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Article

Multiscale Fine Characterization of a Coal Pore–Fracture System Based on SEM, CT, and NMR in the Jingbian Block, Ordos Basin

by
Suping Zhao
1,*,
Rong Ding
2,
Wenguang Tian
1,* and
Jincheng Ye
3
1
PetroChina Research Institute of Petroleum Exploration and Development, Beijing 100083, China
2
PetroChina Coalbed Methane Co., Ltd., Beijing 100028, China
3
School of Energy Resources, China University of Geosciences, Beijing 100083, China
*
Authors to whom correspondence should be addressed.
Energies 2023, 16(14), 5315; https://doi.org/10.3390/en16145315
Submission received: 1 June 2023 / Revised: 16 June 2023 / Accepted: 7 July 2023 / Published: 11 July 2023
(This article belongs to the Special Issue Advances in Unconventional Oil and Gas Accumulation, Reservoir Evaluation and Seepage Mechanism)
Browse Figures
Versions Notes

Abstract

:
To achieve an accurate and comprehensive characterization of the multiscale pore–fracture characteristics of Permian coal in the Jingbian Block, Ordos Basin, a combination of scanning electron microscopy (SEM), X-ray computed tomography (CT), and nuclear magnetic resonance (NMR) techniques was utilized. With these experiments, the mineral composition, pore size distribution (PSD), porosity, and connectivity of pores in coal samples were characterized through qualitative and quantitative methods. The results show that the SEM experiments enabled qualitative identification of pores and mineral types. The coal samples primarily contained gas pores, cell pores, intercrystalline pores, and moldic pores, and clay minerals were the predominant fracture fillings. The 3D reconstruction of the CT experiments shows that the pores and fractures generally expand horizontally, while the minerals show obvious bedding expansion characteristics. Moreover, the estimation of full-size porosity in coal samples can be achieved by combining CT and NMR experiments. The full-size porosity of samples G11-5-1, G11-5-6, G11-5-9, and G11-5-11 was 8.93%, 9.11%, 10.45%, and 11.63%, respectively. The connectivity differences are primarily determined by the throat development degree and the connected pore–fracture count. Samples with more connected pores and larger throat radii exhibit excellent connectivity.

1. Introduction

As a porous medium, coal has complex pores and fractures that are essential for the storage and seepage of coalbed methane (CBM) [1,2,3]. The pore size, pore morphology, porosity, and connectivity in coal have a direct impact on the generation, migration, and accumulation of gas, as well as the adsorption, diffusion, and seepage of CH4 [4,5,6]. Fractures are the main channels for gas migration and seepage, determining the production capacity of CBM wells [7,8,9]. Therefore, studying the pore–fracture structure in coal is essential for optimizing CBM production [10,11,12,13].
Currently, research on pores and fractures focuses on their morphology, genetic type, scale, combination relationship, connectivity, and their relationship with coal’s porosity and permeability, fluid occurrence, migration, and production [14,15,16,17]. Traditional methods such as optical microscopy, scanning electron microscopy (SEM), mercury injection porosimetry (MIP), and gas adsorption techniques (e.g., N2 and CO2) are commonly used for this research [18,19,20]. However, these methods have limitations, such as difficulty in studying pore–fracture connectivity, or damage to pores and fractures during sample preparation and testing [21,22]. Nuclear magnetic resonance (NMR) and X-ray computed tomography (CT) are becoming important technologies for studying pores and fractures in coal, due to their measurement speed and non-destructive testing capabilities, which allow for the construction of three-dimensional (3D) models of coal samples [23,24].
In the current study, we adopted a comprehensive approach that combines optical microscopy, SEM, CT, and NMR to qualitatively and quantitatively characterize the mineral composition, pore size distribution (PSD), and porosity of coal samples. Moreover, the connectivity of pores in three directions was analyzed, and the reasons for the differences were further explained. The objective was to better characterize the pore–fracture structure of a coal seam to assist in the development of CBM in the Jingbian Block, Ordos Basin.

