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Article

Pore Structure and Fractal Characteristics of the Permian Shales in Northeastern Sichuan Basin, China

by
Guanping Wang
1,2,3,*,
Qian Zhang
4,*,†,
Baojian Shen
1,2,3,
Pengwei Wang
1,2,3,
Wei Du
1,2,3,
Lu Wang
4,
Min Li
1,2,3 and
Chengxiang Wan
1,2,3
1
State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Efficient Development, Beijing 102206, China
2
Key Laboratory of Shale Oil/Gas Exploration and Production Technology, Sinopec, Beijing 102206, China
3
Petroleum Exploration and Production Research Institute, Sinopec, Beijing 102206, China
4
Institute of Energy, School of Earth and Space Sciences, Peking University, Beijing 100871, China
*
Authors to whom correspondence should be addressed.
†
Current address: British Geological Survey, Keyworth, Nottingham NG12 5 GG, UK.
Minerals 2025, 15(7), 684; https://doi.org/10.3390/min15070684
Submission received: 30 April 2025 / Revised: 10 June 2025 / Accepted: 20 June 2025 / Published: 27 June 2025
(This article belongs to the Section Mineral Exploration Methods and Applications)
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Abstract

The complexity of the pore system hindered our understanding of the storage and transport properties of organic-rich shales, which in turn brought challenges to the efficient exploration and development of shale oil and gas. This study, based on elemental, mineralogical, petrographic, and petrophysical approaches, attempts to reveal the pore structure and fractal characteristics of a suite of Permian shales collected from the northeastern Sichuan Basin, China. The results showed that meso-pores make up the main proportion of the total pore volume in the Permian shale in this study; most of the pore size distribution patterns for micro pores and meso-macropores are bimodal. Pores related to clay minerals, organic matter pores, and intragranular dissolution pores are the main storage spaces in these shales. In these samples, ink-bottle pores dominate, with some slit and wedge-shaped ones developed. The morphology of the pores in the studied shales is mainly ink-bottle pores, with some slit-shaped and wedge-shaped pores. The fractal dimension D2 is greater than D1, indicating that the homogeneity of pore space is stronger than that of the specific surface area. Quartz in Permian shales inhibits the development of macro- and mesopore spaces and enhances pore heterogeneity, while clay minerals facilitate the development of macro- and mesopore spaces and attenuate pore heterogeneity. The organic matter content shows a negative impact on the macropore volume due to the stripped occurrence and matrix filling. This study has a vital significance for current exploration and development of shale gas in Permian strata in the Sichuan Basin and offers insights for Permian shales in other basins all over the world.

