# Coupling Relationship between Lithofacies and Brittleness of the Shale Oil Reservoir: A Case Study of the Shahejie Formation in the Raoyang Sag

Authors: Zhaojing Song; Junqian Li; Xiaoyan Li; Ketong Chen; Chengyun Wang; Peng Li; Yongbo Wei; Rixin Zhao; Xiaocheng Wang; Shasha Zhang; Mengying Yan; Haiyan Geng
Journal: Geofluids
 (2022)

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## Abstract

Analyzing the characteristics of rock brittleness in low-permeability mudstone and shale (MS) formations is imperative for efficient hydraulic fracturing stimulation. Rock brittleness depends on the mineral composition, organic matter abundance, and bedding structure. Based on the MS from Shahejie Formation mineral composition (clay mineral, felsic mineral, and calcareous mineral contents), total organic content, and bedding structure (laminated, laminar, and massive), six types of lithofacies were identified: clay-rich MS, felsic-rich MS, calcareous-rich MS, clay MS, felsic MS, and calcareous MS. The quartz, feldspar, calcite, and dolomite of the Shahejie Formation are brittle minerals. Consequently, lithofacies with high felsic and calcareous mineral contents are more brittle. In addition, laminated and laminar MS are also conducive to hydraulic fracturing. Therefore, laminated, organic-rich, and calcareous-rich MS are the dominant lithofacies for hydraulic fracturing in the Shahejie Formation. The lithofacies and brittleness index were predicted by the response characteristics between mineral compositions and logging curves. The 3521–3552 m section of well B11x is dominated by calcareous-rich MS with developed laminae, representing a favorable section for hydraulic fracturing. Fragile minerals and oil are widely developed in the lower part of the lower 1st member of the Shahejie Formation (Es1L) in the southwestern part of Zhaohuangzhuang-Suning, where hydraulic fracturing can be used to increase shale oil production.


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## Body

## 1. Introduction

Abundant continental shale oil resources are the focus of current unconventional oil development and research [1–3]. Continental fine-grained sedimentary rocks (CFSR) are primarily developed in lake environments. Owing to the variations in the sedimentary environment, the mineral composition, structure, and organic content of CFSR are highly heterogeneous, which is not conducive to selecting fracturing intervals [4–8]. One of the vital construction factors affecting the recoverability of shale oil is hydraulic fracturing treatment [9–12]. Fracability is a measure of the difficulty of a shale reservoir to be effectively reformed and indicates whether a reservoir may produce a larger reformed volume by fracturing [13, 14]. Chong et al. [15] proposed the concept of using brittleness index (BI) as a norm to evaluate the fracturing ability of shale reservoirs. Fracability is enhanced when the BI of mudstone and shale (MS) is higher.The brittleness of MS can be evaluated based on the mechanical properties or mineral composition of the rock. Young’s modulus and Poisson’s ratio are critical mechanical properties that indicate shale brittleness. Higher Young’s modulus and lower Poisson’s ratio indicate higher shale brittleness, and the shale will be more amenable to hydraulic fracturing treatment [16, 17]. There are two different methods for determining the mechanical properties of rocks: static and dynamic. In the static method, a certain pressure is applied to the rock under laboratory conditions to analyze the stress–strain relationship of the rock, thereby calculating Young’s modulus and Poisson’s ratio. In the dynamic method, the mechanical properties of rocks are determined using sonic wave propagation through the rock. The mechanical properties of a continuous distribution are obtained through logging data, including sonic wave and rock density data. They define the brittle minerals, which is crucial for calculating the BI using the mineral composition method. Based on the mineral composition characteristics of the Barnett Shale, Jarvie et al. [18] considered quartz to be the major brittle mineral. Wang and Gale [19] suggested that dolomite is also a brittle mineral. Additionally, some scholars consider that quartz, feldspar, calcite, and dolomite are brittle minerals in shales with complex mineral compositions [20–22].Static parameters produce several changes in the sample-loading history; however, the mechanical properties of rock under formation conditions can be simulated by applying different forces to the sample. The rock mechanical properties (Young’s modulus, Poisson’s ratio, bulk modulus, and shear modulus) and rock strength (compressive strength, tensile strength, and shear strength) at any formation depth can be easily obtained from sonic logging data. However, shear wave velocity cannot be obtained in most old wells because it is usually obtained from long spacing sonic logging or multipole array logging [23]. Mineral components can be obtained via methods such as whole-rock mineral X-ray diffraction (XRD), energy dispersive spectroscopy, and element capture spectroscopy logging. A practical method can combine the above methods for maximum advantage. The mechanical properties of rock minerals vary considerably. However, by analyzing the relationship between different minerals and mechanical properties, minerals can be divided into brittle and nonbrittle ones. Subsequently, a BI calculation formula can be derived based on the mineral components. This formula has guiding research in target areas with few research data.There are several significant differences between marine shale and CFSR [24–28]. CFSR is strongly affected by the sediment source and sedimentary environments [29, 30]. Consequently, CFSR is highly heterogeneous, which is primarily manifested in the mineral types, rock compositions, structural characteristics, pore space, organic matter (OM) characteristics, and hydrocarbon mobility [31–36]. Some scholars have studied this issue. Jiang et al. [25, 37] proposed a classification scheme for eastern China, which is based on total organic content (TOC), carbonate content, and clay minerals, with TOC contents of 2% and 4% as the boundaries. The rocks are then divided into three categories: low TOC, medium TOC, and high TOC. Additionally, 50% carbonate and clay minerals are used as boundary, and thus, six subcategories are established. Chen et al. [38] analyzed the mineral composition, bedding structure, OM abundance, color, and other contaminants in the Dongying Sag and found that the content of mixed fine-grained sedimentary rocks was >60%. Using this definition, the rock was subdivided into 17 lithofacies based on Jiang et al.’s classification scheme [25].The brittleness characteristics of MS are closely associated with rock characteristics such as mineral composition, bedding structure, and TOC, which can be reflected by lithofacies [39]. Different lithofacies have different brittleness characteristics, and analysis of the coupling relationship between lithofacies and brittleness has great significance for selecting hydraulic fracturing intervals. Research on the shale interval of the Es1L in the Raoyang Sag is relatively limited, and data are insufficient. Therefore, this study first classified the lithofacies of the rocks in Es1L. The classification was based on mineral composition, sedimentary structure, and TOC. Subsequently, the mechanical properties of the rock were obtained through triaxial mechanical experiments. Brittle minerals were defined by analyzing the relationships between different minerals and mechanical parameters of the rocks, and the BI was calculated. The dominant lithofacies were selected by analyzing the brittleness characteristics of the different lithofacies. Finally, hydraulic fracturing intervals conducive to shale oil development were selected.


## 2. Geological Setting

The Raoyang Sag, with an area of approximately 5280 km2, is located in the central region of the Jizhong Depression, Bohai Bay Basin (Figure 1). The sag has the richest hydrocarbon resources in the depression [30, 40, 41]. High-quality hydrocarbon source rocks of the Es1L and 3rd member of the Shahejie Formation, which are an essential material basis for the formation of shale oil, are developed in a semideep and deep lacustrine sedimentary environment [30, 42].Figure 1
Structural map and well locations of the Raoyang Sag (modified after [30]).


## 3. Samples and Methods

### 3.1. Samples

The Es1L in the Raoyang Sag has a large area of continuously distributed organic-rich shale with high OM content. Most areas have entered the oil generation window [30, 40, 42]. In this study, an experimental analysis was performed on 99 MS samples with different secondary structures from Es1L. The experiment included four types of tests: TOC measurement, Rock–Eval pyrolysis analysis, thin section identification, and XRD analysis.


