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Article

Nanopore Structure and Multifractal Characteristics of Continental Shale Oil Reservoir: A Case Study from Ziliujing Shales in the Sichuan Basin

by
Youzhi Wang
1,
Wei Li
2,3,*,
Xiandong Wang
1,
Zhiguo Wang
1,
Weiqi Ma
1,
Yanping Zhu
1,
Mengdi Sun
2,3,*,
Bo Liu
2,3,
Lijuan Cheng
2,3 and
Xiaofei Fu
2,3
1
Exploration and Development Research Institute, PetroChina Daqing Oilfield Company Limited, Daqing 163712, China
2
Key Laboratory of Continental Shale Hydrocarbon Accumulation and Efficient Development, Northeast Petroleum University, Ministry of Education, Daqing 163318, China
3
Institute of Unconventional Oil & Gas, Northeast Petroleum University, Daqing 163318, China
*
Authors to whom correspondence should be addressed.
J. Mar. Sci. Eng. 2023, 11(10), 1989; https://doi.org/10.3390/jmse11101989
Submission received: 25 July 2023 / Revised: 7 October 2023 / Accepted: 10 October 2023 / Published: 15 October 2023
(This article belongs to the Special Issue High-Efficient Exploration and Development of Oil & Gas from Ocean)
Browse Figures
Versions Notes

Abstract

:
Thermal maturity of the shales from the Ziliujing Formation of the Jurassic age in the Sichuan Basin is in the hydrocarbon generation window, which makes it a candidate for shale oil and gas development. The meso- and macropore characteristics and heterogeneity of shales are important factors affecting the occurrence and development of oil and gas. However, the meso- and macropores of the Ziliujing shales have not been systematically studied. Thus, the mineral compositions and total organic carbon (TOC) of samples from this formation, as well as its pore structure, are analyzed by low-temperature N2 adsorption technique. Moreover, the heterogeneity of the pores was determined by multifractal analysis. The results show that the Ziliujing shales can be classified into three types according to the distributions of mineral compositions of carbonate and mixed and argillaceous shales. Results revealed that the smallest meso- and macropore volume (PV), the smallest specific surface area (SSA), and the largest average pore diameter (APD) occur in the carbonate shales. However, the largest PV and SSA and the smallest APD are observed in the argillaceous shales. The porosity of carbonate shales is mainly concentrated between 5 nm and 30 nm. Compared with carbonate shales, the porosity with pore sizes less than 30 nm of mixed and argillaceous shales shows a rapid increase. Furthermore, inorganic minerals are the main factors affecting the pore distributions, while TOC shows a weak effect. Herein, clay minerals significantly increase the mesopore volume and the pore number with a size of less than 30 nm. The Dq-q curves reveal that the meso- and macropore distributions of Ziliujing shales show multifractal behavior, but the multifractal characteristics of pores of various shales are distinctly different. The information dimension D1, the Hurst exponent H, and the width of the right side D0–D10 are key indicators to distinguish the local variations within the pore structure of different types of shales. The carbonate shales have the largest multifractal spectra width and the smallest D1 and H, while the opposite trend is found for the argillaceous shales. Clay minerals reduce the heterogeneity of the meso- and macropore distributions and increase the pore connectivity. Nevertheless, the carbonate minerals exhibit a reverse trend. Finally, it was found that TOC does not impact pore complexity as much. Collectively, this study supports our understanding of the occurrence of shale oil within various reservoir facies, thereby providing a guideline for future explorations in the Ziliujing Formation of the Jurassic age in the Sichuan Basin.

