Next Article in Journal
Design of a Mixed Reality System for Simulating Indoor Disaster Rescue
Next Article in Special Issue
Pore Structure and Fluid Evaluation of Deep Organic-Rich Marine Shale: A Case Study from Wufeng–Longmaxi Formation of Southern Sichuan Basin
Previous Article in Journal
Calcareous Tufa: Deposition and Erosion during Geological Times
Previous Article in Special Issue
Research on the Development Law of Water-Conducting Fracture Zone in the Combined Mining of Jurassic and Carboniferous Coal Seams
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Applied Sciences
Volume 13
Issue 7
10.3390/app13074414
applsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Comparing the Pore Networks of Coal, Shale, and Tight Sandstone Reservoirs of Shanxi Formation, Qinshui Basin: Inspirations for Multi-Superimposed Gas Systems in Coal-Bearing Strata

by
Difei Zhao
1,*,
Jiaming Zhang
2,
Xin Guan
3,
Dandan Liu
4,
Qinxia Wang
1,
Weiwei Jiao
5,
Xueqing Zhou
6,
Yingjie Li
2,
Geoff Wang
7,* and
Yinghai Guo
2
1
Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou 221000, China
2
School of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, China
3
School of Safety Engineering, China University of Mining and Technology, Xuzhou 221116, China
4
Sun Yueqi Honors College, China University of Mining and Technology, Xuzhou 221116, China
5
School of Architecture and Engineering, Chongqing Industry Polytechnic College, Chongqing 401120, China
6
Institute of Deep-Sea Science and Engineering, Chinese Academy of Sciences, Sanya 572000, China
7
School of Chemical Engineering, University of Queensland, Brisbane, QLD 4072, Australia
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2023, 13(7), 4414; https://doi.org/10.3390/app13074414
Submission received: 26 February 2023 / Revised: 23 March 2023 / Accepted: 24 March 2023 / Published: 30 March 2023
(This article belongs to the Special Issue New Progress in Interdisciplinary Research of Geological Energy and Resources)
Browse Figures
Versions Notes

Abstract

:
Transitional upper carboniferous Shanxi Formation coal-bearing strata in Qinshui Basin have been proven to be a set of mixed unconventional gas-bearing reservoirs forming a multi-superimposed gas system that consists of multiple independent fluid pressure systems vertically through the strata. An experimental protocol was designed to compare the pore networks in high-rank coal, shale, and tight sandstone reservoirs from Shanxi Formation using quantitative and qualitative experimental methods, including high-pressure mercury injection porosimetry (MIP), low-pressure nitrogen gas adsorption (LN2GA), and argon ion polishing–field emission scanning electron microscope (AIP-FESEM). The results show that genetic and structural differences in pore types, morphology, abundance, and proportion in coal, shale, and tight sandstone reservoirs are significant, reflecting strong heterogeneity characteristics. Pore networks determine the roles of different types of reservoirs in gas-bearing systems through differentiated pore structure, development degree, and spatial distribution. Due to the differences in nanopore development and connectivity, coal and tight sandstone reservoirs provide important reservoir spaces for adsorbed and free gas in the system. Thus, they become influential factors controlling the relationship between the gas-bearing subsystems with different fluid pressures. The lack of mesopores in shale and relatively weaker heterogeneity between layers lead to the phenomenon that continuously developed shales of a specific thickness are more likely to be the interlayers that divide the superimposed gas-bearing system. Systematic comparison of pore development characteristics will provide scientific support to further explain the formation mechanism of multi-superimposed gas systems in coal-bearing strata from the perspective of pore networks and provide guidance for the development of unconventional natural gas in coal-bearing strata.

1. Introduction

With the deepening of research and exploration on shale gas in China, the co-exploration of conventional natural gas, shale gas, coalbed methane (CBM), and tight gas in coal-bearing strata has attracted increasing attention from scholars [1,2,3,4,5]. If exploration in coal-bearing strata is limited to coal or coal bed methane, massive resources of shale gas and tight sand gas will be wasted. A large number of coal-bearing strata with various lithologies are developed in China’s major energy basins, which mainly includes coal seams, organic-rich mudstones, shales, and tight sandstones. Hence, a comprehensive gas-bearing system with the joint preservation of several types of unconventional gases is discovered, referred to as a multi-superimposed gas system [2]. CBM and shale gas are the major types of natural gas resources in transitional coal-bearing strata and are stored in a mixed reservoir of a coal–mud–sand structure system [6,7]. As revealed by Fu (2016), the gas resource in coal-bearing strata of China (without the Northeast Area) is mainly CBM about 16.8 × 1012 m3, shale gas about 32 × 1012 m3, and tight gas about 2032 × 1012 m3 [7]. Now, it is well acknowledged that multi-superimposed gas systems are widely present in coal-bearing strata and that co-exploration and co-exploitation are crucial to increasing the efficiency of gas production [8,9,10].
Coal, organic-rich shale, and tight sandstone reservoirs of the terrestrial sedimentary environment are interbedded and deposited in the vertical direction to form different gas-bearing systems, which have a significant influence on the evaluation and co-exploration of gases in coal-bearing strata [9]. The transitional upper carboniferous Shanxi Formation in Qinshui Basin is a typical transitional coal-bearing stratum with multiple unconventional gases stored in coal–mud–sand reservoirs that interbedded and deposited in the stratum [11,12]. Scholars have tried to use simulation and modeling methods to explore the feasibility of the co-exploration and co-exploitation potential of unconventional natural gas at a macro scale [3,4,5,11,13,14]. However, the significantly strong heterogeneity in coal-bearing strata increases the necessity of having a deeper understanding of the gas-bearing system of the reservoir and its complex influencing factors. Previous literature has revealed the particularity of multi-superimposed gas systems in coal-bearing strata, showing the existence of different independent fluid pressure systems due to the combination of interbedded reservoirs with other physical and gas-bearing properties [15,16]. In coal-bearing strata, the coal is not the only source of gas, and the shale plays multiple roles of being source rock, reservoir, and cap in the process of gas accumulation and preservation [11]. Extensively developed shale with low permeability generally grew as lithological boundaries of different fluid pressure systems in strata, affecting the comprehensive evaluation of gas reservoirs and the design of co-exploration technologies [8]. Further research on the development characteristics of different types of unconventional reservoirs may provide new insights to help better understand the geological mechanism of gas accumulation and to assist reservoir evaluation and prediction for the complex gas-bearing system.
Unconventional reservoirs are characterized by low porosity, low permeability, and strong microheterogeneity. The characterization of micro-reservoir space has been a hot topic and focus among related investigations of unconventional natural gas. The micro-reservoir space in coal, shale, and tight sandstone reservoirs—mainly pores and micro-fractures—is developed at the nanoscale with complex pore structures and poor connectivity [17,18,19,20,21]. The differences in pore systems, including the type, structure, size distribution, and connectivity, are the direct influencing factors of reservoir permeability and gas-bearing property [21,22]. Despite the similarity of being unconventional reservoirs with low porosity and permeability, these types of reservoirs differ greatly from each other (Figure 1). Influenced by the sedimentary environment, they vary in material component types and characteristics. This leads to differential diagenesis, which further produces variations in the rock structure, pore system, physical properties, and gas-bearing properties. For such fine-grained sediments and nanoscale pore-microfracture systems, many nanotechnologies have been applied to the physical property testing and stimulation of reservoirs [23]. The difference in material composition and pore structure of different reservoirs in strata is the basis of forming multi-superimposed gas systems. Therefore, it is necessary to improve the mining technology to avoid interaction between systems in order to improve the production of the strata. In view of this, it is of great significance to study and make a comparison of the nanoscale characteristics of different reservoirs in coal-bearing strata.
Research on the characterization of nanoscale pores in unconventional reservoirs has increased dramatically since the successfully commercialized exploration of unconventional oil and gas in the North American area [18,24,25]. Many achievements have been realized with the progress of nano-scale characterization technologies, including high-pressure mercury injection porosimetry (MIP), gas adsorption methods (low-pressure nitrogen gas adsorption and carbon dioxide adsorption), argon ion polishing–field emission scanning electron microscope (AIP-FESEM), etc. The pore size distributions (PSDs) and pore structures are commonly quantitatively studied by using MIP, NMR, and gas adsorption methods [20,26,27]. The pore types, pore shape, pore genesis, spatial distribution, and connectivity are qualitatively observed using high-resolution images or three-dimensional models using FESEM, CT, FIB-SEM, AFM, and other observation methods [26,27,28,29,30,31]. Similar methods were also applied to study the pore structure characteristics of coal and tight sandstone reservoirs [19,32,33]. Simulation and modeling methods are applied based on experimental data to further reveal the relationship between pore structure and gas behavior [21,33]. For example, the fractal model, which can be established to study the relationship between pore structure, surface irregularities, and migration of gas, is commonly used as an effective tool to investigate the non-intuitive pore structure in nanoscale using data from MIP, LN2GA, or digital image information [34,35]. As revealed by previous studies, strong fractal characteristics of pores in coal, shale, and tight reservoirs provide a quantitative approach to describing structural features and their influence on gas accumulation and migration [36,37].
The interconnected reservoirs developed in coal-bearing strata and their influence on fluid pressure systems have raised scholars’ attention, but very few studies have been focused on the comparative study of reservoir characteristics. Recently, a few studies have focused on the comparison of different unconventional reservoirs in the aspects of geological conditions [38], reservoir characteristics and gas accumulation [35], flow unit and pore structure [39,40], and adsorption characteristics [41]. As reservoir pore research has progressed, the evaluation of pores has shifted from focusing on several specific structural characteristics (e.g., porosity, permeability, PSDs, etc.) to the comprehensive characterization of pore systems. The influencing mechanism of pore systems on reservoir physical properties and gas-bearing properties is extremely complex, and the description of several aspects of pore structure characteristics cannot meet the demand for understanding it. The characterization and evaluation of the pore system require a combination of qualitative knowledge (pore type, morphology, connectivity, etc.) and quantitative knowledge (PSD, adsorption characteristics, fractal characteristics, etc.). In this study, typical samples of high-rank coal, shale/mudstone, and tight sandstone reservoirs were selected from a coal-bearing stratum of Shanxi Formation to make a comparative study on the pore systems and pore networks of coal, shale, and tight sandstone reservoirs through experimental methods of LN2GA, MIP, and AIP-FESEM. The aims of this research are: (1) to compare the differences in pore types, genesis, PSD, and structure of the three types of reservoirs; (2) to summarize the characteristics of pore systems in the three types of reservoirs; and (3) to provide the scientific basis for co-exploration and co-mining of unconventional gases in coal-bearing strata from the perspective of pore systems.

