Pore Structure and the Multifractal Characteristics of Shale Before and After Extraction: A Case Study of the Triassic Yanchang Formation in the Ordos Basin

Abstract

The shale oil reservoirs of Member 7 of the Triassic Yanchang Formation in the Longdong Area of the Ordos Basin have attracted widespread attention due to their unique geological characteristics and enormous development potential. As the core factor controlling reservoir storage capacity and hydrocarbon flow efficiency, the precise characterization and quantitative analysis of pore structure are the prerequisite and key for reservoir evaluation and development plan optimization. All samples selected in this study were collected from the shale of Member 7 of the Triassic Yanchang Formation and were classified into two categories: medium-organic-rich shales (total organic carbon, TOC: 2–6%; TOC refers to the total organic carbon content in rocks, indicating organic matter abundance; unit: %) and high-organic-rich shales (TOC: >6%). The mineral composition and organic geochemical parameters of the shale were determined via X-ray diffraction (XRD) and Rock-Eval pyrolysis experiments, respectively. Meanwhile, pore structure characteristics were analyzed by combining low-temperature nitrogen adsorption–desorption experiments before and after extraction, and multifractal analysis was used to systematically investigate the differences in pore heterogeneity of shale and their influencing factors. The results show that the specific surface area (SSA) and total pore volume (TPV) of shale increased after extraction, while the change in average pore diameter (APD) varied. Multifractal analysis indicates that the micropores of shale both before and after extraction exhibit significant multifractal characteristics; after extraction, pore connectivity is improved, but the changes in pore heterogeneity are inconsistent. The pore connectivity of shale first increases and then decreases with the increase in TOC content and pyrolysis parameter S₂ content. The better the pore connectivity of shale, the lower the content of light-component saturated hydrocarbons and the relatively higher the content of heavy-component resins in the extractable organic matter (EOM). Brittle minerals can provide a rigid framework to inhibit compaction and are prone to forming natural microfractures under tectonic stress, thereby promoting pore connectivity. In contrast, clay minerals, due to their plasticity, are prone to deformation and filling pore throats during compaction, thus reducing pore connectivity. This study provides a theoretical basis for the evaluation and development of shale reservoirs in the Longdong Area.

1. Introduction

With the increasing global energy demand, shale oil resources have become a significant focus in the field of energy exploration worldwide [1,2,3]. In contrast to conventional sandstone and carbonate reservoirs, shale oil reservoirs are characterized by self-sourcing and self-storage mechanisms, typically exhibiting low porosity and low permeability [4,5]. Shale oil primarily occurs in both adsorbed and free states within the heterogeneous pore systems of shale formations. Owing to their complex pore structures, significant variations exist in pore types, pore size distribution, and connectivity [6,7].

Currently, the characterization methods for micropores in shale reservoirs mainly fall into three categories: fluid injection methods, image analysis methods, and non-fluid injection methods. Fluid injection methods obtain data experimentally and calculate pore structure parameters (e.g., pore volume, specific surface area, pore size distribution) using theoretical models [8], but cannot directly observe pore morphology. Image analysis methods primarily rely on advanced microscopic imaging techniques such as field emission scanning electron microscopy (FE-SEM), atomic force microscopy (AFM), and micro-nano-CT [9]. These methods can visually display structural characteristics such as pore types, morphology, and distribution based on acquired images. Non-fluid injection methods employ techniques like nuclear magnetic resonance (NMR), small-angle neutron scattering (SANS), and ultra-small-angle neutron scattering (USANS). These methods offer the advantage of non-destructive testing with high precision [10,11]. While all three methods yield pore structure parameters, their different principles and applicable ranges result in complementary information with inherent limitations, posing challenges for a systematic understanding of heterogeneous pore structures in shale.

Since its proposal, fractal theory has demonstrated broad application value in the field of geosciences [12,13]. In recent years, fractal theory has been widely applied in the study of micropore structures in tight sandstone, shale, and coal rock, providing an important methodology for quantifying the heterogeneity of pore size distribution in porous media. Many researchers have employed single-fractal models, such as the Frenkel–Halsey–Hill (FHH), Neimark, and Wang–Li theories [14] to investigate pore heterogeneity and its influencing factors. Based on the fractal dimension calculation model of the FHH method, Deng et al. [15] observed that black shales in different layers of the Xujiahe Formation exhibit dual-fractal characteristics, with larger pores displaying more complex structures than smaller ones. Using the Wang–Li theory, Zhang et al. [16] calculated the fractal dimensions of shales from the Fengcheng Formation in the Junggar Basin. Their quantitative characterization of the pore structure before and after extraction revealed that changes in fractal dimension are attributed to multiple factors. However, a single fractal dimension often fails to accurately represent the heterogeneous nature of pore systems, as certain parameters can only reflect partial pore characteristics and do not fully capture pore properties [17]. According to Jelinek et al. [18], Evertsz and Mandelbrot pointed out in 1992 that the multifractal dimension is more suitable than the single-fractal dimension for describing the geometric features of fractal sets. As an extension of fractal theory or a superposition of single fractal dimensions, multifractal analysis can not only examine fractal characteristics across different scales but also thoroughly capture local variations [19]. For instance, Wang P.F. et al. [20] used the multifractal spectrum width Δα to precisely evaluate subtle differences in heterogeneity among different pore types, finding that intergranular pores exhibited the largest Δα and the strongest heterogeneity. In a comparative study of coal and shale pore structures using gas adsorption analysis and multifractal theory, Wang D. et al. [21] demonstrated that both coal from the Benxi Formation and shale from the Longmaxi Formation exhibit multifractal characteristics in micropore and meso–macropore distributions. Their results indicated that coal has better pore connectivity, while shale displays stronger pore heterogeneity.

