Lithofacies analysis system and method based on shale geology-mechanics coupling lithofacies classification system

Abstract

The invention discloses a lithofacies analysis system and method based on a shale geology-mechanics coupling lithofacies classification system, and relates to the field of shale lithofacies analysis. The system construction module determines a geology-mechanics coupling lithofacies classification system according to the information data of the data acquisition module; the prediction module determines single well lithofacies prediction information; the feedback adjustment module performs feedback adjustment on the single well lithofacies prediction information according to a set rock mechanics and ground stress parameter range to obtain adjustment information; the three-dimensional model construction module adopts a genetic algorithm to perform seismic attribute embedding and integrated inversion according to the seismic attribute data and the adjustment information, and adopts a collaborative Kriging method to construct a three-dimensional model of geological-mechanical coupling lithofacies well-seismic collaborative modeling; and the parameter model determination module adopts a layer control-phase control double-control method to control constraints, determines the heterogeneity of shale bed geological-mechanical characteristics in three-dimensional space distribution, and is used for providing a basis for a building and producing area so as to explore and develop artificial oil and gas reservoirs.

Description

Technical Field

[0001] The present application relates to the field of shale lithofacies analysis, and in particular to a lithofacies analysis system and method based on a shale geology-mechanics coupled lithofacies classification system. Background Art

[0002] With the long-term exploitation of conventional oil and gas reservoirs and the continuous reduction of recoverable reserves worldwide, people have gradually invested various resources in the exploration and development of oil and gas for unconventional oil and gas reservoirs, among which shale oil and gas reservoirs have become the focus of research for many scholars and practitioners. The abundant resources of shale oil and gas have created many major shale oil and gas producing countries. For example, the "shale gas revolution" has transformed the United States from a major natural gas importer to a major exporter; as the largest shale gas producing country outside North America, China has deployed a large range of shale gas production areas in multiple regions based on the Sichuan Basin and its adjacent areas, forming a mature large-scale industrial production model of shale gas. Whether it is the rich medium and shallow shale oil and gas in the United States or the deep and ultra-deep shale oil and gas resources with huge development potential in China, ultra-long horizontal wells + large-scale repeated fracturing have always been a common and effective means of shale oil and gas exploration and development. In the process of continuous implementation of this method, the geological-mechanical coupling characteristics of shale have gradually attracted people's attention.

[0003] The three-dimensional geological-mechanical modeling for shale oil and gas reservoirs can provide a large amount of geological-mechanical coupling characteristics for oil and gas development sites, thereby improving the engineering efficiency of large-scale development of shale oil and gas resources. At present, geological modeling can improve the accuracy of the constructed model by sedimentary phase control; however, rock mechanics and geostress parameter modeling has not found an effective phase control scheme to improve the accuracy of the model, and still uses deterministic interpolation or random modeling methods to achieve it. The established geological-mechanical attribute model has poor reliability and does not conform to the geological-mechanical coupling characteristics and laws of the underground rock mass. In order to solve the above problems, it is necessary to deeply explore the controlled factors of the geological-mechanical characteristics of shale rock mass.

[0004] At present, the research on the characteristics of geological-mechanical coupling is less or not thorough enough, and in most cases, the research on geological-mechanical characteristics is artificially separated, that is, the influence of mechanical factors is ignored when studying geological properties or the objective existence of geological factors is not considered when analyzing mechanical characteristics, which leads to a large deviation from the actual underground situation. The article "The Effect of Temperature on Rock Mechanical Properties and Borehole Stability" (published in September 2017; author: Jia Lichun, Chen Dong, Huang Bing) conducted triaxial compression experiments on rocks under different temperature conditions and analyzed the variation between the wellbore collapse pressure, fracture pressure and temperature; this method fully explains the relationship between temperature and rock mechanics by combining theory and practice, but lacks the exploration of the core geological factors. The article "Shale Strength Weakening Law and Its Effect on Wellbore Stability of Horizontal Wells" reveals the weakening law of shale shear strength under the influence of drilling fluid based on direct shear test, and analyzes the influence of shale weakening on the collapse pressure of horizontal wells; this method focuses on the influence of rock mechanical properties on wellbore stability, and less exploration of geological causes, and the only rock mechanical parameter involved is shear strength, lacking discussion of other mechanical parameters. The article "Rock Mechanical Parameter Modeling of Tight Reservoir in Fuyu Oil Layer in T30 Well Area of ​​Daqing Oilfield" (published in October 2020; author: Guo Siqiang) obtains the rock mechanical and geostress parameters of a single well through well logging data calculation, and establishes a three-dimensional model of rock mechanical and geostress parameters using geostatistical methods; this method mainly uses a random modeling method, and the accuracy of the established model has a certain deviation from the actual situation. Therefore, it is crucial to analyze and determine the heterogeneity of the geological-mechanical characteristics of shale layers in three-dimensional space to provide a basis for the construction of production areas and to conduct exploration and development of artificial oil and gas reservoirs. Summary of the invention

[0005] The purpose of this application is to provide a lithofacies analysis system and method based on a shale geological-mechanical coupled lithofacies classification system, which can analyze and determine the heterogeneity of the geological-mechanical characteristics of shale layers in three-dimensional spatial distribution.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In a first aspect, the present application provides a lithofacies analysis system based on a shale geology-mechanics coupled lithofacies classification system, comprising:

[0008] A data acquisition module is used to acquire information data; the information data includes: geological lithofacies data and mechanical in-situ parameter data;

[0009] A system construction module, connected to the data acquisition module, is used to determine the geological-mechanical coupled lithofacies classification system based on the information data;

[0010] The prediction module is connected with the system construction module and is used to perform single-well lithofacies quantitative prediction based on the geological-mechanical coupled lithofacies classification system and the logging data to obtain single-well lithofacies prediction information; the single-well lithofacies prediction information is the prediction result of the geological-mechanical coupled lithofacies of the entire wellbore space within the target layer, which is used to characterize the phase sequence change characteristics of the geological-mechanical coupled lithofacies of the single well along the wellbore;

[0011] A feedback adjustment module is connected to the prediction module and is used to perform feedback adjustment on the single well lithofacies prediction information according to the set rock mechanics and geostress parameter range to obtain adjustment information;

[0012] The three-dimensional model building module is connected to the feedback adjustment module, and is used to use genetic algorithms to perform seismic attribute mosaic and integrated inversion according to seismic attribute data and adjustment information, and to use collaborative Kriging method to build a three-dimensional model of geological-mechanical coupled lithofacies well-seismic collaborative modeling; the three-dimensional model is a three-dimensional physical simulation model constructed based on the intrinsic connection between the properties of geological lithofacies and geological-mechanical coupled lithofacies, and is used to characterize the mechanical heterogeneity differences and distribution laws of geological lithofacies;

[0013] The parameter model determination module is connected to the three-dimensional model construction module, and is used to determine the geological-mechanical coupling lithofacies control rock mechanics and ground stress parameter model based on the three-dimensional model and the layer control-phase control dual control method to control constraints; the parameter model is used to characterize the heterogeneity of the geological-mechanical characteristics of the shale layer in the three-dimensional spatial distribution, and to provide a basis for the construction of production areas for the exploration and development of artificial oil and gas reservoirs.

