Well-seismic analysis system, methods, and equipment based on tight sandstone geomechanical coupled lithofacies system

Abstract

This application discloses a well-seismic analysis system, method, and equipment based on a tight sandstone geomechanical coupled lithofacies system, relating to the field of tight sandstone oil and gas extraction analysis. The system employs a four-element composite triangular diagram method in its classification model construction module to construct a geomechanical coupled lithofacies classification model for tight sandstone reservoirs based on information data. The integration module performs geomechanical coupled lithofacies classification and integrates multi-type lithofacies prediction results at the single-well level based on element difference data. The analysis and determination module, based on a well-seismic collaborative three-dimensional model of the tight sandstone reservoir's geomechanical coupled lithofacies, performs feature extraction, lithofacies classification feedback adjustment, and seismic attribute mapping of the geomechanical coupled lithofacies to determine the distribution information data of the tight sandstone geomechanical coupled lithofacies. This application can determine the heterogeneous distribution law of rock mechanics and geostress characteristics in tight sandstone reservoirs, providing a basis for improving the extraction efficiency of tight sandstone oil and gas.

Description

Technical Field

[0001] This application relates to the field of tight sandstone oil and gas extraction analysis, and in particular to a well seismic analysis system, method and equipment based on a tight sandstone geological-mechanical coupled lithofacies system. Background Technology

[0002] Tight sandstone reservoirs possess enormous oil and gas resource potential and considerable reserves. To extract oil and gas resources concentrated in tight sandstone reservoirs, in addition to using traditional geological lithofacies methods to find geological sweet spots, it is also necessary to identify engineering sweet spots based on geomechanical characteristics to guide drilling and artificial stimulation operations. Ultra-long horizontal wells combined with large-scale repeated fracturing are currently essential methods for tight sandstone oil and gas development. Both are essentially mechanical processes; only by relying on accurate rock mechanics and geostress parameter fields to optimize process parameters can drilling and fracturing efficiency be improved. However, current rock mechanics and geostress parameter field modeling uses deterministic or stochastic interpolation methods, resulting in modeling results that differ significantly from the geomechanical characteristics of tight sandstone reservoirs, thus failing to effectively guide ultra-long horizontal wells combined with large-scale repeated fracturing.

[0003] Essentially, tight sandstone bodies in reservoirs, buried deep underground, are a unity of geological and mechanical characteristics. Therefore, determining what lithofacies system can accurately characterize tight sandstone bodies that unify these geological and mechanical features remains a global challenge that has yet to be solved. Summary of the Invention

[0004] The purpose of this application is to provide a well seismic analysis system, method, and equipment based on the geological-mechanical coupled lithofacies system of tight sandstone, which can determine the heterogeneous distribution law of rock mechanics and geostress characteristics of tight sandstone reservoirs, so as to provide a basis for improving the exploitation effect of tight sandstone oil and gas.

[0005] To achieve the above objectives, this application provides the following solution:

[0006] Firstly, this application provides a well seismic analysis system based on a tight sandstone geo-mechanical coupled lithofacies system, comprising:

[0007] The information data acquisition module is used to acquire information data, including: characteristic core data and rock mechanics data. The characteristic core data includes characteristic data acquired using the three-dimensional nine-point method and parameter data determined based on mechanical experiments. The rock mechanics data is based on the theory of lithofacies discretization and sensitivity analysis. By setting different lithofacies numerical experiments, the influence of lithofacies changes on rock strength and crack propagation behavior is taken as the research object, and parameter data sensitive to the rock mechanics performance of different lithofacies are obtained.

[0008] The classification pattern construction module is connected to the information data acquisition module. It is used to construct a geological-mechanical coupled lithofacies classification pattern for tight sandstone reservoirs based on information data using the four-element composite triangular diagram method.

[0009] The integration module, connected to the classification pattern construction module, is used to integrate the geological-mechanical coupled lithofacies classification of tight sandstone reservoirs at the single-well level, based on the geological-mechanical coupled lithofacies classification pattern, and the prediction results of multiple lithofacies based on the element difference data, to obtain the integrated result; the element difference data includes: particle composition, particle size, brittleness index and horizontal stress difference.

[0010] The analysis and determination module, connected to the integration module, is used to determine the distribution information of the tight sandstone reservoir's geological-mechanical coupled lithofacies well-seismic co-operational three-dimensional model based on the integrated results. This involves feature extraction of the geological-mechanical coupled lithofacies, lithofacies classification feedback adjustment, and seismic attribute mapping. The geological-mechanical coupled lithofacies well-seismic co-operational three-dimensional model of the tight sandstone reservoir is a physical model constructed based on the relationship between seismic data and geological-mechanical data. The distribution information of the tight sandstone reservoir's geological-mechanical coupled lithofacies is used to characterize the heterogeneous distribution of rock mechanics and geostress characteristics, providing a basis for improving the extraction efficiency of tight sandstone oil and gas.

[0011] Secondly, this application provides a well-seismic analysis method based on a tight sandstone geo-mechanical coupled lithofacies system, implemented using the aforementioned system; including:

[0012] Information data is acquired; the information data includes: characteristic core data and rock mechanics data; the characteristic core data includes characteristic data obtained by the three-dimensional nine-point method and parameter data determined based on mechanical experiments; the rock mechanics data is based on the theory of lithofacies discretization and sensitivity analysis, and is obtained by setting different lithofacies numerical tests, taking the influence of lithofacies changes on rock strength and crack propagation behavior as the research object, and obtaining parameter data sensitive to the rock mechanics performance of different lithofacies.

[0013] A four-element composite triangular diagram method was used to construct a geological-mechanical coupled lithofacies classification model for tight sandstone reservoirs based on information data;

[0014] At the single-well level, based on the geological-mechanical coupled lithofacies classification model of tight sandstone reservoirs, the geological-mechanical coupled lithofacies classification and multi-type lithofacies prediction results are integrated according to the element difference data to obtain the integrated results; the element difference data include: particle composition, particle size, brittleness index and horizontal stress difference.

[0015] Based on the geological-mechanical coupled lithofacies well-seismic co-engineered three-dimensional model of tight sandstone reservoirs, feature extraction, lithofacies classification feedback adjustment, and seismic attribute mapping of geological-mechanical coupled lithofacies are performed according to the integrated results to determine the geological-mechanical coupled lithofacies distribution information data of tight sandstone. The geological-mechanical coupled lithofacies well-seismic co-engineered three-dimensional model of tight sandstone reservoirs is a physical model constructed based on the relationship between seismic data and geological-mechanical systems. The geological-mechanical coupled lithofacies distribution information data of tight sandstone is used to characterize the heterogeneous distribution law of rock mechanics and geostress characteristics of tight sandstone reservoirs, so as to provide a basis for improving the exploitation effect of tight sandstone oil and gas.

[0016] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described well seismic analysis method based on a tight sandstone geomechanical coupled lithofacies system.

