Continental facies shale oil reservoir brittleness evaluation method

Abstract

The invention discloses a continental facies shale oil reservoir brittleness evaluation method, which comprises a central control machine, and the central control machine is bidirectionally and electrically connected with a reservoir mineral composition analysis unit, a rock mechanics experiment simulation unit, a cloud memory and a continental facies shale oil reservoir related data collection and uploading module in sequence through wires. The central control machine and the like are arranged, so that when the central control machine runs, the central control machine carries out reservoir mineral composition analysis by controlling the reservoir mineral composition analysis unit to run and controls the rock mechanics experiment simulation unit to run to simulate and run rock mechanics experiment work on the basis of calculation analysis and control effects of the central control machine; and subsequently, intelligent research and analysis of shale oil reservoir brittleness are realized through operation of a shale oil reservoir brittleness research unit, then reasonability of a reservoir transformation scheme is guided, exploration and development of the shale oil reservoir are summarized and guided, and guidance is provided for exploration and development of the shale oil reservoir in the next step.

Description

Technical Field

[0001] The present invention relates to the technical field of evaluation of continental shale oil reservoirs, and specifically to a method for evaluating the brittleness of continental shale oil reservoirs. Background Art

[0002] Continental shale oil reservoirs refer to shale oil reservoirs deposited in continental lake basin environments. The exploitation of such oil reservoirs is relatively difficult and it is necessary to analyze and evaluate the brittleness of continental shale oil reservoirs.

[0003] The methods for evaluating the brittleness of continental shale oil reservoirs often can only conduct simple evaluation and analysis based on the data uploaded by users. It is not easy to conduct classified calculation and analysis according to the different research requirements and experimental conditions of the mineral composition data of the reservoir to be analyzed, nor is it easy to integrate a multi-level analysis and evaluation structure, which results in poor use effects of the system. Based on this, we propose a new method for evaluating the brittleness of continental shale oil reservoirs. By carrying out work such as reservoir mineral composition analysis and rock mechanics experiments, it studies the brittleness of shale oil reservoirs to guide the rationality of reservoir stimulation programs and provides guidance for the exploration and development of shale oil reservoirs in the next step, which improves the analysis and evaluation effects of the system. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for evaluating the brittleness of continental shale oil reservoirs to solve the problems raised in the above background art.

[0005] To achieve the above object, the present invention provides the following technical solutions: A method for evaluating the brittleness of continental shale oil reservoirs, including a central control unit, which is bidirectionally electrically connected to a reservoir mineral composition analysis unit, a rock mechanics experiment simulation unit, a cloud storage device, and a collection and upload module for relevant data of continental shale oil reservoirs in sequence through wires. The cloud storage device is bidirectionally electrically connected to a cloud storage encryption module through a wire. The reservoir mineral composition analysis unit includes a first data receiving and classification module, an analysis module for core experiment statistics, an analysis module based on an optimization calculation method, and an analysis module based on a fluid inclusion method. The rock mechanics experiment simulation unit includes a rock mechanics experiment process shooting and monitoring module, a rock mechanics experiment data entry module, a rock mechanics simulation experiment generation and operation module, and an experiment result upload and analysis module. The output end of the rock mechanics experiment simulation unit is electrically connected to a shale oil reservoir brittleness research unit through a wire. The shale oil reservoir brittleness research unit includes a second data receiving and classification module, a research module for evaluating the brittleness of bedding structures, a research module for the multiplicative synthesis method, and a research module for the in-situ brittleness evaluation method of the reservoir based on the on-site fracturing construction curve. The output end of the shale oil reservoir brittleness research unit is electrically connected to an artificial reservoir transformation plan receiving module through a wire. The output end of the artificial reservoir transformation plan receiving module is electrically connected to a comparison and analysis module for the artificial reservoir transformation plan through a wire. The output end of the comparison and analysis module for the artificial reservoir transformation plan is electrically connected to an exploration and development summary and guidance unit for shale oil reservoirs based on AI through a wire. The exploration and development summary and guidance unit for shale oil reservoirs based on AI includes an analysis and comparison module for the exploration and development plan of artificial shale oil reservoirs, an optimization and summary module for the exploration and development plan of shale oil reservoirs based on AI, and an intelligent guidance and evaluation module based on AI.

[0006] Further, the collection and upload module for relevant data of continental shale oil reservoirs securely uploads relevant pictures, texts, and video data of continental shale oil reservoirs to the cloud storage device confidentially through a scanner, a high-speed camera, and a camera via the cloud storage encryption module, which improves data security.