### 3.2. TOC and Rock–Eval Pyrolysis Analyses

The TOC and Rock–Eval pyrolysis analyses were conducted at the Key Laboratory of Deep Oil and Gas at the China University of Petroleum (East China). For estimating TOC, samples were first crushed to an approximate mesh size of 100 and then reacted with 5% diluted hydrochloric acid to remove inorganic carbon. After oven-drying at 80°C (5%), the TOC content was determined using an Elab-TOC/E2000 analyzer. Rock-Eval pyrolysis analysis required crushing the sample to an approximate mesh size of 100 and then heating the sample from 300 to 600°C (25°C/min) under anaerobic conditions with nitrogen as the carrier gas in a YQ-VIIA Rock–Eval rock pyrometer. The hydrocarbons and carbon dioxide emissions released from the rock were quantified using a flame ionization detector and thermal conductivity detector. The measured parameters were the free hydrocarbon content when vaporized at a temperature of 300°C (S1), residual hydrocarbon generation potential when the temperature was between 300 and 600°C (S2), and temperature of the maximum pyrolysis yield (Tmax).


### 3.3. XRD Tests

XRD tests were performed on the 99 samples to quantitatively analyze their relative mineral contents. All samples used for XRD analysis were powders with a particle size of <40μm. The analytical instrument used was a Panalytical X’Pert PRO MPD X-ray diffractometer with Cu Kα radiation (40 kV, 40 mA) at a scanning speed of 2°/min and testing angle range of 5°–90°.


### 3.4. Mechanical Experiment

Six rock samples from the study area were drilled as regular core plugs with a diameter of approximately 2.5 cm using an SK-5625A cycling CNC diamond wire cutting machine. In natural underground conditions, the rock is always under in situ stress and mostly in a state of stress from three axes. Therefore, when hydraulic fracturing is designed, it is necessary to consider the deformation characteristics of rocks under a certain confining pressure. In this test, a conventional triaxial compression experiment was performed on a plugged specimen. First, the specimen was placed in an autoclave, and lateral pressure (confining pressure) was then applied to the core. Finally, axial stress was applied to the core through the hydraulic cylinder of the press. The confining pressure was maintained at 20 MPa throughout the experiment, and the axial pressure was gradually increased until the specimen was destroyed.


## 3.1. Samples

The Es1L in the Raoyang Sag has a large area of continuously distributed organic-rich shale with high OM content. Most areas have entered the oil generation window [30, 40, 42]. In this study, an experimental analysis was performed on 99 MS samples with different secondary structures from Es1L. The experiment included four types of tests: TOC measurement, Rock–Eval pyrolysis analysis, thin section identification, and XRD analysis.


## 3.2. TOC and Rock–Eval Pyrolysis Analyses

The TOC and Rock–Eval pyrolysis analyses were conducted at the Key Laboratory of Deep Oil and Gas at the China University of Petroleum (East China). For estimating TOC, samples were first crushed to an approximate mesh size of 100 and then reacted with 5% diluted hydrochloric acid to remove inorganic carbon. After oven-drying at 80°C (5%), the TOC content was determined using an Elab-TOC/E2000 analyzer. Rock-Eval pyrolysis analysis required crushing the sample to an approximate mesh size of 100 and then heating the sample from 300 to 600°C (25°C/min) under anaerobic conditions with nitrogen as the carrier gas in a YQ-VIIA Rock–Eval rock pyrometer. The hydrocarbons and carbon dioxide emissions released from the rock were quantified using a flame ionization detector and thermal conductivity detector. The measured parameters were the free hydrocarbon content when vaporized at a temperature of 300°C (S1), residual hydrocarbon generation potential when the temperature was between 300 and 600°C (S2), and temperature of the maximum pyrolysis yield (Tmax).


## 3.3. XRD Tests

XRD tests were performed on the 99 samples to quantitatively analyze their relative mineral contents. All samples used for XRD analysis were powders with a particle size of <40μm. The analytical instrument used was a Panalytical X’Pert PRO MPD X-ray diffractometer with Cu Kα radiation (40 kV, 40 mA) at a scanning speed of 2°/min and testing angle range of 5°–90°.


## 3.4. Mechanical Experiment

Six rock samples from the study area were drilled as regular core plugs with a diameter of approximately 2.5 cm using an SK-5625A cycling CNC diamond wire cutting machine. In natural underground conditions, the rock is always under in situ stress and mostly in a state of stress from three axes. Therefore, when hydraulic fracturing is designed, it is necessary to consider the deformation characteristics of rocks under a certain confining pressure. In this test, a conventional triaxial compression experiment was performed on a plugged specimen. First, the specimen was placed in an autoclave, and lateral pressure (confining pressure) was then applied to the core. Finally, axial stress was applied to the core through the hydraulic cylinder of the press. The confining pressure was maintained at 20 MPa throughout the experiment, and the axial pressure was gradually increased until the specimen was destroyed.


## 4. Results

The TOC values of the MS samples ranged from 0.09% to 5.71%, with an average of 1.36%. Among the samples, 59 had aTOCvalue>1.00% (Figure 2(a)). The S1 value of the 99 samples ranged from 0.01% to 6.27% (average of 0.78%) (Figure 2(b)). Clay minerals and quartz were the major minerals in Es1L in the Raoyang Sag, with average contents of 31.80% (range: 1.30%–58.30%) and 22.30% (range: 1.00%–56.90%) (Figure 3(a)). The clay minerals in Es1L primarily comprised of illite (average: 40.50%) and illite–smectite mixed layer clay (average: 39.70%), followed by kaolinite and chlorite (Figure 3(b)). Other minerals also had various degrees of development (Figure 3(a)).Figure 2
Distribution frequency diagrams of (a) TOC and (b)S1 in the Es1L of the Raoyang Sag.
(a)(b)Figure 3
(a) Inorganic mineral and (b) clay mineral contents of Es1L in the Raoyang Sag. CM: clay minerals; Qtz: quartz; PF: potash feldspar; PI: plagioclase; Cal: calcite; Dol: dolomite; Ara: aragonite; Gyp: gypsum; Py: pyrite; and Sid: siderite.
(a)(b)Figure4 shows the stress–strain relationships of the six specimens under a confining pressure of 20 MPa. Based on the calculated results, Young’s modulus ranged from 9.04 to 28.07 GPa, with an average of 19.40 GPa. Poisson’s ratio was approximately 0.2. The distribution of Poisson’s ratio was relatively narrow with only small perturbations. Young’s modulus values varied significantly among the different samples. Young’s modulus was the major factor affecting the mechanical characteristics of the rock in the study area.Figure 4
Stress–strain curves of mudstone and shale.


## 5. Discussion

### 5.1. Types and Characteristics of Lithofacies

#### 5.1.1. Lithofacies Types

In this study, the minerals of the Es1L were divided into four categories: clay minerals, felsic minerals (quartz and feldspar), calcareous minerals (calcite, aragonite, dolomite, and iron dolomite), and other minerals (pyrite, siderite, and gypsum) according to the properties of each mineral in the MS. The MS were classified based on its mineral content [43]. MS with a clay mineral content>50% were defined as clay-rich MS. When clay minerals were the most abundant minerals, with a content between 33% and 50%, the MS were categorized as clay MS. Felsic-rich MS, felsic MS, calcareous-rich MS, and calcareous MS were also similarly defined (Table 1). The sedimentary structure of the Es1L was also categorized. According to the degree of bedding development, MS of the Es1L were approximately divided into laminated (beddingthickness<1 mm), laminar (1mm<beddingthickness<50cm), and massive (beddingthickness>50 cm) structures. The rock with laminated or laminar structure was classified as shale, and the rock with massive structure was called mudstone.Table 1
Classification of rock types in the Es1L of the Raoyang Sag.
CodenameRock typeMineral content (%)ClayFelsicCalcareousAClay-rich MS≥50<50<50BFelsic-rich MS<50≥50<50CCalcareous-rich MS<50<50≥50DClay MS33~50<Clay mineral content<Clay mineral contentEFelsic MS<Felsic mineral content33~50<Felsic mineral contentFCalcareous MS<Calcareous mineral content<Calcareous mineral content33–50In previous lithofacies classification schemes, the OM boundaries have typically been determined based on subjective experience. However, this method is controversial because of the differences in geological conditions. By analyzing the relationship between shale oil content and TOC, Lu et al. [44] classified shale oil and gas resources into three levels: scattered (ineffective), inefficient, and enriched resources. TOC can be used as a grading standard that is important for the study of shale oil. The oil content of the samples from Es1L exhibited the characteristics of a tripartite division with increases in TOC value and chloroform bitumen “A” (Figure 5). The TOC classification limits were determined to be 0.6% and 1.7%. Based on these classification boundaries, rocks were divided into three types: organic-rich MS (TOC>1.7%), organic-medium MS (0.6%<TOC<1.7%), and organic-poor MS (TOC<0.6%).Figure 5
Relationships between (a) TOC andS1 and (b) TOC and chloroform bitumen “A” in the Es1L of the Raoyang Sag (data from the PetroChina Huabei Oilfield Exploration Development Research Institute).
(a)(b)In summary, the classification rules for the shale in the Es1L in the Raoyang Sag include the following three aspects: structure (laminated, laminar, or massive), characteristics of OM contents (organic-rich, organic-medium, or organic-poor), and rock type (such as clay-rich shale) (Table 1). The lithofacies classification results of the 99 shale samples in the study area are shown in Figure 6.Figure 6
Lithofacies types of the mudstone and shale (MS) in the Es1L of the Raoyang Sag.


