We report apparent permeabilities (k a ) of coal samples from Bansgara, Jharia, and Bokaro collieries from Gondwana coalfield (India) to assess the Klinkenberg coefficients (b) and intrinsic permeabilities (k ∞ ) owing to their characteristic pore-size distributions. All k a values were measured with N 2 at room temperature, constant 6.2 MPa effective stress, and up to 7.6 MPa pore pressures (P p ), except for the Bokaro samples (maximum 3.5 MPa P p ). The permeabilities of the Bansgara samples were also measured with CO 2 up to P p 3.5 MPa. The poresize distributions were measured using low-pressure N 2 adsorption−desorption. We determined the b and k ∞ from the plots of k a versus (1/P p ). The linear and nonlinear sections of k a versus (1/ P p ) plots are modeled using the Klinkenberg and Ashrafi equations, respectively. The Ashrafi model yields lower k ∞ over the Klinkenberg model. The k ∞ and b of the Bansgara sample determined from the Klinkenberg model are nonidentical for N 2 (0.073 md and 0.33 MPa) and CO 2 (0.0052 md and 7.27 MPa). Our data show that b decreases with the total pore volume and interconnected porosity, whereas k ∞ increases with the total pore volume and interconnected porosity. The Bansgara sample with open-ended and well-connected micropores has the lowest b. The Jharia and Bokaro samples have volumetrically higher mesopores; although the pores are semiopen-ended with poor connectivity, they result in higher b compared to the Bansgara sample. The results of this study suggest that gas permeability evolution due to slippage effect is a strong function of the pore characteristics of the concerned reservoir.

Accessible to inaccessible nanopore structure, distribution and complexity controls the storage, transport, and recovery of gas-shale. Fluid- (low-pressure N2 and CO2 adsorption) and radiation-based (small-angle scattering) methods used to characterize the connected and isolated nanopores in the early Tertiary sequence (1043-2573 m) of Cambay basin, India. The obtained continuous pore size distribution was deconvoluted into12 pore families. The mean width (w) of the micropore and mesopore families are w~0.50, 0.61, 0.77, 1.11 nm, and ~4.85, 8.0, 10.5, 14.5, 17.0, 30.0 nm, respectively. The multivariate partial least square regression model estimated the dependency of various dependent pore parameters (e.g., deconvoluted pore families, micro-, meso-, total pore volume, specific surface area – SSA, etc.) on intrinsic, independent variables (e.g., quartz, Fe-bearing minerals, clay and total organic carbon -TOC) of the shale samples. Our result suggests, TOC strongly enhances the storage capacity by increasing SSA and pore volumes (micro-, meso-, total pore volume), which eventually enables higher sorption and free gas storage capacity. Increased clay content increases the inaccessible pores, whereas TOC increases the accessible pore volumes. Families from micropore region (w~0.50, 0.61, 0.77, 1.11 nm) show positive dependency with the TOC, while the Families with w~ 10.5.14.5,17.0 nm show negative dependency with TOC.

Heterogeneous nanopore structure and distribution regulate the gas trapping, desorption kinetics, and diffusion in shale matrices. In shale, pores range from continuous micro- and mesopore size distributions, varying with organic (total organic matter-TOC) and inorganic constituents (clay content, Fe-bearing minerals, quartz, etc.). Previous research only showed a linear relationship of pore parameters with these intrinsic properties of shale, which limits our understanding of the concurrent influence of multiple intrinsic rock properties. As a result, in this work, we established multivariate dependency of nanopore structure, distribution, and complexity (from low-pressure N2 and CO2 sorption and small-angle scattering; SAXS/MSANS) in the previously little-studied Cambay shales and provided a better tool (partial least square regression) for analyzing the simultaneous effect of intrinsic shale properties on multiply connected pore-parameters. Furthermore, we discretized continuous pore-size distribution into individual pore families using deconvolution to understand the pore space better. Additionally, predicted shale formation environment in terms of deposition probability (P+) and dissolution probability (P−) using a dynamic model of the fractal interface by precipitation and dissolution. Our findings indicate that the Cambay shales have a high potential for future hydrocarbon exploration (S2: 2.42–12.04 mg HC/g rock), “very good” (2-4 wt.%) to “excellent” (>4 wt.%) TOC content, and thermally mature type II–III admixed and type III kerogen. Deconvolution of the micro- and mesopore size distributions reveals that pore width (w) ranges ∼15.30–35 nm occupies greater than 50% of the total pore volume, and its pore volume increases with the presence of quartz, Fe-bearing minerals, and clay content. However, pores with w∼ 3.60–15.30 nm increase exclusively with TOC. In the micro- and early mesopore region, pore volume decreases with TOC from w∼ 0.30–0.75 nm and increases with TOC from w∼ 0.75–3.60 nm. Furthermore, TOC in shale increases the specific surface area and pore volume (micro-, meso-, and total pores), enhancing both sorption and free gas storage capacities. Cambay shales were likely deposited in three distinct environments, with precipitation probability (P+) values of 1, 0.7–0.8, and 0.5, as revealed by a fractal dimension (Ds) analysis of multiple samples.

<p>We investigated the nanopore structures of shale samples obtained from Cambay and Krishna-Godavari (KG) basins in India using low-pressure N<sub>2</sub> sorption method. The samples occurred at variable depths (1403-2574m and 2599-2987m for Cambay and KG basins, respectively) and have wide ranges of clay contents (56-90%) both in volume and mineralogy. The results of this study indicate the specific surface area (SSA) and pore diameters of the samples share a non-linear negative correlation. The SSA is a strong function of the clay content over the samples’ depth. The specific micropore volumes of the KG basin have relatively higher (8.29-24.4%) than the Cambay basin (0.1-3.6%), which leads to higher SSA in the KG basin. From different statistical thickness equations, the Harkins Jura equation was found to be most suitable for the computation of BJH pore size distribution and t-plot inversion in shale. Shale samples from Cambay basin show unimodal pore size distribution, with a modal diameter of 4-5nm, while in the KG basin, show bi-modal to multimodal pore size distribution, mostly ranges from 3-12 nm. In the fractal FHH method, fractal exponent D<sub>f</sub>-3 provides a better realistic result than fractal dimensions calculated from (D<sub>f</sub>-3)/3. In our samples, pore surface fractal dimension (D<sub>f1</sub>) show a positive correlation with SSA and a negative correlation with pore diameter, and pore structure fractal dimension (D<sub>f2</sub>) shows a negative correlation both with clay(%) and depth. The experimental data obtained in this study are instrumental in developing the pore-network model to assess the hydrocarbon reserve and recovery in shale.</p>