas, resulting in the development of metamorphic pores [17,20,67].

5.2.3. Proximate Parameters for Determining the Pore Structure

The ash content (Ad) represents the residue remaining after the complete combustion of coal; this ash primarily originates from inorganic minerals within the coal. The ash yield can partially reflect the content of inorganic minerals, which often exist in coal reservoirs as cementing materials or fillers. Therefore, the ash yield serves as an indirect indicator of the influence of inorganic minerals on the pore structure and heterogeneity of coal reservoirs. Figure 19 illustrates the relationship between ash yield and various pore parameters at different scales in the coal samples from the study area. The ash yield shows a significant negative correlation with both the total PV and total SSA (Figure 19a). It also displays a significant negative correlation with the micropore PVs and SSAs (Figure 19b). There is a certain negative correlation with the mesopore PVs and SSAs (Figure 19c), whereas there is no significant correlation with the macropore PVs and SSAs (Figure 19d). Since ash is a derivative of minerals in coal and is formed through complex reactions, such as decomposition and combination, the effect of the ash yield on the pore structure of coal samples is consistent with the influence of minerals [68]. The main reason is that the minerals in the Benxi Formation coal rocks in the study area have a significant effect on pore development. This impact is reflected in two main aspects [24,66]. First, the minerals in coal contain numerous mineral pores, primarily dissolution pores in carbonate minerals and intercrystalline pores in clay minerals and carbonates. The formation of differential shrinkage pores is closely linked to minerals, highlighting the variations in thermoplastic and mechanical properties between minerals and organic matter, which in turn influence the morphology of these pores. Second, many minerals fill pores or fractures, even forming fracture veins, which affect the permeability of pores and fractures. The minerals that fill these pores and fractures are predominantly carbonate minerals, such as calcite, pyrite, and clay minerals.

5.3. The Influence of Different Scales of Pore Structures on the Gas Content

The pore structure of coal is closely related to the presence of CBM. Coal rocks with the same PVs or SSAs may have completely different degrees of pore complexity. The complexity of pore development and heterogeneity inevitably affect the methane adsorption capacity within pores. The gas content of coal mainly consists of adsorbed gas and free gas. The porosity of a coal sample significantly affects both the amount of adsorbed gas and the amount of free gas. Higher porosities in coal generally result in higher total gas contents [22,23]. In addition, the effect of coal rock porosity–SSA on the gas content is reflected mainly in its impact on adsorbed gas. Since adsorbed gas molecules are stored primarily in the pores of coal in an adsorbed state, the SSA of pores in coal significantly influences their capacity to store adsorbed gas. As shown in Figure 20, the VL has a good positive correlation with both the total SSA and micropore SSA but shows no significant correlation with the macropore SSA. The PV of micropores also affects the amount of adsorbed gas. In the research area, the total PV and micropore PV of the Benxi Formation coal are positively correlated with the adsorbed gas amount, whereas the correlation between the macropore PV and adsorbed gas amount is relatively poor (Figure 20). The influence of the PV in coal on the gas content is also reflected in the fact that the size of the PV directly controls the amount of free gas. This is primarily because free gas molecules accumulate in the pores, which serve as their main storage locations. Therefore, as the PV of the coal rock increases, the amount of stored free gas also increases. The PL value reflects the ease of CH4 gas desorption, with a higher value indicating that gas is more easily desorbed when the pore pressure decreases [22,51]. Figure 21 shows that as the micropores, macropores, total PV, and SSA increase, the PL of the Benxi Formation coal in the study area decreases. Conversely, as the volumes of micropores and macropores decrease, the Langmuir pressure gradually increases, making methane desorption in the coal reservoir easier and more favourable for CBM development. When the gas pressure is less than the critical desorption pressure, a larger amount of adsorbed gas can be converted into free gas, thereby achieving more stable long-term production.

6. Conclusions

(1) Analyses via the HPMI, LTN2A, and LPCO2A methods indicate that the pores in coal samples from the Benxi Formation from the study area are primarily micropores, with relatively few mesopores and macropores. The pore structures consist mainly of cylindrical pores, conical pores, and slit-like pores. The PV distribution curve shows a unimodal pattern, with the main peak corresponding to micropores. The total PV values range from 0.046 to 0.092 cm3/g (with an average of 0.073 cm3/g), with micropores being the primary contributors to the total PV, accounting for 87.02% to 96.71% (with an average of 92.26%) of the total PV.
(2) The coal maturity, the vitrinite content, and the ash yield all have complex effects on the pore development of the coal from the Benxi Formation in the study area. As the degree of metamorphism increases, both the micropore PV and SSA significantly increase, whereas the mesopore PV and SSA gradually decrease, and the macropore PV and SSA change slightly. An increase in the vitrinite content is favourable for the development of micropores. The PV and SSA are negatively correlated with the ash yield, indicating that an increase in ash yield leads to a reduction in the PV and SSA.
(3) The SSAs of pores in coal affect the gas content primarily by influencing the amount of adsorbed gas. The SSAs of pores significantly impact the storage capacity of adsorbed gas in coal.