2. Geological Setting and Analytical Procedures

2.1. Geological Setting

The Ordos Basin, which spans an area of 250,000 square kilometers, is situated at the southwest periphery of the North China Plate (Figure 1a) and is recognized as the second-largest sedimentary basin in China [25,26]. The basin’s interior is chiefly characterized by the northern Shanbei slope, whereas the western region is marked by the development of the Tianhuan depression (Figure 1b). In the east, the basin is constrained by the Jinxi fold, while in the west it is bounded by the west thrust belt. Additionally, the northern Yimeng uplift separates the Ordos Basin from the Hetao Basin, and the southern Weibei uplift connects it to the Qinlin thrust belt. The Ordos Basin is a key exploration and development area for unconventional natural gas (CBM, tight sandstone gas, etc.), but current work is mainly focused on the eastern edge of the basin. Therefore, it is urgent to find new blocks to expand the scale of exploration, such as the Jingbian Block.
The Jingbian Block is located in the middle of the northern Shanbei slope, characterized by a dip angle ranging from 0.5° to 1.0° [27,28]. The studied coal seam belongs to the Permian Shanxi Formation, predominantly composed of dark-gray–gray-black mudstone, gray-white–light-gray siltstone, medium-fine sandstone, and coal [29]. Based on the sedimentary cycle, the Shanxi Formation can be segregated into Shan 1 and Shan 2 from top to bottom (Figure 1c). The coal seam of the Shan 2 section is extensively developed in the study area, while that of the Shan 1 section is scarce.
Energies 16 05315 g001 550
Figure 1. (a) Location of the Ordos Basin. (b) Tectonic units of the Ordos Basin and location of the Jingbian Block (modified from [30]). (c) Stratigraphic column of the Permian Shanxi Formation in the Jingbian Block (modified from [29]).
Figure 1. (a) Location of the Ordos Basin. (b) Tectonic units of the Ordos Basin and location of the Jingbian Block (modified from [30]). (c) Stratigraphic column of the Permian Shanxi Formation in the Jingbian Block (modified from [29]).
Energies 16 05315 g001

2.2. Analytical Procedures

2.2.1. Material Composition

Four coal samples were collected from the Jingbian Block, Ordos Basin, and then securely packaged and transported to the laboratory. The coal samples underwent mean vitrinite reflectance (Ro) measurements and maceral analyses (500 points) using a Leitz MPV-3 photometer microscope, in compliance with ISO 7404.3-1994 and ISO 7404.5-1994 standards. Additionally, proximate analysis of the coal samples was conducted in accordance with the Chinese national standard GB/T 2122008, under air-dried conditions.

2.2.2. Pore–Fracture Structure

Firstly, the pores, fractures, and minerals in the coal samples were qualitatively identified using SEM. The sample surface was observed using a Zeiss EVO MA15 field-emission scanning electron microscope from Oberkochen, Germany, with secondary electron imaging, an acceleration voltage range of 1–30 kV, and a maximum resolution of 1.0 nm.
Secondly, X-ray CT experiments were performed to establish 3D models of the pores, fractures, and minerals. The ACTIS-250/320PK/225FFI industrial CT system from New York, USA was utilized, with a spatial resolution close to 50 μm, enabling the identification of macropores and microfractures from the coal samples. The cylindrical samples were scanned for 360° at room temperature to obtain CT two-dimensional images of each sample. Finally, the CT reconstruction software was utilized to reconstruct the scanned images and generate 3D images. Connected pores and unconnected pores were distinguished based on their contact mode. Pores that exhibited face contact with one another were identified as connected pores, whereas those lacking such contact were categorized as unconnected pores.
Finally, the parameters of the pores and fractures were quantitatively characterized through NMR experiments. Coal cylinder plugs with a diameter of approximately 2.5 cm and a length of 5 cm were drilled parallel to the bedding plane, and then the plugs were saturated with water for low-field NMR measurements. A MicroMR12-025 V LF-NMR analyzer instrument from Shanghai, China was utilized to conduct the measurements in accordance with the guidelines [7,31], to obtain a comprehensive identification of the pore structure at different pore sizes.