[0014] In the second aspect, the present application provides a lithofacies analysis method based on a shale geology-mechanics coupled lithofacies classification system, comprising:

[0015] Obtain information data; the information data includes: geological lithofacies data and mechanical in-situ parameter data;

[0016] Determine the geological-mechanical coupled lithofacies classification system based on information data;

[0017] Based on the geological-mechanical coupled lithofacies classification system, single-well lithofacies quantitative prediction is performed according to logging data to obtain single-well lithofacies prediction information; single-well lithofacies prediction information is the prediction result of the geological-mechanical coupled lithofacies of the entire wellbore space within the target layer, which is used to characterize the phase sequence change characteristics of the geological-mechanical coupled lithofacies of a single well along the wellbore;

[0018] According to the set rock mechanics and geostress parameter range, the single well lithofacies prediction information is feedback-adjusted to obtain adjustment information;

[0019] Genetic algorithms are used to perform seismic attribute mosaicking and integrated inversion based on seismic attribute data and adjustment information, and the collaborative Kriging method is used to construct a three-dimensional model of geological-mechanical coupled lithofacies well-seismic collaborative modeling; the three-dimensional model is a three-dimensional physical simulation model constructed based on the intrinsic connection between the properties of geological lithofacies and geological-mechanical coupled lithofacies, which is used to characterize the mechanical heterogeneity differences and distribution laws of geological lithofacies;

[0020] Based on the three-dimensional model, the layer control and phase control dual control method is adopted to control the constraints and determine the parameter model of rock mechanics and geostress controlled by geological-mechanical coupled lithofacies. The parameter model is used to characterize the heterogeneity of the three-dimensional spatial distribution of the geological-mechanical characteristics of the shale layer, and to provide a basis for the construction of production areas for the exploration and development of artificial oil and gas reservoirs.

[0021] According to the specific embodiments provided in this application, this application has the following technical effects:

[0022] The present application provides a lithofacies analysis system and method based on the shale geological-mechanical coupled lithofacies classification system. Since the shale rock mass deeply buried underground is a geological-mechanical coupled body, the study of the shale rock mass needs to take into account the controlled factors of geology and mechanics and explore the internal relationship between the two; and in order to improve the accuracy of the three-dimensional model of rock mechanics and geostress parameters, it is necessary to select mechanical parameters that have obvious correlation with geological properties, and form a geological-mechanical coupling control factor to participate in the modeling of mechanical parameters, so as to achieve the purpose of improving the accuracy of the mechanical model. Based on the classification and identification of shale geological lithofacies, the present application systematically carries out the analysis and identification of the rock mechanics and geostress characteristics of geological lithofacies, further proposes the construction of a classification system for geological-mechanical coupled lithofacies, and forms a comprehensive single well identification and three-dimensional prediction technical process based on this, so as to characterize the heterogeneity of the distribution of geological-mechanical characteristics of shale layers in three-dimensional space, so as to provide a basis for the construction of production areas, so as to carry out the exploration and development of artificial oil and gas reservoirs. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a structural diagram of the lithofacies analysis system based on the shale geological-mechanical coupled lithofacies classification system;

[0024] Figure 2 It is a technical flow chart of lithofacies analysis based on the shale geological-mechanical coupled lithofacies classification system;

[0025] Figure 3 Flow chart for geological lithofacies acquisition and mechanical parameters sorting;

[0026] Figure 4 Flow chart for optimizing differentially sensitive rock mechanics and geostress parameters;

[0027] Figure 5It is a schematic diagram of the geological-mechanical coupled lithofacies classification of carbon-rich medium-high porosity siliceous shale;

[0028] Figure 6 It is a schematic diagram of the geological-mechanical coupled lithofacies classification of carbon-rich medium-high porosity calcium-mud-containing siliceous shale;

[0029] Figure 7 It is a schematic diagram of the geological-mechanical coupled lithofacies classification of medium-high carbon and medium-high porosity siliceous shales;

[0030] Figure 8 It is a schematic diagram of the geological-mechanical coupled lithofacies classification of medium-low carbon medium-low porosity calcic siliceous argillaceous shale;

[0031] Fig. 9 It is a schematic diagram of the geological-mechanical coupled lithofacies classification of medium-high carbon medium-high porosity muddy siliceous shale;

[0032] Fig.10 It is a comprehensive bar chart of the quantitative prediction results of geological-mechanical coupled lithofacies of a typical single well;

[0033] Fig.11 It is the relative depth impedance-genetic inversion profile of single well lithofacies;

[0034] Fig.12 It is a schematic diagram of geological lithofacies three-dimensional inversion data volume;

[0035] Fig.13 It is a schematic diagram of the geological-mechanical coupled lithofacies three-dimensional inversion data volume;

[0036] Fig.14 It is a schematic diagram of the three-dimensional model of the first geological lithofacies;

[0037] Fig.15 It is a schematic diagram of the three-dimensional model of the second geological lithofacies;

[0038] Fig.16 This is the first geological lithofacies section;

[0039] Fig.17 This is the second geological lithofacies cross section;

[0040] Fig.18 This is a schematic diagram of the first geological-mechanical coupled lithofacies three-dimensional model;

[0041] Fig.19 It is a schematic diagram of the second geological-mechanical coupled lithofacies three-dimensional model;

[0042] Fig. 20 It is a schematic diagram of the third geological-mechanical coupled lithofacies three-dimensional model;

[0043] Fig.21 It is a geological-mechanical coupled lithofacies profile;

[0044] Fig. 22 It is a schematic diagram of the random interpolation three-dimensional model;

[0045] Fig.23 It is a schematic diagram of the geological-mechanical coupled lithofacies phase-controlled model;

[0046] Fig.24 is the profile of the random interpolation model;

[0047] Fig.25 This is a cross-sectional view of the phase-controlled model. DETAILED DESCRIPTION

[0048] The content mentioned in this application can actually be applied to a shale gas production area in western China, and can be expanded to cover the entire study area based on a single well, revealing the characteristic differences and distribution patterns of different geological-mechanical coupled lithofacies in the study area, thereby forming a comprehensive and accurate understanding of the stratigraphic lithofacies, rock mechanics and geostress characteristics of the study area, and providing methods and technical support for phase-controlled geological-mechanical modeling. This set of technical processes can fully characterize the geological and mechanical characteristics of shale lithofacies, and the lithofacies-rock mechanics characterization of rock masses such as sandstone and carbonate rock can also refer to this technical process.

[0049] like Figure 1 As shown, the present application provides a lithofacies analysis system based on a shale geology-mechanics coupled lithofacies classification system, including: a data acquisition module, a system construction module, a prediction module, a feedback adjustment module, a three-dimensional model construction module and a parameter model determination module.

[0050] The data acquisition module is used to acquire information data; the information data includes: geological lithofacies data and mechanical in-situ parameter data.