[0007] Furthermore, the central control machine, based on its own calculation analysis and control function, cooperates with the collection and upload module of continental shale oil reservoir related data and the data content collected and saved by the cloud storage, firstly controls the operation of the reservoir mineral composition analysis unit to carry out reservoir mineral composition analysis, and controls the operation of the rock mechanics experiment simulation unit to simulate the operation of the rock mechanics experiment, and then provides data support to the shale oil reservoir brittleness research unit based on the data calculated by the operation of the reservoir mineral composition analysis unit and the rock mechanics experiment simulation unit, and then realizes the intelligent research and analysis of the shale oil reservoir brittleness through the operation of the shale oil reservoir brittleness research unit, and then the reservoir transformation plan formulated by the staff based on the above data will be sent to the artificial reservoir transformation plan receiving module for data conversion, and then sent to the comparison and analysis module for the artificial reservoir transformation plan to simulate and analyze the data and methods in the artificial plan, and then give instructions and guide the rationality of the reservoir transformation plan, and finally, through the exploration and development summary guidance unit of the shale oil reservoir based on AI, the advantages of AI calculation summary analysis are used to summarize and guide the exploration and development of the shale oil reservoir, and provide guidance for the next step of the exploration and development of the shale oil reservoir.

[0008] Furthermore, when the reservoir mineral composition analysis unit is specifically operated, the first data receiving and classification module classifies the reservoir mineral composition data to be analyzed according to different research requirements and experimental conditions, selects the data suitable for occasions requiring detailed mineral composition information, and performs operations on the core experiment statistics analysis module, so that it can perform statistical analysis on the mineral composition of reservoir rocks based on the core analysis data. This method can qualitatively observe the mineral composition of the formation, provide skeleton parameters and basis for the study of reservoir porosity evaluation modeling, and select data suitable for the calculation of logging instrument response, mineral response parameters and the volume of formation mineral composition. , and perform calculations on the analysis module based on the optimization calculation method, so that it can use the optimization program to solve various logging response equations simultaneously and use the optimization technology to invert the formation parameters. This method inverts the formation parameters through the optimization theory and realizes high-precision formation parameter inversion. It also selects the oil and gas reservoir research data and performs calculations on the analysis module based on the fluid inclusion method, so that it can infer the formation time and filling direction of the oil and gas reservoir by measuring the uniform temperature of the fluid inclusions and the maturity of the hydrocarbon components. This enables the device to realize the classification and subsequent analysis of the reservoir mineral components, thereby improving the analysis effect and accuracy of the reservoir mineral components.

[0009] Further, when the rock mechanics experiment simulation unit is running specifically, the rock mechanics experiment process shooting and monitoring module starts shooting to collect the rock mechanics experiment process, and obtains various rock mechanics experiment data manually input by the experimenters through the rock mechanics experiment data entry module. These data are integrated inside the rock mechanics simulation experiment generation and operation module to generate a simulation experiment plan and perform dynamic simulation operation, solving the problems of time-consuming, laborious, and resource waste caused by the experimenters manually changing various rock mechanics experiment variables for multiple experiments. Through the computer-generated simulation of the experiment, the function of intelligent simulation experiment can be realized, which is conducive to obtaining better and more accurate experimental results. Subsequently, the experiment result uploading and analysis module is used to compare and analyze the obtained experimental result data, and then upload it to the experimenters for feedback and upload it to the cloud for storage.

[0010] Further, when the brittle study unit of the shale oil reservoir is running specifically, the second data receiving and classifying module classifies according to different research requirements and experimental conditions of the brittle data of the shale oil reservoir to be studied, and selects the cases suitable for detailed mineral composition analysis, and performs operations on the research module of the brittle evaluation of the bedding structure, enabling it to collect the logging curves of shale oil wells, rock mechanics parameters, and the measured contents of the main minerals in the shale, use the multiple regression method to model the mineral content, predict the brittle mineral content, and calculate the brittleness index through the ratio of the Young's modulus to the Poisson's ratio of the mixed minerals. And it selects the scenarios suitable for comprehensively considering the energy before and after the peak, and performs operations on the research module of the multiplicative synthesis method, enabling it to calculate the brittleness index by the ratio of the elastic deformation energy during the rock compression process to the elastic energy under ideal conditions, and the ratio of the energy provided by the outside after the stress reaches the peak to the fracture energy of the rock fracture, combined with the proposed brittleness index that can comprehensively reflect the energy characteristics before and after the peak.

[0011] Further, the second data receiving and classifying module selects the engineering application data suitable for on-site real-time monitoring and evaluation, and performs operations on the research module of the in-situ brittleness evaluation method of the reservoir based on the on-site fracturing construction curve, enabling it to analyze the construction curve data, extract the in-situ brittleness index of the reservoir, and combine the real-time microseismic data and the fracturing construction curve to monitor the fracture characteristics of the reservoir in real time.