3. Results and Discussion

3.1. Qualitative Characterization of Pores and Fractures

3.1.1. Material Composition

As shown in Table 1, the Ro of the coal samples ranged from 2.04% to 2.16%, with an average of 2.10%. The vitrinite content was the highest among the coal samples (60.68–80.24%, mean 71.14%), followed by inertinite (15.42–25.00%, mean 21.30%) and liptinite (1.14–5.24%, mean 3.49%). Different mineral contents (1.14–9.74%, mean 4.15%) can lead to varying filling degrees of pores or fractures, thereby affecting the pore–fracture structure (Figure 2; [32,33]).
The coal samples had medium ash yields (6.57–27.51%), with relatively low moisture content (1.43–1.71%) and volatile yields (6.08–8.12%). Samples with relatively high ash yields tend to have a relatively high mineral content, with mineral particles more likely to block pores and fractures and reduce connectivity.

3.1.2. Pore Type Identification

The coal samples in the study area developed four distinct types of pores: gas pores, cell pores, intercrystalline pores, and moldic pores (Figure 3). Gas pores are metamorphic pores that form as a result of the generation and accumulation of gas during the coal-forming process [34,35]. These pores are subcircular, ellipsoidal, and irregular in shape, and their distribution is relatively concentrated (Figure 3a,b). Cell pores, on the other hand, are primary pores that consist of cells from the plants that contributed to the formation of the coal [36]. These pores are filled with granular, flaky, and other clastic minerals (Figure 3c). Intercrystalline pores are formed by the development of flaky clay minerals (Figure 3d). The different hardness of mineral and organic matter in coal can lead to the formation of moldic pores under compression stress during the coal-forming process. These pores typically have a diameter of less than 20 μm and are isolated (Figure 3e). Fractures in the coal samples are primarily static pressure fractures, interlayer fractures, and tectonic fractures. Tectonic fractures are oriented in different directions, some of which are X-shaped, filled with kaolinite, pyrite, and other minerals (Figure 3f–i).

3.2. Visual Characterization of Pores and Fractures

CT experiments were conducted to reconstruct the pore and fracture structures in three dimensions, providing a more intuitive description of the interior of the coal samples (Figure 4). The connected and unconnected pores were distinguished separately. As depicted in Figure 4A, the pores and fractures in the coal sample are generally horizontally distributed, with the fractures having small dip angles. Layer-parallel fractures can be observed in samples G11-5-1, G11-5-6, and G11-5-11. Additionally, sample G11-5-1 also has obvious oblique fractures that connect different horizontal fractures. Mineral fillings within the coal samples display clear bedding development characteristics, ultimately reducing the connectivity of the pores and fractures. Sample G11-5-9 has the highest ash yield and exhibits obvious mineral development, while sample G11-5-11 has the second-highest ash yield and less significant mineral development. Samples G11-5-1 and G11-5-6 have the lowest ash yield, so only tabular mineral development can be clearly observed.
Meanwhile, connected pores and fractures in coal samples are characterized in Figure 4B, with different colors showing different connected pores and fractures. The connected pores and fractures of the samples exhibit distinct layering characteristics, e.g., samples G11-5-1 and G11-5-11, with oblique fractures connecting different horizontal fractures. In samples G11-5-6 and G11-5-9, the connected pores and fractures are tabular or flaky, and different pores and fractures are locally connected.
Moreover, unconnected pores and fractures were individually extracted, as shown in Figure 4C. Unconnected pores are scattered throughout the entire model, and the unconnected fractures are plate-like or sheet-like. Large numbers of unconnected pores are visible in all four samples. Unconnected pores in samples G11-5-1 and G11-5-6 are uniformly distributed throughout the sample space, while in samples G11-5-9 and G11-5-11 unconnected pores are relatively scarce in the upper parts of the samples. Unconnected fractures are distributed differently in the samples. Obvious plate-like or sheet-like unconnected fractures are visible in sample G11-5-1, which are segmented due to the layering filling of minerals. In addition, significant unconnected fractures are also visible in sample G11-5-9, which could reduce the connectivity of the pores. Unconnected fractures are less developed in samples G11-5-6 and G11-5-11.

