Multiparameter Logging Evaluation of Chang 7<sub>3</sub> Shale Oil in the Jiyuan Area, Ordos Basin

Liu, Bobiao; Tan, Chengqian; Huai, Yinchao; Tan, Yuhan; Zhang, Han; Feng, Zhao

doi:https://doi.org/10.1155/2023/1672207

Geofluids

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Abstract Introduction Data Results and Discussion Conclusion Data Availability Conflicts of Interest Acknowledgments References Copyright Related Articles

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Mechanisms of Oil and Gas Accumulation in Fine-Grained Sedimentary Systems

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Research Article | Open Access

Volume 2023 | Article ID 1672207 | https://doi.org/10.1155/2023/1672207

Multiparameter Logging Evaluation of Chang 7₃ Shale Oil in the Jiyuan Area, Ordos Basin

Bobiao Liu,^1,2Chengqian Tan,^1,2Yinchao Huai,^1,2Yuhan Tan,³Han Zhang,^1,2and Zhao Feng^1,2

Academic Editor: Yang Wang

Received22 Dec 2022

Revised20 Feb 2023

Accepted15 May 2023

Published01 Jun 2023

Abstract

The accurate evaluation of shale oil and gas reservoirs is of great significance to the integrated development of geology and engineering. Based on the core analysis, conventional logging, and array acoustic logging data, the total organic carbon, hydrocarbon generation potential, brittleness, and anisotropy of shale reservoirs were calculated. The p-wave time difference curves calculated by the artificial neural network (ANN) method and the conventional logging curve fitting method were compared. The multiresolution graph-based clustering (MRGC) method was used to classify shale reservoirs into three categories and evaluate the classification results. Brittle minerals such as quartz and feldspar were mainly found to be present in shale reservoirs and clay minerals which mainly consisted of illite. The Chang 7₃ reservoir is rich in organic matter and has great potential for survival. The p-wave time difference calculated by the fitting formula of the shear wave time difference meter demonstrated high accuracy and did not require a complex ANN model. MRGC method can well classify shale reservoir types. The classification results reduce the interference of human factors and are more scientific and reasonable. This research method is of great significance for the scientific classification and evaluation of shale reservoirs.

1. Introduction

As an unconventional oil and gas resource, shale oil has emerged as a promising spot in global oil and gas exploration. With the deepening of exploration practices and the introduction of new technology, China’s continental shale oil resources could have enormous development potential [1–3]. In terms of reservoir physical properties, pore structure, and fluid occurrence mode, shale oil, conventional oil, and gas reservoirs differ significantly. These differences are mainly manifested in two aspects. First, oil and gas are self-generating and self-storing. Second, there is no production capacity under natural conditions. Volume fracturing must be carried out to obtain industrial oil and gas flow [4–7]. This leads to the difference between the evaluation of shale oil reservoirs and conventional reservoirs. Conventional reservoir mainly focuses on reservoir physical property, porosity, permeability, oil saturation, and other parameters [8, 9].

Shale evaluation methods include the conventional logging curve characteristic method, method, and logging curve overlap method [10–13]. The most common method was proposed by Passey et al. in 1990 and was initially used for the qualitative identification of organic-rich source rocks [14]. Shale has the characteristics of large shale thickness, good continuity, and small change in mineral composition [15, 16]. Therefore, the method has achieved good results in identifying high-quality shale during shale formation [17, 18].

The stress-strain curve of rock, which records the whole process from loading to failure, is simple and easy to learn. It is the most intuitive and effective method for qualitative evaluation of rock brittleness [19, 20]. There are two main methods for the characterization of rock brittleness. One is based on Young’s modulus and Poisson’s ratio. This method does not consider the impact of stress environment and mineral content on the brittleness index. It is not suitable for strata with large changes in burial depth and mineral types. Another one is based on the content proportion of brittle minerals (e.g., quartz, feldspar, and carbonate) in the rock [21, 22]. This method only considers the impact of a single factor of mineral composition on the brittleness of the rock. It does not consider the impact on the brittleness during the process of rock diagenesis and evolution. When using the mineral method, it is necessary to find out the primary types of brittle minerals and diagenesis background in the study area as well as to establish an appropriate evaluation model [20].

The evaluation of shale oil reservoirs should be carried out while considering geological characteristics and engineering transformation. Numerous research has been conducted on shale oil geological characteristics, formation mechanism, enrichment law, volume transformation, and enhanced oil recovery. However, the evaluation of shale was mostly conducted while considering a single factor, and the geological and engineering characteristics of shale oil were rarely combined to comprehensively evaluate its sweet spots. The exploration and development practice of shale oil fields shows that even geological hotspots with high hydrocarbon generation potential are not always the most suitable engineering hotspots for volume fracturing. The main goal of shale oil and gas evaluation is to identify a sweet spot area for shale development that combines geological and engineering factors and achieves integrated geological engineering development [23–34].

We calculated the TOC, hydrocarbon generation potential, and stress of the reservoir while considering the logging and core analysis data. The sweet spot characteristics of the shale oil reservoir were also evaluated by integrating geological engineering factors. This study has a significant impact on the logging evaluation of shale oil reservoirs.

