Logging identification and characteristic analysis of the lacustrine organic-rich shale lithofacies: A case study from the Es3L shale in the Jiyang Depression, Bohai Bay Basin, Eastern China

Journal of Petroleum Science and Engineering 145 (2016) 238–255

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Logging identification and characteristic analysis of the lacustrine organic-rich shale lithofacies: A case study from the Es3L shale in the Jiyang Depression, Bohai Bay Basin, Eastern China Jianhua He a,b,c, Wenlong Ding a,b,c,n, Zaixing Jiang a,b,c, Ang Li a,b,c, Ruyue Wang a,b,c, Yaxiong Sun a,b,c a

School of Energy Resources, China University of Geosciences, Beijing 100083, China Key Laboratory for Marine Reservoir Evolution and Hydrocarbon Abundance Mechanism, Ministry of Education, China University of Geosciences, Beijing 100083, China c Key Laboratory for Shale Gas Exploration and Assessment, Ministry of Land and Resources, China University of Geosciences, Beijing 100083, China b

art ic l e i nf o

a b s t r a c t

Article history: Received 14 September 2015 Received in revised form 13 May 2016 Accepted 18 May 2016 Available online 19 May 2016

The Es3L (lower sub-member of the third member of the Eocene Shahejie Formation) shale of Jiyang depression was a set of relatively thick and widely deposited lacustrine sediments with high organic richness and medium thermal maturation (0.7% rRo r1.1%), which rapidly emerges an important and target shale-oil play in Eastern China. An important component for shale-oil reservoir evaluation is lithofacies that affect hydraulic fracture stimulation and constrain significant organic-matter and oil concentration. Based on core and thin section observation, X-ray diffraction and comparative analysis of well logging data, we can define the types of shale lithofacies in the Es3L and determine log response characteristics of each lithofacies to show effective methods for identifying or predicting the lithofacies from conventional well logs. Organic richness, porosity and permeability measurements and the analysis of scanning electron microscope (SEM) images were used to study the impact of lithofacies on total organic carbon (TOC), reservoirs properties and oil content. The Es3L shale is rich in carbonate minerals (most 450%). According to the mineral composition, sedimentary structure and genesis, the lithofacies can be divided into 5 types and 2 sub-categories: marl (including massive and bedded), calcareous shale (including massive and bedded), laminated bindstone, massive silty mudstone and argillaceous shale. By the analysis of log response characteristics of lithofacies from 17 wells, 8 sensitive logging curves (e.g., density (DEN), acoustic times (AC) and resistance (Rt)) were optimized to construct logging recognition models and conduct cross plots to qualitatively identify lithofacies. On this basis, using the Fisher Discrimination Analysis and Naïve Bayes Classification Function, 5 types of shale lithofacies were quantitatively identified and predicted. Combined with the identification of shale beddings from Formation MicroScanner Imaging (FMI), the 7 sub-categories of shale lithofacies can be further recognized. The statistics of lithofacies indicate that bedded marl and calcareous shale are the dominant lithofacies in the Es3L. TOC content has the wide variation between different lithofacies (0.06–12%). The argillaceous shale has the high TOC content, followed by calcareous shale and bindstone. TOC is positively correlated to clay and pyrite content, and negatively correlated to quartz. The measurements of reservoir properties indicate that bindstone and calcareous shale have high porosity (47.0%) and good permeability. A general positive relationship between porosity and TOC, quartz or carbonate minerals indicates that organic matter (OM), recrystallized intercrystal and interparticle pores in BSE images can improve the porosity. Additionally, the oil accumulation index (OAI) of bindstone, calcareous shale and argillaceous shale is high. Considered reservoir properties and fracability, the laminated bindstone and bedded calcareous shale in the medium-low section of Es3L are the most advantageous lithofacies for shale-oil exploration and exploitation in this area. & 2016 Elsevier B.V. All rights reserved.

Keywords: Organic-rich shale Lithofacies Logging identification Shale reservoir Es3L Jiyang depression

1. Introduction n

Corresponding author at: School of Energy Resources, China University of Geosciences, Beijing 100083, China. E-mail address: [email protected] (W. Ding). http://dx.doi.org/10.1016/j.petrol.2016.05.017 0920-4105/& 2016 Elsevier B.V. All rights reserved.

Lithofacies, an important part of sedimentary facies, is an intrinsic property of rock or rock association formed under certain

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conditions of sedimentation reflecting a particular process or environment, greatly affecting many subsurface reservoir properties (Chang et al., 2000, 2002; Jiang, 2003). Historically, studies of the sedimentary features of shales indicated that thick-bedded shale intervals are the products of deposition in the transgression system tract (TST) with little variation (e.g., Schutter, 1998; Posamentier et al., 1999). So interest in lithofacies research focused on carbonate and siliciclastic reservoirs, including lithofacies classification and description from core data and outcrops (e.g., Berteig et al., 1985; Porta et al., 2002), lithofacies identification and prediction by wireline logs and seismic volumes (e.g., Wong et al., 1995; Yao and Chopra, 2000; Qi and Carr, 2006; Dubois et al., 2007), and the relationships of lithofacies with reservoir properties and gas concentration (e.g., Akatsuka, 2000). A tremendous success of oil-gas exploration and production from the marine shale in America (such as Bakken, EagleFord, Barnett and Marcellus shales) (e.g., Jarvie, 2012) and the marine or lacustrine shale in China (such as the Longmaxi, Shahejie and Yanchang formations) (e.g., Guo et al., 2014; Wang et al., 2014a, 2014b; Yang et al., 2015) has refocused lithofacies research on organic-rich shale (e.g., Javadpour, 2009; Curtis et al., 2010; Aplin and Macquaker, 2011). Primarily, most of the shale lithofacies research focused on the classification and description from core and outcrop observations (e.g., Hickey and Henk , 2007; Loucks and Ruppel, 2007; Singh, 2008; Walker-Milani, 2011; Zhou et al., 2012; Liang et al., 2014; Dong et al., 2015). To date, there are few studies on the prediction of shale lithofacies and lithofacies modeling in two or three dimensions based on petrophysical and geophysical data by logging and seismic attributes methods (Akatsuka, 2000; Qi et al., 2007; Vallejo, 2010; Koesoemadinata et al., 2011; Jonk et al., 2012; Wang et al., 2013) and little research concerning geological characteristics and identification-prediction of lacustrine mudstone lithofacies. Whether shale oil-gas exploration and production can obtain economic benefit depends on finding target shale layers with high oil-gas content and good fracability. The variability of organic richness and mineral composition between different lithofacies can affect reservoir properties and control hydrocarbon-generation potential and oil-gas concentration. A strong positive relationship between total organic carbon (TOC) and gas/oil content is often observed in shale reservoirs. The mineral components of different lithofacies have variable mechanical properties which influence the effect of hydraulic fracture stimulation. Generally, the shale with high carbonate minerals or biogenic silica contents can be easily fractured (Milliken et al., 2012, 2016). However, the increase of clay mineral in shale can enhance the ductility making it difficult to create and maintain extensive and open fractures (Rickman et al., 2008; Mayerhofer et al., 2008). Additionally, mineralogy can also affect the porosity and pore structure with an impact on gas/oil storage capacity and deliverability (Loucks et al., 2007). Shale lithofacies can be defined and described by multi-aspects, including sedimentary features (e.g., texture, color, structure, and genesis) (Hickey and Henk, 2007; Loucks and Ruppel, 2007), petrophysical properties (e.g., porosity and rock mechanic properties) (Jacobi et al., 2008), mineralogy and geochemistry characteristics (Wang et al., 2012; Jiang et al., 2007). However, geochemistry data, mineral composition data or other experimental parameters, which are more expensive than conventional logging data, are available in only a small proportion of recent wells limiting the ability to identify and predict shale lithofacies in lateral or vertical (He et al., 2016). Lower economic cost, higher vertical resolution, better continuity, and geologically abundant information have led to the wide application of logging technology to identify and predict lithofacies of carbonate and sandstone, but rarely for shale lithofacies, let alone lacustrine mudstones. The Es3L shale is the most important target shale oil reservoir, which makes up 56.7% of the key wells which have direct