[0051] The system construction module is connected with the data acquisition module, and the system construction module is used to determine the geological-mechanical coupled lithofacies classification system according to the information data. The geological-mechanical coupled lithofacies classification system includes: a geological-mechanical coupled lithofacies classification plate and a geological-mechanical coupled lithofacies classification system parameter division standard table.

[0052] The prediction module is connected to the system construction module and is used to perform quantitative prediction of single-well lithofacies based on the geological-mechanical coupled lithofacies classification system and logging data to obtain single-well lithofacies prediction information; the single-well lithofacies prediction information is the prediction result of the geological-mechanical coupled lithofacies of the entire wellbore space within the target layer, which is used to characterize the phase sequence change characteristics of the geological-mechanical coupled lithofacies of a single well along the wellbore.

[0053] The feedback adjustment module is connected to the prediction module and is used to perform feedback adjustment on the single well lithofacies prediction information according to the set rock mechanics and geostress parameter range to obtain adjustment information.

[0054] The three-dimensional model construction module is connected to the feedback adjustment module, and is used to use genetic algorithms to perform seismic attribute mosaicking and integrated inversion according to seismic attribute data and adjustment information, and to use the collaborative Kriging method to construct a three-dimensional model of geological-mechanical coupled lithofacies well-seismic collaborative modeling; the three-dimensional model is a three-dimensional physical simulation model constructed based on the intrinsic connection between the respective properties of geological lithofacies and geological-mechanical coupled lithofacies, and is used to characterize the mechanical heterogeneity differences and distribution laws of geological lithofacies.

[0055] The parameter model determination module is connected to the three-dimensional model construction module, and is used to determine the geological-mechanical coupling lithofacies control rock mechanics and ground stress parameter model based on the three-dimensional model and the layer control-phase control dual control method to control constraints; the parameter model is used to characterize the heterogeneity of the geological-mechanical characteristics of the shale layer in the three-dimensional spatial distribution, and to provide a basis for the construction of production areas for the exploration and development of artificial oil and gas reservoirs.

[0056] The system building blocks include:

[0057] The processing submodule is connected to the data acquisition module and is used to perform data combing and singular point elimination processing according to the information data, based on the position correspondence principle or the adjacent similarity principle and the logging response characteristics to obtain combed data.

[0058] The parameter determination submodule is connected to the processing submodule and is used to determine the difference-sensitive rock mechanics parameters according to the sorted data; the difference-sensitive rock mechanics parameters include: tensile strength, shear strength and Young's modulus.

[0059] The cluster core determination submodule is connected to the data acquisition module and the parameter determination submodule respectively, and is used to determine the core of the geological-mechanical coupling lithofacies cluster according to the information data and the differentially sensitive rock mechanical parameters.

[0060] The analysis submodule is connected to the cluster core determination submodule and is used to analyze the boundaries and cross-ranges of the geological-mechanical coupling lithofacies clustering clusters based on the geological-mechanical coupling lithofacies cluster core, and determine the boundaries and ranges of the geological-mechanical coupling lithofacies clusters.

[0061] The system determination submodule is connected to the analysis submodule and is used to draw a geological-mechanical coupled lithofacies classification plate according to the boundaries and ranges of the geological-mechanical coupled lithofacies clusters, and to construct a standard table for the division of lithofacies classification system parameters to determine the geological-mechanical coupled lithofacies classification system.

[0062] The parameter determination submodule includes:

[0063] The characteristic attribute determination unit is connected to the processing submodule and is used to determine the characteristic attributes based on the combing data, single factor cluster feature extraction and multi-well analogy analysis.

[0064] The distribution difference determination unit is connected to the characteristic attribute determination unit, and is used to perform attribute consistency verification of a single well horizontally and multiple wells vertically according to the characteristic attributes, and determine the distribution difference of each mechanical parameter in different geological lithofacies.

[0065] The parameter determination unit is connected to the distribution difference determination unit and is used to determine the difference-sensitive rock mechanics parameters based on the distribution difference.

[0066] In one embodiment, the present application also mentions a lithofacies analysis method based on a shale geology-mechanics coupled lithofacies classification system, including:

[0067] Obtain information data; the information data includes: geological lithofacies data and mechanical in-situ parameter data.

[0068] Determine the geological-mechanical coupled lithofacies classification system based on the information data; based on the geological-mechanical coupled lithofacies classification system, perform quantitative prediction of single-well lithofacies according to logging data to obtain single-well lithofacies prediction information; single-well lithofacies prediction information is the prediction result of the geological-mechanical coupled lithofacies of the entire wellbore space within the target layer, which is used to characterize the phase sequence change characteristics of the geological-mechanical coupled lithofacies of a single well along the wellbore.

[0069] According to the set rock mechanics and geostress parameter range, the single well lithofacies prediction information is feedback-adjusted to obtain adjustment information.

[0070] Genetic algorithm is used to perform seismic attribute mosaicking and integrated inversion according to seismic attribute data and adjustment information, and collaborative Kriging method is used to construct a three-dimensional model of geological-mechanical coupled lithofacies well-seismic collaborative modeling; the three-dimensional model is a three-dimensional physical simulation model constructed based on the intrinsic connection between the properties of geological lithofacies and geological-mechanical coupled lithofacies, which is used to characterize the mechanical heterogeneity differences and distribution laws of geological lithofacies.

[0071] Based on the three-dimensional model, the layer control and phase control dual control method is adopted to control the constraints and determine the parameter model of rock mechanics and geostress controlled by geological-mechanical coupled lithofacies. The parameter model is used to characterize the heterogeneity of the three-dimensional spatial distribution of the geological-mechanical characteristics of the shale layer, and to provide a basis for the construction of production areas for the exploration and development of artificial oil and gas reservoirs.

[0072] Among them, the geological-mechanical coupled lithofacies classification system is determined based on information data, including:

[0073] According to the information data, based on the position correspondence principle or the adjacent similarity principle, data combing and singular point elimination are carried out according to the logging response characteristics to obtain combed data.

[0074] The differentially sensitive rock mechanical parameters are determined based on the sorted data; the differentially sensitive rock mechanical parameters include: tensile strength, shear strength and Young's modulus.

[0075] The core of the geological-mechanical coupled lithofacies cluster is determined based on information data and differentially sensitive rock mechanical parameters.

[0076] Based on the core of geological-mechanical coupled lithofacies cluster, the boundary and cross-range analysis of geological-mechanical coupled lithofacies clustering clusters was carried out to determine the boundary and range of geological-mechanical coupled lithofacies clusters.

[0077] A geological-mechanical coupled lithofacies classification plate is drawn according to the boundaries and ranges of the geological-mechanical coupled lithofacies clusters, and a standard table for the division of lithofacies classification system parameters is constructed to determine the geological-mechanical coupled lithofacies classification system.

[0078] The differentially sensitive rock mechanical parameters are determined based on the sorted data, including:

[0079] According to the sorted data, the characteristic attributes are determined based on single-factor cluster feature extraction and multi-well analogy analysis; according to the characteristic attributes, the single-well horizontal and multi-well vertical attribute consistency checks are carried out to determine the distribution differences of each mechanical parameter in different geological lithofacies; based on the distribution differences, the differentially sensitive rock mechanical parameters are determined.