#### 5.1.2. Lithofacies Characteristics

The rock types of the 99 samples from the Es1L were primarily clay MS (35.35%) and calcareous-rich MS (23.23%), followed by felsic MS (21.21%) and calcareous MS (11.11%) (Figure 7). As shown in Figure 6, the rock type, bedding structure, and OM characteristics have a specific relationship. In calcareous-rich MS, calcareous minerals, siliceous minerals, clay minerals, and OM typically occurred in alternate bands (Figures 8(a) and 8(b)), or calcareous minerals, clay minerals, and OM occurred in alternate bands (Figures 8(c) and 8(d)). The TOC ranged from 0.54% to 5.07%, with an average of 2.45%. The calcareous MS and calcareous-rich MS exhibited the same bedding characteristics (Figures 8(e) and 8(f)). However, clay mineral bands and siliceous mineral bands appeared more frequently in the calcareous MS, and the OM content was slightly lower. Within the clay MS, with an average TOC of 2.44%, organic-rich MS were observed, which can be divided into two types: well-developed bedding type and underdeveloped bedding type. The bedding-developed clay MS had a ternary laminar structure. The ternary laminar layer comprised alternately distributed calcareous minerals, felsic minerals, clay minerals, and OM (Figures 8(g) and 8(h)). The clay MS with underdeveloped bedding were dominated by massive structures (Figures 8(i) and 8(j)). The former was dominated by organic-rich MS, whereas the latter was mostly organic-poor MS with OM content ranging from 0.09% to 1.60% (average: 0.58%). The clay-rich MS mostly had a massive structure and low OM content (Figures 8(k) and 8(l)). The felsic-rich MS (Figures 8(m) and 8(n)) and felsic MS (Figures 8(o) and 8(p)) were dominated by siliceous minerals, followed by clay minerals. Laminae were not developed, and most of them are organic-poor MS or organic-medium MS types.Figure 7
Correspondence between the rock types, bedding structure, and OM characteristics of the MS in the Es1L.Figure 8
(a, b) Calcareous-rich MS, well XL25x, 3540.65 m; (c, d) calcareous-rich MS, well G24, 2617.00 m; (e, f) calcareous MS, well XL25x, 3538.41 m; (g, h) clay MS, well B11x, 3543.90 m; (i, j) clay MS, well G34, 2517.56 m; (k, l) clay-rich MS, well G103, 2754.10 m; (m, n) felsic MS, well XL13, 3220.08 m; (o, p) felsic-rich MS, well G107, 2400.70 m.
(a)(b)(c)(d)(e)(f)(g)(h)(i)(j)(k)(l)(m)(n)(o)(p)


### 5.2. Brittleness Evaluation

#### 5.2.1. Brittleness Evaluation Based on Mineral Composition

Young’s modulus (E) is a physical quantity that reflects the ability of a solid material to resist deformation. It is expressed as the slope of the stress and strain in a uniaxial rock mechanics experiments [45–47].
(1)E=ΔσaΔεa,where Δσa is the axial stress increment and Δεa is the axial strain increment.Poisson’s ratio (υ), also called the transverse deformation coefficient, refers to the ratio of the absolute value of the transverse positive strain to the positive axial strain when the material is under tension or compression in one direction.
(2)v=ΔεrΔεa,where Δεa is the axial strain increment and Δεr is the radial strain increment.The compositions of the six samples are listed in Table2.Table 2
Composition characteristics of mudstone and shale.
Serial numberQtz (%)PF (%)Pl (%)Cal (%)Dol (%)Sid (%)Py (%)Ank (%)Ara (%)CM (%)RA24.15.011.012.30.00.00.42.27.236.9RB22.32.17.410.07.22.20.60.00.048.4RC10.30.01.910.50.00.00.733.223.517.8RD10.30.01.910.50.00.00.733.223.517.8RE32.24.510.815.56.41.30.30.00.029.2RF15.22.40.061.10.00.00.04.20.017.0RA and RE are felsic MS; RB is clay MS; and RC, RD, and RF are calcareous-rich MS.(1) Impact of Material Composition on Brittleness. The mechanical properties (Young’s modulus) of different minerals are extremely different and are closely related to the nature of the minerals [43]. Even for the same mineral, the contribution to brittleness in different regions may have completely opposite effects. The calcite in the Dongying Sag in eastern China has a degradative effect on the shale brittleness [43]. However, the calcite in the Barnett shale of North Texas is an essential contributor to effective reservoir fracturing [48]. Therefore, the contribution of the various minerals to brittleness cannot be generalized. Brittle minerals require specific analyses in different regions. The influence of the different minerals on Young’s modulus was analyzed comprehensively in the Shahejie Formation (Figure 9).Figure 9
Characteristics of mineralogical composition and Young’s modulus for the different samples.Figure10 shows that clay minerals are negatively correlated with Young’s modulus (Figure 10(a)) and positively correlated with calcareous minerals (Figure 10(b)). Three samples, RC, RD, and RF, had low felsic and high calcareous mineral contents. The respective calcareous mineral contents occupying the central part of the rock mineral composition (Figure 10(c)) were 67.28%, 67.28%, and 65.32% and significantly contributed to rock frangibility. For these three samples, the contribution of felsic minerals to brittleness was limited. Nevertheless, felsic minerals did make have a positive contribution to shale brittleness (Figure 10(d)). In addition, the shale oil development interval commonly had a relatively high OM content, and therefore, the impact of OM on the brittleness of the MS cannot be ignored. Eliyahu et al. [49] revealed that Young’s modulus of OM is less than that of inorganic minerals. Furthermore, OM typically occupied the micronanoscale pore spaces between particles, thereby reducing the overall shale brittleness. The trend shown in Figure 10(f) is consistent with the aforementioned research results. The content of other minerals was low, resulting in only a small influence on the rock brittleness (Figure 10(e)).Figure 10
Relationship of Young’s modulus of mudstone and shale with (a) clay, (b) felsic, (c) calcareous, (d) felsic and calcareous minerals, (e) other minerals, and (f) TOC.
(a)(b)(c)(d)(e)(f)(2) Impact of the Bedding Structure on Brittleness. Rock is cemented by various mineral crystals and contains cracks, joints, and other internal defects [50–52]. The bedding surface of shale has an essential influence on fracturing, particularly when the bedding surface is different from the loading direction [53, 54]. The RC and RD samples were obtained from the same shale but in different directions. The RC sample was perpendicular to the bedding, and the RD sample was parallel to the bedding. As shown in Figure 8, the rupture strength and Young’s modulus of the RD sample were lower than those of the RC sample. Fracturing parallel to the bedding plane is more conducive to the reformation of shale.


#### 5.2.2. BI Based on Mineralogy

Referring to previous evaluation methods [18] and considering them along with the mineralogical characteristics of the Shahejie Formation, clay minerals and TOC are negatively correlated with brittleness, and felsic minerals and calcareous minerals are positively correlated. Thus, the formula for calculating the BI of the material composition can be expressed as follows:
(3)B=Cfel+CcaCcl+Cfel+Cca+CTOC,where B is the brittleness index and Ccl, Cfel, Cca, and CTOC are the clay mineral content, felsic mineral content, calcareous mineral content, and TOC, respectively.This equation can more conveniently evaluate the brittleness characteristics of shale oil reservoirs in locations with sparse research data. This equation can help in selecting an interval favorable for hydraulic fracturing.