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hydrocarbon indication in Shahejie formation, with an average daily oil production of 14.7 t/d. According to evaluation result of the third hydrocarbon resources, the shale oil resources of Es3L shale can reach 29.62
108 t. Recently, various researchers have performed the studies on the sedimentary, geochemistry and reservoir properties characteristics in Es3L shale (Li et al., 2006; Jiu et al., 2013; Wang et al., 2014a, 2014b). However, classification of shale lithofacies and systematic analyses on the relationship between lithofacies and reservoir properties or oil content are still lacking. Additionally, little research has been conducted with respect to identification and prediction of lacustrine mudstone lithofacies by wireline logs. The main objectives of this paper are to (1) classify the lacustrine mudstone lithofacies that generally have different mineral composition and TOC content; (2) identify lithofacies by conventional well logs and construct the quantitative prediction models between lithofacies and electrofacies; (3) analysis the impact of lithofacies on hydrocarbon-generation potential, porosity and oil content; (4) optimistic the advantageous shale lithofacies to provide guidance for selecting the target shale oil layers and improving the placement of hydraulic fracture stimulation.

2. Geological setting Jiyang Depression is one of the major petroliferous depressions within the Bohai Bay Basin of eastern China. It covers approximately 2.65
104 km2 that reaches the curved Chenning Uplift in the west and north, the Qiguang Fault and Luxi Uplift in the south and the Tanlu Fault in the east (Fig. 1(a)). Influenced by regional extension movement in the Late Cretaceous, a number of NW-SE trending normal faults were developed in the Jiyang Depression and controlled the structural framework and depositional history of the depression. The depression is comprised of four sub-depressions from south to north: Dongying, Huimin, Chezhen and Zhuanghua Sags separated by Linfanjia-Qingcheng, YihezhuangWudi-Gudao and Chenjiazhuang-Binxian Salients (pale yellow areas in Fig. 1(a)). The Jiyang Depression is a complex Mesozoic-Cenozoic faulted depression, involved by regional extension, strike-slip movements of the NE-SW trending Tan-Lu Fault Zone and tectonic inversion, amongst other tectonism within a Paleozoic cratonic platform setting (Hou et al., 2001; Shi et al., 2005; Qi and Yang, 2010). Based on the distribution of regional unconformity, the Cenozoic can be divided into two tectonic sequences: (1) the syn-rifting tectonic sequence consists of the Paleogene Kongdian (Ek), Shahejie (Es) and Dongying (Ed) formations that comprise fluvial-lacustrine sediments (Fig. 1c); (2) the post-rifting tectonic sequence consists of Neogene and Quaternary coarse clastic fluvial sediments (Fig. 2) (Jiu et al., 2013). Multiple lacustrine organic-rich shale layers have been deposited within the Jiyang depression since the Paleogene, including Es3, Es4, and Es1 members. And the lower parts of the Es3 is one of the most important organic-rich layers and widely distributed in the Dongying sag and Zhanhua sag ( 45000 km2), with a thickness of 300–500 m (Wu et al., 2013). The Es3L organic-rich shale was mainly formed in deep lake and brackish water facies environment within the warm climate (Fig. 2). The analysis of sedimentary structures in this area indicates that the Es3L shale was deposited during expansion of the lacustrine system and deepening of the water within the lake. Under this expansion, abundant terrigenous material nutrient supply and aquatic organism deposition in the lake anoxic environment (Fig. 2) favor the accumulation and preservation of organic matter (OM) (Jiu et al., 2013). The TOC content of Es3L shale in Jiyang Depression mainly ranges from 2.0% to 4.0%, with a maximum value of 46.0% in the Zhanhua sag and Dongying sag. The vitrinte reflectance (Ro)

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Fig. 1. a. Location of the study area; maps show the structural features and well locations; b a N-S trending, structural section with the location shown in a; c a E-W trending, structural section with the location shown in a. (For interpretation of the references to color in this figure, the reader is referred to the web version of this article.)

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Fig. 2. Stratigraphic column and sequence stratigraphic framework of the Jiyang Depression (modified after Jiang et al. (2007b), Pan et al. (2003), Jiu et al. (2013)).

mainly ranges from 0.7% to 1.1% (Wu et al., 2013), indicating the mature stage, with a huge potential for shale oil production.

3. Samples and methods 3.1. Sample sources In this study, core descriptions, experimentally analyzed samples, and thin section photographs have been obtained from the

Es3L shale cores from 8 cored wells (well L69, L67, XYS9, L63, F120, NY1, FY1 and N38) in Zhanhua and Dongying Sags, with a total length of 521.22 m and the depth ranging from 2499.30 m to 3472.00 m (Fig. 1a). In particular, well L69, NY1 and FY1, fully sealed wells designated for coring, were drilled for the study of shale reservoirs after 2011 and provided important data for characterizing lithofacies. Additionally, the conventional well logs, abnormal gas logging, and drilling materials of 17 important prospecting wells and FMI logging data of well L69 with a length of 230 m in Es3L shale were sorted to analyze the log response