[0080] Genetic algorithm is used to perform seismic attribute mosaic and integrated inversion based on seismic attribute data and adjustment information, and the collaborative Kriging method is used to construct a three-dimensional model of geological-mechanical coupled lithofacies well-seismic collaborative modeling, including:

[0081] Determine the genetic neural network architecture parameters; the genetic neural network architecture parameters include: the number of iterations, the correlation threshold, the weight attenuation and the number of nodes in the hidden layer of the neural network; use a genetic algorithm to extract the single well seismic attribute data mosaic based on the genetic neural network architecture parameters and the seismic attribute data; based on the adjustment information and the single well seismic attribute data mosaic, perform seismic attribute mosaic and integrated inversion to obtain an inversion data body; perform data coarsening of single well geological lithofacies and geological-mechanical coupled lithofacies, and lithofacies data analysis to obtain analysis data; based on the common constraint control of the analysis data and the inversion data body, use the collaborative Kriging method to construct a three-dimensional model of geological-mechanical coupled lithofacies well-seismic collaborative modeling.

[0082] Based on the three-dimensional model, the layer control and phase control dual control method is used to control the constraints and determine the geological-mechanical coupling lithofacies control rock mechanics and ground stress parameter model, including:

[0083] Obtain the rock mechanics and geostress parameters of a single well; perform data coarsening and small-layer data analysis based on the rock mechanics and geostress parameters of a single well to obtain analytical parameter data; based on the analytical parameter data, use the layer control-phase control dual control method to control constraints, and based on the rock mechanics and geostress parameters of a single well and the three-dimensional model, use the collaborative Kriging method to determine the geological-mechanical coupling lithofacies control rock mechanics and geostress parameter model.

[0084] The rock mechanics and geostress parameters of a single well are obtained by uniaxial compression, triaxial compression or point load strength test.

[0085] like Figure 2 As shown, the method proposed in this application, namely, the construction of shale geology-mechanics coupled lithofacies classification system, single well identification and three-dimensional prediction method system covers 4 key technologies and 10 key technical links.

[0086] 1. Construction of the four-element classification system of shale geological-mechanical coupled lithofacies: ① First, the classification system needs to be carried out on the basis of the existing geological lithofacies and mechanical in-situ parameters, relying on the one-to-one comparison of geological lithofacies and corresponding rock mechanical parameters; ② Secondly, based on the optimal difference principle, the rock mechanics and geostress parameters with high sensitivity to geological lithofacies are selected as the basis for further subdividing the geological-mechanical coupled lithofacies types; ③ Then, the geological-mechanical coupled lithofacies subdivision work is carried out according to the selected four elements, and the core of each type of geological-mechanical coupled lithofacies cluster is determined from the analysis of the four-element intersection diagram; ④ On this basis, the intersection range between the cluster boundary and different geological-mechanical coupled lithofacies clusters is determined; ⑤ Finally, the geological-mechanical coupled lithofacies classification plate is drawn and the standard table of geological-mechanical coupled lithofacies classification system parameter division is constructed to complete the construction of the geological-mechanical coupled lithofacies classification system.

[0087] 2. Three-stage logging data quality control and single-well quantitative prediction of geological-mechanical coupled lithofacies. According to the parameter division standard of the geological-mechanical coupled lithofacies classification system, single-well identification and quantitative prediction of geological-mechanical coupled lithofacies are carried out in three stages: ① First, the logging data integrity cleaning and reconstruction and homogeneity suppression processing are carried out; ② Secondly, the lithofacies quantitative prediction is carried out based on the single well; ③ Finally, the prediction results are adjusted based on the logging response characteristics to ensure the accuracy and authenticity of the geological-mechanical coupled lithofacies classification.

[0088] 3. Geological-mechanical coupled lithofacies multi-stage seismic attribute mosaic and integrated inversion and well-seismic collaborative modeling, specifically including: ① Geological-mechanical coupled lithofacies multi-stage seismic attribute mosaic and integrated inversion; ② Well-seismic collaborative modeling and visualized three-dimensional prediction.

[0089] 4. Geological-mechanical coupling lithofacies control rock mechanics and geostress parameter modeling.

[0090] The specific implementation steps are as follows:

[0091] 1) Construction of the four-element classification system of shale geology-mechanics coupled lithofacies:

[0092] A. The geological lithology and mechanical in-situ parameters have been sorted out.

[0093] By adopting the patents "A method for classification, identification and three-dimensional characterization of marine shale lithofacies" (patent number 202011398837.8) and "A method for intelligent identification and visualization of continental tight reservoir lithofacies" (patent number 202011393013.1), marine or continental shale geological lithofacies can be obtained; by adopting "A method for three-dimensional visualization characterization of tight rock mass stress" (patent number 202011388424.1), mechanical in-situ parameters can be obtained.

[0094] On the basis of obtaining marine or continental shale geological lithofacies, combined with the mechanical in-situ parameters obtained by sorting out, the mechanical parameter data corresponding to the geological lithofacies are extracted along the wellbore with the wellbore as the core. Based on the principle of position correspondence, the existing geological lithofacies data are sorted and appropriately merged according to the logging response characteristics; or based on the principle of adjacent similarity, the geological lithofacies with small quantity and small distribution thickness are merged into the adjacent dominant geological lithofacies, and the overall geological lithofacies classification system of the area is determined based on the principle of appropriate contiguousness and having more data samples.

[0095] The logging response and the determined geological lithofacies data are recombined to compare and sort out the mechanical in-situ parameters, eliminate the singular points, determine the mechanical in-situ parameter data set, and extract the statistical characteristics of the data set. Figure 3 The steps for obtaining geological lithofacies and combing the in-situ mechanical parameters are demonstrated.

[0096] B. Optimization of rock mechanics and geostress parameters sensitive to geological lithofacies differences.

[0097] On the basis of determining the geological lithofacies type and mechanical in-situ parameter data set, the rock mechanics and geostress parameters that are sensitive to geological lithofacies differences are further analyzed, compared, and selected. First, single-factor cluster feature extraction and multi-well analogy analysis are carried out, that is, the digital characteristic attributes of different rock mechanics and geostress parameter clusters of each geological lithofacies are obtained; on this basis, the "single well vertical-multi-well horizontal" attribute feature consistency is determined, and the characteristic consistency of different mechanical parameters is verified in single wells and multiple wells for the same geological lithofacies, and the distribution difference of each mechanical parameter in different geological lithofacies is determined; finally, the distribution difference of the digital characteristics of each rock mechanics and geostress parameter in different geological lithofacies is compared, and the parameters with the largest distribution difference are selected as differentially sensitive rock mechanics and geostress parameters. After the actual screening operation is carried out using the above method, the differentially sensitive parameters are determined to be tensile strength, shear strength and Young's modulus.

[0098] After conducting a literature and field investigation, it was found that the generation of cracks in the shale fracturing process is highly correlated with the tensile strength, the shear strength of the rock mass has a great influence on the operating efficiency of the drilling process, and the Young's modulus plays a decisive role in evaluating the rock mass's deformation resistance and has a certain linear relationship with the brittleness of the shale. The above three types of parameters can be used to characterize the strength, elasticity and brittleness of shale, and are consistent with the parameter types selected by the differential sensitive parameters, so they can be used as reliable data for subsequent research. Figure 4 Technical flow chart for parameter optimization.