#### 5.2.3. Brittleness Characteristics of Lithofacies

Clay minerals are unfavorable for shale oil, principally because of two aspects. On the one hand, clay minerals are plastic. They react with water during hydraulic fracturing, thereby causing the formation to collapse [55]. The clay-rich shale contains many clay minerals, and majority of it has massive structure. These are the reasons for its low brittleness. In contrast, the brittleness of the clay shale is greater than that of the clay-rich shale. The content of brittle minerals is higher, and some clay shales have developed bedding. These factors are all favorable to increase.Sufficient oil and gas contents are essential for the development of unconventional oil and gas reservoirs [56]. After establishing this, the next step is to improve the original permeability of the shale through fracturing [57]. The shales with a high content of calcareous and felsic minerals were brittle (Figure 11). However, the calcareous-rich MS and calcareous MS, which are the dominant lithofacies for hydraulic fracturing, tended to have laminar structures with high S1 and well-developed pore spaces. In contrast, the felsic-rich MS and felsic MS were relatively poor lithofacies, with low S1 and block structures. Although the clay mineral content of the laminar clay shale was slightly higher, the developed bedding and high S1 caused it to be a secondary favorable lithofacies (Figure 11).Figure 11
Brittleness index andS1 of the different lithofacies.


### 5.3. Establish a Mineral Component Calculation Model Based on Logging Evaluation

#### 5.3.1. Establishment of the Model

The back propagation (BP) neural network is a multilayer feedforward neural network trained by the error backpropagation algorithm, comprising an input layer, output layer, and hidden layer [58, 59]. It has self-learning ability, particularly promotion, generalization, and self-adaptive ability and is one of the most widely used neural network models. It can learn and store many input and output pattern mapping relationships without revealing the mathematical equations describing these mapping relationships in advance [59]. The learning process of the BP neural network involves two parts: forward propagation processing and back propagation processing. In the forward propagation process, the input layer set is processed layer by layer from the input layer through the hidden layer and then transmitted to the output layer. The state of each neuron layer only affects the state of the next neuron layer. If the desired output layer cannot be obtained, the output is transferred to the back propagation. At this time, the error signal propagates from the output layer to the input layer. It adjusts the connection weights of the layers and biases of the neurons of each layer along the way so that the error signal becomes continuously smaller. The basic principle of the BP neural network for predicting mineral components is to find a mapping relationship by continuously modifying the network weights and correction threshold until a satisfactory accuracy is obtained [59–61]. In this study, the input data were the screened logging curves, and the output data were mineral components.Mineral components can be easily obtained from logging curves [62]. The relationships between different mineral components and conventional logging curves were analyzed. Afterward, the BP neural network algorithm was used to establish logging evaluation models for felsic, calcareous, and clay minerals. Finally, the inorganic mineral content was predicted. Taking well B11x in the Xiliu area of the Raoyang Sag as an example, the mineral contents were obtained via XRD analysis. First, the core data must be subjected to homing in with depth and matched with the corresponding logging curve. Conventional logging curves include lithology log (natural gamma log, spontaneous potential log, and caliper log), porosity log (acoustic time difference, density logging, and neutron log), and resistivity log (deep resistivity, medium resistivity, and shallow resistivity).Different logging curves have different logging characteristics for various mineral types. For different types of minerals, logging curves with more significant correlation coefficients should be selected for core depth homing and logging modeling. The correlation analysis results of the measured values of various minerals and the normalized values of the logging curves are presented in Table3. The BP neural network algorithm was used to calculate the continuous distributions of felsic, calcareous, and clay minerals in the Es1L of well B11x. The results showed that the calculated value of the mineral composition in well B11x had a high correlation with the measured value (Figure 12(a)). The calculation model of minerals based on the BP neural network can better predict the continuous distribution of MS minerals in the longitudinal direction (Figure 13).Table 3
Correlation analysis of clay, felsic, and calcareous mineral contents and logging curve for well B11x.
Mineral compositionCALSPGRACCNLDENR04R4R25Clay minerals0.41-0.29-0.330.020.37-0.41-0.55-0.43-0.50Felsic minerals0.48-0.31-0.410.140.39-0.44-0.48-0.54-0.59Calcareous minerals-0.460.330.39-0.07-0.380.440.600.500.57Figure 12
Establishment and application process of the mineral prediction model.
(a)(b)Figure 13
Comprehensive evaluation of longitudinal fractured section in well B11x.


#### 5.3.2. Application

Figure12(b) shows that the established mineral component prediction model predicts the mineral component contents. Subsequently, the lithofacies are divided based on the established lithofacies division scheme. The distribution position of facies favorable to fracturing are determined, and the hydraulic fracturing interval is finally selected.The lithofacies in well B11x were dominated by calcareous-rich MS and clay-rich MS (Figure13). The overall characteristics were as follows: calcareous minerals were highly developed (average 46.90%), with a large BI value (average: 0.654). The shale oil content was rich in well sections 3524–3551 m and well sections 3570–3598 m, with average of 2.68 mg/g and 1.27 mg/g, respectively. The 3521–3552 m well section has included a sizable calcareous-rich shale interval with high BI, which was easy to fracture and reform. This is an optimal configuration for the development of shale oil fracturing. The well section of 3570–3598 m, with complex mineral composition, primarily included clay-rich MS and calcareous-rich MS. Calcareous MS, clay MS, and felsic MS were also developed to varying degrees. In the most-enriched shale oil area, the lithofacies were clay-rich MS with a low BI, which was not conducive to hydraulic fracturing. However, calcareous-rich MS and calcareous MS were developed in the upper and lower parts, which were relatively brittle overall and can be used as alternative fracturing intervals.The profile connected by wells is located in the Zhaohuangzhuang-Suning area of the Raoyang Sag (Figure1). The middle part of the profile is located at the center of the sag, with a stable depositional environment (Figure 14). Multiple lithofacies appear alternately, including clay MS, felsic MS, felsic-rich MS, and calcareous MS. The left slope zone of the profile is close to the sediment provenance and exhibited a transitional trend. Felsic-rich MS with low shale oil content dominate the upper part of this area. A large section of calcareous-rich MS, with rich shale oil content and continuous distribution, exists in the middle and lower parts, representing good areas for shale oil development.Figure 14
Profile connected by wells showing the (a) brittleness index, (b) lithofacies, and (c)S1.
(a)(b)(c)


## 5.1. Types and Characteristics of Lithofacies

### 5.1.1. Lithofacies Types

In this study, the minerals of the Es1L were divided into four categories: clay minerals, felsic minerals (quartz and feldspar), calcareous minerals (calcite, aragonite, dolomite, and iron dolomite), and other minerals (pyrite, siderite, and gypsum) according to the properties of each mineral in the MS. The MS were classified based on its mineral content [43]. MS with a clay mineral content>50% were defined as clay-rich MS. When clay minerals were the most abundant minerals, with a content between 33% and 50%, the MS were categorized as clay MS. Felsic-rich MS, felsic MS, calcareous-rich MS, and calcareous MS were also similarly defined (Table 1). The sedimentary structure of the Es1L was also categorized. According to the degree of bedding development, MS of the Es1L were approximately divided into laminated (beddingthickness<1 mm), laminar (1mm<beddingthickness<50cm), and massive (beddingthickness>50 cm) structures. The rock with laminated or laminar structure was classified as shale, and the rock with massive structure was called mudstone.Table 1
Classification of rock types in the Es1L of the Raoyang Sag.
CodenameRock typeMineral content (%)ClayFelsicCalcareousAClay-rich MS≥50<50<50BFelsic-rich MS<50≥50<50CCalcareous-rich MS<50<50≥50DClay MS33~50<Clay mineral content<Clay mineral contentEFelsic MS<Felsic mineral content33~50<Felsic mineral contentFCalcareous MS<Calcareous mineral content<Calcareous mineral content33–50In previous lithofacies classification schemes, the OM boundaries have typically been determined based on subjective experience. However, this method is controversial because of the differences in geological conditions. By analyzing the relationship between shale oil content and TOC, Lu et al. [44] classified shale oil and gas resources into three levels: scattered (ineffective), inefficient, and enriched resources. TOC can be used as a grading standard that is important for the study of shale oil. The oil content of the samples from Es1L exhibited the characteristics of a tripartite division with increases in TOC value and chloroform bitumen “A” (Figure 5). The TOC classification limits were determined to be 0.6% and 1.7%. Based on these classification boundaries, rocks were divided into three types: organic-rich MS (TOC>1.7%), organic-medium MS (0.6%<TOC<1.7%), and organic-poor MS (TOC<0.6%).Figure 5
Relationships between (a) TOC andS1 and (b) TOC and chloroform bitumen “A” in the Es1L of the Raoyang Sag (data from the PetroChina Huabei Oilfield Exploration Development Research Institute).
(a)(b)In summary, the classification rules for the shale in the Es1L in the Raoyang Sag include the following three aspects: structure (laminated, laminar, or massive), characteristics of OM contents (organic-rich, organic-medium, or organic-poor), and rock type (such as clay-rich shale) (Table 1). The lithofacies classification results of the 99 shale samples in the study area are shown in Figure 6.Figure 6
Lithofacies types of the mudstone and shale (MS) in the Es1L of the Raoyang Sag.