2. Geological Setting

Ordos Basin, which spans across Shaanxi, Ningxia, Gansu, Inner Mongolia, and Shaanxi Provinces, is the second largest oil- and gas-bearing basin in China (Figure 1(a)). The Yanchang Formation in the basin deposited a complete and typical clastic sedimentary system characterized by fluvial-lacustrine facies as a result of the Indonesian movement in the Late Triassic. According to the sedimentary cycle, it can be divided into ten oil-bearing formations (Chang 10-Chang 1) from bottom to top [35, 36]. The Chang 10-Chang 7 is mainly the expansion period of the lake, and the lake shoreline moves outward rapidly. During the Chang 7₃ period, the development of the lake basin reached its peak. The widely deposited deep lake mudstone formed the main source rocks of the Mesozoic basin. During the Chang 6-Chang 4 period, the lake basin entered a shrinking period after experiencing a short lake flooding period. The lake basin gradually disappeared from the Chang 3-Chang 1. Multiple oil-bearing series were formed vertically as a result of the formation of numerous favorable source-reservoir-cap assemblages (Figure 1(b)) [37, 38].

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The Jiyuan Oilfield is located across the Yishan Slope and Tianhuan Sag secondary tectonic units in the western Ordos Basin. Considering the Triassic Chang 7 period, it is located in the sedimentary center of the lake basin. The water depth is large, and the deep lake-semideep lake surfaces are developed. Thick layers of lacustrine organic-rich dark mudstone are deposited, which is the main rock source of the basin (Figure 1(b)). According to the core observation, Chang 7 mainly develops carbonaceous mudstone that contains plant fragments, fish scales, and fish stone (Figure 2). The thickness of the hydrocarbon source rock is generally more than 40 meters. The kerogen is dominated by sapropel formation and vitrinite, mainly types II and I. The type of organic matter is relatively good. The average content of chloroform asphalt A is 0.8% with a higher abundance of organic matter. The organic matter has a maturity level greater than 0.85 while having significant potential for hydrocarbon generation [39].

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3. Data and Methodology

3.1. Data

This study mainly utilized conventional logging data, array acoustic logging, and core analysis data. Conventional logging data was obtained from Jiyuan Oilfield including three wells (Y-285, Y-162, and Y-335). The target layer was Yanchang Formation 7₃. The logging curve included natural gamma, natural potential, well diameter, acoustic transit time, compensation density, compensation neutron, and deep shallow resistivity. Array acoustic logging was obtained from well Y-162 and well Y-335 including density, shear wave slowness, and compressional wave time difference. Core analysis data was obtained from Y-285 including whole rock analysis, clay mineral, and rock pyrolysis as well as organic carbon content test.

3.1.1. Logging Data

The geophysical logging method was used to measure the rock’s physical properties point by point with depth. Conventional logging curves were used to calculate organic carbon content, hydrocarbon generation potential, shear wave time difference, and ground stress. Five curves were mainly selected as follows:

Natural gamma (GR). It measured the total natural radioactivity in the formation.

Acoustic transit time (Ac). It measured the propagation velocity of elastic waves in the stratum.

Compensated neutron (CNL). It calculated the porosity of rock by measuring the degree of neutron energy loss in a porous formation.

Density (Den). It measured the electronic density in the formation.

Resistivity (RT). It measured the conductivity of fluids in the formation and pores.

3.1.2. Core Analysis Data

The core analysis was obtained from the Chang 7₃ shale of well Y-285. Rock pyrolysis analysis and organic carbon content determination were performed on 29 samples. Seven samples were selected for whole rock quantitative analysis and clay mineral analysis.

3.2. Methodology

For the evaluation of shale oil reservoirs, the most direct method is to use core analysis. However, due to the high cost of coring and testing, it is impossible to carry out very well, but every well has conventional logging curve data. Therefore, the parameters of the shale oil reservoir were calculated and evaluated using conventional logging data by combining core and conventional logging data. The workflow is shown in Figure 3. The organic carbon content of the reservoir was first calculated based on conventional logging data. The core analysis results were then used to establish a calculation model between organic carbon and hydrocarbon generation potential. The hydrocarbon generation potential of different depths in the well was calculated. The calculation model of the shear wave time difference curve was subsequently established by using the conventional logging and array acoustic logging data. The shear wave time difference of shale oil was calculated based on conventional logging curves. According to Poisson’s ratio and Young’s modulus, the brittleness index and anisotropy of the formation were calculated by using the p-wave time difference, shear wave time difference, and density curve. Finally, the organic carbon, hydrocarbon generation potential, brittleness index, and anisotropy index were comprehensively used to classify and evaluate shale oil reservoirs by the MRGC method.

3.2.1. Calculation of Total Organic Carbon

Organic carbon is an important index to evaluate the abundance of organic matter in shale reservoirs. The most direct and reliable method is to use core data for laboratory measurement. However, continuous data cannot be obtained due to the high cost of coring. Using conventional logging curves to calculate total organic carbon content is not only convenient but also inexpensive for evaluating shale organic matter content. In this study, the total organic carbon content was calculated by superposition of acoustic time difference and resistivity curve [32]. The total organic carbon of the shale reservoir was calculated by where is the differentiation amplitude of acoustic and resistivity curves, which includes rock properties and source rock characteristics, is the logging (deep) resistivity (Ω·m), is the measured acoustic time difference (μs/ft), and are the resistivity (Ω·m) and acoustic transit time difference base value, and is the index reflecting the maturity of organic matter.

