A lithofacies analysis system and method based on a shale geological-mechanical coupled lithofacies classification system
By constructing a lithofacies analysis system based on a coupled geological-mechanical lithofacies classification system for shale, the problem of insufficient model accuracy in three-dimensional geological-mechanical modeling of shale oil and gas reservoirs has been solved. This system enables three-dimensional spatial distribution analysis of geological-mechanical characteristics of shale layers, improves model accuracy, and provides a basis for oil and gas reservoir exploration and development.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SOUTHWEST PETROLEUM UNIV
- Filing Date
- 2024-11-26
- Publication Date
- 2026-05-26
AI Technical Summary
Existing technologies for three-dimensional geomechanical modeling of shale oil and gas reservoirs suffer from insufficient geological modeling accuracy and a lack of effective phase control schemes for rock mechanics and geostress parameter modeling, resulting in poor model reliability and an inability to accurately characterize the geomechanical coupling features of underground rock masses.
This paper presents a lithofacies analysis system and method based on a shale geological-mechanical coupled lithofacies classification system. Through data acquisition, system construction, prediction, feedback adjustment and three-dimensional model construction, a three-dimensional model of geological-mechanical coupled lithofacies well-seismic co-modeling is constructed using genetic algorithms and co-kriging methods. Combined with the strata-control and facies-control dual control method, the rock mechanics and geostress parameter models are determined.
This study enabled the analysis of the heterogeneity of the geological and mechanical characteristics of shale formations in three-dimensional space, improved the accuracy of rock mechanics and geostress parameter models, and provided a reliable basis for the exploration and development of shale oil and gas reservoirs.
Smart Images
Figure CN119514380B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of shale lithofacies analysis, and in particular to a lithofacies analysis system and method based on a shale geological-mechanical coupled lithofacies classification system. Background Technology
[0002] With the prolonged exploitation of conventional oil and gas reservoirs worldwide and the continuous depletion of recoverable reserves, resources are increasingly being invested in the exploration and development of unconventional oil and gas reservoirs. Shale oil and gas reservoirs have become a key research focus for many scholars and professionals. The abundant resources of shale oil and gas have created many major shale oil and gas producing countries. For example, the "shale gas revolution" transformed the United States from a major natural gas importer to a major exporter. As the largest shale gas producer outside North America, China, based in the Sichuan Basin and its adjacent areas, has deployed large-scale shale gas production areas in multiple regions, forming a mature large-scale industrial production model for shale gas. Whether it's the abundant shallow and medium-depth shale oil and gas in the United States or the deep and ultra-deep shale oil and gas resources in China with enormous development potential, ultra-long horizontal wells combined with large-scale repeated fracturing have always been a common and effective method for shale oil and gas exploration and development. As this method is implemented more deeply, the geological-mechanical coupling characteristics of shale have gradually attracted attention.
[0003] Three-dimensional geomechanical modeling of shale oil and gas reservoirs can provide a wealth of insights into the geological-mechanical coupling characteristics of formations at the oil and gas development site, thereby improving the engineering efficiency of large-scale shale oil and gas resource development. Currently, geological modeling can improve the accuracy of the established models by employing sedimentary facies control methods; however, effective facies control schemes for rock mechanics and geostress parameter modeling have not yet been found to improve model accuracy, and deterministic interpolation or stochastic modeling methods are still used. The resulting geomechanical property models have poor reliability and do not conform to the geological-mechanical coupling characteristics and laws of subsurface rock masses. To solve these problems, it is necessary to further explore the controlling factors of the geological-mechanical characteristics of shale rock masses.
[0004] Current research on the coupling characteristics of geomechanics is limited or insufficiently thorough. Furthermore, studies on geomechanical characteristics are often artificially separated, neglecting the influence of mechanical factors when studying geological properties or ignoring the objective existence of geological factors when analyzing mechanical characteristics. This leads to significant deviations between research results and actual underground conditions. The article "The Influence of Temperature on Rock Mechanical Properties and Wellbore Stability" (published September 2017; authors: Jia Lichun, Chen Dong, Huang Bing) conducted triaxial compression experiments on rocks under different temperature conditions, analyzing the variation patterns between wellbore collapse pressure, fracture pressure, and temperature. This method comprehensively elucidates the relationship between temperature and rock mechanics by combining theory and practice, but it lacks exploration of the core geological factors involved. The article "The Weakening Law of Shale Strength and Its Impact on Wellbore Stability" reveals the weakening law of shale shear strength under the influence of drilling fluid based on direct shear tests, and analyzes the impact of shale weakening on the collapse pressure of horizontal wells. This method focuses on discussing the influence of rock mechanical properties on wellbore stability, with less exploration of geological genesis, and only shear strength is involved in the rock mechanical parameters, lacking discussion of other mechanical parameters. The article "Modeling of Rock Mechanical Parameters in the Tight Reservoir of Fuyu Oil Layer in Daqing Oilfield T30 Well Area" (published in October 2020; author: Guo Siqiang) calculates single-well rock mechanical and geostress parameters using well logging data, and establishes a three-dimensional model of these parameters using geostatistical methods. This method mainly uses stochastic modeling, and the accuracy of the established model deviates somewhat from the actual situation. Therefore, it is crucial to analyze and determine the heterogeneity of the three-dimensional spatial distribution of shale geological and mechanical characteristics to provide a basis for production area construction and for the 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 realize the analysis and determination of the heterogeneity of the geological-mechanical characteristics of shale layers in three-dimensional space.
[0006] To achieve the above objectives, this application provides the following solution:
[0007] Firstly, this application provides a lithofacies analysis system based on a shale geological-mechanical coupled lithofacies classification system, comprising:
[0008] The data acquisition module is used to acquire information data, including geological and lithofacies data and in-situ mechanical parameter data.
[0009] The system construction module, connected to the data acquisition module, is used to determine the geological-mechanical coupled lithofacies classification system based on information data.
[0010] The prediction module, connected to the system construction module, is used to perform quantitative prediction of single-well lithofacies based on well logging data according to the geomechanical coupled lithofacies classification system, and obtain single-well lithofacies prediction information. The single-well lithofacies prediction information is the prediction result of the geomechanical coupled lithofacies of the entire wellbore space within the target layer, which is used to characterize the phase sequence variation characteristics of single-well geomechanical coupled lithofacies along the wellbore.
[0011] The feedback adjustment module, connected to the prediction module, is used to adjust the single-well lithofacies prediction information based on the set range of rock mechanics and geostress parameters, and obtain adjustment information.