1. Introduction

Recent advancements in technology have accelerated exploration and production from continental and lacustrine shale oil reservoirs in China [1,2,3]. The recoverable resource potential of shale oil in China is estimated at 34.98 × 108 t, which offsets the shortage of China’s crude oil storage [4]. Large continental shale oil resources have been discovered in the Songliao, Ordos, Junggar, and Bohai Bay Basins [5]. As for the mentioned basins, significant achievements in occurrence, geological characteristics, sweet spot evaluation, and development technologies of shale oil reservoirs have been obtained [6,7,8,9,10]. Recently, several sets of organic-rich black shales with thermal maturity ranging from 1.3% to 1.5% in the Ziliujing Formation of the Jurassic age in the Sichuan Basin have been discovered [11]. The thermal maturity of the Ziliujing shale oil reservoir is in the oil and gas window, which shows great potential for shale oil and gas production. At present, the research regarding the Ziliujing shale reservoir is focused on the analysis of the main controlling factors of hydrocarbon enrichment [12,13]. Herein, shale oil reservoirs are considered to be fine-grained sediments with a wide range of pore sizes (nm to μm) and ultralow porosity [14]. The nanopores, including meso- and macropores, are important channels for shale oil occurrence and diffusion, which are important parameters to decide the future development of shale oil [15]. Therefore, systematic research on the meso- and macropore characteristics of the Ziliujing shales is needed.
Various methods have been reported to study pore characteristics of shale oil and gas reservoirs [16,17,18,19,20,21]. Jiang et al. [22] studied the pore characteristics of lacustrine shales with vitrinite reflectance (Ro) of 0.68–1.02% in the Ordos Basin through low-temperature nitrogen (N2) adsorption (LNA) and high-pressure mercury intrusion porosimetry (MIP). They concluded that mesopores (2–50 nm) mainly controlled the distributions of pore volume and specific surface area of shales, and clay mineral contents and Ro were the main factors controlling pore development. Zhao et al. [23] employed small-angle neutron scattering, and fluid invasion methods were adopted to characterize the pore structure of typical American shale oil reservoirs. They found that large amounts of inaccessible porosity at pore size <10 nm occurred in typical American shales. Zhang et al. [24] analyzed the pore size distributions of shale oil reservoir samples from Dongying Sag, Bohai Bay Basin, by using LNA, scanning electron microscopy (SEM), MIP, and nuclear magnetic resonance (NMR) techniques. The results showed that a wide range of pore sizes with small-scale throats from nanometers to millimeters existed in shale oil reservoirs. Hou et al. [25] investigated the influences of clay minerals on the pore structure of lacustrine shales by SEM. They found that the type, occurrence, and composition of clay minerals are the key factors affecting pore characteristics. The results revealed that the pore-filling clay minerals, as well as layered ones, played a negative role in the pore development of lacustrine shales. Zheng et al. [26] characterized the multi-scale pore structure of tight shale oil reservoirs from the Triassic Yanchang Formation, Ordos Basin, by combining LNA, MIP, helium pycnometry, field emission scanning electron microscopy (FE-SEM), and nano-CT. They found that the pore sizes of the studied shales varied from 2 nm to >20 μm, but the dominant pores were in the range of 20–100 nm. Among these methods, the LNA technique is simple and convenient and has been widely used to characterize nanopore characteristics of shales [27,28]. Despite the popularity of this method, the evaluation of the nanopore structure of Ziliujing shales by the LNA method has not been carried out so far.
The pore heterogeneity or complexity is another critical parameter affecting the flow characteristics of fluids within pores of shales [29,30,31]. However, the traditional Euclidean geometry theory makes it difficult to characterize pore complexity [32]. Thus, fractal theory provides a scientific tool to quantitatively describe the complexity of the pore system by fractal dimension [33,34,35]. The pore size distributions of shales show fractal characteristics, but the pores with different size ranges exhibit multiple fractal characteristics [36]. The multifractals, described as an extension of single fractals, decompose self-similar measures into intertwined fractal subsets that can characterize the local complexity of measured variables [37]. Thus, in comparison with single fractal, multifractal can better explain the local and differential distributions of the pore structure of shales with an acceptable accuracy [38]. Currently, limited research has been carried out in applying the multifractal approach to analyze the pore heterogeneity of shale oil reservoirs [39,40]. Liu et al. [41] applied multifractal methods to analyze gas adsorption isotherms for meso- and macropore and micropore heterogeneity of the Bakken shale. Their results showed that clay and organic matter reduced micropore complexity but increased meso- and macropore complexity. Likewise, Liu et al. [42] investigated multifractal characteristics of the pore structure of Middle Bakken based on FE-SEM image. They found that the heterogeneity of entire pores in images weas larger than that of the sub-pore groups in the same image. Zhang et al. [43] applied multifractal analysis on the samples that were polished by the ion beam via scanning electron microscopy to quantify the heterogeneity of major pores of shale oil reservoirs from Dongying Sag, Bohai Bay Basin. They reported that the interparticle pores possessed major complexity of pore morphologies and the simplest pore structure, while the dissolution pores had the simplest pore morphologies but had the most complex pore distributions. Guan et al. [44] conducted a multifractal analysis of N2 adsorption and Hg pore size distributions of lacustrine shales from Cangdong Sag, Bohai Bay Basin. The results revealed that the pore distributions obtained from LNA are more homogeneous than those obtained from MIP, which implied that the pores with larger diameters could increase the heterogeneity of pore distributions. In this regard, the thermal maturity (Ro) of shale oil reservoirs described above is mostly less than 1.05%. Nevertheless, the Ro of the shale oil reservoirs in some basins in China is measured over 1.05%. However, the investigation on the multifractal characteristics of nanopore structure of shale oil reservoirs with medium to high thermal maturity (1.05% < Ro < 2.0%) is scarce, which may limit a better understanding of shale oil occurrence with Ro > 1.05%. In this case, the Ro values of the Ziliujing shale oil reservoirs mostly vary from 1.3% to 1.5%, and the research on the multifractal heterogeneity of nanopores from N2 adsorption of this reservoir has not been reported. Hence, this study can help us to understand the occurrence of hydrocarbons and support exploration efforts within reservoirs with Ro > 1.05% in general.
In this paper, the lacustrine shale samples with different depths from the Ziliujing Formation in the Sichuan Basin were investigated. The aims are to (1) investigate the meso- and macropore characteristics, including pore volume (PV), specific surface area (SSA), and pore size distributions of shales by low-temperature N2 adsorption; (2) analyze the effects of mineral compositions and total organic carbon (TOC) on meso- and macropore structures of different types of shales within this formation; (3) address the heterogeneity of meso- and macropore distributions by multifractal theory and discuss the governing factors on meso- and macropore heterogeneity. This research should be beneficial for more successful exploration and development of shale oil in the Ziliujing Formation of the Jurassic age in the Sichuan Basin.

2. Experiments and Methods

2.1. Sample Preparation

The shale samples were collected from drill cores of the Ziliujing Formation of the Jurassic age in the Sichuan Basin, with details listed in Table 1. The depth of the samples varies in the range of 1866.5–2017.0 m. Details about the geological settings of the study area and this formation can be found in our previous study [45]. To better address the meso- and macropore characteristics, a schematic diagram has been presented in Figure 1. First, the studied shale samples were grinded to measure TOC, and inorganic mineral compositions were determined via LECO test and X-ray diffraction (XRD) analysis, while meso- and macropore parameters were obtained by LNA. Then, the roles of TOC and mineral compositions on pore distributions of the studied shale samples were investigated. Finally, the heterogeneity of meso- and macropore distributions of different shale samples was studied by using multifractal theory. Moreover, major identified facies in the samples were chosen for more detailed analysis by quantitative evaluation of minerals through scanning electron microscopy (QEMSCAN).