3.3. Quantitative Characterization of Pores and Fractures

3.3.1. Pore–Fracture Distribution

CT and NMR experiments are effective techniques for analyzing the PSD of coal samples. However, due to the difference in observation scale between the two methods, it may be challenging to accurately characterize the pores using a single technique alone. Therefore, a comprehensive analysis of PSD was conducted using a combination of CT and NMR experiments.
Firstly, the PSD was analyzed based on the 3D reconstructed models using CT experiments (Figure 4), where the relationship between pore radius and percentage of pore volume was plotted (Figure 5). The identified samples G11-5-1, G11-5-6, G11-5-9, and G11-5-11 had minimum pore radii of 44.38 μm, 45.89 μm, 37.10 μm, and 53.06 μm (with an average of 45.11 μm), respectively. The results reveal that the percentage of pore volume exhibits a trend of first decreasing and then increasing with the increase in pore radius. Pores with a radius of less than 250 μm contributed significantly to the pore volume, accounting for 73.64%, 85.83%, 76.33%, and 74.29% of the total pore volume of samples G11-5-1, G11-5-6, G11-5-9, and G11-5-11, respectively. Furthermore, the percentage of pore volume increased with the increase in pore radius, indicating that pores with a radius greater than 1500 μm also significantly contribute to the pore volume.
In addition, the number of pores and their average radius were determined based on the 3D reconstructed models (Figure 6). Due to limitations in the CT experiments’ precision, the pores below the minimum pore radius were not included in the count. The distribution of pores in the samples was similar, with larger numbers of pores having relatively smaller radii. Pores with a radius smaller than 250 μm constituted a significant proportion of the total number of pores, contributing significantly to the pore volume. The average pore radii for samples G11-5-1, G11-5-6, G11-5-9, and G11-5-11 were 74.46 μm, 76.93 μm, 69.34 μm, and 88.01 μm, respectively. Sample G11-5-11 exhibited the largest average pore radius, mainly due to having a greater number of pores with radii larger than 250 μm.
Due to the limitations of the CT experiments’ accuracy, only macropores (>10 μm) and fractures could be observed in the pore–fracture system. Thus, NMR experiments were utilized to characterize the micropores. The PSD of the coal samples was obtained by studying the water signal distribution in the main fractures and micropores of the coal samples under water-saturated conditions.
Previously, the PSD of coal samples was calculated using the “T2 cutoff value” in NMR experiments [31]. For a given coal sample, the pore radius corresponding to the T2C value determined by T2 analysis of saturated water and irreducible water is fixed. Assuming that the pore radius r corresponding to the T2C value is constant, the pore radius rci corresponding to relaxation time i (T2i) can be expressed as follows:
r c i = r T 2 i T 2 C
In this study, the pore radius corresponding to the T2C value of the centrifugation experiment was determined to be 0.1 μm. By using the above equation, the rci values for each period can be calculated, and the PSD can be constructed based on the NMR T2 spectrum analysis.
The PSD based on NMR is presented in Figure 7. The pores with radii less than 10 nm were the most developed and contributed the most to the porosity, accounting for 93.74%, 91.91%, 95.10%, and 94.57% of the porosity of samples G11-5-1, G11-5-6, G11-5-9, and G11-5-11, respectively. Additionally, based on the specific surface area and the role of pores in CBM development, the pores were categorized into adsorption pores (<100 nm) and seepage pores (>100 nm) [7,31]. The coal samples were dominated by adsorption pores, with poorly developed seepage pores, and the contribution of adsorption pores to the porosity was significant.