3.2.2. Calculation of Hydrocarbon Generation Potential

Hydrocarbon generation potential refers to the sum of pyrolytic hydrocarbons and residual hydrocarbons in rocks. It is an important parameter for evaluating the hydrocarbon generation capacity of shale reservoirs. It mainly uses core data to directly measure hydrocarbon generation potential. There are a lot of logging data because of the limited coring data in the study area. Therefore, the hydrocarbon generation potential of rocks is calculated by combining logging with core analysis. Since the hydrocarbon generation potential is closely related to the amount of organic matter, the calculation model was constructed by considering the obtained organic carbon content. The hydrocarbon generation potential was obtained by pyrolysis. Equation (4) represents the calculation formula. where is the total organic carbon content, is the pyrolytic hydrocarbon of rock, is the residual hydrocarbon, and and are the coefficients and constants of fitting, respectively.

3.2.3. Calculation of Terrestrial Stress

Shale oil can obtain industrial oil flow only through fracturing modification. Therefore, rock brittleness evaluation is vital for optimizing fracturing stimulation intervals [40]. The brittleness index is generally calculated based on core analysis or array acoustic logging data. There is no core or array acoustic logging for development wells. Therefore, it is necessary to use conventional logging curves to calculate the brittleness index.

(1) Calculate Shear Wave Time Difference. Conventional logging only measures the p-wave time difference of the formation. It does not measure the s-wave time difference. Therefore, it is necessary to calculate the shear wave time difference curve of the formation before using conventional logging data to calculate the ground stress. The single factor method and ANN method were used to establish the shear wave time difference calculation model while considering the acoustic logging data. The shear wave time difference calculation model was found to be suitable for the study area after comparing the results of each method.

(2) Brittleness Index. (a)Rock brittleness calculation based on logging method

The calculation of the brittleness index is generally based on the Poisson-Young method (Poisson’s ratio and Young’s modulus method) of determining rock mechanical parameters. Poisson’s ratio, Young’s modulus, and other rock mechanical parameters can be calculated by logging curves such as density and p-wave and s-wave time differences. The calculation method followed [41] where is the dynamic Poisson’s ratio of rock (dimensionless), is the bulk density (g/cm³), is the p-wave time difference (μs/m), and is the s-wave time difference (μs/m) ().

The brittleness index calculation model followed [32, 42, 43] where is the brittleness index (%), is Young’s modulus of rock (GPa), and is Poisson’s ratio of rock (dimensionless). The subscripts and represent the maximum and minimum values of this parameter in a certain stratum section, respectively. and are the brittleness indices calculated by Young’s modulus and Poisson’s ratio, respectively. (b)Cock brittleness based on mineral composition content

The whole rock mineral composition (mass fraction) of the rock was determined by the XRD analysis to quantitatively analyze the relative content of brittle minerals and plastic minerals in the reservoir. The higher the content of brittle minerals in shale reservoirs, the easier to form fracture networks during fracturing. After considering other brittle minerals (brittle dolomite), plastic minerals (plastic limestone), and total organic content (TOC), a new formula for calculating the rock brittleness index was proposed. According to the mineral composition of shale in the study area, the following brittle mineral content evaluation model was adopted [44]: where is the content of quartz, is the content of feldspar, is the content of carbonate minerals, is the content of clay minerals, and is the brittleness index calculated by rock mineral composition.

(3) Anisotropy. In situ, stress can often be divided into vertical stress, horizontal maximum stress, horizontal minimum stress, and formation pressure (pore fluid pressure) [45]. The pore fluid pressure can be calculated by the Eaton method based on acoustic transit time logging (Equation (11)) [46]. The vertical stress is mainly generated by the gravity of the overlying strata. Therefore, it can be calculated by the density curve integration method (Equation (12)). where is the overburden formation pressure (MPa), is the formation water hydrostatic column pressure (MPa), is the Eaton index, and are the measured acoustic time difference and normal fluid pressure acoustic time difference, is the vertical stress (MPa), is the depth (m), is the bulk density (kg/m³), and is the gravitational acceleration (m/s²).

In this study, Huang’s model (Equations (13) and (14)) was preferred for horizontal maximum and minimum principal stresses [47]. Huang’s model and its improved model have been widely used in oilfield stress evaluation [48, 49]. where and are the horizontal maximum and horizontal minimum stresses (MPa), respectively, is the pore pressure (MPa), is the static Poisson’s ratio, is the overburden formation pressure (MPa), is the effective stress coefficient, and and are the construction correction amount.

3.2.4. Multiresolution Graph-Based Clustering

Shale reservoirs are unique, and hence, geological factors and development factors are comprehensively considered in reservoir classification. Parameters such as total organic carbon, hydrocarbon generation potential, brittleness index, and anisotropy were selected and comprehensively classified by the MRGC method. MRGC is a multidimensional lattice image recognition method based on the nonparametric -neighbor method and graphic data representation. Since the MRGC method does not rely on the classification of the class domain to determine the category, it relies on the classification of the surrounding limited adjacent samples. The MRGC method is more suitable than the other methods for the core sample set with overlapping class domains. The similarity between sampling points in the MRGC method was determined by the Euclidean distance. The relationship between attract and be attracted between sampling points was judged according to the Euclidean distance matrix. Considering the adsorption relationship between each sampling point and other sampling points, the attraction ability of each sampling point to all other sampling points was expressed by the nearest neighbor index (NI). The sampling point with the largest NI value was considered the final attraction center. The entire sample set was divided into multiple attraction sets, and the center of the attraction set was represented by the kernel representation index (KRI). The number of classifications at each level in the multilevel classification was obtained through the descending arrangement of the kernel representation index. The final classification results were obtained through the multilevel fusion attraction set [50–52]. During the MRGC cluster analysis, the geological and engineering parameters of the rock reservoir were comprehensively considered. The classification results reduce the error caused by human factors and are more reasonable.