2.2. TOC Measurement, XRD Analysis, and QEMSCAN

The organic content and inorganic mineral compositions of the shale samples were characterized by TOC and XRD, respectively. The TOC of the shale samples was determined according to the GB/T 19145-2003 standard [46]. Based on SY/T 5163-2018 [47], the inorganic mineral compositions of the shale samples were determined by using a Japanese RIKEN SmartLab X-ray diffractometer. Before the experiment, the shale samples were grinded to a particle size >200 mesh using an agate mortar. The XRD experiments were performed at 40 kV and 200 mA, and the scanning range of minerals varied from 3° to 80° with a step size of 0.02°. A Quanta 650 equipment of FEI Company was chosen to conduct QEMSCAN analysis following the standard SY/T 6189-2018 [48]. Before the QEMSCAN, the shale samples were polished by an argon ion beam for 4 h to achieve a proper surface roughness of 1 μm in a scanning area of 1.5 × 1.5 mm2.

2.3. Low-Temperature Nitrogen Adsorption Measurements

In this work, the pore size ranges of macropore and mesopore were determined according to the IUPAC classification [49], which has been broadly used in the pore characterization of coals and shales [27,30,36]. Thus, the pore sizes of macropore and mesopore were >50 nm and 2–50 nm, respectively. The meso- and micropore parameters were analyzed using a fully automatic specific surface analysis tester (ASAP 2460, Micromeritics, Norcross, GA, USA). The N2 at 77 K as a molecular probe was chosen to detect the pore characteristics of the shale samples. Before the experiments, the shale samples were placed in a vacuum drying oven and were dried at 110 °C for 12 h to remove the residual moisture and gas from the samples. The relative pressure (P/P0) of the low-temperature N2 adsorption/desorption isotherm was between 0.019 and 0.995. Based on the low-temperature N2 adsorption/desorption curves, the specific surface area (SSA) and pore volume (PV) of the shale samples were obtained by the BET model and BJH model, respectively. The pore size distribution was obtained based on the DFT model, and the average pore diameter (APD) was obtained by the BJH model.

2.4. Multifractal Analysis

The multifractal singularity spectrum (α-f(α)) and generalized dimensional spectrum (q-Dq) are equivalent to multifractal descriptions [50]. In this paper, relatively simple q-Dq spectra were used to characterize the pore heterogeneity of the shale samples [51]. As suggested by the work of Caniego et al. [52], the relative pressure P/P0 is taken as the interval J, and the interval J of length L is divided into N(ε) = 2k boxes of scale ε based on the dyadic scaling down. In order to allow the smallest subinterval to contain measured pore volumes, the k values derived from low-temperature N2 adsorption were from 0 to 3. The generalized dimensional spectra require the determination of four functions: mass probability pi(ε), partition function χ(q, ε), mass exponent τ(q), and generalized fractal dimension Dq.
(1) The probability mass distribution pi(ε) is the key to multifractal analysis to define a mass probability at different scales. These components will help to quantitatively characterize the local features of the pore volume distribution [53] and is defined in each box as:
p i ( ε ) = N i ( ε ) N t
where Ni is N2 adsorption amount of the ith (i = 1, 2, 3…) box, and Nt is the total N2 adsorption amount.
(2) The partition function χ(q, ε) can be calculated from pi(ε) function by using:
χ ( q , ε ) = ∑ i = 1 N ( ε ) p i q ( ε )
where q is the order of statistical moments in the range of [−∞ +∞]. In this paper, q varies from −10 to 10. When q >> 1, the information on the high probability of pore volume is amplified [50]. When q << −1, the information on the low probability of pore volume is amplified [34]. Therefore, the multifractal method divides the pore volume with different pore sizes into local high- or local low-porosity regions.
(3) The mass exponent τ(q) for fractals is a power law relationship between χ(q, ε) and ε [52]:
χ ( q , ε ) ∝ ε − τ ( q )
where τ(q) can be obtained from the slope of the double logarithmic curve of χ(q, ε) versus ε.
(4) The generalized fractal dimension Dq. Dq is related to τ(q) as follows [53]:
τ ( q ) = ( q − 1 ) D q
In combination with Equations (3) and (4), the Dq can be given by:
D q = lim ε → 0 1 q − 1 log [ χ ( q , ε ) ] log ( ε )
where q ≠ 1. For q = 1, D1 can be obtained by L’Hôpital rule [51]:
D 1 = lim ε → 0 ∑ i = 1 N ( ε ) χ i ( 1 , ε ) log [ χ i ( 1 , ε ) ] log ( ε )
The Dq values at q = 0, q = 1, and q = 2 are named capacity dimension D0, information dimension D1, and correlation dimension D2, respectively. For monofractal, D0 = D1 = D2 and the Dq spectrum is a horizontal line. For multifractal, the Dq spectrum is a monotonically decreasing function of q and D0 > D1 > D2. According to Riedi et al. [54], D2 can be replaced by Hurst exponent H as:
D 2 = 2 H − 1
where H indicates the autocorrelation of pore volume distribution over the set of pore sizes related to long-range spatial variation [36,51,55].

