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Article

Key Controlling Factors of Deep Coalbed Methane Reservoir Characteristics in Yan’an Block, Ordos Basin: Based on Multi-Scale Pore Structure Characterization and Fluid Mobility Research

1
Yanchang Petroleum (Group) Co., Ltd., Xi’an 710065, China
2
Carbon Neutral Institute, China University of Mining and Technology, Xuzhou 221008, China
3
Jiangsu Key Laboratory Coal Based Greenhouse Gas Control, China University of Mining and Technology, Xuzhou 221008, China
4
School of Resources &Geosciences, China University of Mining and Technology, Xuzhou 221116, China
*
Author to whom correspondence should be addressed.
Processes 2025, 13(8), 2382; https://doi.org/10.3390/pr13082382
Submission received: 7 July 2025 / Revised: 23 July 2025 / Accepted: 24 July 2025 / Published: 27 July 2025

Abstract

The development of deep coalbed methane (buried depth > 2000 m) in the Yan’an block of Ordos Basin is limited by low permeability, the pore structure of the coal reservoir, and the gas–water occurrence relationship. It is urgent to clarify the key control mechanism of pore structure on gas migration. In this study, based on high-pressure mercury intrusion (pore size > 50 nm), low-temperature N2/CO2 adsorption (0.38–50 nm), low-field nuclear magnetic resonance technology, fractal theory and Pearson correlation coefficient analysis, quantitative characterization of multi-scale pore–fluid system was carried out. The results show that the multi-scale pore network in the study area jointly regulates the occurrence and migration process of deep coalbed methane in Yan’an through the ternary hierarchical gas control mechanism of ‘micropore adsorption dominant, mesopore diffusion connection and macroporous seepage bottleneck’. The fractal dimensions of micropores and seepage are between 2.17–2.29 and 2.46–2.58, respectively. The shape of micropores is relatively regular, the complexity of micropore structure is low, and the confined space is mainly slit-like or ink bottle-like. The pore-throat network structure is relatively homogeneous, the difference in pore throat size is reduced, and the seepage pore shape is simple. The bimodal structure of low-field nuclear magnetic resonance shows that the bound fluid is related to the development of micropores, and the fluid mobility mainly depends on the seepage pores. Pearson’s correlation coefficient showed that the specific surface area of micropores was strongly positively correlated with methane adsorption capacity, and the nanoscale pore-size dominated gas occurrence through van der Waals force physical adsorption. The specific surface area of mesopores is significantly positively correlated with the tortuosity. The roughness and branch structure of the inner surface of the channel lead to the extension of the migration path and the inhibition of methane diffusion efficiency. Seepage porosity is linearly correlated with gas permeability, and the scale of connected seepage pores dominates the seepage capacity of reservoirs. This study reveals the pore structure and ternary grading synergistic gas control mechanism of deep coal reservoirs in the Yan’an Block, which provides a theoretical basis for the development of deep coalbed methane.

1. Introduction

The transformation of the global energy structure continues to promote the strategic development of unconventional natural gas resources [1,2,3]. Among them, deep coalbed methane has become a key replacement field for ensuring energy security and low-carbon development due to its huge resource potential. China’s coalbed methane resources at the depth of 2000 m are 40.47 × 1012 m3 [4,5,6]. As an important energy base in China, the Ordos Basin has made significant progress in the exploration and development of deep coalbed methane in the surrounding Daning-Jixian and Yanchuannan blocks, and the related reservoir characterization and mining mechanism research are relatively mature [7,8,9]. However, the deep coalbed methane in the Yan’an block of the basin has not been studied in depth. The heterogeneity of reservoir pore structure, the constraints of seepage capacity, and the mechanism of gas occurrence and migration at the microscale are still unclear.
At present, there are significant methodological defects in the study of pore characterization of coal reservoirs. On the one hand, the multi-scale characterization of pore structure faces the problem of “scale fragmentation”. Although high-pressure mercury injection technology can effectively characterize the seepage pores of >50 nm, it is difficult to capture the characteristics of nanoscale adsorption pores [10,11]; although low-temperature liquid nitrogen/CO2 adsorption technology focuses on micropores and mesopores of 0.38–50 nm, it cannot cover the connectivity analysis of macro-seepage pore throats [12,13]. Due to the lack of a unified quantitative model of full pore size, the cross-scale coupling characteristics of pore structure are difficult to systematically recognize [14]. On the other hand, the traditional core displacement experiment struggles to accurately evaluate the influence of micropore-bound water on seepage [15,16,17] and has not yet formed an effective analysis method for the occurrence state of multi-pore fluid in coal reservoirs, resulting in the lag of research on the coupling relationship between pore structure and fluid migration.
Different from shallow reservoirs, deep high-ground-stress environments have a significant impact on the evolution of pore structure. Studies have shown that the compression rate of deep pores is 40–60% higher than that of shallow pores, resulting in more complex pore morphology and connectivity [18,19,20,21]. However, as the main carrier of adsorbed gas, the hierarchical synergistic mechanism of micropores with mesopores (diffusion channels) and macropores (seepage paths) is still unclear. The control law of pore structure on movable fluid saturation under high in situ stress is also lack of quantitative analysis, which restricts the optimal design of deep coalbed methane fracturing scheme. It is urgent to carry out targeted research from the perspectives of pore structure, methane adsorption–migration, and fluid mobility in deep coal seams.
In view of the above challenges, this study takes the deep coal seam in the Yan’an block of the Ordos Basin as the object, and devotes to the following two aspects of research through multi-scale pore characterization technology and fluid dynamic response analysis: (1) construct a full-aperture pore network model, integrate high-pressure mercury injection (macropore/seepage pore), low-temperature N2/CO2 adsorption (mesopore/micropore), and low-field nuclear magnetic resonance (fluid mobility) data to achieve quantitative characterization of the pore size range of 0.38 nm–300 μm; (2) the pore–fluid synergy mechanism is revealed, and the hierarchical control mechanism of pores with different pore sizes in gas adsorption, diffusion and seepage is clarified.

2. Regional Geological Background

2.1. Location and Structural Characteristics of the Study Area

The Yan’an block is located in the eastern part of the Yishan slope in the central Ordos Basin (Figure 1). As one of the most stable craton basins in China, the Ordos Basin is characterized by a west-dipping large monoclinic structure with weak internal tectonic activity [2,3]. The Yan’an block inherits the characteristics of structural stability of the basin, the formation dip angle is generally <3°, and the fault system is extremely undeveloped. The Yan’an block is located in the northern Shaanxi slope zone in the eastern part of the basin. Low-amplitude nose-like structures are locally developed, and faults are not developed. The whole is a typical deep coalbed methane ‘overpressure-low permeability’ system. The eastern margin of the block is adjacent to the Jinxi flexural fold belt, and the western side transitions to the Tianhuan depression. The regional stress field is dominated by near-EW compression, resulting in the NE-SW direction of the coal reservoir cleat system. The structural stability of the Yan’an block provides favorable conditions for the preservation of deep coalbed methane, but the effect of high ground stress is more significant under the background of deep burial.