2. Samples and Experimental Methods

2.1. Samples

Qinshui Basin, as one of China’s most important energy basins, is a synclinore located in Shanxi Province with large reserves of natural gas and coal. Shanxi Formation and Taiyuan Formation are the major exploration targets for the exploitation of coal and the co-exploration of coaly unconventional gases in Qinshui Basin [5,9,11,12,22,34]. Carboniferous Shanxi Formation is a set of coal-bearing strata originating in continental–oceanic interactive facies in the study area [22]. The deposits of Shanxi Formation are formed in the delta plain and delta front environment. As revealed by previous studies, Shanxi Formation is a major coal-bearing stratum developed in a regional marine regression depositional environment setting of the epicontinental sea with thickness between 30–70 m and finer particle size upward consisting of conglomerate, sandstone, sandy mudstone, mudstone, and coal [42].
The samples used in the study were taken from Y-4 well in the Yangzhuang exploration area of the Xishan coalfield (Figure 2). In total, 15 representative core samples, including 5 coal samples, 5 shale samples, and 5 tight sandstone samples, were selected from Y-4 well. The basic information of samples is shown in Table 1. The material compositions of coal samples differ greatly with shale and sandstone with OM content higher than 90%. All coal samples are vitrinite-rich and belong to high-rank coal (with the mean maximum reflectance of vitrinite (Ro,max) ≥ 2.50). The average volume fractions (Vol, %) of vitrinite and inertinite were 91.78% and 5.30%, respectively. The average content of minerals ranges between 0.5–6.4%. Kaolinite and quartz minerals were identified using FESEM and EDS. The material compositions of shale samples are mainly clay minerals and quartz with average proportions of 33.66% and 50.94%, respectively. The TOC content of shale samples ranges from 1.65% to 2.38%, with an average value of 2.004%. Some fragment fossils of plants can be found on the cross sections of shale samples. In terms of mineral components, the heterogeneity of material components in shale samples is relatively strong due to the differences in sedimentary environment and sedimentary conditions. Samples T1–T5 belong to tight sandstone reservoir according to porosity (4.76–8.06%) and air permeability data (0.08–0.25 × 10−3 μm2). Among them, sample T1 is argillaceous siltstone, T2–T4 are fine-grained sandstone, and T5 is medium-grained sandstone.

2.2. Experimental Protocol

Pore types, PSDs, and morphological characteristics were investigated by using SEM and FESEM to compare the pore development characteristics and pore network (Figure 3). The images of microfractures and macropores were obtained by using SEM with an EDS. Nanoscale pores were observed by using FESEM images on an argon-ion-polished surface area [28]. The pore size characteristics were obtained via image processing using ImageJ and Matlab software. This technology is helpful for studying the PSDs of different pore types independently based on the precise identification of different pore groups [21]. Structure indexes and PSDs of samples were studied by using low-pressure nitrogen gas adsorption (LP-N2GA) and high-pressure mercury intrusion porosimetry (MIP). In this study, the pores were classified into macropores (>1000 nm), mesopores (100 nm–1000 nm), transitional pores (10 nm–100 nm), and micropores (<10 nm) according to the scheme proposed by Hodot in 1966 [43]. Due to the limitations of MIP and LP-N2GA, various testing techniques were combined for a more accurate and thorough evaluation of pore structure [20,22]. The initial structure of nanoscale pores will be altered by the high pressure of MIP. Therefore MIP is more appropriate for and was thus adopted in the measurement of macro- and mesopores [25,44]. LP-N2GA was adopted to examine the structure and PSDs of micropores and transitional pores of tested samples because it is more precise than MIP in measuring the nanoscale pores of <100 nm. Subsequently, based on the experimental results of structure indexes, PSDs, pore types, descriptions of pores, and pore network characteristics of high-rank coal, shale, and tight sandstone samples, their pore types and classifications, pore networks, pore shape, and connectivity were compared, and their influence on gas storage and flow capacity was analyzed. Among them, pore shape and connectivity characteristics were revealed from the images, N2 adsorption–desorption curves, and mercury injection–withdraw curves. The N2 adsorption–desorption curves reflect the shape of adsorption pores (transitional pores and micropores), while mercury injection–withdraw curves reflect the shape of seepage pores (macropores and mesopores). A FHH fractal model was introduced to study the fractal characteristics and heterogeneity characteristics and make comparisons between reservoirs. The influence of pore development and pore network characteristics on the functions of different reservoirs in strata was discussed based on the comprehensive analysis of qualitative and quantitative experimental results.

2.3. Experimental Methods

Image processing is the most direct approach to studying the microstructure, genesis, and morphology of pores [45]. However, SEM imaging is not suitable to study the nanoscale PSDs of unconventional reservoirs. Argon board ion beam tools were firstly used to study shale nanoscale pores by providing a pseudogaussian surface with extremely high quality, thus making the observation of nanopores possible. Some research also used this method to investigate nanopores in coal and tight sandstone reservoirs [21,33,46]. Bulk samples were selected and polished by using a stand-alone argon beam machine (IB-09020CP Cross-sectioning Polisher, JEOL) and prepared via gold spray treatment, then imaged with a S-4700 field emission scanning electron microscope. Energy-dispersive X-ray spectroscopy (EDS) was used to study the genesis and type of pores in the observation. Note that the borders between clay and brittle minerals and mineralogical edges were determined by combining these two imaging methods during FESEM image processing and data support from an energy dispersive spectrometer (EDS). The secondary electron image mainly reflects the morphological characteristics of the sample surface at about 10 nm, and backscattered electron imaging can not only analyze morphological features as an imaging signal but can also be used to display atomic number contrast and qualitatively analyze components. During image processing, we combined these two types of images to determine the spatial location and types of pores and verified them with EDS data.
LP-N2GA was carried out to study pores with diameters ranging from 3.5 Å to 5000 Å using automated pore size and surface area analyzer (Tristar II 3020). Samples weighing 2 g with particle sizes between 60–80 mesh were dried for 2 h at 105℃ and outgassed for 12 h. High-pressure mercury intrusion porosimetry (MIP) was conducted using an AutoPore IV 9500 V1.09 Mercury intrusion tester as a method for quantifying PSDs at the Key Laboratory of Coalbed Methane Resources and Reservoir Formation Process, Ministry of Education. Selected bulk samples were sized to 0.1–1 cm3 and prepared by drying for 24 h.
The porosity and permeability values were determined using helium porosity measurement and the pressure pulse decay method (PDM), respectively. The testing of sample physical properties is mainly used to detect whether the sample belongs to typical tight sandstone and unconventional gas reservoirs and to support relevant discussions.

2.4. Fractal Models

On the basis of obtaining MIP and LP-N2GA data, the fractal characteristics of pore structures are obtained through the Menger sponge model and the Frenkel–Halsey–Hill (FHH) fractal model, respectively. This study established the Menger sponge model for seepage pores based on mercury intrusion data. The procedure for establishing Menger sponge model using MIP data can be found in [33]. For absorption pores, this study established the FHH fractal model based on LP-N2GA data of selected samples [47]. The FHH fractal model is described as:
Ln(V/V0) = λln(ln(p0/p)) + K
In Equation (1), p is the equilibrium pressure (Mpa) and p0 is the saturated gas pressure. Thus, p/p0 represents the relative pressure of the gas. V is the adsorbed gas volume under p, and V0 is the volume of monolayer molecule adsorbed gas. λ is a power law exponent dependent on fractal dimension (D) and adsorption mechanism. K is a constant value.
According to Equation (1), if the porous material is fractal, there will be a linear relationship between lnV and ln(ln(p0/p)) with a high linear correlation coefficient. The relationship of the power law exponent λ and the fractal dimension D can be derived from the plot as:
D = 3 + λ
From Equation (2), the fractal dimension D can be obtained [46].