[0012] Further, when the exploration and development summary guidance unit for the AI-based shale oil reservoir operates specifically, the analysis and comparison module for the manual shale oil reservoir exploration and development plan uses AI technology to analyze and compare the initial draft of the manual shale oil reservoir exploration and development plan formulated by the staff, and then the exploration and development plan optimization and summary module for the AI-based shale oil reservoir uses AI technology to conduct intelligent optimization and summary of the exploration and development plan for the shale oil reservoir. Finally, the intelligent guidance and evaluation module based on AI provides guidance for the next-step exploration and development of the shale oil reservoir.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0014] 1) By providing a central control unit, etc., when it operates, the central control unit, based on its own calculation, analysis, and control functions, in cooperation with the data collection and upload module for continental shale oil reservoir-related data and the data content collected and stored in the cloud storage, first controls the operation of the reservoir mineral composition analysis unit to carry out reservoir mineral composition analysis, and controls the operation of the rock mechanics experiment simulation unit to simulate the rock mechanics experiment work. Subsequently, the data obtained from the operation and calculation of the reservoir mineral composition analysis unit and the rock mechanics experiment simulation unit provide data support for the shale oil reservoir brittleness research unit, and then the intelligent research and analysis of the shale oil reservoir brittleness are realized through the operation of the shale oil reservoir brittleness research unit. Then, the reservoir stimulation plan formulated by the staff based on the above data will be sent to the manual reservoir stimulation plan receiving module for data conversion, and then sent to the comparison and analysis module for the manual reservoir stimulation plan to simulate and analyze the data and methods in the manual plan, thereby giving instructions to guide the rationality of the reservoir stimulation plan. Finally, through the exploration and development summary guidance unit for the AI-based shale oil reservoir, taking advantage of the AI calculation, summary, and analysis, the exploration and development of the shale oil reservoir are summarized and guided, providing guidance for the next-step exploration and development of the shale oil reservoir;

[0015] 2) By setting up a reservoir mineral composition analysis unit, etc., when the reservoir mineral composition analysis unit is in use, when the reservoir mineral composition analysis unit is specifically running, the first data receiving and classification module classifies the reservoir mineral composition data according to different research needs and experimental conditions to be analyzed, selects the occasions that require detailed mineral composition information, and performs operations on the core experiment statistics analysis module, so that it can perform statistical analysis on the mineral composition of the reservoir rock based on the core analysis data. This method can qualitatively observe the mineral composition of the formation, provide skeleton parameters and basis for the study of reservoir porosity evaluation modeling, and select the appropriate logging instrument response, mineral response parameters and formation mineral composition. The data calculated by volume is operated on the analysis module based on the optimization calculation method, so that it can use the optimization program to solve various logging response equations simultaneously and use the optimization technology to invert the formation parameters. This method uses the optimization theory to invert the formation parameters and realizes high-precision formation parameter inversion. The oil and gas reservoir research data is selected and operated on the analysis module based on the fluid inclusion method, so that the formation time and filling direction of the oil and gas reservoir can be inferred by measuring the uniform temperature of the fluid inclusions and the maturity of the hydrocarbon components. This allows the device to realize the classification and analysis of the reservoir mineral components first, thereby improving the analysis effect and accuracy of the reservoir mineral components.

[0016] 3) By setting up a shale oil reservoir brittleness research unit, etc., when it is used, when the shale oil reservoir brittleness research unit is specifically operated, the second data receiving and classification module classifies the shale oil reservoir brittleness data to be studied according to different research needs and experimental conditions, selects the data suitable for the situation where detailed mineral composition analysis is required, and performs calculations on the research module of bedding structure brittleness evaluation, so that it collects the logging curves of shale oil wells, rock mechanical parameters and the measured content of the main minerals in shale, uses the multivariate regression method to model the mineral content, predicts the brittle mineral content, and calculates the brittleness index through the ratio of the Young's modulus to the Poisson's ratio of the mixed minerals, and selects the data suitable for the situation where the pre-peak For scenarios of pre-peak and post-peak energy, operations are performed on the research module of the multiplicative synthesis method, so that the brittleness index is calculated by the ratio of the elastic deformation energy in the rock compression process to the elastic energy in the ideal situation, and the ratio of the energy provided by the outside world after the stress reaches the peak value to the fracture energy of the rock, and a brittleness index that can comprehensively reflect the pre-peak and post-peak energy characteristics is proposed, thereby calculating the brittleness index, and selecting engineering application data that require on-site real-time monitoring and evaluation, and performing operations on the research module of the reservoir in-situ brittleness assessment method based on the on-site fracturing construction curve, so that the construction curve data is analyzed, the reservoir in-situ brittleness index is extracted, and the reservoir fracture characteristics are monitored in real time by combining real-time microseismic data and fracturing construction curves. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0018] Figure 1 It is the system block diagram of the present invention;

[0019] Figure 2 It is the system block diagram of the reservoir mineral composition analysis unit of the present invention;

[0020] Figure 3 It is the system block diagram of the rock mechanics experiment simulation unit of the present invention;

[0021] Figure 4 It is the system block diagram of the brittle study unit of the shale oil reservoir of the present invention;

[0022] Figure 5 It is the system block diagram of the exploration and development summary guidance unit of the shale oil reservoir based on AI of the present invention.