With the increasing efforts in unconventional oil and gas exploration, the Triassic Yanchang Formation Chang 7 Member shale oil in the Longdong area of the Ordos Basin has garnered significant attention. Scholars have conducted extensive research on its reservoir characteristics, shale oil enrichment patterns, and sedimentary features, yielding abundant achievements: Sun et al. [22] found that under low maturity, the decrease in TOC leads to poor pore connectivity due to pore blockage by heavy pyrolysis products, while under high maturity, the reduction in TOC is accompanied by the cracking of heavy hydrocarbons into light hydrocarbons and their expulsion, resulting in improved pore connectivity. Liu et al. [23] explored the pore structure heterogeneity of Chang 7 Member shale through a coupled analysis using nuclear magnetic resonance (NMR) and multifractal theory, and identified that the multifractal parameters Δα and D₁/D₂ can effectively classify shale oil reservoir quality. However, there is still a lack of studies on heterogeneity differences based on fractal theory combined with variations in source rock TOC content, and the heterogeneity of micro–nano pores in Chang 7 Member shale before and after extraction remains insufficiently investigated. The TOC content of Chang 7 Member shale is highly heterogeneous, ranging from 0.5% to 38% [24]. Xiao Ying et al. [25] proposed another classification with TOC contents of 3% and 6% as thresholds: low-organic-matter shale (TOC < 3%), high-organic-matter shale (3% ≤ TOC ≤ 6%), and organic-rich shale (TOC > 6%). Li et al. [26] divided Chang 7 Member shale into three categories using TOC contents of 2% and 6% as boundaries: low-organic-matter shale (TOC < 2%), moderately organic-rich shale (2% ≤ TOC ≤ 6%), and highly organic-rich shale (TOC > 6%). This study categorizes shales from the Longdong area of the Ordos Basin based on their TOC content, both before and after extraction. Utilizing low-temperature nitrogen adsorption experiments, we investigate whether the microscopic pores of the shale exhibit multifractal characteristics. Furthermore, the relationships between fractal dimensions and gas adsorption characteristics, organic geochemical parameters, and shale mineral composition are explored. The aim is to provide insights into the pore characteristics, heterogeneity, and connectivity of shales in the Longdong area of the Ordos Basin.

2. Geological Background

The Ordos Basin, situated in north-central China and the western part of the North China Craton, represents a typical multi-cyclic superimposed cratonic basin developed on the Archean–Paleoproterozoic metamorphic crystalline basement. It is characterized by a stable internal geological structure and limited fault development, displaying a general tectonic framework marked by uplift in the north and south, thrusting at the western margin, and uplift in the east [27,28]. This tectonic pattern was established by the Indosinian–Yanshan tectonic cycles, influenced by the northward compression of the Yangtze Plate and the sinistral strike-slip movement of the Western Thrust Belt—a belt intensely active in the Late Jurassic that formed a thrust-nappe fault zone over 600 km in length. According to its tectonic evolution history, the basin can be divided into six first-order tectonic units: the Yimeng Uplift, Jinxi Flexural Fold Belt, Weibei Uplift, Tianhuan Depression, Western Thrust Belt, and Yishan Slope (the main body of the basin) [29]. During the Triassic period, the basin was in an overall stable sagging stage and accumulated a complete set of terrestrial stratigraphic sequences from bottom to top: the Liujiagou Formation, Heshanggou Formation, Zhifang Formation, and Yanchang Formation, with gentle lateral thickness variation of each formation reflecting balanced subsidence. The Chang 7 Member of the Yanchang Formation was deposited in a deep to semi-deep lacustrine environment during the late Ladinian Stage of the Middle Triassic, serving as the basin’s main source rock sequence. Its organic matter enrichment is closely related to the outbreak of primary productivity induced by volcanic activities and the anoxic sedimentary environment, and it can be vertically divided into three sub-members (Chang $7_1$ Member, Chang $7_2$ Member, Chang $7_3$ Member) [30]. Dominated by dark mudstone and organic-rich oil shale, interbedded with fine-grained feldspar sandstone and lithic feldspar sandstone, this member features typical tight sandstone reservoirs characterized by low porosity and ultra-low permeability, whose physical properties are controlled by compaction, carbonate cementation, and the degree of microfracture development [31]. The Longdong area, the study area, is located in the southwestern part of the Ordos Basin, spanning the Tianhuan Depression and Yishan Slope. Affected by the Qinling–Qilian Orogenic Belt, it exhibits transitional sedimentary–tectonic characteristics and is a key area for tight oil and gas exploration [32]. All experimental samples involved in this study were selected from shale cores of the Chang 7 Member in the Jiyuan Area of the Ordos Basin (Figure 1), and the comprehensive drilling columnar section is shown in Figure 2.

3. Experimental Methods

3.1. Shales and Experiments

Ten shale cores of Chang 7 Member from the Longdong area were selected for this study. After preparing the rock shales into blocks and powders, the following experimental work was mainly carried out: a CS-344 carbon-sulfur analyzer (LECO Corporation, St. Joseph, MI, USA) was used to determine the total organic carbon (TOC) content; a Rigaku Ultima IV X-ray diffractometer (Rigaku Corporation, Tokyo, Japan) was employed to conduct whole-rock mineral analysis on the extracted shale powders, with XRD data processed using MDI Jade 6.5 software for mineral composition confirmation; a ROCK-EVAL 6 pyrolyzer (Vinci Technologies, Nanterre, France) was used to perform rock pyrolysis analysis on the powder shales to determine key pyrolysis parameters, including Tmax (maximum pyrolysis temperature), S1 (free hydrocarbon content), and S2 (pyrolyzable hydrocarbon content); the powder shales were ground to 40–60 mesh, followed by sealing and drying treatment, and an ASAP 2020 specific surface area analyzer (manufactured by Micromeritics Instrument Corporation, Norcross, GA, USA) was used to carry out nitrogen adsorption–desorption experiments. After this experiment, the rock shales were extracted with a mixed solvent of dichloromethane and acetone, and then a second nitrogen adsorption experiment was conducted. Based on the data obtained from the gas adsorption experiments before and after extraction, pore structure parameters such as the pore volume, specific surface area, pore size distribution, and average pore diameter of reservoir pores were calculated in accordance with the Brunauer–Emmett–Teller (BET) specific surface area detection method and the Barrett–Joyner–Halenda (BJH) pore size distribution theoretical model [34].

3.2. Multifractal Model

The box-counting method is a common approach in multifractal analysis. This method covers the study area with a series of boxes of length $\mathrm{\epsilon }$. For gas adsorption experiments, the relative pressure from the gas adsorption experiment is selected as the measured length. According to the dyadic scaling method, $\mathrm{N}$($\mathrm{\epsilon }$) = $2^{\mathrm{k}}$ (k = 0, 1, 2,…), the normalized distribution of adsorbed gas Δ$\overline{{\mathrm{n}}_{\mathrm{i}}}$  is selected as the measure ${\mathrm{\mu }}_{\mathrm{i}}$($\mathrm{\epsilon }$), expressed as follows [35]:

$$\mathrm{\Delta }\overline{{\mathrm{n}}_{\mathrm{i}}}= (\mathrm{\Delta }{\mathrm{n}}_{\mathrm{i}}- \mathrm{\Delta }{\mathrm{n}}_{\min })/{\sum }_{\mathrm{i} = 1}^{\mathrm{N}}(\mathrm{\Delta }{\mathrm{n}}_{\mathrm{i}} -\mathrm{\Delta }{\mathrm{n}}_{\min }),$$