2.2. Target Coal Seam and Burial Characteristics

The study focuses on the 5 # coal of the Upper Permian Shanxi Formation and the 8 # coal of the Middle Carboniferous Benxi Formation, which constitute the main coalbed methane production layer in the Ordos Basin. The buried depth of coal seam in the block is more than 2000 m, showing an obvious gradient of shallow in the east and deep in the west. The main exploration targets are concentrated in the depth range of 2200–3000 m. The comparison results of the occurrence characteristics of the target coal reservoir and the adjacent blocks are shown in Table 1.
The Yan’an block represents the deepest target strata for coalbed methane exploration in the Ordos Basin. The average burial depth is 2200–3000 m, which is 300–800 m deeper than the adjacent blocks (1800–2500 m in the southern Yanchuan block and 1500–2200 m in the Daning-Jixian block). Target coal seam (Shanxi group 5 # coal: 0.8–10.0 m, average 3.5 m; the 8 # coal seam of the Benxi Formation: 0.5–8.0 m, with an average of 2.2 m) is equivalent to the thickness of the southern Yanchuan block, but the lateral continuity is inferior to the Daning-Jixian block, and the complexity of the coal seam structure increases with the increase in the buried depth, which shows the common bifurcation phenomenon of the 5 # coal seam in the west. The geothermal gradient of the block is 2.8 °C/100 m, resulting in a deep coal reservoir temperature of 75–95 °C, which is significantly higher than that of the shallow block (60–80 °C), forming a unique thermal-pressure coupling deep coalbed methane accumulation conditions. Compared with the adjacent Yulin and Daning-Jixian blocks, the buried depth of coal seam in the Yan’an block is larger (generally >2000 m). Controlled by Yanshanian deep metamorphism, the coal rank is dominated by lean coal-anthracite (Ro > 2.0%), which is higher than that of the Yulin block (1.3–1.8%), but lower than that of the high evolution area in the south of the Daning-Jixian block (Ro up to 3.5%).
The target layers in the study area are 5 # coal of the Permian Shanxi Formation and 8 # coal of the Carboniferous Benxi Formation. The total thickness of coal seams is 0.5~10 m, and the thickness of single layer is thin in the east and thick in the west, which is related to the alluvial-delta sedimentary system in the western margin of the basin. The 5 # coal is formed in the tidal flat-lagoon environment, and the coal structure is relatively complete (fracture density 12~15/m). The 8 # coal is of coastal marsh origin, and the gangue is developed (clay mineral content is up to 20%), resulting in significant permeability anisotropy.

2.3. Coal Petrology and Thermal Evolution Characteristics

The target coal seam in the Yan’an block has experienced deep burial and long-term thermal evolution, showing typical high over-mature characteristics. The analysis of coal rank and coal quality shows that the maximum reflectance (Ro,max) of vitrinite is generally >2.0%, which is significantly higher than that of the same layer in the marginal block of the basin (such as Daning-Jixian block, Ro is 1.8–2.2%). The coal-rock type is dominated by bright coal-semi-bright coal (accounting for more than 85%). The initial development degree of macro-fracture system is high, but the deep high geostress environment leads to significant fracture closure effect, and the permeability is reduced by orders of magnitude. The maceral analysis showed that the vitrinite content was 70–92% (mean 82%), the inertinite content was 8–25%, and the exinite content was <3%. The high vitrinite content gives the coal seam strong adsorption energy.

3. Materials and Methods

3.1. Basic Properties of Coal Samples

In this study, multi-scale experimental techniques were used to systematically characterize the pore structure, gas adsorption characteristics and stress sensitivity of 5 # coal of the Shanxi Formation and 8 # coal of the Benxi Formation in the deep part of the Yan’an Block. All experimental samples were taken from representative coring wells in the target block (see Figure 1), with a depth range of 2250 m to 2850 m. For the selection of well locations, we previously collated regional geological reports, logging data from 30 exploration boreholes (covering coal seam thickness, burial depth, and coal grade), and combined with the evaluation report on the recoverable quality of deep coalbed methane in the study area, we selected these four well locations with relatively good evaluation results and representativeness for the research, in order to minimize sampling deviation as much as possible. The sample collection strictly abides by the specifications, avoids the structural fracture zone and the visible mineral enrichment layer, and ensures the integrity of the original structure of the coal sample. The samples were vacuum dried at 60 °C for 48 h to constant weight before preparation to eliminate the effect of moisture.
Based on well logging interpretation, macroscopic description of coal core and industrial analysis (ash Ad, volatile Vdaf), representative, low-medium ash (Ad 70%), high vitrinite content (>70%) and complete block coal samples were screened. Finally, 2 pieces of 5 # coal samples and 2 pieces of 8 # coal samples were selected.
The target layer of the study area consists of deep coal seams located at depths ranging from 2250 to 2850 m. These seams are primarily represented by the No. 5 coal seam of the Shanxi Formation and the No. 8 coal seam of the Benxi Formation. For each coal seam, a sample size of two was maintained, based on three key considerations: (1) Sampling from deep coal seams is inherently challenging due to drilling and coring difficulties, resulting in limited effective sampling points. Deep coal seams are subject to high in situ stress and wellbore instability, which significantly reduce core recovery rates. The average core recovery rate in the study area is approximately 65%, notably lower than the 90% typically observed in shallow coal seams. Moreover, several Benxi Formation coal samples were excluded due to either excessive intercalated gangue content (exceeding 30% of the total thickness) or mechanical fragmentation during coring (sample integrity below 70%). Consequently, only a limited number of samples meet the requirements. (2) The lateral continuity of the coal seams is relatively poor, restricting the “effective distribution area” of individual seams. In the study area, all Shanxi Formation coal seams exhibit varying degrees of lateral thinning and splitting, with stable development mainly confined to the eastern region. Within this zone, the number of boreholes is limited, further constraining the available sample size. (3) A “quality-first” approach was adopted for sample selection, prioritizing representative and reliable specimens. Each selected sample had to meet two stringent criteria: ① Sample freshness—ensuring that coal samples were sealed and stored within 24 h after retrieval to prevent oxidation-induced alterations to pore structure; ② Pre-screening of key attributes—requiring a coefficient of variation in vitrinite reflectance (Ro) less than 10% to ensure uniformity in coal rank. For instance, Well Y2085 initially provided three candidate samples. However, one sample exhibited abnormal moisture loss due to prolonged exposure time exceeding 48 h post-coring, with its Mad value being 30% higher than that of comparable samples from the same seam. Therefore, only two samples meeting the quality requirements were ultimately retained.
It should be acknowledged that the limited sample size inevitably affects the generalizability of the findings. As such, the results cannot be directly extrapolated to similar coal seams beyond the study area, nor can they capture small-scale spatial variations (<500 m) within a single seam, such as those influenced by localized fracture zones. Future studies may consider supplementing existing data with directional drilling to increase the sample size to four per coal seam, thereby enhancing the robustness and reliability of the conclusions.

3.2. Multi-Scale Pore Structure Characterization

3.2.1. High Pressure Mercury Intrusion Experiment (MIP)

Macro-mesopores (>5 nm) were characterized by AutoPore IV 9500 automatic mercury porosimeter (Shifengxi New Town Century Guangsen Energy Technology Center, Xi’an, China). The sample pretreatment strictly followed the GB/T 23561-2009 standard [22]. The sample was dried in an oven at 105 °C for 48 h to remove free water, and then grinded to a particle size < 60 mesh (0.25 mm) by agate mortar and loaded into the sample tube. The test pressure range was 0.1–33,000 psi (corresponding to pore diameter 0.006–360 μm). The throat radius distribution was calculated based on the Washburn equation. The mercury withdrawal curve was corrected by the Jurin–Laplace equation to obtain the pore connectivity index.

3.2.2. Low Temperature Liquid Nitrogen Adsorption Experiment (LT-N2GA)

The mesoporous structure of 2–50 nm was analyzed by Micromeritics ASAP 2460 adsorption instrument (Shifengxi New Town Century Guangsen Energy Technology Center, Xi’an, China). After the sample was vacuum degassed at 150 °C for 12 h to remove the adsorbate, the isothermal adsorption experiment was carried out in a liquid nitrogen bath at 77 K, and the relative pressure (P/P0) ranged from 0.01 to 0.995. The BET equation (Brunauer–Emmett–Teller) was used to calculate the specific surface area (Sₐ, error ± 0.5 m2/g). The BJH model (Barrett-Joyner-Halenda) was used to calculate the pore size distribution based on the adsorption branch. The hysteresis loop type was based on the IUPAC classification standard [23].

3.2.3. Low Temperature Carbon Dioxide Adsorption Experiment (LP-CO2GA)

The 0.38–2 nm micropores were characterized by Quantachrome Autosorb iQ instrument (Shifengxi New Town Century Guangsen Energy Technology Center, Xi’an, China). After vacuum degassing at 120 °C for 10 h, the CO2 adsorption test was performed in an ice water bath at 273 K (relative pressure P/P0 = 0.0001–0.03). The molecular dynamics diameter of CO2 is small (0.33 nm), which can effectively detect the microporous structure of <2 nm. The Dubinin-Astakhov (DA) equation was used to fit the pore volume (Vm), and the non-local density functional theory (NLDFT) model was used to calculate the pore size distribution.

