Abstract

Despite geochemical properties and pore structures for shale oil extraction, the interplay among shale chemistry, pore network, and hydrocarbon movability still is an open question. Here, by using hybrid experimental methods, including backscatter scanning electron microscopy, X-ray diffraction, adsorption/desorption isotherm, and nuclear magnetic resonance, we provide a general characteristic of geochemistry and pore structures of Chang 7 (C7) shale in Ordos basin and figure out how these properties impact the shale oil extraction. Our results confirmed that the C7 shale in Ordos basin could be divided into three types based on the mineral components with various pore structures, and the proportion of the pores that radius below 4.8 nm (from adsorption branch) or 3.8 nm (from desorption branch) play a dominant role in the determination of pore size distributions. The kerogen index that is derived from maceral types was the most significant geochemical indicator; high kerogen index was corresponding to large pore volumes and surface areas, but low pore diameters. We identify the pore structure mechanism—mesopores predominantly or micropores abundantly (increase free fluid index) and absence of inorganic or organic pores (decrease free fluid index)—responsible for the oil extraction capability and show that the unmovable fluid stained in the voids would lead to the slow rate decay of the curves, which caused the nonsignal disturbance. Our results demonstrate the powerful control of maceral components and pore diameters on shale oil extraction and show the markedly different flow behavior of various types of shale.

1. Introduction

Shale oil and gas reservoirs are typical unconventional hydrocarbon plays that are composed of tightly packed, fine-grained sedimentary rock [13]. Despite large reserves associated with shale oil and gas, these unconventional plays require innovative development technologies and strategies to acquire economical hydrocarbon flow due to subsurface heterogeneity and complexity [4, 5]. Stimulation techniques, such as horizontal drilling and hydraulic fracturing, could create fracture and to engender hydrocarbons migration to the wellhole [6, 7]. Yet, the impressive progress of horizontal drilling and hydraulic fracturing technologies in the field of petroleum development have improved access to oil and gas from source rock formations [2, 3, 810]. As a result, the proportion of hydrocarbons derived from unconventional reservoirs have been on the rise, and the knowledge about these reservoirs is gradually abundant [7, 11].

As the critical parameter of shale reservoir property, the pore structure has attracted tremendous attentions of scholars in recent decades [1214]. Despite its importance, our knowledge about pore structures is still incomplete, owing to the complex pore architecture within shale reservoirs [1517]. Evaluation of pore structure requires a clear understanding of the pore morphology and pore size distribution (PSD), and these parameters can be predicted using a variety of different approaches, including direct observations and indirect methods [3, 12, 1821]. Such methods have allowed pore information to be obtained for the porosity, pore radius, pore-throat radius ratio (PTR), and pore tortuosity [13, 2224]. However, because of the amorphous pore shape with a PSD spanning from a few nanometers to the micrometers range, all of these tests confront different challenges related to resolution or sample size [22, 25, 26]. Among them, the direct observations, such as focus ion beam scanning electron microscopy (FIB-SEM) and field emission scanning electron microscopy (FE-SEM), are strongly dependent on the field of vision, while the indirect methods, mainly based on the combination of adsorption and mercury intrusion in shale reservoirs, are limited by accuracy [12, 18, 21, 27, 28]. It is therefore imperative to combine different experimental data for the propose of determining pore morphology and PSD accurately [27].

From a traditional perspective, the multitype tests could provide insight into the interplay among pore structures, lithology, physical property electric character, and hydrocarbon-bearing conditions [2932]. In contrast to other unconventional reservoirs such as tight sandstone and tight carbonate reservoirs, the hydrocarbons which generated within organic agglomerates (kerogen) play a significant role [8, 23, 33]. Therefore, from a geochemical perspective, the pore structure data may show quantitative correlation with organic matter parameters. Thus, the relationships between pore structures and thermal maturation-related parameters are of interest in previous research [13, 15, 34]. Some scholars believed that the porosity and PSD may be related to the organic geochemistry parameters, such as vitrinite reflectance (Ro), total organic carbon (TOC), and volatile hydrocarbon content (S1) [35, 36]. However, others contended that these relationships were strongly different between geologic formations [37]. As a result, the impacts of organic geochemistry parameters on pore structures remain an open challenge and a very active area of research.

Herein, we adopt hybrid methods, in addition to determining the pore radius with sole test, the geochemical parameters were also analyzed: (1) We image the shale oil reservoirs at high resolution with backscatter scanning electron microscopy (BS-SEM), providing the visualization of the minerals and pores, and the lithofacies and pore types were classified when combined with the X-ray diffraction (XRD) method. (2) We apply the nitrogen adsorption/desorption isotherm (NADI) approach in combination with analysis of geochemical index to disclose the relationships between pore structures and geochemical properties. (3) The nuclear magnetic resonance (NMR) was used for the propose of movable fluid saturation determination. Implications include both a comprehensive perspective regarding shale oil reservoir classification and the potential in understanding how geochemical properties impact on the evolution of pore structures.

2. Materials and Methods

2.1. Ordos Basin Geological Background

The Ordos basin is located in Western China and subdivided into six structural units: Yimeng Uplift in the north, Western Edge Thrust Belt, located adjacent to Tianhuan Depression, in the west, Weibei Uplift in the south, Jinxi Folding Belt in the east, and Yishan Slop in the middle (Figure 1). As a typical cratonic basin which was formed in the Palaeozoic and developed in the Mesozoic and Cenozoic, it has been estimated to hold up to 2×1011 tonnes of shale oil, the vast majority of which occurring at the Yanchang Formation in Upper Triassic age [3840]. The Yanchang Formation could be subdivided into 10 members, and the sediment infilling of which is of continental lacustrine origin, among them, the Chang 7 Member, which is overlain by Chang 8 Member, preserve more than 50% of shale oil in the basin (Figure 1) [39, 41]. The Chang 7 Member was studied in detail using various experimental methods and porous media models, which was also the target in this research [4244].

2.2. Experiments and Methods

2.2.1. BS-SEM and XRD Analyses

The BS-SEM measurement was made using a Zeiss GeminiSEM 500. Before placing the specimens inside the equipment, each specimen was prepared via argon ion milling and wrapped in gold or carbon. The BS-SEM can analyze the minerals qualitatively. The XRD tests were performed using a Bruker D8 ADVANCE Diffractometer with Cu Kα radiation to study the minerals quantitatively.

2.2.2. NADI Test

The NADI test was performed with a Micromeritics ASAP 2020 Plus HD88, and the pore was considered cylindrical shaped. In this test, the equivalent shale surface areas were calculated by the Brunauer-Emmett-Teller (BET) method [45], the values of pore volume were analyzed by Barrett-Joyner-Halenda (BJH) algorithm [46], and the PSDs were determined with the application of the density functional theory (DFT) method [47].

2.2.3. Geochemical Analysis

In this text, maceral types, total organic carbon (TOC), and vitrinite reflectance (Ro) were tested. Before the test, the specimens were powdered and placed into a liquiTOC II Analyzer. In a nonisothermal mode, the TOC were analyzed during one temperature ramp. Before the Ro analysis, the crushed specimens were mounted in epoxy resin and immersed in oil, and the Raman spectra were obtained with results for maceral types and Ro.

2.2.4. NMR Experiment

The NMR experiments were carried on a Bruker AVANCE III HD instrument with a frequency of 23 MHz, testing temperature of 33°C, and 0.1% EB sensitivity for 1H. Before the tests, the samples were dried, vacuumed, and saturated by the brine. The T2 curves were based on CPMG sequence in which the interecho time is initially set at the lowest possible, and gradually up to 0.07 ms for the purpose of nanopores detection [48].

3. Results

3.1. Mineralogical Characterization

Based on the XRD data, the main minerals in C7 shale are clay (av. 41.9%) followed by quartz (av. 22.7%) and feldspar (av. 19.5%). The content of carbonate varies greatly, from 4.1% to 26.2% with an average of 15.9% (Table S1). In terms of the relative clay content, the Illite+I/S mixed layer values of shales were the highest, ranging from 68.0% to 97.0%, with an average value of 84.3%, indicating the high hydrophilic clay minerals content of the selected samples (Table S1).

3.2. Organic Matter Abundance and Bulk Chemistry

As the organism in shales was relatively abundant, determining the organic abundance, types, and shale maturity becomes all the more important [4951]. Thanks to the use of various types of analyzers, our research allows one to acquire geochemical properties. The kerogen types, maturity, TOC, and other relevant data were listed in Table S2. From a geochemical viewpoint, exinite play a critical role in the kerogen type, followed by sapropelinite, while vitrinite and inertinite only occupying a very small proportion, revealing that the organic type in the C7 shale is dominated by II1 [52]. Besides, the kerogen index (KI) was defined as Equation (1), which has the positive relationship with the maturity.
(1)KI=Sap+0.5Exi0.75VitIne,
where Sap is sapropelinite, Exi is exinite, Vit is vitrinite, and Ine is inertinite.

The results also reveal that the average Ro of the sample suite ranges between 0.78% and 0.98% indicating that all samples were overmature with respect to oil generation [53]. The TOC content ranged from 2.40% to 5.91% with an average of 4.25%, indicating an excellent hydrocarbon source rock and shale oil play [54, 55].

3.3. Nitrogen Adsorption/Desorption Isotherms

N2 physisorption isotherms of the C7 shale show different shapes which is commonly observed on other area [37, 56, 57]. Based on the classification proposed by IUPAC [58], the isotherms of C7 shale belong to type IIB and III, and the hysteresis loop were mainly above relative pressures of 0.45 with characteristics of type H3 and H4 (Figures 2 and 3). Capillary condensation (CC stage) was observed from the mesopore regimes, and we define the maximum difference of adsorption and desorption branches as the hysteresis loop index (HLI) (Figure 2). Besides, low-pressure hysteresis (LPH) was widely observed for the C7 shale with relative pressures of 0.14 (Figure 2). Commonly, the adsorption branches could be divided into three parts: micropores, mesopores, and macropores, and the inflection points represented the boundary of different types of pores that proposed by previous research [37, 59, 60]. According to the physisorption isotherms, all shale samples were dominated by nanoscale pores based on the previous findings [15]. The details of the N2 physisorption-related parameters were listed in Table S3.

3.4. NMR Relaxation of Shales

NMR data were acquired for all samples in fully and centrifugation conditions. Their T2 spectra were shown in Supplementary materials, Figure S1, and sample 1 is exemplarily shown in Figure 4. In terms of the saturated state, the NMR T2 distribution showed bimodal shape except sample 6, and the amplitude of the front peak were greater than that of the rear peak. The dominant peak value of the T2 spectra occurred between 0.3 and 1.2 ms, which corresponds with the small or mediate pores in the shale matrix. Nearly all specimens can be detected at longer T2 times, which could correspond to microfractures being present in the specimens. Owing to the similar salinity of simulated water and real formation water, the presence of microfractures has no relevance to clay swelling while can be most likely envisaged for decompression [55]. The NMR amplitude of the T2 distribution decreased slightly or remained mostly unaltered after it was centrifugated, even the larger pores had not been expelled from the shale, indicating that the movable fluid was rare in the samples.

T2 cutoff and movable fluid distributions have been widely used to evaluate porous media properties and flowing disciplining for unconventional reservoirs [28, 61, 62]. In our research, fully saturated and desaturated states were used for the propose of T2 cutoff value determination. The methods are illustrated in Figure 4(b) (see ref. [28, 61] for detailed description). Apart from the T2 cutoff calculation, the estimation of free fluid index (FFI) and bound water index (BWI) were the most common applications of NMR (Figure 4(b)); the former one was the gaps between fully saturated and centrifugated states, while subtracting FFI into a hundred percent, we could obtained BWI.

The NMR decay signal could also estimate the properties of fluids in unconventional reservoirs [63]. Figure 5 shows the NMR T2 signal amplitude at the first echo time in spin echo train of the saturated and desaturated shale (free fluid gap, FWG in short) (sample 1). Although, in the majority cases, the amplitude of saturated states was larger than that of centrifugated states, there still exerted some samples that contained inflection points (Figure 5, Figure S2,). The details of the NMR-related parameters were listed in Table S4.

4. Discussion

4.1. Lithofacies and Pore Type Classification

The measured XRD contents for the C7 shale highlighting the differences of mineral assemblage for different samples and the backscatter electron scanning microscopy (BE-SEM) images were a nice indication of nanomorphology of minerals and pores (Table S1; Figures 6 and 7). We introduce the concept of lithofacies here for the propose of mineral assemblage and pore type classification [15, 6466]. The pore type classification adopted in this research was proposed by Loucks et al. [59].

In this research, three lithofacies were identified, including high rigid particle shale (HRPS), high clay mineral shale (HCMS), and high carbonate shale (HCS) (Table S1; Figure 6). The minerals of HRPS were relative uniformly and are mainly composed of quartz and feldspar, while carbonate was significantly low (Figure 6(a)). The BE-SEM images showed the enrichment degree of nanoscale interparticle pores that display elliptical shape of this lithofacies (Figure 7(a)), with pore sizes in a range from 100 nm to about 1000 nm, and the mineral particles display granular shape (Figures 7(b) and 3(a)). Besides, the random distributed organic matter-related (OM) pores were observed with irregular shape, generally in the middle of the organic matter or in the edge of the organic matter that connected to mineral grains and their diameters vary from few nanometers to around 200 nm (Figure 7(c)). A stronger clay mineral intensity was observed for the HCMS with lower quartz and feldspar (Table S1; Figure 6(a)). BS-SEM images of this lithofacies exhibiting laminated or irregular nanoscale interparticle or intraparticle pores with pore sizes range from 20 nm to 1000 nm (Figures 7(d) and 3(b)), and the clays usually present flake morphology (Figure 7(e)). Abundant small OM pores were randomly distributed with dominant pore diameters lower than 100 nm, while few pores have pore sizes larger than 300 nm (Figure 7(f)). Carbonate play a dominant role in HCS (Table S1; Figure 6(a)), while irregular particle with rough surface were observed in this lithofacies (Figure 7(g)). Limited interparticle pores were observed, while intraparticle pores and OM pores were poorly developed (Figure 7(h)). The sizes of pores of this lithofacies, inorganic and organic, were obviously lower than that of other two, with pore diameters in a range from 20 nm to 250 nm and few nanometers to 90 nm (Figures 7(i) and 3(c)).

4.2. The Relationships between Pore Types and Pore Structures

4.2.1. The Relationships between Pore Types and Pore Volumes

The micropore, mesopore, and macropore volumes are summarized in Figure 8. In all the three kinds of shales, the volumes of the macropore ranked first, followed by that of mesopore and micropore, suggesting that the development of macropores is directly related to the total pore volumes. The superiority of micropore and mesopore volumes is the typical signal of HCS, while the pore volumes of all three types of pores came in at the bottom of the list in HCMS. Macropore volumes in HRPS ranked the top. These findings proved that the development of interparticle pores are important contributors to macropore volumes (Figure 7(b)), whereas abundant clay minerals and OM could not increase the pore volumes significantly (Figures 7(d)–7(f)). As mentioned above, while the pore sizes of HCS were obviously lower than that of other two (Figures 7(h) and 7(i)), poor clay minerals and OM lead to the lack of organic pores; hence, inorganic pores become more important (Figure 7(g)). Previous literatures have already reported that the pore sizes of inorganic pores were generally larger than that of organic pores and the voids among clay aggregate [6769]; therefore, the micropore and mesopore volumes of HCS ranked at the top.

4.2.2. The Relationships between Pore Types and Pore Surface Area

For the unconventional reservoirs, the hydraulic fracturing treatment concurrent with the horizontal well technology provide more surface area for hydrocarbon flow and thus lead to the rapid oil production from shale reservoirs indirectly [7072]. From this perspective, the surface area of the organic and inorganic pores may relate to more fractures and greater pore volume; therefore, this pore structure parameter is considered a vital indicator of shale oil [7375]. In this research, the pore surface areas were calculated by five-point Brunauer, Emmett, and Teller (BET) method. The calculated surface areas and averaged values are shown in Figure 9. It is suggested that the surface areas are higher for the HCS shale than that of the HCMS shales, followed by the HRPS shales in terms of average values. These trends reveal that the increase of carbonate and the decrease of rigid grains may be one of the reasons of increasing surface area in C7 shales. Besides, it is worth noting that the averaged surface areas were the lowest when excluding the values of No. 8, suggesting that the clay minerals play a negligible role in the variation of surface areas because of the strong compactions, same as the previous studies from Zhang et al. [76] and Cao et al. [77].

4.2.3. The Relationships between Pore Types and PSD

The PSD information plays an essential role in the determination of reservoirs complexity, structures, and heterogeneity of the unconventional shales [76, 7880]. Data from nitrogen isotherms for the different types of C7 shales are compiled in Figure 10. From the data of adsorption and desorption branches, when excluding the outlier (No. 8 sample), the averaged pore diameters of HCMS are the highest, followed by HRPS and HCS. Take the typical samples from each type of reservoirs, we believe that the lower proportion of the relative finer pores and the higher percentage of the relative larger pores have higher averaged pore diameters. Specifically, pores lower than 4.8 nm (from adsorption branches) and 3.8 nm (from desorption branches) become more accessible from outlier to the HCMS, indicating the significant contribution of the throats, OM, and the clay-related pores, on the averaged pore size. In other words, for C7 shales, high PSDs were determined by the low proportion of the pores that radius below 4.8 nm (from adsorption branch) or 3.8 nm (from desorption branch).

4.3. The Effects of Geochemical Characteristics on Pore Structures

4.3.1. The Effects of KI, TOC, and Ro on Pore Volumes

Understanding the complex pore networks is one of the important issues in evaluating the storage characteristics of shale, and as one of the most important geochemical parameters, the pore volumes can significantly vary between different types of reservoirs [80, 81]. Previous research found that the geochemical parameters have a significant influence on pore volumes; however, whether the pore types corresponding to volume (micro-, meso-, and macropores) and reservoir types increase, decrease, or have no impact on the geochemical characteristics has long been disputed [76, 82]. For these three geochemical parameters, the KI generally have strong positive correlation with all sorts of pores, while the TOC and Ro showed weak correlation with pore volumes, suggesting that the pore volumes have strong relationships with the types of maceral (Figure 11). Besides, the correlation coefficient of the micropore volumes is higher than other kinds of pores indicating that the majority of micropore volumes are related to geochemical characteristics (Figure 11). This correlation between the TOC, Ro, and pore volumes are contrary to the findings which have been discovered in other literatures [56, 83, 84], revealing that further analysis needs to be done and the types of reservoirs might be the main reason.

4.3.2. The Effects of KI, TOC, and Ro on Pore Surface Area

Although previous research showed that the correlations between geochemical parameters, such as TOC and Ro, and surface areas were obvious; however, these two parameters have no distinct correlations with the surface areas, suggesting that the organic materials, as well as OM, are not the important controlling factor on the evolution of pore surface areas for C7 shale (Figures 12(b) and 12(c)). Nevertheless, the positive relationships between KI and surface areas of C7 shales is significant, suggesting that for the maceral, the exinite and sapropelinite tend to provide more surface areas (Figure 12(a)). Hence, compared to the shales in other areas and formations, the organic materials of C7 shales may play less significant role in the adsorption ability.

4.3.3. The Effects of Kerogen Index (KI), TOC, and Ro on PSD

Following the pore type analysis, the impacts of geochemical characteristics on pore structures were studied. Figure 13 compares the relationships between different geochemical parameters and pore diameters. KI and Ro show distinct and poor positive relationships with pore diameters for all the samples, respectively (Figures 13(a) and 13(c)), while the TOC has no relevance to this parameter (Figure 13(b)). These trends also proved that the abundance of vitrinite and inertinite would deteriorate the development of large pores, whereas the occurrence of the OM has no relationships with the PSDs. These findings proofed that the OM may have done nothing to contribute to the pore sizes in C7 shales; however, only small amount of exinite and sapropelinite decreased the pore diameters significantly.

4.3.4. Evolution of Geochemical Characteristics of Pore Structures

In shale play, geochemical parameters were the significant indicators of maturity, which is directly related to the thermal cracking of OM at elevated temperature [85]. As mentioned above, although different geochemical parameters existed with various correlations with pore structure-related parameters, the determination of the change of geochemical characteristics of pore structures in different types of shale still is an open challenge. In this research, the combination of pore structure-related parameters with N2 physisorption isotherm-related parameters allows the maturity features to be ascertained (Figure 14).

Pore diameters, pore surface areas, and pore volumes in each of the shale types and its geochemical derivative (KI) are plotted in Figure 14(a). The plot illustrates the evolution of KI transformation when pore structure-related parameters alteration, showing the increase of pore volumes at high KI samples in each group. However, no similar trends are observed in diameters and surfaces areas versus KI. As far as the importance of KI on pore structure determination mentioned above, these observations suggest that the development of pore volumes were driven by abundant exinite and sapropelinite with poor vitrinite and inertinite. Meanwhile, consistent with the findings mentioned above, TOC and Ro could not comparable to the investigation of pore structures in C7 shales (Figure 14(b)). The reason for this phenomenon is that although carbon is the fundamental element for the OM [85], the existence of oxygen, hydrogen, and other elements which composed of OM may also influence the pore structures; thus, the TOC have poor relationships with pore network, especially in the continental shales [15, 37], due to the high consistence observed in marine shales [13, 82]. As for the Ro, it is only one of the components of the shale organic materials (Table S2); hence, other OM and inorganic minerals need to be considered in C7 shales.

4.4. Outlier Analysis

To explain the appearance of the outlier, XRD was used as the probing test due to high proportion of clay minerals in the outlier, and nitrogen adsorption/desorption isotherms were included during this analysis. Apart from the values of the outlier, that of HCMS specimens were also included for the propose of comparison and comprehensive analysis. The parameters derived from XRD and nitrogen adsorption/desorption isotherms exhibit increasing chlorite plus illite values with increasing NADI-related parameters, suggesting that the high hydrophilic clay minerals may be the reason of strong adsorption and weak desorption (Figure 15). However, for other types of shale reservoir, there were no distinct relationships between those values. Besides, the value of chlorite plus illite of the outlier is significantly higher than others, revealing the maximum adsorption ability and minimum desorption. Therefore, the amount of chlorite and illite is one of the most serious impacts of high surface area (Figure 15). The hysteresis of the C7 shale may strongly be related to the abundance of hydrophilic clay minerals, which result to great amount of nitrogen detained in the pores (Figure 16).

4.5. Implications for Shale Oil Extraction

FFI, based largely on the NMR tests, are widely used in the evaluation of hydrocarbon movable ability, and the rise of FFI means the increase capability of hydrocarbon extraction [86]. Figure 17 shows how pore structure and geochemical properties impact the shale oil movable ability. Notably, the development of the micropore and mesopore volumes would deteriorate the FFI significantly, while that of the macropore volume exert vague trend (Figure 17(a)). Besides, a trade-off behavior has emerged in the pore proportion, that is, the abundance of micro- and mesopores have negative relationships with FFI, while the occurrence of macropores, even in a small amount, could improve the oil movability dramatically (Figure 17(b)). These phenomena proved that macropores contribute to the C7 shale oil extracted capability a lot when compared with micro- and mesopores, and with the increase of pores with large radius, the proportion of movable fluids would increase observably. Surface area and the hysteresis-related parameters (LPH and HLI and CC stage) have distinct negative correlations with FFI, suggesting that strong adsorption and weak desorption would restrict the improvement of the movable fluid percentage (Figure 17(c)). Similar as the results mentioned above (Figures 12(a) and 13(a)), KI is the most prominent geochemical index; high KI represents a large surface area and small pore diameters, resulting in FFI exhibiting deterioration (Figure 17(d)).