[0017] According to the specific embodiments provided in this application, this application has the following technical effects:

[0018] This application provides a well-seismic analysis system, method, and equipment based on a geomechanical coupled lithofacies system of tight sandstone. Considering the petrological characteristics and stimulation potential of tight sandstone reservoirs, this application identifies factors influencing the potential for horizontal drilling and artificial stimulation, establishes a geomechanical coupled lithofacies classification model for tight sandstone reservoirs, and enables quantitative identification and prediction of geomechanical coupled lithofacies in single wells. It characterizes the heterogeneous distribution characteristics of geomechanical coupled lithofacies in tight sandstone reservoirs, providing technical support for modeling the heterogeneous rock mechanics and geostress controlled by the oil and gas facies in tight sandstone. This facilitates the effective implementation of ultra-long horizontal wells combined with large-scale repeated fracturing, ultimately leading to efficient oil and gas extraction from tight sandstone. Therefore, this application can determine the heterogeneous distribution law of rock mechanics and geostress characteristics in tight sandstone reservoirs, providing a basis for improving the extraction efficiency of tight sandstone oil and gas. Attached Figure Description

[0019] Figure 1 A schematic diagram of an isosceles triangle for lithological classification of tight sandstone reservoirs;

[0020] Figure 2 This is a structural diagram of a well seismic analysis system based on a geomechanical coupled lithofacies system of tight sandstone;

[0021] Figure 3 This is a flowchart of a well seismic analysis scheme based on a geomechanical coupled lithofacies system of tight sandstone.

[0022] Figure 4 A schematic diagram of three-dimensional nine-point sampling for tight sandstone reservoirs;

[0023] Figure 5A schematic diagram of three-dimensional nine-point sampling points in a case study area of ​​tight sandstone reservoir in a certain production area;

[0024] Figure 6 This is a cross-plot of elastic modulus and Poisson's ratio;

[0025] Figure 7 This is a diagram showing the intersection of crack length and density with the difference in horizontal stress.

[0026] Figure 8 A schematic diagram of a composite isosceles triangle for geological-mechanical coupled lithofacies classification of dense sandstone;

[0027] Figure 9 A schematic diagram of the geological-mechanical coupled lithofacies classification results of tight sandstone reservoirs;

[0028] Figure 10 A comprehensive columnar section of the results of quantitative identification of lithofacies through geological-mechanical coupling in tight sandstone gas wells;

[0029] Figure 11 This is a geological-mechanical coupled lithofacies P-wave impedance inversion profile of a tight sandstone reservoir.

[0030] Figure 12 A schematic diagram of a three-dimensional lithofacies model of a tight sandstone reservoir coupled with geological and mechanical processes.

[0031] Figure 13 A schematic diagram of the first lithofacies distribution of a tight sandstone reservoir, involving geological and mechanical coupling.

[0032] Figure 14 This is a schematic diagram of the second distribution of lithofacies in a tight sandstone reservoir, involving geological and mechanical coupling.

[0033] Figure 15 A scatter plot of bar graphs showing the productivity distribution of tight sandstone reservoirs;

[0034] Figure 16 A schematic diagram of the brittleness index model for tight sandstone reservoirs;

[0035] Figure 17 A columnar section of facies-controlled rock mechanics for a control well in a tight sandstone reservoir;

[0036] Figure 18 This is a schematic diagram of the structure of a computer device. Detailed Implementation

[0037] Currently, many scholars have studied the world's major tight sandstone reservoirs. By combining nuclear magnetic resonance logging (NMR) with sonic transit-time logging (SDR), the porosity of low-permeability gas reservoirs is jointly calculated and applied to actual production, improving calculation accuracy. Methods for directly identifying tight sandstone gas reservoirs and quantitative identification methods have been developed. The traditional Stacking model has been improved to better learn the complex characteristics and variation patterns of different reservoir types. A "double sweet spot" reservoir prediction method for tight sandstone reservoirs has been developed, integrating geological and engineering sweet spots to objectively evaluate the production potential of low-permeability reservoirs. Through comprehensive research on reservoir sedimentary microfacies, pore structure, and diagenesis, the main controlling factors for the development of tight sandstone reservoirs have been elucidated.

[0038] The current method for classifying tight sandstone is to select the main minerals in the tight sandstone reservoir, namely quartz (Q), feldspar (F), and rock fragments (R), to form a mineral content triangular diagram. Figure 1 Based on quartz content (q), feldspar content (f), and rock fragment content (r), four auxiliary lines are drawn: q = 0.75, r / (f+r) = 0.25, r / (f+r) = 0.5, and r / (f+r) = 0.75. These correspond to X = r + q / 2 and Y = q when reading values ​​within the phase diagram for q = 0.75, r / f = 1:3, r / f = 1:1, and r / f = 3:1, respectively.

[0039] Different minerals undergo varying hydration reactions during diagenesis. Rocks containing clay minerals expand and contract upon absorbing water, thus affecting the reservoir's pore structure, permeability, and productivity. The interactions between reservoirs with different mineral compositions during the flow of oil and gas influence the distribution and preferred flow paths of hydrocarbons. High-strength sandstone may be more stable under high pressure conditions, thus helping to maintain reservoir structure and vertical connectivity, thereby increasing oil and gas productivity.

[0040] Sedimentary rocks of different sizes (such as sandstone and mudstone) have a significant impact on the characteristics of oil and gas reservoirs. Small-sized rocks may lead to poor flow due to increased fluid resistance, thus affecting extraction efficiency and production capacity. Larger-sized particles tend to make the rock structure more stable, reducing engineering risks and ensuring the normal operation of drilling and production equipment, thus maintaining the continuity of production capacity.

[0041] In integrated geomechanical studies, different lithofacies exhibit significant differences in rock mechanics and geostress characteristics due to their varying depositional environments, burial depths, and petrological properties. In coupled geomechanical lithofacies characterization and modeling, the geological characteristics and mechanical properties of rocks are typically considered to establish lithofacies models. By coupling geological lithofacies with mechanical properties, the mechanical characteristics of each lithofacies are analyzed, enabling accurate prediction of rock deformation, failure, and stability.

[0042] Current research mainly focuses on the specific study of the mechanical properties of different rock facies. In rock mechanics research and three-dimensional modeling, the analysis is relatively independent, and the constraints of rock facies on rock mechanical parameters are relatively vague. The modeling results obtained are quite different from the geomechanical characteristics of tight sandstone reservoirs. There is no further subdivision of geological rock facies coupled with mechanical properties, and no further establishment of a geological-mechanical coupled rock facies classification scheme, which has certain limitations.

[0043] For tight sandstone reservoirs, current rock mechanics and geostress parameter field modeling uses deterministic or stochastic interpolation methods. The modeling results obtained differ significantly from the geomechanical characteristics of tight sandstone reservoirs, and cannot effectively guide ultra-long horizontal wells with large-scale repeated fracturing, which will restrict the further production efficiency of tight sandstone reservoirs.

[0044] like Figure 2 As shown, this application provides a well seismic analysis system based on a tight sandstone geo-mechanical coupled lithofacies system, comprising:

[0045] The information data acquisition module is used to acquire information data, including characteristic core data and rock mechanics data. The characteristic core data includes characteristic data acquired using the three-dimensional nine-point method and parameter data determined based on mechanical experiments. The rock mechanics data is based on the theory of lithofacies discretization and sensitivity analysis. By setting different lithofacies numerical experiments, the influence of lithofacies changes on rock strength and crack propagation behavior is studied, and parameter data sensitive to the rock mechanics performance of different lithofacies are obtained.