(1)

A series of boxes of specific length are placed over the heterogeneous distribution pattern of gas adsorption. The probability mass function describes the proportion of gas adsorption volume in the i-th box relative to the total adsorption volume, expressed as

$${\mathrm{p}}_{\mathrm{i}}(\mathrm{\epsilon }) ={\mathrm{N}}_{\mathrm{i}}(\mathrm{\epsilon })/{\mathrm{N}}_{\mathrm{T}},$$

(2)

The probability mass function and box length are related exponentially. Therefore, ${\mathrm{p}}_{\mathrm{i}}$($\mathrm{\epsilon }$) can also be expressed as

$${\mathrm{p}}_{\mathrm{i}}(\mathrm{\epsilon }) ~{\mathrm{\epsilon }}^{-{\mathrm{\alpha }}_{\mathrm{i}}},$$

(3)

${\mathrm{\alpha }}_{\mathrm{i}}$ is used to represent the degree of local singularity of ${\mathrm{p}}_{\mathrm{i}}$($\mathrm{\epsilon }$), which depends on the box length. A smaller ${\mathrm{\alpha }}_{\mathrm{i}}$ value indicates higher variability or heterogeneity of the data; a larger ${\mathrm{\alpha }}_{\mathrm{i}}$ value indicates higher regularity or uniformity of the data. For an interval with multifractal properties, the number of boxes with the same probability mass value is defined as ${\mathrm{N}}_{\mathrm{\alpha }}$($\mathrm{\epsilon }$). This number increases as $\mathrm{\epsilon }$ decreases, exhibiting a power-law relationship [36]:

$${\mathrm{N}}_{\mathrm{\alpha }}(\mathrm{\epsilon }) ~{\mathrm{\epsilon }}^{-{\mathrm{\alpha }}_{\mathrm{i}}},$$

(4)

where $\mathrm{\epsilon }$ approaches 0. The formulas for α(q) and f(α) are, respectively, as follows [37]:

$$\mathrm{\alpha }\left(\mathrm{q}\right)\propto \left[{\sum }_{\mathrm{i} = 1}^{\mathrm{N}\left(\mathrm{\epsilon }\right)}{\mathrm{u}}_{\mathrm{i}}(\mathrm{q},\mathrm{\epsilon }) \times \lg \mathrm{\epsilon }\right]/\lg \mathrm{\epsilon },$$

(5)

$$\mathrm{f}(\mathrm{\alpha })\propto \left[{\sum }_{\mathrm{i}=1}^{\mathrm{N}\left(\mathrm{\epsilon }\right)}{\mathrm{u}}_{\mathrm{i}}(\mathrm{q},\mathrm{\epsilon }) \times \lg {\mathrm{u}}_{\mathrm{i}}(\mathrm{q},\mathrm{\epsilon })\right]/\lg \mathrm{\epsilon },$$

(6)

where

$${\mathrm{u}}_{\mathrm{i}}(\mathrm{q},\mathrm{\epsilon })={\mathrm{p}}_{\mathrm{i}}^{\mathrm{q}}(\mathrm{\epsilon })/{\sum }_{\mathrm{i}=1}^{\mathrm{N}\left(\mathrm{\epsilon }\right)}{\mathrm{p}}_{\mathrm{i}}^{\mathrm{q}}\left(\mathrm{\epsilon }\right),$$

(7)

In this study, q ranges from −10 to 10 in steps of 1. When q < 1, information from regions of lower concentration is amplified; when q > 1, information from regions of higher concentration is amplified. The parameters α(q) and f(α) are not evaluated for all q values, but only within the range where they can be described by a linear function of $\log \mathrm{\epsilon }$, representing the heterogeneity level of the sample.

The partition function for the q-th moment is defined as

$${\mathrm{u}}_{\mathrm{i}}(\mathrm{q},\mathrm{\epsilon })={\sum }_{\mathrm{i}=1}^{\mathrm{N}\left(\mathrm{\epsilon }\right)}{\mathrm{p}}_{\mathrm{i}}^{\mathrm{q}}\left(\mathrm{\epsilon }\right)~{\mathrm{\epsilon }}^{\mathrm{\tau }\left(\mathrm{q}\right) },$$

(8)

τ(q) can be expressed as

$$\mathrm{\tau }\left(\mathrm{q}\right)=-\underset{\mathrm{\epsilon }\to 0}{\lim }{\displaystyle \frac{{\sum }_{\mathrm{i}=1}^{\mathrm{N}\left(\mathrm{\epsilon }\right)}{\mathrm{p}}_{\mathrm{i}}^{\mathrm{q}}\left(\mathrm{\epsilon }\right)}{\lg \mathrm{\epsilon }}},$$

(9)

${\mathrm{D}}_{\mathrm{q}}$ is another set of parameters used to characterize the local features of multifractals, expressed as follows [38]

$${\mathrm{D}}_{\mathrm{q}}={\displaystyle \frac{\mathrm{\tau }\left(\mathrm{q}\right)}{\mathrm{q}-1}}={\displaystyle \frac{1}{\mathrm{q}-1}}\underset{\mathrm{\epsilon }\to 0}{\lim }{\displaystyle \frac{\lg {\sum }_{\mathrm{i}=1}^{\mathrm{N}\left(\mathrm{\epsilon }\right)}{\mathrm{p}}_{\mathrm{i}}^{\mathrm{q}}\left(\mathrm{\epsilon }\right)}{\lg \mathrm{\epsilon }}}\mathrm{q}\ne 1,$$

(10)

For a monofractal, ${\mathrm{D}}_{\mathrm{q}}$ is constant with respect to q, so examining higher-order moments is unnecessary. For a multifractal, ${\mathrm{D}}_{\mathrm{q}}$ is a monotonically decreasing function of q. When q > 0, ${\mathrm{D}}_{\mathrm{q}}$ reflects the pore distribution characteristics in high-porosity regions; when q < 0, it reflects the characteristics in low-porosity regions [39,40].