As for the oil extraction for different types of shale, there are several possibilities to explain the variations and differences. Both FFI and FWG are representative parameters of movable fluids; the former one refers to the variations between saturation and centrifugation stages, while the later one is derived from the signal differences between those stages [63]. The FFI were generally larger than FWG, except the HCS specimens (Figure 18(a)). This phenomenon may be due to the nonfluid components signal disruption; structural water which is adsorbed at the pore surface would be one of the media; although centrifugation made most of the free fluid moved out, abundant clay minerals sometimes would lead to the remaining water and result to the nonfluid signal. However, as for the HCS with ample hydrophobic carbonate and less clay minerals, there are reverse trends, that is, FFI were smaller than FWG (Figure 18(a)). To sum up, the existence of clay minerals, especially for those hydrophilic ones, would disturb the signal of movable fluids; thus, the gap between FFI and FWG could be seen as a method to determine the wettability: larger FWG represent hydrophobicity while high FFI derive from hydrophilicity.

Apart from the gap between FFI and FWG, the movable fluid distributions in different kinds of pores are worth noting. In general, the signal of the decay curves of saturated state was always higher than that of centrifugated state; however, fewer specimens exert the inflection point (IP) (Figure 18(b)). The existence of the IP may result from the invariant fluid that in the macropores, namely, the neutralized or sustained signal in macropores reveal the unmovable fluid stained in the voids and lead to the slow rate decay of the curves (Figure 18(b), Figure 19, Figure S1, Figure S2). In addition, a relatively good deal of mesopores in HCMS is one of the most important factors of high FFI and large pore diameters (Figures 10(b) and 19; Table S4). Although the pore diameters were relatively low when compared with the HRPS, abundant micropores that contained movable fluid caused medium FFI (Figures 10(a) and 19; Table S4). Nonetheless, the majority of movable fluids distributed in micropores (Figure 19(c)), or lack of effective pore space (Figure 19(d)), would generate a low percentage of movable fluid (Figure 10; Table S4).

5. Conclusions

  • (i)

    We divided the shale reservoirs into three different parts based on XRD data and BS-SEM observations: high rigid particle shale (HRPS), high clay mineral shale (HCMS), and high carbonate shale (HCS). HCMS have the largest pore volume and smallest pore sizes, while the surface area of HCS topped the list while having abundant micropores

  • (ii)

    The KI is the most important geochemical parameters to indicate pore structures, while TOC and Ro could not compare to the investigation in C7 shales. The amount of chlorite and illite is one of the most serious impacts of high surface area, and the hysteresis of the C7 shale may strongly related to the abundance of hydrophilic clay minerals

  • (iii)

    The micropore and mesopore volumes would deteriorate the FFI significantly, while the occurrence of macropores could improve the oil movability dramatically

  • (iv)

    High KI represents large surface area and small pore diameters, resulting in FFI exhibiting deterioration. High FFI and large pore diameters of HCMS were dominated by the peak values of mesopores; abundant micropores that contained movable fluid caused medium FFI in HRPS. Too many movable fluids remaining in micropores, or lack of effective pore space, would deteriorate the FFI significantly

Glossary

     
  • BWI:

    Bound water index

  •  
  • CC stage:

    Capillary condensation

  •  
  • FFI:

    Free fluid index

  •  
  • FWG:

    Free water gap

  •  
  • HCMS:

    High clay mineral shale

  •  
  • HCS:

    High carbonate shale

  •  
  • HLI:

    Hysteresis loop index

  •  
  • HRPS:

    High rigid particle shale

  •  
  • IP:

    Inflection point

  •  
  • LPH:

    Low-pressure hysteresis

  •  
  • PSD:

    Pore size distribution

Data Availability

The experimental data used to support the findings of this study are included in the manuscript and supplementary material.

Conflicts of Interest

The authors declare that there is no conflict of interest regarding the publication of this paper.

Acknowledgments

The authors would like to acknowledge financial support from the Open Fund of Key Laboratory of Coal Resources Exploration and Comprehensive Utilization (No. KF2020-2 and No. KF2019-1) and the National Natural Science Foundation of China (Nos. 11872295, 12072256, and 41702146).

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