Graphical Abstract

1. Introduction

With the changes in political situation and the energy supply all over the world and the energy demands of different countries, unconventional shale oil and gas has turns into a key area for energy independence in many countries [1,2,3,4,5]. The commercial development of Ordovician–Silurian marine shale gas in the Sichuan Basin has stimulated exploration activities in China, and shale gas has become an important support for ensuring China’s energy security [2,5,6]. The exploration and development of shale gas in the Sichuan Basin is moving from the Ordovician–Silurian to other marine shale formations [5]. More and more exploration experiences have shown that the Permian marine shales in the Sichuan Basin have the characteristics of high organic matter content, large gas content, high organic matter maturity and excellent single-well production, which are expected to be the next large-scale commercial shale gas strata [5,7,8]. However, due to insufficient drilling and outcrops, there has been little research on the Permian marine shales in the Sichuan Basin [8,9].
The highly heterogeneous shale pore system directly affects hydrocarbon migration, storage and enrichment, which is affected by many factors, such as mineralogical composition, organic matter content, thermal maturity, and lithofacies [8,9,10,11,12]. According to the classification proposed by Loucks et al. (2012), the pores in the shale matrix can be classified into organic matter pores (OMP), intraparticle pores (intraP) and interparticle pores (interP) [13]. Previous studies on the marine Wufeng Formation–Longmaxi Formation in the Sichuan Basin have shown that organic matter pores are the main storage space, which are affected by the type, content and thermal maturity of organic matter [2,5,12]. High organic matter content and high thermal maturity are conducive for the development of OMP. It should be noted, however, that excessively high thermal maturity can also lead to the collapse of the OMP-induced by the process of graphitization [14]. Secondary organic matter, such as solid bitumen, has been found to contain more pores than primary organic matter [14,15]. In addition, some researchers have proposed that pressure can also affect the development of OMP in shale [15]. IntraP and interP are mainly affected by the source, type, content, formation process of inorganic minerals and the dissolution caused by organic acid [15]. The content and type of clay minerals, due to their porous structure, are important factors affecting the quantity and distribution of intraP and interP [16]. Silica of different origins and formed at different times has varying degrees of dissolution. For example, terrigenous clastic quartz contains dissolution pores; in contrast, silica formed during clay mineral transformation will block pores [10,17,18]. In practical, with the exploration and development of new marine shale formations, new insights have been put forwarded. For example, the Cambrian silt shale in the Sichuan Basin is mainly composed of inorganic pores, while interP and clay mineral-related pores are the main storage spaces [5,18]. Compared with the marine Silurian and Cambrian shales in the Sichuan Basin, the Permian shales have higher TOC and lower porosity, and their pore structure and influencing factors are unclear.
The characterization of the pore structure of the shale mainly comprises qualitative and quantitative methods. From the initial optical microscope to field emission scanning electron microscope and then atomic force microscope, researchers have deepened their understanding of pore morphology and structure in terms of scale and spatial distribution [1,5,9,10,16]. In terms of quantitative characterization, since HUPAC-classified pores into micropores (<2 nm), mesopores (2–50 nm) and macropores (>50 nm) based on CO2 gas adsorption analysis (CO2 GA) and N2 gas adsorption analysis (N2 GA), researchers have proposed a variety of classification standards based on different needs, such as “millipores (>1 mm), micropores (1–1000 μm), submicropores (100–1000 nm) and nanopores (2–100 nm)” and “six categories” characterization based on pore size distribution morphology, etc. [10,16,19,20]. In addition, fractal dimension has been widely used to characterize the heterogeneity of shale’s pore system [10]. Fractal geometry theory is widely used to analyze the complexity and heterogeneity of porous media [10,21,22,23]. This theory was proposed by Mandelbrot (1982) and it overcomes the limitations of traditional Euclidean geometry in characterizing the structure of porous media [24]. According to fractal theory, the fractal dimension of three-dimensional Euclidean space should be between 2 and 3 [10,21,22,23]. Generally speaking, complexity and heterogeneity increase with the increase in fractal dimension. When the fractal dimension exceeds 3 or is less than 2, the corresponding pore structure does not have fractal characteristics. The pore structure and fractal characteristics of shale are the keys affecting the transport and storage properties of shales, which are the hot areas for scientific research [10,21,22,23,24]. The Permian marine shales in the Sichuan Basin are new targets for the exploration and development of shale oil and gas. Therefore, their pore structure and fractal characteristics have attracted much attention and need to be further investigated.
Based on the mineralogy, TOC, qualitative-quantitative pore structure characterization and Spearman correlation analysis of the Permian marine shales in the Sichuan Basin, this study attempts to (1) analyze the pore structure and fractal characteristics; (2) identify the main controlling factors on pore structure and fractal dimension of pores in Permian marine shales. The research findings will deepen our comprehension of pore network characteristics in Permian marine shale formations, which will further help minimize uncertainties regarding shale oil and gas exploration potential in analogous marine shale deposits across global basins.

2. Geological Background

The Sichuan Basin is located in the upper parts of the Yangtze River in South China. It is a tectonic stable and diamond-shaped sedimentary basin with an area of more than 1.8 × 105 square kilometers. It is surrounded by the Micang Mountains, Daba Mountains, Dalou Mountains, Daxiang Mountains, and Longmen Mountains (Figure 1A,B) [12]. From the Permian to the Middle Triassic, the northeastern Sichuan basin experienced a sedimentary succession from marine transgression to regression, and three sets of high-quality shales, the Gufeng Formation, the Wujiaping Formation, and the Dalong Formation, were formed (Figure 1C) [9,11]. In the late stage of Gufeng Formation deposition, under the background of extension, Kaijiang-Liangping continental shelf started to form, and a large area of organic-rich black siliceous shale was deposited. During the deposition of Wujiaping Formation, sedimentary differentiation intensified, and the alternating platform-basin depositional pattern was further strengthened, and a relatively thin organic-rich gray-black siliceous shale developed in the northeastern Sichuan Basin. During the deposition of the Dalong Formation, the sedimentary differentiation of the platform-trough was more obvious, and the water depth in the Kaijiang-Liangping trough was relatively deep, and a sedimentary combination of organic-rich gray-black siliceous shale, led to the formation of gray siliceous shale and thin layers of muddy limestone extending from northwest-southeast to the Puguang-Fuling north area [7,8].