3.3.2. Porosity

CT experiments can be used to determine porosity. The principle is to scan each sample to obtain two-dimensional grayscale slice figures, where black represents the pore, gray represents the matrix, and highlighted colors represent the high-density components (such as calcite and pyrite) in the sample. The grayscale slice figures are then utilized to quantify the number of pores within various size ranges and calculate the overall porosity of the sample [37,38]. As shown in Table 2, the CT porosity of the samples ranged from 4.43% to 5.43%, with an average of 4.80%. The connected porosity of the samples ranged from 1.90% to 4.07%, with an average of 2.51%. Connected pores developed in the horizontal direction (X, Y direction), but not in the vertical direction (Z direction).
NMR experiments can be employed to determine the porosity of coal samples. The fundamental principle is that the quantity of NMR signal obtained in a water-saturated coal sample is directly proportional to the water content. It is widely acknowledged that there exists a linear correlation between porosity and NMR signal volume, and the linear expression of NMR signal volume–porosity can be obtained by calibrating a standard-porosity sample, thereby converting the NMR signal volume of coal samples to porosity [39]. The relationship between porosity and signal volume per unit volume is shown below:
H = k φ
where k is the coefficient obtained by calibration, φ is the porosity, and H is the accumulated NMR signal amount per unit volume of coal sample. The accumulated NMR signal amount of coal samples is measured by NMR experiments and is utilized in the equation to calculate the porosity of coal samples.
The NMR porosity of the coal samples ranged from 4.50% to 6.20%, with an average of 5.23% (Table 2), which is relatively higher than the CT porosity (average 4.80%). Among them, sample G11-5-11 had the greatest porosity. The irreducible water saturation is primarily controlled by the pore structure, which is one of the parameters in reservoir evaluation [40,41]. The irreducible water saturation and movable water saturation can be distinguished based on the T2 cutoff value. The samples possessed a high irreducible water saturation (94.51% on average) and a low movable water saturation (5.49% on average), indicating that fluid flow in the pore space of the samples is difficult.
Based on the results of CT 3D reconstruction and NMR T2 spectra, the PSD and porosity of coal samples can be calculated. In this study, the minimum pore radius of the CT experiments was 10 μm (although the actual value depends on the sample, ranging from about 37.10 μm to 53.06 μm), and the NMR experiments corresponded to pore radii in the range of 0 to 10,000 nm (0 to 10 μm). Previous studies [37,39,42] have shown that the total porosity can be estimated by summing the two porosities, with negligible errors. Therefore, the total porosity of samples G11-5-1, G11-5-6, G11-5-9, and G11-5-11 was calculated to be 8.93%, 9.11%, 10.45%, and 11.63%, respectively. Sample G11-5-11 had the highest porosity, while G11-5-1 had the lowest porosity, which to some extent characterizes the pore–fracture structure of the samples. However, considering the mineral filling shown in Figure 4, further research is needed on the connected and unconnected pores of the samples.