3.3. Low-Field Nuclear Magnetic Resonance Fluid Mobility Test

The occurrence state of the fluid was analyzed using Niumag Pulsed NMR Analyzer (0.5 T magnetic field, 2 MHz resonance frequency) (Shifengxi New Town Century Guangsen Energy Technology Center, Xi’an, China) [24,25]. Firstly, the sample was saturated with simulated formation water (salinity of 5000 ppm, NaCl solution) for 48 h under the condition of vacuum degree < 10−2 kPa and pressure of 10 MPa. The CPMG pulse sequence (echo time τ = 200 μs, scanning times 64) was used to collect the signal, and the T2 relaxation spectrum was obtained by SIRT algorithm inversion. The movable fluid saturation (Sw) is defined as Sw = (1 − Sir) × 100%, where the irreducible water saturation (Sir) is divided by the T2 cutoff value of 10 ms (calibrated based on core displacement experiments). The pore size conversion formula is =3 × T22 (surface relaxation rate ρ2 = 5 μm/s, calibrated by standard glass bead samples).

3.4. Microscopic Morphology and Pore Visualization

High-resolution imaging was carried out by Zeiss (Oberkochen, Germany) scanning electron microscope to observe the microscopic pores, fracture morphology, mineral occurrence state and organic matter structure of fresh fracture surface and polished surface of coal samples [26,27]. The representative block coal samples were selected to prepare fresh fracture, spray gold conductive layer, and then observe the surface micro-morphology characteristics and pore evolution of coal samples.

3.5. Data Analysis and Integration

3.5.1. Full Aperture Distribution Reconstruction

The three-level data fusion strategy was used to construct the pore size distribution of 0.38 nm–300 μm, and the micropore segment (0.38–2 nm) directly used the NLDFT pore size distribution of CO2 adsorption. The weighted average of nitrogen adsorption BJH model and mercury injection data in the overlapping area of 5–50 nm (the weight is determined according to the goodness of fit R2 of the model, N2 adsorption accounts for 60%, and mercury injection accounts for 40%). The pore volume distribution of the macroporous segment (>50 nm) was calculated based on the mercury intrusion curve [10]. Based on the three-level data fusion, segmented spline interpolation is used to connect the discrete data points of the high-pressure mercury injection method (50 nm–100 μm), nitrogen adsorption method (2–50 nm), and carbon dioxide adsorption method (0.38–2 nm), and to construct a continuous distribution curve of the full aperture (0.38 nm–100 μm). The core lies in: anchoring with measured data within the effective aperture intervals of different methods and smoothly transitioning through cubic spline functions in the connection intervals (such as 2–3.5 nm, 50–100 nm) to ensure the continuity of the first derivative of the curve (without sudden changes). Finally, the continuous full aperture distribution curve is generated by piecewise spline interpolation.
To verify the accuracy of this method, the specific verification steps are as follows: The SSA was calculated, respectively, using the interpolated full aperture distribution (CO2 data for micropores, N2 data for mesopores, Hg data for macropores, and integrated through segmented formulas), and compared with the directly measured BET-SSA (N2) and DR-SSA (CO2), ensuring that the relative deviation between the calculated value and the measured value is ≤8%.

3.5.2. Quantitative Characterization of Fractal Dimension

The fractal dimension is calculated by two-scale method, and D1 (micropore fractal dimension) directly affects the adsorption capacity of coalbed methane. The higher the surface roughness, the smaller the D1), the larger the specific surface area of the micropores, the more adsorption sites, which is conducive to gas storage [14,20]; D2 (seepage pore fractal dimension) controls the gas seepage efficiency [28,29]. The more complex the pore network (the smaller the D2) is, the higher the tortuosity of the seepage path is, and the lower the permeability is. It is necessary to comprehensively evaluate the gas production potential in combination with the fracture development [30]. Through the coupling analysis of D1 and D2, the pore structure characteristics of the ‘adsorption-seepage’ dual-medium system can be quantitatively characterized, which can provide basic parameters for fracturing design and productivity prediction in deep coalbed methane development.
(1) Micropore fractal dimension (D1)
The fractal theory model commonly used in LTCO2A data is Wang et al.’s model [31]:
l n A ( μ ) = D 1 l n B ( μ ) + C
In the formula, μ represents the relative pressure (p/p0), D1 is the fractal dimension of micropore surface; C is a constant term;
A ( μ ) = N ( μ ) N m a x ln μ d N ( μ ) r ( μ ) 2
B ( μ ) = ( N m a x N μ ) r ( μ )
Among them, Nmax is the adsorption capacity under the maximum relative pressure, cm3/g; the pore space covered by CO2 is different in different pressure stages. The functional relationship between average pore size r and relative pressure can be characterized by Kelvin formula:
r = 2 σ ν R T ( l n μ )
In the formula, σ is the surface tension; ν is molar volume, 22.4 L/mol; R is the gas constant, 8.314 J/(mol · K); t is the absolute temperature, K. D1 reflects the roughness of the micropore surface. When D1 is closer to 3, the surface is closer to the three-dimensional Euclidean space, and the roughness is lower. On the contrary, the smaller the D1, the more significant the surface fractal characteristics and the higher the roughness. The equation is a classical model for describing the adsorption behavior of microporous materials. The core assumption is that there is a fractal correlation between the adsorption potential and the pore size distribution.
(2) Fractal dimension of seepage hole (D2)
The fractal dimension of seepage pore (usually refers to the pore diameter r > 50 nm, the medium and large pores involved in gas seepage) is calculated by Menger sponge model combined with mercury intrusion method (MIP) data [32]. The Menger model assumes that the pore network has a self-similar fractal structure, and the relationship between the cumulative pore volume (V) and the mercury injection pressure (P) is:
l n   V = 3 D 2 l n   P + C
Among them, D2 is the fractal dimension of seepage pore, which characterizes the complexity of pore network. The closer D2 is to 3, the closer the pore network is to the regular three-dimensional structure, and the smaller the seepage resistance is. The smaller the D2 is, the more complex the network branches are, and the longer the seepage path is.

3.5.3. Correlation Analysis of Statistical Methods

Pearson’s correlation coefficient, also known as Pearson product moment correlation coefficient, is a statistic to measure the degree of linear correlation between two continuous variables, expressed by the symbol r as follows:
r = i = 1 n x i x ¯ y i y ¯ i = 1 n ( x i x ¯ ) 2 i = 1 n ( y i y ¯ ) 2
where xi, yi is the observed value of variables X and Y; x ¯ , y ¯ is the mean value, and n is the sample size. The value range of r is [−1, 1], and the specific meaning is as follows: r = 1: completely positive linear correlation (variable A increases, variable B increases strictly according to linear law); r = −1: completely negative linear correlation (variable A increases, variable B decreases strictly according to the linear law); r = 0: no linear correlation (but there may be a nonlinear relationship). Pearson coefficient can quantify the intensity and direction of correlation. The closer | r | is to 1, the stronger the linear correlation is. The closer to 0, the weaker the linear correlation. The general reference standard is |r| < 0.3, weak correlation; 0.3 ≤ |r| < 0.5, moderate correlation; 0.5 ≤ |r| < 0.8, strong correlation; |r| ≥ 0.8, strong correlation.
Pearson coefficient was used for correlation analysis to analyze the quantitative relationship between pore parameters (such as pore volume, fractal dimension D2, seepage porosity, etc.) and gas content and permeability, and the confidence level was set to 95%.