[0046] The classification pattern construction module, connected to the information data acquisition module, is used to construct a geological-mechanical coupled lithofacies classification pattern for tight sandstone reservoirs based on information data using the four-element composite triangular diagram method.

[0047] The integration module, connected to the classification pattern construction module, is used to integrate the geological-mechanical coupled lithofacies classification of tight sandstone reservoirs at the single-well level, based on the geological-mechanical coupled lithofacies classification pattern, and the prediction results of multiple lithofacies based on the element difference data, to obtain the integrated result; the element difference data includes: particle composition, particle size, brittleness index and horizontal stress difference.

[0048] The analysis and determination module, connected to the integration module, is used to determine the distribution information of the tight sandstone reservoir's geological-mechanical coupled lithofacies well-seismic co-operational three-dimensional model based on the integrated results. This involves feature extraction of the geological-mechanical coupled lithofacies, lithofacies classification feedback adjustment, and seismic attribute mapping. The geological-mechanical coupled lithofacies well-seismic co-operational three-dimensional model of the tight sandstone reservoir is a physical model constructed based on the relationship between seismic data and geological-mechanical data. The distribution information of the tight sandstone reservoir's geological-mechanical coupled lithofacies is used to characterize the heterogeneous distribution of rock mechanics and geostress characteristics, providing a basis for improving the extraction efficiency of tight sandstone oil and gas.

[0049] The classification pattern building module includes:

[0050] The brittleness factor determination submodule, connected to the information data acquisition module, is used to determine the brittleness factor of dense sandstone by using the brittle boundary division method, visualizing the relationship between elastic modulus and Poisson's ratio based on information data, and dividing material properties through negative slope straight lines.

[0051] The geostress factor determination submodule, connected to the information data acquisition module, is used to determine the geo-mechanical coupled lithofacies geostress factors of tight sandstone based on the stress optimization identification model and information data. The stress optimization identification model is a physical model of the influence of horizontal stress difference on fracture propagation in tight sandstone reservoirs, constructed based on the influence of horizontal stress difference on fracture formation, propagation and connection in horizontal multi-stage fracturing operations.

[0052] The map determination submodule is connected to the brittleness factor determination submodule and the geostress factor determination submodule, respectively. It is used to process the composite triangular map model based on the isosceles triangular map of tight sandstone reservoir lithology classification, according to the tight sandstone geological-mechanical coupled lithofacies brittleness factor and tight sandstone geological-mechanical coupled lithofacies geostress factor, coupled grain size, horizontal stress difference and brittleness index, to obtain the tight sandstone geological-mechanical coupled lithofacies classification composite isosceles triangular map.

[0053] The classification pattern construction submodule, connected to the map determination submodule, is used to name the geological-mechanical coupled lithofacies type of the tight sandstone reservoir based on the composite isosceles triangular map of the geological-mechanical coupled lithofacies classification. The naming part is the grain composition and grain size, and the horizontal stress difference and brittleness index are used as modifying prefixes to determine the geological-mechanical coupled lithofacies classification pattern of the tight sandstone reservoir.

[0054] The integration module includes:

[0055] The single-well identification result acquisition submodule is used to acquire the single-well identification results of the geological lithofacies of tight sandstone reservoirs;

[0056] The sub-modules are connected to the single-well identification result acquisition sub-module and the classification pattern construction module, respectively. They are used to perform geomechanical coupled lithofacies classification based on the tight sandstone reservoir geological-mechanical coupled lithofacies classification pattern and the single-well geological lithofacies identification results of tight sandstone reservoir at the single-well level, and obtain the classification results based on the element difference data.

[0057] The integration processing submodule, connected to the partitioning submodule, is used to perform numerical mapping, priority selection, and classification processing based on the partitioning results, so as to integrate the prediction results of multiple types of lithofacies and obtain the integrated result.

[0058] This application also provides a well seismic analysis method based on a tight sandstone geomechanical coupled lithofacies system, which is implemented using the aforementioned system; the method includes:

[0059] Information data is acquired; the information data includes: characteristic core data and rock mechanics data; the characteristic core data includes characteristic data obtained by the three-dimensional nine-point method and parameter data determined based on mechanical experiments; the rock mechanics data is based on the theory of lithofacies discretization and sensitivity analysis, and is obtained by setting different lithofacies numerical experiments, taking the influence of lithofacies changes on rock strength and crack propagation behavior as the research object, and obtaining parameter data sensitive to the rock mechanics performance of different lithofacies.

[0060] Characteristic data includes: lithology, grain type, and grain size. Parameter data includes: elastic modulus, Poisson's ratio, brittleness, cohesion, fracture toughness, fracture pressure, internal friction angle, tensile strength, shear strength, maximum horizontal principal stress, minimum horizontal principal stress, vertical stress, and horizontal stress difference.

[0061] A four-element composite triangular diagram method was used to construct a geological-mechanical coupled lithofacies classification model for tight sandstone reservoirs based on information data.

[0062] At the single-well level, based on the geological-mechanical coupled lithofacies classification model of tight sandstone reservoirs, the geological-mechanical coupled lithofacies classification and multi-type lithofacies prediction results are integrated according to the element difference data to obtain the integrated results; the element difference data include: particle composition, particle size, brittleness index and horizontal stress difference.

[0063] Based on the geological-mechanical coupled lithofacies well-seismic co-engineered three-dimensional model of tight sandstone reservoirs, feature extraction, lithofacies classification feedback adjustment, and seismic attribute mapping of geological-mechanical coupled lithofacies are performed according to the integrated results to determine the geological-mechanical coupled lithofacies distribution information data of tight sandstone. The geological-mechanical coupled lithofacies well-seismic co-engineered three-dimensional model of tight sandstone reservoirs is a physical model constructed based on the relationship between seismic data and geological-mechanical systems. The geological-mechanical coupled lithofacies distribution information data of tight sandstone is used to characterize the heterogeneous distribution law of rock mechanics and geostress characteristics of tight sandstone reservoirs, so as to provide a basis for improving the exploitation effect of tight sandstone oil and gas.

[0064] In one embodiment, a four-element composite triangular diagram method is used to construct a geological-mechanical coupled lithofacies classification model for tight sandstone reservoirs based on information data, specifically including:

[0065] The brittle boundary delineation method was adopted. Based on the information data, the relationship between elastic modulus and Poisson's ratio was visualized, and the material properties were delineated by negative slope straight lines to determine the geological-mechanical coupled lithofacies brittleness factors of dense sandstone.

[0066] Based on the stress optimization identification model, the geological-mechanical coupled lithofacies stress factors of tight sandstone are determined according to information data. The stress optimization identification model is a physical model of the influence of horizontal stress difference on fracture propagation in tight sandstone reservoirs, which is based on the influence of horizontal stress difference on fracture formation, propagation and connection in horizontal multi-stage fracturing operations.

[0067] Based on the isosceles triangular diagram of tight sandstone reservoir lithology classification, and according to the geological-mechanical coupled lithofacies brittleness factors and geological-mechanical coupled lithofacies geostress factors of tight sandstone, coupled grain size, horizontal stress difference and brittleness index are coupled to process the composite triangular diagram model, and obtain the composite isosceles triangular diagram of tight sandstone geological-mechanical coupled lithofacies classification.