3.3.3. Pore Connectivity

The connected and unconnected porosity of the four coal samples is illustrated in Figure 8, based on the 3D reconstruction of the coal samples (Figure 4B) and the data in Table 2. The connected porosity of the samples was mainly developed in the horizontal directions (X, Y direction), whereas the connectivity in the vertical direction (Z direction) was poor. The unconnected porosity of the samples ranged from 1.36% to 3.19%, with an average value of 2.28%. There were significant differences in the development of connected and unconnected pores among different samples. Connected pores were the most developed in sample G11-5-11, while unconnected pores were the most developed in sample G11-5-9.
Sample G11-5-9 displayed the most developed unconnected porosity, which can be attributed to the high mineral content in this sample, where the mineral filled the layers and reduced the connectivity of the pores. Ash yields can reflect the connectivity of pores by reacting with the mineral content. Compared to sample G11-5-9, the highest connected porosity in sample G11-5-11 can be attributed to its having the lowest mineral content, as well as the specific characteristics of its mineral distribution. Sample 5-11 had a relatively high ash yield, but its connected porosity was good, due to the minerals being mostly in the form of blocks or flakes, where the phenomenon of mineral filling of fractures is relatively rare. The 3D reconstruction in Figure 4C also reveals that unconnected pores were less developed and primarily concentrated in the lower part of Sample 5-11. Samples G11-5-1 and G11-5-6 had similar mineral contents and similar proportions of connected and unconnected porosity. Therefore, the mineral content and distribution have a direct impact on the connectivity of the sample.
Based on the 3D reconstruction models, an equivalent pore–fracture ball-and-stick model was established to analyze the connectivity of the coal samples. In this model, the pore is represented as a sphere, the throat channel as a stick, and the connected pores are linked via the throat channel. In addition, the ball-and-stick model demonstrates the equivalent radius rather than the actual radius. The structural parameters of the model were used to investigate the connectivity of the coal sample, and the constructed pore network structural model and parameters are presented in Figure 9. Sample G11-5-11 had a high number of pores and thick channels, the largest throat radius, and the best connectivity. Samples G-11-5-1 and G-11-5-6 had generally larger radii of channels, promoting connectivity between pores, although the number of pores was relatively small. However, sample G-11-5-9 had a small pore radius and relatively undeveloped seepage channels. This further confirms the previous conclusion that mineral filling affects the number of pores and channels in the sample, thereby restricting the connected porosity.
In summary, SEM, NMR, and CT experiments offer complementary characterizations for various aspects of coal reservoir properties. SEM allows for the qualitative identification of pore types, mineral composition, and pore distribution. CT facilitates the calculation of parameters such as porosity, the visualization of pore distribution, and the assessment of pore connectivity in coal samples. NMR can be employed for quantitative analysis of fluid saturation, porosity, pore structure, and other physical properties in coal samples. Additionally, NMR can reveal pore characteristics that cannot be detected by CT experiments alone. Integrating these technologies is beneficial for evaluating physical parameters such as the pore structure, permeability, and fluid migration ability of coal reservoirs.

4. Conclusions

In this study, optical microscopy, SEM, CT, and NMR experiments were used to finely characterize the pore and fracture structure of coal samples. The following conclusions can be drawn:
(1)
The average Ro of the coal samples in the Jingbian Block is 2.10%, meaning that they belong to the middle-rank coal. The pores or fractures are filled with minerals due to the mineral development in the coal samples. The pores are mainly observed as gas pores, cell pores, intercrystalline pores, and moldic pores, and the fractures are mostly filled with kaolinite minerals.
(2)
CT 3D reconstruction shows that the pores and fractures in the coal samples generally exhibit horizontal spreading, with a small inclination. Oblique fractures communicate with different layers. The coal samples are filled with minerals along the layers, and the mineral spreading shows obvious characteristics of subsequent layer development.
(3)
CT 3D reconstruction and NMR T2 spectra can be used calculate the pore distribution and porosity. The total porosity of samples G11-5-1, G11-5-6, G11-5-9, and G11-5-11 was calculated to be 8.93%, 9.11%, 10.45%, and 11.63%, respectively.
(4)
The difference in sample connectivity is mainly determined by the degree of throat development and the number of connected pores and fractures. The mineral content and filling mode of the sample affect the number and size of pores and throats, ultimately affecting the connected and unconnected pores.

Author Contributions

Conceptualization, S.Z. and W.T.; methodology, S.Z. and W.T.; software, R.D.; validation, S.Z. and W.T.; formal analysis, R.D.; investigation, J.Y.; resources, S.Z.; data curation, J.Y.; writing—original draft preparation, S.Z.; writing—review and editing, W.T.; visualization, W.T.; supervision, S.Z.; project administration, W.T.; funding acquisition, S.Z. and W.T. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Research on CBM Exploration and Development Technology, Topic 3 of the “New Bedding System and New Field Strategy and Evaluation Technology for New CBM Regions” (2021DJ2303), and by the Major Projects of Ningxia Key Research and Development Plan (2022CMG02013).

Data Availability Statement

Not applicable.