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characteristics of the organic-rich shale lithofacies and discuss the effective methods to identify lacustrine shale lithofacies. 3.2. Experimental analyses Organic geochemistry data (TOC and rock-eval pyrolysis) were provided by the Geochemistry Laboratory of Yangtze University and Geological Research Institute in Sinopec Shengli Oilfield. A Leco CS-200/440 carbon-sulfur analyzer was used to measure the total organic carbon content (TOC) of 275 samples. Crushed samples (approximately 100 mg of 120-mesh size) were heated to 1200 °C in an induction furnace after removing the carbonates with 5% hydrochloric acid (HCl). The experiments were conducted according to the Chinese standard GB/T19145–2003, “Determination of Total Organic Carbon in Sedimentary Rock”, at a temperature of 25 °C and a relative humidity (RH) of 60%. 216 shale samples were analyzed using a CoreLab OGE-Ⅱ Rock-Eval instrument. Crushed samples (approximately 100 mg of 100-mesh size) were heated at a programmed rate. Then, the hydrogen and carbon dioxide emitting from the heated organic matter in rock can be quantitatively detected by flame ionization detector (FID) and thermal conductivity detector (TCD). Parameters measured include TOC content, free oil or volatile hydrocarbon content expressed as mg HC/g rock (S1) vaporized at a temperature of 300 °C, the remaining hydrocarbon generation potential as mg HC/g rock (S2) between the temperature of 300 and 600 °C, and temperature of maximum pyrolysis yield (Tmax). The testing methods are from the Chinese Standard to GB/T19145–2003, “Rock-Eval Analysis”, at a temperature of 26 °C and a relative humidity (RH) of 55%. X-ray diffraction (XRD) was used on 520 samples to quantitatively analyze the mineral composition at the CNNC Beijing Research Institute of Uranium Geology and Geological Research Institute in Sinopec Shengli Oil field. All of the shale samples were ground into a fine powder (o40 lm) and then analyzed with a Panalytical X’ PertPRO MPD X-ray diffraction with Cu Kα radiation (40 kV, 40 mA) at a scanning speed of 2°/min and a testing angle range of 5–90°. Computer analyses of the diffraction patterns showed the relative abundances of various mineral phases to be determined and a semi-quantitative assessment was performed. The experimental standards and testing methods employed here were in accordance with SY/T6201-1996, the oil and natural gas industry standard of the People's Republic of China entitled “Quantitative Analysis of Total Contents of Clay Minerals and Common Non-clay Minerals in Sedimentary Rocks by X-ray Diffraction”. Porosity and permeability values of 555 samples were determined by the helium expansion principle performed on a CoreLab CMS-300 core measurement system at the Analysis and Testing Center of Geological Research Institute in Shengli Oilfield. Helium molecules can enter pores of variable sizes; therefore, the tested porosities include microscopic pores and range from 0.01% to 40%. Cylinders with diameters of 2.5 cm, heights of 2.5–7.6 cm were made before testing. The standard “API Recommended Practice 40 for Core Analysis: Section 6.4.1.1 and B.8.6.2 Permeability Determination” was used in the test. 10 shale samples were divided into two groups. Pore characteristics of one group were observed using an “FEI Quanta model 200F” field-emission scanning electron microscopy (FE-SEM) with a working current set at 20 kV and distance of 8–9 mm at the Microstructure Laboratory for Energy Materials of China University of Petroleum in Beijing. First, this group of samples were cut into 0.5 cm
1 cm
0.2 mm chips and polished to 0.1 mm thick using “TechnoorgSC-1000″ argon ion beams polishing instrument. Then, the polished samples were observed using the FE-SEM in the backscattered electron model, at a temperature of 24 °C and a relative humidity (RH) of 35% in the laboratory. Pore size and plane

porosity were determined on BSE images using the Image- Pro Plus 6.0 (Bennett et al., 2012; Dong and Harris, 2013). Other unpolished samples were observed by “Quanta 200″ environmental scanning electron microscopy (ESEM) in the secondary electron model. Compared with SEM and BSEM, The moisture content of these samples can be adjusted to a desired humidity with a wide range of 0–90% by a build-in cooling stage in the ESEM chamber. In this way, the various hydrocarbon occurrences in the micro-pores can be studied in its most natural state.

4. Results and discussion 4.1. Lithofacies classification and log response characteristics 4.1.1. Mineralogy and lithofacies classification Mineral components play an important role in lithofacies classification. The various lithofacies deposited in different environment reflect variations of mineral composition. X-ray diffraction analysis from 520 shale samples of cores from five wells indicates that the Es3L shale is dominated by carbonate minerals (on average 58.2%), followed by clay minerals (on average 22.5%) and quartz and feldspars (on average 20.6%), as well as pyrite and siderite minerals (on average 5.0%) (Fig. 3). Calcite is the main component of carbonate minerals with an average of 52.3%. The quartz content mainly ranges from 15% to 23%, with an average of 18.2%. The clay minerals are mainly mixed-layer illite-smectite and illite minerals (on average 11.3% and 6.1%, respectively). Additionally, the Es3L shale also generally contains pyrite (on average 3.8%). Except for minerals, the OM is also the important component of shale. According to analysis data of 275 core samples from four wells, the TOC content of the Es3L shale is high and mainly ranges from 2.0% to 5.0%, with an average of 3.2%. The definition and classification of shale lithofacies is not unified by the domestic and overseas scholars, the basis of which includes color, mineral composition, TOC content, structure, genesis, fossil, texture and lamination characteristics (Loucks and Ruppel, 2007; Hammes et al., 2011; Wang et al., 2013; Liang et al., 2007; Hemmesch et al., 2014; Milliken et al., 2012, 2016; Milliken, 2013, 2014). These classified standards and methods are mostly created for marine shale. Little research concerning the classification of lacustrine organic-rich shale has been published. Based on the core observations, thin section analysis and X-ray diffraction data from 3 wells (L69, L67 and NY1), five types and seven sub-categories are defined by mineral compositions, sedimentary genesis and structure. These are marl (including massive and bedded), calcareous shale (including massive and bedded), laminated bindstone, massive silty mudstone and argillaceous shale. 4.1.2. The geological characteristic of lithofacies and its log response analysis The analysis of log response characteristics of organic-rich shale lithofacies is the most reliable way of qualitatively identifying and quantitatively predicting lithofacies. The various mineral composition, organic-matter and hydrogen saturation degree of different lithofacies reflect difference of log response on the conventional log curves, including spontaneous potential (SP), natural Gamma rays (GR), photoelectric absorption index (PE), acoustic (AC), neutron (CNL), density (DEN), resistivity (Rt) and radioactivity logging (U, TH, K). On the FMI log with higher vertical resolution, geological features (e.g., bed boundaries, fractures and sedimentary structures) can be better identified than the conventional log. By the comparative analysis of log response to five types of the defined shale lithofacies, log response characteristics of various lithofacies are obtained from the abundant conventional log data of 17 wells and limited FMI data of L69 well. Additionally,