[0068] Based on the composite isosceles triangular diagram of geological-mechanical coupled lithofacies classification of tight sandstone, the geological-mechanical coupled lithofacies types are named with grain composition and grain size as naming parts and horizontal stress difference and brittleness index as modifying prefixes, thus determining the geological-mechanical coupled lithofacies classification model of tight sandstone reservoirs.

[0069] At the single-well level, based on the geomechanical coupled lithofacies classification model of tight sandstone reservoirs, the geomechanical coupled lithofacies classification and multi-type lithofacies prediction results are integrated according to the element difference data to obtain the integrated results, which specifically include:

[0070] Obtain the geological facies identification results of tight sandstone reservoirs from single wells; at the single well level, based on the geological-mechanical coupled facies classification model of tight sandstone reservoirs and the geological facies identification results of tight sandstone reservoirs from single wells, perform geological-mechanical coupled facies division according to the difference data of elements, and obtain the division results; based on the division results, perform numerical mapping, priority selection and classification processing, and integrate the prediction results of multiple types of facies to obtain the integrated results.

[0071] The method for determining the geological-mechanical coupled lithofacies well-seismic co-operation three-dimensional model of tight sandstone reservoirs specifically includes:

[0072] Obtain raw data; the raw data contains geological, seismic, and mechanical properties; the raw data includes geological profiles, lithological data, seismic wave velocity, porosity, and permeability; perform data preprocessing on the raw data to obtain processed data; preprocessing includes: interpolation, denoising, and normalization; construct a three-dimensional mesh model; the three-dimensional mesh model is a physical model constructed based on the spatial distribution and characteristics of geological bodies, used to characterize the relationship between geological and mechanical features.

[0073] Geological-mechanical coupled lithofacies feature extraction was performed on the processed data to obtain extracted data; based on the geological attribute data and the extracted data, geological-mechanical coupled lithofacies sensitive attribute analysis was performed to obtain analytical data.

[0074] Based on the analysis data, inversion is performed on a three-dimensional mesh model to determine the inversion information data, which includes seismic wave velocity and impedance. The inversion information data is compared with the actual downhole sampling results. With the goal of minimizing the error in the comparison results, the inversion parameters of the three-dimensional mesh model are trained and adjusted to obtain the adjusted three-dimensional mesh model.

[0075] The adjusted three-dimensional mesh model was determined as a geological-mechanical coupled lithofacies well-seismic co-model for tight sandstone reservoirs.

[0076] like Figure 3 As shown, the core of the technical solution in this application lies in the construction of a geological-mechanical coupled lithofacies classification model for tight sandstone reservoirs based on a four-element composite triangular diagram method. This includes six key steps: multi-level three-dimensional nine-point characteristic core sampling and parameter sorting for tight sandstone reservoirs; rock mechanics parameter optimization based on lithofacies discretization and sensitivity analysis; determination of brittle factors in tight sandstone geological-mechanical coupled lithofacies based on brittle boundary delineation method; determination of geostress factors in tight sandstone geological-mechanical coupled lithofacies based on stress optimization identification model; division of geological-mechanical coupled lithofacies composite triangular facies diagrams with coupled four elements; and construction of a geological-mechanical coupled lithofacies classification model for tight sandstone reservoirs. Based on this, quantitative identification and prediction of geological-mechanical coupled lithofacies in single wells of tight sandstone reservoirs are carried out, including two steps: identification of geological-mechanical coupled lithofacies in single wells of tight sandstone reservoirs based on intelligent lithofacies identification and construction of a three-step fusion prediction method for geological-mechanical coupled lithofacies. Finally, a three-dimensional visualization characterization of the tight sandstone reservoir was performed, which included two steps: feature extraction and visualization of the geological-mechanical coupled lithofacies based on a three-dimensional mesh model, and lithofacies classification feedback adjustment and seismic attribute mapping based on three-dimensional modeling.

[0077] When studying tight sandstone reservoirs, selecting representative sample points is crucial, as these samples directly impact the understanding of geological and mechanical characteristics and the construction of models. Firstly, using... Figure 4The three-dimensional nine-point method uses a 45-degree angle for core sampling. This helps to minimize damage to the original rock structure during sampling. By using inclined sampling, stress concentration is reduced during core collection, minimizing the possibility of cracks or breakage upon sample removal, thus better preserving the natural state of the rock. Sampling the core using the three-dimensional nine-point method effectively captures information related to fracture development. This inclined sampling allows the sample to contain micro-fracture data from multiple directions, more accurately reflecting the mechanical characteristics of the rock mass. The spatially inclined sampling angle increases sample diversity and representativeness. Such sampling can cover a wider range of structural features, providing a more comprehensive data foundation for subsequent analysis. Furthermore, the three-dimensional nine-point method can, to some extent, balance the stress distribution in different directions, making the obtained experimental data more accurately reflect the mechanical properties of the rock.

[0078] Multi-level three-dimensional nine-point method for core sampling and parameter analysis of tight sandstone reservoirs specifically includes four steps:

[0079] First, a three-dimensional nine-point method was used to select nine potential study areas within the study area to maximize the representativeness of the sample. The selection of sampling areas should include areas with special geological structures or anomalies, which typically exhibit significant differences in mechanical properties. For example, selecting sampling points near faults, folds, or other geological structures allows for a more comprehensive understanding of the geostress state and its impact on reservoir mechanical properties. This method can help identify potential mechanically weak zones and their impact on horizontal drilling in the study.

[0080] Second, nine potential sampling points were selected again within the sampling area using the three-dimensional nine-point method. When selecting sampling points within the sampling area, it should be ensured that the distribution of the points reflects the heterogeneity of the entire reservoir. Considering that tight sandstone reservoirs typically exhibit different lithologies, grain types, and pore structures, sample points at different depths and geological units within the area were selected to ensure that the sampling covers a variety of rock characteristics. Preliminary screening was conducted using geological profile analysis, existing drilling data, and geophysical logging data to ensure the breadth and diversity of the samples.

[0081] Third, adjust the angle and distance spatially, adding or removing sampling points based on actual drilling conditions. When selecting specific sampling locations, the feasibility and safety of the sampling technology, as well as the current drilling and development status of the oil and gas field, must be considered. The selected locations should facilitate drilling and sample collection while avoiding negative impacts on the environment and ecosystem.

[0082] Fourth, if necessary, the three-dimensional nine-point method can be used to increase the number of samples based on the existing sampling points. If the study area is small, the three-dimensional nine-point method can be used only once to determine the sampling points.

[0083] Fifth, for the final obtained samples, basic characteristic data such as lithology, particle type, and particle size are obtained through laboratory testing. At the same time, through detailed mechanical experiments, parameters such as elastic modulus, Poisson's ratio, brittleness, cohesion, fracture toughness, fracture pressure, internal friction angle, tensile strength, shear strength, maximum horizontal principal stress, minimum horizontal principal stress, vertical stress, and horizontal stress difference are measured.

[0084] Figure 5 The sampled points are obtained using the three-dimensional nine-point method in a specific tight sandstone reservoir area. There are two stress singularities in the area, resulting in the addition of eight sampling points; there is one risk point, leading to the removal of one sampling point. A total of 16 sampling points were selected.