Author Contributions

Conceptualization, G.Z.; Methodology, X.C., J.L., Z.R. and W.F.; Software, Tao Wang and Z.R.; Validation, J.L. and G.Z.; Formal analysis, Z.R.; Investigation, W.F.; Resources, S.W. and Z.D.; Data curation, X.C., S.W. and G.Z.; Writing—original draft, X.C., T.W. and S.W.; Writing—review & editing, X.C. and T.W.; Visualization, J.L. and D.H.; Supervision, D.H. and W.F.; Project administration, Z.D.; Funding acquisition, Z.D. All authors have read and agreed to the published version of the manuscript.

Funding

This work is financially supported by China Petroleum Sciencge and Technology Project titled “Research on Coal rock gas Enrichment and Development Mechanism” (No.: 2024DJ23). Petrochina Science and Technology Project titled “Research on Deep Coalbed Methane Reservoir Formation Theory and Benefit Development Technology” (No.: 2023ZZ18). Key Core Technology Research Project of Petrochina Changqing Oilfield Company titled “Research on Occurrence Mechanism, Enrichment Law and Key Technology of Effective Production of Deep Coal rock gas in Ordos Basin” (No.: 2023DZZ01). Youth Guide Project of 2024 Basic Research Plan (Natural Science) of Guizhou Provincial Science and Technology Department “Study on gas bearing and enrichment law of shale gas reservoirs of Wufeng-Longmaxi Formation in Yangxi area, northern Guizhou” (No.: Basic Science of Guizhou-(2024) Youth 379).

Data Availability Statement

Data are contained within the article.

Acknowledgments

The authors thank PetroChina Research Institute of Petroleum Exploration & Development for the support with the project experimental equipment and samples.