4. Results and Discussion

4.1. Core Analysis Results

The distribution of total organic carbon (TOC) was between 1.33 and 12.8% with an average of 7.3%. is distributed in 2.08~6.13 mg/g with an average of 3.93 mg/g. is distributed in 4.78~37.33 mg/g with an average of 20.43 mg/g. is distributed in 0.03-0.67 mg/g with an average of 0.24 mg/g. The maximum pyrolysis peak temperature was 442~454°C with an average of 449.1°C (Table 1).

The shale is mainly composed of quartz, clay minerals, and feldspar with 41.1%, 32.5%, and 14.9% average contents, respectively (Figure 4(a) and Table 2). The clay mineral analysis illustrated illite as the main mineral with an average relative content of 54.2%. The average content of kaolinite was 18.4%. The average content of chlorite was 13.7%. The average content of mixed-layer illite/smectite was 13.7% (Figure 4(b) and Table 3).

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4.2. Analysis of Calculation Results of Reservoir Parameters

Figure 5 represents the logging diagram of Chang 7₃ of well Y-335. The ninth curve shows the lithologic characteristics. The black shale was developed at a depth of 2480~2540 meters. The first curve is GR. The two curves in the shale development section showed obvious mirror-image characteristics. The sixth channel represented the superposition diagram of the acoustic time difference and resistivity. The seventh represented the superposition diagram of density and resistivity. The eighth channel represented the superposition of the neutron curve and resistivity. The shale reservoir showed the characteristics of “four high and one low” on the conventional logging response curve (high GR, high NPHI, high DT, high RT, and low DEN). The high GR was attributed to the shale sedimentary environment having a deep lake and a semideep lake. The sediment particles were relatively fine, and the clay mineral content was relatively high. A higher number of radioactive substances were adsorbed which showed a higher value in the GR logging response. The high value of NPHI was attributed to the reservoir being dominated by fine-grained sediments. As the bound water content was high, it leads to the rapid attenuation of neutrons in the reservoir and the high response value of the NPHI logging curve. The high DT was attributed to the shale reservoir being composed of clay minerals and organic matter. As the content of light minerals was high, it resulted in low density and high DT. High RT indicated that the reservoir was rich in organic matter. The conductivity of organic matter was poor, and the reservoir showed high resistivity. According to the logging response characteristics of shale oil reservoirs, conventional logging curves can be used to qualitatively identify shale reservoirs (Table 4).

Figure 6 shows the intersection of TOC and hydrocarbon generation potential of core analysis. The correlation between the two parameters was high while having a correlation coefficient of 0.87. The values of and in Equation (4) were 2.8021 and 3.9297, respectively.

Figure 7 shows the crossplot of shale content, neutron logging, density logging, p-wave time difference, and s-wave time difference. They are all positively correlated with the s-wave time difference. Different s-wave time difference calculation models were established by the curve fitting method. The ANN method calculated the p-wave time difference while using the face image module of Geolog 2019 software. ANN is a neural network modeling method, which is widely used in the field of classification and recognition. The neural network consists of the input layer, hidden layer, and output layer. It is a multilayer feed-forward network trained by the error backpropagation algorithm. Its main feature is to learn and train the network according to the error backpropagation method. Its network learning can store a large number of input and output mode mapping relations [51]. The modeling curve was selected from well Y-335, which included shale content, neutron logging, density logging, and p-wave time difference. The prediction curve was the s-wave time difference. To eliminate the dimensional error between different modeling curves, the curves were normalized before modeling. Figure 8(a) shows the original curve distribution, and Figure 8(b) shows the normalized curve distribution. The ANN method was used to model the s-wave time difference curve, and the model was applied to other wells [52].

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Figure 9 shows the calculation outcome of the s-wave time difference of the inspection well. Track 5 shows the superposition of the s-wave time difference calculated by shale content and measured s-wave time difference. The 6th to 9th channels represented the comparison between the measured s-wave time difference curve and the s-wave time difference curve calculated by neutron, density, p-wave time difference, and ANN method. The black curve represented the s-wave time difference measured by the array of acoustic waves. The red curve was obtained by using the logging curve. The error of the s-wave time difference curve calculated by the mud content was found to be significant while considering the comparison results of the fifth curve. The s-wave time difference curve calculated by the neutron curve and density curve demonstrated to have a significant error in the nonshale section. The error in the shale section was relatively low. The s-wave time difference curve calculated by the ANN method and the s-wave time difference curve calculated by the p-wave time difference curve demonstrated high consistency with the measured curve. The tenth track represented the relative error analysis. The green track represented the relative error of the p-wave time difference calculation curve, and the red track illustrated the relative error of the ANN calculation result. The error calculated by the p-wave time difference and the ANN method was controlled within 0.05%. The calculation accuracy was found to be accurate.

Figure 10 shows the calculation results of TOC and hydrocarbon generation potential. The sixth channel represented the comparison between the total organic carbon content calculated by the conventional logging curve and the organic carbon content of the core analysis. The blue curve illustrated the total organic carbon calculated by logging, and the red bar chart represented the total organic carbon of core analysis. Both of the curves were consistent, which indicated that the total organic carbon content of the shale reservoir calculated by the method was reliable. The seventh track showed the comparison between the hydrocarbon generation potential calculated by logging and the core analysis results. The green curve showed the hydrocarbon generation potential calculated by logging, and the red rod showed the core analysis results. They were found to be highly consistent.