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Fig. 3. The mineral composition of Es3L organic-rich shale from X-ray diffraction and geochemical data obtained from core samples. The central white line in the colored box is the average value of each mineral and TOC; the two edges of box indicate the 25th and 75th percentiles of cumulative percentage; the whiskers imply the boundary of normal values (2s); the black crosses stand for the atypical values. The percentage of minerals is by volume, while the TOC is by weight. Table 1. Summary of the petrology features and log responses of the five lithofacies defined from core and conventional well logs in the Es3L of Jiyang Depression (QKP ¼ Quartz, K-feldspar and Plagioclase; CARB¼ carbonate mineral; PS ¼ Pyrite and Siderite; RQC¼ ratio of quartz to carbonate; TOC ¼ total organic carbon).

these lithofacies can be further characterized by color, sedimentary structure, fossil fragments, fracture features and mechanical properties to better differentiate lithofacies and clarify their basic geological characteristics (Table 1). 4.1.2.1. Marl (ML) Lithofacies. The ML lithofacies is represented by grey color and massive or bedded structure. It is the predominant

lithofacies within the low section of Es3L shale. Based on core and thin-section observations, the bedded marl is the main sub-lithofacies. The light laminae mainly lenticular calcite is interbedded with the dark laminae which is mainly clay and rich in OM. Moreover, the thickness of light laminae is distinctly larger than that of dark laminae, with a value of 1.0–5.0 mm. The unflat and discontinuous laminae surface reflects the impact of turbulent

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flow on lithofacies. The massive marl lithofacies is rare and also grey-color without the laminae structures. The carbonate content of ML lithofacies is the most highest among five types of lithofacies (most 470%) with an average of 75.40%. The clay and quartz mineral content is less than 15%, and ratio of quartz to carbonate (Q/C) is less than 1/3. Moreover, TOC content of ML lithofacies is lowest and mostly less than 2.0% (on average 1.55%). The lithofacies with a high content of brittle minerals easily lead to fractures under external forces (Table 1). The ML lithofacies is rich in carbonate mineral and has little clay and OM. Therefore, it features lower GR, AC, K and CNL, medium Rt, higher DEN, PE and TH/U from the conventional well logs, which have the characteristic values of 45API rGR r56API, CNL o15%, DEN4 2.60 g/cm3, PE4 3.0 b/e, TH/U 41.8. The texture of the bedded marl lithofacies seen on FMI logs reflects sub-equal, thin-medium, light brown conductive beds interbedded with orange resistive beds. 4.1.2.2. Bindstone (BS) lithofacies. The formation of the BS lithofacies has the particular sedimentary genesis, which is produced where the organisms (such as algae) secrete or capture the carbonate grains during deposition and bind them together (Jiang, 2003). The BS lithofacies is restricted to the medium-low section of the Es3L shale intervals (4 3050 m). The BS lithofacies is darkgrey-colored and clearly represents the alternatively light and dark laminated structures. The thickness of light laminae mainly carbonate minerals is slightly larger than that of dark laminae rich in OM, with a value of 0.02–0.2 mm. The carbonate content of BS lithofacies is lower than that of ML lithofacies and ranges from 50% to 70%, with an average of 63.51%. The calcite comprising most of the carbonate mineral represents recrystallized texture and distributes along the horizontal laminae with the columnar or particulate forms under the microscope. The clay and quartz content is less than 20%, and ratio of quartz to carbonate (Q/C) is less than 1/3. But the TOC content of bindstone is more than that of ML lithofacies and mainly ranges from 2.0% to 4.0%, with a maximum value of 5.73% and an average of 3.25%. The high-angle fractures of this lithofacies can be observed in core and on FMI logs (Table 1). Additionally, it has high oil content and appears oil-rich in the core. However, the TOC content increases. It features the higher Rt, medium AC, DEN, CNL, PE and GR, and lower TH/U and K values. The Rt value is the highest among 5 types of lithofacies and ranges from 80 Ω  M to 180 Ω M (on average 130 Ω M). The AC value generally ranges from 70ms/m to 86 ms/m. However, influenced by a higher TOC content, the TH/U value of bindstone is distinctly lower than that of marl, which varies between 1.0 and 1.9 (on average 1.7). Additionally, the SP curve shows the obvious negative anomaly. The texture of the laminated bindstone lithofacies seen on FMI logs reflects sub-equal, thin, orange conductive beds and laminations-thin yellow resistive beds (can have smooth appearance). 4.1.2.3. Calcareous shale (CES) lithofacies. The CES lithofacies is characterized by dark grey to black-colored, massive or not obviously bedded structures. It is the most abundant lithofacies in the Es3L, mainly occurring in the upper-medium Es3L shale intervals. The bedded calcareous shale lithofacies is the main sub-lithofacies. The stratification and continuity of beddings of calcareous shale are much worse than that of bindstone. The boundary between the light and dark laminae is faint. The light laminae, mainly carbonate minerals, is always mixed with argillaceous components, the thickness of which is slightly smaller than that of the dark laminae, with a value of 0.5–1.0 mm (Table 1). However, the massive calcareous shale lithofacies is relatively rare and darkgrey-colored with massive structures. The micrite calcite is mixed with argillaceous components. The carbonate mineral of CES lithofacies is also the dominant composition and mainly ranges

from 35% to 50%, with a maximum of 60%, followed by the clay mineral ( o30.0%). The ratio of quartz to carbonate (Q/C) varies between 1/3 and 1/2. Additionally, CES lithofacies generally contains pyrite (on average 3.96%). TOC content of CES is higher than ML and BS and mainly ranges from 3.0% to 4.0% (on average 3.45%). The degree of fracture development is lower than in the ML and BS. The oil-saturation characteristics of CES lithofacies can be observed from the core. Compared with the ML and BS, the carbonate mineral content decreases, however, the clay mineral and TOC content increases. It features higher GR, CNL and AC, and medium DEN, Rt and K and lower PE and TH/U from the conventional log curves, which have the characteristic values of 60 APIrGR r75 API, 82 ms/mrAC r93 ms/m, CNL 4 20% and PEo 3.0 b/e. Additionally, the SP curve also shows the obvious negative anomaly. The texture of the bedded CES lithofacies seen on FMI logs reflects sub-equal medium brown conductive beds interbedded with pale yellow resistive beds. 4.1.2.4. Silty mudstone (STM) lithofacies. The STM lithofacies is represented by light-grey or grey-colored, massive structures. The massive STM lithofacies is much rarer and limited to the upper part of the Es3L. On the core, rare carbonized plant debris is scattered within argillaceous components. The aphanitic calcite is mixed with clay and quartz minerals under the microscope. The sand-sized quartz and shell fragments are observed in some samples. The quartz and feldspar minerals of STM lithofacies range from 30% to 45%, followed by the carbonate and clay minerals with a value of o30%. Moreover, the increase of quartz mineral content (25–30%) reflects the enhancement of terrestrial input and hydrodynamic force. The Q/C is more than 2. The TOC content of massive STM lithofacies is relatively low but higher than ML lithofacies (on average 2.35%). High-angle fractures can be observed in core and on FMI logs. Compared with the first three types of shale lithofacies, the quartz and feldspar content increases, and TOC content is medium. It features high GR, CNL, AC, PE, TH/U and K, medium DEN, and lower Rt. The Rt value is much lower (on average 16 Ω M). However, the CNL and AC values are much higher, with a value of 25–32% and 86ms/m-97 ms/m, respectively. The texture seen on FMI logs appears thick brown-black conductive beds. 4.1.2.5. Argillaceous shale (ACS) lithofacies. The ACS lithofacies mainly occur the upper Es3L shale intervals. It is dark-grey colored, massive structures. Under the microscope, the argillaceous components are mixed with other minerals, not showing the laminae or beds structures. Additionally, lack of oriented distribution of rare conferva carbon cutting and shell fragments indicate that ACS lithofacies was deposited in a sedimentary environment with weak hydropower (Table 1). The clay mineral content of massive ACS is high and ranges from 30% to 50%, followed by the carbonate mineral (o 30%), as well as a little quartz and feldspar (10–25%). The Q/C varies between 1/2 and 2. Pyrite in ACS lithofacies is common, with an average and maximum value of 5.32% and 15%, respectively. TOC content of massive ACS is the highest among 5 types of lithofacies and ranges from 2.5% to 4.5%, with a maximum value of 9.32%. Because of the high ductility of ACS, the degree of fracture development is lower. Compared with the above four types of lithofacies, the clay mineral and TOC content have increased significantly. ACS Lithofacies features high GR, AC, Rt and K, medium CNL and TH/U, and low DEN and PE values. The DEN and PE values are low, with an average of 2.43 g/cm3 and 2.0 b/e, respectively. However, the AC and Rt values are high, influenced by high TOC content. The texture seen on FMI logs reflects thick light brown-orange resistive beds.