3. Results

3.1. Pore Types and Genesis

The genetic types and shape of nanoscale pores were qualitatively studied using FESEM in combination with EDS data. Coal, shale, and tight reservoirs have significantly different pore genesis, especially in the nanoscale. The genetic types of nanopores were identified according to the differences in material compositions, morphological characteristics, and spatial locations of pores.
The genetic types of nanoscale pores in high-rank coal are mainly organic matter (OM) pores. Mineral pores are not well-developed because of their low mineral content. However, mineral pores have an important influence on pore networks and micropermeability [21]. At the microscale, the predominate genetic types of OM pores in coal are gas pores, which are similar to OM pores of shale/mudstone in shape and pore size because of the similar thermal process (Figure 4a), but some gas pores were filled with autogenous minerals during the reservoir diagenesis (Figure 4b). Few residual structure pores were observed at the microscale (Figure 4c). At the nanoscale, phyteral residual nanopores and microscopic organic constituents interparticle nanopores with smaller pore sizes (usually <100 nm) and various shapes, including triangular, slit-shaped, irregular, or quadrilateral shapes, predominate among OM pores in coal (Figure 4d–f). On a molecular scale, OM pores were proved to be metamorphic pores and intermolecular pores using an atomic force microscope (AFM) [21,48]. Thus, OM pores in coal have different types and formation mechanisms in different scales. Gas pores developed at the microscale have similar characteristics as OM pores in shale. It altered the understanding gained from the previous comparison between coal and shale pores under the insufficient resolution of coal nanoscale pores [35]. Mineral-related pores have relatively larger pore sizes (Figure 4g). Due to the special structure and mechanical properties, cleats and fractures are well-developed in high-rank coals (Figure 4h,i).
The size of pores in shale reservoirs was concentrated at the nanoscale. Complex genetic types of nanoscale pores were identified and divided into four major groups including OM pores, clay mineral pores, granular mineral pores, and (micro)fractures (Figure 5). The diameter of gas pores in OM ranges from 10 nm to 500 nm according to image processing results (Figure 5a,b). However, the development degree of OM pores is highly controlled by the OM type and thermal evolution process. In the observation, OM pores are absent in some samples (Figure 5c), showing the strong heterogeneity of pore development degree. The limited development of OM pores in shale increases the proportion of mineral-related pores in the reservoir space. Intergranular pores between lamellar clay crystals, pores between clay–mineral aggregates (Figure 5d,e), and pores on mineralogical edge (pores formed by dissolution or microfractures, Figure 5f,h) are well-developed and make a higher contribution to microscopic reservoir space compared with high-rank coals. Shale is characterized by low porosity and permeability. Thus, microfractures on the mechanical weak surface between different material compositions have exerted an important influence on improving the reservoir’s physical characteristics, serving as the seepage channels from nanoscale pores to fractures (Figure 5i).
The genetic types of pores in tight sandstone are almost all mineral-related pores. There is significant heterogeneity in the physical characteristics of tight sandstone because of the differences in sedimentary structure and material compositions. Three major groups of pores are observed and identified in tight sandstone: residual interparticle pores (primary pores) (RIPs) (Figure 6a,g,h); intercrystalline pores in clay interstitial substance (ICPs) (Figure 6b,d,f,g,i); and grain dissolution pores formed in soluble minerals (GDPs), such as calcite and feldspar (Figure 6b,e). Residual interparticle pores usually have larger pore sizes, which are followed by grain dissolution pores. Macropores and mesopores make a major contribution to the reservoir space. The pore size of intercrystalline pores is smaller compared to interparticle pores and dissolution pores, and thus, intercrystalline pores mostly belong to transitional pores. Pores in tight sandstone with complex genetic origins and PSDs are combined to form a complex pore–throat network. Due to the lack of organic matter, gases from the adjacent source rocks of coal or shale were enriched in tight reservoirs. Under the influence of a sedimentary environment, there is basically no organic matter originally deposited in tight sandstone reservoirs. Through thin rock section microscope observation, X-ray diffraction (XRD), and other tests, no organic matter components were found in the sample. Previous studies have also shown that the gas source of tight sandstone is mainly from adjacent coal seams, with a small amount coming from organic shale [11]. These gases are formed during reservoir diagenesis and the thermal evolution of organic matter and enter tight sandstone reservoirs through fractures and pore systems.
According to the above research and comparison, the coal reservoir is mainly composed of organic-matter-related pores. The pore types of shale are more complex, and both the mineral-related pores and OM-related pores have a certain degree of development. However, the pores are concentrated on the nanometer scale, with poor connectivity, according to the observation images. The pores of tight sandstone are predominantly composed of mineral pores and a small number of microfractures. The images of tight sandstone samples show that the pore size is relatively larger and that the connectivity between pores is significantly better.

3.2. Pore Size Distributions (PSDs)

The MIP method has been widely used to analyze pore throats with incremental intrusion plots [34]. PSDs of macro- and mesopores of coal, shale, and tight sandstone samples were obtained using MIP (Figure 7). As shown in Figure 7, coal, shale, and tight sandstone samples show various pore volumes and PSDs. It should be noted that coal samples and tight sandstone samples show significant volumes and multimodal distributions in macro- and mesopore size ranges, while shale samples are dominated by macropores within this range.
PSDs of absorption pores (micro- and mesopores) with diameters smaller than 100 nm were obtained using LP-N2GA. PSDs of transitional pores and micropores of coal, shale, and tight sandstone samples using LP-N2GA are illustrated in Figure 8. All samples have shown the bimodal PSD characteristics of two major peaks. It can be observed that high-rank coal, shale, and tight sandstone have similar pore size peaks at 20 nm and 40 nm. However, most of the tight sandstone samples have larger pore volumes than coal and shale. It is notable that shale samples have another minor but prominent peak at the pore size range between 1 and 4 nm, especially in samples of S-1 and S-5.

3.3. Pore Shape and Connectivity

Pore shape can be studied by using two approaches: image observation and the N2 adsorption–desorption curves or mercury injection–withdraw curves (Figure 9) [49]. By observing the SEM images of the sample cross section, specific descriptions of pore shapes of different types can be provided. In shale samples from Shanxi Formation, clay-mineral-related pores are mostly intergranular pores between lamellar clay and have slit-like shapes (Figure 4). OM pores with elliptical shapes or nonspherical shapes are the predominant pore type in coal and an important pore type in shale (Figure 4 and Figure 5). On the contrary, the pores related to minerals present complex and irregular pore morphology in coal and shale (Figure 4 and Figure 5). Thus, pores in coal and shale have some similarities in pore shape, especially the OM pores with similar genetic mechanisms. Reservoir space networks in tight sandstone are greatly different from coal and shale due to the special material compositions and microstructure. Tight sandstone does not contain OM, and material compositions can be significantly divided into two groups of sand-sized mineral particles and clay-sized particles occupying the pore spaces between the sand-sized particles. Pore networks in tight sandstone contain pores and throats. Different types of pores have different predominant shapes. For example, residual interparticle pores (RIPs) usually have a polygonal shape with larger pore sizes, while intercrystalline pores in clay interstitial substance (ICPs) show silt-like shapes with nanoscale pore sizes (Figure 6).
Figure 9 shows the corresponding pore shapes of mercury injection-withdraw curves and N2 adsorption–desorption curves. The mercury injection and withdrawal curves of the samples studied are shown in Figure 10. According to Figure 9a, the shapes of mercury injection and withdrawal curves are influenced by the spatial development characteristics of pores, including the shape and connectivity. The curves of coal, shale, and tight sandstone show huge differences in accumulative pore volume and morphology (Figure 10). Injection and withdrawal curves of shale are more open than coal samples, and therefore, flask pores are well-developed in shale considering there is a correspondence between curve shapes and pore morphology (Figure 10a). The coincidence degree between the mercury injection curve and the mercury withdrawal curve is relatively high in coal samples, showing that open pores are the predominant pores in high-rank coal (Figure 10b). Pore shapes in tight sandstone are complex, and open pores, flask pores, and semi-open pores are all developed there (Figure 10c). It should be noted that coal samples have the highest accumulative pore volume, which is followed by tight sandstone samples. Shale samples have the lowest accumulative pore volume.
The microreservoir space in unconventional reservoirs is mainly contributed by transitional pores (10 nm–100 nm) and micropores (<10 nm). Therefore, it is of significance to study the pore shape and connectivity in this size range. N2 adsorption–desorption curves of shale, coal, and tight sandstone samples are shown in Figure 11. According to Figure 8b, considering the corresponding relationship between adsorption–desorption loop morphology and pore morphology, transitional and micropores can be divided into four groups: cylindrical pores, fracture-shape pores, conical pores, and inkbottle-shape pores. From Figure 11a,b, it can be found that the pore morphology of transitional and micropores in shale and tight sandstone is similar in having mixed characteristics of types A, B, C, and F, as described in Figure 8b, which indicates that complex types of pores with different shapes are developed in these two groups of samples. The limited OM pores in shale and similar predominant pore type of intergranular pores between lamellar clay crystals may lead to the similarity of shale and tight sandstone in this pore size range. C-3 and C-5 high-rank coal samples have similar adsorption–desorption loops to shale and tight sandstone samples. Sample C-1, C-2, and C-4, however, are characterized by being more similar to type B in Figure 9b (Figure 12). The differences in adsorption–desorption loop morphology also show the heterogeneity of the pore structure of coal samples.