[0085] When conducting rock mechanics sensitivity analysis, different lithofacies numerical tests can be set up to observe the effects of these lithofacies variations on rock strength and fracture propagation behavior. This method can help identify parameters that are more sensitive to the rock mechanics performance of different lithofacies.

[0086] Sensitivity analysis of geological lithofacies rock mechanical parameters based on numerical simulation includes five steps:

[0087] First, based on the logging curves, the static rock mechanics results measured in the laboratory are calibrated, the rock mechanics curve of a single well is calculated, the dynamic rock mechanics of the core is obtained through wave velocity testing, the dynamic-static parameter conversion relationship is established, and the rock mechanics logging interpretation results are corrected.

[0088] Second, the continuous single-well lithofacies data is discretized, and specific codes are used to represent the same lithofacies. Lithofacies with similar mechanical properties use codes of similar size.

[0089] Third, correlation analysis. Statistical methods (such as regression analysis) are used to examine the relationship between lithofacies codes and rock mechanical parameters. Parameters with significant fluctuations in mechanical properties across different lithofacies are identified.

[0090] Fourth, sensitivity analysis. Sensitivity analysis methods, such as analysis of variance (ANOVA) or local sensitivity analysis, are used to further reveal the extent to which changes in rock mechanical parameters in different lithofacies affect the overall behavior. This quantifies the response of different lithofacies to different mechanical parameters.

[0091] Fifth, select the most sensitive parameters. Rank all rock mechanics parameters and select the parameters with the largest variations across different lithofacies for further analysis. Use statistical methods such as paired t-tests to perform multiple comparisons of the candidate parameters to determine their significant differences across different lithofacies. Construct an optimized model between lithofacies and rock mechanics parameters to accurately predict the mechanical behavior under different lithofacies.

[0092] Within the example area, the lithofacies are numbered as follows: siltstone facies: 1; fine sandstone facies: 2; medium sandstone facies: 3; coarse sandstone facies: 4; mudstone facies: 5; limestone facies: 6. After optimization of rock mechanics parameters, four of the most sensitive rock mechanics parameters were selected: elastic modulus, Poisson's ratio, brittleness index, and horizontal stress difference. Table 1 shows the thermodynamic table of the correlation between lithofacies and rock mechanics of tight sandstone reservoirs in a certain production area.

[0093] Table 1. Thermodynamic correlation between lithofacies and rock mechanics of tight sandstone reservoirs in a certain production area.

[0094] Correlation siltstone facies Fine sandstone facies medium sandstone facies coarse sandstone facies mudstone facies limestone facies elastic modulus 0.71 0.77 0.76 0.73 0.84 0.70 Poisson's ratio 0.79 0.82 0.75 0.76 0.84 0.77 Brittleness Index 0.61 0.74 0.76 0.66 0.7 0.72 Cohesion 0.73 0.79 0.57 0.52 0.56 0.57 fracture toughness 0.63 0.68 0.34 0.54 0.5 0.4 Rupture pressure 0.61 0.77 0.36 0.58 0.41 0.46 internal friction angle 0.38 0.61 0.38 0.39 0.47 0.38 tensile strength 0.48 0.77 0.31 0.44 0.36 0.39 shear strength 0.48 0.35 0.3 0.43 0.38 0.44 Maximum horizontal principal stress 0.4 0.5 0.53 0.59 0.41 0.58 Minimum horizontal principal stress 0.47 0.43 0.43 0.43 0.52 0.56 Vertical stress 0.34 0.35 0.31 0.21 0.36 0.21 Horizontal stress difference 0.88 0.83 0.86 0.71 0.85 0.9

[0095] The brittle boundary delineation method is used to visualize the relationship between elastic modulus and Poisson's ratio, and material properties are effectively delineated using negative slope lines. This involves two steps:

[0096] First, construct a cross-plot by collecting and selecting appropriate sample materials, and measuring the elastic modulus (E) and Poisson's ratio (v) for each material. Plot the measured data points in the Ev coordinate system to form a scatter plot.

[0097] Second, draw the boundary line: Using regression analysis or empirical rules, draw a straight line with the properties of a linear function, with the formula ν = -kE + b, where k is the positive slope and b is the bias. The brittleness index can comprehensively consider the elastic modulus and Poisson's ratio of rocks, and is usually used to measure whether rocks are more prone to brittle fracture or plastic deformation when subjected to stress. Determine appropriate parameters so that the line effectively divides the sample data into a brittle region (lower left region) and a ductile region (upper right region).

[0098] Figure 6 The diagram shown is a cross-plot of the elastic modulus and Poisson's ratio of a tight sandstone reservoir in a certain production area. Statistical analysis reveals that when μ... c ≥-3.695E c ×10 -3 At +0.425, it is a brittle lithofacies, μ c <-3.695E c ×10 -3 A value of +0.425 indicates a ductile rock facies; therefore, the brittleness index is used to evaluate the strain characteristics of the rock facies. Rock facies with a higher brittleness index have a higher content of brittle minerals, are more likely to form fracture networks, and have more stable fractures. They can maintain the open state of fractures after pressure release, reducing the energy consumed and the amount of horizontal fracturing fluid during fracturing, and improving construction efficiency.

[0099] Horizontal stress differences in tight sandstone reservoirs have a significant impact on fracture propagation. In horizontal multi-stage fracturing operations, the magnitude of the horizontal stress difference affects fracture formation, propagation, and connectivity, thus influencing oil and gas extraction efficiency. A larger stress difference can lead to more energy concentration at the fracture front, resulting in a simpler fracture pattern and allowing the fracture to propagate to greater depths within the rock. A smaller horizontal stress difference helps control the direction of fracture propagation, increases fracture network complexity, and reduces the risk of formation failure. The determination of geo-mechanical coupled lithofacies stress factors in tight sandstone based on a stress optimization identification model specifically includes two steps:

[0100] First, the construction of the intersection plot. Scattered data for crack length and crack density are plotted on the horizontal stress difference (x-axis) and crack length / density (y-axis) plots, respectively, forming two types of scatter points. An intersection region is designed, corresponding to the intersection of crack length and crack density, i.e., the combined region of decreasing crack length and increasing crack density.

[0101] Second, the optimal selection of common areas. A convergence zone in a balanced position is selected, with its left and right boundaries determined by actual conditions. At this point, the fracture length and density reach their optimal state, potentially corresponding to the best fracturing effect of the formation and reducing the risk of formation failure. The horizontal stress difference corresponding to this zone is the optimal horizontal stress difference. This approach ensures fracture depth while increasing the complexity of the fracture network, thereby improving oil and gas flow and exchange.

[0102] Figure 7 The diagram shows the intersection of fracture length and density with horizontal stress difference in a tight sandstone reservoir in a certain production area. When the horizontal stress difference in a tight sandstone reservoir is less than 5 MPa, it usually has the geological conditions for volumetric fracturing and is prone to forming complex far-well fracture networks.

[0103] Four factors were selected: tight sandstone particle composition, particle size, brittleness index, and horizontal stress difference. These factors can represent the differences in geological conditions of tight sandstone reservoirs, as well as reflect the different degrees of brittleness, stress differences, and the effects of fracture network construction. Therefore, they can be the main controlling factors for the differences in tight gas potential of tight sandstone reservoirs in production areas.