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Fig. 4. The logging recognition models of five types of lithofacies. Lithofacies coded as marl (ML), bindstone (BS), calcareous shale (CES), silty mudstone (STM), argillaceous shale (ACS).

4.2. Logging identification methods of lithofacies 4.2.1. The qualitative identification of lithofacies By analyzing the log response characteristics of different lithofacies, the conventional log curves sensitive to minerals, OM, porosity and oil content variability can be selected and combined for constructing qualitatively logging recognition models of lithofacies. Considering characteristics of the Es3L shale (e.g., high carbonate mineral and TOC content), the eight logging parameters (including GR, CNL, AC, DEN, PE, Rt, TH/U and K) are selected to construct recognition models of five types of lithofacies (Fig. 4). The increase of TOC content generally results in the increase of GR, CNL, AC and Rt and the decrease of DEN, PE and TH/U. With the increase of the carbonate minerals, the GR, CNL, AC, and K values decrease. However, the DEN, PE and TH/U values distinctively increase (Berteig et al., 1985; Wang et al., 2012). Additionally, the higher AC, CNL and lower DEN values reflects good porosity in the shale intervals. Moreover, increased oil saturation also contributes to a high resistivity. To visually differentiate logging recognition models of various lithofacies, using the value “0.3”, “0.6” and “0.9” respectively represents the low, medium, and high value of each logging parameters. For example, with recognition model of BS lithofacies (Fig. 4b), the BS lithofacies has high TOC content and oil content but also influenced by high carbonate content. Therefore, it features medium GR, CNL, DEN, and PE, high Rt and low TH/U and K values, which is defined as “MMMMMHLL” Mode. These sensitive log curves can be also used for cross plot analyses. The logging data points, corresponding to the defined lithofacies of Es3L shale intervals in the L69 and the L67 wells, are used in cross plot analysis to qualitatively identify five types of lithofacies. As shown in Fig. 5, the AC-Rt-GR and DEN-CNL-GR

logging suites can best distinguish five types of shale lithofacies, followed by the DEN-PE-GR logging cross, whereas the TH-K-U cross plot can only distinguish between the lithofacies with high carbonate mineral content and high clay mineral content. 4.2.2. The quantitative identification and prediction of lithofacies On the basis of the qualitative identification of lithofacies, a quantitative identification and prediction of shale lithofacies is significant to evaluate shale gas/oil resource potential and design hydraulic fracture stimulation. Although cross plot analysis and recognition models are capable for identifying the lithofacies, cross plot analysis using few variables leads to limited accuracy. Logging recognition models have a relatively strong subjectivity and a significant increase of complexity. Moreover, both have no advantage for continuously identifying and automatically predicting shale lithofacies in vertical. However, considering the limited testing data and abundant conventional well logs in this area, the Fisher Discrimination Analysis (FDA) and Naïve Bayes Classification Function (NCF) can be effectively applied to identify and predict shale lithofacies. The 590 samples of data from three wells (L69, L67 and NY1 wells) were selected on the basis of geographic integrity and availability of appropriate well logs and core testing data. The seven sensitive logging parameters (including GR, CNL, AC, DEN, PE, Rt and U) are optimized to construct the standard Fisher canonical discriminant functions from the training set (Table 2). Among the four discriminant functions, the contribution rate of eigenvalue of the first two discriminant functions respectively reaches 74.1% and 16.5%, with a cumulative value of 90.6% (Function (1) and (2)). Therefore, only two discriminant functions can be used to quantitatively identify the five types of shale lithofacies

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Fig. 5. Cross plots for qualitatively identifying five types of typical lithofacies in the Es3L shale.

Table 2. The contribution rate of eigenvalues of four standard Fisher canonical discriminant functions. Function

Eigenvalue

% of variance

Cumulative/%

Canonical correlation

1 2 3 4

3.594 0.697 0.438 0.118

74.1 16.5 7.0 2.4

74.1 90.6 97.6 100.0

0.885 0.641 0.552 0.325

(Fig. 6).

F1 = −0.19· GR + 1.65· CNL−0.48· AC + 0.22· DEN + 0.14· PE −0.36·Rt−0.11·U

(1)

F2 = 0.19·GR + 0.54·CNL + 0.03·AC + 1.07·DEN + 0.42·PE −0.38·Rt−0.44·U

(2)

Compared with the FDA, the NCF considering the prior probability of the occurrence of a certain lithofacies can construct numeric discriminant patterns of each lithofacies from sensitive logging data sets and provide right rate of discriminant (Rs) for testing the significance of difference between the cores-define lithofacies and the NCF-predicted lithofacies. Moreover, it may also give the predicted lithofacies that are at least as accurate and

Fig. 6. Fisher discriminant analysis for five types of shale lithofacies recognition.