To ensure the continuity of ${\mathrm{D}}_{\mathrm{q}}$, L’Hôpital’s rule is applied to Equation (10) to obtain ${\mathrm{D}}_1$:

$${\mathrm{D}}_1= \underset{\mathrm{\epsilon }\to 0}{\lim }{\displaystyle \frac{{\sum }_{\mathrm{i} = 1}^{\mathrm{N}\left(\mathrm{\epsilon }\right)}{\mathrm{p}}_{\mathrm{i}}\left(\mathrm{\epsilon }\right) \times \lg {\mathrm{p}}_{\mathrm{i}}\left(\mathrm{\epsilon }\right)}{\lg \mathrm{\epsilon }}},$$

(11)

The generalized fractal dimension and the multifractal spectrum satisfy the Legendre transform, expressed as follows [36]:

$$\mathrm{\alpha }\left(\mathrm{q}\right)= {\displaystyle \frac{\mathrm{d}\mathrm{\tau }\left(\mathrm{q}\right)}{\mathrm{dq}}},$$

(12)

$$\mathrm{f}(\mathrm{\alpha })=\mathrm{q} \times \mathrm{\alpha }\left(\mathrm{q}\right)- \mathrm{\tau }\left(\mathrm{q}\right),$$

(13)

The multifractal structure is represented by three sets of multifractal parameters: ${\mathrm{D}}_{\mathrm{q}}$, α(q), and f(α). Additional parameters related to multifractal analysis are defined as follows:

$$\mathrm{H} = ({\mathrm{D}}_2+ 1)/2,$$

(14)

where H is the Hurst exponent, ranging from 0.5 to 1 [41]. It can describe the autocorrelation of local pore size distributions across different pore size ranges. When H approaches 1, it corresponds to better connectivity between different pores of different sizes [42].

$$\mathrm{\Delta }\mathrm{\alpha }={\mathrm{\alpha }}_{-10}-{\mathrm{\alpha }}_{10},$$

(15)

Δα is the width of the singularity spectrum, reflecting the degree of heterogeneity in the pore size distribution of the shale sample. A higher Δα value indicates greater heterogeneity in the pore structure.

$${\mathrm{R}}_{\mathrm{d}}=({\mathrm{\alpha }}_0-{\mathrm{\alpha }}_{10})-({\mathrm{\alpha }}_{-10}-{\mathrm{\alpha }}_0),$$

(16)

${\mathrm{R}}_{\mathrm{d}}$ describes the asymmetry of the spectrum within the range [${\mathrm{\alpha }}_{10}$,${\mathrm{\alpha }}_{-10}$] relative to ${\mathrm{\alpha }}_0$. A positive ${\mathrm{R}}_{\mathrm{d}}$ value means that sparse regions of the pore distribution dominate, and vice versa.

4. Results and Analysis

4.1. Organic Geochemical Characteristics and Mineral Composition

TOC (total organic carbon, the total content of organic carbon in rocks, characterizing organic matter abundance; unit: %) content measurements on 10 shales showed values ranging from 3.17% to 12.24%. Shales were classified as medium organic-rich (2% < TOC < 6%) or highly organic-rich (TOC > 6%), as shown in

Table 1. Organic geochemical parameters of shales from the study area.

Organic Matter Type	Sample	ω(TOC)/%	${\mathbf{T}}_{\mathbf{max}}/°C$	${\mathbf{S}}_1/(\mathbf{mg}·{\mathbf{g}}^{-1})$	${\mathbf{S}}_2/(\mathbf{mg}·{\mathbf{g}}^{-1})$	$\mathbf{HI}/(\mathbf{mg}·{\mathbf{g}}^{-1})$
Medium Organic-Rich	5–109	3.17	446	1.51	6.82	214.93
	5–60	4.01	445	2.54	9.84	245.50
	4–12	4.10	444	2.82	11.03	268.84
Highly Organic-Rich	9–10	6.44	453	1.98	20.80	323.21
	10–8	6.95	450	2.40	22.66	326.22
	11–9	7.10	457	3.06	17.27	243.27
	9–14	9.80	453	2.17	18.02	183.97
	11–59	10.11	454	3.81	31.64	313.09
	11–123	10.78	454	3.68	31.77	294.84
	7–17	12.24	456	4.38	36.49	298.13

. For medium-organic-rich shales, ${\mathrm{T}}_{\max }$ (maximum pyrolysis temperature, characterizing organic matter maturity; unit: °C) ranged from 444 °C to 446 °C; ${\mathrm{S}}_1$ (free hydrocarbon content, representing the total amount of generated and unmigrated liquid hydrocarbons in shale; unit: mg/g) ranged from 1.51 mg/g to 2.82 mg/g (average of 2.29 mg/g); ${\mathrm{S}}_2$ (pyrolyzable hydrocarbon content, characterizing the potential hydrocarbon generation capacity of organic matter; unit: mg/g) ranged from 6.82 mg/g to 11.03 mg/g (average of 9.23 mg/g); HI (Hydrogen Index, reflecting kerogen hydrogen content and type and hydrocarbon generation potential; unit: mg/g) ranged from 214.93 mg/g to 268.84 mg/g (average of 243.09 mg/g). For highly organic-rich shales, ${\mathrm{T}}_{\max }$ ranged from 450 °C to 457 °C; ${\mathrm{S}}_1$ ranged from 1.98 mg/g to 4.38 mg/g (average of 3.07 mg/g); ${\mathrm{S}}_2$ ranged from 17.27 mg/g to 36.49 mg/g (average of 25.52 mg/g); HI ranged from 183.97 mg/g to 326.22 mg/g (average of 283.25 mg/g). The organic matter in the 10 shales was primarily Type II₁ and II₂, with a small amount of Type I (Figure 3). The highly organic-rich shales are dominated by Type II₁ organic matter, exhibiting sapropelic–mixed characteristics, which aligns with a deep lacustrine facies depositional environment known for its strong hydrocarbon generation potential. In contrast, shales with medium-organic-rich content show a higher proportion of Type II₂ organic matter, leaning towards mixed characteristics.

Extractable organic matter (EOM) refers to mobile liquid hydrocarbons that can be extracted by organic solvents, serving as a direct indicator of the shale oil-bearing property, with a unit of mg/g. The extraction amounts of EOM and its components for all shales are listed in

Table 2. Extraction ratios of EOM and its composition in the Chang 7 shale sample.

Organic Matter Type	Sample	$\mathbf{EOM}/(\mathbf{mg}·{\mathbf{g}}^{-1})$	Sat./%	Aro./%	Res./%	Asp./%
Medium Organic-Rich	5–109	3.47	51.89	18.19	17.10	12.82
	5–60	5.54	61.66	16.48	13.55	8.31
	4–12	6.12	71.55	17.51	4.90	6.04
Highly Organic-Rich	9–10	5.39	27.51	19.38	35.84	17.27
	10–8	4.12	25.34	1.21	55.44	18.01
	11–9	4.86	41.76	19.98	27.90	10.35
	9–14	5.11	22.97	17.93	38.02	21.09
	11–59	7.61	42.13	20.17	30.63	7.07
	11–123	5.80	39.30	22.75	27.20	10.75
	7–17	7.55	24.76	22.73	25.70	26.81

Sat.: saturated hydrocarbon; Aro.: aromatic hydrocarbon; Res.: resins; Asp.: asphaltenes.