3. Results and Discussion

3.1. TOC Content and Mineral Compositions of Shale Samples

The TOC contents and mineral compositions of the shale samples are listed in Table 1. The TOC values vary from 0.396% to 2.417%, with an average value of 1.285%. This means both the organic-rich and organic-poor shale samples can be identified. Furthermore, previous studies [56,57,58,59,60] have generally categorized the shale types based on the contents of siliceous minerals (quartz and feldspar) and carbonate and clay minerals from XRD analysis. Thus, the studied shale samples can be classified into three types, as presented in the ternary plot (Figure 2), namely carbonate and mixed and argillaceous shales. This is consistent with the findings by He et al. [13], who have categorized the primary types of Ziliujing shales into the same three groups based on XRD results of 70 samples. Hence, the 12 samples chosen are considered to be representative of the studied interval in the reservoir. For carbonate shales, the calcite contents are larger than 50%, while both the contents of quartz and clay are less than 20% (Table 1). The contents of calcite, quartz, and clay in mixed shales are close, within the ranges of 26.9–35.3%, 27.8–30.7%, and 28.2–29.5%, respectively (Table 1). For argillaceous shales, the clay contents are higher than 50% (51.2–59.0%), and the quartz contents vary from 26.3% to 29.6%, while calcite contents are below 5% (Table 1). The mineral compositions of different samples show a significant impact on pore distributions and pore heterogeneity, which will be discussed in the following Section 3.3.
The QEMSCAN images of the three typical types of shales can be seen in Figure 3. The carbonate shale (sample D97) is mainly composed of calcite (65.18%) with scarce organic matter (0.47%) (Figure 3a). This result is consistent with the XRD analysis. However, the content of quartz from QEMSCAN in sample D97 is only 7.9%, which is less than that obtained from XRD analysis. The heterogeneity of shale is strong at the nano and micro scale, which means, in comparison to QEMSCAN, XRD analysis of the power samples will be more accurate and free from such heterogeneity. In this regard, QEMSCAN can directly observe the distributions of mineral compositions, except for the quantitative contents of minerals. For carbonate shale (Figure 3a), the calcites exhibit a layered structure with illite embedded in calcite particles, and the pyrite aggregates are randomly distributed in the samples. In terms of the mixed shale (sample D123), the clay and quartz contents are 29.45% and 28.93%, respectively (Figure 3b), while the clay and quartz contents of the argillaceous shale (sample D71) with a lesser amount of calcite (3.80%) is 50.38% and 23.85%, respectively (Figure 3c). Both the mixed and argillaceous shales exhibit dense massive textures, which demonstrates an ultralow porosity and permeability in the shale samples should be expected.

3.2. Meso- and Macropore Characteristics of the Shale Samples

Figure 4 shows the low-temperature N2 adsorption/desorption isotherms of the shale samples. According to the gas adsorption classification [61], the isotherms of all the samples belong to type IV curves. The desorption isotherms of all the samples are located in the upper part of the adsorption isotherms, and a clear inflection point occurs at a relative pressure of 0.5, where the adsorption hysteresis occurs. The types of the adsorption hysteresis loop can be used to determine the pore shape of the adsorbent [62]. The adsorption hysteresis loops of all the samples are of H3 type (Figure 4), suggesting that the pore morphology is dominated by wide slit-type pores. However, there are obvious differences in the N2 adsorption amounts of different types of shales. The N2 adsorption values of both carbonate and mixed shales are less than 11 cm3/g (Figure 4a), while those of argillaceous shales are higher than 15 cm3/g (Figure 4b). The variations of N2 adsorption amounts indicate that obvious differences exist in the pore structure of different shale samples. The pore structure parameters of the shale samples derived from the low-temperature N2 adsorption/desorption isotherms are presented in Table 2.
As shown in Table 2, the mesopore volume of all the samples is higher than the macropore volume. This change is similar to the pore variations in samples from the Longmaxi Formation [22,35]. However, there are clear differences in the pore parameters of different types of shales. The carbonate shales exhibited the lowest SSA (2.23–2.32 m2/g) and PV (0.0109–0.0125 cm3/g) but the largest APD (14.50 nm on average). Compared with carbonate shale, the SSA and PV of mixed shales were found to increase by 1.13 m2/g and 0.003 cm3/g on average, respectively, and the meso- and macropore volumes increased by 25.46% and 25.08%, respectively, while the APD decreased to 11.62 nm on average. The SSA and PV of argillaceous shales showed a significant increase within the ranges of 6.48–8.07 m2/g and 0.0246–0.0376 cm3/g, respectively. In comparison with mixed shales, the mesopore volume of argillaceous shales increased by 2–4 times, and the macropore volume increased by 71.77% on average, while the APD was found to be the lowest (10.88 nm on average).
The pore size distributions (PSDs) can reveal subtle variations in the pore structure of different shale samples (Figure 5). From Figure 5, the peaks of PSDs of carbonate shales are concentrated at 9 nm, and the porosity is found between 2 nm and 30 nm. The PSDs of mixed shales become wider, while the peaks are left shifted to 3.4 nm. Except for the D123 sample, the number of pores with a diameter of less than 35 nm of mixed shales increased significantly, resulting in an increased mesopore volume and SSA. The pore number with a diameter less than 8 nm in D123 samples increased, but the opposite trend is recorded for pore diameter >8 nm. As a result, the meso- and macroporosity increased slightly. The PSDs of argillaceous shales are similar to those of mixed shales. However, the PV in the whole pore size range of clay shales increased, especially for the pores with size <30 nm. Thus, the mesopore volume and SSA sharply increased, but the APD decreased. The results indicate that the increase in mesopore volume of continental shale will prompt the formation of pores with small size and enlarged SSA, which is consistent with the previous studies [16,22,35].