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efficient as those from neural networks without the burden of lengthy training required by neural networks (Kapur et al., 2000). Additionally, the point-by-point identification of shale lithofacies can be continuously made in the Es3L of whole well by the NCF, which was more advantageous than other methods. The 590 samples of data from the core-defined shale lithofacies, corresponding to the seven logging parameters sensitive to the variation of lithofacies, are used to construct the numeric discriminant patterns of five types of lithofacies:

Y1 = −0.306· GR + 4.530· CNL + 3.136· AC + 856.973· DEN (3)

+ 9.909·PE + 0.047· Rt + 0.410·U −1267.140 Y2 = −0.313· GR + 4.518· CNL + 3.134· AC + 852.171· DEN

(4)

+ 9.930· PE + 0.048·Rt + 0.422·U −1254.318 Y3 = −0.319· GR + 4.663· CNL + 3.117· AC + 854.477· DEN

(5)

+ 9.813· PE + 0.045· Rt + 0.514·U −1260.774 Y4 = −0.324· GR + 4.848· CNL + 3.099· AC + 859.525· DEN

(6)

+ 10.214·PE + 0.044·Rt + 0.226·U −1275.808 Y5 = −0.303· GR + 4.624· CNL + 3.115· AC + 853.272· DEN

(7)

+ 9.886· PE + 0.046·Rt + 0.236·U −1256.929

Y1 = ML; Y2 = BS; Y3 = CES; Y4 = STM; Y5 = ACS. These discriminant functions (Ys, s ¼1, 2, 3, 4, 5) can be used to calculate discriminant scores from well logs data. We can predict that a new observation sample s comes from the facies s* that achieves the highest discriminant cores:

Ys* ( L) = max { Y s (L ) }

(8)

1≤ s≤ 5

Here Ys (L) is the discriminant score of the sth facies obtained from the Ys discriminant function. Y s*(L) is the maximum value among these discriminant functions. The s is the number of lithofacies, ranging from 1 to 5. The s* is the identified lithofacies associated with the highest discriminant cores. To ensure the consistency and validity of prediction models, they should be tested, including data validation and classification performance. The former can be tested by the statistic value of Wilks'sλ and significant value before discriminant models are constructed. Moreover, the smaller Wilks'sλ value ( o0.50) and significant value ( o0.05) indicate that the chosen seven sensitive logging parameters have more significance on the models and the constructed models are more effective. After the logging discriminant models have been constructed, the classification accuracy should be tested by right rate of classification (Rs), include cross-validation and hold out prediction validation. The higher Rs is, and the more accurate lithofacies classification is. p

Rs=

∑i = 0 Ni Ns

× 100%

(9)

In which Rs is right rate of discriminant in each type of lithofacies. The Ns is the number of core-defined lithofacies samples.

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The Ni is the count of correctly identified lithofacies samples. The P is the number of the correctly discriminant lithofacies samples among each type of lithofacies data, ranging from 0 to Ns. As shown in Table 3, the significant value of NCF method is much less than 0.05, with the small statistic value of Wilks'sλ (o0.50), which indicates that the discriminant models based on the seven sensitive logging parameters can effectively distinguish five types of lithofacies. The result of cross-validation indicates that right rate of cross-validation (Rsc) is mostly more than 75% (on average 83.6%). Additionally, four wells (L67, L69, NY1 and FY1) with enough geological data were held out as test sets to study the consistency of the prediction lithofacies by NCF method. As shown in Figs. 7 and 8, the NCF-predicted lithofacies are consistent with the core-defined lithofacies obtained from thin section observation and X-ray diffraction analysis. The right rate of hold-out prediction validation (Rsh) averagely reaches 80.9%. Moreover, the ML lithofacies, the most prevalent shale lithofacies, are for the most part correctly classified, with a Rsh value of 93.5%, whereas the argillaceous shale can barely be identified by the NCF method with a Rsh value of 75.4% (Fig. 8). Additionally, to make up a deficiency of not identifying lamination structure from the conventional well log data, the lamination density of shale can be automatically picked by computer from the dynamic FMI logs data of L69 well. Laminated lithofacies have a “Lamination density” of 410/0.5 m, whereas, for massive lithofacies it is o5/0.5 m and bedded lithofacies have a “Lamination density” of 5–10/0.5 m. Therefore, combined with the quantitative identification of shale bedding from FMI logs, the 7 sub-categories of shale lithofacies can be further determined. As shown in Fig. 7, the vertical distribution of the Es3L shale lithofacies has a strong anisotropy. The bedded calcareous shale and marl lithofacies are the most dominant lithofacies, followed by the laminated bindstone and massive argillaceous shale lithofacies, consistent with the statistical results of core-defined and NCF-predicted lithofacies types from other hold-out wells (Fig. 8). 4.3. Characteristic analysis of different lithofacies 4.3.1. Organic matter abundance The sedimentary environment, OM productivity, and terrigenous sediment input rate of various lithofacies controlling deposition and accumulation of OM reflect variation of OM richness. TOC measured in 275 shale samples from four wells, show a wide range of values (0.06–12.0%) (Fig. 3). Argillaceous shale has the highest TOC content (avg. 3.78%), followed by calcareous shale (avg. 3.45%) and bindstone (avg. 3.25%), whereas TOC content of silty mudstone and marl is low, with an average value of 2.35% and 1.55%, respectively. According to XRD data from 275 samples, corresponding with TOC data, the relationship between TOC content and mineral composition reflects differences among various lithofacies. TOC content of all lithofacies is positively to clay minerals (Fig. 9a). Moreover, the relationship between TOC content and clay mineral of ACS, CES and STM is much stronger (R2 4 0.25). Because clay minerals (especially montmorillonite) with a large surface area have a strong absorptivity for OM (Ross and Bustin, 2009). In

Table 3. The basic parameters of Naïve Bayes Classification Functions for five types of lithofacies and right rate of cross-validation (Rsc). Lithology type

Number of samples

Prior Probability/%

Classification functions

Lithofacies codes

Wilks's λ

Significant value

Right rate of Discriminant (Rsc)/%

ML BS CES STM ACS

158 156 162 64 50

0.268 0.264 0.275 0.108 0.085

Y1 Y2 Y3 Y4 Y5

1.0 0.8 0.6 0.5 0.3

0.48

1.72e-118

98.8 75.5 89.4 79.7 74.8

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Fig. 7. An example of quantitatively predicting the Es3L shale lithofacies by the Naïve Bayes Classification Function (NCF) method from conventional well logs data and FMI in L69 well of Zhanhua sag, Jiyang Depression. The defined lithofacies based on core data (12th track) and the predicted lithofacies (13th track) are similar with slight differences. Lithofacies coded as laminated bindstone (LBS), massive marl (MML), bedded marl (BML), massive calcareous shale (MCES), bedded calcareous shale (BCES), massive silty mudstone (MSTM) and massive argillaceous shale (MACS).

contrast, there is no correlation between TOC and quartz (Fig. 9b). Except for bindstone, there is a strong negative relationship between TOC and calcite (Fig. 9c). Calcite of bindstone can be

positively correlated to TOC content, because the genesis of calcite in this lithofacies is associated with OM. In this lithofacies, increased calcite content usually reflects stronger input of calcareous

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whereas silty mudstone and marl are organic-lean lithofacies, with a S1 þS2 and HI of 10.85–20.08 mg/g and 310.29–422.36 mg/g, respectively. In vertical, organic-rich shale with high OM richness is restricted to the middle section of the Es3L, the lithofacies of which is mainly laminated bindstone and bedded calcareous shale (Fig. 10).