. For medium-organic-rich shales, the EOM content ranges from 3.47 to 6.12 mg/g (average of 5.04 mg/g). EOM is composed of saturated hydrocarbons, resins, aromatic hydrocarbons, and asphaltenes: among these, saturated hydrocarbons account for 51.89% to 71.55% (average of 61.70%), which contain no unsaturated bonds, act as the main component of crude oil, and belong to light components; resins, which are viscous liquids, account for 4.90% to 17.10% (average of 11.85%), serve as intermediate products of organic matter evolution, play a transitional role, and fall into heavy components; aromatic hydrocarbons account for 16.48% to 18.19% (average of 17.39%) and have a benzene ring structure; asphaltenes account for 6.04% to 12.82% (average of 9.06%) and are heavy components. For highly organic-rich shales, the EOM content ranges from 4.12 to 7.61 mg/g (average of 5.78 mg/g), with saturated hydrocarbons accounting for 22.97% to 42.13% (average of 31.97%), resins for 25.70% to 55.44% (average of 34.39%), aromatic hydrocarbons for 1.21% to 22.75% (average of 17.74%), and asphaltenes for 7.07% to 26.81% (average of 15.91%).

Whole-rock X-ray diffraction analysis reveals that in medium-organic-rich shales (

Table 3. Mineral composition characteristics of shale in study area (%).

Organic Matter Type	Sample	Quartz	K-Feldspar	Plagioclase	Calcite	Ankerite	Siderite	Pyrite	Clay
Medium Organic-Rich	5–109	35.4	2.8	10.5	1.4	1.6	3.6	4.7	40
	5–60	37.9	2.3	9.9	0	2.3	2.4	5	40.2
	4–12	33.9	2.1	10	1.4	1	3.6	4.8	43.3
Highly Organic-Rich	9–10	33.3	10.3	7	2.8	2	0	13.5	31.1
	10–8	37	3	5	0	1.6	3.9	11.3	38.3
	11–9	52.6	1	5.4	0	3.2	2.5	6.5	28.8
	9–14	34.8	0	13.7	0	3.3	2.9	11.4	33.9
	11–59	38.2	2.9	10.8	0	1.4	1.8	8.1	36.9
	11–123	33.5	3.1	12	1.2	0	0	13.3	37
	7–17	26.5	5.8	6.7	0	1.4	1.7	25.6	32.3

), quartz content ranges from 33.9% to 37.9% (average of 35.7%); feldspar, categorized as potassium feldspar and plagioclase, shows contents of 2.1–2.8% (average of 2.4%) and 9.9–10.5% (average of 10.1%), respectively; carbonate minerals including calcite, ankerite, and siderite all occur at relatively low levels; pyrite content varies between 4.7% and 5% (average of 4.8%); and clay minerals constitute 40.0–43.3% (average of 41.2%). Conversely, in highly organic-rich shales, quartz content spans 26.5–52.6% (average of 36.6%); potassium feldspar and plagioclase contents range from 0 to 10.3% (average of 3.7%) and 5–13.7% (average of 8.7%), respectively; carbonate minerals (calcite, ankerite, and siderite) similarly exhibit low abundances; pyrite content fluctuates between 6.5% and 25.6% (average of 12.8%); and clay minerals account for 28.8–38.3% (average of 34.0%). Zhang et al. [44] classified rock minerals into three categories, which are used for the quantitative evaluation of shale brittleness: quartz + feldspar + pyrite, carbonate minerals, and clay minerals. Therefore, we also adopt this rock mineral classification method and have plotted a triangular diagram of shale mineral composition (Figure 4), from which it is found that the selected shales are all brittle minerals.

4.2. Reservoir Microscopic Pore Structure

4.2.1. Nitrogen Adsorption Isotherm Characteristics

According to the classification principles of the International Union of Pure and Applied Chemistry (IUPAC), the curve formed between the adsorption amount and relative pressure is called an isotherm, currently classified into six types (Figure 5a). Analysis of the isotherms of shales before extraction revealed that all shales belong to the typical Type IV isotherm. Due to differences in the internal pore structure of the shale, the shapes of the hysteresis loops differ. Therefore, the characteristics of the hysteresis loop can be used to identify pore morphology, specifically including four types: H1, H2, H3, and H4 (Figure 5b), corresponding to open cylindrical pores, ink-bottle pores, slit-shaped pores, and one-side-open slit-shaped pores, respectively. For pore size classification, the IUPAC scheme is commonly used: micropores (<2 nm), mesopores (2–50 nm), and macropores (>50 nm). Nitrogen adsorption isotherms are particularly accurate for characterizing mesopores.

A comparison of nitrogen adsorption–desorption isotherms before and after extraction is shown in Figure 6. Extracted shales exhibit greater adsorption capacity than unextracted shales.

All adsorption curves demonstrate similar evolutionary patterns: When the relative pressure (${\mathrm{P}/\mathrm{P}}_0$) ranges between 0 and 0.4, the adsorption curves display gentle variation with slight convexity, indicating the presence of micropores and minimal hysteresis loop width; within the 0.4~0.8 relative pressure interval, the curves gradually ascend with sustained adsorption increase, reflecting progressive pore enlargement; at relative pressures of 0.8~1, the curves surge steeply with pronounced concavity and expanded hysteresis loops, signifying the transition from monolayer to multilayer adsorption within shale pores, suggesting pore evolution from micropores to macropores.

Two primary hysteresis morphologies emerge: Type I exhibits a distinct steep decline in the desorption branch at ${\mathrm{P}/\mathrm{P}}_0$ = 0.5, with a sharply increased slope; when pressure decreases to ${\mathrm{P}/\mathrm{P}}_0$ = 0.4, the slope diminishes until isotherm closure, differing primarily in maximum adsorption capacity (e.g., sample 11-9). This represents an H2-H3 transitional pore configuration combining ink-bottle and parallel plate-slit geometries. Type II demonstrates rapid desorption decline with a steep slope at ${\mathrm{P}/\mathrm{P}}_0$ > 0.8; below ${\mathrm{P}/\mathrm{P}}_0$ = 0.8, desorption moderates with a reduced slope, resulting in either closed or separated curves with limited adsorption capacity (e.g., sample 11-123). Such configurations correspond to H3-H4 transitional pores featuring parallel plate-slits and open-ended slit-shaped morphologies.