Figure 11 shows the calculated brittleness value and anisotropy of the shale reservoir. The fifth track showed the calculated result of Young’s modulus and Poisson’s ratio. The sixth channel showed the calculated pore fluid pressure. The seventh track showed the calculated maximum and minimum horizontal principal stress values. The eighth track showed the calculated brittleness index. The higher the organic matter content of the reservoir, the lower the brittleness index as well as the relatively poor compressibility.

Figure 12 shows the crossplot of the brittleness index calculated by logging and the brittleness index calculated by mineral content. The results calculated by the two methods had a good correlation. However, there were also certain differences. The error caused by the brittleness evaluation method based on mineral content was mainly caused by two reasons. This method depends on the content of brittle minerals in rocks, but different mineral components have different effects on rock brittleness in different regions. On the other hand, this method only considers the content of mineral components while ignoring the influence of diagenesis, cementation, and confining pressure. The brittleness evaluation method based on elastic parameters also has certain shortcomings. The formula for calculating the brittleness index using elastic parameters has strong regional applicability. The calculation formula is mainly based on rock samples in different regions with poor universality. When calculating the dynamic elastic parameters by considering the time difference between p-waves and s-waves, the acoustic time difference is easily affected by TOC. Moreover, the static elastic parameters are determined by laboratory experiments, and hence, their brittleness characteristics are easily affected by confining pressure, temperature, and other factors.

As there is no in situ stress test in the laboratory, it is impossible to directly compare the rationality of the in situ stress and anisotropy values calculated by array acoustic logging. However, considering the calculation of in situ stress by array acoustic logging, the rock in the formation is in a three-dimensional stress state. When drilling coring, the core is out of the original stress state and can release its stress. During the process of stress release, numerous small cracks could be formed. The development degree of microcracks has an intrinsic genetic relationship with the size and direction of ground stress. The acoustic wave propagation velocity is the slowest in the direction of the maximum horizontal principal stress of the rock. On the contrary, the propagation speed of the sound wave is the fastest in the direction of the minimum horizontal principal stress. The difference in wave velocity (time difference) in different directions, namely, anisotropy, is brought on by the existence of ground stress. The use of orthogonal dipole array logging can determine the direction of in situ stress based on the shear wave anisotropy effect caused by stress. The shear wave polarized along the maximum stress direction is faster than the shear wave polarized perpendicular to this direction. Therefore, the state and orientation of in situ stress can be inferred. The intersection of the fast and slow bending wave dispersion curves in the orthogonal dipole data was found to be significant. The direction of the minimum principal stress was determined. Therefore, the maximum and minimum principal stresses and anisotropy calculated by array acoustic logging are reliable.

Figure 13(a) shows the distribution characteristics of TOC, , BI, , and before normalization. Figure 13(b) shows the distribution characteristics after normalization. Normalization eliminated the error caused by the dimensional difference between curves. The phase clusters and histograms derived from the normalized self-organizing map (SOM) of each parameter in Figure 14 showed that the clustering effect was relatively good.

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Figure 15 shows the classification results of shale reservoirs in well Y-335. The fourth channel showed the total organic carbon, and the fifth channel showed the hydrocarbon generation potential. The sixth channel represented the brittleness index, and the seventh channel showed maximum and minimum horizontal principal stresses. The eighth channel showed lithologic profile, and the ninth channel represented reservoir classification results. Shale reservoirs were divided into three facies. Facies_1 reservoirs were marked in red, Facies_2 in pink, and Facies_3 in yellow. The Chang 7₃ shale oil reservoirs mainly consisted of Facies_1 and Facies_2 reservoirs. The average total organic carbon of the Facies_1 reservoir was 10.99. The hydrocarbon generation potential was 34.72 mg/g, the brittleness index was 24.75, and the maximum horizontal principal stress was 45.61 MPa. The reservoir was rich in organic matter, and the compressibility was relatively low. The average total organic carbon of the Facies_2 reservoir was 7.22, the hydrocarbon generation potential was 24.17 mg/g, the brittleness index was 28.34, and the maximum horizontal principal stress was 45.4 MPa. The average total organic carbon of the Facies_3 of reservoirs was 3.46, the hydrocarbon generation potential was 13.61 mg/g, the brittleness index was 34.02, and the maximum horizontal principal stress was 44.27 MPa. The organic matter content of the reservoir was poor (Table 5).

5. Conclusion

(1)The average content of brittle minerals in the shale reservoir was 58.8%, which mainly consisted of quartz and feldspar with an average content of 41.1% and 14.9%, respectively. The content of clay minerals was relatively high while reaching 32.5% (mainly illite) with a relative content of 54.2%(2)The total organic carbon of the Chang 7₃ shale reservoir in the Jiyuan area ranged from 1.33% to 12.8% with an average value of 7.3%. The average was 3.93 mg/g. The average distribution of was 20.43 mg/g. The average was 0.24 mg/g. The average maximum pyrolysis peak temperature was 449.1°C. The reservoir is generally a good shale oil reservoir(3)The error of the p-wave time difference curve and the p-wave time difference curve calculated by the ANN method was controlled within 0.05%. The calculation accuracy was high. Therefore, the p-wave time difference curve fitting formula can be directly used to calculate the s-wave time difference without using the complex neural network model(4)The Facies_1 of the reservoir was rich in organic matter, which was considered to be the best reservoir. The average total organic carbon was 10.99%, the hydrocarbon generation potential was 34.72 mg/g, and the brittleness index was 24.75. The total organic carbon and hydrocarbon generation potential of the Facies_2 reservoir were relatively low. The average content of TOC was 7.22, the hydrocarbon generation potential was 24.17 mg/g, and the brittleness index was 28.34. The Facies_3 reservoirs were poor with an average total organic carbon of 3.46, hydrocarbon generation potential of 13.61 mg/g, and brittleness index of 34.02

Data Availability

The data used to support the findings of this study are included within the article.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

This paper was supported by the Scientific Research Program funded by Shaanxi Provincial Education Department (Program No. 22JK0504) and Natural Science Basic Research Plan in Shaanxi Province of China (Program No. 2023JCQN0314).