Conflicts of Interest

Author Song Wu was employed by the company Guizhou Wujiang Energy Investment Co., Ltd. Author Daojun Huang was employed by the company Changqing Oilfield Company of PetroChina. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Figure 1. Regional location and coal-bearing strata of the Daning–Jixian block. (a) Location of the study area; (b) Tectonic location of the Daning-Jixian block; (c) General stratigraphic column.
Figure 1. Regional location and coal-bearing strata of the Daning–Jixian block. (a) Location of the study area; (b) Tectonic location of the Daning-Jixian block; (c) General stratigraphic column.
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Figure 2. Mercury intrusion-extrusion curves of the coal samples.
Figure 2. Mercury intrusion-extrusion curves of the coal samples.
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Figure 3. PV distribution curves based on HPMI.
Figure 3. PV distribution curves based on HPMI.
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Figure 4. SSA distribution curves based on HPMI.
Figure 4. SSA distribution curves based on HPMI.
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Figure 5. N2 adsorption-desorption isotherms.
Figure 5. N2 adsorption-desorption isotherms.
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Figure 6. PV distribution curves based on LTN2A experiments.
Figure 6. PV distribution curves based on LTN2A experiments.
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Figure 7. SSA distribution curves based on LTN2A experiments.
Figure 7. SSA distribution curves based on LTN2A experiments.
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Figure 8. CO2 adsorption isotherms.
Figure 8. CO2 adsorption isotherms.
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Figure 9. PV distribution curves from the LPCO2A experiments.
Figure 9. PV distribution curves from the LPCO2A experiments.
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Figure 10. SSA distribution curves based on LPCO2A experiments.
Figure 10. SSA distribution curves based on LPCO2A experiments.
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Figure 11. CH4 adsorption isotherms.
Figure 11. CH4 adsorption isotherms.
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Figure 12. Principle of dominant pore size segments for LPCO2A, LTN2A, and HPMI [58].
Figure 12. Principle of dominant pore size segments for LPCO2A, LTN2A, and HPMI [58].
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Figure 13. PV distributions of different apertures in the three experiments.
Figure 13. PV distributions of different apertures in the three experiments.
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Figure 14. SSA distributions of different apertures combined with the three experiments.
Figure 14. SSA distributions of different apertures combined with the three experiments.
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Figure 15. PV (a) and SSA (b) ratios for different pore sizes.
Figure 15. PV (a) and SSA (b) ratios for different pore sizes.
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Figure 16. Relationships between Ro,max and total PV and SSA (a), micropore PV and SSA (b), mesopore PV and SSA (c), and macropore PV and SSA (d).
Figure 16. Relationships between Ro,max and total PV and SSA (a), micropore PV and SSA (b), mesopore PV and SSA (c), and macropore PV and SSA (d).
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Figure 17. Relationships between vitrinite content and total PV and SSA (a), micropore PV and SSA (b), mesopore PV and SSA (c), and macropore PV and SSA (d).
Figure 17. Relationships between vitrinite content and total PV and SSA (a), micropore PV and SSA (b), mesopore PV and SSA (c), and macropore PV and SSA (d).
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Figure 18. Relationships between inertinite content and total PV and SSA (a), micropore PV and SSA (b), mesopore PV and SSA (c), and macropore PV and SSA (d).
Figure 18. Relationships between inertinite content and total PV and SSA (a), micropore PV and SSA (b), mesopore PV and SSA (c), and macropore PV and SSA (d).
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Figure 19. Relationships between Ad and total PV and SSA (a), micropore PV and SSA (b), mesopore PV and SSA (c), and macropore PV and SSA (d).
Figure 19. Relationships between Ad and total PV and SSA (a), micropore PV and SSA (b), mesopore PV and SSA (c), and macropore PV and SSA (d).
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Figure 20. Relationships between the VL and total PV and micropore PV (a), mesopore PV and macropore PV (b), total SSA and micropore SSA (c), and mesopore SSA and macropore SSA (d).
Figure 20. Relationships between the VL and total PV and micropore PV (a), mesopore PV and macropore PV (b), total SSA and micropore SSA (c), and mesopore SSA and macropore SSA (d).
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Figure 21. Relationships between the PL and total PV and micropore PV (a), mesopore PV and macropore PV (b), total SSA and micropore SSA (c), and mesopore SSA and macropore SSA (d).
Figure 21. Relationships between the PL and total PV and micropore PV (a), mesopore PV and macropore PV (b), total SSA and micropore SSA (c), and mesopore SSA and macropore SSA (d).
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Table 1. Results of Ro,max, maceral, and proximate analyses of the coal samples.
Table 1. Results of Ro,max, maceral, and proximate analyses of the coal samples.
Sample ID Depth (m)Ro,max (%)Maceral (%) Proximate Analysis (%)
VIEMMadAdVdafFCad
DJ57-11884.22.1577.3619.090.203.351.4710.216.9482.33
DJ57-21885.81.8244.0641.340.0014.600.7729.9612.9260.52
DJ57-31886.42.1752.4732.150.0015.371.0126.2110.4965.38
DJ63-11961.82.5282.0110.250.007.741.255.506.2987.45
DJ63-21964.51.9151.9833.920.0014.100.7533.8712.4357.48
DJ63-31965.62.1970.3313.871.9313.871.0819.828.8172.32
G11-12068.22.0081.5912.900.005.501.716.826.5185.62
G11-22071.02.1277.2712.883.796.061.3714.148.1877.76
G11-32072.12.1663.9130.083.382.632.137.557.2083.96
P20-12276.03.0586.0010.000.004.001.1612.848.1179.16
P20-22277.02.9780.0016.000.004.000.7015.158.4577.14
P20-32278.43.1972.0022.000.006.001.266.497.1085.78
Note: Ro,max = maximum oil vitrinite reflectance, V = vitrinite, I = inertinite, E = exinite, M = minerals, Mad = moisture content, Ad = ash content, Vdaf = volatile matter content, and FCad = fixed carbon content.
Table 2. Pore structure parameters of the coal samples.
Table 2. Pore structure parameters of the coal samples.
Sample IDLPCO2ALTN2AHPMI
VDFTSDFTDCO2SBETVDFTSDFTDN2VHPMISHPMI
(cm3/g)(m2/g)(nm)(m2/g)(cm3/g)(m2/g)(nm)(cm3/g)(m2/g)
DJ57-10.072247.050.5011.610.0032.301.2730.0530.65
DJ57-20.042143.640.5012.100.0042.461.220.01810.09
DJ57-30.059202.150.5011.820.0042.106.0790.02714.40
DJ63-10.078263.870.5240.390.0010.456.7940.05932.37
DJ63-20.049165.390.5010.920.0030.866.0790.02313.92
DJ63-30.066221.810.5241.230.0031.194.8870.01812.69
G11-10.070236.720.5241.310.0031.306.0790.03115.45
G11-20.065219.920.5240.650.0020.816.0790.03215.42
G11-30.073247.270.5010.810.0020.806.0790.03618.45
P20-10.077260.320.5010.520.0020.597.0320.03819.27
P20-20.072239.420.5240.720.0020.696.0790.03216.53
P20-30.081275.760.5240.290.0010.396.7940.05124.77
Table 3. Langmuir parameters of the coal samples.
Table 3. Langmuir parameters of the coal samples.
Sample ID VL (cm3/g)PL (MPa)
DJ57-129.313.27
DJ57-218.443.79
DJ57-321.903.66
DJ63-129.753.43
DJ63-219.093.40
DJ63-323.613.15
G11-127.522.66
G11-224.622.78
G11-329.013.31
P20-124.463.04
P20-228.653.25
P20-332.282.91
Table 4. Statistical results of the PV at different scales.
Table 4. Statistical results of the PV at different scales.
Sample ID Total PV (cm3/g)MicroporesMesoporesMacropores
V1 (cm3/g)α1 (%)V2 (cm3/g)α2 (%)V3 (cm3/g)α3 (%)
DJ57-10.0770.07294.030.0023.120.0022.86
DJ57-20.0460.04290.950.0047.530.0011.52
DJ57-30.0660.05990.380.0045.500.0034.12
DJ63-10.0810.07896.710.0011.560.0011.73
DJ63-20.0530.04992.850.0024.520.0012.64
DJ63-30.0690.06694.780.0034.060.0011.16
G11-10.0770.07091.600.0034.220.0034.17
G11-20.0710.06591.480.0023.300.0045.22
G11-30.0790.07392.800.0022.500.0044.70
P20-10.0830.07792.510.0022.090.0055.40
P20-20.0830.07792.510.0022.090.0055.40
P20-30.0920.08188.940.0011.230.0099.83
Table 5. Statistical results of the SSA at different scales.
Table 5. Statistical results of the SSA at different scales.
Sample IDTotal SSA (m2/g)MicroporesMesoporesMacropores
S1 (m2/g)β1(%)S2 (m2/g)β2(%)S3 (m2/g)β3(%)
DJ57-1248.04247.1099.620.930.370.020.01
DJ57-2145.02143.6699.071.340.920.020.01
DJ57-3203.55202.1799.321.310.640.070.03
DJ63-1264.32263.8799.830.450.170.010.00
DJ63-2166.28165.3999.470.850.510.040.02
DJ63-3222.88221.8199.521.070.480.000.00
G11-1238.09236.7299.421.290.540.080.04
G11-2220.87219.9299.570.800.360.150.07
G11-3248.16247.2799.640.780.320.110.04
P20-1260.93260.3499.780.460.180.120.05
P20-2240.13239.4299.710.670.280.040.02
P20-3276.20275.7699.840.380.140.060.02
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MDPI and ACS Style