4.2.2. Nitrogen Adsorption Pore Size Distribution Characteristics

Pore size distribution curves of shale before and after extraction, plotted based on low-temperature nitrogen adsorption results, are shown in Figure 7. Comparing pore size distributions before and after extraction reveals that extracted shales show significant changes predominantly in pores < 10 nm. This indicates that movable oil primarily resides in pores < 10 nm, as extraction releases pore space by removing trapped movable oil.

4.2.3. Characterization of Pore Structure Parameters

Nitrogen adsorption experiments enable quantification of key pore structure parameters—including specific surface area (SSA), total pore volume (TPV), and average pore diameter (APD)—as documented in

Table 4. Pore structure characteristic parameters of shale before and after extraction in the study area.

Organic Matter Type	Sample		Non-Extracted			Extracted
		$\mathbf{SSA} {\mathbf{m}}^2$/g	$\mathbf{TPV} \mathbf{c}{\mathbf{m}}^3$/g	APD nm	$\mathbf{SSA} {\mathbf{m}}^2$/g	$\mathbf{TPV} \mathbf{c}{\mathbf{m}}^3$/g	APD nm
Medium Organic-Rich	5–109	1.25	0.0035	10.94	1.94	0.0061	13.32
	5–60	1.94	0.0048	9.55	2.26	0.0054	10.82
	4–12	1.47	0.0052	13.94	3.81	0.0091	10.74
Highly Organic-Rich	9–10	1.13	0.0018	7.17	3.09	0.0042	6.79
	10–8	1.11	0.0018	6.96	1.35	0.0023	7.99
	11–9	1.94	0.0029	6.81	5.57	0.0075	6.26
	9–14	1.24	0.0020	7.30	2.44	0.0032	6.88
	11–59	1.06	0.0027	11.40	3.83	0.0053	6.90
	11–123	0.83	0.0019	10.53	1.54	0.0035	11.40
	7–17	1.09	0.0017	7.21	1.84	0.0025	7.18

. For medium-organic-rich shales, BET analysis revealed pre-extraction SSA ranging from 1.25 to 1.94 m²/g (average of 1.55 m²/g), while post-extraction SSA increased significantly to 1.94–3.81 m²/g (average of 2.67 m²/g). BJH pore size distribution measurements showed pre-extraction TPV of 0.0035–0.0052 cm³/g (average of 0.0045 cm³/g), expanding to 0.0054–0.0091 cm³/g (average of 0.0069 cm³/g) post-extraction. Pre-extraction APD spanned 9.55–13.94 nm (average of 11.48 nm), contrasting with post-extraction values of 10.74–13.32 nm (average of 11.63 nm), demonstrating consistent SSA/TPV increases but variable APD changes after solvent treatment. Highly organic-rich shales exhibited lower baselines: pre-extraction SSA of 0.83–1.94 m²/g (average of 1.20 m²/g) rising to 1.35–5.57 m²/g (average of 2.81 m²/g) post-extraction; pre-extraction TPV of 0.0017–0.0029 cm³/g (average of 0.0021 cm³/g) increasing to 0.0023–0.0075 cm³/g (average of 0.0041 cm³/g); while APD decreased from 6.81 to 11.40 nm (average of 8.20 nm) to 6.26–11.40 nm (average of 7.63 nm). Post-extraction universally had enhanced SSA and TPV, whereas APD responses diverged—notably, the APD reduction may stem from nanopore liberation during hydrocarbon removal by organic solvents [45].

5. Discussion

5.1. Relationship Between Fractal Dimensions and Pore Structure Parameters

For a research subject to exhibit multifractal characteristics, three conditions must be satisfied: ① D(q) and α(q) show a strictly monotonically decreasing trend with increasing q; ② τ(q) forms a strictly increasing convex function with q; ③ f(α) exhibits convex function characteristics with α. Multifractal calculations from the low-temperature nitrogen adsorption data of shales before and after extraction demonstrate that χ(q,ε) and lgε display a strong linear correlation (Figure 8a), confirming multifractal characteristics in the pore size distribution obtained through low-temperature nitrogen adsorption. When q > 0, χ(q,ε) negatively correlates with lgε; when q < 0, positive correlation occurs. This indicates that as the order of statistical moments increases from low to high, the mass exponent function (i.e., slope of the fitted equation) transitions gradually from negative to positive values, while the fitted lines between χ(q,ε) and lgε progressively converge, reflecting relatively concentrated pore size distributions predominantly distributed in smaller pore ranges.

As shown in Figure 8b, τ(q) strictly increases with increasing q, exhibiting convex characteristics. D(q) strictly decreases monotonically with increasing q (Figure 8c), displaying an inverse-S shape. For homogeneous fractals, the D(q)-q relationship is linear; for heterogeneous fractals, it manifests as a decreasing function with a transition, where steeper D(q)-q curves correspond to larger D(q) value ranges—indicating broader distribution ranges of fractal structures with different singularity intensities and greater fractal heterogeneity, and vice versa. f(α) exhibits convex function characteristics with α (Figure 8d). These phenomena in Figure 8 align with the defining features of multifractals, confirming the objective existence of multifractal characteristics in shale micropores both before and after extraction.

Table 5. Generalized fractal dimensions and multifractal singularity spectrum calculated from pre-extraction nitrogen adsorption isotherms for shale in the study area.

Organic Matter Type	Sample	${\mathbf{D}}_{\left(0\right)}$	${\mathbf{D}}_{\left(1\right)}$	${\mathbf{D}}_{\left(0\right)}$$\mathbf{–}{\mathbf{D}}_{\left(1\right)}$	${\mathbf{D}}_{\left(2\right)}$	${\mathbf{D}}_{(-10)}$	${\mathbf{D}}_{\left(10\right)}$	Hurst	${\mathsf{\alpha }}_{\left(0\right)}$	${\mathsf{\alpha }}_{(-10)}$	${\mathsf{\alpha }}_{\left(10\right)}$	${\mathsf{\alpha }}_{(-10)}$$–{\mathsf{\alpha }}_{\left(10\right)}$	${\mathbf{R}}_{\mathbf{d}}$
Medium Organic-Rich	5–109	1.000	0.850	0.150	0.685	1.184	0.449	0.843	1.106	1.258	0.404	0.854	0.550
	5–60	1.000	0.863	0.137	0.714	1.289	0.483	0.857	1.097	1.405	0.435	0.970	0.354
	4–12	1.000	0.818	0.182	0.631	1.212	0.404	0.816	1.123	1.304	0.364	0.940	0.579
Highly Organic-Rich	9–10	1.000	0.886	0.114	0.756	1.191	0.514	0.878	1.089	1.266	0.463	0.803	0.450
	10–8	1.000	0.892	0.108	0.770	1.186	0.529	0.885	1.084	1.258	0.476	0.782	0.434
	11–9	1.000	0.898	0.102	0.783	1.219	0.552	0.891	1.079	1.315	0.498	0.817	0.346
	9–14	1.000	0.887	0.113	0.759	1.180	0.518	0.879	1.088	1.246	0.466	0.780	0.463
	11–59	1.000	0.862	0.138	0.705	1.225	0.465	0.853	1.099	1.324	0.418	0.905	0.456
	11–123	1.000	0.869	0.131	0.720	1.216	0.478	0.860	1.095	1.311	0.430	0.881	0.449
	7–17	1.000	0.889	0.111	0.762	1.197	0.522	0.881	1.086	1.278	0.470	0.808	0.424

and

Table 6. Generalized fractal dimensions and multifractal singularity spectrum calculated from post-extraction nitrogen adsorption isotherms for shale in the study area.