3.4. Fractal Characteristics

Based on MIP data, the fractal model of seepage pores is established. The fractal dimension obtained by the Menger sponge model is shown in Table 2. The high-pressure mercury intrusion fractal model of porous media can generally be divided into two stages—the pores are deformed or damaged in the high-pressure stage (corresponding D2 value, generally >3), and the low-pressure stage generally has significant fractal characteristics (D1 represents its fractal dimension) [33]. The fractal dimension D1 of the pore (mainly corresponding to macro- and mesopore) in the low-pressure stage of the test sample is shown in Table 3. The fractal dimensions of coal samples C-1–C-5 fluctuates greatly, ranging from 2.7335 to 3.1366. The variance of D1 value of the coal sample is 0.0188283464. The D1 value of the shale sample is between 2.9164 and 2.9829, with a variance of 0.000526418. The D1 value of tight sandstone samples also fluctuates significantly, between 2.8722 and 3.1820, with a variance of 0.011885722. From the D1 parameter value and its variance, it can be seen that the complexity of macroporous and mesoporous pore structures of different shale samples is similar, with the smallest change, so the structure is the most stable. However, coal samples and tight sandstone samples have significant changes, reflecting the significant differences in pore structure between samples.
The MIP method is not accurate when measuring nanoscale pores. Thus, the data of gas absorption was used to measure the fractal characteristics of micropores. The Frenkel–Halsey–Hill (FHH) fractal model [48], which has been proven to be effective in the fractal analysis of dense porous geologic materials, was used in this research. Gas adsorption data of relative pressure p/p0 >0.45 with a loop formed by the uncoincidence of adsorption and desorption curves were selected to build the FHH fractal model. The data of LP-N2GA were treated according to Equation (1), with ln (ln (p0/p)) being the x-axis and lnV being the y-axis. The curve and slope (R2) of each sample were obtained by using the principle of least squares (Figure 13) and the fractal dimension value D3 of each sample was calculated according to Equation (2) (Table 3).
Figure 12a shows the FHH plots of shale samples. The correlation coefficients of shale samples are all >0.95, and the S-2, S-4, and S-5 samples are larger than 0.98 (Table 3). All shale samples show a good linear relationship indicating that shale samples have obvious fractal characteristics. The D3 values of shale samples calculated using Equation (2) range from 2.7545 to 2.8601. The FHH plots of high-rank coal samples are shown in Figure 12b, with correlation coefficients ranging from 0.9314 to 0.9945 and D3 values from 2.3016 to 2.9192 (Table 3). Tight sandstone samples show a good linear relationship in FHH plots, with correlation coefficients ranging from 0.984 to 0.9993 and D3 values from 2.6328 to 2.751 (Table 3).
The fractal dimension value reflects the structure and spatial development characteristics of micropores in porous media [33]. For porous media, the fractal dimension value varies from 2 to 3. Value 2 represents an absolutely smooth surface, and if it is closer to 3, the pore structure will be more complex with stronger heterogeneity characteristics. By comparing the fractal characteristics and dimension values of coal, shale, and tight sandstone samples, the following results can be drawn. All the samples are obviously fractal, but the tight sandstone samples (average correlation coefficient of 0.99248) have a better linear relationship than shale (0.9819) and coal (0.96708). The shale samples (average D3 of 2.79592) have stronger fractal characteristics than coal (2.62244) and tight sandstone (2.68544), indicating that pores in shale are more complex in structure, type, and spatial development characteristics than the other two. Additionally, the higher D values indicate that the heterogeneity characteristics and the complexity of pore structure of shale are stronger. Pores in shale are developed in or between the complex material compositions of various clay minerals, clastic minerals, and OM. Pores in high-rank coal have a predominant type of OM pores. Tight sandstone does not contain OM, and pores and throats are controlled by inorganic minerals. Therefore, the pore networks in shale are more complex and more irregular than in high-rank coal and tight sandstone. The complex pore types and structure are also observed in FESEM images with good consistency with the fractal modeling results, which gives direct proof of the complex types, proportions, structure, pore composition, and spatial distribution of different reservoirs.
Overall, the test samples are significantly fractal within the different pore size ranges. From the processing results of mercury intrusion data, the slope of the fitting curve is different between the relatively low-pressure section and the higher-pressure section. The fractal dimensions obtained by the FHH model based on low-temperature nitrogen adsorption data are also different from those obtained by mercury intrusion. Therefore, although pores developed within different pore size ranges have fractal characteristics, their corresponding fractal dimension values are different. The fractal dimension can represent the irregularity of the sample surface or the complexity of the pore structure. The larger the fractal dimension, the rougher the surface for adsorption pores. For seepage pores, the larger the fractal dimension, the more complex the pore structure of the sample and the stronger the heterogeneity. Thus, the non-intuitive reflection of fractal dimension on pore surface structure and pore spatial structure can be used to establish evaluation schemes for pore complexity and heterogeneity.

4. Discussion

4.1. Comparison of Pore Networks

As shown in Table 4, a comparison of pore characteristics and physical characteristics was conducted between coal, shale, tight sandstone, and conventional sandstone, which illustrates the differences in lithology, material compositions, porosity, permeability, pore distribution, gas occurrence, and pore types. Shale and mudstone with exceptionally low permeability are the gas-bearing reservoirs as well as the cap rocks between other reservoirs in strata since coal and conventional sandstone have substantially better permeability, which is followed by tight sandstone reservoirs. In terms of pore networks, coal, shale, and tight sandstone reservoirs are significantly different in pore type, PSD, and connectivity at different scales. Therefore, a systematic classification and comparison of reservoir space are needed to better understand the similarities and differences between these unconventional reservoirs.
The systematic classification of reservoir space from the nanoscale to fracture scale of high-rank coal, shale/mudstone, and tight gas reservoirs is shown in Table 5. High-rank coal reservoirs are characterized by well-developed nanoscale OM pores (concentrated <100 nm) in coal matrix and well-connected fracture–cleat networks because of the special mechanical properties of coal, but macropores and mesopores are underdeveloped with the more isolated spatial distribution. The major pore types in coal are molecular pores of <10 nm, phyteral residual nanopores of 10 nm–100 nm, and fractures (including cleats) at the μm–mm scale, respectively. Shale has similar structural characteristics to coal, with well-developed nanoscale OM pores, but its microfractures and fractures are less developed compared to coal. This indicates a lower permeability and better sealing ability of shale reservoirs considering that the development of microfractures and fractures are the major influencing factors of nanoscale reservoirs. OM nanopores, clay mineral pores, and microfractures are the most important and developed pore types in shale. However, compared to coal and shale, tight sandstone reservoirs have higher porosity and permeability controlled by different pore types and PSD. Mineral-related pores, represented by intergranular pores, are the major pore types in tight sandstone, while intergranular pores develop better at the nanoscale and worse at the mesoscale. The connectivity between pores is mainly controlled by original fillings between particles and their changes during diagenesis.

4.2. Fractal Characteristics and Heterogeneity of Pores

Pore structure parameters (PSDs, pore morphology, pore type, etc.) and physical parameters, such as porosity and permeability, are commonly used for the evaluation of unconventional reservoirs. However, practical problems during exploration show that it is difficult to build a sufficiently accurate reservoir prediction scheme based on these parameters. This is mainly because non-intuitive parameters, such as heterogeneity, are ignored, especially when reservoir evaluation or prediction is carried out in the gas-bearing system composed of layers with different properties or lithology. As a parameter for evaluating the self-similarity of structure, fractal dimension can measure the similarity between different scales and further characterize the complexity and heterogeneity of pore structure to a certain extent.
Table 2 and Table 3 list the fractal parameters of the samples. Fractal modeling results show that adsorption pores and seepage pores are significantly fractal. The difference between the D1 value and D3 value of each sample can reflect the relative complexity of its pore structure. However, we found that the fractal dimension parameters of different lithologic samples are also significantly different. This is mainly manifested in (1) the degree of change between samples of the same lithology and (2) the stability of these data for different lithology samples.
D1 values are calculated based on data from seepage pores (Table 2). Comparing the D1 values of coal, shale, and tight sandstone samples, it can be seen that the data difference between shale samples and the fluctuation is the smallest. Under the control of reservoir diagenesis, all shale samples mainly preserve nanoscale pores, and the characteristics of pore structure are more stable. This may be affected by the particle size of the material components and the scale effect.
Nanoscale pores in unconventional reservoirs have obvious heterogeneity characteristics that significantly affect the enrichment, storage, and transportation of gases as well as the spatial distribution, brittleness, and productivity of reservoirs [30]. Nano-scale pores are the predominant reservoir space for gas absorption and storage in tested samples. The development of transitional and microabsorption pores controls the gas-bearing and microscale seepage capacity of the matrix. In terms of the variance of the three sets of data, the variance of D3 value of the shale sample is 0.0012046136, that of coal is 0.0515439196, and that of tight sandstone is 0.0017734504. Therefore, the random variable of the shale sample has the smallest deviation from the mean value, indicating a relatively stable structure and little fluctuation in samples. The random variable of D3 value of the coal sample has the largest deviation from the mean value, showing that the data is unstable, and the samples vary significantly. The data fluctuation of D3 value of the tight sandstone sample is higher than that of shale but lower than that of the coal sample. Therefore, in addition to the pore structure of shale being more complicated and the connectivity being relatively poorer, the difference and heterogeneity between shale samples are relatively weaker. The adsorption pore structure of coal and tight sandstone samples is relatively simpler compared to shale, but the difference between samples is more significant.
In the stratum, the lithology changes obviously, and the stratigraphic superposition of coal, shale, and sandstone leads to strong interlayer heterogeneity. However, shale samples from different intervals have similar fractal dimensions, indicating that under the influence of common factors, their pore structures are similar in complexity and connectivity. Compared to the other two types of samples, shale has the most complicated pore structure, the poorest connectivity, and the weakest heterogeneity between each interval. The differences in the pore structure of coal, shale, and tight sandstone contribute to the formation of multi-superimposed gas systems in the coal measure strata.
The fractal dimension values are influenced by many different factors, such as methods for obtaining experimental data, models for processing data, and sample preparation procedures. Note that different fractal dimensions obtained from different models or methods are not suitable for direct comparison with each other due to the impact of data significance and scale issues [50].