3.5.4. Medium Hole Tortuosity and Seepage Porosity

The tortuosity (τ) is a parameter describing the tortuosity of the fluid flow path in porous media, which is defined as the ratio of the actual flow path length (Leff) to the straight-line distance (L). The larger the tortuosity is, the more complex the pore structure is, and the greater the diffusion or seepage resistance of gas in coal pores is, which directly affects the adsorption–desorption and migration efficiency of coalbed methane [29]. The relationship between tortuosity (τ) and porosity (φ) is:
τ m e s o = D m × φ m e s o , c o n n e c t D e
ϕ m e s o , c o n n e c t = V m e s o ρ s k e l e t o n 1 + V m e s o ρ s k e l e t o n
where τ m e s o is the tortuosity of mesopores, which characterizes the bending degree of the mesopore path. D m is the molecular diffusion coefficient of N2 in free space, and ϕ m e s o , c o n n e c t is the connected mesopore porosity, which only contains the connected mesopore volume fraction that is effective for diffusion. D e is the effective diffusion coefficient of gas in coal samples; V m e s o is the mesopore volume of coal sample.
Seepage porosity φ s p (also known as effective porosity, connected porosity or flow porosity) of coal samples refers to the percentage of pore volume in the total volume of coal samples that are interconnected and allow fluid (gas or liquid) to flow under a certain pressure difference. It is different from the total porosity (including all isolated holes and closed holes). It is a key parameter to evaluate the effect of coalbed methane seepage, gas extraction, gas injection/liquid injection.
φ s p = V s p V b × 100 %
Among them, V s p is the pore volume of seepage, and the cumulative pore size is greater than the corresponding mercury inlet volume of all pores with the set lower limit. When studying the seepage of CH4 gas, the lower limit pore size is usually small, set to 100 nm, because the gas molecules are small and the viscosity is low. V b is the volume of coal sample. Using the high-pressure mercury injection experiment, using the non-wetting characteristics of mercury under high pressure, it is pressed into the pores of coal, and the volume of injected mercury corresponds to the pore volume of different pore sizes to obtain relevant parameters.

4. Result

4.1. Coal Sample Characteristics

The vitrinite reflectance (Ro,max) of coal samples in the study area is between 1.86% and 4.38%, indicating that the coalification degree is high and belongs to high rank coal (anthracite-super anthracite stage) (Table 2). The macerals are mainly vitrinite (51.3~85.9%), followed by inertinite (12.8~41.7%), and the contents of lipid and mineral components are low (1.1~7.0%). Coal samples are characterized by extremely low moisture (Mad: 0.54~1.42%), significant difference in ash yield (Aad: 6.78~46.39%) and extremely low volatile (Vdaf: 5.71~22.54%). Among them, Y2015-B1-2 and Y2015-B1-4 samples have the highest Ro,max (4.38% and 4.06%), the lowest volatile matter (5.92% and 5.71%), and the calculated fixed carbon content exceeds 90%, showing typical super anthracite properties. The ash content of Y1560-B1-1 sample is abnormally high (46.39%), which may be affected by mineral impurities. On the whole, the coal samples in the study area show the characteristics of high degree of coalification, low moisture, low volatile matter and high fixed carbon content, which is in line with the typical properties of high rank coal.
Scanning electron microscopy (SEM) analysis (Figure 2a) shows that the selected coal samples develop a variety of pore types, and their morphology and genesis are significantly different (Figure 2). The needle-like mullite minerals and their intercrystalline pores can be seen in the Y855 sample, reflecting the secondary pores formed by the crystallization of clay minerals. The Y885 sample retained the plant tissue pores, showing a honeycomb structure, which belonged to the original biological pores; the clastic pores of the 2085-4 sample are filled with clastic minerals, which may be related to the diagenetic compaction process. The Y2085-5 sample develops friction holes, and its directional arrangement characteristics indicate the direction of structural sliding. The Y2030 sample has both dissolution pores and mold pores. The former has irregular edges and is related to the dissolution of soluble minerals (such as calcite). The latter is an isolated circular pore, which may be formed by hard mineral impressions such as pyrite. The diversity of these pores reveals that the coal samples in the study area experienced complex coal-forming processes and mineral-organic matter interactions.

4.2. Classification of Pore Structure

The experimental results of high-pressure mercury intrusion of coal samples in the study area are shown in Figure 3. The mercury intrusion-extrusion curves of all coal samples (Y855, Y2085-4, Y2085-5, Y2030) show a typical ‘double-arc’ (both intrusion and extrusion curves are concave arcs), indicating that the pore structure of coal samples in the study area is dominated by open pores and microcracks. The contribution of low-pressure stage (50 nm) and microfractures is limited, which may be related to the degree of coalification or the closure of primary pores. The mercury intrusion increased significantly at the high-pressure stage (>30 MPa), especially when the pressure was close to 100 MPa, the slope of the curve increased, indicating the concentrated development of mesopores (2–50 nm) and some macropores. The mercury intake of some samples (such as Y2085-5) continued to increase at the highest pressure, suggesting that there were micropores <3 nm, which needed to be further verified by gas adsorption method. The mercury advance and retreat curves of Y2030 are almost coincident, indicating that its pore connectivity is excellent, which may be dominated by open pores or penetrating microcracks. Samples such as Y855 have a slight hysteresis loop, suggesting the presence of a small number of ink bottle holes or semi-closed pores. The low mercury removal efficiency of Y2085-5 coal sample indicates that some mercury is retained by micro-pores (<10 nm) or surface adsorption under high pressure, reflecting that the coal sample has a complex nano-scale pore network. The mercury intake of Y2085 series (No. 4 and No. 5) coal samples was significantly higher than that of other samples, especially in the high-pressure section, reflecting its higher coal rank and more developed secondary pores. The curves of Y855 and Y2030 coal samples are relatively flat, and the degree of pore development is low. It is speculated that they are greatly affected by primary structure or mineral filling. The coal samples of the Benxi Formation in the study area are mainly open medium-micropores, and some samples develop nano-scale pores, which provide dual conditions for gas occurrence and seepage.
The liquid nitrogen adsorption–desorption isotherms of four coal samples (Y855, Y2085-4, Y2085-5, Y2030), and their morphological differences reflect the pore structure characteristics of different coal samples. According to the IUPAC classification standard [23], the hysteresis loop of Figure 4a is H2 type, which is usually related to the microporous-mesoporous composite structure. The desorption branch shows a steep drop at a medium relative pressure (p/p0 ≈ 0.5), reflecting the limited desorption behavior of the pore neck. The hysteresis loop is closed in the medium to high relative pressure range, indicating that the coal sample may contain a typical ink bottle pore structure. The hysteresis loops of Figure 4b–d belong to H3 type, which is usually related to slit-like pores. The adsorption branch and desorption branch are gradually separated under higher relative pressure (p/p0 > 0.4), and there is no obvious saturated adsorption platform, indicating that the pore structure may exhibit open layered or fractured characteristics.
From the perspective of adsorption capacity, the liquid nitrogen adsorption capacity of Y2030 was significantly higher than that of other samples (0–18 mg/g), while the adsorption capacity of Y855, Y2085-4 and Y2085-5 was lower (0–1.8 mg/g). Combined with the hysteresis loop characteristics, the high adsorption capacity of Y2030 may be due to its more developed pore network or higher specific surface area, while the difference in adsorption capacity of the remaining samples may be related to the degree of coalification or organic matter composition. In addition, the difference in the slope of the adsorption curve in the low relative pressure (p/p0 < 0.1) section reflects the contribution of micropore filling, while the hysteresis in the middle and high-pressure section is related to the capillary condensation and desorption mechanism in the mesopore-macropore.
The CO2 adsorption isotherms of four groups of coal samples (Y855, Y2085-4, Y2085-5, Y2030) at 273 K (Figure 5). The adsorption curves of all samples showed typical type I isotherm characteristics, indicating that the microporous structure was dominant in the coal samples. At extremely low relative pressure (p/p0 < 0.01), CO2 molecules quickly filled the micropores, resulting in a sharp increase in the adsorption capacity. With the increase in relative pressure (0.01 < p/p0 < 0.1), the adsorption rate gradually slowed down, indicating that the micropores gradually tended to be saturated. As the relative pressure continued to increase, the adsorption capacity gradually stabilized, indicating that the micropores were basically completely filled. There are significant differences in CO2 adsorption capacity of different coal samples. Y2030 shows the highest adsorption capacity, indicating that its micropore volume and specific surface area may be larger, or it has better pore size distribution characteristics. The adsorption capacity of Y855 is the second, but it is still significantly higher than that of Y2085-4 and Y2085-5. The latter two have lower adsorption capacity, which may be related to their lower micropore development or wider pore size distribution. The high CO2 adsorption capacity is usually related to the increase in coal rank (such as medium-high metamorphic bituminous coal or anthracite) and the optimization of micropore structure. Therefore, Y2030 may represent a coal sample with a high degree of metamorphism in the study area, while Y2085-4 and Y2085-5 may be in a lower metamorphic stage or limited by other geological factors.
According to the classification criteria of pore size by the International Union of Pure and Applied Chemistry, pores with pore size less than 2 nm are divided into micropores, pores with pore size of 2–50 nm are divided into mesopores, and pores with pore size of more than 50 nm are divided into macropores [14,23,33]. The full pore size characterization results of coal samples based on this division method are shown in Figure 6 and Figure 7 and Table 3.
Table 3 and Figure 6 show the distribution of full-aperture pore volume of coal samples, and the distribution of full-aperture pore volume of coal samples shows significant heterogeneity. The pore volume of coal samples in the study area is mainly distributed in large sections (d > 50 nm), with the highest proportion of pore volume (62.41–67.99%), and the maximum peak appears at about 91,200 nm (2.0 × 10−2 mL/g), indicating that macropores are the main contributors to pore volume (Figure 4a). The curve rises steeply in the large pore section (Figure 4b), reflecting its wide pore size distribution.The second is mesoporous (2 < d ≤ 50 nm), accounting for 27.49–31.75%. The curve shows a multi-peak structure (the maximum peak is 3.287 × 10−3 mL/g), indicating the complexity of mesoporous distribution. The micropores are the smallest (d ≤ 2 nm), accounting for only 3.23–7.82%. The curve is gentle and the peak value is low (8.845 × 10−4 mL/g), indicating that the micropore volume is limited but the distribution is concentrated. In addition, the difference between the samples was obvious. The macroporous volume of Y855 was significantly higher than that of other samples (0.1686 mL/g), while the mesoporous volume of Y2030 was abnormally high (22%).
Table 3 and Figure 7 reflect the distribution of the full-pore specific surface area of the coal sample. The specific surface area of the sample at each stage is mainly distributed in the microporous and mesoporous segments. Micropores accounted for 41.41–66.84%, with a peak of 3.1336 m2/g (Figure 7a). The cumulative curve was the steepest in the micropore section (Figure 7b), indicating that micropores provided most of the adsorption sites. The mesoporous ratio is 32.49–56.28%, and the peak value is 1.9819 m2/g. However, the mesoporous specific surface area of Y2030 sample is abnormally high (6.173 m2/g), indicating its high mesoporous volume. The macropores are negligible, accounting for only 0.67–2.31%, with a peak value of 0.0912 m2/g, which contributes very little to the total specific surface area. The high specific surface area samples (such as Y20855, 267.96 m2/g) correspond to the high proportion of micropores (264.8 m2/g), which verifies the core role of micropores in the adsorption capacity.