3.3. Effect of Mineral Compositions on Meso- and Macropore Structures

Many studies have shown that mineral compositions and TOC are the main factors that would affect shale pore structure parameters [16,22,63,64]. Figure 6 depicts the relationship between the mineral compositions with SSA, VT, Vmeso, and Vmacro. A strong positive correlation can be seen between clay mineral contents and SSA, TPV, Vmeso, and Vmacro. All the complex correlation coefficients, R2, are greater than 0.89 (Figure 6(a1,a2)). Nevertheless, a significant negative correlation (R2 > 0.70) is observed between carbonate content and the above pore parameters (Figure 6(b1,b2)). Furthermore, we did not observe any clear relationship between the amounts of quartz and feldspar (QF) and pore parameters (Figure 6(c1,c2)). As described in Figure 6(a2,b2), the variations of R2 demonstrate that the influence of clay and carbonate on Vmeso is greater than that on Vmacro. The mentioned results illustrate that clay and carbonate mineral content are the main factors affecting meso- and macropore distributions, but negatively. Previous works [22,27,35,43] have stated that shales with high clay mineral content generally own high meso- and macropore volumes. In this regard, Rexer et al. [65] explained that carbonate precipitation would hinder pore development, which means the higher the content of carbonate minerals, the lower the volume of mesopores and macropores should be. In this study, the amounts of QF among different shale samples were found to be similar, which leads to a very weak correlation between QF and pore parameters.
The relationships between TOC and SSA and pore volume are plotted in Figure 7. Ambrose et al. [66] found that TOC played an important role in the distributions of PV and SSA. Liu et al. [67] analyzed the relationships of TOC with Vmeso and Vmacro of continental shales from the Yanchang Formation in the Ordos Basin by low-temperature N2 adsorption method. They found that TOC encouraged the development of Vmeso, Vmacro, and SSA. Similar trends were observed in marine shales by Wu et al. [68]. However, no correlations can be observed here between TOC and pore parameters. Jiang et al. [22] considered that the weak correlations between TOC and pore parameters might originate from six different factors, that is, organic matter preservation process to form TOC, organic matter biogenic origin, thermal maturity, intercrystalline pores between pyrites, secondary enlargement of quartz, and the occurrence of irreducible fluids. Therefore, the ambiguous relationships between TOC and pore parameters from N2 adsorption might be due to multiple factors that need further study.

3.4. Multifractal Characteristics of Meso- and Macropore Structures

According to Equations (1)–(6), the generalized dimension spectra (Dq vs. q) of the shale samples in the range of moment q = −10 to q = 10 are plotted in Figure 8. The relevant multifractal parameters are listed in Table 3. It can be seen that the Dq spectra of all the samples are monotonically decreasing as a function of q, and D0 > D1 > D2. The results suggest that the pore size distributions of meso- and macropores of shales exhibit multifractal behavior. According to multifractal theory, it is known that the generalized spectra Dq and the corresponding characteristic parameters can describe the inner complexity or heterogeneity in the size-dependent distribution of pore volume [36,51].
As seen in Figure 8 and Table 3, the multifractal characteristics of pores in different types of shales demonstrate significant discrepancies. The Dq spectra morphology and the width D−10–D10 depict the variability of local porosity along the pore size intervals [53]. The wider the Dq spectra, the greater the local fluctuation in PSDs and the higher the complexity in pore structure. The carbonate shales exhibited the largest curvature of Dq spectra (Figure 8) and the largest values of D−10–D10 (Table 3), indicating that carbonate minerals increase the multifractality of PSDs as well as the complexity of pore structure. The average values of D−10–D10 for mixed and argillaceous shales are 1.575 and 1.499, respectively, denoting a higher degree of heterogeneity in PSDs of mixed shales.
Figure 8 shows that the left branches in D−10–D0 of the mixed shales and argillaceous shales are crossed and overlapped and cannot be distinguished well. However, the right branch D0–D10 of the argillaceous shale is located above the mixed shales, and the Dq values of the right branch of carbonate shales are the smallest (Figure 8). As listed in Table 3, the D0–D10 values of carbonate shales are greater than 0.82, and the average values for mixed shales and argillaceous shales are 0.81 and 0.72, respectively. Thus, D0–D10 can be used as an effective parameter to distinguish the inner variations in PSDs of different types of shales. The left and right branches of the Dq spectrum represent different information on the variables. The right branch D0–D10 (q > 0) and the left branch D−10–D0 (q < 0) correspond to the dominance of high and low concentrations of pore volume, respectively [36,51]. In combination with Figure 5, the pore volume distributions with size <50 nm (mesopores) lead to differential distributions among various shale samples. Thus, the right side D0–D10 for q > 0 reflects the various distributions of mesopores and the left side D−10–D0 for q < 0 represents the inner complexity in the distribution of macropores.
D1 and H characterize the concentration of PSDs and the autocorrelation of pore connectivity, respectively [36,51]. The smaller D1 and H, the greater the fluctuation in the inner distributions of pores would be, the narrower the PSDs, and the lower pore connectivity along pore size intervals should be expected. On the contrary, uniformity in the PSDs in each pore size interval and stronger connectivity among different pore size intervals can be understood. The D1 and H values of carbonate shale are the smallest, indicative of concentrated PSDs. This is compatible with the results in Section 3.3. Compared with carbonate shale, the D1 and H values of mixed shales increased, varying from 0.52–0.58 and 0.65–0.68, respectively. This infers weakened pore clustering and enhanced pore connectivity. Combined with Figure 5, the pore volume with a size less than 10 nm in mixed shale obviously increased, thus widening the PSDs and enhancing the connectivity between the pores. As a result, the values of D1 and H increase. The D1 and H values of argillaceous shales continue to increase with the average values of 0.64 and 0.72, respectively. The results show that argillaceous shales have a relatively homogeneous distribution of pore volume over different pore diameter intervals. The PSDs of argillaceous shales in Figure 5 suggest that the rapid increase in mesopore volume may be attributed to the strong autocorrelation and pore connectivity among the pore size range, which is the main reason for the difference in D1 and H of different types of shales.