References

Y. Donghua, Z. Lihong, W. Wen'ge et al., “Logging evaluation of composite sweet spots for shale oil: a case study of member 2 of Kongdian formation in Candong sag,” Acta Petrolei Sinica, vol. 43, no. 7, pp. 912–924, 2022.
View at: Publisher Site | Google Scholar
F. Xiaodong, C. Ya’na, L. Bing et al., “Evaluation of source rocks and petroleum system of the Lower Cambrian Maidiping formation-Qiongzhusi formation in the middle-upper Yangtze region,” China Petroleum Exploration, vol. 27, no. 4, pp. 103–120, 2022.
View at: Publisher Site | Google Scholar
L. Ma, X. Jiang, and J. Gong, “Analysis of logging characteristics of high quality shale has reservoirs,” Petroleum Reservoir evaluation and Development, vol. 12, no. 3, pp. 445–454, 2022.
View at: Google Scholar
L. Yahui, W. Caizhi, and L. Zhonghua, “A logging method for evaluating oil-bearing property of shale oil: case study of Jimusar sag in Junggar basin,” Natural Gas Geoscience, vol. 32, no. 7, pp. 1084–1091, 2021.
View at: Google Scholar
P. Huang, “Study on log evaluation method of key parameters for shale gas reserve calculation,” Energy and Energy Conservation, vol. 5, p. 2-6+83, 2021.
View at: Google Scholar
F. Xie, W. Ding, and S. Yin, “Petrophysical properties of continental shale reservoir and the influence factors of its gas contents,” Science Technology and Engineering, vol. 17, no. 5, pp. 20–28, 2017.
View at: Google Scholar
L. I. Jinbu, L. U. Shuangfang, C. H. Guohui, S. Junfeng, H. Yingjie, and M. Junli, “Friability evaluation for the mud shale reservoirs based on the mineralogy and rock mechanics,” Petroleum geology and oilfield development in Daqing, vol. 34, no. 6, pp. 159–164, 2015.
View at: Google Scholar
L. I. Xia, C. H. Xiangzhi, and Z. H. Cancan, “Technology and application of well logging evaluation of shale oil and gas reservoirs,” Natural Gas Geoscience, vol. 26, no. 5, pp. 904–914, 2015.
View at: Google Scholar
Z. He, “Well logging evaluation on effective source rocks for mud shale reservoirs and its application in M sag,” Journal of yangtze university (natural science edition), vol. 13, no. 29, p. 18-23+4, 2016.
View at: Google Scholar
Q. Wu, X. Li, H. Liu, and X. Chen, “Log interpretations and the application of core testing technology in the shale-gas: taking the exploration and development of the Sichuan basin as an example,” Acta Petrolei Sinica, vol. 32, no. 3, pp. 484–488, 2011.
View at: Publisher Site | Google Scholar
W. Zhong, L. Xiong, H. Li, X. Dong, and J. Zhou, “Application of log evaluation technology in Weirong deep shale gas field,” Petroleum Reservoir Evaluation and Development, vol. 11, no. 1, pp. 38–46, 2021.
View at: Google Scholar
S. Chen, W. Z. Zhao, Y. L. Ouyang et al., “Comprehensive prediction of shale gas sweet spots based on geophysical proper-ties: a case study of the Lower Silurian Longmaxi Fm in Changning block, Sichuan basin,” Natural Gas Industry, vol. 37, no. 5, pp. 20–30, 2017.
View at: Google Scholar
S. Luo, X. Chen, and F. Ni, “Application of natural gamma spectroscopy logging in evaluation and guidance of black shale,” Well Logging Technology, vol. 45, no. 3, pp. 297–304, 2021.
View at: Google Scholar
Q. R. Passey, S. Creaney, J. B. Kulla, F. J. Moretti, and J. D. Stroud, “A practical model for organic richness from porosity and resistivity logs,” AAPG Bulletin, vol. 74, no. 12, pp. 1777–1794, 1990.
View at: Publisher Site | Google Scholar
Z. D. Xi, S. H. Tang, J. Wang et al., “Evoluation parameters study of selecting favorable shale gas areas in southern China,” Acta Geologica Sinica, vol. 92, no. 6, pp. 1313–1323, 2018.
View at: Google Scholar
Z. Jiang, S. O. Yan, T. A. Xianglu et al., “Controlling factors of marine shale gas differential enrichment in southern China,” Petroleum Exploration and Development, vol. 47, no. 3, pp. 661–673, 2020.
View at: Publisher Site | Google Scholar
J. Xie, H. Lu, L. Chen, X. Peng, D. Wang, and G. Fu, “Microscopic heterogeneity and mechanical anisotropy of the Longmaxilaminated shale,” Bulletin of Geological Science and Technology, vol. 40, no. 3, pp. 67–77, 2021.
View at: Google Scholar
B. Shi, F. Wu, S. Li et al., “Logging identification of high-quality shale of marine-continent transitional facies; an example of Shan 2 member of Daning-Jixian area in Ordos basin,” Bulletin of Geological Science and Technology, vol. 42, no. 2, pp. 115–126, 2022.
View at: Publisher Site | Google Scholar
J. V. Howell, Glossary of Geology and Related Sciences, vol. 99-102, American Geological Institute, Washington, D.C., 1960.