3. Experiments and Analytical Methods

Twelve Permian marine shale samples were selected from Well L-1 located in the northeastern Sichuan Basin (Table 1). These samples included siliceous shale, argillaceous shale, and carbonate shale from the Dalong, Wujiaping, and Gufeng Formations. The samples were obtained from depths ranging between 4237.92 m and 4335.57 m.
X-ray diffraction analysis (XRD) is a crucial technique in analyzing mineralogical composition. Before conducting the analysis, the bulk rock sample was crushed into fine powder and thoroughly homogenized. Subsequently, XRD analysis was performed using a D8A25 diffractometer (German), adhering to the Chinese Oil and Gas Industry Standard (SY/T) 5163-2010 [6,25]. Total organic carbon (TOC) analyses were performed by a LECO CS230 analyzer (USA) in line with the Chinese Oil and Gas Industry Standard (GB/T) 19145-2003 [10,26]. The procedure for the porosity experiment can be found in Wang et al. [16].
A Helios 5CX high-resolution field emission scanning electron microscope (USA) was employed to capture images of the pore morphology. Prior to imaging, the samples were precisely cut perpendicular to their bedding, followed by a rigorous mechanical polishing process. Subsequently, an argon-ion cross-section polisher (Technoorg SC-100, USA) was utilized for an additional hour to achieve an even smoother observational surface. As the last step for sample preparation, the polished surfaces were coated with gold to enhance image quality. Subsequently, the measurements were carried out under precisely controlled conditions, maintaining a temperature of 293.15 K and a humidity level of 40%.
Low-temperature nitrogen gas adsorption (N2) experiments were conducted using a micrometric analyzer (ASAP 2460, USA) at a temperature of 77 K. The samples were ground to a particle size of 60–80 mesh and then degassed in a vacuum oven at a pressure below 10 mm Hg and a temperature of 383 K for a duration of 12 h. The pore volume (PV) and pore size distribution (PSD) were calculated based on the BJH theory, as outlined by Gregg and Sing in 1982 [27]. The specific surface area (SSA) was computed using data from the N2 gas adsorption experiments and the multipoint BET model, as described by Brunauer et al. in 1938 [28]. For low-temperature carbon dioxide gas adsorption (CO2) measurements, an SSA and pore size analyzer (JWBK-200C, USA) was employed at a temperature of 273 K. The sample preparation and measurement protocols were identical to those used for the N2 adsorption experiments. The pore size distribution (PSD) was interpreted based on adsorption isotherms, utilizing the nonlocal density functional theory (NLDFT) model proposed by Fan and Ziegler in 1992 [29].
The fractal dimension (D) is widely utilized to quantitatively characterize the heterogeneity and complexity of pore structures in porous media. This study employed LT-N2 adsorption experimental data to achieve fractal analysis of pore structures using the Frenkel–Halsey–Hill (FHH) model. Typically, D values range from 2 to 3, where a D value of 2 indicates an extremely smooth pore surface, while a D value of 3 signifies a rough surface with irregular pore morphology. In the FHH plot, data are divided into two parts: D1 represents monolayer adsorption (P/P0 < 0.45), and D2 represents multilayer adsorption (0.45 < P/P0 < 1), reflecting the fractal dimension of pore surface roughness and pore structure, respectively.
To investigate the influence of mineral compositions on the pore system and its heterogeneity, this study conducted the analysis using Spearman’s correlation coefficient approach, excluding the effects of outliers on the results [30,31]. The analysis employs Spearman’s rank correlation coefficient—a nonparametric statistical method designed to quantify monotonic relationships between two variables [30]. This approach offers several advantages, including broad applicability, robustness against outliers, and the capacity to detect nonlinear associations. Its utility is particularly pronounced when analyzing small sample sizes or datasets with skewed distributions, where it can effectively uncover underlying patterns [31].

4. Results

4.1. Mineralogy and TOC

The mineralogical composition of 12 samples is listed in Table 1. A predominance of quartz, followed by calcite and clays, makes up most of the minerals in the Dalong Formation. The Wujiaping Formation is primarily composed of clay minerals, followed by quartz, while the Gufeng Formation is dominated by quartz, with very low proportions of other minerals. Additionally, some samples exhibit a notably high content of dolomite, such as Sample L1-3. Other minerals present include siderite and pyrite to varying degrees. It is worth noting that, in Permian shales, the abundance of feldspar is generally low. The total organic carbon (TOC) content of these samples ranges from 2.74% to 9.19%, with an average value exceeding 5%.

4.2. Pore Types and Porosity

Figure 2 shows that organic matter pores, intergranular pores, intragranular pores and microcracks are all developed in the Permian shales in the Sichuan Basin. The heterogeneity of organic matter pores is relatively strong, and organic matter pores are more developed in the solid bitumen near pyrite particles (Figure 2a–j). For dispersed organic matter with relatively large sizes, there are fewer organic matter pores. While for those with relatively small sizes and filled among rigid minerals, organic pores are more extensively developed (Figure 2a–d,i,j). The observed sizes of organic pores in this study range from mesopore to macropore scales (Figure 2a–d,g,h). Intergranular pores are mostly developed between rigid minerals and clay, and between rigid minerals and organic matter, with a small amount of which also developed among rigid minerals (Figure 2c–e). The intragranular pores are mainly composed of intragranular dissolution pores and clay intragranular pores (Figure 2k–p). Clay minerals, on the one hand, offer intragranular pores; on the other hand, they reduce pore space by filling between other rigid minerals. The intragranular dissolution pores are mostly developed in quartz and carbonate minerals (Figure 2m–p). Overall, pores related to clay minerals, organic matter, and mineral dissolution are the main reservoir spaces in the Permian shales. Table 2 shows that the porosity of Permian shales ranges from 0.6 to 7.7%, most of which are less than 4%. The samples L1-8 and L1-10 exhibit invalid test results due to rock breakdown during the experiment, resulting in excessively large test results.

4.3. N2 GA and CO2 GA

4.3.1. N2 GA

The N2 GA experimental results of the Permian shales are shown in Figure 3 and Figure 4 and Table 2. The adsorption isotherms exhibit predominantly Type IV characteristics according to the IUPAC classification system, featuring a distinct hysteresis loop, as clearly shown in Figure 3. Some of the samples show H2 type of hysteresis loops, indicating that the pore morphology in these samples is mostly ink-bottle-type pore. There are also some samples with hysteresis loops of H3 type, which, combined with the unclosed adsorption and desorption curves, indicate that the pore morphology in these samples is mostly slit-shaped and wedge-shaped pores (samples L-1, L1-11, and L1-12). The SSA and PSD are listed in Table 2 and shown in Figure 4. The SSA values range from 6.362 (L1-12) to 25.309 m2/g (L1-6). Pores with a size of 20–50 nm contribute to the main storage space for these samples. PSD shows a bimodal distribution based on nitrogen adsorption data (Figure 4).

4.3.2. CO2 GA

The CO2 gas adsorption isotherms for the Permian shale samples exhibit Type I characteristics (Figure 5), demonstrating microporous properties across all analyzed specimens. The experimental results demonstrated that under conditions of maximum relative pressure, CO2 adsorption capacities reach values between 1.04 and 3.09 cm2/g (STP), as evidenced by the data presented in Figure 5 and Table 2. Concurrent measurements revealed that microporous volumes span from 0.002 to 0.006 cm2/g, with these structural characteristics being systematically documented in Table 2 for comparative analysis. All these samples show bimodality with peaks around 0.5 and 0.7 nm in the pore size distribution curve for micropores (Figure 6). The majority of pore volume within micropores is attributed to pores exhibiting diameters between 0.5 nm and 1.2 nm, as clearly demonstrated in Figure 6. This size range appears to dominate the pore structure characteristics observed in the analyzed system.

4.4. Fractal Dimensions

The fractal characteristics of the studied samples are presented in Table 3 and Figure 7. All analyzed samples have large correlation coefficients (R12 and R22) ranging from 0.8122 to 0.9996, and all values exceed the 0.80 threshold. These strong correlations provide clear evidence that the pore structures within the Permian shale have distinct fractal geometry features. The high consistency observed across the dataset (R2 > 0.8) confirmed the validity of fractal theory in describing these complex pore network configurations. The D1 values range from 2.5848 (L1-8) to 2.8584 (L1-12), with an average of 2.7097. The D2 values vary between 2.6129 (L1-10) and 2.9435 (L-11), with an average of 2.8393. The D2 is larger than D1 for these samples.

5. Discussion

5.1. Pore System and Fractal Dimensions

The total pore volume (TPV) in the Permian shales is predominantly composed of mesopores (Figure 8 and Figure 9 and Table 2). The gas adsorption analysis revealed that meso-pores account for over 55% of the total pore volume (TPV) across all samples, with the exception of samples L1-1, L1-11, and L1-12. In terms of micropores, the results from nitrogen are slightly smaller than those from carbon dioxide adsorption. In fact, the pore volume of micropores is roughly the same as that of macropores. The average pore volume of macropores derived from nitrogen adsorption is 0.0036 cm3/g, while the average pore volume of micropores determined from carbon dioxide adsorption is 0.004 cm3/g. The results in this study are slightly different from those in Wu et al. (2022) on the Dalong Formation shale [9]. The average pore volume of micropores in Wu et al. (2022) is also 0.0036 cm3/g, while that for macropores is 0.015 cm3/g, suggesting that these samples are mainly composed of macropores [9]. In addition, this study is also inconsistent with Zhang et al. (2018), in which the authors claimed that the macropores are the main reservoir space for the Dalong Formation [12]. Notably, Zhang et al. (2018) employed both gas adsorption and high-pressure mercury injection and showed many pores with sizes larger than 10 µm [12]. This study suggests that pore spaces larger than 10 µm are likely due to artificial influence-caused microcracks. In addition, samples used in Zhang et al. (2018) and Wu et al. (2022) were collected either from outcrops or from shallow boreholes, which probably can be blamed for the difference between the two and this study [9,12]. Considering the range of pore sizes derived from nitrogen gas adsorption, this study assumes that pores with a mesopore size occupy the majority of the storage space within pore sizes less than 200 nm.
Figure 10 shows that micropore and mesopore volumes contribute significantly to the SSA of these samples, and the more micropores and mesopores there are, the smaller the heterogeneity of pore volume and SSA. The micropore and mesopore volumes based on nitrogen adsorption showed a positive correlation with the SSA, indicating that micropores and mesopores contributed significantly to the SSA (Figure 10a). The negative correlation between mesopore and total pore volumes and fractal dimension D1 indicates that the more pores at or close to mesopore sizes, the smaller the heterogeneity of SSA (Figure 10b,c). The negative correlations between total pore volume, mesopore volume, micropore volume, and D2 suggest that the more mesopores and micropores, the smaller the heterogeneity of pore volume, which also indicates that the mesopores and micropores in these shales are relatively regular in geometry (Figure 10d–f). Compared to mesopores and macropores, the micropores have less heterogeneity in pore volume. In addition, it should be noted that there is a more intensive heterogeneity in pore volume than SSA if D2 is larger than D1. The fractal dimensions of these samples are larger than those for also for the Permian shales in previous studies [9,12]. This is because the fractal dimensions of SSA and pore volume increase with maturity [8]. The maturity of the samples used in this study is mostly in the highly mature to over maturity stages, with an average vitrinite reflectance greater than 2% [7]. The spearman rank correlation coefficients and single-tail significance test of pore system with rock components are listed in Table 4.

5.2. Effect of Quartz on Pore Structure and Heterogeneity

Table 4 and Figure 2 show that quartz in the Permian shales has the effect of suppressing the heterogeneity of macropore and mesopore space, increasing SSA and pore volume. The impact of quartz of different origins on pore space varies in marine shales. For example, dispersed quartz cement (mostly biogenic siliceous) increases the overall porosity and permeability of rocks [17]. However, some researchers claim that biogenic silica nanospheres may also destroy porosity [18]. The role of terrestrial detrital quartz in protecting pores against compaction is limited [17,18]. The negative effects of quartz on reservoir space in the Permian marine shales can be summarized into two aspects: (1) the cementation of dispersed self-generated quartz blocking the pores; (2) the diverse genesis of siliceous rocks in the Permian marine shale leads to an insufficient contribution of overall quartz to resist compaction. Table 4 shows that quartz will increase the SSA of reservoir space and the heterogeneity of pore volume, indicating that the size, type, and arrangement of quartz of different origins would damage shale reservoir space. Wu et al. (2022) claimed that there is no relationship between quartz and pore volume or heterogeneity in the Dalong Formation shale in the Sichuan Basin, which is different from the role of quartz in the Silurian Longmaxi shale in the Sichuan Basin [9]. The different roles of quartz in the Dalong and the Longmaxi shales indicate that the complex sources of siliceous materials in the Dalong Formation shale result in different effects of quartz on pore development.

5.3. Effect of Clay Minerals on Pore Structure and Heterogeneity

Table 4 and Figure 2 show that clay minerals in the Permian marine shales facilitate the development of macropore and mesopore spaces, reducing pore SSA and pore space heterogeneity. Clay mineral-related pores in shale have always been one of the main types of reservoir spaces in shale [16,31]. Although the proportion of clay minerals within the Permian shales is relatively low, their overall distribution is notably clustered among other brittle particles. It is rare to see scattered clay minerals among terrestrial debris (Table 1 and Figure 2). The Permian marine shale is mainly composed of siliceous and calcareous materials, which are randomly distributed (Table 1) [8,11,12]. Therefore, the pores related to clay minerals are mostly centrally distributed. Due to the protection of brittle minerals in the periphery, the compaction effect on clay minerals is relatively small, ultimately leading to clay minerals as the main contributor to the Permian marine shale reservoir space. This is consistent with the positive effect of clay minerals on reservoir space in the Silurian Longmaxi marine shale in the Sichuan Basin and the Triassic Chang 7 lacustrine shale in the Ordos Basin [2,5,10,16,19].

5.4. Effect of TOC on Pore Structure and Heterogeneity

Figure 4 and Table 2 show that TOC in the Permian shales has a certain negative effect on macropore volume. Organic matter pores comprise the main storage spaces in marine shales. Nevertheless, the size and quantity of organic matter vary with the types of macerals, the maturity and occurrence of organic matter [10,14,15]. Yang et al. (2016) believed that the pore volume of micropores and mesopores in shale increases with organic matter content, based on the study of the Wufeng-Longmaxi marine shales [32]. Wu et al. (2022) deemed that high organic matter content will inhibit the development of pore space in shale based on the study of the Dalong Formation marine shale [9]. The results in this study indicate that the compaction-induced decrease in macropore can be ascribed to the overall high organic matter content in the Permian marine shale. Scanning electron microscopy observation revealed that a significant amount of organic matter in the studied samples was present in striped and matrix-filling patterns, and the development of organic matter pores exhibited strong heterogeneity. Therefore, an increase in the organic matter content in shale will, to some extent, enhance the vertical compaction effect and reduce a certain amount of reservoir space. Compared to micropores and mesopores, the compaction effect is more pronounced for macropores. This is somewhat similar to Wu et al. (2022), where the weak impact of organic matter on fractal dimension was documented [9]. On the one hand, organic matter provides a certain amount of pore space, and on the other hand, it fills larger pore spaces to increase the heterogeneity of SSA. Therefore, due to the particular occurrence of organic matter and the development of heterogeneous organic matter pores, the increase in organic matter content in the Permian shales only shows a certain level of negative effect on the development of macropores.

6. Conclusions

Twelve Permian shale samples collected from the northeastern Sichuan Basin were subjected to comprehensive analysis on pore structure characterization, fractal feature evaluation, and controlling factor identification. This investigation yielded the following principal findings:
(1) Pores related to clay minerals, OMP, and intraP are the main storage spaces in the Permian shales in the Sichuan Basin. The pore morphology is mainly ink-bottle pores, with some slit-shaped and wedge-shaped pores. Mesopores make up the main TPV, and pore size distributions are mostly bimodal at sizes from micropores to medium-large pores.
(2) The fractal dimension D2 is greater than D1, indicating that the homogeneity of pore space is stronger than that of the SSA. The more pores sized close to mesopore, the smaller the heterogeneity of the SSA. The more pores sized as macropore, the smaller the heterogeneity of the pore volume.
(3) Quartz in the Permian marine shales has the effects of suppressing the macropore and mesopore volumes and increasing the heterogeneity of SSA and pore volume. On the one hand, the cementation of dispersed authigenic quartz blocks the pores. On the other hand, due to the diverse origins of silica in the Permian marine shale, the overall contribution of silica to resisting compaction is insufficient.
(4) Clay minerals in the Permian marine shales could promote the development of macropores and mesopores and reduce the SSA and the heterogeneity of pore volume. Siliceous and calcareous materials are composed in majority of rock fabric, and they are randomly distributed, which resulted in the concentrated distribution of pores related to clay minerals. The protective role of surrounding brittle minerals significantly prohibited the compaction of clay minerals, leading to clay components as the primary contributors to the storage capacity of Permian marine shale formations. This mineralogical arrangement effectively preserves clay-related pore networks during diagenetic processes.
(5) TOC in the Permian shales has a certain level of negative effect on macropore volume. Most of the organic matter occurred as strip and matrix fillings, resulting in the strong heterogeneity of OMP. The increase in organic matter content will enhance the effect of compaction to a certain extent and reduce a certain amount of storage space.

Author Contributions

Conceptualization, G.W.; Methodology, Q.Z. and L.W.; Validation, Q.Z., B.S., P.W. and W.D.; Formal analysis, G.W., W.D., L.W. and C.W.; Investigation, P.W., L.W. and C.W.; Resources, B.S., P.W., W.D. and M.L.; Data curation, G.W. and M.L.; Writing—original draft, G.W.; Writing—review & editing, Q.Z. and B.S.; Project administration, B.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (Grant No. 42090020 and No. 42090025) and the Technical Development (Entrusted) Project of Science and Department of SINOPEC (Grant Nos. P22137 and KLP25015).