3.5. Relationships of Pore Parameters, Mineral Compositions, TOC, and Multifractal Parameters from N2 Adsorption

The correlations of pore parameters (VT, Vmeso, Vmacro), mineral compositions, TOC, and multifractal parameters were conducted by a linear regressive analysis (Table 4). At the p = 0.01 level, VT, Vmeso, Vmacro, and clay show strong linear positive correlations to D1 and H and negative correlations to D0–D10 (R > 0.8). On the contrary, carbonate content displays a strongly negative correlation to D1 and H and a positive correlation to D0–D10 (R > 0.9). QF also exhibits some positive or negative correlation to the above-mentioned multifractal parameters, although the degree of correlation decreases with correlation coefficients R ranging from 0.582–0.645 at the p = 0.1 level. VT, Vmeso, Vmacro, and QF exhibit strong negative correlations to D−10–D10 at the p = 0.05 level, while carbonate and clay are positively and negatively correlated to D−10–D10 at the p = 0.01 level, respectively. A lower correlation (0.392 < R < 0.525) can be found between VT, Vmeso, Vmacro, and clay and D−10–D0. Carbonate shows a somewhat positive correlation to D−10–D0 at the p = 0.1 level, while a strong negative correlation exists between QF and D−10–D0. Moreover, a weak relationship between TOC and multifractal parameters is recorded (0.394 < R < 0.442).
The multifractal parameters (D1, H, and D0–D10) for moment q > 0 denote the fractal characteristics of high concentrations of pore volume. As analyzed in Section 3.3, the variations in mesopore volume are the main factor affecting the pore distributions of different shale samples. Consequently, a strongly positive relationship between mesopore volume and multifractal parameters (D1, H and D0–D10) for q > 0 is observed. The changes in pore distributions and regression results suggest that, in the shale samples studied, the increasing mesopore volume due to increased clay contents is accompanied by lower fluctuation, lower clustering, higher autocorrelation, and lower heterogeneity in the inner size-dependent distribution of pores as indicated by multifractal parameters for q > 0. However, the increased carbonate mineral contents play a reverse role.
Although a lower correlation between QF and the multifractal parameters for q > 0 exists when compared with clay and carbonate, the increased QF contents contribute to the widening of pore distribution, improve pore connectivity, and reduce the complexity of pore distribution. The multifractal parameters (D−10–D0) for q < 0 describe the complexity of small concentrations of pore volume, which characterizes the inner heterogeneous distribution of macropores. Although the correlation between macropore volume and D−10–D0 is low, the increasing macropore volume caused by clay mineral content still reduces the complexity in the inner macropore distributions as estimated by the negative correlation coefficient. However, carbonate enhances the complexity and fluctuation of the macropore distributions. In summary, D1, H, and D0–D10 can be considered to be the parameters that are important to distinguish the internal differences in meso- and macropore distributions along pore size intervals of the Ziliujing shales.
As mentioned above, limited parameters are not enough to demonstrate the multifractal heterogeneity of macropores. Previous research [69,70] showed that the pore size range of shale oil reservoirs varied from nanometers to millimeters, and the pores with a diameter larger than 100 nm (seepage-pores) are crucial to the transport and development of shale oil [71,72,73]. Thus, it is necessary to fully characterize the pore properties of seepage-pores by the combination of fluid-invasion methods and image analysis techniques, including MIP, low-field nuclear magnetic resonance, micro-CT, SEM, and others. Moreover, further comparison of the multifractal characteristics of pore structure from different methods of shale oil reservoirs is required.

3.6. Comparision of Multifractal Parameters of N2 Adsorption among Different Shale Oil Reservoirs

Figure 9 depicts the comparison of multifractal parameters of N2 adsorption among different shale oil reservoirs. The D1 values of the Bakken shales in the US vary from 0.702 to 0.920, with an average of 0.797 [41]. The Ek2 shales in Cangdong Sag, Bohai Bay Basin, possess the largest D1 values ranging from 0.927 to 0.982 [44]. The smallest D1 values are recorded in the Ziliujing shales, and all the D1 values are less than 0.7. On the contrary, the largest D−10–D10 values can be observed in the Ziliujing shales, and the increasing order for the average value of D−10–D10 is followed by Ek2 shales, Bakken shales, and Ziliujing shales. The results indicate that the meso- and macropore structures of the Ziliujing shales are much more heterogeneous than those of the Bakken shales and Ek2 shales.
The reasons for this discrepancy of nanopore heterogeneity of the shale oil reservoirs in different basins can be attributed to thermal maturity (Ro), mineral compositions, sedimentary environment, etc. [41,44]. Guan et al. [44] illustrated that the nanopore complexity subsequently increased when Ro is greater than 0.65%. Both the Ro of the Bakken shales from Liu et al. [41] and the Ek2 shales from Guan et al. [44] are less than 1.0%. The Ro of the Ziliujing shales in our study mostly varies from 1.3% to 1.5% [11]. Therefore, compared with the Bakken and Ek2 shales, the higher Ro may tend to increase the meso- and macropore heterogeneity of the Ziliujing shales. The impact on nanopore heterogeneity from inorganic mineral compositions of shale oil reservoirs from different basins is generally a complex inner relation. In this regard, Liu et al. [44] reported that quartz and clay played an opposite effect on the meso- and macropore heterogeneity of the Upper Bakken and Middle Bakken shales, which may be related to depositional sub-facies [70]. Thus, the effect of mineral compositions on nanopore heterogeneity of shale oil reservoirs could vary in different basins. This means that the impact of such components on pore heterogeneity, specifically composition, should be analyzed based on the geological setting and lithofacies in different basins.