### 5.1.2. Lithofacies Characteristics

The rock types of the 99 samples from the Es1L were primarily clay MS (35.35%) and calcareous-rich MS (23.23%), followed by felsic MS (21.21%) and calcareous MS (11.11%) (Figure 7). As shown in Figure 6, the rock type, bedding structure, and OM characteristics have a specific relationship. In calcareous-rich MS, calcareous minerals, siliceous minerals, clay minerals, and OM typically occurred in alternate bands (Figures 8(a) and 8(b)), or calcareous minerals, clay minerals, and OM occurred in alternate bands (Figures 8(c) and 8(d)). The TOC ranged from 0.54% to 5.07%, with an average of 2.45%. The calcareous MS and calcareous-rich MS exhibited the same bedding characteristics (Figures 8(e) and 8(f)). However, clay mineral bands and siliceous mineral bands appeared more frequently in the calcareous MS, and the OM content was slightly lower. Within the clay MS, with an average TOC of 2.44%, organic-rich MS were observed, which can be divided into two types: well-developed bedding type and underdeveloped bedding type. The bedding-developed clay MS had a ternary laminar structure. The ternary laminar layer comprised alternately distributed calcareous minerals, felsic minerals, clay minerals, and OM (Figures 8(g) and 8(h)). The clay MS with underdeveloped bedding were dominated by massive structures (Figures 8(i) and 8(j)). The former was dominated by organic-rich MS, whereas the latter was mostly organic-poor MS with OM content ranging from 0.09% to 1.60% (average: 0.58%). The clay-rich MS mostly had a massive structure and low OM content (Figures 8(k) and 8(l)). The felsic-rich MS (Figures 8(m) and 8(n)) and felsic MS (Figures 8(o) and 8(p)) were dominated by siliceous minerals, followed by clay minerals. Laminae were not developed, and most of them are organic-poor MS or organic-medium MS types.Figure 7
Correspondence between the rock types, bedding structure, and OM characteristics of the MS in the Es1L.Figure 8
(a, b) Calcareous-rich MS, well XL25x, 3540.65 m; (c, d) calcareous-rich MS, well G24, 2617.00 m; (e, f) calcareous MS, well XL25x, 3538.41 m; (g, h) clay MS, well B11x, 3543.90 m; (i, j) clay MS, well G34, 2517.56 m; (k, l) clay-rich MS, well G103, 2754.10 m; (m, n) felsic MS, well XL13, 3220.08 m; (o, p) felsic-rich MS, well G107, 2400.70 m.
(a)(b)(c)(d)(e)(f)(g)(h)(i)(j)(k)(l)(m)(n)(o)(p)


## 5.1.1. Lithofacies Types

In this study, the minerals of the Es1L were divided into four categories: clay minerals, felsic minerals (quartz and feldspar), calcareous minerals (calcite, aragonite, dolomite, and iron dolomite), and other minerals (pyrite, siderite, and gypsum) according to the properties of each mineral in the MS. The MS were classified based on its mineral content [43]. MS with a clay mineral content>50% were defined as clay-rich MS. When clay minerals were the most abundant minerals, with a content between 33% and 50%, the MS were categorized as clay MS. Felsic-rich MS, felsic MS, calcareous-rich MS, and calcareous MS were also similarly defined (Table 1). The sedimentary structure of the Es1L was also categorized. According to the degree of bedding development, MS of the Es1L were approximately divided into laminated (beddingthickness<1 mm), laminar (1mm<beddingthickness<50cm), and massive (beddingthickness>50 cm) structures. The rock with laminated or laminar structure was classified as shale, and the rock with massive structure was called mudstone.Table 1
Classification of rock types in the Es1L of the Raoyang Sag.
CodenameRock typeMineral content (%)ClayFelsicCalcareousAClay-rich MS≥50<50<50BFelsic-rich MS<50≥50<50CCalcareous-rich MS<50<50≥50DClay MS33~50<Clay mineral content<Clay mineral contentEFelsic MS<Felsic mineral content33~50<Felsic mineral contentFCalcareous MS<Calcareous mineral content<Calcareous mineral content33–50In previous lithofacies classification schemes, the OM boundaries have typically been determined based on subjective experience. However, this method is controversial because of the differences in geological conditions. By analyzing the relationship between shale oil content and TOC, Lu et al. [44] classified shale oil and gas resources into three levels: scattered (ineffective), inefficient, and enriched resources. TOC can be used as a grading standard that is important for the study of shale oil. The oil content of the samples from Es1L exhibited the characteristics of a tripartite division with increases in TOC value and chloroform bitumen “A” (Figure 5). The TOC classification limits were determined to be 0.6% and 1.7%. Based on these classification boundaries, rocks were divided into three types: organic-rich MS (TOC>1.7%), organic-medium MS (0.6%<TOC<1.7%), and organic-poor MS (TOC<0.6%).Figure 5
Relationships between (a) TOC andS1 and (b) TOC and chloroform bitumen “A” in the Es1L of the Raoyang Sag (data from the PetroChina Huabei Oilfield Exploration Development Research Institute).
(a)(b)In summary, the classification rules for the shale in the Es1L in the Raoyang Sag include the following three aspects: structure (laminated, laminar, or massive), characteristics of OM contents (organic-rich, organic-medium, or organic-poor), and rock type (such as clay-rich shale) (Table 1). The lithofacies classification results of the 99 shale samples in the study area are shown in Figure 6.Figure 6
Lithofacies types of the mudstone and shale (MS) in the Es1L of the Raoyang Sag.


## 5.1.2. Lithofacies Characteristics

The rock types of the 99 samples from the Es1L were primarily clay MS (35.35%) and calcareous-rich MS (23.23%), followed by felsic MS (21.21%) and calcareous MS (11.11%) (Figure 7). As shown in Figure 6, the rock type, bedding structure, and OM characteristics have a specific relationship. In calcareous-rich MS, calcareous minerals, siliceous minerals, clay minerals, and OM typically occurred in alternate bands (Figures 8(a) and 8(b)), or calcareous minerals, clay minerals, and OM occurred in alternate bands (Figures 8(c) and 8(d)). The TOC ranged from 0.54% to 5.07%, with an average of 2.45%. The calcareous MS and calcareous-rich MS exhibited the same bedding characteristics (Figures 8(e) and 8(f)). However, clay mineral bands and siliceous mineral bands appeared more frequently in the calcareous MS, and the OM content was slightly lower. Within the clay MS, with an average TOC of 2.44%, organic-rich MS were observed, which can be divided into two types: well-developed bedding type and underdeveloped bedding type. The bedding-developed clay MS had a ternary laminar structure. The ternary laminar layer comprised alternately distributed calcareous minerals, felsic minerals, clay minerals, and OM (Figures 8(g) and 8(h)). The clay MS with underdeveloped bedding were dominated by massive structures (Figures 8(i) and 8(j)). The former was dominated by organic-rich MS, whereas the latter was mostly organic-poor MS with OM content ranging from 0.09% to 1.60% (average: 0.58%). The clay-rich MS mostly had a massive structure and low OM content (Figures 8(k) and 8(l)). The felsic-rich MS (Figures 8(m) and 8(n)) and felsic MS (Figures 8(o) and 8(p)) were dominated by siliceous minerals, followed by clay minerals. Laminae were not developed, and most of them are organic-poor MS or organic-medium MS types.Figure 7
Correspondence between the rock types, bedding structure, and OM characteristics of the MS in the Es1L.Figure 8
(a, b) Calcareous-rich MS, well XL25x, 3540.65 m; (c, d) calcareous-rich MS, well G24, 2617.00 m; (e, f) calcareous MS, well XL25x, 3538.41 m; (g, h) clay MS, well B11x, 3543.90 m; (i, j) clay MS, well G34, 2517.56 m; (k, l) clay-rich MS, well G103, 2754.10 m; (m, n) felsic MS, well XL13, 3220.08 m; (o, p) felsic-rich MS, well G107, 2400.70 m.
(a)(b)(c)(d)(e)(f)(g)(h)(i)(j)(k)(l)(m)(n)(o)(p)


