Prediction of multiscale lamina structure and high quality reservoirs in shale: A case study of the Lianggaoshan Formation in northeastern Sichuan Basin, China

Prediction of multiscale lamina structure and high quality reservoirs in shale: A case study of the Lianggaoshan Formation in northeastern Sichuan Basin, China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Prediction of multiscale lamina structure and high quality reservoirs in shale: A case study of the Lianggaoshan Formation in northeastern Sichuan Basin, China wang youzhi, mao cui, bai xuefeng, wang xiaodong, wang zhiguo, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3738133/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Shale has become an important reservoir and source rock for unconventional oil and gas development. The Lianggaoshan Formation in the Sichuan Basin comprises a set of shales located under a lacustrine rock layer, where alternating silt, mud, and carbonate laminae exist, demonstrating strong heterogeneity. Reservoir quality and oil-bearing potential aredetermined using shale lamina structures. Therefore, the accurate and precise identification of lamina structures plays an essential role in the successful exploration and development of shale oil. In this study, shales were classified into laminated, layered, and massive rocks based on the density of laminae. The meter-scale layers were identified using conventional logs, whereas µm-to-cm scales were identified through image logs and related slabs. The mineral composition of laminae was further revealed based on thin-section observation and quantitativeassessment of minerals usingQEMSCAN technology. High quartz and clay contents were found for the silt laminated type, high calcite and clay contents were observed for the carbonate laminated type, and varying clay and organic matter contents were found for the mud laminated type. Typical alternating band characteristics were observed in the image logs; The dark, orange, and light layers were identified as mud,, silt, and carbonate in the slabs, respectively. The relations between the types of lamina structures, nuclear magnetic resonance logs, and oil test data were also analyzed. The development of the layered type fundamentally influenced the quality of shale reservoirs, and the proportion of the layered type was strongly associated with the production capacity of shale oil. The layered rocks were better than the massive and laminated rocks in terms of reservoir quality and oil-bearing potential. The results of this study provide a basis for predicting multiscale lamina structures from log data, facilitating the exploration and development of shale oil not only in the Lianggaoshan Formation but also worldwide. shale oil lamina structure image logs NMR logs Lianggaoshan Formation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 1 INTRODUCTION With considerable progress in the exploration and development of unconventional oil and gas resources, the global focus on oil and gas has gradually shifted from conventional to unconventional reservoirs, such as shale oil and gas(Gao et al., 2023a ; Wu et al., 2022 ; Wang et al., 2022 ; Loucks et al., 2009 ; Curtis, 2002 ). While marine shale oil and gas exploration and development have been successful in the United States (US), China is anticipated to encounter challenges in exploring and developing its dominant lake shales. This is because lacustrine shales are more heterogeneous than marine shales due to variable hydrodynamic conditions, terrigenous clastic input, and evaporation (Liu et al., 2019 ; Moretti and Ronchi, 2011 ). Thus, complex mineral compositions and abundant lamina structures have developed in lacustrine shales (Hammes et al., 2011 ) .The lamina structure is the basic deposition unit and one of the distinctive sedimentary structures in lacustrine shales (Xiugang et al., 2019 ). Because of its complex heterogeneity, integrated studies between geology and petrophysics are required to reveal and identify lamina structures. Lamina structures, as distinctive structural features, the lamina structures exert an considerably influence on the comprehensive evaluation of shale oil, including quality of hydrocarbon source rock quality, reservoir characteristics, and engineering attributes (Wang et al., 2019a ; Wu et al., 2019 ). Studies are currently focused on the following three areas of lamina structure (Kelai et al., 2020 ; Wang et al., 2019b ; Yawar and Schieber, 2017 ) : the type of laminae, development mechanisms, and impacts on shale quality. The types of laminae were divided into the following four main categories based on the mineral composition and organic matter (OM) content: feldspar-quartz, carbonate, organic, and clay (Zhensheng et al., 2020 ). Because grain (particle) size also plays a crucial role in the classification of laminae, they were classified into the following two types: mud and silt (Pang et al., 2022 ). Laminae formation of laminae is often studied based on basin evolution (Zhang et al., 2019 ) and the Milankovitch cycle (Herbert and Fischer, 1986 ). In general, the more developed the laminae are, the better the physical properties of the shales are. The OM contentin the mud laminae is often higher than that in the silt laminae, with the shale oil occurrence and mobility being controlled by the laminae (Davies et al., 1991 ; Xu et al., 2021a ; Bai et al., 2022 ). This study primarily focused on examining the combined of the patterns and characteristics of laminae, an aspect that has often been overlooked in the accurate identification of these structural features. This is because accurate identification of the structure of laminae is essential for evaluating the shale oil potential in this the studied region. The identification and development of multiscale (µm-to-cm scale) lamina structures in shale has resulted in the need for high-resolution data, processing, and capabilities (Wang et al., 2021a ). Conventional log data (e.g., resistivity tool, sound log, gamma log, and density log) and outcrops can be used to identify cm-scale lamina structures (Shiyue et al., 2017 ). Core observation can demonstrate mm-to-cm-scale laminae structures (Li et al., 2020 ). Lamina structures on the mm-to-µm scale can be obtained using thin-section observation and scanning electron microscopy(SEM) (Xin et al., 2022 ). However, thin sections and SEM are costly and limited because it is almost impossible to identify the structures of a whole well based on them (Xu et al., 2021b ; Lei et al., 2015 ). Because of technological advancements, a series of special logs have shown great advantages for continuous evaluation, such as nuclear magnetic resonance (NMR) logs (Tan et al., 2015 ; Castro and Lupinacci, 2022 ), image logs (He et al., 2016 ; Waters et al., 2006 ; Li et al., 2022 ) and lithology scanning logs (Abel et al., 2012 ; Delavar, 2022 ). In particular, mm-scale changes in the lamina structure can be measured via image logs (vertical resolutions of up to 6 mm) (Mcginnis et al., 2017 ). As a special technique that originated in the US, image logging includes electrical logs and sonic image logs, among which electrical ones play an important role in borehole wall imaging. In 1991, a new generation of full-bore microimaging (FMI) was developed by Schlumberger; further, Halliburton’s electrical MI (EMI) and X-tended Range Micro Imager (XRMI) log instruments were commercially used. In 2012, an electrical resistance MI (ERMI) log instrument was invented by the China National Offshore Oil Corporation, which provided thus providing a higher resolution. Subsequently, the latest generation instrument of Quanta Geo image logs, also referred to as “the microscope of the formation” in the subsurface, has been made commercially available at vertical and horizontal resolutions of 6 and 3 mm, respectively (Lai et al., 2018 ). In addition, sonic image logs are mainly used to evaluate rock engineering features, rock fractures, and geological structures (Lai et al., 2018 ). Thus, image logs can be used to identify the structures of laminae in shale reservoirs, complement conventional logs, and fill gaps in the identification of structures and characteristics of shale laminae. This study combined core tests, thin sections, conventional logs, NMR logs, and image logs to reveal the multiscale structures of the shale laminae of the Lianggaoshan Formation in the northeastern Sichuan Basin, south China and provided a new method to identify macroscopic-to-microscopic scale structures of laminae using log data. The findings of this study provide new insights into shale laminae research and provide a basis for the “sweet spot” predictions of shale oil. 2 GEOLOGICAL SETTING Sichuan Basin is located in south China (Fig. 1 A) and a crucial hydrocarbon-bearing foreland basin that can be divided into five first-level tectonic units: low and steep fold belt in the south Sichuan, high and steep fold belt in the east Sichuan, low and gentle belt in the central Sichuan uplift, low and steep fold belt in the west Sichuan, and Micangshan Daba front thrust belt (Fig. 1 B) (Chen et al., 2014). The Yilong-Pingchang area is located in the northeastern Sichuan Basin, whose tectonic direction is mainly north-west, with extrusive and torsional reverse faults developing and with the structural pattern being “a depression between two uplift” in the plane (Fig. 1 C) (Xu-Sheng et al., 2016 ). Jurassic Lianggaoshan Formation is underlined by Ziliujing Formation overlies Shaximiao Formation. According to the lithology, combined log features, and sedimentary cycles, Lianggaoshan Formation in the northeastern Sichuan Basin was divided into Liangshang and Liangxia members from top to bottom (Fig. 1 D) (Yang et al., 2015 ). Jurassic Lianggaoshan Formation in the northeastern Sichuan Basin is mainly composed of fine-grained deposits located under a layer of shallow shore to semi-deep lakes. Liangshang was classified into Liangshang 3, Liangshang 2, and Liangshang 1. Liangshang 3 is mainly composed of shale and argillaceous siltstone in the upper section and sandy mudstone and argillaceous siltstone in the lower section with the increased exogenous source material. While Liangshang 2 includes shale and sandy mudstone, Liangshang 1 is mainly composed of shale rich in OM in the lower section and sandy mudstone and siltstone in the upper section. Liangxia is not similar to Liangshang although the section changes from argillaceous siltstone and sandy mudstone to limestone and sandy mudstone with the increased salinity of the depositional environment (He et al., 2022) . For the last twenty years, the exploration of conventional oil has not led to the discovery of large oil reservoirs, which has in turn limited the exploration of unconventional oil resources in Sichuan Basin (He et al., 2022). In 2020, referring to the exploration practices of Daqing Gulong Shale Oil(Gong et al., 2021 ), based on the exploration principle of “grand strategy, great layout, and large scale discovery,” shales of Lianggaoshan Formation were systematically studied to evaluate their oil potential. For the shale reservoirs of Lianggaoshan Formation, the risk exploration well (A1) was deployed in the northeastern Sichuan Basin, thus officially initiating the exploration of shale oil in Sichuan Basin. 3 DATA AND METHODS Core plug samples of shale were obtained from well A1 in Lianggaoshan Formation in Sichuan Basin (Fig. 2 ). To further identify the microscopic features of the shale reservoirs, some samples were selected to conduct experiments to obtain geological properties (e.g., mineral composition, porosity, permeability, oil content, and pore types) based on thin section, X-ray diffraction (XRD), laser scanning confocal microscopy (LSCM), QEMSCAN, and SEM analyses. The mineralogical composition was obtained via XRD and QEMSCAN, pore types were acquired via SEM, oil content information was revealed via LSCM, and laminae structure characteristics were detected via thin sections. The samples were made to a thickness of 30 µm and then observed via microscopy under plane- and cross-polarized light views. Thin sections were stained with Alizarin Red S to identify carbonate minerals via microscopy. The microstructural characteristics (laminar structures, pores, and microfractures) of the shale samples were effectively identified by adding blue or red epoxy to the core plugs before thin-section preparation. The shale samples were analyzed by using a FEI Quanta 250 field emission-scanning electron microscope at 25°C and relative humidity of 60% according to the GB/T18395-2001 standard for the SEM test (Zhao et al., 2022 ). These samples were analyzed for mineral composition by using a Rigaku D/max-2600 XRD according to the Chinese oil and gas industry standard (SY/T 5163 − 2010) (Zhang et al., 2019 ), and QEMSCAN according to the Chinese oil and gas industry standard (SY/T 5162 − 2014) (Li et al., 2022 ). These samples were analyzed for their oil content by using Leica SP5 according to the Chinese oil and gas standard (Q/SY DQ6007-2022) (Gao et al., 2023b ). Since well A1 constitutes the primary risk-exploration well, the log data were complete. In addition to the conventional logs, acoustic logs (AC), compensated neutron porosity (CNL), bulk density (DEN), gamma-ray (GR), spontaneous potential (SP), and caliper (CAL) were included as the tests for well A1. Some unconventional log data of combinable magnetic resonance (CMR-Magni PHI) (CMR-NG) and high-resolution borehole micro-resistivity image logs (FMI-HD) (Quanta Geo) were also used. Logs of CMR-Magni PHI (CMR-NG) have always played a crucial role in the exploration and development of unconventional oil and gas resources, where pore structure and oil-bearing information can be effectively obtained for a series of tight reservoirs, such as shale (Wang et al., 2021b ). The high-resolution borehole micro-resistivity image logs (Quanta Geo) are critical for realizing sedimentary fabric interpretation for shale reservoirs, which provides highly accurate sedimentary and structural information. In the FMI images, the dark shaded areas indicate low-resistance conductive clays and shale-like rocks, whereas the light-shaded areas represent high-resistance, dense, and OM (or hydrocarbon occurrence). The dominate minerals were quartz, clay minerals, and OM in Lianggaoshan Formation in Sichuan Basin. Therefore, the color of the FMI images was used to identify the mineral types and structures of laminae. Because the sedimentary fabric features of the shale reservoirs could be effectively identified, and the pore distribution characteristics and mobility properties in different sedimentary fabrics of the shale reservoirs could be accurately obtained, the lamina structure, reservoir quality, and “sweet spot” were successfully evaluated and predicted based on the logging methods. 4 RESULTS AND DISCUSSION 4.1 Reservoir characteristics of The Lianggaoshan Formation The mineralogical compositions of the shale reservoirs in the Lianggaoshan Formation in the Sichuan Basin were obtained using XRD analysis (Fig. 3 ). Siliceous (quartz and feldspar) and clay minerals were dominant in the samples, with an average of 57.13 wt% (38.9–76.3 wt%) and 34.185 wt% (11.3–52 wt%), respectively, thus showing the siliceous-rich and clay-rich characteristics of the shale reservoirs in Lianggaoshan Formation. The calcareous mineral content (calcite and dolomite) ranged from 1.5 wt% to 42.2 wt% with an average of 12.1 wt%. The clay mineral results indicated that the shale reservoirs were predominantly illite (Fig. 3 C) with an average of 49.5 wt% (34–70 wt%), while kaolinite, chlorite, and illite-montmorillonite mixed-layer on average accounted for 12.5 wt% (5–31 wt%), 20.33 wt% (10–28 wt%), and 17.66 wt% (9–27 wt%), respectively. The mineralogical characteristics indicated that the shale reservoirs of the Lianggaoshan Formation were brittle. The lithologies of the Lianggaoshan Formation were classified based on the mineral compositions (He et al., 2022). The mineral triangle diagram and core observation results demonstrated that the northeastern Lianggaoshan Formation in the Sichuan Basin primarily included comprised siliceous shale, argillaceous shale, siltstone, mudstone, and minimal calcareous shale. Interbedded siltstones and shales were typically in a vertical contact, and their complex and variable lithologies were attributed to the hydrodynamic conditions, provenance, and depositional environments (Wang et al., 2020 ). The siltstones were mainly comprised feldspar and quartz, the colors of which were usually gray or grayish-white. Mud and quartz layers frequently alternated on the µm scale. The mudstones were primarily massive and, with virtually no internal structure, and comprised quartz, and clay, whose colors were usually gray-green and dark purpleShales Shales were mainly characterized by a laminated structure, where silicone, clay, and minimal bioclastic laminae alternated frequently, and the colors of the shale were usually gray-black and gray. When the mechanically deposited hydrodynamic conditions changed, and provenance increased, the rock demonstrated a frequently layered structure with siltstone and shale lithology. In contrast, the rock appeared as a massive structure, and the lithology was mudstone. Developed by Loucks et al. (2012, 2014), the classification scheme of the shale pore types includes the following three categories: (1) OM pores, (2) intraparticle pores (pores within a mineral grain) (IntraP), and (3) interparticle pores (pores between mineral grains; InterP). Based on this classification scheme, this study analyzed and classified the pore types of the shale samples in the Lianggaoshan Formation using SEM. The results showed that the presence of OM pores, mineral IntraP, particle InterP, and microfractures in the shale samples. InterP were a critically important pore type in the study area, was mainly found between quartz and clay (Fig. 5 A) and existed between stiff mineral grains and clay minerals. Spongy OM pores (Fig. 5 C), which were observed using SEM in the shale samples were considered to be related to hydrocarbon cracking that occurred during the thermal maturation of OM (Ko et al., 2018 ). IntraP, which was formed in the shale, including quartz IntraP (Fig. 5 D), pyrite IntraP (Fig. 5 B), and clay (mainly illite IntraP) (Fig. 5 E), also improved the reservoir quality of the shale. The presence of numerous microfractures (Fig. 5 F) increased the permeability of the rocks. Despite their considerable importance in shale oil fracturing, etermining the presence and developmental status of natural fractures in the subsurface usingSEM is challenging. 4.2 Multiscale laminae structure characteristics of the shale reservoir The lamina structure, the most basic unit of sedimentation, is one of the characteristic sedimentary structures in sediments or sedimentary rocks that can be identified through observation, with a main body thickness of ˂1 1 cm (Pang et al., 2022 ). The multiscale structure of laminae, which is a unique characteristic of shale, is significantly related to the hydrocarbon source, reservoir, and engineering quality and is of considerable importance for the exploration and development of shale oil. The cm-to-mm-scale lamina structure can be identified via core observation, and the mm- to-µm-scale lamina structure must be identified via thin sections. Based on the core observations, laminated rocks with rhythmic bedding were common in the Lianggaoshan Formation. Rocks with mud, silt, and carbonate alternated vertically. The rock structures were divided into massive, layered, and laminated cores. The massive structures were often formed in a weakly hydrodynamic sedimentary environment (low-energy) with a fine grain size (the overall lithology was massive mudstone) and no obvious bedding (no layer or layer spacing of > 0.1 m). The layered structures (the thickness of an individual layer was 0.01–0.1 m) and the laminated structures (the thickness of an individual layer was < 0.01 m) were formed in a dynamic sedimentary environment with seasonal climatic and sediment variations and rapid mineral changes. The silt, mud, and carbonate layers were identified based on the core observations of the Lianggaoshan Formation. Based on the thin-section observations, mm-to-µm-scale lamina structures and mineral superimposition patterns were effectively identified. The following four lamina types (Fig. 6 ) were revealed in the rocks of the Lianggaoshan Formation in the northeastern Sichuan Basin: (1) silt, (2) mud, (3) organic and (4) carbonate. Based on the QEMSCAN technology, this study identified the mineral composition of the lamina structures (Li et al., 2022 ). The silt laminae contained quartz, feldspar, clay, and a minor amount of dolomite (higher quartz and clay contents than other minerals), and the clay minerals were dominated by illite (Fig. 7 ). Interlaminar cracks were was observed in the silt laminae when the quartz and feldspar contents changed within a specific range (Fig. 6 B and D). The higher the feldspar and quartz contents, the better the brittleness of the rock and the more likely the formation of network fracture The mud laminae contained clay, OM, and other minerals, and the clay minerals were dominated by illite (Fig. 7 ). The mud laminae were commonly combined with silt and carbonate laminae (Figs. 6 A and E). Rocks with mud laminae showed poor pore structures and low brittleness; the reservoir quality was generally poor and unfavorable for reservoir modification. The organic laminae were often observed in a mixture of mud and silt laminae (Figs. 6 C and F), and the thickness of a single organic laminae was very thin. Typically, the OM shape was striped and formed by extrusion due to compaction (Figs. 6 F). The carbonate lamina primarily comprised calcite and clay, and OM was stripped and wavy with clay minerals in a superimposed distribution (Fig. 7 ). Although carbonate minerals showed good resistance to compaction, the pores were cemented with increasing carbonate mineral contents. 4.3 Slab and well log characteristics of the shale reservoir Lamina structures are easily identified based on the core and thin-section observations, which have limitations (such as high cost and the risk of destruction of cores); therefore, log data are used to successfully identify lamina structures. Because unconventional reservoirs are extremely heterogeneous in the vertical direction, reservoir characteristics are not truly reflected byconventional log data. Thus, higher-resolution image log data were used to reveal the structures of the shale reservoirs. The slab image was obtained from image logs via a computer and used for comparison with borehole images, slabbed cores, or high-resolution CT scans. The primary goal was to obtain information contained within the cylinders around a borehole and project this information onto any cross section of a borehole (the slab view; Fig. 8 ) (Kivi et al., 2018 ). Much critical information about the rocks was reflected in the slab images, such as tectonics, lithology, and minerals. Slab images are particularly helpful in observing complex geological events. Dynamic, static, slab, and core slab view images are shown in Figs. 9 and 10 . These results indiate that incomplete sine lines of the dynamic and static images occurred, as shown in Figs. 9 and 10 , respectively. Alternating light and dark laminated rocks are observed on the dynamic slabs of the image logs shown in Figs. 9 and 10 . The dynamic slab image changed from bright to dark colors from top to bottom in the blue box line, as shown in Fig. 9 . This phenomenon was typical of the massive sedimentary characteristics on the image logs because resistivity increased with increasing carbonate mineral content. In addition, the colors of this image were affected by the OM content, and the resistivity increased with increasing OM content. Millimeter-level light and dark laminae were clearly observed in the slab image. However, this correspondence was occasionally poor between the slab image and the core slab view, mainly controlled by the measurement angle and image resolution (Masoudi et al., 2017 ). The alternating mm-level light and dark laminae were identified in the slab images shown in Figs. 9 and 10 . This phenomenon was typical of the layered sedimentary characteristics. With the alternate development of silt and mud layers in the core, bright and dark layers appeared in the slab images. 4.4 Identification of lamina structure and prediction of high-quality shale reservoir 4.4.1 Identification of lamina structure of shale reservoir The colors of the image logs were influenced by the mineral composition of the rock (Fig. 11 ), such as laminated rocks characterized by dark, orange-yellow, and bright colors, layered rocks recognized by alternating dark and bright bands, and massive rocks identified as bright or dark. The most pronounced characteristics of the shale reservoirs are that they are highly heterogeneous and primarily influenced by hydrodynamic conditions, terrigenous clastic inputs, and climate variations (Bohacs et al., 2000 ). The structure response characteristics of the image logs were used to identify the multiscale lamina structure in the shale reservoir of well A1. The results showed that the layered and massive types were the most developed, followed by the laminated types in Lianggaoshan Formation (Figs. 11 and 12 ). The multiple lamina structures (binary, ternary, and multiple laminae) were recognized from the color changes of the image during the identification process. The binary laminated type was dominated by silt and mud and exhibited high quartz and clay concentrations and low carbonate contents, as evidenced by the QEMSCAN data shown in Fig. 7 . The core observations showed that the gray–white silt (the main mineral was quartz) was intermittently and thinly layered with dark-gray clay laminae (Fig. 4 ). Typical alternating band characteristics were observed in the image logs; the dark layer was identified as a mud layer (the main mineral was illite in Fig. 7 ), and the orange layer was recognized as a silt layer, as shown in Fig. 11 . Another binary laminated type was dominated by carbonate and mud and showed calcite and clay and low quartz content, as evidenced by the QEMSCAN data shown in Fig. 7 . The light, and dark layers were identified as carbonate and mud layers in the slab image in, respectively (Fig. 12 ). The ternary laminae contents (mud, carbonate, and OM) and the multiple laminae contents (mud, silt, carbonate, and OM) showed high illite, moderate quartz and calcite, and low OM contents. The appearance of the silt layer proved the presence of terrestrial input, destroying the deep-lake surface, and the complexity of the laminae was generally dominated by the season. moreover, the organic and carbonate layers were similar and showed bright colors on the slab image; however, the GR value was generally high for the organic layers (the OM content is higher with the increased clay minerals as clay mineral content can be reflected by GR). The results showed that in the Lianggaoshan Formation in the northeastern Sichuan Basin of south China, ternary layers that are rich in OM and binary layers typically dominated the structure types of laminae, while carbonate layers were typically less developed. 4.4.2 Relationship between laminae structure and quality of shale reservoir The reservoir-quality parameters of porosity and permeability and the geochemical parameters of total organic carbon (TOC) and free hydrocarbons (S1) play an essential role in the shale oil accumulation (Hou et al., 2017 ). The Lianggaoshan Formation in the northeastern Sichuan Basin showed the following three main reservoirs: sweet spots in Liangshang 3, Liangshang 2, and Liangshang 1. The reservoir-quality and geochemical parameters are presented shown in Fig. 12 , with the samples having porosity less than 7.0% and permeability less than 0.03 mD. It was observed that a lower reservoir quality corresponded to the lower TOC and S1 values in the massive rocks, while a higher reservoir quality aligned with the higher TOC and S1 values in the layered rocks. Therefore, the layered rocks showed greater potential for exploration. Shale reservoir quality also has some differences in microscopic pore structure. First, layered rock reservoirs are of the best quality, with pore types dominated by InterP, IntraP, and OM pores; pore diameters ranging from 2 nm to 10 µm; and pore sizes greater than 400 nm, more than 20 percent. Laminated rock reservoirs are of the secondary quality, with pore types dominated by IntraP; pore diameters ranging from 2 nm to 2 µm; and pore sizes greater than 400 nm, more than 15 percent. Massive rock reservoirs are of the poor quality, with pore types dominated by IntraP; pore diameters ranging from 2 nm to 1 µm; and pore sizes greater than 400 nm, more than 8 percent (Fig. 13 ). The state characteristics of oil content and occurrence are also key parameters for the exploration and development of shale oil (Li et al., 2015 ; Wang et al., 2022 ). Laser scanning confocal microscopy combined with fluorescence detection technology and layered scanning was initially used for the analysis of the light and heavy components of shale oil (Gao et al., 2023b ). The findings showed that the layered shale showed rich oil content, whereas the massive rock showed poor oil content (Fig. 13 ). However, among the layered rocks, the siliceous rocks showed a higher light oil content (red represents light oil, while blue represents heavy oil) than the calcareous rocks. These findings are attributed to the fact that the oil absorption capacity of the different minerals was influenced by wettability; thus, the calcareous minerals exhibited a stronger oil-wet property than the siliceous minerals, and the layered calcareous shale was more strongly adsorbed. For the laminated rock, some band-shaped light oil also remained in the microfractures and interlaminar cracks. Therefore, the oil content of the layered rocks was better than that of the massive and laminated rocks. In addition, NMR logs were analyzed in detail to obtain information about reservoir quality, such as pore size distribution, fluid state, and type (Wang et al., 2021b ). For NMR logs, T2 relaxation times were associated with the pore size; the long relaxation times corresponded to the macropore sizes, and the short relaxation times represented the micropore sizes. Overall, the high reservoir quality exhibited a relatively long relaxation time. In Fig. 15 , in accordance with according to the division result of the rock fabric obtained using the image logs, the NMR logs were used to compare the three structure types of laminae. Thus, the pore distributions of the lamina structures were determined. Significantly different characteristics were observed for the three types of lamina structure. The layered rocks showed high total and effective porosity and oil saturation, whereas the massive rocks exhibited low porosity and oil saturation. The high T2 amplitudes and long distributions were observed in the layered rocks. The massive rocks showed low T2 amplitudes. The T2 distributions were associated with the pore types. The layered rocks were characterized by a bimodal distribution; the left peak represented IntraP, and the right peak was associated with InterP. The massive rocks were characterized by poor reservoir quality and pore structure. Therefore, the layered rocks presented the best potential for exploration and development in the Lianggaoshan Formation. 4.4.3 The high-quality reservoir and prediction of sweet spots As shown in Fig. 14 , the shale with different lamina structures presented different porosities and permeabilities. Most of the shale samples contained porosities of < 6 and permeability of < 0.02 mD, and the layered shales showed better reservoir quality than the massive and laminated shales. In addition, the laminated and layered rocks were related to shale and siltstone, and bedding fractures tended to occur along the bedding planes. Stylolite and irregular high-angle microfractures were found in the laminated and layered rocks, which are the channels for oil and gas transport and can assist in oil and gas drainage. The massive rocks were related to mudstones and limestones, which showed poor geochemical parameters and reservoir quality (Fig. 14 ). InterP and IntraP could not be found in the most massive rocks due to compaction and cementation. Therefore, the laminated and layered rocks proved to be the more favorable exploration objectives for shale oil than did massive rocks. The oil test data provided important verification information for the reservoir prediction process. Well A1 in the Lianggaoshan Formation (2856–3100.7 m) adopted a vertical well-staged fracturing process. The 10 mm choke was used for trial production, and the daily totals of 112.8 m 3 and 11.45 × 104 m 3 for oil and gas, respectively, were obtained (total production: 3895 m3 for oil and 472 × 104 m 3 for gas), with a flowback ratio of 11.01%. Based on the single-layer productivity analysis, the physical properties were found to influence the level of production in the laminated type. As shown in Fig. 15 , the total yield of the 19th century was much higher than that of the 18th century. The layered type, whose production has been considerably improved, accounted for a high proportion in the 19th century, and the highly effective and total porosity was revealed by the NMR logs and the images. Therefore, the development of the layered-type fundamentally influences the quality of the shale reservoirs. The proportion of the layered type was highly correlated with the production capacity of shale oil, and the layered rocks were the more promising exploration targets for shale oil in the Lianggaoshan Formation in the northeastern Sichuan Basin, south China. 5 CONCLUSION The lithological characteristics, lamina types, oil content, and pore types of shales in Lianggaoshan Formation of the northeastern Sichuan Basin, south China were characterized based on the core, mineralogy, thin-section, test, log, and drilling geologic data. The following conclusions were drawn: The shales of Lianggaoshan Formation were primarily composed of quartz, clay, feldspar, and low amounts of calcite and dolomite. In addition, pyrite and siderite were occasionally detected. The core observation results showed that the structure types were classified as the massive, layered, and laminated structures based on the density and thickness of the individual laminae. The laminated structures were further divided into silt (mainly composed of quartz and clay), carbonate (mainly composed of calcite and clay), and mud (mainly composed of clay and organic). The reservoirs were of good quality (the best physical and oil content) in the layered type owing to the presence of silt bands, whereas the reservoirs were of poor quality in the massive type due to compaction and cementation. The multi-scale lamina structures were effectively identified via the image logs and slab images. In general, mm-scale changes could only be detected via the slab image; the silt laminae appeared orange on the slabs, the carbonate lamina appeared light, and the mud lamina appeared dark. For some wells without core data in the study area, the types of sedimentary fabrics could validly be evaluated based on the methods presented in this study. In addition, the method developed in this study can be generalized to other basins for the sedimentary-fabric classification and identification. Declarations ACKNOWLEDGEMENTS This study was partly funded by the Engineering and Technology Major Project of Heilongjiang Province (SC2020ZX05A0023). Conflict of interest: We declare that we have no financial or personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled, " Prediction of multiscale lamina structure and reservoir quality in shale reservoir: A case study of Lianggaoshan Formation in Northeastern Sichuan Basin, China ". Ethical approval: Not required DATA AVAILABILITY STATEMENT The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. All data generated or analysed during this study are included in this published article AUTHOR CONTRIBUTIONS Youzhi W ang designed the project and wrote the main manuscript. Xuefeng Bai and Cui Mao help to draw the figures and to draft the manuscript. X iandong Wang defined the statement of problem. Zhiguo Wang and Cui Mao help to discuss the problems and revise the manuscript. Ce An help to discuss the main idea and help to draft the manuscript. Youzhi W ang help to calculate the data and draw the figures. Ce An help to revise the figures. All authors reviewed the manuscript. References Abel, M., Lorenzatti, A., Ros, L., Da Silva, O.P., Bernardes, A., Goldberg, K., Scherer, C., 2012. Lithologic logs in the tablet through ontology-based facies description, AAPG Annual Convention and Exhibition. 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Zhensheng, S., Dazhong, D., Hongyan, W., Shasha, S., Jin, W.U., 2020. Reservoir characteristics and genetic mechanisms of gas-bearing shales with different laminae and laminae combinations: A case study of Member 1 of the Lower Silurian Longmaxi shale in Sichuan Basin, SW China. Petroleum Explor. Dev. 47 (4) , 888-900. Additional Declarations No competing interests reported. Supplementary Files Data.pptx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3738133","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":264637856,"identity":"3f895b46-f7af-4342-b801-5b0f9496d4d8","order_by":0,"name":"wang youzhi","email":"","orcid":"","institution":"Chengdu University of Technology","correspondingAuthor":false,"prefix":"","firstName":"wang","middleName":"","lastName":"youzhi","suffix":""},{"id":264637857,"identity":"1f634260-d828-45e6-b0c2-50ec7c6c7f6c","order_by":1,"name":"mao cui","email":"","orcid":"","institution":"Northeast Petroleum University","correspondingAuthor":false,"prefix":"","firstName":"mao","middleName":"","lastName":"cui","suffix":""},{"id":264637858,"identity":"91a01bf9-40ee-4686-9207-efab5ae463e1","order_by":2,"name":"bai xuefeng","email":"","orcid":"","institution":"Exploration and Development Research Institute of Daqing Oilfield Co Ltd","correspondingAuthor":false,"prefix":"","firstName":"bai","middleName":"","lastName":"xuefeng","suffix":""},{"id":264637859,"identity":"d2788257-615b-4d2a-9b43-7d62c226f916","order_by":3,"name":"wang xiaodong","email":"","orcid":"","institution":"Exploration and Development Research Institute of Daqing Oilfield Co Ltd","correspondingAuthor":false,"prefix":"","firstName":"wang","middleName":"","lastName":"xiaodong","suffix":""},{"id":264637860,"identity":"4ef07023-db87-4462-a01d-d0327da7389a","order_by":4,"name":"wang zhiguo","email":"","orcid":"","institution":"Exploration and Development Research Institute of Daqing Oilfield Company Ltd","correspondingAuthor":false,"prefix":"","firstName":"wang","middleName":"","lastName":"zhiguo","suffix":""},{"id":264637861,"identity":"a1b48de5-4679-4786-bae3-f4607b2fb5f7","order_by":5,"name":"An Ce","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYLCCDxCKjSGBWB2MMxgYJEjTwswD00IU4J/d/PCx7Q6bOvP25mcPHu6wY+Bv705g+LkDtxaJO8eMjXPPpEnInDlmbpB4JplB4szZDYy9Z/BYcyOHTTq37bCEhEQOm0RiGzODgUTuBmbGNtw65G/ksP+2bPsP01JPWIsB0BagggMwLYcJazG8kWYs2XsmWXIGzzEzoJbjPCC/HOzFo0XuRvLDDz932PFLsDc/k/zZVi3H39678cFPPFrAgLEBweYBEQcIaEDVMgpGwSgYBaMAAwAAS4dK2q6AVgMAAAAASUVORK5CYII=","orcid":"","institution":"Exploration and Development Research Institute of Daqing Oilfield Company Ltd","correspondingAuthor":true,"prefix":"","firstName":"An","middleName":"","lastName":"Ce","suffix":""}],"badges":[],"createdAt":"2023-12-11 10:29:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3738133/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3738133/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49136565,"identity":"f8969b76-972e-4b37-a879-15d1bf26e9d9","added_by":"auto","created_at":"2024-01-03 17:22:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":246159,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of the Yilong-Pingchang block and the distribution of A1 well sites in the northeastern Sichuan Basin, South China. (Modified from references He et al., 2022)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3738133/v1/a356b35fbc0bf521b8e11d3e.png"},{"id":49136566,"identity":"b6f98f6c-542a-40bb-9654-e628242f063d","added_by":"auto","created_at":"2024-01-03 17:22:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":816475,"visible":true,"origin":"","legend":"\u003cp\u003eCore photos show the lithology and multi-scale laminae structure in the Lianggaoshan Formation. (A) and (B) Massive mudstone. (C) Shale. (D) Mudstone,. (E) Siltstone.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3738133/v1/505fd0356ff8c317b4d9403c.png"},{"id":49137512,"identity":"e7750a07-9f4a-4808-a889-b0bcd5ee1d21","added_by":"auto","created_at":"2024-01-03 17:30:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":79593,"visible":true,"origin":"","legend":"\u003cp\u003eMineralogical composition of shale samples and triangle diagram of mineral composition. I = illite; I/S = illite-montmorillonite mixed-layer; C = chlorite; K = kaolinite.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3738133/v1/c5d09cadaa4bebae3da67b0a.png"},{"id":49137802,"identity":"f92a4d26-c3ca-40f1-bed3-b69a0e53e0e9","added_by":"auto","created_at":"2024-01-03 17:38:36","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":860243,"visible":true,"origin":"","legend":"\u003cp\u003eCore photos and thin sections show the lithology and multi-scale structure. (A) Shale, silicone layer and clay layer, 2908.66m - 2908.75m, A1well. (B) Siltstone, mud layer, 2864.32m-2864.40m, A1well. (C) Shale, silicone layer, 3001.38 m, A1well. (D) Mudstone, massive, 3405m, DY1well. (E) Shale, clay layer and silicone layer, 3001.38 m, A1well. (F) Siltstone, clay layer, 2867m, A1well.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3738133/v1/35d40a2a657735f020f0d437.png"},{"id":49137513,"identity":"ba629b29-8c6d-47ab-be58-584ad07f8f45","added_by":"auto","created_at":"2024-01-03 17:30:36","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":468933,"visible":true,"origin":"","legend":"\u003cp\u003eThe SEM photos show the pore system of the Lianggaoshan Formation in the northeastern Sichuan Basin. (A) InterP, 2904.02m, A1well. (B) Pyrite IntraP, 3007.08m, A1well. (C) OM pores, 3007.05m, A1well. (D) Quartz IntraP, 3015.47m, A1well. (E) Illite IntraP, 3001.38 m, A1well. (F) Microfractures, 3001.45m, A1well.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3738133/v1/1caa6452c8046443a4bc6bbe.png"},{"id":49137516,"identity":"fbe23bd1-d171-4472-9ab2-a6a46414ee5f","added_by":"auto","created_at":"2024-01-03 17:30:36","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":565358,"visible":true,"origin":"","legend":"\u003cp\u003eThe types of lamina composition in the Lianggaoshan Formation in the northeastern Sichuan Basin. S: silt laminae, M: mud laminae, C: carbonate laminae, O: organic laminae, I: inter-laminar crack.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-3738133/v1/cffb80498a4aeb0059ccfff1.png"},{"id":49136573,"identity":"2e294e71-fa85-4c74-b08b-896ccce1eea0","added_by":"auto","created_at":"2024-01-03 17:22:36","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":443675,"visible":true,"origin":"","legend":"\u003cp\u003eThe mineralogy composition of laminae types in the Lianggaoshan Formation in the northeastern Sichuan Basin.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-3738133/v1/d8f1225bd6e4048014ace9b5.png"},{"id":49137514,"identity":"9518faf1-9dd8-4625-ae2b-9913feada2c8","added_by":"auto","created_at":"2024-01-03 17:30:36","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":94238,"visible":true,"origin":"","legend":"\u003cp\u003eThe process of slab image obtaining. (Modified from references Wang et al., 2022)\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-3738133/v1/0fe026d617e27bd69c9226a2.png"},{"id":49136570,"identity":"4bf74e97-4b9f-44b4-9048-8c848d3a58cf","added_by":"auto","created_at":"2024-01-03 17:22:36","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":532896,"visible":true,"origin":"","legend":"\u003cp\u003eImage and slab responses and related cores of rocks.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-3738133/v1/a1203563776ba9a25c527763.png"},{"id":49136567,"identity":"162e71fa-b547-4c52-98ec-e96d8e288a6a","added_by":"auto","created_at":"2024-01-03 17:22:36","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":421431,"visible":true,"origin":"","legend":"\u003cp\u003eImage and slab responses and related cores of rocks.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-3738133/v1/7849810fa96e71cc2ef61c31.png"},{"id":49136576,"identity":"e857f709-8889-4435-9bb5-6642106aa878","added_by":"auto","created_at":"2024-01-03 17:22:36","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":434143,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification and classification of laminae structure of the Lianggaoshan Formation in A1 well.\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-3738133/v1/8b11136b761a6bef51c27b87.png"},{"id":49137801,"identity":"3e61656b-97c9-4111-96c1-fdfc26b5d205","added_by":"auto","created_at":"2024-01-03 17:38:36","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":499529,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of laminae identification at various scales in the Lianggaoshan Formation in A1 well.\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-3738133/v1/8df801e865655c88713cf1a6.png"},{"id":49138266,"identity":"86973269-2e8a-41af-bf7b-daa24e10922b","added_by":"auto","created_at":"2024-01-03 17:46:36","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":50970,"visible":true,"origin":"","legend":"\u003cp\u003eFig. 12. Cross-plots of permeability and porosity and TOC and S\u003csub\u003e1\u003c/sub\u003e for multi-scale lamina structure in the Lianggaoshan Formation in A1 well.\u003c/p\u003e","description":"","filename":"13.png","url":"https://assets-eu.researchsquare.com/files/rs-3738133/v1/d13e8bb67d6f68d9a78d3082.png"},{"id":49136575,"identity":"9302b321-e454-4c40-baa0-d06b1ce6a9fc","added_by":"auto","created_at":"2024-01-03 17:22:36","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":302800,"visible":true,"origin":"","legend":"\u003cp\u003eFig. 13. The variability of shale reservoir quality.\u003c/p\u003e","description":"","filename":"14.png","url":"https://assets-eu.researchsquare.com/files/rs-3738133/v1/98eb60db716e4998a97448e0.png"},{"id":49136578,"identity":"939a7ff2-84cf-4b40-b52d-e7da297e19e7","added_by":"auto","created_at":"2024-01-03 17:22:36","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":444847,"visible":true,"origin":"","legend":"\u003cp\u003eFig. 14. Lamina structure type and its relationship with laser scanning confocal microscopy in the Lianggaoshan Formation in A1 well. (A) Laminate siliceous shale, 3005.6m, A1well. (B) Layer siliceous shale, 3002.9m, A1well. (C) Massive calcareous shale, 2904.67m, A1well. (D) Laminate siliceous shale, 2902.93m, A1well. (E) Layer calcareous shale, 3001.38 m, A1well. (F) Massive calcareous shale, 2892.68m, A1well.\u003c/p\u003e","description":"","filename":"15.png","url":"https://assets-eu.researchsquare.com/files/rs-3738133/v1/71cdf5fb2f367b573ef56a61.png"},{"id":49137518,"identity":"6cb29c2d-a040-46f9-a560-fd6da72aa01d","added_by":"auto","created_at":"2024-01-03 17:30:36","extension":"png","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":394227,"visible":true,"origin":"","legend":"\u003cp\u003eFig. 15. Well log expression of the sweet spot in the Liaogaoshan Formation in A1 well.\u003c/p\u003e","description":"","filename":"16.png","url":"https://assets-eu.researchsquare.com/files/rs-3738133/v1/8c3a16fb729c8a4be1300a8f.png"},{"id":49737295,"identity":"835d34f1-7c71-4988-b671-42593cd7748f","added_by":"auto","created_at":"2024-01-17 07:52:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6590600,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3738133/v1/67a246b2-82e7-421f-8bcf-4d452f6d31fc.pdf"},{"id":49136562,"identity":"20ccace5-8631-40f9-aa8c-4397a9f90428","added_by":"auto","created_at":"2024-01-03 17:22:36","extension":"pptx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":59364,"visible":true,"origin":"","legend":"","description":"","filename":"Data.pptx","url":"https://assets-eu.researchsquare.com/files/rs-3738133/v1/9f77ed5aae7a74b8baaa6452.pptx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prediction of multiscale lamina structure and high quality reservoirs in shale: A case study of the Lianggaoshan Formation in northeastern Sichuan Basin, China","fulltext":[{"header":"1 INTRODUCTION","content":"\u003cp\u003eWith considerable progress in the exploration and development of unconventional oil and gas resources, the global focus on oil and gas has gradually shifted from conventional to unconventional reservoirs, such as shale oil and gas(Gao et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Loucks et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Curtis, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). While marine shale oil and gas exploration and development have been successful in the United States (US), China is anticipated to encounter challenges in exploring and developing its dominant lake shales. This is because lacustrine shales are more heterogeneous than marine shales due to variable hydrodynamic conditions, terrigenous clastic input, and evaporation (Liu et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Moretti and Ronchi, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Thus, complex mineral compositions and abundant lamina structures have developed in lacustrine shales (Hammes et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) .The lamina structure is the basic deposition unit and one of the distinctive sedimentary structures in lacustrine shales (Xiugang et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Because of its complex heterogeneity, integrated studies between geology and petrophysics are required to reveal and identify lamina structures.\u003c/p\u003e \u003cp\u003eLamina structures, as distinctive structural features, the lamina structures exert an considerably influence on the comprehensive evaluation of shale oil, including quality of hydrocarbon source rock quality, reservoir characteristics, and engineering attributes (Wang et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Studies are currently focused on the following three areas of lamina structure (Kelai et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019b\u003c/span\u003e; Yawar and Schieber, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) : the type of laminae, development mechanisms, and impacts on shale quality. The types of laminae were divided into the following four main categories based on the mineral composition and organic matter (OM) content: feldspar-quartz, carbonate, organic, and clay (Zhensheng et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Because grain (particle) size also plays a crucial role in the classification of laminae, they were classified into the following two types: mud and silt (Pang et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Laminae formation of laminae is often studied based on basin evolution (Zhang et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and the Milankovitch cycle (Herbert and Fischer, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). In general, the more developed the laminae are, the better the physical properties of the shales are. The OM contentin the mud laminae is often higher than that in the silt laminae, with the shale oil occurrence and mobility being controlled by the laminae (Davies et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e; Bai et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This study primarily focused on examining the combined of the patterns and characteristics of laminae, an aspect that has often been overlooked in the accurate identification of these structural features. This is because accurate identification of the structure of laminae is essential for evaluating the shale oil potential in this the studied region.\u003c/p\u003e \u003cp\u003eThe identification and development of multiscale (\u0026micro;m-to-cm scale) lamina structures in shale has resulted in the need for high-resolution data, processing, and capabilities (Wang et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e). Conventional log data (e.g., resistivity tool, sound log, gamma log, and density log) and outcrops can be used to identify cm-scale lamina structures (Shiyue et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Core observation can demonstrate mm-to-cm-scale laminae structures (Li et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Lamina structures on the mm-to-\u0026micro;m scale can be obtained using thin-section observation and scanning electron microscopy(SEM) (Xin et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, thin sections and SEM are costly and limited because it is almost impossible to identify the structures of a whole well based on them (Xu et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e; Lei et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Because of technological advancements, a series of special logs have shown great advantages for continuous evaluation, such as nuclear magnetic resonance (NMR) logs (Tan et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Castro and Lupinacci, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), image logs (He et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Waters et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and lithology scanning logs (Abel et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Delavar, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In particular, mm-scale changes in the lamina structure can be measured via image logs (vertical resolutions of up to 6 mm) (Mcginnis et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). As a special technique that originated in the US, image logging includes electrical logs and sonic image logs, among which electrical ones play an important role in borehole wall imaging. In 1991, a new generation of full-bore microimaging (FMI) was developed by Schlumberger; further, Halliburton\u0026rsquo;s electrical MI (EMI) and X-tended Range Micro Imager (XRMI) log instruments were commercially used. In 2012, an electrical resistance MI (ERMI) log instrument was invented by the China National Offshore Oil Corporation, which provided thus providing a higher resolution. Subsequently, the latest generation instrument of Quanta Geo image logs, also referred to as \u0026ldquo;the microscope of the formation\u0026rdquo; in the subsurface, has been made commercially available at vertical and horizontal resolutions of 6 and 3 mm, respectively (Lai et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In addition, sonic image logs are mainly used to evaluate rock engineering features, rock fractures, and geological structures (Lai et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Thus, image logs can be used to identify the structures of laminae in shale reservoirs, complement conventional logs, and fill gaps in the identification of structures and characteristics of shale laminae.\u003c/p\u003e \u003cp\u003eThis study combined core tests, thin sections, conventional logs, NMR logs, and image logs to reveal the multiscale structures of the shale laminae of the Lianggaoshan Formation in the northeastern Sichuan Basin, south China and provided a new method to identify macroscopic-to-microscopic scale structures of laminae using log data. The findings of this study provide new insights into shale laminae research and provide a basis for the \u0026ldquo;sweet spot\u0026rdquo; predictions of shale oil.\u003c/p\u003e"},{"header":"2 GEOLOGICAL SETTING","content":"\u003cp\u003eSichuan Basin is located in south China (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA) and a crucial hydrocarbon-bearing foreland basin that can be divided into five first-level tectonic units: low and steep fold belt in the south Sichuan, high and steep fold belt in the east Sichuan, low and gentle belt in the central Sichuan uplift, low and steep fold belt in the west Sichuan, and Micangshan Daba front thrust belt (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB) (Chen et al., 2014). The Yilong-Pingchang area is located in the northeastern Sichuan Basin, whose tectonic direction is mainly north-west, with extrusive and torsional reverse faults developing and with the structural pattern being \u0026ldquo;a depression between two uplift\u0026rdquo; in the plane (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC) (Xu-Sheng et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Jurassic Lianggaoshan Formation is underlined by Ziliujing Formation overlies Shaximiao Formation. According to the lithology, combined log features, and sedimentary cycles, Lianggaoshan Formation in the northeastern Sichuan Basin was divided into Liangshang and Liangxia members from top to bottom (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD) (Yang et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Jurassic Lianggaoshan Formation in the northeastern Sichuan Basin is mainly composed of fine-grained deposits located under a layer of shallow shore to semi-deep lakes. Liangshang was classified into Liangshang 3, Liangshang 2, and Liangshang 1. Liangshang 3 is mainly composed of shale and argillaceous siltstone in the upper section and sandy mudstone and argillaceous siltstone in the lower section with the increased exogenous source material. While Liangshang 2 includes shale and sandy mudstone, Liangshang 1 is mainly composed of shale rich in OM in the lower section and sandy mudstone and siltstone in the upper section. Liangxia is not similar to Liangshang although the section changes from argillaceous siltstone and sandy mudstone to limestone and sandy mudstone with the increased salinity of the depositional environment (He et al., 2022) .\u003c/p\u003e \u003cp\u003eFor the last twenty years, the exploration of conventional oil has not led to the discovery of large oil reservoirs, which has in turn limited the exploration of unconventional oil resources in Sichuan Basin (He et al., 2022). In 2020, referring to the exploration practices of Daqing Gulong Shale Oil(Gong et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), based on the exploration principle of \u0026ldquo;grand strategy, great layout, and large scale discovery,\u0026rdquo; shales of Lianggaoshan Formation were systematically studied to evaluate their oil potential. For the shale reservoirs of Lianggaoshan Formation, the risk exploration well (A1) was deployed in the northeastern Sichuan Basin, thus officially initiating the exploration of shale oil in Sichuan Basin.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"3 DATA AND METHODS","content":"\u003cp\u003eCore plug samples of shale were obtained from well A1 in Lianggaoshan Formation in Sichuan Basin (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). To further identify the microscopic features of the shale reservoirs, some samples were selected to conduct experiments to obtain geological properties (e.g., mineral composition, porosity, permeability, oil content, and pore types) based on thin section, X-ray diffraction (XRD), laser scanning confocal microscopy (LSCM), QEMSCAN, and SEM analyses. The mineralogical composition was obtained via XRD and QEMSCAN, pore types were acquired via SEM, oil content information was revealed via LSCM, and laminae structure characteristics were detected via thin sections.\u003c/p\u003e \u003cp\u003eThe samples were made to a thickness of 30 \u0026micro;m and then observed via microscopy under plane- and cross-polarized light views. Thin sections were stained with Alizarin Red S to identify carbonate minerals via microscopy. The microstructural characteristics (laminar structures, pores, and microfractures) of the shale samples were effectively identified by adding blue or red epoxy to the core plugs before thin-section preparation. The shale samples were analyzed by using a FEI Quanta 250 field emission-scanning electron microscope at 25\u0026deg;C and relative humidity of 60% according to the GB/T18395-2001 standard for the SEM test (Zhao et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These samples were analyzed for mineral composition by using a Rigaku D/max-2600 XRD according to the Chinese oil and gas industry standard (SY/T 5163\u0026thinsp;\u0026minus;\u0026thinsp;2010) (Zhang et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and QEMSCAN according to the Chinese oil and gas industry standard (SY/T 5162\u0026thinsp;\u0026minus;\u0026thinsp;2014) (Li et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These samples were analyzed for their oil content by using Leica SP5 according to the Chinese oil and gas standard (Q/SY DQ6007-2022) (Gao et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSince well A1 constitutes the primary risk-exploration well, the log data were complete. In addition to the conventional logs, acoustic logs (AC), compensated neutron porosity (CNL), bulk density (DEN), gamma-ray (GR), spontaneous potential (SP), and caliper (CAL) were included as the tests for well A1. Some unconventional log data of combinable magnetic resonance (CMR-Magni PHI) (CMR-NG) and high-resolution borehole micro-resistivity image logs (FMI-HD) (Quanta Geo) were also used.\u003c/p\u003e \u003cp\u003eLogs of CMR-Magni PHI (CMR-NG) have always played a crucial role in the exploration and development of unconventional oil and gas resources, where pore structure and oil-bearing information can be effectively obtained for a series of tight reservoirs, such as shale (Wang et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e). The high-resolution borehole micro-resistivity image logs (Quanta Geo) are critical for realizing sedimentary fabric interpretation for shale reservoirs, which provides highly accurate sedimentary and structural information. In the FMI images, the dark shaded areas indicate low-resistance conductive clays and shale-like rocks, whereas the light-shaded areas represent high-resistance, dense, and OM (or hydrocarbon occurrence). The dominate minerals were quartz, clay minerals, and OM in Lianggaoshan Formation in Sichuan Basin. Therefore, the color of the FMI images was used to identify the mineral types and structures of laminae.\u003c/p\u003e \u003cp\u003eBecause the sedimentary fabric features of the shale reservoirs could be effectively identified, and the pore distribution characteristics and mobility properties in different sedimentary fabrics of the shale reservoirs could be accurately obtained, the lamina structure, reservoir quality, and \u0026ldquo;sweet spot\u0026rdquo; were successfully evaluated and predicted based on the logging methods.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"4 RESULTS AND DISCUSSION","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Reservoir characteristics of The Lianggaoshan Formation\u003c/h2\u003e \u003cp\u003eThe mineralogical compositions of the shale reservoirs in the Lianggaoshan Formation in the Sichuan Basin were obtained using XRD analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Siliceous (quartz and feldspar) and clay minerals were dominant in the samples, with an average of 57.13 wt% (38.9\u0026ndash;76.3 wt%) and 34.185 wt% (11.3\u0026ndash;52 wt%), respectively, thus showing the siliceous-rich and clay-rich characteristics of the shale reservoirs in Lianggaoshan Formation. The calcareous mineral content (calcite and dolomite) ranged from 1.5 wt% to 42.2 wt% with an average of 12.1 wt%. The clay mineral results indicated that the shale reservoirs were predominantly illite (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC) with an average of 49.5 wt% (34\u0026ndash;70 wt%), while kaolinite, chlorite, and illite-montmorillonite mixed-layer on average accounted for 12.5 wt% (5\u0026ndash;31 wt%), 20.33 wt% (10\u0026ndash;28 wt%), and 17.66 wt% (9\u0026ndash;27 wt%), respectively. The mineralogical characteristics indicated that the shale reservoirs of the Lianggaoshan Formation were brittle.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe lithologies of the Lianggaoshan Formation were classified based on the mineral compositions (He et al., 2022). The mineral triangle diagram and core observation results demonstrated that the northeastern Lianggaoshan Formation in the Sichuan Basin primarily included comprised siliceous shale, argillaceous shale, siltstone, mudstone, and minimal calcareous shale. Interbedded siltstones and shales were typically in a vertical contact, and their complex and variable lithologies were attributed to the hydrodynamic conditions, provenance, and depositional environments (Wang et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The siltstones were mainly comprised feldspar and quartz, the colors of which were usually gray or grayish-white. Mud and quartz layers frequently alternated on the \u0026micro;m scale. The mudstones were primarily massive and, with virtually no internal structure, and comprised quartz, and clay, whose colors were usually gray-green and dark purpleShales Shales were mainly characterized by a laminated structure, where silicone, clay, and minimal bioclastic laminae alternated frequently, and the colors of the shale were usually gray-black and gray. When the mechanically deposited hydrodynamic conditions changed, and provenance increased, the rock demonstrated a frequently layered structure with siltstone and shale lithology. In contrast, the rock appeared as a massive structure, and the lithology was mudstone.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDeveloped by Loucks et al. (2012, 2014), the classification scheme of the shale pore types includes the following three categories: (1) OM pores, (2) intraparticle pores (pores within a mineral grain) (IntraP), and (3) interparticle pores (pores between mineral grains; InterP). Based on this classification scheme, this study analyzed and classified the pore types of the shale samples in the Lianggaoshan Formation using SEM. The results showed that the presence of OM pores, mineral IntraP, particle InterP, and microfractures in the shale samples. InterP were a critically important pore type in the study area, was mainly found between quartz and clay (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA) and existed between stiff mineral grains and clay minerals. Spongy OM pores (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC), which were observed using SEM in the shale samples were considered to be related to hydrocarbon cracking that occurred during the thermal maturation of OM (Ko et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). IntraP, which was formed in the shale, including quartz IntraP (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD), pyrite IntraP (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB), and clay (mainly illite IntraP) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE), also improved the reservoir quality of the shale. The presence of numerous microfractures (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF) increased the permeability of the rocks. Despite their considerable importance in shale oil fracturing, etermining the presence and developmental status of natural fractures in the subsurface usingSEM is challenging.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Multiscale laminae structure characteristics of the shale reservoir\u003c/h2\u003e \u003cp\u003eThe lamina structure, the most basic unit of sedimentation, is one of the characteristic sedimentary structures in sediments or sedimentary rocks that can be identified through observation, with a main body thickness of ˂1 1 cm (Pang et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The multiscale structure of laminae, which is a unique characteristic of shale, is significantly related to the hydrocarbon source, reservoir, and engineering quality and is of considerable importance for the exploration and development of shale oil. The cm-to-mm-scale lamina structure can be identified via core observation, and the mm- to-\u0026micro;m-scale lamina structure must be identified via thin sections.\u003c/p\u003e \u003cp\u003eBased on the core observations, laminated rocks with rhythmic bedding were common in the Lianggaoshan Formation. Rocks with mud, silt, and carbonate alternated vertically. The rock structures were divided into massive, layered, and laminated cores. The massive structures were often formed in a weakly hydrodynamic sedimentary environment (low-energy) with a fine grain size (the overall lithology was massive mudstone) and no obvious bedding (no layer or layer spacing of \u0026gt;\u0026thinsp;0.1 m). The layered structures (the thickness of an individual layer was 0.01\u0026ndash;0.1 m) and the laminated structures (the thickness of an individual layer was \u0026lt;\u0026thinsp;0.01 m) were formed in a dynamic sedimentary environment with seasonal climatic and sediment variations and rapid mineral changes. The silt, mud, and carbonate layers were identified based on the core observations of the Lianggaoshan Formation.\u003c/p\u003e \u003cp\u003eBased on the thin-section observations, mm-to-\u0026micro;m-scale lamina structures and mineral superimposition patterns were effectively identified. The following four lamina types (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) were revealed in the rocks of the Lianggaoshan Formation in the northeastern Sichuan Basin: (1) silt, (2) mud, (3) organic and (4) carbonate. Based on the QEMSCAN technology, this study identified the mineral composition of the lamina structures (Li et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The silt laminae contained quartz, feldspar, clay, and a minor amount of dolomite (higher quartz and clay contents than other minerals), and the clay minerals were dominated by illite (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Interlaminar cracks were was observed in the silt laminae when the quartz and feldspar contents changed within a specific range (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB and D). The higher the feldspar and quartz contents, the better the brittleness of the rock and the more likely the formation of network fracture The mud laminae contained clay, OM, and other minerals, and the clay minerals were dominated by illite (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The mud laminae were commonly combined with silt and carbonate laminae (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA and E). Rocks with mud laminae showed poor pore structures and low brittleness; the reservoir quality was generally poor and unfavorable for reservoir modification. The organic laminae were often observed in a mixture of mud and silt laminae (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC and F), and the thickness of a single organic laminae was very thin. Typically, the OM shape was striped and formed by extrusion due to compaction (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). The carbonate lamina primarily comprised calcite and clay, and OM was stripped and wavy with clay minerals in a superimposed distribution (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Although carbonate minerals showed good resistance to compaction, the pores were cemented with increasing carbonate mineral contents.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Slab and well log characteristics of the shale reservoir\u003c/h2\u003e \u003cp\u003eLamina structures are easily identified based on the core and thin-section observations, which have limitations (such as high cost and the risk of destruction of cores); therefore, log data are used to successfully identify lamina structures. Because unconventional reservoirs are extremely heterogeneous in the vertical direction, reservoir characteristics are not truly reflected byconventional log data. Thus, higher-resolution image log data were used to reveal the structures of the shale reservoirs. The slab image was obtained from image logs via a computer and used for comparison with borehole images, slabbed cores, or high-resolution CT scans. The primary goal was to obtain information contained within the cylinders around a borehole and project this information onto any cross section of a borehole (the slab view; Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e) (Kivi et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Much critical information about the rocks was reflected in the slab images, such as tectonics, lithology, and minerals. Slab images are particularly helpful in observing complex geological events.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDynamic, static, slab, and core slab view images are shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e and \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e. These results indiate that incomplete sine lines of the dynamic and static images occurred, as shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e and \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e, respectively. Alternating light and dark laminated rocks are observed on the dynamic slabs of the image logs shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e and \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e. The dynamic slab image changed from bright to dark colors from top to bottom in the blue box line, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e. This phenomenon was typical of the massive sedimentary characteristics on the image logs because resistivity increased with increasing carbonate mineral content. In addition, the colors of this image were affected by the OM content, and the resistivity increased with increasing OM content. Millimeter-level light and dark laminae were clearly observed in the slab image. However, this correspondence was occasionally poor between the slab image and the core slab view, mainly controlled by the measurement angle and image resolution (Masoudi et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The alternating mm-level light and dark laminae were identified in the slab images shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e and \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e. This phenomenon was typical of the layered sedimentary characteristics. With the alternate development of silt and mud layers in the core, bright and dark layers appeared in the slab images.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Identification of lamina structure and prediction of high-quality shale reservoir\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e4.4.1 Identification of lamina structure of shale reservoir\u003c/h2\u003e \u003cp\u003eThe colors of the image logs were influenced by the mineral composition of the rock (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e), such as laminated rocks characterized by dark, orange-yellow, and bright colors, layered rocks recognized by alternating dark and bright bands, and massive rocks identified as bright or dark. The most pronounced characteristics of the shale reservoirs are that they are highly heterogeneous and primarily influenced by hydrodynamic conditions, terrigenous clastic inputs, and climate variations (Bohacs et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). The structure response characteristics of the image logs were used to identify the multiscale lamina structure in the shale reservoir of well A1. The results showed that the layered and massive types were the most developed, followed by the laminated types in Lianggaoshan Formation (Figs.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e and \u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe multiple lamina structures (binary, ternary, and multiple laminae) were recognized from the color changes of the image during the identification process. The binary laminated type was dominated by silt and mud and exhibited high quartz and clay concentrations and low carbonate contents, as evidenced by the QEMSCAN data shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. The core observations showed that the gray\u0026ndash;white silt (the main mineral was quartz) was intermittently and thinly layered with dark-gray clay laminae (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Typical alternating band characteristics were observed in the image logs; the dark layer was identified as a mud layer (the main mineral was illite in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), and the orange layer was recognized as a silt layer, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e. Another binary laminated type was dominated by carbonate and mud and showed calcite and clay and low quartz content, as evidenced by the QEMSCAN data shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. The light, and dark layers were identified as carbonate and mud layers in the slab image in, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e12\u003c/span\u003e). The ternary laminae contents (mud, carbonate, and OM) and the multiple laminae contents (mud, silt, carbonate, and OM) showed high illite, moderate quartz and calcite, and low OM contents. The appearance of the silt layer proved the presence of terrestrial input, destroying the deep-lake surface, and the complexity of the laminae was generally dominated by the season. moreover, the organic and carbonate layers were similar and showed bright colors on the slab image; however, the GR value was generally high for the organic layers (the OM content is higher with the increased clay minerals as clay mineral content can be reflected by GR). The results showed that in the Lianggaoshan Formation in the northeastern Sichuan Basin of south China, ternary layers that are rich in OM and binary layers typically dominated the structure types of laminae, while carbonate layers were typically less developed.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e4.4.2 Relationship between laminae structure and quality of shale reservoir\u003c/h2\u003e \u003cp\u003eThe reservoir-quality parameters of porosity and permeability and the geochemical parameters of total organic carbon (TOC) and free hydrocarbons (S1) play an essential role in the shale oil accumulation (Hou et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The Lianggaoshan Formation in the northeastern Sichuan Basin showed the following three main reservoirs: sweet spots in Liangshang 3, Liangshang 2, and Liangshang 1. The reservoir-quality and geochemical parameters are presented shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e12\u003c/span\u003e, with the samples having porosity less than 7.0% and permeability less than 0.03 mD. It was observed that a lower reservoir quality corresponded to the lower TOC and S1 values in the massive rocks, while a higher reservoir quality aligned with the higher TOC and S1 values in the layered rocks. Therefore, the layered rocks showed greater potential for exploration.\u003c/p\u003e \u003cp\u003eShale reservoir quality also has some differences in microscopic pore structure. First, layered rock reservoirs are of the best quality, with pore types dominated by InterP, IntraP, and OM pores; pore diameters ranging from 2 nm to 10 \u0026micro;m; and pore sizes greater than 400 nm, more than 20 percent. Laminated rock reservoirs are of the secondary quality, with pore types dominated by IntraP; pore diameters ranging from 2 nm to 2 \u0026micro;m; and pore sizes greater than 400 nm, more than 15 percent. Massive rock reservoirs are of the poor quality, with pore types dominated by IntraP; pore diameters ranging from 2 nm to 1 \u0026micro;m; and pore sizes greater than 400 nm, more than 8 percent (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe state characteristics of oil content and occurrence are also key parameters for the exploration and development of shale oil (Li et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Laser scanning confocal microscopy combined with fluorescence detection technology and layered scanning was initially used for the analysis of the light and heavy components of shale oil (Gao et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e). The findings showed that the layered shale showed rich oil content, whereas the massive rock showed poor oil content (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e13\u003c/span\u003e). However, among the layered rocks, the siliceous rocks showed a higher light oil content (red represents light oil, while blue represents heavy oil) than the calcareous rocks. These findings are attributed to the fact that the oil absorption capacity of the different minerals was influenced by wettability; thus, the calcareous minerals exhibited a stronger oil-wet property than the siliceous minerals, and the layered calcareous shale was more strongly adsorbed. For the laminated rock, some band-shaped light oil also remained in the microfractures and interlaminar cracks. Therefore, the oil content of the layered rocks was better than that of the massive and laminated rocks.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn addition, NMR logs were analyzed in detail to obtain information about reservoir quality, such as pore size distribution, fluid state, and type (Wang et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e). For NMR logs, T2 relaxation times were associated with the pore size; the long relaxation times corresponded to the macropore sizes, and the short relaxation times represented the micropore sizes. Overall, the high reservoir quality exhibited a relatively long relaxation time. In Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e15\u003c/span\u003e, in accordance with according to the division result of the rock fabric obtained using the image logs, the NMR logs were used to compare the three structure types of laminae. Thus, the pore distributions of the lamina structures were determined. Significantly different characteristics were observed for the three types of lamina structure. The layered rocks showed high total and effective porosity and oil saturation, whereas the massive rocks exhibited low porosity and oil saturation. The high T2 amplitudes and long distributions were observed in the layered rocks. The massive rocks showed low T2 amplitudes. The T2 distributions were associated with the pore types. The layered rocks were characterized by a bimodal distribution; the left peak represented IntraP, and the right peak was associated with InterP. The massive rocks were characterized by poor reservoir quality and pore structure. Therefore, the layered rocks presented the best potential for exploration and development in the Lianggaoshan Formation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e4.4.3 The high-quality reservoir and prediction of sweet spots\u003c/h2\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e14\u003c/span\u003e, the shale with different lamina structures presented different porosities and permeabilities. Most of the shale samples contained porosities of \u0026lt;\u0026thinsp;6 and permeability of \u0026lt;\u0026thinsp;0.02 mD, and the layered shales showed better reservoir quality than the massive and laminated shales. In addition, the laminated and layered rocks were related to shale and siltstone, and bedding fractures tended to occur along the bedding planes. Stylolite and irregular high-angle microfractures were found in the laminated and layered rocks, which are the channels for oil and gas transport and can assist in oil and gas drainage. The massive rocks were related to mudstones and limestones, which showed poor geochemical parameters and reservoir quality (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e14\u003c/span\u003e). InterP and IntraP could not be found in the most massive rocks due to compaction and cementation. Therefore, the laminated and layered rocks proved to be the more favorable exploration objectives for shale oil than did massive rocks.\u003c/p\u003e \u003cp\u003eThe oil test data provided important verification information for the reservoir prediction process. Well A1 in the Lianggaoshan Formation (2856\u0026ndash;3100.7 m) adopted a vertical well-staged fracturing process. The 10 mm choke was used for trial production, and the daily totals of 112.8 m\u003csup\u003e3\u003c/sup\u003e and 11.45 \u0026times; 104 m\u003csup\u003e3\u003c/sup\u003e for oil and gas, respectively, were obtained (total production: 3895 m3 for oil and 472 \u0026times; 104 m\u003csup\u003e3\u003c/sup\u003e for gas), with a flowback ratio of 11.01%. Based on the single-layer productivity analysis, the physical properties were found to influence the level of production in the laminated type. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e15\u003c/span\u003e, the total yield of the 19th century was much higher than that of the 18th century. The layered type, whose production has been considerably improved, accounted for a high proportion in the 19th century, and the highly effective and total porosity was revealed by the NMR logs and the images. Therefore, the development of the layered-type fundamentally influences the quality of the shale reservoirs. The proportion of the layered type was highly correlated with the production capacity of shale oil, and the layered rocks were the more promising exploration targets for shale oil in the Lianggaoshan Formation in the northeastern Sichuan Basin, south China.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"5 CONCLUSION","content":"\u003cp\u003eThe lithological characteristics, lamina types, oil content, and pore types of shales in Lianggaoshan Formation of the northeastern Sichuan Basin, south China were characterized based on the core, mineralogy, thin-section, test, log, and drilling geologic data. The following conclusions were drawn:\u003c/p\u003e \u003cp\u003eThe shales of Lianggaoshan Formation were primarily composed of quartz, clay, feldspar, and low amounts of calcite and dolomite. In addition, pyrite and siderite were occasionally detected. The core observation results showed that the structure types were classified as the massive, layered, and laminated structures based on the density and thickness of the individual laminae. The laminated structures were further divided into silt (mainly composed of quartz and clay), carbonate (mainly composed of calcite and clay), and mud (mainly composed of clay and organic). The reservoirs were of good quality (the best physical and oil content) in the layered type owing to the presence of silt bands, whereas the reservoirs were of poor quality in the massive type due to compaction and cementation. The multi-scale lamina structures were effectively identified via the image logs and slab images. In general, mm-scale changes could only be detected via the slab image; the silt laminae appeared orange on the slabs, the carbonate lamina appeared light, and the mud lamina appeared dark.\u003c/p\u003e \u003cp\u003eFor some wells without core data in the study area, the types of sedimentary fabrics could validly be evaluated based on the methods presented in this study. In addition, the method developed in this study can be generalized to other basins for the sedimentary-fabric classification and identification.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThis study was partly funded by the Engineering and Technology Major Project of Heilongjiang Province (SC2020ZX05A0023).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConflict of interest: We declare that we have no financial or personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled, \u0026quot;\u003c/em\u003e \u003cem\u003ePrediction of multiscale lamina structure and reservoir quality in shale reservoir: A case study of Lianggaoshan Formation in Northeastern Sichuan Basin, China\u003c/em\u003e\u003cem\u003e\u0026quot;.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEthical approval: Not required\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY STATEMENT\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe datasets used and/or analysed during the current study available from the corresponding author on reasonable request. All data generated or analysed during this study are included in this published article\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eYouzhi W\u003c/em\u003e\u003cem\u003eang\u003c/em\u003e\u003cem\u003e\u0026nbsp;designed the project and wrote the main manuscript. Xuefeng Bai and Cui Mao help to draw the figures and to draft the manuscript. X\u003c/em\u003e\u003cem\u003eiandong Wang\u003c/em\u003e\u003cem\u003e\u0026nbsp;defined the statement of problem. Zhiguo Wang and Cui Mao help to discuss the problems and revise the manuscript. Ce An help to discuss the main idea and help to draft the manuscript. Youzhi W\u003c/em\u003e\u003cem\u003eang\u003c/em\u003e\u003cem\u003e\u0026nbsp;help to calculate the data and draw the figures. Ce An help to revise the figures. All authors reviewed the manuscript.\u003c/em\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbel, M., Lorenzatti, A., Ros, L., Da Silva, O.P., Bernardes, A., Goldberg, K., Scherer, C., 2012. Lithologic logs in the tablet through ontology-based facies description, AAPG Annual Convention and Exhibition.\u003c/li\u003e\n\u003cli\u003eAwan, R.S., Liu, C., Aadil, N., Yasin, Q., Salaam, A., Hussain, A., Yang, S., Jadoon, A.K., Wu, Y., Gul, M.A., 2021. Organic geochemical evaluation of Cretaceous Talhar Shale for shale oil and gas potential from Lower Indus Basin, Pakistan. J. Pet. Sci. Eng.\u003cem\u003e \u003c/em\u003e200\u003cstrong\u003e,\u003c/strong\u003e 108404.\u003c/li\u003e\n\u003cli\u003eBai, L., Liu, B., Du, Y., Wang, B., Tian, S., Wang, L., Xue, Z., 2022. Distribution characteristics and oil mobility thresholds in lacustrine shale reservoir: Insights from N2 adsorption experiments on samples prior to and following hydrocarbon extraction. 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Dev.\u003cem\u003e \u003c/em\u003e47 (4)\u003cstrong\u003e,\u003c/strong\u003e 888-900.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"shale oil, lamina structure, image logs, NMR logs, Lianggaoshan Formation","lastPublishedDoi":"10.21203/rs.3.rs-3738133/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3738133/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eShale has become an important reservoir and source rock for unconventional oil and gas development. The Lianggaoshan Formation in the Sichuan Basin comprises a set of shales located under a lacustrine rock layer, where alternating silt, mud, and carbonate laminae exist, demonstrating strong heterogeneity. Reservoir quality and oil-bearing potential aredetermined using shale lamina structures. Therefore, the accurate and precise identification of lamina structures plays an essential role in the successful exploration and development of shale oil. In this study, shales were classified into laminated, layered, and massive rocks based on the density of laminae. The meter-scale layers were identified using conventional logs, whereas \u0026micro;m-to-cm scales were identified through image logs and related slabs. The mineral composition of laminae was further revealed based on thin-section observation and quantitativeassessment of minerals usingQEMSCAN technology. High quartz and clay contents were found for the silt laminated type, high calcite and clay contents were observed for the carbonate laminated type, and varying clay and organic matter contents were found for the mud laminated type. Typical alternating band characteristics were observed in the image logs; The dark, orange, and light layers were identified as mud,, silt, and carbonate in the slabs, respectively. The relations between the types of lamina structures, nuclear magnetic resonance logs, and oil test data were also analyzed. The development of the layered type fundamentally influenced the quality of shale reservoirs, and the proportion of the layered type was strongly associated with the production capacity of shale oil. The layered rocks were better than the massive and laminated rocks in terms of reservoir quality and oil-bearing potential. The results of this study provide a basis for predicting multiscale lamina structures from log data, facilitating the exploration and development of shale oil not only in the Lianggaoshan Formation but also worldwide.\u003c/p\u003e","manuscriptTitle":"Prediction of multiscale lamina structure and high quality reservoirs in shale: A case study of the Lianggaoshan Formation in northeastern Sichuan Basin, China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-03 17:22:31","doi":"10.21203/rs.3.rs-3738133/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"157df6cd-cdbe-4629-8ee5-9f5ceadc7c64","owner":[],"postedDate":"January 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-01-17T07:44:25+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-03 17:22:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3738133","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3738133","identity":"rs-3738133","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}