[0012] The 3D model construction module, connected to the feedback adjustment module, is used to perform seismic attribute embedding and integrated inversion based on seismic attribute data and adjustment information using a genetic algorithm, and to construct a 3D model for geological-mechanical coupled lithofacies well-seismic co-modeling using the co-kriging method. The 3D model is a 3D physical simulation model constructed based on the intrinsic relationship between the properties of geological lithofacies and geological-mechanical coupled lithofacies, used to characterize the mechanical heterogeneity differences and distribution patterns of geological lithofacies.
[0013] The parameter model determination module, connected to the 3D model construction module, is used to determine the geological-mechanical coupled lithofacies control rock mechanics and geostress parameter model based on the 3D model and using the layer-control-facies dual control method. The parameter model is used to characterize the heterogeneity of the geological-mechanical characteristics of shale layers in three-dimensional space, and to provide a basis for the establishment of production areas for the exploration and development of artificial oil and gas reservoirs.
[0014] Secondly, this application provides a lithofacies analysis method based on a shale geological-mechanical coupled lithofacies classification system, including:
[0015] Acquire information and data; the information and data include: geological and lithofacies data and in-situ mechanical parameter data;
[0016] Determine a geological-mechanical coupled lithofacies classification system based on information data;
[0017] Based on the geomechanical coupled lithofacies classification system, quantitative prediction of lithofacies in a single well is performed according to well logging data to obtain single well lithofacies prediction information. The single well lithofacies prediction information is the prediction result of the geomechanical coupled lithofacies of the entire wellbore space within the target layer, which is used to characterize the facies sequence variation characteristics of the single well geomechanical coupled lithofacies along the wellbore.
[0018] Based on the set range of rock mechanics and geostress parameters, the single-well lithofacies prediction information is adjusted by feedback to obtain the adjustment information;
[0019] A genetic algorithm is used to perform seismic attribute embedding and integrated inversion based on seismic attribute data and adjustment information. A three-dimensional model of geological-mechanical coupled lithofacies well-seismic co-modeling is constructed using the co-kriging method. The three-dimensional model is a three-dimensional physical simulation model constructed based on the intrinsic relationship between the properties of geological lithofacies and geological-mechanical coupled lithofacies, which is used to characterize the mechanical heterogeneity differences and distribution patterns of geological lithofacies.
[0020] Based on the three-dimensional model, the stratigraphic-facies dual-control method is used to control constraints and determine the geological-mechanical coupled lithofacies control rock mechanics and geostress parameter model. The parameter model is used to characterize the heterogeneity of the geological-mechanical characteristics of shale layers in three-dimensional space, and to provide a basis for the establishment 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] This application provides a lithofacies analysis system and method based on a shale geological-mechanical coupled lithofacies classification system. Since deeply buried shale bodies are geological-mechanical coupled entities, studying shale bodies requires considering both the controlling factors of geology and mechanics, as well as exploring their intrinsic relationship. Furthermore, to improve the accuracy of three-dimensional models of rock mechanics and geostress parameters, it is necessary to select mechanical parameters that are significantly correlated with geological properties, forming a geological-mechanical coupled control factor to participate in the modeling of mechanical parameters, thereby improving the accuracy of the mechanical model. Based on the classification and identification of shale geological lithofacies, this application systematically conducts rock mechanics and geostress characteristic analysis and identification of geological lithofacies, further proposes the construction of a geological-mechanical coupled lithofacies classification system, and forms a comprehensive single-well identification and three-dimensional prediction technology process based on this system. This process characterizes the heterogeneity of the geological-mechanical characteristics of shale layers in three-dimensional space, providing a basis for establishing production areas and conducting exploration and development of artificial oil and gas reservoirs. Attached Figure Description
[0023] Figure 1 This is a structural diagram of a lithofacies analysis system based on a shale geological-mechanical coupled lithofacies classification system.
[0024] Figure 2 This is a flowchart illustrating the technical process of lithofacies analysis based on a shale geological-mechanical coupled lithofacies classification system.
[0025] Figure 3 Flowchart for obtaining geological lithofacies and sorting mechanical parameters;
[0026] Figure 4 Flowchart for optimizing differentially sensitive rock mechanics and geostress parameters;
[0027] Figure 5A schematic diagram of a geological-mechanical coupled lithofacies classification plate for carbon-rich medium- to high-porosity siliceous shale;
[0028] Figure 6 A schematic diagram of the geological-mechanical coupled lithofacies classification of carbon-rich, medium- to high-porosity, calcium- and muddy siliceous shale;
[0029] Figure 7 A schematic diagram of the geological-mechanical coupled lithofacies classification of medium- to high-carbon, medium- to high-porosity siliceous shale;
[0030] Figure 8 A schematic diagram of a geological-mechanical coupled lithofacies classification plate for medium-low carbon, medium-low porosity, calcium-bearing siliceous argillaceous shale;
[0031] Figure 9 A schematic diagram of a geological-mechanical coupled lithofacies classification plate for medium- to high-carbon, medium- to high-porosity argillaceous siliceous shale;
[0032] Figure 10 A comprehensive columnar section of typical single-well geological-mechanical coupled lithofacies quantitative prediction results;
[0033] Figure 11 This is a profile of a single well's relative deep impedance to lithology, derived from genetic inversion.
[0034] Figure 12 This is a schematic diagram of a three-dimensional inversion data volume of geological lithofacies.
[0035] Figure 13 This is a schematic diagram of a geomechanical coupled three-dimensional inversion data volume of lithofacies.
[0036] Figure 14 This is a schematic diagram of the first geological lithofacies three-dimensional model;
[0037] Figure 15 This is a schematic diagram of the three-dimensional model of the second geological lithofacies.
[0038] Figure 16 This is a first geological lithofacies profile.
[0039] Figure 17 This is a second geological lithofacies profile.
[0040] Figure 18 This is a schematic diagram of the first geomechanical coupled lithofacies three-dimensional model;
[0041] Figure 19 This is a schematic diagram of the second geomechanical coupled lithofacies three-dimensional model;
[0042] Figure 20 This is a schematic diagram of the third geological-mechanical coupled three-dimensional lithofacies model;
[0043] Figure 21 This is a geological-mechanical coupled lithofacies profile.
[0044] Figure 22 This is a schematic diagram of a three-dimensional model with random interpolation.
[0045] Figure 23 This is a schematic diagram of a geomechanical coupled lithofacies facies control model;
[0046] Figure 24 This is a cross-sectional view of the random interpolation model;
[0047] Figure 25 This is a cross-sectional view of the phased array model. Detailed Implementation
[0048] The content mentioned in this application can be practically applied to a shale gas production area in western China. Using a single well as a baseline, the application can be extended to cover the entire study area, revealing the characteristic differences and distribution patterns of different geomechanical coupled lithofacies. This leads to a comprehensive and accurate understanding of the stratigraphic lithofacies, rock mechanics, and geostress characteristics of the study area, providing methodological and technical support for facies-controlled geomechanical modeling. This technical process can fully characterize the geological and mechanical characteristics of shale lithofacies; the lithofacies-rock mechanics characterization of sandstone, carbonate rocks, and other rock masses can also refer to this technical process.