W. Wang, Z. Wang, C. Liu et al., “Key technology of shale oil sweet spot evaluation and sweet spot type division in Fengcheng formation of Mahu sag,” Earth Science, vol. 48, no. 1, pp. 223–234, 2023.
View at: Google Scholar
V. Hucka and B. Das, “Brittleness determination of rocks by different methods,” International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts, vol. 11, no. 10, pp. 389–392, 1974.
View at: Publisher Site | Google Scholar
D. M. Jarvie, R. J. Hill, T. E. Ruble, and R. M. Pollastro, “Unconventional shale-gas systems: the Mississippian Bar-Nett shale of North-Central Texas as one model for thermogenic shale-gas assessment,” AAPG Bulletin, vol. 91, no. 4, pp. 475–499, 2007.
View at: Publisher Site | Google Scholar
T. L. Guo, L. Xiong, W. Lei, Y. Zhao, and H. Q. Pang, “Deep shale gas exploration and development in the Weirong and Yongchuan areas, South Sichuan basin: progress, challenges and prospect,” Natural Gas Industry, vol. 42, no. 8, pp. 45–59, 2022.
View at: Google Scholar
Q. Yu, “Logging facies analysis and lithology identification based on MRGC clustering method-by taking buried hill formation of Z oilfield as an example,” Petroleum Geology And Engineering, vol. 36, no. 1, pp. 31–38, 2022.
View at: Google Scholar
W. Cai, “Logging identification technology for mud shale in L block of Ordos basin,” Scientific and Technological Innovations, vol. 23, pp. 43–46, 2022.
View at: Google Scholar
B. Li, J. Li, Q. Hou, X. Bao, X. Jiang, and P. Qu, “Log evaluation tecnology of Duvernay shale gas reservoirs in western Canada basin,” Well Logging Technology, vol. 45, no. 6, pp. 609–616, 2021.
View at: Google Scholar
H. Xia, J. Lai, G. Li, and Y. Yang, “Sweet sport prediction of shale oil reservoir based on loogging data,” Journal of Southwest Petroleum University (Science & Technology Edition), vol. 43, no. 4, pp. 199–207, 2021.
View at: Publisher Site | Google Scholar
X. Pu, Z. Jiang, H. Huang, H. Cao, and Y. Qi, “Logging sedimentology analysis of shale reservoir: a case study of Biye HF-1 well of Hetaoyuan formation in Biyang depression,” Journal oF guangdong ocean university, vol. 39, no. 5, pp. 115–121, 2019.
View at: Google Scholar
L. Liu, C. Lin, C. Dong, and C. Ma, “Logging data interpretation of the muddy reservoir in E1f2 of Subei basin,” Marine geology frontiers, vol. 34, no. 2, pp. 53–60, 2018.
View at: Google Scholar
Z. Wang, S. Chen, H. Li et al., “The interpretation method of geophysical well logging for the brittle characteristics of shale gas reservoirs,” Chinese Journal of Engineering Geophysics, vol. 13, no. 1, pp. 7–13, 2016.
View at: Google Scholar
L. Q. Sima, D. N. Wen, J. P. Yan, M. Tan, and H. Deng, “Fracturing hierarchy analysis and fracturing height predication method in shale reservoir,” Well Logging Technology, vol. 39, no. 5, p. 622-627+639, 2015.
View at: Google Scholar
Y. Xin, G. Wang, B. Liu et al., “Well logging evaluation of “three quality” of Jurassic tight gas sandstone reservoirs in Kuqa depression,” Earth Science, vol. 5, no. 4, pp. 1–28, 2022.
View at: Google Scholar
T. Liu, K. Xu, and X. Wei, “Evaluation research of shale gas pre-exploration wells based on logging data,” Coal Technology, vol. 41, no. 4, pp. 75–77, 2022.
View at: Google Scholar
X. Liu, Y. Liu, and Z. Guo, “Key sweet spot factors seismic characterization of continental shale oil,” Progress in Geophysics, vol. 37, no. 4, pp. 1576–1584, 2022.
View at: Google Scholar
B. Liu, C. Tan, R. Li et al., “Study on the correlation between occurrence characteristics of movable fluids and microscopic pore structure of Chang 8 formation reservoir in Central Ordos basin,” Fresenius Environmental Bulletin, vol. 28, no. 4, pp. 3131–3140, 2019.
View at: Google Scholar
B. Liu, C. Tan, Y. Zhang et al., “Investigation of pore throat structure and fractal characteristic of tight sandstone of Chang 8 reservoir in Jiyuan area, Ordos basin,” Fresenius Environmental Bulletin, vol. 28, no. 12, pp. 9865–9876, 2019.
View at: Google Scholar
H. Huang, R. Li, Z. Lyu et al., “Comparative study of methane adsorption of Middle-Upper Ordovician marine shales in the western Ordos basin, NW China: insights into impacts of moisture on thermodynamics and kinetics of adsorption,” Chemical Engineering Journal, vol. 446, article 137411, 2022.
View at: Publisher Site | Google Scholar
B. Zhao, R. Li, X. Qin et al., “Geochemical characteristics and mechanism of organic matter accumulation of marine-continental transitional shale of the Lower Permian Shanxi formation, southeastern Ordos basin, North China,” Journal of Petroleum Science and Engineering, vol. 205, article 108815, 2021.
View at: Publisher Site | Google Scholar