[0099] C. Determination of the core of the geological-mechanical coupled lithofacies cluster.

[0100] Focusing on the determined mechanical in-situ parameters sensitive to geological lithofacies differences, the classification and identification of geological-mechanical coupled lithofacies is carried out in combination with geological lithofacies. That is, with geological lithofacies as the core and mechanical parameters as classification parameters, the geological lithofacies are classified in multiple directions and dimensions, and multiple geological-mechanical coupled lithofacies clusters within the same geological lithofacies are determined. Based on the differences in geological and mechanical properties, one or more representative points of the same cluster are identified, so as to determine the core of the geological-mechanical coupled lithofacies cluster.

[0101] D. Determination of the boundaries and scope of geological-mechanical coupled lithofacies clusters.

[0102] Focusing on the core of geological-mechanical coupled lithofacies clustering clusters, the boundary and cross-range analysis of geological-mechanical coupled lithofacies clustering clusters is further carried out. The boundary of the lithofacies cluster can be defined by analyzing the changes in the mechanical characteristics of the geological-mechanical coupled lithofacies, or some logging curves can be screened and the change threshold can be set to set the boundary where the geological-mechanical coupled lithofacies characteristics change significantly. On the basis of the preliminary determination of the cluster boundary, the cross-region range between different lithofacies clusters is collected according to the size of the mechanical curve response range. The position of the boundary area is adjusted according to the principle that the range of each lithofacies cluster is the largest and the cross-region range between different lithofacies is the smallest. On the basis of completing the optimization of cluster boundaries and cross-ranges, the reliability of the cluster core is reversely verified, and the cluster core is appropriately adjusted according to the verification results, so as to ensure the reliability of the cluster core, boundary and cross-range as a whole.

[0103] E. Drawing of geological-mechanical coupled lithofacies classification maps and construction of classification system tables.

[0104] The above analysis results are plotted into a geological-mechanical coupled lithofacies classification chart, and a standard table for the division of lithofacies classification system parameters is constructed to complete the construction of the geological-mechanical coupled lithofacies classification system. According to the above process, the accurate division and identification of shale geological-mechanical coupled lithofacies can be achieved.

[0105] Figures 5 to 9The classification chart of the geological-mechanical coupled lithofacies of the main production layer of a marine shale production area is displayed. It can be seen that for the five types of geological lithofacies in the study area, further three types of geological lithofacies are achieved according to the mechanical characteristics - brittleness, strong shear toughness and strong tensile toughness, and 15 types of geological-mechanical coupled lithofacies based on geological lithofacies are constructed. The cluster cores of different geological-mechanical coupled lithofacies remain independent, and the boundaries and cross-range distributions of different clusters are relatively obvious. It can be seen that the geological-mechanical coupled lithofacies classification system of the shale layer in this area constructed according to the above method can be well applied to field production research and provide good guidance for the understanding of the geological-mechanical coupling characteristics on site.

[0106] According to the classification plate, a classification system table of different geological-mechanical coupled lithofacies can be further constructed. Table 1 shows Figures 5 to 9 The 5 types of geological lithofacies, 15 types of geological-mechanical coupled lithofacies classification systems, and the corresponding classification parameter standards for each geological-mechanical coupled rock. It can be seen that different geological lithofacies are divided into different geological-mechanical coupled lithofacies according to the corresponding standards. The mechanical parameter characteristics of each type of lithofacies are very different. The classification standards of mechanical parameters include but are not limited to constants and functions, which depend on the geological or mechanical characteristics of the core of each type of lithofacies.

[0107] Table 1 List of geological-mechanical coupled lithofacies classification system and corresponding type classification parameter standards for a shale layer

[0108]

[0109]

[0110] X′ in Table 1 抗剪 is the shear strength value; X′ 抗拉 is the tensile strength value.

[0111] (2) Quality control of three-stage logging data and quantitative prediction of geological-mechanical coupled lithofacies for single wells.

[0112] On the basis of the above-mentioned shale geological-mechanical coupled lithofacies classification system, further single-well lithofacies quantitative prediction is carried out to lay a good data foundation for geological-mechanical coupled lithofacies three-dimensional modeling.

[0113] A. Phase 1: Logging data integrity cleaning, reconstruction and homogeneity suppression processing.

[0114] Integrity cleaning, reconstruction and homogeneity suppression of well logging data are carried out to provide a reliable data source for the calculation of mechanical parameters: (1) Data integrity cleaning and reconstruction is a very important step in data processing, which aims to ensure the quality and reliability of data. This stage mainly checks whether there are missing values ​​or abnormal values ​​in the well logging data, and performs secondary processing of the well logging data by filling, deleting or interpolating, so as to ensure the reliability and authenticity of the well logging data; (2) Data homogeneity suppression processing is mainly aimed at the influence of similar data in the analysis and modeling process. The goal is to reduce duplication or redundancy in the well logging data set, so as to ensure the accuracy of the mechanical parameters and geological-mechanical coupled lithofacies classification results constructed based on the original well logging data.

[0115] B. The second stage: single well quantitative prediction using the two-step geological-mechanical coupled lithofacies method.

[0116] Single-well quantitative prediction of geological-mechanical coupled lithofacies is performed in two steps.

[0117] In the first step, the patented "A method for classification, identification and three-dimensional characterization of marine shale lithofacies" (patent number ZL202011398837.8) and "A method for intelligent identification and visualization of continental tight reservoir lithofacies" (patent number ZL202011393013.1) are used to obtain marine or continental shale geological lithofacies after division and identification; the "A method for three-dimensional visualization characterization of tight rock mass stress" (patent number ZL202011388424.1) is used to calculate and obtain the single well differential sensitive mechanical parameter curve, thus completing the preparation of geological lithofacies, rock mechanics and geostress data for single well prediction of geological-mechanical coupled lithofacies.

[0118] In the second step, relying on the obtained geological-mechanical coupled lithofacies system parameter division standards, focusing on different categories of geological lithofacies, and using the calculated single-well differential sensitive mechanical parameter curves, the geological-mechanical coupled lithofacies single-well subdivision prediction is completed.

[0119] For the actual case area, firstly, the selected shale single well differential sensitive mechanical parameters - tensile strength, shear strength, and Young's modulus curves were calculated; then, based on the original five types of geological lithofacies, the geological-mechanical coupled lithofacies of the entire wellbore space in the target layer were predicted according to the classification parameter standards corresponding to each geological-mechanical coupled lithofacies. Finally, a comprehensive columnar diagram of geological-mechanical coupled lithofacies single well was drawn ( Fig.10 ), showing the phase sequence variation characteristics of the geological-mechanical coupled lithofacies along the wellbore of a typical single well in the actual case area.

[0120] C. The third stage: feedback adjustment of geological-mechanical coupled lithofacies prediction results.