[0049] like Figure 1 As shown, this application provides a lithofacies analysis system based on a shale geological-mechanical 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, including geological and lithofacies data and in-situ mechanical parameter data.
[0051] The system construction module is connected to the data acquisition module. The system construction module is used to determine the geological-mechanical coupled lithofacies classification system based on the information data. The geological-mechanical coupled lithofacies classification system includes: a geological-mechanical coupled lithofacies classification map and a table of parameter division standards for the geological-mechanical coupled lithofacies classification system.
[0052] The prediction module, connected to the system construction module, is used to perform quantitative prediction of single-well lithofacies based on well logging data using a geomechanical coupled lithofacies classification system, and obtain single-well lithofacies prediction information. The single-well lithofacies prediction information is the prediction result of the geomechanical coupled lithofacies of the entire wellbore space within the target layer, which is used to characterize the facies sequence variation characteristics of single-well geomechanical coupled lithofacies along the wellbore.
[0053] The feedback adjustment module, connected to the prediction module, is used to adjust the single-well lithofacies prediction information based on the set range of rock mechanics and geostress parameters, and obtain adjustment information.
[0054] The 3D model construction module, connected to the feedback adjustment module, is used to perform seismic attribute embedding and integrated inversion based on seismic attribute data and adjustment information using a genetic algorithm, and to construct a 3D model for geological-mechanical coupled lithofacies well-seismic co-modeling using the co-kriging method. The 3D model is a 3D physical simulation model constructed based on the intrinsic relationship between the properties of geological lithofacies and geological-mechanical coupled lithofacies, used to characterize the mechanical heterogeneity differences and distribution patterns of geological lithofacies.
[0055] The parameter model determination module, connected to the 3D model construction module, is used to determine the geological-mechanical coupled lithofacies control rock mechanics and geostress parameter model based on the 3D model and using the layer-control-facies dual control method. The parameter model is used to characterize the heterogeneity of the geological-mechanical characteristics of shale layers in three-dimensional space, and to provide a basis for the establishment of production areas for the exploration and development of artificial oil and gas reservoirs.
[0056] The system construction modules include:
[0057] The processing submodule, connected to the data acquisition module, is used to sort out the data and remove singular points based on the information data, according to the principle of location correspondence or adjacent similarity, and based on the logging response characteristics, to obtain sorted data.
[0058] The parameter determination submodule, connected to the processing submodule, is used to 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.
[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 geological-mechanical coupled lithofacies cluster core based on information data and differentially sensitive rock mechanical parameters.
[0060] The analysis submodule, connected to the cluster core determination submodule, is used to analyze the boundaries and intersection ranges of geological-mechanical coupled lithofacies clusters based on the geological-mechanical coupled lithofacies cluster core, and to determine the boundaries and ranges of the geological-mechanical coupled lithofacies clusters.
[0061] The system determination submodule, connected to the analysis submodule, is used to draw a geological-mechanical coupled lithofacies classification map based on the boundaries and ranges of the geological-mechanical coupled lithofacies clusters, and to construct a parameter classification standard table for the lithofacies classification system in order to determine the geological-mechanical coupled lithofacies classification system.
[0062] The parameter determination submodule includes:
[0063] The feature attribute determination unit, connected to the processing submodule, is used to determine feature attributes based on the sorted data, single-factor cluster feature extraction, and multi-well analogy analysis.
[0064] The distribution difference determination unit, connected to the characteristic attribute determination unit, is used to perform attribute consistency verification for single-well lateral and multi-well longitudinal operations based on the characteristic attributes, and to determine the distribution difference of each mechanical parameter in different geological lithofacies.
[0065] The parameter determination unit, connected to the distribution difference determination unit, is used to determine difference-sensitive rock mechanical parameters based on distribution differences.
[0066] In one embodiment, this application also mentions a lithofacies analysis method based on a shale geological-mechanical coupled lithofacies classification system, comprising:
[0067] Acquire information and data; the information and data include: geological and lithofacies data and in-situ mechanical parameter data.
[0068] A geological-mechanical coupled lithofacies classification system is determined based on information data. Based on the geological-mechanical coupled lithofacies classification system, quantitative prediction of lithofacies in a single well is carried out according to well logging data to obtain single-well lithofacies prediction information. The single-well lithofacies prediction information is the prediction result of geological-mechanical coupled lithofacies in the entire wellbore space within the target layer, which is used to characterize the facies sequence variation characteristics of single-well geological-mechanical coupled lithofacies along the wellbore.
[0069] Based on the set range of rock mechanics and geostress parameters, the single-well lithofacies prediction information is adjusted by feedback to obtain the adjustment information.
[0070] A genetic algorithm is used to perform seismic attribute embedding and integrated inversion based on seismic attribute data and adjustment information. A three-dimensional model of geological-mechanical coupled lithofacies well-seismic co-modeling is constructed using the co-kriging method. The three-dimensional model is a three-dimensional physical simulation model constructed based on the intrinsic relationship between the properties of geological lithofacies and geological-mechanical coupled lithofacies, which is used to characterize the mechanical heterogeneity differences and distribution patterns of geological lithofacies.
[0071] Based on the three-dimensional model, the stratigraphic-facies dual-control method is used to control constraints and determine the geological-mechanical coupled lithofacies control rock mechanics and geostress parameter model. The parameter model is used to characterize the heterogeneity of the geological-mechanical characteristics of shale layers in three-dimensional space, and to provide a basis for the establishment of production areas for the exploration and development of artificial oil and gas reservoirs.
[0072] Among them, the geological-mechanical coupled lithofacies classification system determined based on information data specifically includes:
[0073] Based on the information data, and according to the principle of location correspondence or adjacent similarity, the data is sorted and singular points are removed according to the logging response characteristics to obtain sorted data.
[0074] Based on the analyzed data, the differentially sensitive rock mechanical parameters were determined; these parameters include tensile strength, shear strength, and Young's modulus.
[0075] The core of the geomechanical coupled lithofacies cluster was determined based on information data and differentially sensitive rock mechanical parameters.
[0076] Based on the core of the geomechanical coupled lithofacies cluster, the boundary and intersection range of the geomechanical coupled lithofacies cluster are analyzed to determine the boundary and range of the geomechanical coupled lithofacies cluster.
[0077] Based on the boundaries and extent of the geological-mechanical coupled lithofacies clusters, a geological-mechanical coupled lithofacies classification map was drawn, and a parameter classification standard table for the lithofacies classification system was constructed to determine the geological-mechanical coupled lithofacies classification system.