4.3. Inspirations for Multi-Superimposed Gas System

The physical properties of rocks determine their role as a reservoir, interlayers, or source rock in complex gas-bearing systems. The microscopic pore structure controls the physical properties of the sample, and the heterogeneity of the sample affects its physical properties. Therefore, pore structure data can provide important information for exploring the formation mechanism of multi-superposed gas systems. The following information can be obtained from the study on the pore structure characteristics of coal, shale, and tight sandstone in coal measure strata (Figure 14):
(1)
In terms of pore development degree, tight sandstone samples have the most developed pores and more reservoir space than shale and coal at nano- and micrometer scales. The pores of coal samples are more developed than those of shale samples.
(2)
In terms of the pore development scale, the pores of shale and coal are relatively developed in the nanoscale, and the pores of tight sandstone are relatively developed in the macropore and mesopore size ranges. It should be noted that the pores of shale samples are poorly developed in the range of mesopores, which shows the connectivity of intersecting pore systems.
(3)
In terms of pore development type, the nanopores developed in coal are mainly composed of organic matter pores with strong adsorption and storage capacity. The shale pore system consists of organic matter pores and mineral pores, but the pore development is poor and the structure is complex. The pores of tight sandstone are mainly composed of mineral pores, and the reservoir space is better developed, but the adsorption is relatively poor.
(4)
In terms of pore connectivity, the shale pore system has the worst connectivity, followed by coal, and the tight sandstone pore system has the best connectivity.
(5)
In terms of stability and heterogeneity of pore system structure, shale samples from different intervals have the most similar overall pore characteristics, with good stability and relatively weak heterogeneity between intervals and samples. Coal samples take second place, and the heterogeneity between the dense sandstone samples is the strongest.
In transitional coal-bearing strata, the coal seam, organic-rich mudstone or shale, and tight sandstone reservoirs are interbedded, forming an unconventional gas-bearing system [16,51,52]. Pore development and network characteristics greatly influence the roles of different reservoirs in strata. OM pores are proven to be the best microreservoir space in unconventional reservoirs, with strong aerophilic and adsorption capacity. Therefore, the predominant pore type of OM pores and well-developed nanoscale reservoir space in high-rank coal reservoirs are beneficial to the gas adsorption and storage in strata. It is notable that there are special mechanical properties in high-rank coal reservoirs, and its pore–fracture–cleat structure with mesopores and micropores is not developed as well as nanopores and cleats. Shale reservoirs with more complex material compositions have similar pore network characteristics in the aspect that they have well-developed nanopores and are undeveloped in the pore size range of mesopores. However, the fracture scale reservoir space is observed less developed than coal, which limits the flow capacity through shale. Because of the undeveloped mesopores in coal and shale, the reservoirs lack seepage channels between nanopores and fractures. Thus, high-rank coal is the major source rock with strong adsorption and storage capacity and well-developed shale is the separating layer of different pressure systems in strata with relatively lower storage capacity. Tight sandstone is different from coal and shale. Due to the lack of OM, tight sandstone is not the source rock in strata. The nanoscale pores in tight sandstone are less developed compared to coal and shale, resulting in a higher content of free gas there and the inability to be the separating layer. Thus, in transitional coal-bearing strata, the coal is the major source rock, and the well-developed shale is the key separating layer between different gas pressure systems. The systematic comparison of the pore development characteristics and pore network characteristics of high-rank coal, shale, and tight sandstone will help to reveal the mechanism controlling the functions of different reservoirs in strata. Due to differences in stratum thickness, organic matter content, and thermal evolution degree, the importance of gas sources of coal and shale varies in different strata. The relative importance of coal versus shale as a gas source in other strata requires an analysis based on corresponding data and background.
Pore network characteristics are comprehensively reflected by pore type, proportion, morphological characteristics, and spatial distribution. The systematic comparison of pore network characteristics can provide theoretical support for analyzing the role and genesis of different reservoirs in coal-bearing strata in the gas-bearing system. Serving as the primary gas source, coal is a better reservoir that has stronger storage capacity and hydrocarbon generation capacity than shale because of its enrichment of organic matter. A tight sandstone reservoir has a higher degree of pore development, which is an important potential factor for better overall gas-bearing properties in the strata. However, due to the absence of organic matter, it tends to become the high-free-gas-content section in the gas-bearing system. Systematic comparison of pore development characteristics will provide scientific support to further explain the genesis of multi-superimposed gas systems in coal-bearing strata from the perspective of pore networks. There are multiple independent subsystems within the multi-superimposed gas system in coal-bearing strata, which affect the engineering measures and effects of gas development. These new findings can also provide guidance for the development of unconventional natural gas in coal-bearing strata. Note that the pore system is one of the most important control factors.

5. Conclusions

(1) The nanoscale pores of high-rank coal are mainly organic matter pores. The nanoscale pore types in the shale are complex, the connectivity is poor, and the overall pore development degree is low—mainly clay intergranular pores and OM nano-pores. Mineral-related pores, such as intercrystalline pores, in clay minerals occupy the major reservoir space in tight sandstone.
(2) The mesopores of shale and coal are poorly developed compared to tight sandstone. The nanopores of tight sandstone have lower proportion of reservoir space than shale and coal. Significant differences in connectivity and storage performance, as well as stability of structural characteristics between different samples (heterogeneity), lead to pore systems with different physical properties and gas-bearing capacities.
(3) According to the comprehensive analysis of pore structure, heterogeneity, and other factors, coal is a better reservoir in strata that have stronger storage capacities and hydrocarbon generation capacities. A tight sandstone reservoir has a higher degree of pore development, which is an important potential factor for better overall gas-bearing properties in the strata. The pore structure and heterogeneity characteristics of shale determine that it is more likely to become an interlayer between gas-bearing subsystems. Continuously developed shale of a specific thickness is more likely to grow into interlayers between gas-bearing systems.
(4) From the point of view of pore system and heterogeneity, the study of the difference comparison of different lithological intervals in coal measures can provide a scientific basis for the study of the formation mechanism of multi-superposed gas systems. It can be concluded that in addition to the pore structure characteristics of the sample itself, the differences and heterogeneity between samples can also be important evaluation and research parameters.

Author Contributions

Conceptualization, D.Z. and W.J.; Methodology, D.Z.; Validation, Q.W. and Y.L.; Formal analysis, D.Z. and Q.W.; Investigation, D.Z. and X.G.; Data curation, J.Z. and X.Z.; Writing—original draft, D.Z., X.G. and W.J.; Writing—review & editing, X.G. and D.Z.; Visualization, D.L.; Supervision, G.W. and Y.G.; Project administration, D.Z., G.W. and Y.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Natural Science Foundation of Jiangsu Province (No. BK20210521), the science and technology research program of Chongqing Municipal Education Commission (No.KJZD-K202103201), the Fundamental Research Funds for the Central Universities (No. 2021QN1061) and Provincial Innovation Training Plan for College Students of Jiangsu Province (202210290394H).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All data needed to evaluate the conclusions in the paper are present in the paper. Additional data related to this paper are available from the authors upon request.

Acknowledgments

The author Z.D. appreciates the QIHANG Project of China University of Mining and Technology for its help. The authors appreciate Zhang Qing and Lu Mengyu for their help in language editing.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

MIPmercury injection porosimetry
LN2GAlow-pressure nitrogen gas adsorption
AIPargon ion polishing
FESEMfield emission scanning electron microscope
CBMcoalbed methane
PSDspore size distributions
Ro,maxmean maximum reflectance of vitrinite
CTcomputed tomography
FIBfocused ion beam
SEMscanning electron microscope
AFMatomic force microscope
EDSenergy dispersive spectrometer
FHH modelFrenkel–Halsey–Hill model
OMorganic matter
RIPsresidual interparticle pores
ICPsintercrystalline pores in clay interstitial substance
GDPsgrain dissolution pores
XRDX-ray diffraction