[0121] Based on the prediction results of the single-well geological-mechanical coupled lithofacies, in accordance with the principles of reasonable classification, sufficient data volume, and reasonable uniformity of adjacent categories, combined with the rock mechanics and geostress parameter ranges of the geological-mechanical coupled lithofacies proposed in the present invention, feedback adjustment is performed on the prediction results to make the prediction results more reasonable and reliable and meet the geological laws, thereby providing reliable data support for subsequent geological-mechanical coupled lithofacies three-dimensional prediction and modeling.

[0122] (3) Geological-mechanical coupling lithofacies multi-stage seismic attribute embedding and integrated inversion and well-seismic collaborative modeling.

[0123] The seismic attribute extraction and optimization method provided by the "Invention Patent: A Three-Dimensional In-Situ Characterization Method for the Heterogeneity of Shale Generation and Reservoir Properties (Patent No. 202011393012.7)" is used to carry out A. Multi-stage seismic attribute mosaicking and integrated inversion of geological-mechanical coupled lithofacies, and then obtain the geological-mechanical coupled lithofacies evolution data body; combined with the result data obtained by the above technical process, the well-seismic collaborative modeling and characterization method provided by the "Invention Patent: A Three-Dimensional In-Situ Characterization Method for the Heterogeneity of Shale Generation and Reservoir Properties (Patent No. 202011393012.7)" is further adopted to carry out B. Geological-mechanical coupled lithofacies well-seismic collaborative modeling and visualized three-dimensional prediction, and obtain the three-dimensional model of the geological-mechanical coupled lithofacies, thereby completing the three-dimensional attribute and distribution prediction of the geological-mechanical coupled lithofacies.

[0124] A. Geological-mechanical coupled lithofacies multi-stage seismic attribute mosaicking and integrated inversion.

[0125] The "multi-stage" mentioned in this method mainly includes the following parts: ① Optimize and determine the preliminary extracted seismic attributes to lay the data foundation for subsequent attribute extraction and inversion; ② Based on the extracted seismic attributes, continue to perform "multi-level extraction" of attributes, and finally extract the single well seismic attribute data mosaic through genetic algorithm; ③ According to the learning correlation coefficient presented after attribute extraction, judge the quality of the extraction effect, appropriately adjust the variable parameters in the genetic neural network architecture to extract attributes again, and select the genetic neural network structure with the highest learning correlation coefficient. Based on it, generate a three-dimensional inversion data body as the input data for the final well-seismic collaborative modeling. The "integration" in integrated inversion is manifested as the reciprocating and continuous optimization of attribute extraction mosaic and genetic network structure parameter adjustment. Here, it means that the geological-mechanical coupled lithofacies of a single well along the wellbore space are obtained as the goal, and the seismic attribute data body consistent with the statistical characteristics of the geological-mechanical coupled lithofacies in the wellbore space is extracted by relying on multi-stage seismic attribute mosaic, so as to provide reliable basic data for three-dimensional modeling characterization.

[0126] Combined with the actual case, the specific operation steps are as follows: ① First, extract the relative acoustic impedance attribute of a single well to provide a data basis for the subsequent multi-stage extraction, embedding other attributes and integrated inversion (Note: After the actual embedding of seismic attributes in this case, it was found that the learning correlation coefficient was the highest after only extracting the relative acoustic impedance attribute for inversion); ② The seismic data volume with extracted relative acoustic impedance and the lithofacies of the single well were used as input data, and the vertical range, inline, cross-line half-range and other seismic sub-value variables and seismic resampling parameters were adjusted at the same time; ③ The genetic neural network architecture parameters were set, that is, the number of iterations, correlation threshold, weight attenuation and the number of nodes in the hidden layer of the neural network were set, and the genetic algorithm was used to extract the seismic attribute data volume of the single well on the above basis; ④ The extraction effect was judged according to the numerical value of the learning correlation coefficient, and the attribute extraction was repeated by adjusting the seismic data volume parameters and the genetic neural network learning parameters, and finally the single well lithofacies seismic attribute data volume with the best effect was obtained; ⑤ On the basis of completing the single well attribute extraction, integrated inversion was carried out to obtain the three-dimensional seismic inversion volume of the area where the single well was located, which provided reliable data for the subsequent well-seismic collaborative modeling. Since the analysis process of geological-mechanical coupled lithofacies needs to include the geological and mechanical characteristics of the lithofacies and the correlation between the two, single-well seismic attribute data volume extraction is also carried out for the geological lithofacies in actual operation.

[0127] Table 2 shows the various parameter settings for extracting attributes of geological-mechanical coupled lithofacies through genetic algorithms. It can be seen that when the vertical range is 50, the inline and crossline half-ranges and resampling parameters are 3 respectively, the number of iterations is 20,000 times, the correlation threshold is 0.001, and the number of hidden layer nodes of the neural network is 4, the learning correlation coefficient is the highest, which is 0.9005.

[0128] Fig.11 This is the inversion profile generated after the attribute extraction of a single well lithofacies. It can be seen that the single well lithofacies has a good correlation with the seismic profile. Fig.12 and Fig.13 The three-dimensional inversion data bodies of geological lithofacies and geological-mechanical coupled lithofacies after attribute extraction and integrated inversion are displayed. It can be seen that there is a certain correlation in the distribution of the two, and the latter is more complex than the former.

[0129] Table 2 Statistics of geological-mechanical coupled lithofacies multi-stage seismic attribute mosaic and integrated inversion parameter settings

[0130]

[0131] B. Geological-mechanical coupled lithofacies well-seismic collaborative modeling and visualized three-dimensional prediction.

[0132] On the basis of multi-stage seismic attribute mosaic and integrated inversion, well-seismic collaborative modeling is further carried out through single well lithofacies to achieve the purpose of predicting the three-dimensional spatial distribution of lithofacies. The specific steps are as follows: ① Carry out data coarsening of single well geological lithofacies and geological-mechanical coupled lithofacies, and analyze small-layer lithofacies data layer by layer; ② Under the common constraints of single well lithofacies data and the inversion data body formed by multi-stage nesting and superimposed inversion of seismic attributes of A. geological-mechanical coupled lithofacies, the "cooperative Kriging" method is used to establish a three-dimensional model of geological lithofacies and geological-mechanical coupled lithofacies.

[0133] Figure 14-17 The three-dimensional model of a certain layer in a shale production area containing five geological lithofacies and a single well lithofacies profile are shown. It can be seen that the geological lithofacies are dispersed and mixed in the plane, and the distribution is relatively uniform in the vertical direction, and the lithofacies types are clearly distinguished. The geological lithofacies shown in the figure mainly include carbon-rich medium-high porosity siliceous shale, carbon-rich medium-high porosity calcium-containing mud-containing siliceous shale, medium-high carbon medium-high porosity siliceous shale, medium-low carbon medium-low porosity calcium-containing siliceous mud shale and medium-high carbon medium-high porosity mud-containing siliceous shale.