[0078] Based on the analyzed data, the differentially sensitive rock mechanical parameters were determined, specifically including:
[0079] Based on the analyzed data, characteristic attributes were determined through single-factor cluster feature extraction and multi-well analogy analysis. The consistency of attributes in single-well horizontal and multi-well vertical operations was verified based on the characteristic attributes to determine the distribution differences of each mechanical parameter in different geological lithofacies. Based on the distribution differences, the difference-sensitive rock mechanical parameters were determined.
[0080] A genetic algorithm is used to perform seismic attribute embedding and ensemble inversion based on seismic attribute data and adjustment information. A co-kriging method is then employed to construct a 3D model for geological-mechanical coupled lithofacies-well-seismic co-modeling, specifically including:
[0081] The genetic neural network architecture parameters were determined, including the number of iterations, correlation threshold, weight decay, and the number of hidden layer nodes. Based on these parameters, a genetic algorithm was used to extract single-well seismic attribute data chimeras from the seismic attribute data. Seismic attribute chimeras and ensemble inversion were performed using adjustment information and the single-well seismic attribute data chimeras to obtain the inversion data volume. Data coarsening of single-well geological lithofacies and geomechanical coupled lithofacies, as well as lithofacies data analysis, were conducted to obtain analytical data. Based on the common constraints of the analytical data and the inversion data volume, a three-dimensional model of well-seismic co-modeling of geomechanical coupled lithofacies was constructed using the co-kriging method.
[0082] Based on a three-dimensional model, a layer-control and facies-control dual-control method is used to determine the geological-mechanical coupled lithofacies-controlled rock mechanics and geostress parameter model, specifically including:
[0083] Obtain the rock mechanics and geostress parameters of a single well; based on the rock mechanics and geostress parameters of the single well, perform data coarsening and small-layer data analysis to obtain analytical parameter data; based on the analytical parameter data, adopt the layer-control-facies dual-control method for control constraints; based on the rock mechanics and geostress parameters of the single well and the three-dimensional model, use the co-kriging method to determine the geological-mechanical coupled lithofacies-controlled 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 tests.
[0085] like Figure 2 As shown, the method proposed in this application, namely the construction of a shale geological-mechanical 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 a four-element classification system for shale geological-mechanical coupled lithofacies: ① First, this classification system needs to be built on the basis of the existing geological lithofacies and in-situ mechanical parameters, relying on the one-to-one comparison between geological lithofacies and corresponding rock mechanical parameters; ② Second, based on the principle of optimal difference, rock mechanical and geostress parameters with high sensitivity to geological lithofacies are selected as the basis for further subdivision of geological-mechanical coupled lithofacies types; ③ Then, based on the selected four elements, the geological-mechanical coupled lithofacies subdivision work is carried out, and the core of each geological-mechanical coupled lithofacies cluster is determined from the cross-plot of the four elements; ④ On this basis, the cluster boundary and the intersection range between different geological-mechanical coupled lithofacies clusters are determined; ⑤ Finally, a geological-mechanical coupled lithofacies classification map is drawn and a parameter division standard table for the geological-mechanical coupled lithofacies classification system is constructed, thus completing the construction of the geological-mechanical coupled lithofacies classification system.
[0087] 2. Three-stage well logging data quality control and quantitative prediction of geomechanical coupled lithofacies in a single well. Based on the classification standards of the geomechanical coupled lithofacies classification system, single-well identification and quantitative prediction of geomechanical coupled lithofacies are carried out in three stages: ① First, well logging data integrity cleaning and reconstruction and homogeneity suppression processing are performed; ② Second, lithofacies quantitative prediction is carried out on a single-well basis; ③ Finally, the prediction results are adjusted based on the well logging response characteristics to ensure the accuracy and authenticity of the geomechanical coupled lithofacies classification.
[0088] 3. Geological-mechanical coupled lithofacies multi-stage seismic attribute embedding and integrated inversion and well-seismic co-modeling, specifically including: ① geological-mechanical coupled lithofacies multi-stage seismic attribute embedding and integrated inversion; ② well-seismic co-modeling and visualized 3D prediction.
[0089] 4. Geological-mechanical coupled facies control of rock mechanics and geostress parameters modeling.
[0090] The specific implementation steps are as follows:
[0091] 1) Construction of a four-element classification system for shale geological-mechanical coupled lithofacies:
[0092] A. Existing geological lithofacies and in-situ mechanical parameters have been reviewed.
[0093] The geological facies of marine or continental shale can be obtained by using the patents “A method for classification, identification and three-dimensional characterization of marine shale facies” (Patent No. 202011398837.8) and “A method for intelligent identification and visualization of facies of continental tight reservoirs” (Patent No. 202011393013.1); and the in-situ mechanical parameters can be obtained by using the “A method for three-dimensional visualization characterization of in-situ stress in tight rock masses” (Patent No. 202011388424.1).
[0094] Based on the obtained marine or terrestrial shale geological lithofacies, and combined with the in-situ mechanical parameters obtained from the analysis, mechanical parameter data corresponding to the geological lithofacies are extracted along the wellbore, with the wellbore as the core. Based on the principle of location correspondence, existing geological lithofacies data are organized and appropriately merged according to the logging response characteristics; or based on the principle of adjacency and similarity, geological lithofacies with fewer numbers and thinner distributions are merged into adjacent dominant geological lithofacies. The overall geological lithofacies classification system for the region is determined based on the principle of appropriate contiguous areas and a large number of data samples.
[0095] By recombining the well logging response and the determined geological lithofacies data, comparing and sorting out the in-situ mechanical parameters, eliminating outliers, determining the in-situ mechanical parameter dataset, and extracting the statistical characteristics of the dataset. Figure 3 The process of obtaining geological lithofacies and sorting out in-situ mechanical parameters is demonstrated.
[0096] B. Optimization of rock mechanics and geostress parameters sensitive to differences in geological facies.
[0097] Based on the established geological lithofacies types and in-situ mechanical parameter datasets, further analysis, comparison, and selection of rock mechanics and geostress parameters sensitive to differences in geological lithofacies were conducted. First, single-factor cluster feature extraction and multi-well analogy analysis were performed to obtain the numerical characteristic attributes of different rock mechanics and geostress parameter clusters for each geological lithofacies. Then, the consistency of attribute characteristics between single-well longitudinal and multi-well lateral measurements was determined. For the same geological lithofacies, the consistency of characteristics of different mechanical parameters was verified in single and multi-well measurements, identifying the distribution differences of each mechanical parameter across different geological lithofacies. Finally, the distribution differences of the numerical characteristics of each rock mechanics and geostress parameter across different geological lithofacies were compared, and the parameters with the greatest distribution differences were selected as the difference-sensitive rock mechanics and geostress parameters. After practical screening using the above methods, the difference-sensitive parameters were determined to be tensile strength, shear strength, and Young's modulus.