[0104] Based on the above analysis, relying on the differences in four factors—particle composition, particle size, brittleness index, and horizontal stress difference—of tight sandstone reservoirs, the following steps are used to determine the geological-mechanical coupled lithofacies classification model of tight sandstone reservoirs.

[0105] Based on the isosceles triangular diagram for lithological classification of tight sandstone reservoirs, grain size, horizontal stress difference, and brittleness index are further coupled into the triangular diagram to form a composite triangular diagram model. Grain size is classified as silt (0.002 mm–0.0625 mm), fine sand (0.0625 mm–0.25 mm), medium sand (0.25 mm–0.5 mm), and coarse sand (greater than 0.5 mm). Brittleness index is classified as toughness (0–0.5), low brittleness (0.5–0.65), medium brittleness (0.5–0.75), and high brittleness (greater than 0.75). Horizontal stress difference is classified as low stress difference (less than or equal to 5 MPa) and high stress difference (greater than 5 MPa). A composite isosceles triangular diagram model for the geological-mechanical coupled lithological classification of tight sandstone is shown below. Figure 8 .

[0106] The names are mainly based on particle composition and particle size, and modified by horizontal stress difference and brittleness index, as shown in Table 2.

[0107] Table 2. Components of the Geological-Mechanical Coupled Lithofacies Classification Nomenclature of Tight Sandstone Reservoirs

[0108]

[0109] The geological-mechanical coupled lithofacies types were named, and a geological-mechanical coupled lithofacies classification model for tight sandstone reservoirs was finally established, as shown in Table 3.

[0110] Figure 9 This presentation showcases the geomechanical coupled lithofacies classification results of a tight sandstone reservoir in a certain production area. The tight sandstone reservoir in this area is dominated by feldspathic lithic sandstone, with a content reaching up to 78%, followed by lithic feldspathic sandstone and lithic sandstone, with contents of 13% and 9%, respectively. Geomechanical coupled lithofacies classification of this group reveals that the more prevalent geomechanical coupled lithofacies in the reservoir facies are: A) low-stress-difference, low-brittle feldspathic lithic fine sandstone; B) low-stress-difference, low-brittle lithic fine sandstone; C) low-stress-difference, ductile feldspathic lithic fine sandstone; D) low-stress-difference, low-brittle feldspathic lithic medium sandstone; E) low-stress-difference, low-brittle quartz fine sandstone; and F) low-stress-difference, ductile feldspathic lithic medium sandstone. Among these, lithofacies A, B, D, and E all exhibit fine-grained, low-stress-difference, and low-brittle characteristics, representing potential lithofacies for fracturing and stimulation of the tight sandstone reservoir in this production area.

[0111] Table 3. Geological-Mechanical Coupled Lithofacies Classification Model of Tight Sandstone Reservoirs

[0112]

[0113] The method of "Intelligent Identification and Visualization Method for Lithofacies of Continental Tight Reservoirs" (Patent No. 202011393013.1) was used to obtain the single-well geological facies identification results of tight sandstone reservoirs. Based on this, considering the differences in four factors—particle composition, particle size, brittleness index, and horizontal stress difference—further geomechanical coupled facies classification was carried out at the single-well level, based on the established geological-mechanical coupled facies classification model of tight sandstone reservoirs.

[0114] When using the geomechanical coupled lithofacies prediction method, there are usually two or more results. In this case, the integration of multiple lithofacies prediction results can be completed through three steps: numerical mapping, priority selection, and classification processing.

[0115] First, numerical mapping: merge the two columns of lithofacies numerical codes into a new column by using summation.

[0116] Second, select priority: Among the prediction results under two different conditions, select one column according to priority. For example, prioritize lithofacies 1, and if the value is 0, select lithofacies 2.

[0117] Third, multi-category processing: If multi-category processing is required after merging, a new column can be created and values ​​can be assigned through conditional judgment.

[0118] Using the above methods, a single-well geological-mechanical coupled lithofacies quantitative prediction was completed in the example area. Based on the prediction results, a comprehensive columnar section of the geological-mechanical coupled lithofacies quantitative identification results of a well in a tight sandstone production area was drawn. Figure 10 .

[0119] Feature extraction and visualization of geomechanical coupled lithofacies based on 3D mesh model:

[0120] First, data collection and preparation: Collect geological, seismic, and mechanical property data, including geological profiles, lithological data, seismic wave velocity, porosity, and permeability. Perform data preprocessing, such as interpolation, noise reduction, and normalization, to ensure the accuracy and completeness of the data.

[0121] Second, construct a three-dimensional mesh model: Based on the spatial distribution and characteristics of the geological body, establish a three-dimensional mesh model. Finite element meshes or other types of mesh models can be used. Subdivide the mesh model into different geological units to better represent the geomechanical characteristics.

[0122] Third, geological-mechanical coupled lithofacies feature extraction: extract the geological features (such as physical properties, elastic modulus, etc.) of each lithofacies based on geological data and assign them to the corresponding cells in the three-dimensional model.

[0123] Fourth, geomechanical coupled lithofacies sensitivity analysis: This considers the influence of geological properties (such as stress field, fracture distribution, etc.) on seismic wave propagation. Sensitive property selection: This determines the types of seismic sensitive properties, which typically include the following categories:

[0124] Elastic wave velocity (P-wave, S-wave velocity): closely related to the elastic characteristics of rock facies.

[0125] Impedance: Calculated by multiplying density and wave velocity, it reflects the physical state of the rock.

[0126] Amplitude: During the propagation of seismic waves, it reflects the characteristics of the reflecting interface and changes in material properties.

[0127] Frequency characteristics: Different rock formations respond differently to seismic wave frequencies, which can reflect the details of the rock strata.

[0128] Fifth, seismic data processing and visualization: Processing seismic waveform data and performing inversion to obtain information such as seismic wave velocity and impedance. Visualizing the relationship between seismic data and the geomechanical model in a 3D model to create a geomechanical coupled lithofacies seismic 3D sculpting effect.

[0129] Figure 11 The image shown is a longitudinal wave impedance inversion profile of tight sandstone in a certain production area, based on the feature extraction and visualization of geological-mechanical coupled lithofacies using a 3D mesh model.

[0130] Lithofacies classification feedback adjustment and seismic attribute mapping based on 3D modeling:

[0131] Geological modeling software, such as Petrel, is used to integrate well data, seismic inversion results, and lithofacies information.

[0132] First, 3D modeling: A 3D framework is established based on known geological features such as structures and faults. Using interpolation, kriging methods, and other techniques, the distribution of different lithofacies in single wells and seismic sculpting results are modeled in 3D space. The lithofacies distribution predicted by the model is compared with the actual downhole sampling results to analyze errors. The deviation between the model prediction and the actual values ​​is visually presented using 3D visualization software, facilitating analysis and adjustment.

[0133] Second, feedback and adjustment: Based on the model validation results, adjust the lithofacies classification criteria (e.g., add new classification parameters) or optimize the parameter settings in the inversion algorithm. This can be achieved by adding more training samples or readjusting parameters to improve model accuracy. Through continuous validation and correction, the optimal model can be reached. It is necessary to record the data input and output at each step for easy tracking and improvement.

[0134] Third, results analysis: show the distribution characteristics of different lithofacies in three-dimensional space, with color or shadow representing different lithofacies types.