## 5.2. Brittleness Evaluation

### 5.2.1. Brittleness Evaluation Based on Mineral Composition

Young’s modulus (E) is a physical quantity that reflects the ability of a solid material to resist deformation. It is expressed as the slope of the stress and strain in a uniaxial rock mechanics experiments [45–47].
(1)E=ΔσaΔεa,where Δσa is the axial stress increment and Δεa is the axial strain increment.Poisson’s ratio (υ), also called the transverse deformation coefficient, refers to the ratio of the absolute value of the transverse positive strain to the positive axial strain when the material is under tension or compression in one direction.
(2)v=ΔεrΔεa,where Δεa is the axial strain increment and Δεr is the radial strain increment.The compositions of the six samples are listed in Table2.Table 2
Composition characteristics of mudstone and shale.
Serial numberQtz (%)PF (%)Pl (%)Cal (%)Dol (%)Sid (%)Py (%)Ank (%)Ara (%)CM (%)RA24.15.011.012.30.00.00.42.27.236.9RB22.32.17.410.07.22.20.60.00.048.4RC10.30.01.910.50.00.00.733.223.517.8RD10.30.01.910.50.00.00.733.223.517.8RE32.24.510.815.56.41.30.30.00.029.2RF15.22.40.061.10.00.00.04.20.017.0RA and RE are felsic MS; RB is clay MS; and RC, RD, and RF are calcareous-rich MS.(1) Impact of Material Composition on Brittleness. The mechanical properties (Young’s modulus) of different minerals are extremely different and are closely related to the nature of the minerals [43]. Even for the same mineral, the contribution to brittleness in different regions may have completely opposite effects. The calcite in the Dongying Sag in eastern China has a degradative effect on the shale brittleness [43]. However, the calcite in the Barnett shale of North Texas is an essential contributor to effective reservoir fracturing [48]. Therefore, the contribution of the various minerals to brittleness cannot be generalized. Brittle minerals require specific analyses in different regions. The influence of the different minerals on Young’s modulus was analyzed comprehensively in the Shahejie Formation (Figure 9).Figure 9
Characteristics of mineralogical composition and Young’s modulus for the different samples.Figure10 shows that clay minerals are negatively correlated with Young’s modulus (Figure 10(a)) and positively correlated with calcareous minerals (Figure 10(b)). Three samples, RC, RD, and RF, had low felsic and high calcareous mineral contents. The respective calcareous mineral contents occupying the central part of the rock mineral composition (Figure 10(c)) were 67.28%, 67.28%, and 65.32% and significantly contributed to rock frangibility. For these three samples, the contribution of felsic minerals to brittleness was limited. Nevertheless, felsic minerals did make have a positive contribution to shale brittleness (Figure 10(d)). In addition, the shale oil development interval commonly had a relatively high OM content, and therefore, the impact of OM on the brittleness of the MS cannot be ignored. Eliyahu et al. [49] revealed that Young’s modulus of OM is less than that of inorganic minerals. Furthermore, OM typically occupied the micronanoscale pore spaces between particles, thereby reducing the overall shale brittleness. The trend shown in Figure 10(f) is consistent with the aforementioned research results. The content of other minerals was low, resulting in only a small influence on the rock brittleness (Figure 10(e)).Figure 10
Relationship of Young’s modulus of mudstone and shale with (a) clay, (b) felsic, (c) calcareous, (d) felsic and calcareous minerals, (e) other minerals, and (f) TOC.
(a)(b)(c)(d)(e)(f)(2) Impact of the Bedding Structure on Brittleness. Rock is cemented by various mineral crystals and contains cracks, joints, and other internal defects [50–52]. The bedding surface of shale has an essential influence on fracturing, particularly when the bedding surface is different from the loading direction [53, 54]. The RC and RD samples were obtained from the same shale but in different directions. The RC sample was perpendicular to the bedding, and the RD sample was parallel to the bedding. As shown in Figure 8, the rupture strength and Young’s modulus of the RD sample were lower than those of the RC sample. Fracturing parallel to the bedding plane is more conducive to the reformation of shale.


### 5.2.2. BI Based on Mineralogy

Referring to previous evaluation methods [18] and considering them along with the mineralogical characteristics of the Shahejie Formation, clay minerals and TOC are negatively correlated with brittleness, and felsic minerals and calcareous minerals are positively correlated. Thus, the formula for calculating the BI of the material composition can be expressed as follows:
(3)B=Cfel+CcaCcl+Cfel+Cca+CTOC,where B is the brittleness index and Ccl, Cfel, Cca, and CTOC are the clay mineral content, felsic mineral content, calcareous mineral content, and TOC, respectively.This equation can more conveniently evaluate the brittleness characteristics of shale oil reservoirs in locations with sparse research data. This equation can help in selecting an interval favorable for hydraulic fracturing.


### 5.2.3. Brittleness Characteristics of Lithofacies

Clay minerals are unfavorable for shale oil, principally because of two aspects. On the one hand, clay minerals are plastic. They react with water during hydraulic fracturing, thereby causing the formation to collapse [55]. The clay-rich shale contains many clay minerals, and majority of it has massive structure. These are the reasons for its low brittleness. In contrast, the brittleness of the clay shale is greater than that of the clay-rich shale. The content of brittle minerals is higher, and some clay shales have developed bedding. These factors are all favorable to increase.Sufficient oil and gas contents are essential for the development of unconventional oil and gas reservoirs [56]. After establishing this, the next step is to improve the original permeability of the shale through fracturing [57]. The shales with a high content of calcareous and felsic minerals were brittle (Figure 11). However, the calcareous-rich MS and calcareous MS, which are the dominant lithofacies for hydraulic fracturing, tended to have laminar structures with high S1 and well-developed pore spaces. In contrast, the felsic-rich MS and felsic MS were relatively poor lithofacies, with low S1 and block structures. Although the clay mineral content of the laminar clay shale was slightly higher, the developed bedding and high S1 caused it to be a secondary favorable lithofacies (Figure 11).Figure 11
Brittleness index andS1 of the different lithofacies.