References

  1. Cao, D.Y.; Yao, Z.; Li, J. Evaluation Status and Development Trend of Unconventional Gas in Coal Measure. Coal Sci. Technol. 2014, 42, 89–92. [Google Scholar]
  2. He, J.; Zhan, X.; Ma, L.; Wu, H.; Ashraf, M.A. Geological Characteristics of Unconventional Gas in Coal Measure of Upper Paleozoic Coal Measures in Ordos Basin, China. Earth Sci. Res. J. 2016, 20, Q1–Q5. [Google Scholar] [CrossRef]
  3. Zhao, S.H.; Wang, Y.B.; Li, Y.; Wu, X.; Hu, Y.J.; Ni, X.M.; Liu, D. Co-production of Tight Gas and Coalbed Methane from Single Wellbore: A Simulation Study from Northeastern Ordos Basin, China. Nat. Resour. Res. 2021, 30, 1595–1612. [Google Scholar] [CrossRef]
  4. Bi, C.Q.; Zhang, J.Q.; Shan, Y.S.; Hu, Z.F.; Wang, F.G.; Chi, H.P.; Tang, Y.; Yuan, Y.; Liu, Y.R. Geological characteristics and co-exploration and co-production methods of Upper Permian Longtan coal measure gas in Yangmeishu Syncline, Western Guizhou Province, China. China Geol. 2020, 3, 38–51. [Google Scholar] [CrossRef]
  5. Xie, W.D.; Gan, H.J.; Chen, C.Y.; Vandeginste, V.; Chen, S.; Wang, M.; Wang, J.Y.; Yu, Z.H. A model for superimposed coalbed methane, shale gas and tight sandstone reservoirs, Taiyuan Formation, Yushe-Wuxiang Block, eastern Qinshui Basin. Sci. Rep. 2022, 12, 11455. [Google Scholar] [CrossRef] [PubMed]
  6. Tang, X.; Zhang, J.C.; Shan, Y.S.; Xiong, J.Y. Upper Paleozoic coal measures and unconventional natural gas systems of the Ordos Basin, China. Geosci. Front. 2012, 3, 863–873. [Google Scholar] [CrossRef] [Green Version]
  7. Fu, X.H.; Jianatayi, D.; Zhu, Y.M.; Li, G. Resources characteristics and separated reservoirs’ drainage of unconventional gas in coal measures. Earth Sci. Front. 2016, 23, 36–40. [Google Scholar]
  8. Hu, H.J.; Sang, S.X.; Yang, J.F.; Jin, J.; Liu, H.H.; Zhou, X.Z.; Gao, W. Evaluation of coal and shale reservoir in Permian coal-bearing strata for development potential: A case study from well LC-1# in the northern Guizhou, China. Energy Explor. Exploit. 2019, 37, 194–218. [Google Scholar]
  9. Qin, Y.; Liang, J.S.; Shen, J.; Liu, Y.G.; Wang, C.W. Gas logging shows and gas reservoir types in tight sandstones and shales from Southern Qinshui Basin. J. China Coal Soc. 2014, 39, 1559–1565. [Google Scholar]
  10. Gao, W.; Tian, W.J.; Qin, W.; Kong, W.M.; Chen, M. Geological optimization of coalbed methane and shale gas co-exploration and concurrent production in Guizhou Province (in Chinese). Fault-Block Oil Gas Field 2014, 21, 36–38. [Google Scholar]
  11. Shao, L.Y.; Xiao, Z.H.; Lu, J.; He, Z.P.; Wang, H.; Zhang, P.F. Permo-Carboniferous coal measures in Qinshui Basin: Lithofacies paleogeography and its control on coal accumulation. Front. Earth Sci. China 2007, 1, 106–115. [Google Scholar] [CrossRef]
  12. Zhao, D.; Guo, Y.; Yin, S.; Ren, C.; Wang, Y. Prediction of geomechanical sweet spots in a tight gas sandstone reservoir: A case study of lower Permian strata in the southern Qinshui Basin, China. Interpret. A J. Subsurf. Charact. 2019, 7, 207–219. [Google Scholar] [CrossRef]
  13. Luo, W.; Hou, M.C.; Liu, X.C.; Huang, S.G.; Chao, H.; Zhang, R.; Deng, X. Geological and geochemical characteristics of marine-continental transitional shale from the Upper Permian Longtan formation, Northwestern Guizhou, China. Mar. Pet. Geol. 2018, 89 Pt 1, 58–67. [Google Scholar] [CrossRef]
  14. Yang, Z.B.; Zhang, Z.G.; Qin, Y.; Wu, C.C.; Yi, T.S.; Li, Y.Y.; Tang, J.; Chen, J. Optimization methods of production layer combination for coalbed methane development in multi-coal seams. Pet. Explor. Dev. 2018, 45, 312–320. [Google Scholar] [CrossRef]
  15. Shen, Y.L.; Qin, Y.; Wang, G.G.; Guo, Y.H.; Shen, J.; Gu, J.Y.; Xiao, Q.; Zhang, T.; Zhang, C.L.; Tong, G.C. Sedimentary Control on the Formation of a Multi-Superimposed Gas System in the Development of Key Layers in the Sequence Framework. Mar. Pet. Geol. 2017, 88, 268–281. [Google Scholar] [CrossRef]
  16. Shen, Y.L.; Qin, Y.; Guo, Y.H.; Yi, T.S.; Yuan, X.X.; Shao, Y.B. Characteristics and Sedimentary Control of a Coalbed Methane-bearing System in Coal-bearing Strata in the Lopingian of the Permian, Western Guizhou Province. J. Nat. Gas Sci. Eng. 2016, 33, 8–17. [Google Scholar] [CrossRef]
  17. Loucks, R.G.; Reed, R.M.; Ruppel, S.C.; Hammes, U. Spectrum of pore types and networks in mudrocks and a descriptive classification for matrix-related mudrock pores. AAPG Bull. 2012, 96, 1071–1098. [Google Scholar] [CrossRef] [Green Version]
  18. Chalmers, G.R.; Bustin, R.M.; Power, I.M. Characterization of gas shale pore systems by porosimetry, pycnometry, surface area, and field emission scanning electron microscopy/transmission electron microscopy image analyses: Examples from the Barnett, Woodford, Haynesville, Marcellus, and Doig units. AAPG Bull. 2012, 96, 1099–1119. [Google Scholar]
  19. Mastalerz, M.; He, L.; Melnichenko, Y.B.; Rupp, J.A. Porosity of coal and shale: Insights from gas adsorption and SANS/USANS techniques. Energy Fuels 2012, 26, 5109–5120. [Google Scholar] [CrossRef]
  20. Clarkson, C.R.; Solano, N.; Bustin, R.M.; Bustin, A.M.M.; Chalmers, G.R.L.; He, L.; Melnichenko, Y.B.; Radlinski, A.P.; Blach, T.P. Pore structure characterization of North American shale gas reservoirs using USANS/SANS, gas adsorption, and mercury intrusion. Fuel 2013, 103, 606–616. [Google Scholar] [CrossRef]
  21. Zhao, D.F.; Guo, Y.H.; Wang, G.; Mao, X.X. Characteristizing nanoscale pores and its structure in coal–Experimental investigation. Energy Explor. Exploit. 2019, 37, 1–28. [Google Scholar] [CrossRef] [Green Version]
  22. Kenomore, M.; Hassan, M.; Malakooti, R.; Dhakal, H.; Shah, A. Shale gas production decline trend over time in the Barnett Shale. J. Pet. Sci. Eng. 2018, 165, 691–710. [Google Scholar] [CrossRef] [Green Version]
  23. Khormali, A.; Koochi, M.R.; Varfolomeev, M.A.; Ahmadi, S. Experimental study of the low salinity water injection process in the presence of scale inhibitor and various nanoparticles. J. Pet. Explor. Prod. Technol. 2023, 13, 903–916. [Google Scholar] [CrossRef]
  24. Cui, G.D.; Niu, Z.; Zhao, D.F.; Kong, Y.L.; Feng, B. High-temperature hydrothermal resource exploration and development: Comparison with oil & gas resource. Gondwana Res. 2022; in press. [Google Scholar]
  25. Okolo, G.N.; Everson, R.C.; Neomagus, H.W.J.P.; Roberts, M.J.; Sakurovs, R. Comparing the porosity and surface areas of coal as measured by gas adsorption, mercury intrusion and SAXS techniques. Fuel 2015, 141, 293–304. [Google Scholar] [CrossRef]
  26. Sun, M.; Yu, B.; Hu, Q.; Chen, S.; Xia, W.; Ye, R. Nanoscale pore characteristics of the Lower Cambrian Niutitang Formation Shale: A case study from Well Yuke# 1 in the Southeast of Chongqing, China. Int. J. Coal Geol. 2016, 154, 16–29. [Google Scholar]
  27. Loucks, R.G.; Reed, R.M.; Ruppel, S.C.; Jarvie, D.M. Morphology, genesis, and distribution of nanometer-scale pores in siliceous mudstones of the Mississippian Barnett Shale. J. Sediment. Res. 2009, 79, 848–861. [Google Scholar] [CrossRef] [Green Version]
  28. Bernard, S.; Wirth, R.; Schreiber, A.; Mathia, E.J. FIB-SEM and TEM investigations of an organic-rich shale maturation series from the lower Toarcian Posidonia Shale, Germany: Nanoscale pore system and fluid-rock interactions. Electron Microsc. Shale Hydrocarb. Reserv. AAPG Mem. 2013, 102, 53–66. [Google Scholar]
  29. Jiao, K.; Yao, S.; Liu, C.; Gao, Y.Q.; Wu, H.; Li, M.C.; Tang, Z.Y. The characterization and quantitative analysis of nanopores in unconventional gas reservoirs utilizing FESEM–FIB and image processing: An example from the lower Silurian Longmaxi Shale, upper Yangtze region, China. Int. J. Coal Geol. 2014, 128, 1–11. [Google Scholar] [CrossRef]
  30. Zhao, D.F.; Yin, S.; Guo, Y.H.; Ren, C.Y.; Wang, R.Y.; Ding, W.L.; Liu, J.D. Investigation of pore structure characteristics of marine organic-rich shales using low-pressure N2 adsorption experiments and fractal theory. Interpret.-A J. Subsurf. Charact. 2019, 7, 671–685. [Google Scholar] [CrossRef]
  31. Nelson, P.H. Pore-throat sizes in sandstones, tight sandstones, and shales. AAPG Bull. 2009, 93, 329–340. [Google Scholar] [CrossRef]
  32. Clarkson, C.R.; Freeman, M.; He, L.; Agamalian, M.; Melnichenko, Y.B.; Mastalerz, M.; Bustin, R.M.; Radlinski, A.P.; Blach, T.P. Characterization of tight gas reservoir pore structure using USANS/SANS and gas adsorption analysis. Fuel 2012, 95, 371–385. [Google Scholar] [CrossRef]
  33. Zhao, D.; Guo, Y.; Wang, G.; Guan, X.; Zhou, X.; Liu, J. Fractal Analysis and Classification of Pore Structures of High-Rank Coal in Qinshui Basin, China. Energies 2022, 15, 6766. [Google Scholar] [CrossRef]