[0134] Figure 18-Figure 21 The three-dimensional model of geological-mechanical coupled lithofacies and the lithofacies profile of a single well of the shale production area are displayed. It can be seen that the geological-mechanical coupled lithofacies are more dispersed and complex than the geological lithofacies on the plane, and are more heterogeneous in the vertical distribution, reflecting the mechanical heterogeneity differences of the same geological lithofacies.

[0135] On the basis of establishing geological lithofacies and geological-mechanical coupled lithofacies, the proportion of different geological-mechanical coupled lithofacies of the same geological lithofacies was statistically analyzed. Table 3 shows the proportion of geological-mechanical coupled lithofacies of each of the five geological lithofacies. Carbon-rich medium-high porosity siliceous shale has the highest proportion of brittle carbon-rich medium-high porosity siliceous shale due to its high organic carbon content; carbon-rich medium-high porosity calcium-mud-containing siliceous shale and medium-low carbon medium-low porosity calcium-siliceous mud shale contain more geological-mechanical coupled lithofacies with strong shear toughness and strong tensile toughness due to their high calcium and mud content. The statistical results in Table 3 fully demonstrate the intrinsic connection between the properties of geological lithofacies and geological-mechanical coupled lithofacies, and further confirm the feasibility of constructing a geological-mechanical coupled lithofacies classification system, single well identification and three-dimensional prediction.

[0136] Table 3 Statistics of geological-mechanical coupled lithofacies proportions

[0137]

[0138] (4) Geological-mechanical coupling lithofacies control rock mechanics and geostress parameter modeling.

[0139] On the basis of completing the construction of shale geological-mechanical coupled lithofacies classification system, single well identification and three-dimensional prediction to obtain geological-mechanical coupled lithofacies three-dimensional model, the geological-mechanical coupled lithofacies is used as the "phase control" factor, combined with the fine small layer structure and structural model as the "layer control" factor, and the "layer control-phase control" dual control method is used to control and constrain the establishment of a three-dimensional model of rock mechanics and geostress parameters. The specific implementation steps are as follows: ① Obtaining single-well rock mechanics and geostress parameters: Single-well rock mechanics and geostress parameters can be obtained through experimental means such as uniaxial compression, triaxial compression, point load strength, etc., or dynamic three-dimensional rock mechanics parameters can be calculated through conventional logging data to obtain static parameter values. This patent adopts a method based on conventional logging data calculation to obtain rock mechanics and geostress parameters; ② Carry out data coarsening of single-well rock mechanics and geostress parameters and small layer data analysis; ③ Taking geological-mechanical coupled lithofacies as the "phase control" factor and fine small layer structure as the "layer control" factor, a "layer control-phase control" dual control constraint is formed, combined with single-well rock mechanics and geostress parameters, and a "cooperative Kriging" method is used to establish a three-dimensional model of mechanical parameters.

[0140] Table 4 shows the value range and average value of the Young’s modulus parameters of random interpolation modeling and geological-mechanical coupled lithofacies phase-controlled modeling. It can be seen that due to the lack of geological-mechanical coupled lithofacies control constraints, the value range of the mechanical parameters of random interpolation modeling is larger than that of the mechanical parameters under phase-controlled modeling, and there is a certain degree of deviation from the actual value. Figure 22-Figure 25 The random interpolation, geological-mechanical coupled lithofacies phase control, and established Young's modulus parameter three-dimensional models and profiles are shown respectively. It can be seen that the model established by random interpolation is too homogenized and cannot represent the complex heterogeneity of the strata; the model established by phase control can clearly represent the strong heterogeneity of the strata in the vertical and plane directions. From the range of table parameter values ​​and model results, the phase control model is more accurate than the random interpolation model, which directly confirms the feasibility and effectiveness of the technical system and method proposed in the present invention.

[0141] Table 4 Statistical table of numerical ranges of random interpolation and geological-mechanical coupled lithofacies phase-controlled Young's modulus parameter modeling

[0142]

[0143]

[0144] Based on the classification and identification of shale geological lithofacies, this application systematically carried out rock mechanics and geostress characteristic analysis and identification of geological lithofacies, further proposed the construction of a classification system for geological-mechanical coupled lithofacies, and based on this, formed a comprehensive and comprehensive single well identification and three-dimensional prediction technical process to characterize the heterogeneity of the three-dimensional spatial distribution of the geological-mechanical characteristics of the shale layer, so as to provide a basis for the construction of production areas and the exploration and development of artificial oil and gas reservoirs.

Claims

1. A lithofacies analysis system based on a shale geology-mechanics coupled lithofacies classification system, characterized in that: The lithofacies analysis system based on the shale geology-mechanics coupled lithofacies classification system includes: A data acquisition module is used to acquire information data; the information data includes: geological lithofacies data and mechanical in-situ parameter data; A system construction module, connected to the data acquisition module, is used to determine the geological-mechanical coupled lithofacies classification system based on the information data; The prediction module is connected with the system construction module and is used to perform single-well lithofacies quantitative prediction based on the geological-mechanical coupled lithofacies classification system and the logging data to obtain single-well lithofacies prediction information; the single-well lithofacies prediction information is the prediction result of the geological-mechanical coupled lithofacies of the entire wellbore space within the target layer, which is used to characterize the phase sequence change characteristics of the geological-mechanical coupled lithofacies of the single well along the wellbore; A feedback adjustment module is connected to the prediction module and is used to perform feedback adjustment on the single well lithofacies prediction information according to the set rock mechanics and geostress parameter range to obtain adjustment information; The three-dimensional model building module is connected to the feedback adjustment module, and is used to use genetic algorithms to perform seismic attribute mosaic and integrated inversion according to seismic attribute data and adjustment information, and to use collaborative Kriging method to build a three-dimensional model of geological-mechanical coupled lithofacies well-seismic collaborative modeling; the three-dimensional model is a three-dimensional physical simulation model constructed based on the intrinsic connection between the properties of geological lithofacies and geological-mechanical coupled lithofacies, and is used to characterize the mechanical heterogeneity differences and distribution laws of geological lithofacies; The parameter model determination module is connected to the three-dimensional model construction module, and is used to determine the geological-mechanical coupling lithofacies control rock mechanics and ground stress parameter model based on the three-dimensional model and the layer control-phase control dual control method to control constraints; the parameter model is used to characterize the heterogeneity of the geological-mechanical characteristics of the shale layer in the three-dimensional spatial distribution, and to provide a basis for the construction of production areas for the exploration and development of artificial oil and gas reservoirs.

2. The petrographic analysis system based on shale geology-mechanics coupled petrographic classification system according to claim 1 is characterized in that: The geological-mechanical coupled lithofacies classification system includes: a geological-mechanical coupled lithofacies classification plate and a standard table for the division of geological-mechanical coupled lithofacies classification system parameters.