Fig. 8. Relative proportions of the Es3L shale lithofacies defined directly by core data and predicted by NCF method with the right rate of hold out prediction validation (Rsh).

algal biomass. The pyrite content of all lithofacies (especially argillaceous shale) is positively related to TOC (Fig. 9d). Increased pyrite deposited in the anoxic environment reflects better conditions of preservation for OM. Additionally, according to Rock-Eval analysis of 216 samples from L69 well, the S1 þS2 and hydrogen index (HI) of calcareous shale is much higher, with a value of 23.02–36.08 mg/g and 456.11–755.45 mg/g, followed by bindstone and argillaceous shale,

4.3.2. Porosity and permeability Porosity and permeability analyses of 555 shale samples from three wells (L69, L67 and XYS9 wells) indicates that different lithofacies vary greatly in porosity and permeability. Porosity and permeability have a weak positive correlation (Fig. 11). The permeability of different lithofacies influenced by micro fractures shows a wide range of 0.004–86 mD. Most of samples with a permeability of 0.1–10 mD account for 78.34% in all samples. The permeability of bindstone and calcareous shale is relatively high. Additionally, their porosities are also much higher than other lithofacies, with an average value of 7.60% and 7.09%, respectively, followed by argillaceous shale (on average 5.96%) and silty mudstone (on average 5.47%), whereas porosity of marl is relatively poor (on average 4.85%). The analyses of relationship among porosity and TOC, clay, quartz or carbonate minerals were performed on porosity measurements of 216 samples, corresponding with XRD and TOC data. There is a positive relationship between porosity and TOC content, although the correlation coefficient is relatively low (Fig. 12a). In the OM, organic pores observed under the BSE image (Fig. 13h) are

Fig. 9. The relationship between TOC content and clay (a), quartz (b), calcite (c) and pyrite (d) content, respectively.

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Fig. 10. Vertical variation of organic richness measured (TOC, S1 and S2) or calculated (S1 þ S2, S1/TOC and HI) for well L69 in the Es3L various lithofacies. The blue and red dash lines show the classification borders of general, good, excellent qualities of source rock. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

considered to develop as a result of thermal maturation and hydrocarbon generation (Loucks et al., 2009). But compared with marine high-maturity shale, this type of pore is absent or less developed in the Es3L shale, related to the relatively lower thermal evolution degree (Ro mainly 0.7–0.9%). The plane porosity (Si is the percentage of pore area and total area under the same magnification in the BSE images) of OM pore in the BSE image is 1.53% (Fig. 13h). Although OM pore has contributed only modestly to total porosity of the Es3L shale, the increased OM originated from

calcareous algae blooms can promote the formation of primary carbonate minerals (Liu et al., 2001). Thus, the increased carbonate mineral greatly improves the brittleness of shale, which is positively related to the degree of fracture development (Jiu et al., 2013). Moreover, OM of laminated or bedded shale lithofacies (laminated bindstone and bedded calcareous shale) with high TOC content is usually concentrated in laterally continuous layers, it may produce relatively good permeability fracture-pathways between OM layer and carbonate-rich layer (Fig. 13a) due to the

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Fig. 11. The relationship between porosity and permeability of different lithofacies.

difference in mechanical properties (Loucks et al., 2009). Additionally, Bedding-parallel microfractures are always favored under overpressure during expulsion in thermally matured, organic-rich shales (Duhailan et al., 2015). And in the ESE images, the hydrocarbon is mainly free in those horizontal fractures (Fig. 13c), which can form effective pore spaces and also conduct the good permeability pathways. No clear relationship between porosity

251

and clay content of shale samples is obvious. Only clay content of ACS and STM is weak-positively related to porosity (Fig. 12b). Because there are commonly some floccule pores in flocculated clay microfabric produced by electrostatic flocculation (Slatt et al., 2011), with a shape of network or cardhouse that are larger than 3 mm (Fig. 13c, f). The plane porosity of these pores can reach 3.89%, which are partly filled with bitumen (Fig. 13f). Additionally, some intergranular pores of clay mineral (mainly illite) are produced by synaeresis during diagenetic process (Fig. 13c). A weak positive correlation exists between porosity and quartz content (Fig. 12c). A part of interparticle pores are preserved around silt grains during compaction (Schneider et al., 2011). On the observation of the ESEM, crude oil is adsorbed on the surface of quartz crystallites or free in the interparticles pore between quartzes (Fig. 13e). But it is not the main contributor to total porosity because the quartz content of the Es3L shale is relatively low. In contrast, carbonate mineral content of the Es3L shale is high. And the occurrence and crystallization state of carbonate mineral have a great influence on porosity. The correlation between carbonate content and porosity is different among various lithofacies (Fig. 12d). The carbonate mineral (mainly calcite) of carbonate-lean shale lithofacies (CES, STM and ACS) is mainly micrite or aphanitic and mixed with argillaceous components showing poor stratification. With calcite content increased, the degree of calcite cementation seems much stronger, which leads to the reduction of porosity. In contrast, carbonate crystals of carbonate-rich ( 450%) shale lithofacies (MLS and BS) is usually

Fig. 12. The porosity versus TOC content (a), clay (b), quartz (c) and carbonate (d) content, respectively.

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Fig. 13. Reservoir space types of the Es3L shale and hydrocarbon distribution characteristics in the pore spaces. (a) Well FY1, 3423.6 m, laminated bindstone, interlaminar fractures between organic-rich argillaceous and calcite (Cc) layers, floccule intercrystal pores, Si ¼ 3.35%, BSEM; (b) Well L69, 3127.25 m, bedded marl, recrystallized intercrystal pores between calcite or dolomite crystals (D) minerals, Si ¼2.85%, BSEM; (c) Well NY1, 3436.23 m, massive calcareous shale, floccule intercrystal pores in flocculated clay microfabric, Si ¼3.89%, BSEM; (d) Well NY1, 3295.8 m, bedding-parallel and continuous micro-fractures form under overpressure (white arrow), saturated with crude oil, ESEM; (e) Well NY1, 3230 m, bedded calcareous shale, the interparticles pore between quartz crystals trapped with crude oil, ESEM; (f) Well NY1, 3436.23 m, massive calcareous shale, crystal lining pores along the boundaries of calcite crystals, floccule intercrystal pores half-filled with solid bitumen, Si ¼ 3.45%, BSEM; (g) Well N38, 3297.50 m, bedded calcareous shale, intercrystal pores within a pyrite (Pr) framboid, Si ¼ 0.75%, BSEM; (h) Well N38, 3251.8 m, massive argillaceous shale, a contiguous spongy textured OM-pore network, Si ¼1.53%, BSEM; (i) Well NY1, 3295.8 m, laminated bindstone, pyrite intercrystal pores, OM and clay pores, saturated with crude oil, ESEM (Fig. 13(a–c, f modified from Liang et al., 2015).