  34. Liu, X.; Xiong, J.; Liang, L. Investigation of pore structure and fractal characteristics of organic-rich Yanchang formation shale in central China by nitrogen adsorption/desorption analysis. J. Nat. Gas Sci. Eng. 2015, 22, 62–72. [Google Scholar] [CrossRef]
  35. Liu, Y.; Zhu, Y. Comparison of pore characteristics in the coal and shale reservoirs of Taiyuan Formation, Qinshui Basin, China. Int. J. Coal Sci. Technol. 2016, 3, 330–338. [Google Scholar] [CrossRef] [Green Version]
  36. Fu, X.H.; Qin, Y.; Zhang, W.H.; Wei, C.T.; Zhou, R.F. Fractal classification and natural classification of coal pore structure based on migration of coal bed methane. Chin. Sci. Bull. 2005, 50, 66–71. [Google Scholar] [CrossRef]
  37. Meng, Z.P.; Liu, C.L.; Ji, Y.M. Geological conditions of coaled methane and shale gas exploitation and their comparison analysis. J. China Coal Soc. 2013, 38, 728–736. [Google Scholar]
  38. Wang, Y.; Zhu, Y.M.; Liu, Y.; Chen, S.B. Reservoir characteristics of coal-shale sedimentary sequence in coal-bearing strata and their implications for the accumulation of unconventional gas. J. Geophys. Eng. 2018, 15, 411–420. [Google Scholar] [CrossRef] [Green Version]
  39. Clarkson, C.R.; Jensen, J.L.; Pedersen, P.K.; Freeman, M. Innovative methods for flow-unit and pore-structure analyses in a tight siltstone and shale gas reservoir. AAPG Bull. 2012, 96, 355–374. [Google Scholar] [CrossRef]
  40. Li, X.F.; Pu, Y.C.; Sun, C.Y.; Ren, W.; Li, Y.; Zhang, Y.; Jing, L.; Zang, J.; Hu, A.; Wen, S. Recognition of absorption/desorption theory in coalbed methane reservoir and shale gas reservoir. Acta Pet. Sin. 2014, 35, 1113–1129. [Google Scholar]
  41. Su, X.B.; Lin, X.Y.; Zhao, M.J.; Song, Y.; Liu, S.B. The upper Paleozoic coalbed methane system in Qinshui Basin, China. AAPG Bull. 2005, 89, 81–100. [Google Scholar] [CrossRef]
  42. Song, S.Z.; Wang, Y.A.; Huoduote, B.B. Coal and Gas Outburst Mechanism; China Industry Press: Beijing, China, 1966. [Google Scholar]
  43. Giesche, H. Mercury porosimetry: A general (practical) overview. Part. Part. Syst. Charact. 2006, 23, 9–19. [Google Scholar] [CrossRef]
  44. Ziegler, K. Clay minerals of the permian Rotliegend group in the North Sea and adjacent areas. Clay Min. 2006, 41, 355–393. [Google Scholar] [CrossRef]
  45. Zou, C.N.; Zhu, R.K.; Liu, K.Y.; Su, L.; Bai, B.; Zhang, X.X.; Yuan, X.J.; Wang, J. Tight gas sandstone reservoirs in China: Characteristics and recognition criteria. J. Pet. Sci. Eng. 2018, 88–89, 82–91. [Google Scholar] [CrossRef]
  46. Pfeiferper, P.; Avnir, D. Chemistry nonintegral dimensions between two and three. J. Chem. Phys. 1983, 79, 3369–3558. [Google Scholar]
  47. Wang, G.C.; Ju, Y.W.; Yan, Z.F.; Li, Q.G. ore structure characteristics of coal-bearing shale using fluid invasion methods: A case study in the Huainan–Huaibei Coalfield in China. Mar. Pet. Geol. 2015, 62, 1–13. [Google Scholar] [CrossRef]
  48. Yao, S.; Jiao, K.; Zhang, K.; Hu, W.X.; Ding, H.; Li, M.C.; Pei, W.M. An atomic force microscopy study of coal nanopore structure. Chin. Sci. Bull. 2011, 56, 2706–2712. [Google Scholar] [CrossRef] [Green Version]
  49. Qin, Y. Micropetrology and Structural Evolution of High-Rank Coals in P. R. China; China University of Mining and Technology Press: Xuzhou, China, 1994. [Google Scholar]
  50. Munawar, M.J.; Vega, S.; Lin, C.Y.; Alsuwaidi, M.; Ahsan, N.; Bhakta, R.R. Upscaling Reservoir Rock Porosity by Fractal Dimension Using Three-Dimensional Micro-Computed Tomography and Two-Dimensional Scanning Electron Microscope Image. J. Energy Resour. Technol.-Trans. ASME. 2020, 143, 013003. [Google Scholar] [CrossRef]
  51. Rigby, S.P. Predicting surface diffusivities of molecules from equilibrium adsorption isotherms. Colloids Surf. Physicochem. Eng. Asp 2005, 262, 139–149. [Google Scholar] [CrossRef]
  52. Zhao, D.; Guo, Y.; Liu, J.; Cao, L.; Chong, X.; Li, G.; Wang, Y.; Wei, Y. Review on the Current Situation, Progress and Direction of the Researchon the Geological Theory of Shale Reservoir Heterogeneity. Unconv. Oil Gas 2020, 7, 1–4. [Google Scholar]
Applsci 13 04414 g001 550
Figure 1. Three types of unconventional reservoirs in a coal-bearing stratum.
Figure 1. Three types of unconventional reservoirs in a coal-bearing stratum.
Applsci 13 04414 g001
Applsci 13 04414 g002 550
Figure 2. The study area of the Yangzhuang exploration area, the Xishan coalfield, and sampling locations of the Transitional Upper Carboniferous Shanxi Formation in Y-4 well.
Figure 2. The study area of the Yangzhuang exploration area, the Xishan coalfield, and sampling locations of the Transitional Upper Carboniferous Shanxi Formation in Y-4 well.
Applsci 13 04414 g002
Applsci 13 04414 g003 550
Figure 3. Comparison of pore systems of three types of unconventional reservoirs in coal-bearing strata.
Figure 3. Comparison of pore systems of three types of unconventional reservoirs in coal-bearing strata.
Applsci 13 04414 g003
Applsci 13 04414 g004 550
Figure 4. Microreservoir space of nano–micro pores and small-scale fractures in high-rank coal matrix. (a) Gas pores in microscale; (b) gas pores with autogenous minerals; (c) residual structure pores in microscale; (d,e) organic constituents interparticle nanopores; (f) pore size processing of nanopores; (g) mineral pores in coal; (h) fractures; (i) cleats.
Figure 4. Microreservoir space of nano–micro pores and small-scale fractures in high-rank coal matrix. (a) Gas pores in microscale; (b) gas pores with autogenous minerals; (c) residual structure pores in microscale; (d,e) organic constituents interparticle nanopores; (f) pore size processing of nanopores; (g) mineral pores in coal; (h) fractures; (i) cleats.
Applsci 13 04414 g004
Applsci 13 04414 g005 550
Figure 5. Microreservoir space of nano–micro pores and small-scale fractures in transitional shale gas reservoirs. (a) Pores and pyrite developed within organic matter; (b) Gas pores in OM; (c) OM with pores not developed; (d) intergranular pores between lamellar clay crystals; (e) pores between clay mineral aggregates; (f–h) pores on mineralogical edge; (i) microfractures on the mechanical weak surface.
Figure 5. Microreservoir space of nano–micro pores and small-scale fractures in transitional shale gas reservoirs. (a) Pores and pyrite developed within organic matter; (b) Gas pores in OM; (c) OM with pores not developed; (d) intergranular pores between lamellar clay crystals; (e) pores between clay mineral aggregates; (f–h) pores on mineralogical edge; (i) microfractures on the mechanical weak surface.
Applsci 13 04414 g005
Applsci 13 04414 g006 550
Figure 6. Microreservoir space of nano–micro pores in tight sandstone reservoirs. (a) RIPs and ICPs under high-resolution microscope; (b) GDPs under high-resolution microscope; (c) ICPs under high-resolution microscope; (d) RIPs, GDPs and ICPs under FESEM; (e) GDPs under FESEM; (f) ICPs under FESEM; (g) RIPs and secondary quartz under SEM; (h) RIPs and ICPs under SEM; (i) ICPs and authigenic kaolinte under SEM.
Figure 6. Microreservoir space of nano–micro pores in tight sandstone reservoirs. (a) RIPs and ICPs under high-resolution microscope; (b) GDPs under high-resolution microscope; (c) ICPs under high-resolution microscope; (d) RIPs, GDPs and ICPs under FESEM; (e) GDPs under FESEM; (f) ICPs under FESEM; (g) RIPs and secondary quartz under SEM; (h) RIPs and ICPs under SEM; (i) ICPs and authigenic kaolinte under SEM.
Applsci 13 04414 g006
Applsci 13 04414 g007 550
Figure 7. MIP incremental pore volume plots of macropores and mesopores of coal, shale, and tight sandstone samples. (a) Macro- and mesopore size distribution in high-rank coal samples. (b) Macro- and mesopore size distribution in shale samples. (c) Macro- and mesopore size distribution in tight sandstone samples.
Figure 7. MIP incremental pore volume plots of macropores and mesopores of coal, shale, and tight sandstone samples. (a) Macro- and mesopore size distribution in high-rank coal samples. (b) Macro- and mesopore size distribution in shale samples. (c) Macro- and mesopore size distribution in tight sandstone samples.
Applsci 13 04414 g007
Applsci 13 04414 g008 550
Figure 8. Transitional and micropores coal, shale and tight sandstone samples via LP-N2GA. (a) Transitional pores and micropores of shale samples via LP-N2GA. (b) Transitional pores and micropores of high-rank coal samples via LP-N2GA. (c) Transitional pores and micropores of tight sandstone samples via LP-N2GA.