[0023] In the figure: 1. Reservoir mineral composition analysis unit; 101. First data receiving and classification module; 102. Analysis module for core experiment statistics; 103. Analysis module based on the optimization calculation method; 104. Analysis module based on the fluid inclusion method; 2. Rock mechanics experiment simulation unit; 201. Rock mechanics experiment process shooting and monitoring module; 202. Rock mechanics experiment data input module; 203. Rock mechanics simulation experiment generation and operation module; 204. Experiment result uploading and analysis module; 3. Central control machine; 4. Shale oil reservoir brittle study unit; 401. Second data receiving and classification module; 402. Research module for bedding structure brittle evaluation; 403. Research module for multiplicative synthesis method; 404. Research module for reservoir in-situ brittleness evaluation method based on on-site fracturing construction curve; 5. Artificial reservoir transformation plan receiving module; 6. Comparison and analysis module for artificial reservoir transformation plan; 7. Cloud storage; 8. Cloud storage encryption module; 9. Exploration and development summary guidance unit of the shale oil reservoir based on AI; 901. Analysis and comparison module for artificial shale oil reservoir exploration and development plan; 902. Optimization and summary module for exploration and development plan of the shale oil reservoir based on AI; 903. Intelligent guidance and evaluation module based on AI; 10. Collection and uploading module for relevant data of continental shale oil reservoir. Specific embodiments

[0024] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] See also Figures 1-5 , an embodiment provided by the present invention: a method for evaluating the brittleness of a continental shale oil reservoir, comprising a central control machine 3, the central control machine 3 is bidirectionally electrically connected to a reservoir mineral composition analysis unit 1, a rock mechanics experimental simulation unit 2, a cloud storage 7 and a collection and uploading module 10 for collecting and uploading data related to a continental shale oil reservoir in sequence through a wire, and the cloud storage 7 is bidirectionally electrically connected to a cloud storage encryption module 8 through a wire;

[0026] The reservoir mineral composition analysis unit 1 includes a first data receiving and classification module 101, a core experiment statistics analysis module 102, an analysis module based on an optimization calculation method 103, and an analysis module based on a fluid inclusion method 104;

[0027] When in use, when the reservoir mineral composition analysis unit 1 is specifically running, the first data receiving and classification module 101 classifies the reservoir mineral composition data to be analyzed according to different research requirements and experimental conditions, selects the data suitable for occasions requiring detailed mineral composition information, and performs operations on the core experiment statistics analysis module 102, so that it can perform statistical analysis on the mineral composition of reservoir rocks based on the core analysis data. This method can qualitatively observe the mineral composition of the formation, provide skeleton parameters and basis for the study of reservoir porosity evaluation modeling, and select data suitable for the calculation of logging instrument response, mineral response parameters and the volume of formation mineral composition. The analysis module 103 based on the optimization calculation method is operated, so that it uses the optimization program to solve various logging response equations simultaneously and uses the optimization technology to invert the formation parameters. This method uses the optimization theory to invert the formation parameters and realizes high-precision formation parameter inversion, and selects the oil and gas reservoir research data. The analysis module 104 based on the fluid inclusion method is operated, so that it can infer the formation time and filling direction of the oil and gas reservoir by measuring the uniform temperature of the fluid inclusion and the maturity of the hydrocarbon components. This allows the device to realize the classification and analysis of the reservoir mineral components first, thereby improving the analysis effect and accuracy of the reservoir mineral components;

[0028] The rock mechanics experiment simulation unit 2 includes a rock mechanics experiment process shooting monitoring module 201, a rock mechanics experiment data entry module 202, a rock mechanics simulation experiment generation and operation module 203 and an experiment result upload and analysis module 204;

[0029] During use, when the rock mechanics experiment simulation unit 2 is operating specifically, the rock mechanics experiment process shooting and monitoring module 201 starts shooting to collect the rock mechanics experiment process, and obtains various rock mechanics experiment data manually input by the experimenters through the rock mechanics experiment data entry module 202. These data are integrated inside the rock mechanics simulation experiment generation and operation module 203 to generate a simulation experiment plan and conduct dynamic simulation operation, solving the time-consuming, laborious, and resource-wasting problems caused by the experimenters manually changing various rock mechanics experiment variables for multiple experiments. Through computer-generated simulation experiments, the function of intelligent simulation experiments can be realized, which is conducive to obtaining better and more accurate experimental results. Subsequently, the experiment result uploading and analysis module 204 analyzes and compares the obtained experimental result data, and then uploads it to the experimenters for feedback and uploads it to the cloud for storage;