## 5.2.1. Brittleness Evaluation Based on Mineral Composition

Young’s modulus (E) is a physical quantity that reflects the ability of a solid material to resist deformation. It is expressed as the slope of the stress and strain in a uniaxial rock mechanics experiments [45–47].
(1)E=ΔσaΔεa,where Δσa is the axial stress increment and Δεa is the axial strain increment.Poisson’s ratio (υ), also called the transverse deformation coefficient, refers to the ratio of the absolute value of the transverse positive strain to the positive axial strain when the material is under tension or compression in one direction.
(2)v=ΔεrΔεa,where Δεa is the axial strain increment and Δεr is the radial strain increment.The compositions of the six samples are listed in Table2.Table 2
Composition characteristics of mudstone and shale.
Serial numberQtz (%)PF (%)Pl (%)Cal (%)Dol (%)Sid (%)Py (%)Ank (%)Ara (%)CM (%)RA24.15.011.012.30.00.00.42.27.236.9RB22.32.17.410.07.22.20.60.00.048.4RC10.30.01.910.50.00.00.733.223.517.8RD10.30.01.910.50.00.00.733.223.517.8RE32.24.510.815.56.41.30.30.00.029.2RF15.22.40.061.10.00.00.04.20.017.0RA and RE are felsic MS; RB is clay MS; and RC, RD, and RF are calcareous-rich MS.(1) Impact of Material Composition on Brittleness. The mechanical properties (Young’s modulus) of different minerals are extremely different and are closely related to the nature of the minerals [43]. Even for the same mineral, the contribution to brittleness in different regions may have completely opposite effects. The calcite in the Dongying Sag in eastern China has a degradative effect on the shale brittleness [43]. However, the calcite in the Barnett shale of North Texas is an essential contributor to effective reservoir fracturing [48]. Therefore, the contribution of the various minerals to brittleness cannot be generalized. Brittle minerals require specific analyses in different regions. The influence of the different minerals on Young’s modulus was analyzed comprehensively in the Shahejie Formation (Figure 9).Figure 9
Characteristics of mineralogical composition and Young’s modulus for the different samples.Figure10 shows that clay minerals are negatively correlated with Young’s modulus (Figure 10(a)) and positively correlated with calcareous minerals (Figure 10(b)). Three samples, RC, RD, and RF, had low felsic and high calcareous mineral contents. The respective calcareous mineral contents occupying the central part of the rock mineral composition (Figure 10(c)) were 67.28%, 67.28%, and 65.32% and significantly contributed to rock frangibility. For these three samples, the contribution of felsic minerals to brittleness was limited. Nevertheless, felsic minerals did make have a positive contribution to shale brittleness (Figure 10(d)). In addition, the shale oil development interval commonly had a relatively high OM content, and therefore, the impact of OM on the brittleness of the MS cannot be ignored. Eliyahu et al. [49] revealed that Young’s modulus of OM is less than that of inorganic minerals. Furthermore, OM typically occupied the micronanoscale pore spaces between particles, thereby reducing the overall shale brittleness. The trend shown in Figure 10(f) is consistent with the aforementioned research results. The content of other minerals was low, resulting in only a small influence on the rock brittleness (Figure 10(e)).Figure 10
Relationship of Young’s modulus of mudstone and shale with (a) clay, (b) felsic, (c) calcareous, (d) felsic and calcareous minerals, (e) other minerals, and (f) TOC.
(a)(b)(c)(d)(e)(f)(2) Impact of the Bedding Structure on Brittleness. Rock is cemented by various mineral crystals and contains cracks, joints, and other internal defects [50–52]. The bedding surface of shale has an essential influence on fracturing, particularly when the bedding surface is different from the loading direction [53, 54]. The RC and RD samples were obtained from the same shale but in different directions. The RC sample was perpendicular to the bedding, and the RD sample was parallel to the bedding. As shown in Figure 8, the rupture strength and Young’s modulus of the RD sample were lower than those of the RC sample. Fracturing parallel to the bedding plane is more conducive to the reformation of shale.


## 5.2.2. BI Based on Mineralogy

Referring to previous evaluation methods [18] and considering them along with the mineralogical characteristics of the Shahejie Formation, clay minerals and TOC are negatively correlated with brittleness, and felsic minerals and calcareous minerals are positively correlated. Thus, the formula for calculating the BI of the material composition can be expressed as follows:
(3)B=Cfel+CcaCcl+Cfel+Cca+CTOC,where B is the brittleness index and Ccl, Cfel, Cca, and CTOC are the clay mineral content, felsic mineral content, calcareous mineral content, and TOC, respectively.This equation can more conveniently evaluate the brittleness characteristics of shale oil reservoirs in locations with sparse research data. This equation can help in selecting an interval favorable for hydraulic fracturing.


## 5.2.3. Brittleness Characteristics of Lithofacies

Clay minerals are unfavorable for shale oil, principally because of two aspects. On the one hand, clay minerals are plastic. They react with water during hydraulic fracturing, thereby causing the formation to collapse [55]. The clay-rich shale contains many clay minerals, and majority of it has massive structure. These are the reasons for its low brittleness. In contrast, the brittleness of the clay shale is greater than that of the clay-rich shale. The content of brittle minerals is higher, and some clay shales have developed bedding. These factors are all favorable to increase.Sufficient oil and gas contents are essential for the development of unconventional oil and gas reservoirs [56]. After establishing this, the next step is to improve the original permeability of the shale through fracturing [57]. The shales with a high content of calcareous and felsic minerals were brittle (Figure 11). However, the calcareous-rich MS and calcareous MS, which are the dominant lithofacies for hydraulic fracturing, tended to have laminar structures with high S1 and well-developed pore spaces. In contrast, the felsic-rich MS and felsic MS were relatively poor lithofacies, with low S1 and block structures. Although the clay mineral content of the laminar clay shale was slightly higher, the developed bedding and high S1 caused it to be a secondary favorable lithofacies (Figure 11).Figure 11
Brittleness index andS1 of the different lithofacies.


## 5.3. Establish a Mineral Component Calculation Model Based on Logging Evaluation

### 5.3.1. Establishment of the Model

The back propagation (BP) neural network is a multilayer feedforward neural network trained by the error backpropagation algorithm, comprising an input layer, output layer, and hidden layer [58, 59]. It has self-learning ability, particularly promotion, generalization, and self-adaptive ability and is one of the most widely used neural network models. It can learn and store many input and output pattern mapping relationships without revealing the mathematical equations describing these mapping relationships in advance [59]. The learning process of the BP neural network involves two parts: forward propagation processing and back propagation processing. In the forward propagation process, the input layer set is processed layer by layer from the input layer through the hidden layer and then transmitted to the output layer. The state of each neuron layer only affects the state of the next neuron layer. If the desired output layer cannot be obtained, the output is transferred to the back propagation. At this time, the error signal propagates from the output layer to the input layer. It adjusts the connection weights of the layers and biases of the neurons of each layer along the way so that the error signal becomes continuously smaller. The basic principle of the BP neural network for predicting mineral components is to find a mapping relationship by continuously modifying the network weights and correction threshold until a satisfactory accuracy is obtained [59–61]. In this study, the input data were the screened logging curves, and the output data were mineral components.Mineral components can be easily obtained from logging curves [62]. The relationships between different mineral components and conventional logging curves were analyzed. Afterward, the BP neural network algorithm was used to establish logging evaluation models for felsic, calcareous, and clay minerals. Finally, the inorganic mineral content was predicted. Taking well B11x in the Xiliu area of the Raoyang Sag as an example, the mineral contents were obtained via XRD analysis. First, the core data must be subjected to homing in with depth and matched with the corresponding logging curve. Conventional logging curves include lithology log (natural gamma log, spontaneous potential log, and caliper log), porosity log (acoustic time difference, density logging, and neutron log), and resistivity log (deep resistivity, medium resistivity, and shallow resistivity).Different logging curves have different logging characteristics for various mineral types. For different types of minerals, logging curves with more significant correlation coefficients should be selected for core depth homing and logging modeling. The correlation analysis results of the measured values of various minerals and the normalized values of the logging curves are presented in Table3. The BP neural network algorithm was used to calculate the continuous distributions of felsic, calcareous, and clay minerals in the Es1L of well B11x. The results showed that the calculated value of the mineral composition in well B11x had a high correlation with the measured value (Figure 12(a)). The calculation model of minerals based on the BP neural network can better predict the continuous distribution of MS minerals in the longitudinal direction (Figure 13).Table 3
Correlation analysis of clay, felsic, and calcareous mineral contents and logging curve for well B11x.
Mineral compositionCALSPGRACCNLDENR04R4R25Clay minerals0.41-0.29-0.330.020.37-0.41-0.55-0.43-0.50Felsic minerals0.48-0.31-0.410.140.39-0.44-0.48-0.54-0.59Calcareous minerals-0.460.330.39-0.07-0.380.440.600.500.57Figure 12
Establishment and application process of the mineral prediction model.
(a)(b)Figure 13
Comprehensive evaluation of longitudinal fractured section in well B11x.