Figure 8. Transitional and micropores coal, shale and tight sandstone samples via LP-N2GA. (a) Transitional pores and micropores of shale samples via LP-N2GA. (b) Transitional pores and micropores of high-rank coal samples via LP-N2GA. (c) Transitional pores and micropores of tight sandstone samples via LP-N2GA.
Applsci 13 04414 g008
Applsci 13 04414 g009 550
Figure 9. Mercury injection–withdraw curves, N2 adsorption–desorption curves with their corresponding pore shapes. (a) Corresponding pore shapes of mercury injection–withdraw curves. (b) Corresponding pore shapes of N2 adsorption–desorption curves (Red line: adsorption curve; Green line: desorption curve).
Figure 9. Mercury injection–withdraw curves, N2 adsorption–desorption curves with their corresponding pore shapes. (a) Corresponding pore shapes of mercury injection–withdraw curves. (b) Corresponding pore shapes of N2 adsorption–desorption curves (Red line: adsorption curve; Green line: desorption curve).
Applsci 13 04414 g009
Applsci 13 04414 g010 550
Figure 10. Mercury injection–withdraw curves of shale, coal, and tight sandstone samples. (a) Shale samples. (b) Coal samples. (c) Tight sandstone samples.
Figure 10. Mercury injection–withdraw curves of shale, coal, and tight sandstone samples. (a) Shale samples. (b) Coal samples. (c) Tight sandstone samples.
Applsci 13 04414 g010
Applsci 13 04414 g011 550
Figure 11. N2 adsorption–desorption curves of shale, coal, and tight sandstone samples. (a) Shale samples. (b) Coal samples. (c) Tight sandstone samples.
Figure 11. N2 adsorption–desorption curves of shale, coal, and tight sandstone samples. (a) Shale samples. (b) Coal samples. (c) Tight sandstone samples.
Applsci 13 04414 g011
Applsci 13 04414 g012 550
Figure 12. N2 adsorption–desorption curves of high-rank coal samples of C-1, C-2, and C-4. (a) N2 adsorption–desorption curve of sample C-2. (b) N2 adsorption–desorption curves of sample C-1 and C-4.
Figure 12. N2 adsorption–desorption curves of high-rank coal samples of C-1, C-2, and C-4. (a) N2 adsorption–desorption curve of sample C-2. (b) N2 adsorption–desorption curves of sample C-1 and C-4.
Applsci 13 04414 g012
Applsci 13 04414 g013 550
Figure 13. FHH fractal models of shale, coal, and tight sandstone samples using LP-N2GA data. (a) Shale samples. (b) Coal samples. (c) Tight sandstone samples.
Figure 13. FHH fractal models of shale, coal, and tight sandstone samples using LP-N2GA data. (a) Shale samples. (b) Coal samples. (c) Tight sandstone samples.
Applsci 13 04414 g013
Applsci 13 04414 g014 550
Figure 14. The systematic comparison of reservoir space of high-rank coal, shale, and tight reservoirs at the nanoscale to fracture scale. (a–e) Representative pore types of pores with different size range.
Figure 14. The systematic comparison of reservoir space of high-rank coal, shale, and tight reservoirs at the nanoscale to fracture scale. (a–e) Representative pore types of pores with different size range.
Applsci 13 04414 g014
Table
Table 1. Sample properties of high-rank coal, shale and tight sandstone in strata.
Table 1. Sample properties of high-rank coal, shale and tight sandstone in strata.
Sample IDFormationLocationTOC
(wt%)		Vitrinite
(Vol.%)		Inertinite
(Vol.%)		Mineral
(Vol.%)		Ro,max
(%)
High-rank coalC-1Carboniferous Shanxi formationXishan coalfield>90%91.54.53.13.05
C-293.26.61.92.79
C-392.66.13.33.41
C-489.24.22.62.49
C-592.45.15.42.44
Sample IDFormationLocationTOC
(wt%)		Clay (%)Quartz (%)Siderite + Feldspar (%)Ro,max
(%)
ShaleS-1Carboniferous Shanxi formationXishan coalfield1.6557.9533.064.71 + 4.282.54
S-22.0321.7068.845.31 + 4.162.02
S-32.383950.36.30 + 3.172.31
S-42.0130.05624.75 + 4.361.97
S-51.9549.540.54.21 + 3.742.87
Sample IDFormationLocationSample LithologyPorosity (%)Air permeability
(10−3 μm2)
Tight sandstoneT-1Carboniferous Shanxi formationXishan coalfieldArgillaceous siltstone8.060.16
T-2Fine-grained sandstone4.760.09
T-3Fine-grained sandstone6.370.22
T-4Fine-grained sandstone5.360.25
T-5Medium-grained sandstone7.550.08
Table
Table 2. The fractal dimension of seepage pores of Munger sponge model.
Table 2. The fractal dimension of seepage pores of Munger sponge model.
SampleD1SampleD1SampleD1
C-12.8521S-12.9829T-12.9022
C-23.1366S-22.9650T-22.8722
C-32.8114S-32.9164T-33.0203
C-42.8416S-42.9622T-43.1820
C-52.7335S-52.9733T-52.9814
Table
Table 3. FHH fractal model and fractal dimension of shale, coal, and tight sandstone samples.
Table 3. FHH fractal model and fractal dimension of shale, coal, and tight sandstone samples.
SampleFitting FormulaR2D3SampleFitting FormulaR2D3SampleFitting FormulaR2D3
S-1y = −0.1399x + 1.42490.95632.8601C-1y = −0.5761x − 1.93710.98252.4239T-1y = −0.2864x + 0.3890.98872.7135
S-2y = −0.2108x + 0.52270.99962.7892C-2y = −1.0108x − 5.41220.93142.9192T-2y = −0.249x + 0.89450.99932.7510
S-3y = −0.2148x + 0.84140.98992.7852C-3y = −0.3014x + 0.79820.99452.6986T-3y = −0.3258x − 1.6410.9842.6742
S-4y = −0.2455x + 1.19850.97932.7545C-4y = −0.6984x − 4.23240.94062.3016T-4y = −0.3672x + 0.33180.99562.6328
S-5y = −0.2094x + 1.12150.98442.7906C-5y = −0.2311x + 0.74640.98642.7689T-5y = −0.343x + 0.7810.99482.6557
Average-0.98192.79592--0.967082.62244--0.992482.68544
Table
Table 4. A comparison of pore characteristics of coal, shale, tight sandstone, and conventional sandstone gas reservoirs.
Table 4. A comparison of pore characteristics of coal, shale, tight sandstone, and conventional sandstone gas reservoirs.
ItemCoal ReservoirsShale ReservoirsTight Sandstone ReservoirsConventional Sandstone Reservoirs
LithologyCoalShale/mudstone and other lithologies in shaleTight sandstone and carbonate rocksSandstone
Material compositionsMainly OM with a small amount of mineral composition.Mainly mineral compositions and a small amount of OMmineral compositionsmineral compositions
PorosityGenerally less than 5% (high-rank coal)<10%<10%Generally larger than 10% with great variation
Permeability (10−15 m2)<1.0 with great variation<0.001<0.1Generally larger than 0.1 with great variation
Pore distributionMainly nanopores (high-rank coal)Mainly nanoporesMainly nanoporesMainly micron pores
Gas occurrenceAdsorbedFree and adsorbedFreeFree
Pore typesMainly OM nanoporesMainly OM nanopores and clay mineral poresIntergranular pores, dissolution pores, etc.Intergranular pores, dissolution pores, etc.
Table
Table 5. The systematic classification and comparison of reservoir space of high-rank coal, shale, and tight reservoirs at nanoscale to fracture scale.
Table 5. The systematic classification and comparison of reservoir space of high-rank coal, shale, and tight reservoirs at nanoscale to fracture scale.
Pore SizeCoalShale/MudstoneTight Sandstone
<10 nm*Metamorphic pores
Intermorthic pores
Intermolecular pores		*OM nanopores
Clay-related pores
Pores in mineralogical edge		 Intercrystalline pores in clay interstitial substance
10–100 nm*Phyteral residual pores
Microorganic constituents interparticle nanopores
Pores in mineralogical edge		* Intercrystalline pores in clay interstitial substance; grain dissolution pores
100–1000 nm Thermally pores
Mineral related pores		 Brittle mineral pores*Residual interparticle pores; grain dissolution pores
0.1–10μm Mineral related pores
Microfractures 		Brittle mineral pores
Microfractures		*
>10μm*Fractures and cleats Fractures Fractures
* represents the major pore size distribution range.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Zhao, D.; Zhang, J.; Guan, X.; Liu, D.; Wang, Q.; Jiao, W.; Zhou, X.; Li, Y.; Wang, G.; Guo, Y. Comparing the Pore Networks of Coal, Shale, and Tight Sandstone Reservoirs of Shanxi Formation, Qinshui Basin: Inspirations for Multi-Superimposed Gas Systems in Coal-Bearing Strata. Appl. Sci. 2023, 13, 4414. https://doi.org/10.3390/app13074414

AMA Style

Zhao D, Zhang J, Guan X, Liu D, Wang Q, Jiao W, Zhou X, Li Y, Wang G, Guo Y. Comparing the Pore Networks of Coal, Shale, and Tight Sandstone Reservoirs of Shanxi Formation, Qinshui Basin: Inspirations for Multi-Superimposed Gas Systems in Coal-Bearing Strata. Applied Sciences. 2023; 13(7):4414. https://doi.org/10.3390/app13074414

Chicago/Turabian Style

Zhao, Difei, Jiaming Zhang, Xin Guan, Dandan Liu, Qinxia Wang, Weiwei Jiao, Xueqing Zhou, Yingjie Li, Geoff Wang, and Yinghai Guo. 2023. "Comparing the Pore Networks of Coal, Shale, and Tight Sandstone Reservoirs of Shanxi Formation, Qinshui Basin: Inspirations for Multi-Superimposed Gas Systems in Coal-Bearing Strata" Applied Sciences 13, no. 7: 4414. https://doi.org/10.3390/app13074414

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop