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. 2025 Feb 2;10(5):4395–4405. doi: 10.1021/acsomega.4c07214

Investigating Pore Characteristics and Their Dependence on Shale Composition: Case Study from a Permian Basin in India

Saheli Ghosh Dastidar a, Kripamoy Sarkar a, Debanjan Chandra b, Bodhisatwa Hazra c, Vikram Vishal d,e,f,*
PMCID: PMC11822721  PMID: 39959104

Abstract

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Shale reservoirs, often acting as caprocks for conventional hydrocarbon reservoirs, exhibit moderate to high porosity and remarkably low permeability. Organic-rich shales serve as reservoirs for unconventional hydrocarbons. This study focused on evaluating the characteristics of the source rocks and the factors influencing pore parameters in organic-rich shale from a Permian Basin in India, exploring its feasibility as both a CO2 sink and a natural gas source. Experimental techniques were employed to explore the mineral and the organic matter characteristics along with attributes of the pores hosted within them. The investigated shales displayed diverse thermal maturity levels, spanning from that in oil-prone to gas-prone zones, with the total organic carbon content varying from 0.72 to 24.98 wt %, indicating substantial organic richness. Rock-Eval pyrolysis results revealed a range of thermal maturity (Tmax) values between 426 and 474 °C, while X-ray diffraction analysis indicated significant quantities of illite and kaolinite, along with trace amounts of pyrite in certain samples. Field-emission scanning electron microscopy imaging and its detailed interpretation provided valuable insights into the pore structure and arrangement. In our study, we found that both the clay content and the organic matter significantly contribute to gas adsorption. While clay content strongly influences mesopore attributes, the organic matter predominantly affects micropore attributes. Furthermore, a direct relationship among fractal dimension, surface area, and pore volume, indicating increased complexities with these variables. Our examination of mesopore fractal attributes revealed that smaller mesopores exhibit a more convoluted and irregular configuration in comparison to the larger ones. These findings provide significant insights into the pore morphology of the analyzed shale sample.

1. Introduction

Understanding pore morphology, sorption properties, and gas storage capacities within organic-rich shale formations is crucial due to the potential significant reserves of trapped gas.1 Traditionally, shale formations are commonly seen as impermeable barriers or cap rocks. However, progress in drilling methods and hydraulic fracturing has facilitated the extraction of gas confined within shale matrix.2 In addition, in situ thermal stimulation of shale enhances hydrocarbon mobility and release, especially in low-permeability formations, thereby improving extraction efficiency.3−5 Shale formations containing high organic matter act as both source and reservoir rocks, owing to their organic content and intricate pore matrix, facilitating gas generation, transport and storage.6 Complexities within the shale formations arise from the variability in depositional conditions and the blending of organic and inorganic constituents, necessitating scientific analysis.7,8 Most of the gas in shale is stored via adsorption on pore surfaces,9−12 with pores classified as macro-, meso-, or nanopores based on their size, each impacting gas transport and storage differently.13 Recently, CO2-enhanced shale gas production has emerged as a method for the extraction of gas while also enabling CO2 storage in depleted reservoir.14 Shale pore characteristics—such as porosity, pore volume, specific surface area, and connectivity—significantly influence the storage and transport potential of both methane and CO2, making their accurate characterization crucial for reserve estimation, production forecasting, and CO2 sequestration planning.15,16

Given the inherent complexity and variability, extensive research is essential to understand the pore network in shale formations.6,8,17 Organic content plays a crucial part in gas storage and production by contributing to the internal surface area as well as controlling the heterogeneity and anisotropy of the shale matrix.18−20 Parameters such as thermal maturity, mineral composition, kerogen types, and total organic carbon influence gas production and storage within the shale reservoirs.8,21−24 Many studies have also experimentally illustrated how changes in temperature and treatment conditions alter pore structural properties in shales.25−27

Various methods, such as microscopy and gas adsorption techniques, are employed to investigate the attributes of pore structure, offering insights into pore size distribution in shale.28−34 These methods offer detailed observations of the pore geometry, size, volume, surface area, and width, contributing to a comprehensive understanding of shale pore systems. Mandelbrot35 introduced the concept of the fractal dimension (Ds) to assess surface pore irregularity and roughness in porous media. This theory has been applied to determine surface roughness in shales using N2 adsorption isothermal curves, as evidenced by several studies.22,36−39 The roughness of pore surfaces plays a crucial role in oil and gas flow within shale reservoirs, underscoring the significance of fractal dimension in characterizing the shale pore structure. Fractal models such as the Frenkel–Halsey–Hill (FHH) equations have been employed to analyze surface irregularities of solid surfaces using the adsorption isotherm.37−40 The goal of this study is to analyze the pore characteristics of shales from the Mand-Raigarh basin, Chhattisgarh. To enhance our understanding of the morphology of pores and delineate the impact of mineral composition and total organic carbon (TOC) content on the properties of shale gas reservoirs, we employed various techniques including X-ray diffraction (XRD) analysis, field-emission scanning electron microscopy (FE-SEM) imaging, and low-pressure N2 and CO2 adsorption. These methods helped us characterize the pore structures, pore size distribution, specific surface area (SSA), and total pore volume (TPV) of shale cores. We examined how mineral composition and TOC content influence pore structure and used the FHH theory41 to investigate the fractal dimensions derived from N2 adsorption data to estimate the heterogeneity of the pore surface and structure. The findings provide valuable insights into the distribution of the pore structure and its influencing factors on shale gas adsorption, aiding in the exploration of shale gas potential in the study region.

Several studies have examined the pore characterization of Indian shales, highlighting their gas storage potential.9,42,43 However, the pore attributes of the Mand-Raigarh basin in Central India remain unexplored. Understanding these characteristics will provide valuable insights into shale gas storage and exploration, alongside opening avenues for CO2 sequestration.44,45 This alternative could reduce the nation’s dependence on conventional energy sources.

2. Study Area

India’s Mand-Raigarh basin, which spans an area of more than 900 km2, is located within the Mahanadi Basin and is bordered by 21°45′00″ and 22°42′00″ latitudes and 83°01′00″ and 83°44′00″ longitudes.46 The Talaipalli Coal Mine is situated close to Talaipalli village in the Gahrghoda block of Raigarh district, Chhattisgarh, India, at latitude 22°14′26″N and longitude 83°27′39″E. It is located about 55 km from Raigarh town and has a 21.13 km2 area. The Talaipalli coal block is located in the eastern half of the Mand-Raigarh coalfield and predominantly relies on the Barakar Formation’s coal-bearing sedimentary layers, with Barren Measure coal strata dominating the block’s southern region. Shale strata with varying lateral thicknesses make up the Lower Permian Barakar Formation in the basin. Figure 1 displays a geological map, indicating the positions of the test borehole drilled in shale formations. Table 1 provides an overview of the lithology and associated geological ages, illustrating a comprehensive stratigraphic sequence.

Figure 1.

Figure 1

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A geological map of the Mand-Raigarh basin is presented, indicating the location of the study area.47 Figure reproduced with permission from American Chemical Society, copyright 2023.

Table 1. Mand-Raigarh Basin’s Lithostratigraphic Succession in India47.

geologic age formation lithology
recent to subrecent Upper Kamthi alluvium and laterite
Upper Permian to Jurassic Lower Kamthi fine- to medium-grained sandstone, carbonaceous shale, and coal bands with greenish sandstone
unconformity
Upper Permian Raniganj fine- to coarse-grained sandstone, gray and carbonaceous shales, persistent coal seams
Middle Permian Barren Measures fine to coarse medium-grained sandstone, grayish to carbonaceous shales
Lower Permian Barakar coarse- to fine-grained sandstone, shales, carbonaceous shales, coal seams
Karharbari medium- to coarse-grained white arkosic sandstone, carbonaceous shales and coal seams
Upper Carboniferous to Lower Permian Talchir dimictite, green shale, rhythmites, and sandstone
unconformity
Precambrian unclassified Precambrian rocks gneisses, schists, pegmatite, etc.
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Table reproduced with permission from American Chemical Society, copyright 2023.

3. Sampling and Methodology

The research work was carried out on five variety of specimens belonging to the Barakar Formation. These shale samples were sourced from depths ranging between 345 and 886 m (Table 2). The nomenclature included a “MR” prefix followed by sequential numbers, such as MR1 for the sample obtained from 345 m and MR5 for the one collected from 886 m. To preserve the integrity of the cores, they were sealed in labeled bags and brought to the lab. For further analysis, a portion of each core was sectioned into flat chips (5 × 10 × 10 mm) for FE-SEM imaging, while the remaining material was crushed and passed through a 212 μm (72 mesh) sieve. The sieved fraction were used for XRD, Rock-Eval pyrolysis, and low-pressure gas adsorption (LPGA) analyses, with each technique described in the following subsections.

Table 2. Shale Samples and Their Depths.

sample ID depth (m)
MR1 345
MR2 513
MR3 610
MR4 776
MR5 886
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3.1. X-ray Diffraction Studies

Mineral and organic components stand out as the most critical parameters in understanding shale’s origins and diagenetic processes. XRD analysis was carried out to quantify mineralogy of the shales using a PANalytical’s X’Pert Pro system equipped with a Cu anode. In this analysis, 5 mg of the sieved shale powder fraction was utilized. The scanning was conducted with a step size of 0.0130°/s and covered a 2θ range from 5 to 70°. Rietveld refinement was followed to identify the mineral peaks.48 Highscore X’Pert Pro software was employed to evaluate the relative mineral abundance.

3.2. High-Resolution Imaging

FE-SEM visualizes and identifies pores and organic matter at nanometer scales. In this study, FE-SEM equipment was utilized for SEM, enabling imaging at magnifications up to 450,000× with a maximum operating voltage of 30 kV. To enhance surface conductivity, chip samples were sputter-coated with gold before imaging. Various magnifications were employed to identify pores of varied sizes.

3.3. Rock-Eval Pyrolysis

The Rock-Eval 6 analyzer was utilized to evaluate the source-rock potential of the shale samples. To analyze the shales, we followed a revised protocols for accurate estimation of TOC.7,49,50 To study the characterization of potential source rock, 5–10 mg of samples was used in the analysis. Initially, the crucibles filled with powdered shales were inserted into the pyrolysis chamber and exposed to isothermal heating at 300 °C. During this process, hydrocarbons released were carried by N2 and detected using a flame ionization detector (FID), represented by the “S1” peak. Subsequently, the shales were heated from 300 to 650 °C at a rate of 25 °C/min, causing the decomposition of organic matter into hydrocarbon. These hydrocarbons, also transported by N2, were detected by the flame ionization detector (FID) and is identified as the “S2” peak. The Tmax, indicates the temperature at which peak hydrocarbon generation during the S2 stage was recorded, aiding in the assessment of organic matter thermal maturity within the shale samples. Additionally, oxygenated compounds within the organic matter decomposed during the pyrolysis stage, yielding CO2 and CO, detected as the “S3” peak. The carbon generated during stages S1, S2, and S3 collectively contributed to the pyrolyzable carbon (PC) fraction. Following the pyrolysis phase, the samples were heated within the oxidation chamber. This process provided data on the quantity of residual carbon (RC) present in the shale samples. The sum of both the PC and RC fractions gives the total organic carbon (TOC) . Furthermore, empirical formulas were applied to determine additional indices, specifically, the hydrogen index (HI) and oxygen index (OI).

3.3. 1
3.3. 2

3.4. Low-Pressure Gas Adsorption (LPGA)

To examine the studied shale samples, N2 and CO2 gases were used as probing agents to explore the mesopores and micropores, respectively. A quantity of 2–3 g of powdered shale underwent degassing under a pressure of 10–4 Torr for 12 h at 110 °C12,51−53 to remove moisture and volatiles present within the pores of shale samples. Chandra et al.25 emphasized the importance of selecting an appropriate degassing temperature to minimize errors in determining pore attributes. Research has shown that a degassing temperature of 110 °C effectively removes moisture and lowers hydrocarbons without altering shale pores.52,53 Research conducted by Singh et al.54 and Chandra et al.25 has demonstrated that raising the degassing temperature from 110 to 200 °C and 300 °C leads to changes in pore attributes due to the breakdown of organic matter. Some researchers have used a degassing temperature of 250 °C,36 noting a minimal mass change (approximately 1–2%) in solid organic matter, but this approach is less effective for shales with lower thermal maturity. To maintain consistency, 110 °C is widely accepted as it causes a minimal mass change in solid organic matter.

Quantachrome Autosorb iQ physisorption analyzer was used to carry out the adsorption studies. It gives us information about the quantity of gas adsorbed or desorbed at specific pressure levels. N2 molecules, characterized by their quadrupolar and non-reactive nature, exhibit preferential adsorption within selective functional groups. This characteristic renders N2 highly suitable as a probe gas for conducting physisorption experiments. Adsorption of N2 at a temperature of 77 K aligns with the point at which pure N2 condenses at standard temperature and pressure. The examination of adsorption and desorption behaviors extends over a spectrum of relative pressures (P/P0) ranging from 0.001 to 0.99. In this case, P is the pressure of probe gas and P0 represents the condensation pressure of liquid N2 at 77 K (760 Torr). The desorption branch of the isotherm is obtained by reversing the adsorption process, during which the liquid adsorbate is released, resulting in a decrease in the equilibrium relative pressure. The notable hysteresis loop generated by adsorption and desorption isotherms, which remain consistent across consecutive cycles, emphasizes the importance of understanding hysteresis behaviors. Such comprehension is vital for effectively tackling practical challenges characterizing pore structures. Despite its limitations in probing micropores smaller than 1.3 nm, N2 was chosen for adsorption measurements due to its ability to bind to specific activation sites and effectively explore pores larger than 1.3 nm in diameter. The N2 adsorption model was applied to obtain various parameters including the Brunauer–Emmett–Teller (BET) surface area and pore size distribution (PSD) determined through density functional theory (DFT). Additionally, the fractal dimension of mesopores was calculated using the FHH method.

CO2 adsorption was carried out at a temperature of 273 K using a water bath, examining relative pressures between 0.0005 and 0.03, with P0 fixed at 26,610 Torr. CO2 was chosen as the adsorbate for micropore characterization due to its strong affinity for organic carbon, allowing it to penetrate shale micropores. This process calculates the Dubinin–Radushkevich (D-R) surface area, Dubinin–Astakhov pore volume, and PSD for the CO2 adsorption. The CO2 adsorption isotherms were analyzed by using the DFT method. In summary, the integration of N2 and CO2 adsorption methods allowed for a thorough investigation of the pore properties. This comprehensive analysis provided valuable information about the structure of the pores of the shale samples.

4. Results

The experimental procedures were used to evaluate the pore characteristics and the PSD, and how they affect the effective surface area of the shale samples. Mineral and organic matter quantification, LPGA measurements, and FE-SEM analyses were done to characterize and illustrate various categories of pores, as elaborated in this section.

4.1. Mineral Composition

The mineral content of the samples was examined using XRD analysis, with the results presented in Figure 2. Quartz and clay minerals constitute the primary mineral components, with illite being the dominant clay mineral, ranging from 16 to 49%. It is worth noting that illite-rich shale exhibits a significant volume of micropores.55 Among the samples, MR5 exhibits the highest quartz content, while MR3 shows the lowest one. Conversely, MR3 has the highest clay content, while MR5 has the lowest one. Feldspar is identified in MR1, MR3, and MR4, while it is absent in MR2 and MR5. Pyrite, as a heavy mineral, is present in MR2 only among the examined shale samples.

Figure 2.

Figure 2

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Mineral composition of the studied shale samples from Mand-Raigarh.

4.2. Thermal Maturity and Organic Content

The Rock-Eval experiment results for the shale samples are outlined in Table 3. S1 and S2 values are combined to give the genetic potential (GP). Tmax shows different thermal levels within the studied samples. MR1, MR2, and MR3 show “immature” thermal maturity, MR4 demonstrate a “late mature” level, and MR5 represents a “postmature” condition.56 The HI is a metric for evaluating the type of kerogen present and the thermal maturity level of organic materials.57 The HI and OI of the studied samples range between 36 and 451 mg HC/g of TOC and 5 and 11 mg CO2/g of TOC, respectively. These findings suggest that the majority of the samples fall under the kerogen types II and III and type III. The GP of source rocks is characterized as follows: values ranging from 2 to 5 signify low production potential, those falling between 5 and 10 are deemed moderate, and values surpassing 10 suggest high to very strong production potential.58 The analyzed samples demonstrate a genetic potential (GP) ranging from 0.30 to 82.59 mg/g, representing a spectrum from poor to exceptional source potential for the source rocks (Table 3).

Table 3. Rock-Eval Results of the Studied Shale Samples.

sample number depth (m) S1 (mg/g) S2 (mg/g) S3 (mg/g) Tmax (°C) TOC (%) HI (mg HC/g TOC) OI (mg CO2/g TOC) S1 + S2 (mg/g)
MR1 345 0.42 82.17 2.08 426 24.98 329 8 82.59
MR2 513 0.35 63 0.69 431 13.97 451 5 63.35
MR3 610 0.17 24.74 0.66 431 13.07 189 5 24.91
MR4 776 0.01 0.29 0.08 454 0.72 40 11 0.30
MR5 886 0.08 2.04 0.31 474 5.7 36 5 2.12
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4.3. Morphology of Pores

The representative images are provided to illustrate various pore types present within the shale samples (Figure 3). In these images, empty pores are depicted as black, while those filled with organic matter appear gray, and mineral-filled pores are portrayed as bright. Loucks et al.30 proposed a classification for matrix-related pores, dividing them into three primary types: (i) organic matter (OM) pores (Figure 3a), (ii) interparticle pores, found between mineral particles (intergranular) (Figure 3b), and (iii) intraparticle pores, present within mineral particles (intragranular) (Figure 3d). These types are all observed in the studied samples. Additionally, natural fractures, visible under an electron microscope, display various forms, primarily at the micrometer scale, and are often observed within quartz grains or OM–clay aggregates (Figure 3c).

Figure 3.

Figure 3

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FE-SEM images illustrating various types of pores in shale samples. (a) Macropores in organic matter, (b) interparticle pores in pyrite framboid, (c) natural fractures in shale samples, and (d) intraparticle pores in quartz and OM pores.

4.4. Characteristics of Pores

Figure 4 shows the sorption isotherms of the shale samples. The low-pressure N2 adsorption profile of the shale samples (Figure 4a) exhibited typical features of type IV adsorption and H3 hysteresis according to IUPAC classification standards. The presence of hysteresis suggests the existence of mesopores and macropores within our shale samples. The H3 hysteresis pattern observed indicates the influence of tensile strength on the samples, likely owing to the presence of cavitation and ink-bottle pores.59 Notably, among all of the samples, MR3 exhibited the highest adsorption potential at 24.57 cc/g, while MR4 demonstrated the lowest one at 10.94 cc/g.

Figure 4.

Figure 4

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(a) Low-pressure N2 adsorption isotherm and (b) low-pressure CO2 adsorption isotherm of the studied shales.

The micropore attributes of the samples were assessed through CO2 adsorption (Figure 4b). The observed isotherms exhibit characteristic features of type II, with a notable increase in the P/P0 range from 0.0005 to 0.01. Among the analyzed shale samples, MR1 demonstrated the highest CO2 adsorption potential at 5.95 cc/g, while MR4 displayed the lowest potential at 0.95 cc/g. This suggests that MR1 has a greater extent of micropore filling, while MR4 has the least.

4.4.1. Pore Attributes

The mesopore characteristics are studied using N2 gas adsorption, while the micropore properties are examined using CO2 gas adsorption. The mesopore surface area is evaluated using the BET equation. From the N2 adsorption isotherm analysis, it was found that sample MR3 had the maximum BET surface area of 31.96 m2/g, whereas sample MR4 exhibited the lowest value of 9.69 m2/g (Table 4). To analyze PSD for CO2 and N2 adsorption, the adsorption curves are scrutinized using CO2 DFT and quenched solid DFT models, respectively. The TPV is determined by combining the values of meso- and micropore volume. Among the samples, MR1 demonstrates the highest pore volume at 0.059 cc/g, while MR4 exhibits the lowest pore volume at 0.021 cc/g. Additionally, MR4 demonstrates the highest average pore width of 6.98 nm, compared to MR3, which has the smallest average pore width of 4.74 nm.

Table 4. Meso- and Micropore Attributes of the Shale Samples.
  specific surface area (SSA) (m2/g)
 average pore width (nm)
 total pore volume (TPV) (cc/g)
sample ID micropore mesopore micropore mesopore micropore mesopore micro + meso
MR1 65.51 21.58 0.882 6.309 0.025 0.034 0.059
MR2 48.85 31.79 0.956 4.779 0.018 0.037 0.055
MR3 50.85 31.96 0.890 4.744 0.019 0.038 0.057
MR4 14.06 9.69 1.072 6.984 0.005 0.016 0.021
MR5 28.71 18.97 0.904 5.801 0.011 0.028 0.039
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4.4.2. Pore Size Distribution (PSD)

The PSD curves depict how the pore volume is distributed relative to the pore size. In these curves, those derived from CO2 adsorption end at 2 nm, while those from N2 adsorption commence at 2 nm, indicating a smooth transition between the two. Consequently, merging these PSD curves into a single curve (Figure 5) provides comprehensive insight into the pore structure of shale. In the CO2 PSD, each shale sample shows a multimodal distribution characterized by noticeable peaks typically ranging from 0.4 to 0.9 nm. MR1 displays the highest peak around 0.5 nm, consistent with those of other samples. On the other hand, the N2 PSD reveals distinct sharp peaks between 4 and 30 nm for all samples. MR2 exhibits the highest peak within this range, similar to those of the other shale samples.

Figure 5.

Figure 5

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Combined CO2 and N2 DFT plot of the shale samples showing pore size distribution.

4.4.3. Fractal Dimension

Fractal dimension (Ds) was proposed by Mandelbrot to characterize surface roughness. With an increase in surface roughness, Ds increases. The range varies between 2 and 3, with 2 indicating a perfectly smooth surface and 3 indicating a highly rough surface. Regardless of the scale, fractal surfaces exhibit a recurring pattern of irregularities, as first described by Avnir et al.60 The FHH theory61 is utilized in the formulation of the multilayer adsorptive model. The expression for this model is as follows:

4.4.3. 3

In the formula, V represents the volume of N2 adsorbed at equilibrium pressure P, the gas volume in the monolayer is denoted by Vm, P0 is the saturation pressure of N2 at 77 K, C is a constant, and A is the power-law exponent that depends on Ds. The interaction between the adsorbent and the adsorbate is mainly controlled by van der Waals forces due to the very low surface tension at extremely low relative pressures. This results in a well-established correlation between A and Ds, formulated as follows:

4.4.3. 4

Capillary condensation becomes more significant at elevated P/P0 ratios as the surface tension rises, causing a modification in eq 2 to

4.4.3. 5

The fractal dimension, which is studied from the N2 adsorption isotherm, is categorized into two segments. The initial segment, where the P/P0 ranges between 0.01 and 0.5, is denoted as D1, while the subsequent portion, where P/P0 ranges between 0.5 and 0.99, is represented as D2. In the low-pressure regime, the primary interaction between the solid surface and the gas interface is dictated by the prevailing van der Waals force of attraction. This interaction results in the fluid interface mirroring the surface roughness, leading to the computation of Ds using eq 4 in this context. Conversely, in the high-pressure regime, the interaction between the solid and gas phases is predominantly influenced by surface tension and capillary condensation. Here, the gas interface moves away from the solid surface, causing a reduction in the interface area, and subsequently, the Ds has been determined using eq 5. The FHH approach was employed for the analyzed shale samples, with detailed information on the fractal dimension calculations provided in Table 5.

Table 5. Fractal Dimension of the Shale Samples Assessed across Varying Ranges of Relative Pressure.
sample number D1(3 + S1)(0.01 < P/P0 < 0.5) R12 D2(3 + 3S2)(0.5 < P/P0 < 0.99) R22
MR1 2.48 0.99 2.11 0.99
MR2 2.51 0.99 2.35 0.99
MR3 2.57 0.99 2.34 0.99
MR4 2.20 0.97 2.08 0.99
MR5 2.41 0.98 2.17 0.99
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5. Discussion

Based on the data obtained from the experiments, it has been observed that sample MR1 exhibited the highest computed D-R surface area, while MR4 displayed the lowest one, reflecting a similar trend observed in the DA total pore volume. The PSD determined from the combined CO2–N2 curve depicted multimodal peaks for the analyzed samples. In N2 DFT, the highest peak is shown by MR2 around 4 nm, whereas in CO2 DFT, the highest peak is shown by MR1 around 0.5 nm.

MR1 exhibits a higher micropore SSA, potentially attributed to its elevated organic content. Conversely, MR4, with the lowest organic content, tends to display a lower micropore SSA. This suggests a notable relation between the composition of organic matter and the attributes of microporosity. Furthermore, the MR3 sample, characterized by the highest clay content, contributes the highest BET SSA.

Figure 6 illustrates that the clay mineral and TOC content display a positive correlation with pore parameters, indicating their primary role in pore formation. Specifically, Figure 6a shows that clay minerals correlate more strongly with mesopore volume than with TOC. Conversely, Figure 6c reveals a stronger correlation between TOC and micropore volume compared to clay minerals. In Figure 6b, clay minerals contribute significantly to achieving a good fit with the mesopore surface area, whereas the TOC content results in a weaker fit for this parameter. However, the D-R micropore surface area derived from CO2 adsorption demonstrates an excellent fit with the TOC content, in contrast to a lower fit with clay content (Figure 6d). TOC content and micropore parameters align with patterns commonly suggested in earlier research.31,62,63

Figure 6.

Figure 6

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Correlation of (a) mesopore volume with TOC and clay mineral, (b) mesopore surface area with TOC and clay mineral, (c) micropore volume with TOC and clay mineral, and (d) micropore surface area with TOC and clay mineral.

This suggests that while clay minerals have a greater influence in the mesopore region, the TOC content plays a more significant role in the micropore region. Considering the significant roles of clay minerals and organic carbon in adsorption, our results indicate that both clay minerals and organic matter have a major impact on gas adsorption. This challenges the traditional notion that gas adsorption in shale is governed primarily by organic matter alone. Instead, our results suggest that clay minerals also play a significant role in adsorption, thereby enhancing the gas storage capacity.

Figure 7 illustrates how the fractal dimensions of shale pores are influenced by primary minerals and TOC. The correlation between D1 and D2 with clay mineral content (Figure 7a) exhibits a stronger influence than that with TOC (Figure 7c), indicating the predominance of smaller mesopores associated with clay minerals. In contrast, the values of D1 and D2 show no clear trend with increasing quartz content (Figure 7b), suggesting that quartz has a negligible effect on the pore surface and structure.