[0098] After conducting literature reviews and field investigations, it was found that crack formation in shale fracturing is strongly correlated with tensile strength, rock mass shear strength significantly impacts drilling efficiency, and Young's modulus, besides playing a decisive role in assessing rock mass deformation resistance, also exhibits a certain linear relationship with shale brittleness. These three types of parameters can be used to characterize the strength, elasticity, and brittleness of shale, and are consistent with the parameter types selected from the difference-sensitive parameter optimization, thus serving as reliable data for subsequent research. Figure 4 A flowchart for the technology of parameter optimization.
[0099] C. Determination of the core of the geological-mechanical coupled lithofacies cluster.
[0100] Based on identified in-situ mechanical parameters sensitive to differences in geological lithofacies, a geomechanical coupled lithofacies classification and identification process is conducted. This involves using geological lithofacies as the core and mechanical parameters as classification parameters to classify geological lithofacies in multiple directions and dimensions. This identifies multiple geomechanical coupled lithofacies clusters within the same geological lithofacies and, based on differences in geological and mechanical properties, identifies one or more representative points within each cluster, thereby determining the core of the geomechanical coupled lithofacies cluster.
[0101] D. Determination of the boundary and extent of the geological-mechanical coupled lithofacies cluster.
[0102] Focusing on the core of the geomechanical coupled lithofacies clusters, further analysis of the clusters' boundaries and intersection ranges is conducted. The boundaries of lithofacies clusters can be defined by analyzing changes in the mechanical characteristics of the coupled lithofacies, or by selecting specific well logging curves and setting change thresholds to establish boundaries in areas of significant change in the characteristics of the coupled lithofacies. Based on the preliminary determination of the cluster boundaries, the intersection ranges between different lithofacies clusters are collected according to the size of the mechanical curve response range. The boundary zone positions are adjusted according to the principle of maximizing the range of each lithofacies cluster while minimizing the intersection range between different lithofacies. After optimizing the cluster boundaries and intersection ranges, the reliability of the cluster core is verified in reverse, and the cluster core is adjusted appropriately based on the verification results, thereby ensuring the overall reliability of the cluster core, boundaries, and intersection ranges.
[0103] E. Drawing of geo-mechanical coupled lithofacies classification plates and construction of classification system tables.
[0104] The above analysis results were plotted into a geomechanical coupled lithofacies classification chart, and a parameter classification standard table for the lithofacies classification system was constructed, thus completing the construction of the geomechanical coupled lithofacies classification system. Following this process, accurate classification and identification of shale geomechanical coupled lithofacies can be achieved.
[0105] Figures 5 to 9This paper presents a classification chart of the geomechanical coupled lithofacies of the main producing strata in a marine shale production area. It shows that, based on mechanical characteristics, the five geological lithofacies in the study area were further subdivided into three categories: brittleness, strong shear toughness, and strong tensile toughness. This resulted in the construction of 15 geomechanical coupled lithofacies based on the geological lithofacies. Furthermore, the cluster cores of each geomechanical coupled lithofacies remain independent, and the boundaries and overlaps between different clusters are clearly defined. Therefore, the geomechanical coupled lithofacies classification system for the shale strata in this region, constructed using the above method, can be effectively applied to field production research, providing valuable guidance for understanding the geomechanical coupling characteristics in the field.
[0106] Based on the classification charts, a classification system table for different geomechanical coupled lithofacies can be further constructed. Table 1 shows the classification system table. Figures 5 to 9 The classification system includes 5 geological lithofacies, 15 geomechanical coupled lithofacies, and corresponding classification parameter standards for each type of geomechanical coupled lithofacies. It can be seen that different geological lithofacies are classified into different geomechanical coupled lithofacies according to the corresponding standards. The mechanical parameter characteristics of each lithofacies are quite different. The classification standards for mechanical parameters include, but are not limited to, constants and functions, and depend on the core geological or mechanical characteristics of each lithofacies.
[0107] Table 1. Overview of the geological-mechanical coupled lithofacies classification system and corresponding type classification parameters for a certain shale stratum.
[0108]
[0109]
[0110] X′ in Table 1 抗剪 X′ represents the shear strength value. 抗拉 This represents the tensile strength value.
[0111] (2) Three-stage logging data quality control and geological-mechanical coupled lithological single-well quantitative prediction.
[0112] Based on the aforementioned shale geological-mechanical coupled lithofacies classification system, further quantitative prediction of lithofacies from single wells will be carried out to lay a solid data foundation for geological-mechanical coupled lithofacies three-dimensional modeling.
[0113] A. First stage: Cleaning and reconstruction of well logging data integrity and homogeneity suppression processing.
[0114] To carry out integrity cleaning and reconstruction and homogeneity suppression processing of well logging data, a reliable data source for the calculation of mechanical parameters is provided: (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 or outlier 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 impact of similar data in the analysis and modeling process. The goal is to reduce the central duplication or redundancy of well logging data, 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. Second stage: Geomechanical coupled lithofacies two-step method for single-well quantitative prediction.
[0116] The quantitative prediction of single-well facies is carried out in two steps, combining geological and mechanical analysis.
[0117] The first step involves using the patented methods "A Method for Classifying and Identifying Marine Shale Facies and Three-Dimensional Characterization" (Patent No. ZL202011398837.8) and "A Method for Intelligent Identification and Visualization of Terrestrial Tight Reservoir Facies" (Patent No. ZL202011393013.1) to classify and identify marine or terrestrial shale geological facies. The second step involves using "A Method for Three-Dimensional Visualization Characterization of Tight Rock Mass In-situ Stress" (Patent No. ZL202011388424.1) to calculate and obtain the single-well differential-sensitive mechanical parameter curves. This completes the preparation of geological facies, rock mechanics, and in-situ stress data for single-well prediction of geological-mechanical coupled facies.
[0118] The second step involves using the obtained geological-mechanical coupled lithofacies system parameter classification standard to complete the subdivision prediction of geological-mechanical coupled lithofacies single wells based on different categories of geological lithofacies and the calculated single-well difference-sensitive mechanical parameter curves.
[0119] For the actual case area, firstly, the optimal shale single-well differentially sensitive mechanical parameters—tensile strength, shear strength, and Young's modulus curves—were calculated; then, based on the original five geological facies, the geological-mechanical coupled facies of the entire wellbore space within the target layer were predicted according to the classification parameter standards corresponding to each geological-mechanical coupled rock type; finally, a comprehensive columnar section of the geological-mechanical coupled facies of a single well was drawn. Figure 10 This paper demonstrates the phase sequence variation characteristics of typical single-well geological-mechanical coupled rock facies along the wellbore in a real-world case study area.