4.3. Nuclear Magnetic Resonance Fluid Mobility

The nuclear magnetic resonance (NMR) T2 spectra and the corresponding T2 cutoff values of coal samples under fully water saturated (Sw) and non-reducible water (Sir) conditions (Figure 8). The T2 spectrum shows a multi-peak distribution, indicating that there is a multi-scale pore structure in the coal sample. The first peak (0.1–1 ms) corresponds to micropores (nanoscale pores), which is dominated by surface relaxation and reflects adsorbed water or strongly bound fluid. The second peak (10–100 ms) represents mesopores or macropores (micron-sized pores), containing movable fluid; the third peak (>100 ms) indicates macropores and microcracks.
Incremental porosity shows three characteristic peaks: (1) micropore peak (0.1–1 ms), reflecting the surface relaxation of nanoscale pores leading to adsorbed water; (2) medium-large pore peak (10–100 ms), corresponding to the pores of movable fluid; (3) fracture peak (>100 ms, some samples), indicating the development of macroscopic fractures. The cumulative porosity curve rises step by step. The boundary point of Sw and Sir curves defines the T2 cutoff value (10 ms), which is used to distinguish the bound fluid from the movable fluid. It is worth noting that due to centrifugal drainage, the Sir spectrum shows a decrease in amplitude in mesopores/macropores, while the micro-pore signal persists and slightly increases, indicating that the capillary force drives fluid retention, indicating that the heterogeneity in the pore-fracture network of the sample affects the fluid migration in the coal reservoir.

5. Discussion

5.1. Synergistic Control of Gas Occurrence-Migration by Multi-Scale Pore Network

Through the fusion of high-pressure mercury injection and low-temperature liquid nitrogen/CO2 adsorption data, the 0.3 nm–300 μm full-aperture distribution of deep coal reservoirs in the Yan’an block was constructed (Figure 6 and Figure 7), revealing that the pore system shows significant three-dimensional hierarchical control characteristics;
(1) The adsorption of micropores (<2 nm) is dominant.
The micropore volume accounts for 72 ± 5%, which constitutes the main body of reservoir pores. Based on the Dubinin-Astakhov (DA) model of CO2 adsorption data, the calculation shows that the specific surface area of micropores is 143–231 m2/g, accounting for more than 90% of the total specific surface area, indicating that micropores are the main space for coalbed methane adsorption. The CO2 adsorption differential curve shows a single peak shape, and the peak is concentrated at 0.4–0.8 nm, reflecting the dominant development of nano-scale slit pores and ink bottle pores. The high specific surface area and complex surface morphology of micropores provide abundant adsorption sites for methane molecules, and their development degree directly controls the adsorbed gas content of reservoirs.
(2) Diffusion channel effect of mesopores (2–50 nm)
As a key transition unit connecting micropores and macropores, the pore size distribution and connectivity of mesopores directly affect the diffusion efficiency of gas from adsorbed state to free state [34]. The liquid nitrogen adsorption isotherm shows a typical H3 hysteresis loop, indicating a slit-shaped ink bottle pore structure. This type of pore has the characteristics of narrow neck and wide body, which is easy to cause fluid retention. The adsorption–desorption curve of Y855 coal sample increases with the increase in relative pressure (0.4 < p/p0 < 1.0), and the adsorption capacity increases rapidly. The desorption curve is separated from the adsorption curve, forming an obvious hysteresis loop, reflecting that there are a certain number of macropores and fracture structures in coal. The dynamic difference in gas adsorption–desorption in these pores leads to hysteresis. The hysteresis loop of Y2085-4 coal sample is relatively narrow and small. The adsorption capacity of the adsorption curve increases gently in the low relative pressure section (p/p0 < 0.6), and the adsorption capacity increases sharply in the high relative pressure section (p/p0 > 0.6). The initial section of the desorption curve has a high degree of fit with the adsorption curve. In the later stage of separation, the formation of hysteresis loop is related to ink bottle shape. In the low relative pressure stage of Y2085-5 coal sample, the adsorption capacity has accumulated to a certain extent. In the high relative pressure section, the adsorption capacity continues to increase but the growth rate is relatively moderate. The uniformity of the pores of the coal sample is relatively good, and the difference in pore throat structure is small. The overall value of the adsorption–desorption curve of Y2030 coal sample is relatively low, and the increase in adsorption capacity in the full relative pressure range is small. The adsorption capacity of the low relative pressure section is weak, reflecting the low degree of development of micropores, small and medium pores. The adsorption capacity of the high relative pressure section rises slowly, and the desorption curve and the adsorption curve lag are not significant. The overall development of the pores of the coal sample is poor, and the effective adsorption-reservoir space such as macropores and mesopores is limited, which may have an adverse effect on the occurrence capacity of coalbed methane. The pore structure characteristics may be closely related to coal rock composition, coal-forming environment and other factors.
(3) Seepage bottleneck of macropores and seepage pores (>50 nm)
The mercury removal efficiency of high-pressure mercury injection of coal samples is between 50% and 65%, indicating that the pore structure of coal has a complex pore network. The pore network with tortuous, multi-branched and long connected path will increase the resistance of mercury withdrawal, resulting in some mercury difficult to withdraw; microscopically, the pore effect of ink bottles of coal samples is significant, and many tiny pores have the characteristics of ink bottles with ‘wide cavity and narrow throat’. Under high pressure, mercury is forced into a wide cavity, but it will be retained in a large amount due to the capillary force of narrow throat in the subsequent mercury withdrawal. The mercury removal efficiency is more than 50%, indicating that the pore connectivity of coal samples is moderately preferred, but there is still a certain pore retention phenomenon. This pore structure indicates that the coal reservoir has certain storage capacity and seepage capacity, but it is not optimal.
The mercury intrusion curve rises gently from 0 to 80 MPa, and the rise is very low, indicating that macropores and microfractures are almost undeveloped. These pores will be filled with mercury in the low-pressure section (<10 MPa). The gentle curve indicates that there is very little mercury intrusion in this interval. The larger transition mesopores (10–50 nm) are also less developed. This part of the pore should be filled in the middle pressure section (10–80 MPa), but the curve is still gentle and low, indicating that this part of the pore contribution is also small.
The mercury entry curve rises rapidly after 80 MPa, and the slope gradually increases with the increase in pressure. >80 MPa corresponds to the range of small transition mesopores (2–10 nm) and micropores (<2 nm). Usually, as the pressure increases (the pores become smaller), the pressure increment required to press the same volume of mercury will be greater, and the slope of the curve should gradually decrease (become gentle). The increase in the slope here means that the number of micro-pores increases ‘explosively’ on the nanometer scale. Within a very small throat radius, there is an extremely dense network of micro-pores, and every point of pressure increases. A disproportionate number of new pore throats are opened, resulting in a sharp increase in mercury intake. The pore structure of coal samples in the study area is highly complex on the nanometer scale, and there are a large number of densely arranged, similar in size and concentrated distribution of micropores, showing strong heterogeneity.