[0030] The output end of the rock mechanics experiment simulation unit 2 is electrically connected to the shale oil reservoir brittleness research unit 4 through a wire. The shale oil reservoir brittleness research unit 4 includes a second data receiving and classifying module 401, a research module 402 for bedding structure brittleness evaluation, a research module 403 for multiplicative comprehensive method, and a research module 404 for reservoir in-situ brittleness evaluation method based on on-site fracturing construction curve;

[0031] During use, when the shale oil reservoir brittleness research unit 4 is operating specifically, the second data receiving and classifying module 401 classifies according to different research requirements and experimental conditions of the shale oil reservoir brittleness data to be studied, and selects the situation suitable for detailed mineral composition analysis for operation on the research module 402 for bedding structure brittleness evaluation. It collects the logging curves of shale oil wells, rock mechanics parameters, and the measured contents of main minerals in shale, uses multiple regression method to model the mineral content, predicts the brittle mineral content, and calculates the brittleness index through the ratio of Young's modulus to Poisson's ratio of the mixed minerals. And it selects the scenario suitable for considering the energy before and after the peak comprehensively for operation on the research module 403 for multiplicative comprehensive method. It calculates the brittleness index by the ratio of the elastic deformation energy during rock compression to the elastic energy under ideal conditions, and the ratio of the energy provided by the outside world after the stress reaches the peak to the fracture energy of rock fracture, and combines to propose a brittleness index that can comprehensively reflect the energy characteristics before and after the peak. Furthermore, the second data receiving and classifying module 401 selects the engineering application data suitable for on-site real-time monitoring and evaluation for operation on the research module 404 for reservoir in-situ brittleness evaluation method based on on-site fracturing construction curve. It analyzes the construction curve data, extracts the reservoir in-situ brittleness index, and combines the real-time microseismic data and fracturing construction curve to monitor the reservoir fracture characteristics in real time;

[0032] The output end of the brittle research unit 4 of the shale oil reservoir is electrically connected to the artificial reservoir transformation plan receiving module 5 through a wire. The output end of the artificial reservoir transformation plan receiving module 5 is electrically connected to the comparison and analysis module 6 for the artificial reservoir transformation plan through a wire. The output end of the comparison and analysis module 6 for the artificial reservoir transformation plan is electrically connected to the exploration and development summary guidance unit 9 of the shale oil reservoir based on AI;

[0033] The exploration and development summary guidance unit 9 of the shale oil reservoir based on AI includes the analysis and comparison module 901 of the artificial shale oil reservoir exploration and development plan, the exploration and development plan optimization and summary module 902 of the shale oil reservoir based on AI, and the intelligent guidance and evaluation module 903 based on AI;

[0034] When in use, when the exploration and development summary guidance unit 9 of the shale oil reservoir based on AI runs specifically, the analysis and comparison module 901 of the artificial shale oil reservoir exploration and development plan is used to analyze and compare the draft of the artificial shale oil reservoir exploration and development plan formulated by the staff through AI technology. Then, the exploration and development plan optimization and summary module 902 of the shale oil reservoir based on AI is used to conduct intelligent optimization and summary of the exploration and development plan of the shale oil reservoir through AI technology. Finally, the intelligent guidance and evaluation module 903 based on AI provides guidance for the next-step exploration and development of the shale oil reservoir;

[0035] The collection and upload module 10 of the continental shale oil reservoir related materials securely uploads the relevant pictures, texts, and video materials of the continental shale oil reservoir to the cloud storage 7 in a confidential manner through a scanner, a high-definition camera, and a camera via the cloud storage encryption module 8, which improves the data security;

[0036] Based on its own computing, analysis and control functions, and in cooperation with the data collection and upload module 10 related to continental shale oil reservoirs and the data content collected and stored by the cloud storage 7, the central control unit 3 first conducts reservoir mineral composition analysis by controlling the operation of the reservoir mineral composition analysis unit 1, and simulates the operation of rock mechanics experiments by controlling the operation of the rock mechanics experiment simulation unit 2. Subsequently, based on the data obtained from the operation and calculation of the reservoir mineral composition analysis unit 1 and the rock mechanics experiment simulation unit 2, data support is provided to the shale oil reservoir brittleness research unit 4, and then the intelligent research and analysis of the shale oil reservoir brittleness is realized through the operation of the shale oil reservoir brittleness research unit 4. Then, the reservoir transformation plan formulated by the staff based on the above data will be sent to the artificial reservoir transformation plan receiving module 5 for data conversion, and then sent to the comparison and analysis module 6 for the artificial reservoir transformation plan to simulate and analyze the data and methods in the artificial plan, and then make instructions to guide the rationality of the reservoir transformation plan. Finally, through the exploration and development summary and guidance unit 9 of the shale oil reservoir based on AI, using the advantages of AI calculation and summary analysis, the exploration and development of the shale oil reservoir is summarized and guided, providing guidance for the next step of the exploration and development of the shale oil reservoir.

[0037] A method for evaluating the brittleness of continental shale oil reservoirs: First, the central control machine 3, based on its own calculation analysis and control function, cooperates with the continental shale oil reservoir related data collection and upload module 10 and the cloud storage 7 to collect and save data content, firstly controls the operation of the reservoir mineral composition analysis unit 1 to carry out reservoir mineral composition analysis, and controls the operation of the rock mechanics experiment simulation unit 2 to simulate the operation of the rock mechanics experiment, and then provides data support to the shale oil reservoir brittleness research unit 4 based on the data calculated by the operation of the reservoir mineral composition analysis unit 1 and the rock mechanics experiment simulation unit 2, and then implements the brittleness of the shale oil reservoir through the operation of the shale oil reservoir brittleness research unit 4. The intelligent research and analysis of the brittleness of shale oil reservoirs is carried out, and then the reservoir transformation plan formulated by the staff based on the above data will be sent to the artificial reservoir transformation plan receiving module 5 for data conversion, and then sent to the comparison and analysis module 6 for the artificial reservoir transformation plan to simulate and analyze the data and methods in the artificial plan, and then give instructions and guide the rationality of the reservoir transformation plan. Finally, through the AI-based shale oil reservoir exploration and development summary guidance unit 9, the advantages of AI calculation summary analysis are used to summarize and guide the exploration and development of shale oil reservoirs, and provide guidance for the next step of shale oil reservoir exploration and development. Specifically, step one, reservoir mineral When the material composition analysis unit 1 is specifically operated, the first data receiving and classification module 101 classifies the reservoir mineral composition data to be analyzed according to different research requirements and experimental conditions, selects the data suitable for occasions requiring detailed mineral composition information, and performs operations on the core experiment statistics analysis module 102, so that it can perform statistical analysis on the mineral composition of reservoir rocks based on the core analysis data. This method can qualitatively observe the mineral composition of the formation, provide skeleton parameters and basis for the study of reservoir porosity evaluation modeling, and select data suitable for the calculation of logging instrument response, mineral response parameters and the volume of formation mineral composition. The analysis module 103 of the optimization calculation method is operated, so that it uses the optimization program to solve various logging response equations simultaneously and uses the optimization technology to invert the formation parameters. This method uses the optimization theory to invert the formation parameters and realizes high-precision formation parameter inversion. The oil and gas reservoir research data is selected and operated on the analysis module 104 based on the fluid inclusion method, so that the formation time and filling direction of the oil and gas reservoir can be inferred by measuring the uniform temperature of the fluid inclusion and the maturity of the hydrocarbon components. This enables the device to realize the classification and analysis of the reservoir mineral components first, thereby improving the analysis effect and accuracy of the reservoir mineral components.In Step 2, when the rock mechanics experiment simulation unit 2 is running, the rock mechanics experiment process shooting and monitoring module 201 starts shooting to collect the rock mechanics experiment process, and collects various rock mechanics experiment data manually input by the experimenters through the rock mechanics experiment data entry module 202. These data are integrated inside the rock mechanics simulation experiment generation and operation module 203 to generate a simulation experiment plan and perform dynamic simulation operation, which solves the time-consuming, laborious and resource waste caused by the experimenters manually changing various rock mechanics experiment variables for multiple experiments. Through the computer-generated simulation of the experiment, the function of intelligent simulation experiment can be realized, which is conducive to obtaining better and more accurate experimental results. Subsequently, the experiment result upload and analysis module 204 compares and analyzes the obtained experimental results, and then uploads them to the experimenters for feedback and uploads them to the cloud for storage; In Step 3, when the shale oil reservoir brittleness research unit 4 is running, the second data receiving and classifying module 401 classifies according to different research requirements and experimental conditions of the shale oil reservoir brittleness data to be studied, and selects the cases applicable to the situation that requires detailed mineral composition analysis, and performs calculations on the research module 402 for bedding structure brittleness evaluation. It collects the logging curves, rock mechanics parameters of the shale oil well and the measured contents of the main minerals of the shale, uses the multiple regression method to model the mineral content, predicts the brittle mineral content, and calculates the brittleness index through the ratio of the Young's modulus to the Poisson's ratio of the mixed minerals. And it selects the scenarios applicable to the situation that requires comprehensive consideration of the energy before and after the peak, and performs calculations on the research module 403 of the multiplicative synthesis method. It calculates the brittleness index by the ratio of the elastic deformation energy during the rock compression process to the elastic energy under ideal conditions, and the ratio of the energy provided by the outside world after the stress reaches the peak to the fracture energy of the rock fracture, and combines to propose a brittleness index that can comprehensively reflect the energy characteristics before and after the peak. Furthermore, the second data receiving and classifying module 401 selects the engineering application data applicable to on-site real-time monitoring and evaluation, and performs calculations on the research module 404 of the in-situ reservoir brittleness evaluation method based on the on-site fracturing construction curve. It analyzes the construction curve data, extracts the in-situ reservoir brittleness index, and combines the real-time microseismic data and the fracturing construction curve to monitor the reservoir fracture characteristics in real time; In Step 4, when the AI-based shale oil reservoir exploration and development summary and guidance unit 9 is running, the analysis and comparison module 901 of the artificial shale oil reservoir exploration and development plan uses AI technology to analyze and compare the initial draft of the artificial shale oil reservoir exploration and development plan formulated by the staff, and then the optimization and summary module 902 of the AI-based shale oil reservoir exploration and development plan uses AI technology to perform intelligent optimization and summary of the shale oil reservoir exploration and development plan. Finally, the AI-based intelligent guidance and evaluation module 903 provides guidance for the next-step shale oil reservoir exploration and development.;

[0038] Obviously, the embodiments described above are only some of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

Claims

1. A method for evaluating the brittleness of continental shale oil reservoirs, characterized in that: The invention comprises a central control machine (3), wherein the central control machine (3) is bidirectionally electrically connected to a reservoir mineral composition analysis unit (1), a rock mechanics experiment simulation unit (2), a cloud storage device (7) and a module (10) for collecting and uploading data related to continental shale oil reservoirs through wires; the cloud storage device (7) is bidirectionally electrically connected to a cloud storage encryption module (8) through wires; the reservoir mineral composition analysis unit (1) comprises a first data receiving and classification module (101), a core experiment statistics analysis module (102), an analysis module (103) based on an optimization calculation method and an analysis module (104) based on a fluid inclusion method; the rock mechanics experiment simulation unit (2) comprises a rock mechanics experiment process shooting monitoring module (201), a rock mechanics experiment data entry module (202), a rock mechanics simulation experiment generation and operation module (203) and an experiment result uploading and analysis module (204); the output end of the rock mechanics experiment simulation unit (2) is electrically connected to a shale oil reservoir brittleness research unit (4) through wires; the shale oil reservoir brittleness analysis module (104) comprises a first data receiving and classification module (101), a core experiment statistics analysis module (102), an analysis module (103) based on an optimization calculation method and an analysis module (104) based on a fluid inclusion method; The shale oil reservoir brittleness research unit (4) comprises a second data receiving and classification module (401), a bedding structure brittleness evaluation research module (402), a multiplication synthesis method research module (403) and a reservoir in-situ brittleness evaluation method research module (404) based on a field fracturing construction curve. The output end of the shale oil reservoir brittleness research unit (4) is electrically connected to an artificial reservoir transformation scheme receiving module (5) through a wire. The output end of the artificial reservoir transformation scheme receiving module (5) is electrically connected to a signal receiving module for artificial reservoir transformation through a wire. The output end of the comparison and analysis module (6) for the artificial reservoir transformation plan is electrically connected to an AI-based shale oil reservoir exploration and development summary guidance unit (9) through a wire, and the AI-based shale oil reservoir exploration and development summary guidance unit (9) includes an artificial shale oil reservoir exploration and development plan analysis and comparison module (901), an AI-based shale oil reservoir exploration and development plan optimization summary module (902) and an AI-based intelligent guidance and evaluation module (903).

2. The method for evaluating the brittleness of continental shale oil reservoirs according to claim 1, characterized in that: The module (10) for collecting and uploading data related to continental shale oil reservoirs uses a scanner, a high-definition camera and a camera to upload pictures, text and video data related to continental shale oil reservoirs to the cloud storage (7) in a confidential and secure manner via a cloud storage encryption module (8), thereby improving data security.

3. The method for evaluating the brittleness of continental shale oil reservoirs according to claim 1, characterized in that: The central control machine (3) based on its own calculation analysis and control function, cooperates with the continental shale oil reservoir related data collection and upload module (10) and the cloud storage (7) to collect and store data content, firstly controls the operation of the reservoir mineral composition analysis unit (1) to carry out reservoir mineral composition analysis, and controls the operation of the rock mechanics experiment simulation unit (2) to simulate the operation of the rock mechanics experiment, and then provides data support to the shale oil reservoir brittleness research unit (4) based on the data calculated by the operation of the reservoir mineral composition analysis unit (1) and the rock mechanics experiment simulation unit (2), and then controls the operation of the shale oil reservoir brittleness research unit (4) to simulate the operation of the rock mechanics experiment. The AI ​​​​based shale oil reservoir exploration and development summary guidance unit (9) is used to summarize and guide the exploration and development of shale oil reservoirs, and then the AI ​​​​based shale oil reservoir exploration and development summary guidance unit (9) is used to utilize the advantages of AI calculation summary analysis to summarize and guide the exploration and development of shale oil reservoirs, and provide guidance for the next step of shale oil reservoir exploration and development.