[0135] Fourth, seismic attribute mapping: Seismic attributes (such as velocity and impedance) are displayed using color mapping combined with lithofacies information to analyze their correlation. Hierarchical analysis: Key levels in the model are defined to identify major geological units and features, helping to understand geological evolution and tectonic environments.

[0136] Figure 12 The model shown is a typical geological-mechanical coupled 3D model of tight sandstone in a certain production area, after 3D modeling, facies classification feedback adjustment, and seismic attribute mapping. Statistical analysis revealed six facies types with the largest volume proportions in the reservoir facies within the 3D space: A) low-stress-difference, low-brittle feldspathic fine sandstone; B) low-stress-difference, low-brittle feldspathic fine sandstone; C) low-stress-difference, ductile feldspathic fine sandstone; D) low-stress-difference, low-brittle feldspathic medium sandstone; E) low-stress-difference, low-brittle quartz fine sandstone; and F) low-stress-difference, ductile feldspathic medium sandstone. Among these, the low-stress-difference, low-brittle feldspathic fine sandstone is the most typical reservoir facies. Figures 13-14 These rock facies possess good porosity and permeability, providing a foundation for fluid storage and flow; their low stress differential ensures that the rock is not easily damaged during operations such as hydraulic fracturing, better maintaining its structural integrity and reducing unnecessary fracture closure; their low brittleness allows the rock to form an effective fracture network during modification and better withstand internal pressure, helping to maintain the conductivity of fractures. The relationship between tight gas production capacity and geomechanical coupled rock facies in a typical tight sandstone of a certain production area is statistically analyzed. Figure 15 The study found that the production capacity of four geomechanical coupled rock facies (B, D, E, and F) was significantly higher than that of other rock facies. Combining geological and engineering factors, the suitable grain size and composition, along with reasonable stress differences and brittleness levels, of the B, D, E, and F rock facies collectively contributed to their higher production capacity. Under typical geological environments and technical conditions in a certain production area, these four rock facies can efficiently unleash their development potential.

[0137] In a typical tight sandstone reservoir in a certain production area, a healthy rock mechanical parameter model was established using the established geomechanical coupled lithofacies model. A three-dimensional model of the brittleness index before and after geomechanical coupled lithofacies facies control was extracted. Figure 16 The results show that the facies-controlled brittleness index distribution model better reflects the heterogeneity of sedimentary facies in both longitudinal and lateral characteristics, outperforming traditional Kriging or Gaussian interpolation three-dimensional models. By projecting rock mechanics parameters onto a control well, the facies-controlled three-dimensional model and the interpolation three-dimensional model were compared. Figure 17The study found that parameters such as elastic modulus, Poisson's ratio, brittleness index, maximum and minimum horizontal principal stresses, and horizontal stress difference all exhibited distribution patterns consistent with the model. This means that the rock mechanics parameters after facies control all conformed to the parameter ranges in the geomechanical coupled lithofacies classification model table for tight sandstone reservoirs (Table 3). This accurately characterized the heterogeneous distribution of rock mechanics and geostress characteristics in the tight sandstone reservoir of the production area, providing technical support for the effective implementation of ultra-long horizontal wells and large-scale repeated fracturing in tight sandstone oil and gas in this area, ultimately improving the extraction efficiency of tight sandstone oil and gas.

[0138] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 18 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and databases. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media to run. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network. When the computer program is executed by the processor, it implements a well-seismic analysis method based on a tight sandstone geomechanical coupled lithofacies system.

Claims

1. A well seismic analysis system based on a tight sandstone geo-mechanical coupled lithofacies system, characterized in that, Well-seismic analysis systems based on tight sandstone geomechanical coupled lithofacies systems include: The information data acquisition module is used to acquire information data, including: characteristic core data and rock mechanics data. The characteristic core data includes characteristic data acquired using the three-dimensional nine-point method and parameter data determined based on mechanical experiments. The rock mechanics data is based on the theory of lithofacies discretization and sensitivity analysis. By setting different lithofacies numerical experiments, the influence of lithofacies changes on rock strength and crack propagation behavior is taken as the research object, and parameter data sensitive to the rock mechanics performance of different lithofacies are obtained. The classification pattern construction module is connected to the information data acquisition module. It is used to construct a geological-mechanical coupled lithofacies classification pattern for tight sandstone reservoirs based on information data using the four-element composite triangular diagram method. The integration module, connected to the classification pattern construction module, is used to integrate the geological-mechanical coupled lithofacies classification of tight sandstone reservoirs at the single-well level, based on the geological-mechanical coupled lithofacies classification pattern, and the prediction results of multiple lithofacies based on the element difference data, to obtain the integrated result; the element difference data includes: particle composition, particle size, brittleness index and horizontal stress difference. The analysis and determination module, connected to the integration module, is used to determine the distribution information of the tight sandstone reservoir's geological-mechanical coupled lithofacies well-seismic co-operational three-dimensional model based on the integrated results. This involves feature extraction of the geological-mechanical coupled lithofacies, lithofacies classification feedback adjustment, and seismic attribute mapping. The geological-mechanical coupled lithofacies well-seismic co-operational three-dimensional model of the tight sandstone reservoir is a physical model constructed based on the relationship between seismic data and geological-mechanical data. The distribution information of the tight sandstone reservoir's geological-mechanical coupled lithofacies is used to characterize the heterogeneous distribution of rock mechanics and geostress characteristics, providing a basis for improving the extraction efficiency of tight sandstone oil and gas.

2. The well-seismic analysis system based on a tight sandstone geo-mechanical coupled lithofacies system according to claim 1, characterized in that, The classification pattern building module includes: The brittleness factor determination submodule is connected to the information data acquisition module. It is used to determine the brittleness factor of dense sandstone by using the brittle boundary division method, visualizing the relationship between elastic modulus and Poisson's ratio based on information data, and dividing material properties through negative slope straight lines. The geostress factor determination submodule, connected to the information data acquisition module, is used to determine the geo-mechanical coupled lithofacies geostress factors of tight sandstone based on the stress optimization identification model and information data. The stress optimization identification model is a physical model of the influence of horizontal stress difference on fracture propagation in tight sandstone reservoirs, based on the influence of horizontal stress difference on fracture formation, propagation and connection in horizontal multi-stage fracturing operations. The diagram determination submodule is connected to the brittleness factor determination submodule and the geostress factor determination submodule, respectively. It is used to process the composite triangular diagram model based on the tight sandstone reservoir lithology classification isosceles triangular diagram, according to the tight sandstone geological-mechanical coupled lithofacies brittleness factor and tight sandstone geological-mechanical coupled lithofacies geostress factor, coupled grain size, horizontal stress difference and brittleness index, to obtain the tight sandstone geological-mechanical coupled lithofacies classification composite isosceles triangular diagram. The classification pattern construction submodule, connected to the map determination submodule, is used to name the geological-mechanical coupled lithofacies type of the tight sandstone reservoir based on the composite isosceles triangular map of the geological-mechanical coupled lithofacies classification. The naming part is the grain composition and grain size, and the horizontal stress difference and brittleness index are used as modifying prefixes to determine the geological-mechanical coupled lithofacies classification pattern of the tight sandstone reservoir.