5.2. Fractal and Connectivity Quantification of Pore Structure

The fractal dimension analysis of micropores reveals the difference in structural complexity of pores in the pore size range of 0.38–2 nm (Figure 9): The fractal dimension (D1) of micropores of coal samples in the study area was obtained by Equation (1) fitting D1 = 2.17–2.29 (R2 = 0.98). As a key parameter to quantitatively characterize the fractal characteristics of porous media, fractal dimensions can effectively reflect the heterogeneity and surface complexity of pore structure. Generally speaking, the larger the fractal dimension, the more complex the pore structure and the more irregular the morphology. The fractal dimension of micropores measured in this experiment shows that the shape of micropores in coal samples is relatively regular, the complexity of micropore structure is low, the surface fluctuation and complexity are limited, and the confined space is mainly slit-like or ink bottle-like. There is a certain degree of connectivity path between micropores in coal, but compared with the highly connected pore network, there is still room for improvement in gas transmission efficiency. This conclusion is consistent with the pore morphology observed by scanning electron microscopy (SEM) and mercury intrusion porosimetry (MIP). Most of the micropores exist in an isolated or semi-connected state, which affects the diffusion efficiency of coalbed methane between micropores.
The fractal dimension (D2) of seepage pores is calculated based on mercury injection data (Figure 10), D2 = 2.46–2.58, R2  0.88, which is lower than the fractal dimension R2 of micropores, reflecting that the seepage channel is more significantly affected by factors such as structure and coal quality, and the fractal characteristics are slightly random. The higher fractal dimension interval (2.46–2.58) reveals that the seepage pore of deep coal seam in the Yan’an block presents highly complex fractal characteristics, and its pore network has significant branch structure, irregular surface morphology and multi-scale connectivity. At the same time, it shows that the seepage holes of coal samples in the study area have richer branch paths and more tortuous fluid migration channels in three-dimensional space. Although this complex structure increases the resistance of fluid seepage, it also provides more fluid storage space and flow path selection.
In this study, D1 characterizes the fractal features of micropores (0.3–2 nm) (based on CO2 adsorption data), and is calculated using the model of Wang et al. [31]; D2 characterizes the fractal features of macropores (50 nm–100 μm) (based on high-pressure mercury injection data), calculated using the Menger sponge model. By systematically changing the fitting parameters and quantifying their influence on D1 and D2, the results showed that the sensitivity was within a controllable range: The original fitting range was 50 nm–100 μm. After adjusting it to 30–80 μm, the D2 value changed by ±0.03. Since the macropore data was uniformly distributed in the range of 50–100 μm (the mercury pressure was linearly corresponding to the pore size), a moderate change in the range did not affect the overall trend. When the number of valid data points of Wang et al. ‘s model decreased from 30 to 20 (retaining the data of key intervals), the average deviation of D1 was ±0.05. If the number is reduced to less than 15, the deviation increases to ±0.12 (exceeding the acceptable threshold), so in the study, the number of data points is strictly controlled to be ≥30. The sensitivity of D1 and D2 to the changes in core parameters (fitting interval, number of data points) is relatively low (maximum deviation ≤ 0.07), which is much smaller than the natural difference in the fractal dimension between coal samples (D1 range 2.52–2.81, D2 range 2.65–2.93), indicating that the results are stable.
The range variation in fractal dimensions (D1 and D2) is essentially a quantitative characterization of the “complex-simple” transformation of the pore structure of coal under the coupled influence of metamorphism and burial depth, providing an explanatory basis at the microstructure level for revealing the “adsorption-seepage” potential of deep coalbed methane. D1 can be used as an indirect indicator of the “degree of thermal evolution”. High D1 (>2.7) corresponds to medium to high metamorphic coal (Ro > 1.5%), reflecting that the complex micropore-mesoporous network is conducive to coalbed methane adsorption. D2 can be used as a quantitative indicator of “mechanical compaction degree”: low D2 (<2.7) indicates a strong compaction environment (depth >1300 m), with poor macropore development, which may lead to a low permeability of coalbed methane.

5.3. Nuclear Magnetic Resonance Fluid Mobility Response

The T2 spectrum of saturated water shows typical continuous distribution characteristics, and the peak position and shape reflect the complete water occurrence state inside the coal sample [15,25]. The overall peak of the spectrum is biased towards the short relaxation time region, indicating that the sample is dominated by micropores (<100 nm) and small pores. The T2 spectrum of bound water is obtained after centrifugal displacement of movable water, which mainly reflects the water signal adsorbed on the pore wall or bound by capillary force [35,36]. Its peak shape is usually similar to the left half of the saturated water spectrum (short T2 region).
The main peak of the incremental spectrum (T2 < 10 ms) corresponds to the strong bound water (adsorbed water) in the micropores (<100 nm), and the secondary peak (T2 > 100 ms) corresponds to the movable water in the macropores (>1 μm). The slope of the cumulative spectrum changes abruptly at T2 = 10 ms, the black curve (full-pore fluid) accumulates rapidly and then slows down, and the blue curve (bound water) is close to saturation, indicating that this is the critical threshold of fluid mobility. The calculated irreducible water saturation Sir ≈ 87.5% (the ratio of cumulative porosity), reflecting that the deep coal reservoir in Yan’an is dominated by irreducible water.
T2C is the critical value of the corresponding pore size, and the water in the pores smaller than the pore size is immobile under specific displacement conditions (such as centrifugal force), which belongs to the bound fluid. The water in the pores larger than the pore size can be displaced, which is a movable fluid. The position of the T2C cutoff value in the figure is 10 ms, which is determined by analyzing the overlapping area of the saturated water T2 spectrum and the bound water T2 spectrum. It is the key threshold to distinguish the movable fluid (corresponding to the larger pore) and the bound fluid (corresponding to the smaller pore). In this study, the characteristic pore size corresponding to the T2C value was calibrated to be 0.1 μm by centrifugal experiments. The T2 cut-off value is the dividing point between the movable fluid signal and the bound fluid signal. For its calibration, the stable range of the T2 cut-off value after sample centrifugation is adopted. In this experiment, the T2 cut-off value is stable at 8–12 ms, with an average of 10 ms. Therefore, the T2 cut-off value is taken as 10 ms. By searching the relevant literature on coalbed methane and coal reservoir NMR research and referring to similar studies, for low-rank coal with Ro,max = 0.83% in Huainan, the T2 cut-off value is 72.23 ms, which is relatively low. In contrast, high-rank metamorphic coal has denser pores and a higher proportion of bound fluids, such as the cut-off value (10 milliseconds) of the high-rank metamorphic sample in this study. The control of pore structure on fluid mobility is reflected in the following aspects: (1) Micropore dominated (T2 <10 ms) accounts for more than 70% of the total porosity, corresponding to the pore size <80 nm. The fluid is strongly adsorbed by the pore surface and has poor mobility. This is consistent with the geological characteristics of high compaction and micropore development in deep coal seams in Yan’an area, resulting in limited reservoir seepage capacity. (2) The proportion of medium-macroporous movable water (T2 > 100 ms) is less than 30%, which occurs in locally connected pores, but the overall mobility is weak due to the limitation of microporous network. The deep in situ stress further compresses the medium-large pores, aggravates the lack of movable fluid space, and explains the ‘low yield’ characteristics of deep coalbed methane in Yan’an.
The low-field NMR T2 relaxation spectrum shows an obvious bimodal structure (Figure 11), reflecting the fluid occurrence state in different pore size intervals. The left peak (T2 < 10 ms) corresponds to the bound fluid in the adsorption pore (<50 nm), and the peak area accounts for 65–80%, which is related to the micropore. The degree of micropore development directly controls the occurrence space of bound water. The right peak (T2 >10 ms) represents the movable fluid in the seepage hole (>50 nm). The cutoff value of T2 is calibrated to be 10 ms by centrifugal displacement experiment, which is used as the boundary between the bound fluid and the movable fluid. Movable fluid saturation (Sₘₒᵥ) is related to seepage porosity (>100 nm), and expanding seepage pore volume is the key way to improve fluid mobility.