4. The method for evaluating the brittleness of continental shale oil reservoirs according to claim 1, characterized in that: When the reservoir mineral composition analysis unit (1) is specifically operated, the first data receiving and classification module (101) classifies the reservoir mineral composition data to be analyzed according to different research requirements and experimental conditions, selects the data suitable for occasions requiring detailed mineral composition information, and performs operations on the core experiment statistical analysis module (102), so that it can perform statistical analysis on the mineral composition of reservoir rocks based on the core analysis data. This method can qualitatively observe the mineral composition of the formation, provide skeleton parameters and basis for the study of reservoir porosity evaluation modeling, and select data suitable for the calculation of logging instrument response, mineral response parameters and the volume of formation mineral composition. The analysis module (103) based on the optimization calculation method is operated, so that it can use the optimization program to solve various logging response equations simultaneously and use the optimization technology to invert the formation parameters. This method uses the optimization theory to invert the formation parameters and realizes high-precision formation parameter inversion, and selects the oil and gas reservoir research data. The analysis module (104) based on the fluid inclusion method is operated, so that it can infer the formation time and filling direction of the oil and gas reservoir by measuring the uniform temperature of the fluid inclusion and the maturity of the hydrocarbon components. This allows the device to realize the classification and analysis of the reservoir mineral components first, thereby improving the analysis effect and accuracy of the reservoir mineral components.

5. The method for evaluating the brittleness of continental shale oil reservoirs according to claim 1, characterized in that: When the rock mechanics experiment simulation unit (2) is in operation, the rock mechanics experiment process shooting monitoring module (201) starts shooting and collecting the rock mechanics experiment process, and collects various rock mechanics experiment data manually entered by the experimenter through the rock mechanics experiment data entry module (202). These data are integrated into the rock mechanics simulation experiment generation and operation module (203), and a simulation experiment plan is generated and dynamically simulated and operated, thereby solving the time-consuming, labor-intensive and resource-wasting problem caused by the experimenter manually changing various rock mechanics experiment variables to conduct multiple experiments. Through the computer experiment generation simulation, the function of intelligent simulation experiment can be realized, which is conducive to obtaining better and more accurate experimental results. Subsequently, the experimental result uploading and analysis module (204) is used to analyze and compare the obtained experimental result data, upload them to the experimenter for feedback, and upload them to the cloud for storage.

6. A method for evaluating brittleness of continental shale oil reservoirs according to claim 1, characterized in that: When the shale oil reservoir brittleness research unit (4) is specifically operated, the second data receiving and classification module (401) classifies the shale oil reservoir brittleness data to be studied according to different research requirements and experimental conditions, selects the data suitable for the situation where detailed mineral composition analysis is required, and performs calculations on the research module (402) of bedding structure brittleness evaluation, so that it collects the logging curves of shale oil wells, rock mechanical parameters and the measured content of the main minerals in shale, uses the multivariate regression method to model the mineral content, predicts the brittle mineral content, and calculates the brittleness index through the ratio of the Young's modulus to the Poisson's ratio of the mixed minerals, and selects the data suitable for the scene where the pre-peak and post-peak energies need to be comprehensively considered, and performs calculations on the research module (403) of the multiplicative synthesis method, so that it calculates the brittleness index through the ratio of the elastic deformation energy in the rock compression process to the elastic energy in the ideal situation, and the ratio of the energy provided by the outside world after the stress reaches the peak value to the fracture energy of the rock, and proposes a brittleness index that can comprehensively reflect the pre-peak and post-peak energy characteristics.

7. A method for evaluating brittleness of continental shale oil reservoirs according to claim 1, characterized in that: The second data receiving and classification module (401) selects engineering application data suitable for on-site real-time monitoring and evaluation, and performs operations on the research module (404) of the reservoir in-situ brittleness assessment method based on the on-site fracturing construction curve, so that it analyzes the construction curve data, extracts the reservoir in-situ brittleness index, and combines the real-time microseismic data and the fracturing construction curve to monitor the reservoir fracture characteristics in real time.

8. A method for evaluating brittleness of continental shale oil reservoirs according to claim 1, characterized in that: When the AI-based shale oil reservoir exploration and development summary guidance unit (9) is specifically operated, the artificial shale oil reservoir exploration and development plan analysis and comparison module (901) uses AI technology to analyze and compare the draft of the artificial shale oil reservoir exploration and development plan formulated by the staff, and then uses the AI-based shale oil reservoir exploration and development plan optimization summary module (902) to intelligently optimize and summarize the shale oil reservoir exploration and development plan through AI technology. Finally, the AI-based intelligent guidance and evaluation module (903) provides guidance for the next step of shale oil reservoir exploration and development.

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