3. The well-seismic analysis system based on a tight sandstone geo-mechanical coupled lithofacies system according to claim 1, characterized in that, The integration module includes: The single-well identification result acquisition submodule is used to acquire the single-well identification results of the geological lithofacies of tight sandstone reservoirs; The sub-module is divided and connected to the single-well identification result acquisition sub-module and the classification pattern construction module, respectively. It is used to perform geomechanical coupled lithofacies classification based on the tight sandstone reservoir geological-mechanical coupled lithofacies classification pattern and the tight sandstone reservoir geological lithofacies single-well identification results at the single-well level, and obtain the classification result based on the element difference data. The integration processing submodule, connected to the partitioning submodule, is used to perform numerical mapping, priority selection, and classification processing based on the partitioning results, so as to integrate the prediction results of multiple types of lithofacies and obtain the integrated result.

4. A well-seismic analysis method based on a geomechanical coupled lithofacies system of tight sandstone, characterized in that, The method is implemented using the system described in any one of claims 1-3; the method includes: Information data is acquired; the information data includes: characteristic core data and rock mechanics data; the characteristic core data includes characteristic data obtained by the three-dimensional nine-point method and parameter data determined based on mechanical experiments; the rock mechanics data is based on the theory of lithofacies discretization and sensitivity analysis, and is obtained by setting different lithofacies numerical tests, taking the influence of lithofacies changes on rock strength and crack propagation behavior as the research object, and obtaining parameter data sensitive to the rock mechanics performance of different lithofacies. A four-element composite triangular diagram method was used to construct a geological-mechanical coupled lithofacies classification model for tight sandstone reservoirs based on information data; At the single-well level, based on the geological-mechanical coupled lithofacies classification model of tight sandstone reservoirs, the geological-mechanical coupled lithofacies classification and multi-type lithofacies prediction results are integrated according to the element difference data to obtain the integrated results; the element difference data include: particle composition, particle size, brittleness index and horizontal stress difference. Based on the geological-mechanical coupled lithofacies well-seismic co-engineered three-dimensional model of tight sandstone reservoirs, feature extraction, lithofacies classification feedback adjustment, and seismic attribute mapping of geological-mechanical coupled lithofacies are performed according to the integrated results to determine the geological-mechanical coupled lithofacies distribution information data of tight sandstone. The geological-mechanical coupled lithofacies well-seismic co-engineered three-dimensional model of tight sandstone reservoirs is a physical model constructed based on the relationship between seismic data and geological-mechanical systems. The geological-mechanical coupled lithofacies distribution information data of tight sandstone is used to characterize the heterogeneous distribution law of rock mechanics and geostress characteristics of tight sandstone reservoirs, so as to provide a basis for improving the exploitation effect of tight sandstone oil and gas.

5. The well-seismic analysis method based on the geological-mechanical coupled lithofacies system of tight sandstone according to claim 4, characterized in that, Using a four-element composite triangulation method, a geological-mechanical coupled lithofacies classification model for tight sandstone reservoirs is constructed based on information data, specifically including: The brittle boundary delineation method was adopted. Based on the information data, the relationship between elastic modulus and Poisson's ratio was visualized, and the material properties were delineated by negative slope straight lines to determine the geological-mechanical coupled lithofacies brittleness factors of dense sandstone. Based on the stress optimization identification model, the geological-mechanical coupled lithofacies stress factors of tight sandstone were determined according to information data. The stress optimization identification model is a physical model of the influence of horizontal stress difference on fracture propagation in tight sandstone reservoirs, which is based on the influence of horizontal stress difference on fracture formation, propagation and connection in horizontal multi-stage fracturing operations. Based on the isosceles triangular diagram of tight sandstone reservoir lithology classification, and according to the geological-mechanical coupled lithofacies brittleness factors and geological-mechanical coupled lithofacies geostress factors of tight sandstone, the composite triangular diagram model is processed by coupling grain size, horizontal stress difference and brittleness index to obtain the composite isosceles triangular diagram of tight sandstone geological-mechanical coupled lithofacies classification. Based on the composite isosceles triangular diagram of geological-mechanical coupled lithofacies classification of tight sandstone, the geological-mechanical coupled lithofacies types are named with grain composition and grain size as naming parts and horizontal stress difference and brittleness index as modifying prefixes, thus determining the geological-mechanical coupled lithofacies classification model of tight sandstone reservoirs.

6. The well-seismic analysis method based on the geomechanical coupled lithofacies system of tight sandstone according to claim 4, characterized in that, At the single-well level, based on the geomechanical coupled lithofacies classification model of tight sandstone reservoirs, the geomechanical coupled lithofacies classification and multi-type lithofacies prediction results are integrated according to the element difference data to obtain the integrated results, which specifically include: Obtain single-well identification results of geological lithofacies in tight sandstone reservoirs; At the single-well level, based on the geological-mechanical coupled lithofacies classification model of tight sandstone reservoirs and the single-well identification results of geological lithofacies of tight sandstone reservoirs, the geological-mechanical coupled lithofacies are divided according to the element difference data to obtain the division results. Based on the division results, numerical mapping, priority selection, and classification processing are performed to integrate the prediction results of multiple lithofacies types, resulting in an integrated result.

7. The well-seismic analysis method based on the geological-mechanical coupled lithofacies system of tight sandstone according to claim 4, characterized in that, The method for determining the geological-mechanical coupled lithofacies well-seismic co-operation three-dimensional model of tight sandstone reservoirs specifically includes: Obtain raw data; raw data contains geological, seismic, and mechanical properties; raw data includes geological profiles, lithological data, seismic wave velocity, porosity, and permeability; The raw data is preprocessed to obtain the processed data; the preprocessing includes: interpolation, noise reduction and normalization; Constructing a three-dimensional mesh model; a three-dimensional mesh model is a physical model constructed based on the spatial distribution and characteristics of geological bodies, used to characterize the relationship between geological and mechanical features; Geological-mechanical coupled lithofacies feature extraction was performed on the processed data to obtain the extracted data; Based on geological attribute data and extracted data, a geological-mechanical coupled lithofacies sensitive attribute analysis was conducted to obtain analytical data; Based on the analysis data, inversion is performed using a three-dimensional mesh model to determine the inversion information data, which includes seismic wave velocity and impedance. The inversion information data is compared with the actual downhole sampling results. With the goal of minimizing the error of the comparison results, the inversion parameters of the three-dimensional mesh model are trained and adjusted to obtain the adjusted three-dimensional mesh model. The adjusted three-dimensional mesh model was determined as a geological-mechanical coupled lithofacies well-seismic co-model for tight sandstone reservoirs.

8. The well-seismic analysis method based on the geological-mechanical coupled lithofacies system of tight sandstone according to claim 4, characterized in that, Characteristic data include: lithology, grain type, and grain size.

9. The well-seismic analysis method based on the geological-mechanical coupled lithofacies system of tight sandstone according to claim 4, characterized in that, The parameter data include: elastic modulus, Poisson's ratio, brittleness, cohesion, fracture toughness, fracture pressure, internal friction angle, tensile strength, shear strength, maximum horizontal principal stress, minimum horizontal principal stress, vertical stress, and horizontal stress difference.

10. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the well seismic analysis method based on the geomechanical coupled lithofacies system of tight sandstone as described in any one of claims 4-9.

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