4. Conclusions

In this study, the meso- and macropore characteristics of shales of the Ziliujing Formation of the Jurassic age in the Sichuan Basin were investigated by combining low-temperature N2 adsorption techniques and multifractal method. Based on the results, the conclusions are as follows:
(1) The mineral compositions of the studied Ziliujing shale samples reveal that the shales can be divided into three types: carbonate shale, mixed shale, and argillaceous shale. The calcite contents of carbonate shale were found to be higher than 50%. The differences in calcite, quartz, and clay mineral contents were found to be small for mixed shale. For argillaceous shale, the clay mineral contents were found to be larger than 50%, and the calcite contents were less than 5%.
(2) There are clear differences in meso- and macropore characteristics among different types of shales. The carbonate shale exhibited the lowest specific surface area and pore volume but the largest average pore diameter. The argillaceous shale had the highest specific surface area and pore volume but the smallest average pore size. The pore parameters of the mixed shale were found to be in between. The mesopore volume is the main control of the differential distribution of pores in different types of shales. Clay minerals significantly increased the mesopore contents, while carbonate minerals had the opposite effect. TOC and the combined contents of quartz and feldspar had a weak effect on the pore distributions.
(3) The meso- and macropore distributions of the Ziliujing shales exhibited multifractal behaviors. The information dimension D1, the Hurst exponent H, and the width of the right side D0–D10 of the Dq spectrum were found effective parameters to distinguish the local variations within the pore distributions of different types of Ziliujing shale samples. As indicated by the change of the multifractal parameters, for the Ziliujing shales studied, clay minerals and quartz and feldspar contents caused a reduction in the pore clustering, enhanced pore connectivity, and weakened the complexity of pore distribution, while carbonate minerals played the opposite role. Therefore, the multifractal approach helps to improve the understanding of internal heterogeneity and differences in meso- and macropore distributions of different types of shales.
(4) A further step is needed to characterize the pores of the Ziliujing shales with sizes varying from nanometers to micrometers in combination with different methods, such as mercury injection and gas adsorption (CO2 and N2), an integration of small-angle neutron scattering techniques (SANS and USANS), and micro-CT. Additionally, the analysis of multifractal properties of seepage-pores heterogeneity is necessary, which has implications for permeability calculation, fluid transport, and future development plans.

Author Contributions

Conceptualization and methodology, W.L.; validation, Z.W., W.L. and W.M.; formal analysis, M.S. and L.C.; investigation, Y.Z.; resources, X.W. and X.F.; data curation, X.W.; writing—original draft preparation, Y.W.; writing—review and editing, Y.W. and W.L.; visualization, B.L. and M.S.; supervision, X.F.; project administration, Y.Z.; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.

Funding

The research work is funded by Heilongjiang Province “Hundred Million” Engineering of Major Projects in Science and Technology (SC2020ZX05A0023), The Scientific Research and Technological Development Project of China National Petroleum Corporation (2022DJ1809).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

This work is supported by the National Natural Science Foundation of China (No. 41902176), Outstanding Youth Fund of (No. YQ2021D005), University Nursing Program for Young Scholars with Creative Talents in Heilongjiang Province (No. UNPYSCT-2020145), Heilongjiang Postdoctoral Financial Assistance (No. LBH-Z19121), and Youth Science Fund of Northeast Petroleum University (No. 2018QNL-24).