L. Shixiang, G. Qiheng, Z. Xinping, L. Yuanbo, and L. Jiangyan, “Reservoir characteristics and exploration direction of pure shale-type shale oil in the 3^rd sub-member, 7th member of Yanchang formation in Ordos basin,” Acta Petrolei Sinica, vol. 43, no. 11, pp. 1509–1519, 2022.
View at: Publisher Site | Google Scholar
H. Gao, X. Zhang, M. Q. He et al., “Study on evaluation of shale oil reservoir fracability based on well logging data volume,” Progress in Geophysics, vol. 33, no. 2, pp. 0603–0612, 2018.
View at: Google Scholar
S. Wang, D. Sun, W. Du, G. Dai, R. Bai, and Y. Wang, “Evaluation method of shale brittleness index,” Well Logging Technology, vol. 44, no. 3, pp. 294–299, 2020.
View at: Google Scholar
O. Iqbal, M. Ahmad, and A. Kadir, “Effective evaluation of shale gas reservoirs by means of an integrated approach to petrophysics and geomechanics for the optimization of hydraulic fracturing: a case study of the Permian Roseneath and Murteree shale gas reservoirs, Cooper basin, Australia,” Journal of Natural Gas Science and Engineering, vol. 58, pp. 34–58, 2018.
View at: Publisher Site | Google Scholar
J. Lai, G. Wang, L. Huang et al., “Brittleness index estimation in a tight Shaly sandstone reservoir using well logs,” Journal of Natural Gas Science and Engineering, vol. 27, pp. 1536–1545, 2015.
View at: Publisher Site | Google Scholar
J. Guo, X. Guo, M. Zhao, J. Yu-xin, L. Qi-qi, and Y. Shuai, “Conventional logging evaluation method of brittleness index of tight sandstone based on multi-mineral model: a case study of Chang 7 member in Tongchuan area, Ordos basin,” Progress in Geophysics, vol. 10, no. 6, 2022, https://kns.cnki.net/kcms/detail/11.2982.P.20221101.1816.010.html.
View at: Google Scholar
J. Lai, X. Pang, F. Xu et al., “Origin and formation mechanisms of low oil saturation reservoirs in Nanpu sag, Bohai Bay basin, China,” Marine and Petroleum Geology, vol. 110, pp. 317–334, 2019.
View at: Publisher Site | Google Scholar
B. A. Eaton, “Fracture gradient prediction and its application in oilfield operations,” Journal of Petroleum Technology, vol. 21, no. 10, pp. 1353–1360, 1969.
View at: Publisher Site | Google Scholar
R. Huang, M. Chen, J. Deng, K. P. Wang, and Z. X. Chen, “Study on shale stability of wellbore by mechanics coupling with chemistry method,” Drilling Fluid & Completion Fluid, vol. 12, no. 3, pp. 56–61, 1995.
View at: Google Scholar
Z. H. Jun, Q. I. Wei-qiang, Z. Li, and D. E. Ning, “Anisotropy of dipole shear wave and its application to ground stress evaluation,” Acta Petrolei Sinica, vol. 26, no. 4, pp. 54–57, 2005.
View at: Google Scholar
J. Zhao, W. Pu, G. Wang, and J. Li, “Application of logging information in the analysis of the ground stress in the foreland compressive area,” Journal of Geomechanics, vol. 11, no. 1, pp. 53–59, 2005.
View at: Google Scholar
B. Liu, X. Zhao, X. Fu et al., “Petrophysical characteristics and log identification of lacustrine shale lithofacies: a case study of the first member of Qingshankou formation in the Songliao basin, Northeast China,” Interpretation, vol. 8, no. 3, pp. SL45–SL57, 2020.
View at: Publisher Site | Google Scholar
L. I. Chang, S. H. Anjiang, and C. H. Shaoying, “Application and contrast of machine learning in carbonate lithofacies log identification: a case study of Longwangmiao formation of MX area in Sichuan basin,” Petroleum Reservoir Evaluation and Development, vol. 11, no. 4, pp. 586–596, 2021.
View at: Google Scholar
D. Nilesh, M. Paul, and K. Kimberly, “Machine learning-based probabilistic lithofacies prediction from conventional well logs: a case from the Umiat oil field of Alaska,” Energies, vol. 13, no. 18, p. 4862, 2020.
View at: Publisher Site | Google Scholar

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Copyright © 2023 Bobiao Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Geofluids

Mechanisms of Oil and Gas Accumulation in Fine-Grained Sedimentary Systems

Multiparameter Logging Evaluation of Chang 73 Shale Oil in the Jiyuan Area, Ordos Basin

Abstract

1. Introduction

2. Geological Setting

3. Data and Methodology

3.1. Data

3.1.1. Logging Data

3.1.2. Core Analysis Data

3.2. Methodology

3.2.1. Calculation of Total Organic Carbon

3.2.2. Calculation of Hydrocarbon Generation Potential

3.2.3. Calculation of Terrestrial Stress

3.2.4. Multiresolution Graph-Based Clustering

4. Results and Discussion

4.1. Core Analysis Results

4.2. Analysis of Calculation Results of Reservoir Parameters

5. Conclusion

Data Availability

Conflicts of Interest

Acknowledgments

References

Copyright

Multiparameter Logging Evaluation of Chang 7₃ Shale Oil in the Jiyuan Area, Ordos Basin