Chen, X.; Wang, T.; Wu, S.; Deng, Z.; Li, J.; Ren, Z.; Huang, D.; Fan, W.; Zhu, G. Characterisation of the Full Pore Size Distribution of and Factors Influencing Deep Coal Reservoirs: A Case Study of the Benxi Formation in the Daning–Jixian Block at the Southeastern Margin of the Ordos Basin. Processes 2024, 12, 2364. https://doi.org/10.3390/pr12112364

AMA Style

Chen X, Wang T, Wu S, Deng Z, Li J, Ren Z, Huang D, Fan W, Zhu G. Characterisation of the Full Pore Size Distribution of and Factors Influencing Deep Coal Reservoirs: A Case Study of the Benxi Formation in the Daning–Jixian Block at the Southeastern Margin of the Ordos Basin. Processes. 2024; 12(11):2364. https://doi.org/10.3390/pr12112364

Chicago/Turabian Style

Chen, Xiaoming, Tao Wang, Song Wu, Ze Deng, Julu Li, Zhicheng Ren, Daojun Huang, Wentian Fan, and Gengen Zhu. 2024. "Characterisation of the Full Pore Size Distribution of and Factors Influencing Deep Coal Reservoirs: A Case Study of the Benxi Formation in the Daning–Jixian Block at the Southeastern Margin of the Ordos Basin" Processes 12, no. 11: 2364. https://doi.org/10.3390/pr12112364

APA Style

Chen, X., Wang, T., Wu, S., Deng, Z., Li, J., Ren, Z., Huang, D., Fan, W., & Zhu, G. (2024). Characterisation of the Full Pore Size Distribution of and Factors Influencing Deep Coal Reservoirs: A Case Study of the Benxi Formation in the Daning–Jixian Block at the Southeastern Margin of the Ordos Basin. Processes, 12(11), 2364. https://doi.org/10.3390/pr12112364

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