[0120] C. Third stage: Feedback and adjustment of geological-mechanical coupled lithofacies prediction results.
[0121] Based on the prediction results of single-well geomechanical coupled lithofacies, and in accordance with the principles of reasonable classification, sufficient data volume, and reasonable uniformity of adjacent classes, the prediction results are adjusted by feedback in combination with the rock mechanics and geostress parameter ranges of the geomechanical coupled lithofacies proposed in this invention, so that the prediction results are more reasonable and reliable and meet geological laws, thereby providing reliable data support for subsequent three-dimensional prediction and modeling of geomechanical coupled lithofacies.
[0122] (3) Geological-mechanical coupled lithofacies multi-stage seismic attribute embedding and integrated inversion and well-seismic collaborative modeling.
[0123] Using the seismic attribute extraction and optimization method provided by the invention patent "A Three-Dimensional In-Situ Characterization Method for Heterogeneous Shale Generation and Reservoir Performance (Patent No. 202011393012.7)," we conduct A. Geological-Mechanical Coupled Lithofacies Multi-Stage Seismic Attribute Embedding and Integrated Inversion, thereby obtaining the geological-mechanical coupled lithofacies inversion data volume; Combining the data obtained from the above technical process, we further use the well-seismic co-modeling and characterization method provided by the invention patent "A Three-Dimensional In-Situ Characterization Method for Heterogeneous Shale Generation and Reservoir Performance (Patent No. 202011393012.7)," to conduct B. Geological-Mechanical Coupled Lithofacies Well-Seismic Co-modeling and Visualized Three-Dimensional Prediction, obtaining the three-dimensional model of the geological-mechanical coupled lithofacies, thereby completing the prediction of the three-dimensional attributes and distribution of the geological-mechanical coupled lithofacies.
[0124] A. Geological-mechanical coupled multi-stage seismic attribute embedding and integrated inversion of lithofacies.
[0125] The "multi-stage" approach mentioned in this method mainly includes the following parts: ① Selecting and determining the initially extracted seismic attributes to lay the data foundation for subsequent attribute extraction and inversion; ② Continuously performing "multi-level extraction" of attributes based on the extracted seismic attributes, and finally extracting a single-well seismic attribute data chimera using a genetic algorithm; ③ Judging the quality of the extraction effect based on the learning correlation coefficients presented after attribute extraction, appropriately adjusting the variable parameters in the genetic neural network architecture, and extracting attributes again, selecting the genetic neural network structure with the highest learning correlation coefficient, and generating a 3D inversion data volume based on it as the input data for the final well-seismic co-modeling. The "integration" in the integrated inversion is manifested in the iterative and continuous optimization of attribute extraction chimera and genetic network structure parameter adjustment. Here, it refers to using the obtained geological-mechanical coupled lithofacies along the wellbore space as the target, relying on multi-stage seismic attribute chimera to extract a seismic attribute data volume consistent with the statistical characteristics of the geological-mechanical coupled lithofacies in the wellbore space, thereby providing reliable basic data for 3D modeling and characterization.
[0126] Based on a practical case, the specific operational steps are as follows: ① First, extract the relative acoustic impedance attribute of a single well to provide a data foundation for subsequent multi-stage extraction and integration of other attributes and ensemble inversion (Note: In this case, after actually integrating seismic attributes, it was found that the highest learning correlation coefficient was obtained after extracting only the relative acoustic impedance attribute for inversion); ② Use the seismic data volume with extracted relative acoustic impedance and the lithofacies of the single well as the input data, and adjust the vertical range, inline, cross-line half-range and other seismic sub-value variables and seismic resampling parameters; ③ Set the genetic neural network architecture parameters, i.e., set the iteration number, correlation threshold, weight decay and the number of hidden layer nodes of the neural network, and use the genetic algorithm to extract the seismic attribute data volume of the single well; ④ Evaluate the extraction effect according to the value of the learning correlation coefficient, and repeatedly extract attributes by adjusting the seismic data volume parameters and the genetic neural network learning parameters, and finally obtain the single well lithofacies seismic attribute data volume with the best effect; ⑤ On the basis of completing the single well attribute extraction, carry out ensemble inversion to obtain the three-dimensional seismic inversion volume of the area where the single well is located, providing reliable data for subsequent well-seismic collaborative modeling. Since the analysis process of coupled geological and mechanical lithofacies needs to include the geological and mechanical characteristics of lithofacies and the correlation between the two, in actual operation, single-well seismic attribute data volume extraction is also performed for geological lithofacies.
[0127] Table 2 shows the parameter settings for extracting attributes of geomechanical coupled lithofacies using a genetic algorithm. It can be seen that when the vertical range is 50, the inline, cross-line half-range and resampling parameters are 3 respectively, the number of iterations is 20,000, the correlation threshold is 0.001, and the number of hidden layer nodes in the neural network is 4, the learning correlation coefficient is the highest, which is 0.9005.
[0128] Figure 11 The image shows an inversion profile generated after attribute extraction of a single well lithofacies. It can be seen that the lithofacies of the single well have a good correlation with the seismic profile. Figure 12 and Figure 13 The three-dimensional inversion data volumes of geological lithofacies and geomechanical coupled lithofacies after attribute extraction and integrated inversion are shown. 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 on parameter settings for multi-stage seismic attribute embedding and integrated inversion of geological-mechanical coupled lithofacies.
[0130]
[0131] B. Geological-mechanical coupled lithofacies well-seismic co-modeling and visualization 3D prediction.
[0132] Based on multi-stage seismic attribute embedding and integrated inversion, well-seismic co-modeling is further conducted through single-well lithofacies to predict the three-dimensional spatial distribution of lithofacies. The specific steps are as follows: ① Data coarsening of single-well geological lithofacies and geomechanical coupled lithofacies is carried out, and layer-by-layer lithofacies data analysis is performed; ② Under the joint constraint control of single-well lithofacies data and the inversion data volume formed by multi-stage overlay and superposition inversion of A. geomechanical coupled lithofacies seismic attributes, the "co-kriging" method is used to establish three-dimensional models of geological lithofacies and geomechanical coupled lithofacies.
[0133] Figures 14-17 This document presents a 3D model and a single-well lithofacies profile of a stratum within a shale production area, showcasing five geological facies types. The geological facies are observed to be dispersed and mixed in plan view, but relatively uniformly distributed vertically, with clear distinctions between facies types. The geological facies shown primarily include carbon-rich medium- to high-porosity siliceous shale, carbon-rich medium- to high-porosity calc- and argillaceous siliceous shale, medium- to high-carbon medium- to high-porosity siliceous shale, medium- to low-carbon medium- to low-porosity calc- and argillaceous siliceous shale, and medium- to high-carbon medium- to high-porosity argillaceous siliceous shale.