powder crystal (4 mm o D(crystal diameter)o 60 mm) or cryptomere (60 mm o Do250 mm) with euhedral shape by recrystallization or replacement and distributes along laterally continuous layers with a good permeability. The intercrystal pore between carbonate minerals is large ranging from 1 mm to 10 mm. The plane porosity can reach 2.85%, which greatly increase total porosity (Fig. 13(b)). Additionally, these types of lithofacies generally develop the tectonic and overpressure fractures (Table 1 and Fig. 13a, d). Therefore, there is a weakly positive relationship between carbonate content and porosity of these lithofacies (Fig. 12d). The intercrystal pore of pyrite framboids is usually observed in the BSE or ESE images, with a small pore diameter of o0.5 mm (Fig. 13g). Although plane porosity is only 0.75%, which makes a limited contribution to porosity, the pyrite is usually

associated with OM or clay minerals (Fig. 13i), which can be connected together to form complex permeability pathways. 4.3.3. Shale oil content and resource potential Oil content within organic-rich shale varies greatly, and is influenced by many complex factors, such as lithofacies types and the richness, types and expulsion efficiency of OM (Li et al., 2015). Therefore, the shale oil resource potential should be evaluated to determine whether or which part of shale oil resources can be effectively exploited. Fewer scholars have attempted to quantitatively evaluate shale oil resource potential. Lu et al. (2012) first divided the shale oil resources into three types (i.e. ineffective resources, potential resources and enriched resources), on the basis of the relationship between oil content (free hydrocarbon

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of marl is over-estimated and most of lithofacies are underestimated by this index. So this method also is inapplicable to estimation of lacustrine shale oil resource potential. A more appropriate parameter of oil accumulation index (OAI), S1
(1 þTOC2)
100/TOC, is established to estimate shale oil resource potential of lacustrine shale, based on the relationship between S1 and TOC content. Combined with the grading evaluation results from Fig. 14, when the OAI value of shale lithofacies is greater than or equal to 900 mg/g, this shale lithofacies has high oil content for shale oil exploitation. As shown in Fig. 15, most of CES and BS and part of ACS lithofacies samples with a value of 4900 mg/g have high potentially producible oil content, whereas, considering the reservoir and mechanical properties of shale lithofacies, the bedded calcareous shale and laminated bindstone lithofacies in the medium-low section of the Es3L are the most advantageous lithofacies for shale oil exploitation.

Fig. 14. Free hydrocarbon (S1) versus TOC for five types of shale lithofacies in the Es3L. The samples within the area① are enriched resources with the greatest oilbearing potential (S1 42.8 mg/g, TOC 4 3.2%); Samples that are within area ② are potential resources with medium oil content (1.0 mg/g o S1 o3.2 mg/g, 0.9% o TOC o 1.0%); Samples in area ③ are ineffective resources with low relative oil content (S1 o3.2 mg/g, TOC o 0.9%).

(S1) and chloroform asphalt “A”) and TOC content under similar organic matters type and thermal maturity conditions. However, this classification method does not consider the production capacity of shale reservoirs. According to this classification method, most of ACS and CES lithofacies samples in the Es3L are assigned to the enriched resources with high relative oil content (TOC 43.2%, S1 42.8 mg/g) (Fig. 14). Whereas, shale oil recoverability of ACS lithofacies with low fracability and BS lithofacies are wrongly estimated. So this method has some limitations regarding the recoverability of shale oil. Jarvie (2012) argued that oil saturation index (OSI) can be considered as an effective indication for determining potentially producible oil in North American marine shale. He also deemed that OSI (S1
100/TOC) value was greater than 100 mg/g in the shale intervals with high potentially producible oil content. As shown in Figs. 10 and 14, the OSI value of only a few CES and ML samples is over 100 mg/g, which has an obvious oil crossover effect. Obviously, shale oil resource potential

5. Conclusions

1) The Es3L shale lithofacies can be distinguished from core, conventional well log and FMI log in terms of mineral composition, TOC content, carbonate percentage, the ratio of quartz and carbonate (Q/C) and sedimentary structure. These parameters are the key criteria for recognizing and defining five types and two sub-categories of shale lithofacies. 2) Logging recognition models and cross plots analysis can be used to qualitatively recognize shale lithofacies on the basis of eight sensitive logging curves (e.g., CNL, AC, and DEN). Using the FDA and NCF methods, five types of shale lithofacies was quantitatively identified and predicted. Combined with the identification of rock beddings from FMI logs, two sub-categories of shale lithofacies can be further determined with high right rate of discriminant (480%). The statistics indicates that bedded marl and calcareous shale are the dominant lithofacies in the Es3L. 3) The organic richness (S1 þS2 and TOC) of ACS is much higher, followed by CES and BS. TOC content is positively related to clay minerals and pyrite, and negatively correlated to quartz of the ML. And porosity and permeability of BS and CES is relatively high, associated with development of OM, recrystallized and intercrystal pores system. Cracking of OM to hydrocarbon and

Fig. 15. The relationship among shale oil accumulation index (OAI), TOC content and free hydrocarbon (S1) for various lithofacies showing the potentially producible oil.

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recrystallization of carbonate mineral can greatly improve shale reservoir properties. 4) Using a new index of OAI, combined with the grading evaluation results of shale oil resource, most of CES and BS and part of ACS lithofacies have high potentially producible oil content with an OAI value of 4900 mg/g. The OAI, reservoir properties and brittle mineral data suggest the bedded calcareous shale and laminated bindstone lithofacies in the medium-low section of the Es3L are the most advantageous lithofacies for shale oil potential production.

Acknowledgments The study was sponsored jointly by the National Natural Science Foundation Project (41372139, 41072098, and 41002072), Major Special Project for National Science and Technology “Geological characteristics and evaluation of reservoir in new oil and gas exploration field” (2011ZX05009–002-205) and “Evaluation on Shale Gas Resources in Bohai Bay Basin” (2011ZX05018–001-002, 2011ZX05033–004 and 2016ZX050406-003-001). We are grateful to the Analysis and Testing Center of Geological Research Institute in Shengli Oilfield, the Geochemistry Laboratory of Yangtze University and the CNNC Beijing Research Institute of Uranium Geology, which provided core samples and helped to test and analyze samples. We thank Profs. Ding Wenlong and Jiang Zaixing and other colleagues who have significantly contributed to this study. We also thank editors and two anonymous reviews for their revisions and comments.

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