Acknowledgments

The authors thank the reviewers and editors for their constructive comments and suggestions on improving this manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 2. Mineral filling in coal samples based on photometer microscopy: (a) Linear clay mineral bands are developed in vitrinite, with visible fractures present in sample G11-5-1. (b) Sample G11-5-1 exhibits lenticular fusinites, which are surrounded by vitrinite and display a developed fracture. (c) In sample G11-5-6, the cell pores present an irregular shape and are filled with clay minerals. (d) Sample G11-5-6 displays banded vitrinite and irregular cell pores that are both filled with clay minerals. (e) Linear clay mineral bands are also observed in the vitrinite of sample G11-5-9. (f) The cell pores in sample G11-5-9 are filled with clay minerals. (g) The fractures in sample G11-5-11 are filled with clay minerals. (h) Sample G11-5-11 exhibits interlayers of banded vitrinite and lenticular clay minerals.
Figure 2. Mineral filling in coal samples based on photometer microscopy: (a) Linear clay mineral bands are developed in vitrinite, with visible fractures present in sample G11-5-1. (b) Sample G11-5-1 exhibits lenticular fusinites, which are surrounded by vitrinite and display a developed fracture. (c) In sample G11-5-6, the cell pores present an irregular shape and are filled with clay minerals. (d) Sample G11-5-6 displays banded vitrinite and irregular cell pores that are both filled with clay minerals. (e) Linear clay mineral bands are also observed in the vitrinite of sample G11-5-9. (f) The cell pores in sample G11-5-9 are filled with clay minerals. (g) The fractures in sample G11-5-11 are filled with clay minerals. (h) Sample G11-5-11 exhibits interlayers of banded vitrinite and lenticular clay minerals.
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Figure 3. Pores, fractures, and minerals in coal based on SEM: (a) Clay minerals and gas pores in sample G11-5-1. (b) Residual cavities and component gaps in sample G11-5-9. (c) Vitrinite and cell pores’ filling material in sample G11-5-1. (d) Flaky clay minerals and their intercrystalline pores and fractures in sample G11-5-11. (e) Kaolinite and moldic pores in sample G11-5-6. (f) X-shaped fractures and tufted calcite in sample G11-5-6. (g) Carbonate minerals in vitrinite fill fractures in sample G11-5-6. (h) Intercrystalline pores and intercrystalline fractures in sample G11-5-9. (i) Lenticular minerals in sample G11-5-11.
Figure 3. Pores, fractures, and minerals in coal based on SEM: (a) Clay minerals and gas pores in sample G11-5-1. (b) Residual cavities and component gaps in sample G11-5-9. (c) Vitrinite and cell pores’ filling material in sample G11-5-1. (d) Flaky clay minerals and their intercrystalline pores and fractures in sample G11-5-11. (e) Kaolinite and moldic pores in sample G11-5-6. (f) X-shaped fractures and tufted calcite in sample G11-5-6. (g) Carbonate minerals in vitrinite fill fractures in sample G11-5-6. (h) Intercrystalline pores and intercrystalline fractures in sample G11-5-9. (i) Lenticular minerals in sample G11-5-11.
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Figure 4. Three-dimensional (3D) reconstruction of coal samples based on CT experiments: The bottom left coordinate axis (x direction (red), y direction (green)) represents the horizontal direction, and the z direction (blue) represents the vertical direction. (A) The mineral components are represented in yellow, while the pores and fractures are displayed in blue (matrix is not shown). (B) Different colors are used to distinguish various connected pores and fractures. (C) Different colors are used to differentiate unconnected pores and fractures. (a–c) represent the distribution of minerals, the network of pores and fractures, the network of connected pores and fractures, and the network of unconnected pores and fractures of coal sample G11-5-1, respectively. (d–f) represent the distribution of minerals, the network of pores and fractures, the network of connected pores and fractures, and the network of unconnected pores and fractures of coal sample G11-5-6, respectively. (g–i) represent the distribution of minerals, the network of pores and fractures, the network of connected pores and fractures, and the network of unconnected pores and fractures of coal sample G11-5-9, respectively. (j–l) represent the distribution of minerals, the network of pores and fractures, the network of connected pores and fractures, and the network of unconnected pores and fractures of coal sample G11-5-11, respectively.