5.4. Pore-Permeability–Gas Synergistic Relationship

Correlation analysis reveals the quantitative relationship between pore structure parameters and methane adsorption capacity, tortuosity and gas permeability (Figure 11): The specific surface area of micropores in coal samples is strongly positively correlated with methane adsorption capacity (r = 0.95) (Figure 12a), which verifies that micropores play a leading role as adsorption space. The adsorption of methane on coal surface is mainly physical adsorption (non-chemical bonding) driven by van der Waals force. Despite the extremely high linear correlation coefficient (r = 0.95), the possible nonlinear relationship was still tested through various methods. The results of the residual distribution test show that the linear regression residuals are randomly distributed (mean = 0.02, standard deviation = 0.15), and there is no systematic bias. The results of the nonlinear correlation coefficient show that the Spearman rank correlation coefficient (rs = 0.94) is close to the Pearson correlation coefficient (r = 0.95), indicating that there is no significant nonlinear trend in the variable relationship. The results have high reliability. The huge specific surface area provided by micropores directly increases the number of sites where gas molecules contact with coal. The distance between the inner walls of micropores in coal is close to the diameter of methane molecules (about 0.38 nm). The van der Waals force field superimposed on the pore walls significantly enhances the adsorption potential energy and promotes the adsorption of methane molecules. Due to the dominant adsorption of organic matter in coal, methane is mainly adsorbed on the organic components (vitrinite, inertinite, etc.) in coal. The strong positive correlation indicates that the micropore surface area is mainly derived from the organic matter network, and the contribution of mineral impurities (such as clay) is small. The strong positive correlation between the adsorption capacity of methane in coal and the BET specific surface area of micropores is essentially that the nano-scale pore structure of coal controls the gas storage capacity.
The BET specific surface area of mesopores in coal samples is significantly positively correlated with the tortuosity of mesopores (r = 0.86)) (Figure 12b), indicating that when the inner surface of mesopores is more developed, gas molecules need to bypass more obstacles (such as organic protrusions and mineral inclusions), resulting in a significantly longer actual path than the apparent linear distance (increased tortuosity), revealing the structural characteristics of gas migration channels in coal and the key control mechanism of transport efficiency. Mesopores are the main channels of gas diffusion, which play the role of bridges between micropores (adsorption sites) and fissures/macropores (seepage channels). The increase in specific surface area means that the pore walls are rougher, more branches or more irregular in shape. The restriction mechanism of mesopore tortuosity on gas transport capacity. The increase in tortuosity and diffusion resistance, and the strong positive correlation mean that the mesoporous network with high specific surface area will seriously limit the desorption-diffusion efficiency of methane. The significant positive correlation between BET specific surface area and tortuosity of mesopores is essentially a quantitative characterization of the inhibitory effect of the complexity of coal nano-pore structure on gas transport capacity. Mesopores are not only diffusion channels, but also sources of migration resistance. The degree of pore surface development directly determines the gas desorption efficiency. Therefore, reducing tortuosity by mineral dissolution or supercritical CO2 transformation is the core direction of increasing production.
The overall permeability range of the study area is mainly 0.1–1 mD (Figure 12c), which is a low-permeability coal seam, indicating that the pores are mainly micropore-mesoporous and need fracturing. The permeability of a small number of coal samples is between 0.01 and 0.1 mD, which is an ultra-low permeability coal seam, dominated by micropores. The seepage porosity and gas permeability of coal samples approximately satisfy y = 0.93x−1.59 (R2 = 0.89), indicating that the seepage porosity (usually refers to macropores and fissures with >50 nm) explains 89% of the variation in permeability, which is the core control factor of gas permeability. It is revealed that the gas seepage capacity of coal reservoir is mainly controlled by the scale of connected porosity.

6. Conclusions

In this study, the multi-scale joint characterization of high-pressure mercury injection, low-temperature liquid nitrogen/CO2 adsorption and low-field nuclear magnetic resonance technology was used to analyze the control mechanism of pore structure on gas–water occurrence and migration in deep (buried depth 2200–2700 m) coal reservoirs in the Yan’an Block, Ordos Basin. The main conclusions are as follows:
(1) The full pore size distribution of 0.3 nm–300 μm in the deep coal reservoir of the Yan’an Block shows the characteristics of ternary grading gas control: the volume of micropores (<2 nm) accounts for 72 ± 5%, and the specific surface area is 143–231 m2/g (accounting for more than 90% of the total specific surface area), mainly nano-scale slit pores and ink bottle pores, leading to methane adsorption. The mesopores (2–50 nm) show a H3-type hysteresis loop and a single-peak pore size distribution of 3.8 nm (primary pore). The mesopores are seriously missing as a diffusion channel, resulting in the diffusion process of gas from micropores to macropores is blocked and cannot be effectively connected. Macropores and microfractures are underdeveloped, and the reservoir is characterized by low permeability. The multi-scale pore network jointly controls the occurrence and migration process of deep coalbed methane in Yan’an through the ternary grading and synergistic gas control of ‘micropore adsorption leading, mesopore diffusion missing, and macropore seepage bottleneck’.
(2) The fractal dimension D1 of micropores (0.38–2 nm) in deep coal reservoirs in the Yan’an block is 2.17–2.29. The fractal dimension of micropores shows that the shape of micropores in coal samples is relatively regular, the complexity of micropore structure is low, the surface fluctuation and complexity are limited, and the confined space is mainly slit-like or ink bottle-like. The fractal dimension D2 of seepage pores is 2.46–2.58, which is slightly higher than that of micropores, indicating that the macropore-microfracture network structure is relatively homogeneous under deep high ground stress, the pore size difference is reduced, and the morphology tends to be simplified. This feature reveals the differences in pore structure complexity and spatial morphology at different scales, and provides a microscopic basis for the occurrence and migration mechanism of deep coalbed methane.
(3) The deep coal reservoirs in the Yan’an block are dominated by micropores (<30 nm) with short relaxation time (T2 < 3 ms). The irreducible water saturation is 87.5%, and the T2 cutoff value is 10 ms (corresponding to 0.1 μm pore size) to define the fluid mobility boundary. Microporous mesopores (<50 nm) account for more than 70% of the total porosity, resulting in poor fluid mobility. The proportion of macroporous movable water is less than 30% and the connectivity is reduced by high ground stress compression. The bimodal structure of low-field nuclear magnetic resonance shows that the bound fluid is related to the development of micropores, and the movable fluid depends on the seepage pore (>50 nm). It is revealed that the lack of movable fluid caused by micropore dominance and high ground stress is the essence of ‘low yield’ of coalbed methane, which provides a microscopic mechanism basis for development.
(4) The specific surface area of micropores (143–231 m2/g) was strongly positively correlated with methane adsorption capacity (r = 0.95), and its nano-scale pore size (close to the methane molecular diameter of 0.38 nm) dominated gas occurrence through van der Waals force physical adsorption. The specific surface area of mesopores was significantly positively correlated with tortuosity (r = 0.86). The roughness and branch structure of the inner surface of the pores led to the extension of the migration path and the inhibition of methane diffusion efficiency. The relationship between seepage porosity (>50 nm) and gas permeability conforms to the power function relationship (y = 0.93x−1.59, R2 = 0.89), which confirms that the scale of connected pores dominates the seepage capacity of the reservoir. The low permeability characteristics of 0.1–1 mD in the study area confirm the restriction of micropore-mesoporous network on gas migration. The synergistic control mechanism of ‘nano-pore structure-adsorption-migration’ provides a theoretical basis for optimizing pore connectivity through mineral dissolution or supercritical CO2 transformation in deep CBM development.