[0134] Figures 18-21 The paper presents a three-dimensional model of the geological-mechanical coupled lithofacies of the corresponding strata in the shale production area, as well as a single-well lithofacies profile. It can be seen that the geological-mechanical coupled lithofacies are more dispersed and complex in the plane than the geological lithofacies, and have stronger heterogeneity in the vertical distribution, reflecting the mechanical heterogeneity differences of the same geological lithofacies.
[0135] Based on the establishment of geological lithofacies and geomechanical coupled lithofacies, the proportion of different geomechanical coupled lithofacies within the same geological lithofacies was statistically analyzed. Table 3 shows the proportion of geomechanical coupled lithofacies for each of the five geological lithofacies. Due to its high organic carbon content, the carbon-rich medium-to-high porosity siliceous shale exhibits the highest proportion of brittle carbon-rich medium-to-high porosity siliceous shale. The carbon-rich medium-to-high porosity calc- and argillaceous siliceous shale and the medium-to-low carbon medium-to-low porosity calc- and argillaceous shale contain a significant amount of geomechanical coupled lithofacies with strong shear toughness and tensile toughness due to their high calcium and argillaceous content. The statistical results in Table 3 fully demonstrate the intrinsic relationship between the properties of geological lithofacies and geomechanical coupled lithofacies, further confirming the feasibility of constructing a geomechanical coupled lithofacies classification system, single-well identification, and three-dimensional prediction.
[0136] Table 3. Statistical table of the proportion of geological-mechanical coupled lithofacies.
[0137]
[0138] (4) Geological-mechanical coupled facies control rock mechanics and geostress parameter modeling.
[0139] Based on the completion of the shale geological-mechanical coupled lithofacies classification system construction, single-well identification, and three-dimensional prediction to obtain a geological-mechanical coupled lithofacies three-dimensional model, this paper utilizes the geological-mechanical coupled lithofacies as the "facies control" factor, combined with the fine sub-layer structure and tectonic model as the "layer control" factor, and adopts the "layer control-facies control" dual control method 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 Obtain single-well rock mechanics and geostress parameters: Single-well rock mechanics and geostress parameters can be obtained through experimental methods such as uniaxial compression, triaxial compression, and point load strength, or by calculating dynamic three-dimensional rock mechanics parameters from conventional well logging data, thereby obtaining static parameter values. This patent adopts a method based on conventional well logging data to obtain rock mechanics and geostress parameters; ② Conduct data coarsening and sub-layer data analysis of single-well rock mechanics and geostress parameters; ③ Using the geological-mechanical coupled lithofacies as the "facies control" factor and the fine sub-layer structure as the "layer control" factor, a "layer control-facies control" dual control constraint is formed. Combined with the single-well rock mechanics and geostress parameters, the "co-kriging" method is used to establish a three-dimensional model of mechanical parameters.
[0140] Table 4 shows the range and average value of Young's modulus parameters for random interpolation modeling and geomechanical coupled lithofacies facies control modeling. It can be seen that, due to the lack of control constraints from geomechanical coupled lithofacies, the range of mechanical parameters for random interpolation modeling is larger than that for facies control modeling, and there is a certain degree of deviation from the actual values. Figures 22-25 The paper presents three-dimensional models and cross-sectional views of Young's modulus parameters established by random interpolation, geomechanical coupled lithofacies facies control, and Young's modulus parameter establishment. It can be seen that the model established by random interpolation is too homogeneous and cannot characterize the complex heterogeneity of the strata; the model established by facies control can clearly characterize the strong heterogeneity of the strata both vertically and horizontally. Based on the parameter value ranges in the table and the model results, the facies control model has higher accuracy than the random interpolation model, directly confirming the feasibility and effectiveness of the technical system and method proposed in this invention.
[0141] Table 4. Statistical table of numerical ranges for random interpolation and geomechanical coupled lithofacies-controlled Young's modulus parameter modeling.
[0142]
[0143]
[0144] Based on the classification and identification of shale geological facies, this application systematically conducts rock mechanics and geostress characteristic analysis and identification of geological facies. It further proposes the construction of a classification system for geological-mechanical coupled facies, and on this basis, forms a comprehensive single-well identification and three-dimensional prediction technology process. This process characterizes the heterogeneity of the geological-mechanical characteristics of shale layers in three-dimensional space, providing a basis for the establishment of production areas and for the exploration and development of artificial oil and gas reservoirs.
Claims
1. A lithofacies analysis system based on a shale geomechanics-coupled lithofacies classification system, characterized in that, The lithofacies analysis system based on the shale geological-mechanical coupled lithofacies classification system includes: The data acquisition module is used to acquire information data, including geological and lithofacies data and in-situ mechanical parameter data. The system construction module, connected to the data acquisition module, is used to determine the geological-mechanical coupled lithofacies classification system based on information data. The prediction module, connected to the system construction module, is used to perform quantitative prediction of single-well lithofacies based on well logging data according to the geomechanical coupled lithofacies classification system, and obtain single-well lithofacies prediction information. The single-well lithofacies prediction information is the prediction result of the geomechanical coupled lithofacies of the entire wellbore space within the target layer, which is used to characterize the phase sequence variation characteristics of single-well geomechanical coupled lithofacies along the wellbore. The feedback adjustment module, connected to the prediction module, is used to adjust the single-well lithofacies prediction information based on the set range of rock mechanics and geostress parameters, and obtain adjustment information. The 3D model construction module, connected to the feedback adjustment module, is used to perform seismic attribute embedding and integrated inversion based on seismic attribute data and adjustment information using a genetic algorithm, and to construct a 3D model for geological-mechanical coupled lithofacies well-seismic co-modeling using the co-kriging method. The 3D model is a 3D physical simulation model constructed based on the intrinsic relationship between the properties of geological lithofacies and geological-mechanical coupled lithofacies, used to characterize the mechanical heterogeneity differences and distribution patterns of geological lithofacies. The parameter model determination module, connected to the 3D model construction module, is used to determine the rock mechanics and geostress parameter model controlled by the geological-mechanical coupled lithofacies based on the 3D model using the layer-control-facies dual control method. The parameter model is used to characterize the heterogeneity of the geological-mechanical characteristics of shale layers in three-dimensional space, and to provide a basis for the establishment of production areas for the exploration and development of artificial oil and gas reservoirs. The system construction modules include: The processing submodule, connected to the data acquisition module, is used to sort the data and remove singular points based on the information data, according to the location correspondence principle or the adjacent similarity principle, and based on the logging response characteristics, to obtain sorted data. The parameter determination submodule, connected to the processing submodule, is used to 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. The cluster core determination submodule is connected to the data acquisition module and the parameter determination submodule, respectively, and is used to determine the geological-mechanical coupled lithofacies cluster core based on information data and differentially sensitive rock mechanical parameters. The analysis submodule, connected to the cluster core determination submodule, is used to analyze the boundaries and intersection ranges of geological-mechanical coupled lithofacies clusters based on the geological-mechanical coupled lithofacies cluster core, and to determine the boundaries and ranges of the geological-mechanical coupled lithofacies clusters. The system determination submodule, connected to the analysis submodule, is used to draw a geological-mechanical coupled lithofacies classification map based on the boundaries and ranges of the geological-mechanical coupled lithofacies clusters, and to construct a lithofacies classification system parameter division standard table to determine the geological-mechanical coupled lithofacies classification system; The parameter determination submodule includes: The feature attribute determination unit, connected to the processing submodule, is used to determine feature attributes based on the sorted data, single-factor cluster feature extraction, and multi-well analogy analysis. The distribution difference determination unit, connected to the feature attribute determination unit, is used to perform attribute consistency verification for single-well horizontal and multi-well vertical based on the feature attributes, and to determine the distribution difference of each mechanical parameter in different geological lithofacies. The parameter determination unit, connected to the distribution difference determination unit, is used to determine difference-sensitive rock mechanical parameters based on distribution differences.