Organic Matter Type	Sample	${\mathbf{D}}_{\left(0\right)}$	${\mathbf{D}}_{\left(1\right)}$	${\mathbf{D}}_{\left(0\right)}$$\mathbf{–}{\mathbf{D}}_{\left(1\right)}$	${\mathbf{D}}_{\left(2\right)}$	${\mathbf{D}}_{(-10)}$	${\mathbf{D}}_{\left(10\right)}$	Hurst	${\mathsf{\alpha }}_{\left(0\right)}$	${\mathsf{\alpha }}_{(-10)}$	${\mathsf{\alpha }}_{\left(10\right)}$	${\mathsf{\alpha }}_{(-10)}$$–{\mathsf{\alpha }}_{\left(10\right)}$	${\mathbf{R}}_{\mathbf{d}}$
Medium Organic-Rich	5–109	1.000	0.853	0.147	0.690	1.190	0.452	0.845	1.103	1.269	0.407	0.862	0.531
	5–60	1.000	0.866	0.134	0.717	1.229	0.480	0.859	1.097	1.332	0.432	0.900	0.431
	4–12	1.000	0.863	0.137	0.711	1.192	0.473	0.856	1.099	1.272	0.426	0.846	0.500
Highly Organic-Rich	9–10	1.000	0.899	0.101	0.785	1.214	0.552	0.893	1.080	1.309	0.497	0.811	0.353
	10–8	1.000	0.885	0.115	0.755	1.187	0.515	0.878	1.088	1.261	0.464	0.797	0.451
	11–9	1.000	0.909	0.091	0.808	1.175	0.578	0.904	1.075	1.237	0.521	0.716	0.392
	9–14	1.000	0.899	0.101	0.785	1.199	0.552	0.893	1.079	1.285	0.497	0.787	0.376
	11–59	1.000	0.901	0.099	0.787	1.229	0.553	0.893	1.078	1.332	0.498	0.834	0.327
	11–123	1.000	0.877	0.123	0.736	1.257	0.495	0.868	1.091	1.368	0.446	0.923	0.368
	7–17	1.000	0.896	0.104	0.779	1.215	0.544	0.889	1.082	1.311	0.490	0.821	0.363

display the adsorption characteristic multifractal parameters derived from nitrogen adsorption data for both medium-organic-rich and highly organic-rich shales before and after extraction. The capacity dimension ${\mathrm{D}}_{\left(0\right)}$ indicates the range of pore size distribution, where larger values correspond to broader distributions; all shales exhibit ${\mathrm{D}}_{\left(0\right)}$ = 1 before and after extraction, confirming pore presence in every measurement box and aligning with the fractal dimension for one-dimensional distributions. The information dimension ${\mathrm{D}}_{\left(1\right)}$ characterizes the local scaling properties of pore size distributions across the entire range, while the correlation dimension ${\mathrm{D}}_{\left(2\right) }$ represents scaling behavior of second-order sampling moments. The parameter ${\mathrm{D}}_{\left(0\right)}$−${\mathrm{D}}_{\left(1\right)}$ reflects the degree of discrete pore size distribution in shale: for medium-organic-rich shales, ${\mathrm{D}}_{\left(0\right)}$−${\mathrm{D}}_{\left(1\right)}$ ranges from 0.137 to 0.182 (average of 0.156) before extraction, narrowing to 0.134–0.147 (average of 0.139) post-extraction, indicating more concentrated pore size distributions after solvent treatment. The H exponent (Hurst) quantifies pore connectivity, with pre-extraction values spanning 0.816–0.857 (average of 0.839) and post-extraction values ranging from 0.845 to 0.859 (average of 0.853), demonstrating enhanced pore connectivity after extraction. The width of the f(α) spectrum ${\mathrm{\alpha }}_{(-10)}$–${\mathrm{\alpha }}_{\left(10\right)}$ measures heterogeneity and complexity across the measured pore size range, where larger ${\mathrm{\alpha }}_{(-10)}$–${\mathrm{\alpha }}_{\left(10\right)}$ values indicate stronger heterogeneity: pre-extraction shales show ${\mathrm{\alpha }}_{(-10)}$–${\mathrm{\alpha }}_{\left(10\right)}$ values of 0.854–0.970 (average of 0.921), while post-extraction values range from 0.846 to 0.900 (average of 0.869). For highly organic-rich shales, the capacity dimension ${\mathrm{D}}_{\left(0\right)}$ consistently equals one, both before and after extraction. Pre-extraction ${\mathrm{D}}_{\left(0\right)}$−${\mathrm{D}}_{\left(1\right)}$ values range from 0.102 to 0.138 (average of 0.117), while post-extraction values decrease to between 0.091 and 0.123 (average of 0.105), indicating more concentrated pore size distributions after solvent extraction. The Hurst exponent (H) characterizes pore connectivity, with pre-extraction shales exhibiting H-values between 0.853 and 0.891 (average of 0.875), compared to post-extraction values spanning 0.868 to 0.904 (average of 0.888)—demonstrating enhanced pore connectivity following extraction. Larger ${\mathrm{\alpha }}_{(-10)}$–${\mathrm{\alpha }}_{\left(10\right)}$ values correspond to greater heterogeneity in pore size distribution: pre-extraction ${\mathrm{\alpha }}_{(-10)}$–${\mathrm{\alpha }}_{\left(10\right)}$ ranges from 0.780 to 0.905 (average of 0.825), while post-extraction values vary between 0.716 and 0.923 (average of 0.813). Shales 9-10 and 9-14 show negligible changes, whereas sample 11-123 developed increased heterogeneity post-extraction; other shales exhibit homogenization trends. All shales maintain ${\mathrm{R}}_{\mathrm{d}}$ > 0 both pre- and post-extraction, confirming that high-probability regions dominate overall heterogeneity. The changes in pore heterogeneity are inconsistent after extraction, which may be due to the fact that the EOM is mostly filling micropores and mesopores—some shales release a large number of micropores smaller than 2 nm after extraction, leading to the nitrogen adsorption detection ignoring this part. A positive ${\mathrm{R}}_{\mathrm{d}}$ value indicates that the f(α) spectrum exhibits a right-skewed characteristic, meaning high-probability measurement regions dominate the pore distribution; if ${\mathrm{R}}_{\mathrm{d}}$ is negative, low-probability measurement regions dominate. All samples show ${\mathrm{R}}_{\mathrm{d}}$ > 0 both before and after extraction, which demonstrates that high-probability-measurement regions contribute most significantly to the overall heterogeneity. Collectively, compared with medium-organic-rich shales, high-organic-rich shales have more concentrated pore distributions, better connectivity, and superior homogeneity. After extraction, the pore distributions of shale formations in the study area are generally more concentrated, the connectivity is somewhat improved, and the homogeneity is significantly enhanced.