Data Availability Statement

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

Conflicts of Interest

Authors Guanping Wang, Baojian Shen, Pengwei Wang, Wei Du, Min Li and Chengxiang Wan were employed by the company Sinopec. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Figure 1. A geological map depicting the well locations (marked as (A,B)) and the stratigraphic columns (labeled (C)) of the Permian shales within the Sichuan Basin [7,12].
Figure 1. A geological map depicting the well locations (marked as (A,B)) and the stratigraphic columns (labeled (C)) of the Permian shales within the Sichuan Basin [7,12].
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Figure 2. FE-SEM images showing the characteristics of pores and minerals in the Permian marine shales. (a) OM pores, 4 255.61 m, Dalong Formation; (b) amplified area marked in the red rectangle of (a); (c) intergranular pores and intragranular dissolution pores, 4 255.61 m; Dalong Formation; (d) OM Pores, intergranular pores and intragranular dissolution pores, enlarged area marked in the red rectangle of (c); (e): intergranular pores, L1-8, Wujiapingzu Formation; (f) clay intercrystalline pores, enlarged area in the red rectangle of (e); (g) OM pores developed in the solid bitumen between pyrite grains, 4 266.45 m, Wujiaping Formation; (h) enlarged area marked in the red rectangle of (g); (i) abundant intergranular pores, L1-9, Wujiaping Formation; (j) no OM pores were observed, but showed microcracks between organic matter and other minerals, enlarged area marked in the red rectangle of (i); (k) abundant clay intercrystalline pores, enlarged area marked in the red rectangle of (i); (l) enlarged area marked in the red rectangle of (k); (m) intragranular dissolution pores, L1-11, Gufeng Formation; (n) enlarged area marked in the red rectangle of (m); (o) intragranular dissolution pores in quartz, L1-11, Gufeng Formation; (p) intergranular pores and intragranular dissolution pores in calcite, L1-11, Gufeng Formation. Note: Q = Quartz; Cl = Clay; Ca: Calcite; P = Pyrite; F = Feldspar.
Figure 2. FE-SEM images showing the characteristics of pores and minerals in the Permian marine shales. (a) OM pores, 4 255.61 m, Dalong Formation; (b) amplified area marked in the red rectangle of (a); (c) intergranular pores and intragranular dissolution pores, 4 255.61 m; Dalong Formation; (d) OM Pores, intergranular pores and intragranular dissolution pores, enlarged area marked in the red rectangle of (c); (e): intergranular pores, L1-8, Wujiapingzu Formation; (f) clay intercrystalline pores, enlarged area in the red rectangle of (e); (g) OM pores developed in the solid bitumen between pyrite grains, 4 266.45 m, Wujiaping Formation; (h) enlarged area marked in the red rectangle of (g); (i) abundant intergranular pores, L1-9, Wujiaping Formation; (j) no OM pores were observed, but showed microcracks between organic matter and other minerals, enlarged area marked in the red rectangle of (i); (k) abundant clay intercrystalline pores, enlarged area marked in the red rectangle of (i); (l) enlarged area marked in the red rectangle of (k); (m) intragranular dissolution pores, L1-11, Gufeng Formation; (n) enlarged area marked in the red rectangle of (m); (o) intragranular dissolution pores in quartz, L1-11, Gufeng Formation; (p) intergranular pores and intragranular dissolution pores in calcite, L1-11, Gufeng Formation. Note: Q = Quartz; Cl = Clay; Ca: Calcite; P = Pyrite; F = Feldspar.
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Figure 3. N2 GA isotherms of the Permian marine shale samples.
Figure 3. N2 GA isotherms of the Permian marine shale samples.
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Figure 4. PSDs for the Permian marine shale samples are based on N2 adsorption isotherms.
Figure 4. PSDs for the Permian marine shale samples are based on N2 adsorption isotherms.
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Figure 5. CO2 GA isotherms of the Permian marine shale samples. The adsorption capacity of sample L1-8 exhibited the highest level, while that of sample L1-2 showed the lowest value.
Figure 5. CO2 GA isotherms of the Permian marine shale samples. The adsorption capacity of sample L1-8 exhibited the highest level, while that of sample L1-2 showed the lowest value.
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Figure 6. PSDs for the Permian marine shale samples are derived from CO2 adsorption experiments.
Figure 6. PSDs for the Permian marine shale samples are derived from CO2 adsorption experiments.
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Figure 7. Plots for ln (V/V0) vs. ln(ln(P0/P)) derived from N2 GA isotherms for selected two samples. Both the samples L-1 and L-2 demonstrated fractal characteristics, which can be categorized into two distinct types: D1 and D2.
Figure 7. Plots for ln (V/V0) vs. ln(ln(P0/P)) derived from N2 GA isotherms for selected two samples. Both the samples L-1 and L-2 demonstrated fractal characteristics, which can be categorized into two distinct types: D1 and D2.
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Figure 8. Histogram of the pore volumes for the Permian marine shale samples based on N2 GA and CO2 GA analysis. The calculation of micropore volumes was carried out utilizing the data from CO2 gas adsorption (CO2 GA), while the determination of meso- and macropore volumes was based on the results of N2 gas adsorption (N2 GA).
Figure 8. Histogram of the pore volumes for the Permian marine shale samples based on N2 GA and CO2 GA analysis. The calculation of micropore volumes was carried out utilizing the data from CO2 gas adsorption (CO2 GA), while the determination of meso- and macropore volumes was based on the results of N2 gas adsorption (N2 GA).