### 5.3.2. Application

Figure12(b) shows that the established mineral component prediction model predicts the mineral component contents. Subsequently, the lithofacies are divided based on the established lithofacies division scheme. The distribution position of facies favorable to fracturing are determined, and the hydraulic fracturing interval is finally selected.The lithofacies in well B11x were dominated by calcareous-rich MS and clay-rich MS (Figure13). The overall characteristics were as follows: calcareous minerals were highly developed (average 46.90%), with a large BI value (average: 0.654). The shale oil content was rich in well sections 3524–3551 m and well sections 3570–3598 m, with average of 2.68 mg/g and 1.27 mg/g, respectively. The 3521–3552 m well section has included a sizable calcareous-rich shale interval with high BI, which was easy to fracture and reform. This is an optimal configuration for the development of shale oil fracturing. The well section of 3570–3598 m, with complex mineral composition, primarily included clay-rich MS and calcareous-rich MS. Calcareous MS, clay MS, and felsic MS were also developed to varying degrees. In the most-enriched shale oil area, the lithofacies were clay-rich MS with a low BI, which was not conducive to hydraulic fracturing. However, calcareous-rich MS and calcareous MS were developed in the upper and lower parts, which were relatively brittle overall and can be used as alternative fracturing intervals.The profile connected by wells is located in the Zhaohuangzhuang-Suning area of the Raoyang Sag (Figure1). The middle part of the profile is located at the center of the sag, with a stable depositional environment (Figure 14). Multiple lithofacies appear alternately, including clay MS, felsic MS, felsic-rich MS, and calcareous MS. The left slope zone of the profile is close to the sediment provenance and exhibited a transitional trend. Felsic-rich MS with low shale oil content dominate the upper part of this area. A large section of calcareous-rich MS, with rich shale oil content and continuous distribution, exists in the middle and lower parts, representing good areas for shale oil development.Figure 14
Profile connected by wells showing the (a) brittleness index, (b) lithofacies, and (c)S1.
(a)(b)(c)


## 5.3.1. Establishment of the Model

The back propagation (BP) neural network is a multilayer feedforward neural network trained by the error backpropagation algorithm, comprising an input layer, output layer, and hidden layer [58, 59]. It has self-learning ability, particularly promotion, generalization, and self-adaptive ability and is one of the most widely used neural network models. It can learn and store many input and output pattern mapping relationships without revealing the mathematical equations describing these mapping relationships in advance [59]. The learning process of the BP neural network involves two parts: forward propagation processing and back propagation processing. In the forward propagation process, the input layer set is processed layer by layer from the input layer through the hidden layer and then transmitted to the output layer. The state of each neuron layer only affects the state of the next neuron layer. If the desired output layer cannot be obtained, the output is transferred to the back propagation. At this time, the error signal propagates from the output layer to the input layer. It adjusts the connection weights of the layers and biases of the neurons of each layer along the way so that the error signal becomes continuously smaller. The basic principle of the BP neural network for predicting mineral components is to find a mapping relationship by continuously modifying the network weights and correction threshold until a satisfactory accuracy is obtained [59–61]. In this study, the input data were the screened logging curves, and the output data were mineral components.Mineral components can be easily obtained from logging curves [62]. The relationships between different mineral components and conventional logging curves were analyzed. Afterward, the BP neural network algorithm was used to establish logging evaluation models for felsic, calcareous, and clay minerals. Finally, the inorganic mineral content was predicted. Taking well B11x in the Xiliu area of the Raoyang Sag as an example, the mineral contents were obtained via XRD analysis. First, the core data must be subjected to homing in with depth and matched with the corresponding logging curve. Conventional logging curves include lithology log (natural gamma log, spontaneous potential log, and caliper log), porosity log (acoustic time difference, density logging, and neutron log), and resistivity log (deep resistivity, medium resistivity, and shallow resistivity).Different logging curves have different logging characteristics for various mineral types. For different types of minerals, logging curves with more significant correlation coefficients should be selected for core depth homing and logging modeling. The correlation analysis results of the measured values of various minerals and the normalized values of the logging curves are presented in Table3. The BP neural network algorithm was used to calculate the continuous distributions of felsic, calcareous, and clay minerals in the Es1L of well B11x. The results showed that the calculated value of the mineral composition in well B11x had a high correlation with the measured value (Figure 12(a)). The calculation model of minerals based on the BP neural network can better predict the continuous distribution of MS minerals in the longitudinal direction (Figure 13).Table 3
Correlation analysis of clay, felsic, and calcareous mineral contents and logging curve for well B11x.
Mineral compositionCALSPGRACCNLDENR04R4R25Clay minerals0.41-0.29-0.330.020.37-0.41-0.55-0.43-0.50Felsic minerals0.48-0.31-0.410.140.39-0.44-0.48-0.54-0.59Calcareous minerals-0.460.330.39-0.07-0.380.440.600.500.57Figure 12
Establishment and application process of the mineral prediction model.
(a)(b)Figure 13
Comprehensive evaluation of longitudinal fractured section in well B11x.


## 5.3.2. Application

Figure12(b) shows that the established mineral component prediction model predicts the mineral component contents. Subsequently, the lithofacies are divided based on the established lithofacies division scheme. The distribution position of facies favorable to fracturing are determined, and the hydraulic fracturing interval is finally selected.The lithofacies in well B11x were dominated by calcareous-rich MS and clay-rich MS (Figure13). The overall characteristics were as follows: calcareous minerals were highly developed (average 46.90%), with a large BI value (average: 0.654). The shale oil content was rich in well sections 3524–3551 m and well sections 3570–3598 m, with average of 2.68 mg/g and 1.27 mg/g, respectively. The 3521–3552 m well section has included a sizable calcareous-rich shale interval with high BI, which was easy to fracture and reform. This is an optimal configuration for the development of shale oil fracturing. The well section of 3570–3598 m, with complex mineral composition, primarily included clay-rich MS and calcareous-rich MS. Calcareous MS, clay MS, and felsic MS were also developed to varying degrees. In the most-enriched shale oil area, the lithofacies were clay-rich MS with a low BI, which was not conducive to hydraulic fracturing. However, calcareous-rich MS and calcareous MS were developed in the upper and lower parts, which were relatively brittle overall and can be used as alternative fracturing intervals.The profile connected by wells is located in the Zhaohuangzhuang-Suning area of the Raoyang Sag (Figure1). The middle part of the profile is located at the center of the sag, with a stable depositional environment (Figure 14). Multiple lithofacies appear alternately, including clay MS, felsic MS, felsic-rich MS, and calcareous MS. The left slope zone of the profile is close to the sediment provenance and exhibited a transitional trend. Felsic-rich MS with low shale oil content dominate the upper part of this area. A large section of calcareous-rich MS, with rich shale oil content and continuous distribution, exists in the middle and lower parts, representing good areas for shale oil development.Figure 14
Profile connected by wells showing the (a) brittleness index, (b) lithofacies, and (c)S1.
(a)(b)(c)


## 6. Conclusions

(1)
The lithofacies of Es1L were divided into six types. Calcareous-rich MS and calcareous MS had layered structures and developed OM. The laminated clay MS had a high TOC value. The clay-rich MS, felsic-rich MS, and felsic MS primarily had massive structures and low TOC(2)
Minerals such as quartz, feldspar, calcite, and dolomite in the Es1L of the Raoyang Sag positively contributed to the shale brittleness. In contrast, clay and OM had an adverse effect on shale brittleness. The BP neural network mineral calculation model can better predict the continuous distribution of the lengthwise mineral composition of MS. The lengthwise distribution of the brittleness of the entire well can be obtained(3)
Lithofacies with high brittle mineral contents and developed laminae exhibited better brittleness. Although OM is a plastic substance, a high TOC value is a prerequisite for shale oil development. Therefore, calcareous-rich MS and calcareous MS are the dominant lithofacies for shale oil development in the Es1L in the Raoyang Sag. Their enrichment area can be considered as hydraulic fracturing area for shale oil reservoirs



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*Source: 2729597-2022-01-15.xml*