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Schematic diagram for this study on the Ziliujing shale samples.
Figure 1. Schematic diagram for this study on the Ziliujing shale samples.
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Figure 2. Mineral composition characteristics of the shale samples. (I: siliceous shales; II: argillaceous shales; III: mixed shales; IV: carbonate shales. The red dots represent carbonate shales, the green dots represent mixed shales, and the blue dots represent argillaceous shales).
Figure 2. Mineral composition characteristics of the shale samples. (I: siliceous shales; II: argillaceous shales; III: mixed shales; IV: carbonate shales. The red dots represent carbonate shales, the green dots represent mixed shales, and the blue dots represent argillaceous shales).
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Figure 3. QEMSCAN images of different types of shale samples. (a) the carbonate shale of sample D97, (b) the mixed shale of sample D123, (c) the argillaceous shale of sample D71.
Figure 3. QEMSCAN images of different types of shale samples. (a) the carbonate shale of sample D97, (b) the mixed shale of sample D123, (c) the argillaceous shale of sample D71.
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Figure 4. Low-temperature N2 adsorption/desorption isotherms of the shale samples. (a) LNA curves of carbonate and mixed shale samples, (b) LNA curves of argillaceous shale samples.
Figure 4. Low-temperature N2 adsorption/desorption isotherms of the shale samples. (a) LNA curves of carbonate and mixed shale samples, (b) LNA curves of argillaceous shale samples.
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Figure 5. Pore size distributions of the shale samples derived from N2 adsorption methods.
Figure 5. Pore size distributions of the shale samples derived from N2 adsorption methods.
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Jmse 11 01989 g006aJmse 11 01989 g006b
Figure 6. Relationships of mineral compositions and SSA, TPV, Vmeso, and Vmacro. (a1–c1) Relationships of clay, carbonate, and QF with SSA and TPV, respectively. (a2–c2) Relationships of clay, carbonate, and QF with meso- and macropore volumes, respectively. (Dotted boxes represent pore parameters of carbonate shales).
Figure 6. Relationships of mineral compositions and SSA, TPV, Vmeso, and Vmacro. (a1–c1) Relationships of clay, carbonate, and QF with SSA and TPV, respectively. (a2–c2) Relationships of clay, carbonate, and QF with meso- and macropore volumes, respectively. (Dotted boxes represent pore parameters of carbonate shales).
Jmse 11 01989 g006aJmse 11 01989 g006b
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Figure 7. Relationships of TOC with SSA, VT, Vmeso, and Vmacro. (a) Relationships of TOC with SSA and VT, respectively. (b) Relationships of TOC with Vmeso and Vmacro, respectively.
Figure 7. Relationships of TOC with SSA, VT, Vmeso, and Vmacro. (a) Relationships of TOC with SSA and VT, respectively. (b) Relationships of TOC with Vmeso and Vmacro, respectively.
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Figure 8. Generalized dimension Dq, versus q from q = −10 to q = +10 for PSDs of the shale samples.
Figure 8. Generalized dimension Dq, versus q from q = −10 to q = +10 for PSDs of the shale samples.
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Figure 9. Comparison of multifractal parameters of nanopore heterogeneity of shale oil reservoirs from different basins (a) D1 data of shale samples from different basins. (b) D−10–D10 data of shale samples from different basins. (The red circle represents the maximum value, the green circle represents the minimum value, and the blue triangle represents the average value. The D1 and D−10–D10 data of the Bakken shale from Liu et al. [41], and the D1 and D−10–D10 data of the Ek2 shale from Guan et al. [44]).
Figure 9. Comparison of multifractal parameters of nanopore heterogeneity of shale oil reservoirs from different basins (a) D1 data of shale samples from different basins. (b) D−10–D10 data of shale samples from different basins. (The red circle represents the maximum value, the green circle represents the minimum value, and the blue triangle represents the average value. The D1 and D−10–D10 data of the Bakken shale from Liu et al. [41], and the D1 and D−10–D10 data of the Ek2 shale from Guan et al. [44]).
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Table 1. Depths and mineral compositions of shale samples.
Table 1. Depths and mineral compositions of shale samples.
SamplesDepth (m)TOC (%)Mineral Contents (%)
QFSPCCASRPYMCCL
Dn91866.51.13818.11.62.353.80.63.81.218.6
D972006.90.39616.21.32.157.90.42.11.019.0
D621997.41.64629.21.62.629.61.05.81.229.0
D1232014.71.01127.80.01.835.32.73.40.828.2
D1302016.60.74430.72.02.928.80.94.50.729.5
D1322017.01.88930.12.13.926.91.05.01.529.5
Dn171890.50.42726.82.53.82.41.45.11.956.1
D171983.91.45925.22.64.92.41.32.42.259.0
D261986.80.73129.63.13.42.00.64.01.356.0
D711999.72.41727.62.04.04.61.67.21.851.2
D802002.81.71926.32.84.13.73.32.51.655.7
D912005.61.84330.72.74.31.11.42.12.755.0
Note: Q—quartz; FS—feldspar; PC—plagioclase; CA—calcite; SR—siderite; PY—pyrite; MC—muscovite; CL—clay.
Table 2. Pore parameters of the shale samples derived from LNA methods.
Table 2. Pore parameters of the shale samples derived from LNA methods.
SamplesShale TypeSBET (m2/g)Pore Volume (cm3/g)APS (nm)
VTVmesoVmacro
Dn9carbonate2.320.01250.00830.004114.01
D972.230.01090.00730.003614.99
D62mixed3.680.01810.01240.005712.07
D1232.710.01150.00750.004111.43
D1303.500.01460.00940.005211.97
D1323.660.01430.00990.004411.03
Dn17argillaceous7.460.02770.02060.007110.09
D178.070.03760.02650.011111.98
D267.620.03100.02270.008310.92
D716.480.02460.01810.006510.15
D807.520.03380.02540.008511.27
D917.770.03190.02370.008310.85
Note: VT—total pore volume; Vmeso—mesopore volume (2–50 nm); Vmacro—macropore volume (>50 nm); APS—average pore size.
Table 3. Multifractal parameters of pores derived from N2 adsorption isotherms for shale samples.
Table 3. Multifractal parameters of pores derived from N2 adsorption isotherms for shale samples.
ShalesSamplesD0D1HD10D−10D0–D10D−10–D0D−10–D10
carbonateDn91.0000.4890.6380.1601.8460.8400.8461.686
D971.0000.5120.6470.1711.8310.8290.8311.660
mixedD621.0000.5260.6540.1791.7810.8210.7811.602
D1231.0000.5520.6650.1931.7680.8070.7681.575
D1301.0000.5510.6650.1931.7810.8070.7811.588
D1321.0000.5750.6770.2081.7430.7920.7431.535
argillaceousDn171.0000.6550.7290.2841.7480.7160.7481.464
D171.0000.6380.7170.2641.7380.7360.7381.474
D261.0000.6380.7190.2691.7650.7310.7651.496
D711.0000.6090.7000.2421.7960.7580.7961.554
D801.0000.6340.7150.2631.7960.7370.7961.533
D911.0000.6430.7210.2721.7500.7280.7501.478
Table 4. Relationships of VT, Vmeso, Vmacro, mineral compositions, TOC, and multifractal parameters.
Table 4. Relationships of VT, Vmeso, Vmacro, mineral compositions, TOC, and multifractal parameters.
D1HD0–D10D−10–D10D−10–D0
VT0.875 **0.893 **−0.895 **−0.782 *−0.414
Vmeso0.886 **0.906 **−0.909 **−0.782 *−0.392
Vmacro0.807 **0.820 **−0.819 **−0.754 *−0.463
carbonate−0.952 **−0.946 **0.941 **0.912 **0.646 †
clay0.966 **0.974 **−0.974 **−0.879 **−0.525
QF0.645 †0.601 †−0.582 †−0.744 *−0.764 *
TOC0.4420.415−0.398−0.416−0.394
Note: **, significant at p = 0.01 confidence level; *, significant at p = 0.05 confidence level; †, significant at p = 0.1 confidence level.
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Wang, Y.; Li, W.; Wang, X.; Wang, Z.; Ma, W.; Zhu, Y.; Sun, M.; Liu, B.; Cheng, L.; Fu, X. Nanopore Structure and Multifractal Characteristics of Continental Shale Oil Reservoir: A Case Study from Ziliujing Shales in the Sichuan Basin. J. Mar. Sci. Eng. 2023, 11, 1989. https://doi.org/10.3390/jmse11101989

AMA Style

Wang Y, Li W, Wang X, Wang Z, Ma W, Zhu Y, Sun M, Liu B, Cheng L, Fu X. Nanopore Structure and Multifractal Characteristics of Continental Shale Oil Reservoir: A Case Study from Ziliujing Shales in the Sichuan Basin. Journal of Marine Science and Engineering. 2023; 11(10):1989. https://doi.org/10.3390/jmse11101989

Chicago/Turabian Style

Wang, Youzhi, Wei Li, Xiandong Wang, Zhiguo Wang, Weiqi Ma, Yanping Zhu, Mengdi Sun, Bo Liu, Lijuan Cheng, and Xiaofei Fu. 2023. "Nanopore Structure and Multifractal Characteristics of Continental Shale Oil Reservoir: A Case Study from Ziliujing Shales in the Sichuan Basin" Journal of Marine Science and Engineering 11, no. 10: 1989. https://doi.org/10.3390/jmse11101989

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