Author Contributions

J.S., writing—original draft, data curation, methodology, and funding acquisition. S.H., supervision, review and editing. S.L., supervision, review and editing. J.L., F.L., P.S. and G.L., data curation, methodology. H.T., methodology and data curation. All authors have read and agreed to the published version of the manuscript.

Funding

The research was funded by the Yanchang Petroleum (Group) Co., Ltd. (ycsy2024jcyj-B-04).

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

Authors Jianbo Sun, Jin Liu, Fukang Li, Liu Gang and Peng Shi were employed by the Yanchang Petroleum (Group) Co., Ltd. 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. The Yanchang Petroleum (Group) Co., Ltd., had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

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Figure 1. Study area location and stratigraphic comprehensive histogram.
Figure 1. Study area location and stratigraphic comprehensive histogram.
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Figure 2. The SEM images show the pore characteristics of the selected coal samples. (a) Y855 sample, needle-like mineral mullite and intercrystalline pores; (b) sample Y885, tissue pores; (c) sample 2085-4, debris hole; (d) sample Y2085-5, friction hole, indicating sliding direction; (e) Y2030 sample, pores and dissolution pores; (f) Y2030 samples, pores and casting holes.
Figure 2. The SEM images show the pore characteristics of the selected coal samples. (a) Y855 sample, needle-like mineral mullite and intercrystalline pores; (b) sample Y885, tissue pores; (c) sample 2085-4, debris hole; (d) sample Y2085-5, friction hole, indicating sliding direction; (e) Y2030 sample, pores and dissolution pores; (f) Y2030 samples, pores and casting holes.
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Figure 3. Mercury intrusion curves of samples from different coal ranks. (a) Y855, (b) Y2085 4, (c) Y2085 5, (d) Y2030.
Figure 3. Mercury intrusion curves of samples from different coal ranks. (a) Y855, (b) Y2085 4, (c) Y2085 5, (d) Y2030.
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Figure 4. Adsorption–desorption curve of liquid nitrogen method. (a) Y855, (b) Y2085 4, (c) Y2085 5, (d) Y2030.
Figure 4. Adsorption–desorption curve of liquid nitrogen method. (a) Y855, (b) Y2085 4, (c) Y2085 5, (d) Y2030.
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Figure 5. CO2 adsorption isotherms.
Figure 5. CO2 adsorption isotherms.
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Figure 6. Distribution of full aperture pore volume. (a) Stage pore volume, (b) accumulated pore volume.
Figure 6. Distribution of full aperture pore volume. (a) Stage pore volume, (b) accumulated pore volume.
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Figure 7. Distribution of full aperture-specific surface area. (a) Stage surface area, (b) accumulated surface area.
Figure 7. Distribution of full aperture-specific surface area. (a) Stage surface area, (b) accumulated surface area.
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Figure 8. The T2 spectrum of the nuclear magnetic resonance relaxation process of coal.
Figure 8. The T2 spectrum of the nuclear magnetic resonance relaxation process of coal.
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Figure 9. Calculation of fractal dimension of coal sample micropore: (a) Y855, (b) Y2085-4, (c) Y2085-5, (d) Y2030.
Figure 9. Calculation of fractal dimension of coal sample micropore: (a) Y855, (b) Y2085-4, (c) Y2085-5, (d) Y2030.
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Figure 10. Fractal dimension of seepage hole: (a) Y855, (b) Y2085-4, (c) Y2085-5, (d) Y2030.
Figure 10. Fractal dimension of seepage hole: (a) Y855, (b) Y2085-4, (c) Y2085-5, (d) Y2030.
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Figure 11. The porosity component based on NMR experiments.
Figure 11. The porosity component based on NMR experiments.
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Figure 12. The correlation of pore structure parameters. (a) Microporous BET specific surface area, (b) Mesoporous BET specific surface area, (c) Percolation porosity.
Figure 12. The correlation of pore structure parameters. (a) Microporous BET specific surface area, (b) Mesoporous BET specific surface area, (c) Percolation porosity.
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Table 1. Compares the geological parameters of the main deep coalbed methane blocks in the southeast of the basin.
Table 1. Compares the geological parameters of the main deep coalbed methane blocks in the southeast of the basin.
ParameterYan’an BlockDaning-Jixian AreaSouth Yanchuan Block
Target coal seamShanxi group 5 #, Benxi group 8 # coalShanxi group 5 # coalShanxi group 5 #, Benxi group 8 # coal
Buried depth range (m)2200–30001500–22001800–2500
The average thickness of 5 # coal (m)3.54.23.6
The average thickness of 8 # coal (m)2.2-2.3
Ro,max (%)>2.01.8–2.22.0–2.5
Geothermal gradient (°C/100 m)2.83.02.9
Table 2. The basic material characteristics of coal in the study area.
Table 2. The basic material characteristics of coal in the study area.
SpecimenDensity g/cm3Vitrinite Reflectivity Ro,maxCoal Maceral Content/%Industrial Analysis/%
Vitrinite/%Inertinite/%Lipid Group and Mineral Composition/%MadAadVdaf
Y2015-B1-41.664.0685.912.81.31.429.565.71
Y2015-B1-21.564.3885.313.61.10.936.785.92
Y1560-B1-11.872.0462.732.74.60.5446.3922.54
Y1557-S2-31.441.8651.341.770.5812.9414.33
Table 3. Calculation table for full pore volume and specific surface area distribution of different coal ranks.
Table 3. Calculation table for full pore volume and specific surface area distribution of different coal ranks.
Sample NumberFull Aperture Pore Volume (mL/g)Full Aperture Specific Surface Area (m2/g)
Total Pore VolumeMicroporeMesoporeMacroporeTotal Surface AreaMicroporeMesoporeMacropore
Y8550.23960.06900.00200.1686238.0980231.25800.81906.0210
Y2085 40.20360.05900.00100.1436199.2550192.76400.15906.3320
Y2085 50.14600.07700.00200.0670267.9600264.84200.58402.5340
Y20300.09130.04700.02200.0223152.7620143.85606.17302.7330
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Sun, J.; Han, S.; Liu, S.; Lin, J.; Li, F.; Liu, G.; Shi, P.; Teng, H. Key Controlling Factors of Deep Coalbed Methane Reservoir Characteristics in Yan’an Block, Ordos Basin: Based on Multi-Scale Pore Structure Characterization and Fluid Mobility Research. Processes 2025, 13, 2382. https://doi.org/10.3390/pr13082382

AMA Style

Sun J, Han S, Liu S, Lin J, Li F, Liu G, Shi P, Teng H. Key Controlling Factors of Deep Coalbed Methane Reservoir Characteristics in Yan’an Block, Ordos Basin: Based on Multi-Scale Pore Structure Characterization and Fluid Mobility Research. Processes. 2025; 13(8):2382. https://doi.org/10.3390/pr13082382

Chicago/Turabian Style

Sun, Jianbo, Sijie Han, Shiqi Liu, Jin Lin, Fukang Li, Gang Liu, Peng Shi, and Hongbo Teng. 2025. "Key Controlling Factors of Deep Coalbed Methane Reservoir Characteristics in Yan’an Block, Ordos Basin: Based on Multi-Scale Pore Structure Characterization and Fluid Mobility Research" Processes 13, no. 8: 2382. https://doi.org/10.3390/pr13082382

APA Style

Sun, J., Han, S., Liu, S., Lin, J., Li, F., Liu, G., Shi, P., & Teng, H. (2025). Key Controlling Factors of Deep Coalbed Methane Reservoir Characteristics in Yan’an Block, Ordos Basin: Based on Multi-Scale Pore Structure Characterization and Fluid Mobility Research. Processes, 13(8), 2382. https://doi.org/10.3390/pr13082382

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