2. The lithofacies analysis system based on the shale geological-mechanical coupled lithofacies classification system according to claim 1, characterized in that, The geomechanical coupled lithofacies classification system includes: a geomechanical coupled lithofacies classification chart and a table of parameters for the geomechanical coupled lithofacies classification system.
3. A lithofacies analysis method based on a shale geological-mechanical coupled lithofacies classification system, characterized in that, The lithofacies analysis method based on the shale geological-mechanical coupled lithofacies classification system includes: Acquire information and data; the information and data include: geological and lithofacies data and in-situ mechanical parameter data; Determine a geological-mechanical coupled lithofacies classification system based on information data; Based on the geomechanical coupled lithofacies classification system, quantitative prediction of lithofacies in a single well is performed according to well logging data to obtain single well lithofacies prediction information. The single well lithofacies prediction information is the prediction result of the geomechanical coupled lithofacies of the entire wellbore space within the target layer, which is used to characterize the facies sequence variation characteristics of the single well geomechanical coupled lithofacies along the wellbore. Based on the set range of rock mechanics and geostress parameters, the single-well lithofacies prediction information is adjusted by feedback to obtain the adjustment information; A genetic algorithm is used to perform seismic attribute embedding and integrated inversion based on seismic attribute data and adjustment information. A three-dimensional model of geological-mechanical coupled lithofacies well-seismic co-modeling is constructed using the co-kriging method. The three-dimensional model is a three-dimensional physical simulation model constructed based on the intrinsic relationship between the properties of geological lithofacies and geological-mechanical coupled lithofacies, which is used to characterize the mechanical heterogeneity differences and distribution patterns of geological lithofacies. Based on the three-dimensional model, the stratigraphic-facies dual control method is used to control the constraints and determine the geological-mechanical coupled lithofacies control rock mechanics and geostress parameter model. The parameter model is used to characterize the heterogeneity of the geological-mechanical characteristics of shale layer in three-dimensional space, and to provide a basis for the establishment of production areas for the exploration and development of artificial oil and gas reservoirs. Based on information data, a geological-mechanical coupled lithofacies classification system is determined, specifically including: Based on the information data, and according to the principle of location correspondence or adjacent similarity, the data is sorted and singular points are removed according to the logging response characteristics to obtain sorted data. Based on the analyzed data, the differentially sensitive rock mechanical parameters were determined; these parameters include tensile strength, shear strength, and Young's modulus. The core of the geological-mechanical coupled lithofacies cluster was determined based on information data and differentially sensitive rock mechanical parameters. Based on the core of the geomechanical coupled lithofacies cluster, the boundary and intersection range of the geomechanical coupled lithofacies cluster are analyzed to determine the boundary and range of the geomechanical coupled lithofacies cluster. Based on the boundaries and extent of the geological-mechanical coupled lithofacies clusters, a geological-mechanical coupled lithofacies classification map was drawn, and a standard table of lithofacies classification system parameters was constructed to determine the geological-mechanical coupled lithofacies classification system. Based on the analyzed data, the differentially sensitive rock mechanical parameters were determined, specifically including: Based on the analyzed data, characteristic attributes were determined using single-factor cluster feature extraction and multi-well analogy analysis. Based on the characteristic attributes, the consistency of attributes in the horizontal direction of a single well and the vertical direction of multiple wells are verified to determine the distribution differences of each mechanical parameter in different geological lithofacies. Determining difference-sensitive rock mechanical parameters based on distribution differences.
4. The lithofacies analysis method based on the shale geological-mechanical coupled lithofacies classification system according to claim 3, characterized in that, A genetic algorithm is used to perform seismic attribute embedding and ensemble inversion based on seismic attribute data and adjustment information. A co-kriging method is then employed to construct a 3D model for geological-mechanical coupled lithofacies-well-seismic co-modeling, specifically including: Determine the parameters of the genetic neural network architecture; these parameters include: number of iterations, relevant threshold, weight decay, and number of hidden layer nodes. A genetic algorithm based on the genetic neural network architecture parameters was used to extract single-well seismic attribute data chimeras from seismic attribute data. Based on the chimerism of regulation information and single-well seismic attribute data, seismic attribute chimerism and integrated inversion are performed to obtain the inversion data volume; Data coarsening of single-well geological lithofacies and geomechanical coupled lithofacies, as well as lithofacies data analysis, were performed to obtain analytical data; Based on the combined constraints of analytical data and inversion data volumes, a three-dimensional model for geological-mechanical coupled lithofacies-well-seismic co-modeling is constructed using the co-kriging method.
5. The lithofacies analysis method based on the shale geological-mechanical coupled lithofacies classification system according to claim 3, characterized in that, Based on a three-dimensional model, a layer-control and facies-control dual-control method is used to determine the geological-mechanical coupled lithofacies-controlled rock mechanics and geostress parameter model, specifically including: Obtain rock mechanics and geostress parameters from a single well; Based on the rock mechanics and geostress parameters of a single well, data coarsening and small-layer data analysis were performed to obtain analytical parameter data. Based on the analysis parameter data, a layer-control-facies dual-control method is adopted to control the constraints. Based on the rock mechanics and geostress parameters of a single well and a three-dimensional model, a co-kriging method is used to determine the geological-mechanical coupled rock facies control rock mechanics and geostress parameter model.
6. The lithofacies analysis method based on the shale geological-mechanical coupled lithofacies classification system according to claim 5, characterized in that, The rock mechanics and geostress parameters of a single well are obtained by uniaxial compression, triaxial compression or point load strength tests.