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Figure 9. The normalized proportions of micro-, meso-, and macropore volumes for the Permian marine shale samples. The calculation of micropore volumes was carried out utilizing the data from CO2 gas adsorption (CO2 GA), while the determination of meso- and macropore volumes was based on the results of N2 gas adsorption (N2 GA).
Figure 9. The normalized proportions of micro-, meso-, and macropore volumes for the Permian marine shale samples. The calculation of micropore volumes was carried out utilizing the data from CO2 gas adsorption (CO2 GA), while the determination of meso- and macropore volumes was based on the results of N2 gas adsorption (N2 GA).
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Figure 10. Relationship between pore structure parameters and fractal dimensions. BET SSA vs. micro- and meso-pores PV (a); D1 vs. mesopores PV (b); D1 vs. TPV (c); D2 vs. TPV (d); D2 vs. mesopores PV (e); D2 vs. macropores PV (f).
Figure 10. Relationship between pore structure parameters and fractal dimensions. BET SSA vs. micro- and meso-pores PV (a); D1 vs. mesopores PV (b); D1 vs. TPV (c); D2 vs. TPV (d); D2 vs. mesopores PV (e); D2 vs. macropores PV (f).
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Table 1. Mineralogical composition and TOC of the Permian shale in the Sichuan Basin.
Table 1. Mineralogical composition and TOC of the Permian shale in the Sichuan Basin.
FormationsSample IDDepthMineral Composition (%)Total Clay (%)TOC
(m)QuartzK-FeldsparPlagioclaseCalciteDolomiteSideritePyrite(wt%)
DalongL1-14237.9251.20.31.235.2--6.25.96.3
DalongL1-24247.334.8-21341-2.66.63.59
DalongL1-34248.5667.50.23.89.61.6-6.410.99.19
DalongL1-4425338.90.77.920.4--5.226.96.76
DalongL1-54254.1445.30.43.7331.2-3.912.56.37
DalongL1-64268.1827-5.736.4-2.47.820.73.19
WujiapingL1-7428252.90.13.125.42-2.5146.21
WujiapingL1-84290.0519.50.24.4-20.26.33.545.96.65
WujiapingL1-94310.9914.30.11.8-5.27.34.666.73.52
WujiapingL1-104312.096.90.50.7-3.611.86.370.22.74
GufengL1-114334.7182.30.40.23.21.7-1.610.68.13
GufengL1-124335.5791.50.20.23.81.1--3.26.88
Table 2. PSD, SSA, and porosity of the studied Permian shale samples.
Table 2. PSD, SSA, and porosity of the studied Permian shale samples.
Sample IDN2Helium PorosityCO2TPV
(cm3/g)
BET SSABJH PSD (10−4 cm3/g) BJH PSD (10−4 cm3/g)CO2-A (cm3/g)BJH TPV (cm3/g)
(m2/g)<2nm2–50 nm>50 nm<0.5 nm0.5–1.0 nm>1.0 nm
L1-17.92438.62125.11724.5671.61224.25.52.220.0040.009
L1-29.20541.19669.39631.1602.25.511.42.71.040.0020.012
L1-322.034102.351119.71522.2944.11024.65.62.140.0040.018
L1-413.97864.15270.71227.7122.65.416.64.41.410.0030.012
L1-521.827102.58596.73930.6853.89.126.57.22.280.0040.017
L1-625.309116.872138.47733.6414.114.231.77.42.830.0050.023
L1-714.62067.32888.46624.5013.99.120.751.850.0030.015
L1-811.99751.518156.26160.445-16.933.47.73.090.0060.027
L1-915.20366.444164.14751.2187.714.129.78.22.740.0050.027
L1-106.50526.749127.26571.092-1225.26.71.080.0040.024
L1-1115.95977.22115.12531.3710.613.229.36.92.630.0050.010
L1-126.36231.0217.94527.4040.6923.962.070.0040.007
Table 3. Fractal dimensions were calculated based on N2 adsorption data.
Table 3. Fractal dimensions were calculated based on N2 adsorption data.
SamplesN2
P/P0: 0–0.45P/P0: 0.45–1.0
R12D1R22D2
L1-10.90162.79430.98432.8710
L1-20.98402.72440.99452.8285
L1-30.98342.72440.95672.8863
L1-40.98492.74850.99622.8668
L1-50.97992.75140.99142.8918
L1-60.97642.72030.98682.8856
L1-70.98232.70730.98042.8657
L1-80.99442.58480.99572.7345
L1-90.99702.62940.98222.7673
L1-100.98662.50560.99962.6129
L1-110.81222.75990.92072.9435
L1-120.84502.85840.90792.9190
Table 4. Spearman rank correlation coefficients and single-tail significance test of pore system with rock components in the Permian shale in the Sichuan Basin.
Table 4. Spearman rank correlation coefficients and single-tail significance test of pore system with rock components in the Permian shale in the Sichuan Basin.
Spearman Correlation CoefficientTOC (wt.%)Clay (%)Quartz (%)Carbonate Minerals (%)Feldspar (%)Pyrite (%)
Vmic//////
Vmes/R = 0.790; P = 0.002R = −0.769; P = 0.003///
VmacR = −0.608; P = 0.036R = 0.692; P = 0.031R = −0.776; P = 0.003///
SSA//////
D1/R = −0.830; P = 0.001R = 0.788; P = 0.02///
D2/R = −0.671; P = 0.017R = 0.832; P = 0.001///
Porosity/R = 0.689; P = 0.028////
Note: R: Spearman’s rank correlation coefficient; P: One-tail significance test; / means that there is no correlation between the two parameters.
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Wang, G.; Zhang, Q.; Shen, B.; Wang, P.; Du, W.; Wang, L.; Li, M.; Wan, C. Pore Structure and Fractal Characteristics of the Permian Shales in Northeastern Sichuan Basin, China. Minerals 2025, 15, 684. https://doi.org/10.3390/min15070684

AMA Style

Wang G, Zhang Q, Shen B, Wang P, Du W, Wang L, Li M, Wan C. Pore Structure and Fractal Characteristics of the Permian Shales in Northeastern Sichuan Basin, China. Minerals. 2025; 15(7):684. https://doi.org/10.3390/min15070684

Chicago/Turabian Style

Wang, Guanping, Qian Zhang, Baojian Shen, Pengwei Wang, Wei Du, Lu Wang, Min Li, and Chengxiang Wan. 2025. "Pore Structure and Fractal Characteristics of the Permian Shales in Northeastern Sichuan Basin, China" Minerals 15, no. 7: 684. https://doi.org/10.3390/min15070684

APA Style

Wang, G., Zhang, Q., Shen, B., Wang, P., Du, W., Wang, L., Li, M., & Wan, C. (2025). Pore Structure and Fractal Characteristics of the Permian Shales in Northeastern Sichuan Basin, China. Minerals, 15(7), 684. https://doi.org/10.3390/min15070684

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