5.2. Influencing Factors of Pore Development

Representative multifractal parameters (${\mathrm{\alpha }}_{(-10)}$–${\mathrm{\alpha }}_{\left(10\right)}$, the width of the singularity spectrum, and the Hurst index) were selected to study the relationships between nitrogen adsorption parameters, organic geochemistry, mineral composition, and multifractal characteristics.

The results of the correlation between nitrogen adsorption parameters and multifractal parameters are shown in Figure 9. The study results indicate that before extraction, the specific surface area shows no correlation with pore connectivity or pore heterogeneity; the total pore volume and average pore diameter exhibit a positive correlation with pore heterogeneity and a negative correlation with pore connectivity. After extraction, the specific surface area shows a weak positive correlation with pore connectivity and a negative correlation with pore heterogeneity; the total pore volume has no correlation with pore connectivity or pore heterogeneity; the average pore diameter still presents a strong negative correlation with pore connectivity and a strong positive correlation with pore heterogeneity.

It can be seen from Figure 10 that TOC has a good correlation with the pore connectivity of shale: pore connectivity first increases with the increase in TOC content and begins to decrease when TOC reaches around 6%. This is similar to the results of Wang, D. et al. [21]. This may be due to the fact that within the range of high organic matter abundance, organic pores develop extensively in rocks, and the heterogeneity of these pores in terms of morphology, size, and distribution increases. Organic matter with medium organic matter abundance may undergo physicochemical changes such as expansion, which can squeeze and deform the surrounding pore structure. The enrichment of organic matter may reduce the rock’s resistance to compaction, leading to an increase in the number of isolated pores, thereby enhancing the overall heterogeneity of the reservoir and ultimately affecting pore connectivity. In contrast, within the range of medium organic matter abundance, due to the relatively low organic matter content, the pores in the rock are mainly composed of inorganic pores and a small number of organic pores. As the total organic carbon (TOC) content increases, organic pores gradually become more abundant. Most of these organic pores are micropores and interweave with inorganic pores. At this stage, the pore structure of the rock is relatively simple, with a higher concentration in pore size distribution than that of highly organic-rich shale and weaker heterogeneity. The interconnection between organic pores and other types of pores provides better flow channels for fluids, thereby enhancing pore connectivity [46,47,48]. ${\mathrm{S}}_1$ shows no correlation with pore connectivity and pore heterogeneity, while the correlation between ${\mathrm{S}}_2$ and pore connectivity, as well as between ${\mathrm{S}}_2$ and pore heterogeneity, is similar to that of TOC. EOM has no correlation with pore connectivity and pore heterogeneity; however, among its components, the content of light-component saturated hydrocarbons shows a strong negative correlation with pore connectivity and a strong positive correlation with pore heterogeneity, while the heavy-component resins show the opposite trend. This may be because better pore connectivity provides effective migration channels for light components, leading to the preferential loss of saturated hydrocarbons and thus the relative enrichment of heavy-component resins remaining in EOM.

It can be seen from Figure 11 that brittle minerals show a strong positive correlation with pore connectivity and a certain negative correlation with pore heterogeneity; carbonate minerals show no correlation with pore connectivity and pore heterogeneity; clay minerals show a strong negative correlation with pore connectivity and a positive correlation with pore heterogeneity. This is similar to the results of Zhong et al. [49]. This is likely because brittle minerals inhibit compaction by providing a rigid framework and are prone to forming natural microfractures under tectonic stress, thereby significantly improving the connectivity between pores. In contrast, the plastic nature of clay minerals causes them to easily deform during compaction, which in turn fills pore throats and blocks the connectivity paths between pores. Meanwhile, the higher the clay content, the more significant the matrix compression effect of the sample; compression leads to the closure of isolated pores and changes in the morphology of primary pores, further damaging pore connectivity [23].

6. Conclusions

(1)

After extraction, the specific surface area (SSA) and total pore volume (TPV) of shale samples in the study area are both higher than those before extraction, while the average pore diameter (APD) exhibits inconsistent changes. Additionally, the pore size distribution becomes more concentrated, with improved pore homogeneity and connectivity. Compared to shale with medium organic matter abundance, shale with high organic matter abundance shows a more concentrated pore size distribution, as well as better pore connectivity and homogeneity.

(2)

Before extraction, the pore connectivity of shale in the study area is positively correlated with APD and TPV, whereas the opposite is true for pore heterogeneity. Furthermore, there is no correlation between pore connectivity/heterogeneity and SSA. After extraction, pore connectivity displays a strong positive correlation with APD and a weak positive correlation with SSA, with the inverse trend observed for pore heterogeneity. Moreover, pore connectivity and heterogeneity no longer show a correlation with TPV.

(3)

The pore connectivity of shale in the study area first increases and then decreases with the increase in total organic carbon (TOC) content and pyrolysis parameter S₂ content. The better the pore connectivity of shale, the lower the content of light-component saturated hydrocarbons and the relatively higher the content of heavy-component resins in extractable organic matter (EOM). Brittle minerals can provide a rigid framework to inhibit compaction and are prone to forming natural microfractures under tectonic stress, thereby enhancing pore connectivity. In contrast, clay minerals, due to their plasticity, tend to deform and fill pore throats during compaction, thus reducing pore connectivity.

(4)

By establishing a multifractal analysis method and obtaining key parameters, this study systematically investigates the evolutionary patterns and controlling factors of shale pore structure before and after extraction. This work deepens the understanding of the complexity of storage spaces in continental shale oil reservoirs and provides new perspectives and a basis for evaluating reservoir quality in the Ordos Basin and other similar lacustrine shale formations.