Figure 4. Three-dimensional (3D) reconstruction of coal samples based on CT experiments: The bottom left coordinate axis (x direction (red), y direction (green)) represents the horizontal direction, and the z direction (blue) represents the vertical direction. (A) The mineral components are represented in yellow, while the pores and fractures are displayed in blue (matrix is not shown). (B) Different colors are used to distinguish various connected pores and fractures. (C) Different colors are used to differentiate unconnected pores and fractures. (a–c) represent the distribution of minerals, the network of pores and fractures, the network of connected pores and fractures, and the network of unconnected pores and fractures of coal sample G11-5-1, respectively. (d–f) represent the distribution of minerals, the network of pores and fractures, the network of connected pores and fractures, and the network of unconnected pores and fractures of coal sample G11-5-6, respectively. (g–i) represent the distribution of minerals, the network of pores and fractures, the network of connected pores and fractures, and the network of unconnected pores and fractures of coal sample G11-5-9, respectively. (j–l) represent the distribution of minerals, the network of pores and fractures, the network of connected pores and fractures, and the network of unconnected pores and fractures of coal sample G11-5-11, respectively.
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Figure 5. PSD based on CT experiments.
Figure 5. PSD based on CT experiments.
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Figure 6. Numbers of sample pores and average pore radius based on CT experiments.
Figure 6. Numbers of sample pores and average pore radius based on CT experiments.
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Figure 7. The porosity component and cumulative porosity based on NMR experiments.
Figure 7. The porosity component and cumulative porosity based on NMR experiments.
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Figure 8. Connected porosity and unconnected porosity based on CT experiments.
Figure 8. Connected porosity and unconnected porosity based on CT experiments.
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Figure 9. Ball-and-stick models by CT 3D reconstruction.
Figure 9. Ball-and-stick models by CT 3D reconstruction.
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Table
Table 1. Results of the proximate and maceral analyses.
Table 1. Results of the proximate and maceral analyses.
Sample No.FormationCoal Composition (%)Ro
(%)		Proximate Analysis (%)
VitriniteInertiniteLiptiniteMineralMadAadVadFCad
G11-5-1Shanxi
Formation		68.2725.004.492.562.041.716.576.6185.11
G11-5-680.2415.421.193.162.121.566.746.0885.62
G11-5-975.3820.453.031.142.071.4327.518.1262.94
G11-5-1160.6724.345.249.742.161.5418.286.9073.28
Notes: Mad = moisture content; Aad = ash yield; Vad = volatile yield; FCad = fixed carbon; ad = air-dried basis.
Table
Table 2. Porosity and other parameters based on NMR and X-ray CT.
Table 2. Porosity and other parameters based on NMR and X-ray CT.
Sample No.NMRX-ray CT
φ1 (%)Sir (%)Sw (%)φ2 (%)φ3 (%)Direction
G11-5-14.5093.866.144.431.90X, Y
G11-5-64.9393.576.434.182.15X, Y
G11-5-95.2895.904.105.171.98X, Y
G11-5-116.2094.725.285.434.07X, Y
Note: φ1, calculated porosity based on NMR; Sir, irreducible water saturation; Sw, water saturation; φ2, calculated porosity based on X-ray CT; φ3, connected porosity based on X-ray CT; X and Y represent the horizontal direction, while Z represents the vertical direction.
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Zhao, S.; Ding, R.; Tian, W.; Ye, J. Multiscale Fine Characterization of a Coal Pore–Fracture System Based on SEM, CT, and NMR in the Jingbian Block, Ordos Basin. Energies 2023, 16, 5315. https://doi.org/10.3390/en16145315

AMA Style

Zhao S, Ding R, Tian W, Ye J. Multiscale Fine Characterization of a Coal Pore–Fracture System Based on SEM, CT, and NMR in the Jingbian Block, Ordos Basin. Energies. 2023; 16(14):5315. https://doi.org/10.3390/en16145315

Chicago/Turabian Style

Zhao, Suping, Rong Ding, Wenguang Tian, and Jincheng Ye. 2023. "Multiscale Fine Characterization of a Coal Pore–Fracture System Based on SEM, CT, and NMR in the Jingbian Block, Ordos Basin" Energies 16, no. 14: 5315. https://doi.org/10.3390/en16145315

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