3. The petrographic analysis system based on shale geology-mechanics coupled petrographic classification system according to claim 2 is characterized in that: The system building blocks include: The processing submodule is connected to the data acquisition module and is used to perform data combing and singular point elimination processing according to the information data, based on the position correspondence principle or the adjacent similarity principle and the logging response characteristics to obtain combed data; The parameter determination submodule is connected to the processing submodule and is used to determine the differentially sensitive rock mechanical parameters according to the combed data; the differentially sensitive rock mechanical parameters include: tensile strength, shear strength and Young's modulus; A cluster core determination submodule, connected to the data acquisition module and the parameter determination submodule respectively, for determining the core of the geological-mechanical coupling lithofacies cluster according to the information data and the differentially sensitive rock mechanical parameters; The analysis submodule is connected to the cluster core determination submodule, and is used to analyze the boundaries and cross-ranges of the geological-mechanical coupled lithofacies clusters based on the geological-mechanical coupled lithofacies cluster core, and determine the boundaries and ranges of the geological-mechanical coupled lithofacies clusters; The system determination submodule is connected to the analysis submodule and is used to draw a geological-mechanical coupled lithofacies classification plate according to the boundaries and ranges of the geological-mechanical coupled lithofacies clusters, and to construct a standard table for the division of lithofacies classification system parameters to determine the geological-mechanical coupled lithofacies classification system.

4. The petrographic analysis system based on shale geology-mechanics coupled petrographic classification system according to claim 3 is characterized in that: The parameter determination submodule includes: A characteristic attribute determination unit is connected to the processing submodule and is used to determine characteristic attributes based on the combed data, single factor cluster feature extraction and multi-well analogy analysis; The distribution difference determination unit is connected to the characteristic attribute determination unit, and is used to perform attribute consistency verification in the horizontal direction of a single well and in the vertical direction of multiple wells according to the characteristic attributes, and determine the distribution difference of each mechanical parameter in different geological lithofacies; The parameter determination unit is connected to the distribution difference determination unit and is used to determine the difference-sensitive rock mechanics parameters based on the distribution difference.

5. A lithofacies analysis method based on a shale geology-mechanics coupled lithofacies classification system, characterized in that: The lithofacies analysis method based on the shale geology-mechanics coupled lithofacies classification system includes: Obtain information data; the information data includes: geological lithofacies data and mechanical in-situ parameter data; Determine the geological-mechanical coupled lithofacies classification system based on information data; Based on the geological-mechanical coupled lithofacies classification system, single-well lithofacies quantitative prediction is performed according to logging data to obtain single-well lithofacies prediction information; single-well lithofacies prediction information is the prediction result of the geological-mechanical coupled lithofacies of the entire wellbore space within the target layer, which is used to characterize the phase sequence change characteristics of the geological-mechanical coupled lithofacies of a single well along the wellbore; According to the set rock mechanics and geostress parameter range, the single well lithofacies prediction information is feedback-adjusted to obtain adjustment information; Genetic algorithms are used to perform seismic attribute mosaicking and integrated inversion based on seismic attribute data and adjustment information, and the collaborative Kriging method is used to construct a three-dimensional model of geological-mechanical coupled lithofacies well-seismic collaborative modeling; the three-dimensional model is a three-dimensional physical simulation model constructed based on the intrinsic connection between the properties of geological lithofacies and geological-mechanical coupled lithofacies, which is used to characterize the mechanical heterogeneity differences and distribution laws of geological lithofacies; Based on the three-dimensional model, the layer control and phase control dual control method is adopted to control the constraints and determine the parameter model of rock mechanics and geostress controlled by geological-mechanical coupled lithofacies. The parameter model is used to characterize the heterogeneity of the three-dimensional spatial distribution of the geological-mechanical characteristics of the shale layer, and to provide a basis for the construction of production areas for the exploration and development of artificial oil and gas reservoirs.

6. The petrographic analysis method based on the shale geology-mechanics coupled petrographic classification system according to claim 5 is characterized in that: The geological-mechanical coupled lithofacies classification system is determined based on the information data, including: According to the information data, based on the position correspondence principle or the adjacent similarity principle, data combing and singular point elimination are performed according to the logging response characteristics to obtain combed data; Determine the differentially sensitive rock mechanical parameters based on the sorted data; the differentially sensitive rock mechanical parameters include: tensile strength, shear strength and Young's modulus; Determine the core of geological-mechanical coupled lithofacies clusters based on information data and differentially sensitive rock mechanical parameters; Based on the core of geological-mechanical coupled lithofacies cluster, the boundary and cross-range analysis of geological-mechanical coupled lithofacies cluster clusters is carried out to determine the boundary and range of geological-mechanical coupled lithofacies clusters; A geological-mechanical coupled lithofacies classification plate is drawn according to the boundaries and ranges of the geological-mechanical coupled lithofacies clusters, and a standard table for the division of lithofacies classification system parameters is constructed to determine the geological-mechanical coupled lithofacies classification system.

7. The petrographic analysis method based on the shale geology-mechanics coupled petrographic classification system according to claim 6 is characterized in that: The differentially sensitive rock mechanical parameters are determined based on the sorted data, including: According to the combed data, the characteristic attributes are determined based on single-factor cluster feature extraction and multi-well analogy analysis; According to the characteristic attributes, the consistency check of single-well horizontal and multi-well vertical attributes is carried out to determine the distribution difference of each mechanical parameter in different geological lithofacies; Determine differentially sensitive rock mechanical parameters based on distribution differences.

8. The petrographic analysis method based on the shale geology-mechanics coupled petrographic classification system according to claim 5, characterized in that: Genetic algorithm is used to perform seismic attribute mosaic and integrated inversion based on seismic attribute data and adjustment information, and the collaborative Kriging method is used to construct a three-dimensional model of geological-mechanical coupled lithofacies well-seismic collaborative modeling, including: Determine the genetic neural network architecture parameters; the genetic neural network architecture parameters include: number of iterations, correlation threshold, weight decay and number of hidden layer nodes of the neural network; Genetic algorithm is used to extract single well seismic attribute data mosaic based on the parameters of genetic neural network architecture according to seismic attribute data; Based on the adjustment information and the mosaic of single well seismic attribute data, seismic attribute mosaic and integrated inversion are performed to obtain an inversion data body; Carry out data coarsening of single well geological lithofacies, geological-mechanical coupled lithofacies, and lithofacies data analysis to obtain analytical data; Based on the joint constraint control of analytical data and inversion data volume, the collaborative Kriging method is used to construct a three-dimensional model of geological-mechanical coupled lithofacies well-seismic collaborative modeling.

9. The petrographic analysis method based on the shale geology-mechanics coupled petrographic classification system according to claim 5, characterized in that: Based on the three-dimensional model, the layer control and phase control dual control method is used to control the constraints and determine the geological-mechanical coupling lithofacies control rock mechanics and ground stress parameter model, including: Obtain rock mechanics and geostress parameters of a single well; According to the rock mechanics and geostress parameters of a single well, data coarsening and small layer data analysis are performed to obtain analytical parameter data; According to the analysis parameter data, the layer control-phase control dual control method is adopted to control the constraints. Based on the single well rock mechanics and geostress parameters and three-dimensional model, the collaborative Kriging method is used to determine the geological-mechanical coupling lithofacies control rock mechanics and geostress parameter model.

10. The petrographic analysis method based on the shale geology-mechanics coupled petrographic classification system according to claim 9, characterized in that: The rock mechanics and geostress parameters of a single well are obtained by uniaxial compression, triaxial compression or point load strength test.

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