Figure 7.

Figure 7

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Relationships between fractal dimensions and (a) clay content, (b) quartz, and (c) TOC depicted in the diagram.

Figure 8 illustrates the correlations between pore attributes and fractal dimensions (D1 and D2). In Figure 8a, the mesopore volume demonstrates an excellent fitting in the low-pressure regime (D1) and a good fitting in the high-pressure regime (D2). The fractal parameters D1 and D2 exhibit notable significance concerning mesoporous SSA (Figure 8b). The BET surface area has a stronger correlation with the fractal dimension D1 than with D2, suggesting that smaller mesopores have a greater influence on the BET surface area and pore volume. A negative correlation exists between fractal dimension and average pore diameter (Figure 8c). The average pore diameter decreases as the fractal dimension increases. Shales with smaller average pore diameters generally exhibit a higher number of mesopores and a more complex pore structure.

Figure 8.

Figure 8

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Relationship between fractal dimension and (a) mesopore volume, (b) mesopore SSA, and (c) average pore width.

Numerous researchers have identified comparable correlations and fitted curves, assessing their accuracy with the coefficient of determination (R2). Similar correlations between fractal dimensions and organic matter, mineral content, and pore parameters were noted by Li et al.64 They found a positive relationship with mesopore SSA and a negative relationship with average pore width. The study highlighted that the clay mineral content has a stronger influence compared with organic matter in shale samples. Similarly, Xu et al.65 demonstrated correlations between pore structure parameters, TOC, and mineral content. Their study revealed a significant relationship with the total organic content, with a minimal correlation with mineral matter. Xu et al.65 noted a relationship between pore structure attributes and mineral content, indicating a positive correlation with TOC and a negative correlation with clay. Given the consistency of the correlations observed in our study with other global examples, we have confidence in the validity of these associations within our experimental constraints.

6. Conclusions

This research aimed to assess the gas storage capacity of shale samples from the Mand-Raigarh basin and explore the relationship between pore attributes and mineral/organic matter content. The primary findings of this investigation are outlined below.

  • 1.

    TOC and clay minerals are the primary contributors to shale porosity. The micropore SSA ranges from 14.06 to 65.51 m2/g, while the mesopore SSA spans 9.69–21.58 m2/g, with the total pore volume varying from 0.021 to 0.059 cm3/g. Notably, mesopore parameters correlate more strongly with clay content, while micropore parameters align well with the organic carbon content, explaining the influence of both on gas storage in shale formations.

  • 2.

    N2 gas adsorption analysis shows that mesopores dominate the shale samples, comprising 57.63–76.19% of the pore volume, while CO2 gas adsorption reveals that micropores account for 23.81–42.37%. Among the samples, MR3 has the highest mesopore volume, and MR1 has the highest micropore volume.

  • 3.

    The fractal characteristics of mesopores indicate that smaller mesopores have rougher surface textures, while larger ones are smoother, as reflected in the average fractal dimensions (D1 = 2.43 and D2 = 2.21). Additionally, the fractal dimension rises with increased BET SSA and mesopore volume but decreases with larger average pore widths, suggesting that narrower mesopores have a more complex structure that may enhance gas adsorption.

Acknowledgments

The authors gratefully acknowledge IIT (ISM) Dhanbad, and IIT Bombay for providing the necessary laboratory facilities to conduct the research works. No additional funding was received by any of the authors to complete this work.

Glossary

Abbreviations

P

vapor pressure (in Torr)

P0

condensation pressure of nitrogen at 77 K (in Torr)

V

nitrogen adsorption volume on the adsorbent at equilibrium pressure (P/P0) (in cc)

Vm

volume of gas at monolayer adsorption (in cc)

A

power-law exponent depending on Ds

Tmax

thermal maturity

PC

pyrolyzable carbon

RC

residual carbon

TOC

total organic carbon

HI

hydrogen index

OI

oxygen index

The authors declare no competing financial interest.

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