Reservoir natural fracture three-dimensional geological modeling method and system and medium

Abstract

The invention relates to the technical field of geological modeling, in particular to a reservoir natural fracture three-dimensional geological modeling method and system and a medium. The method comprises the following steps: acquiring reservoir characteristic parameter data, performing crack identification and characterization according to the reservoir characteristic parameter data, and performing digital processing to generate crack characteristic data; performing multi-scale analysis of geometric features, physical attributes and formation mechanisms on the fracture feature data, and performing fracture density calculation to generate fracture density distribution data; performing dynamic stress field analysis according to the fracture density distribution data to obtain stress field distribution data; and performing construction partition processing according to the stress field distribution data to generate construction unit partition data. Through multi-scale analysis of fracture density distribution and a stress field, zoning processing of a construction unit is carried out, so that the model can effectively reflect the coupling relation between the fracture and the stress field, and a foundation is laid for more accurate zoning and layering modeling.

Description

Technical Field

[0001] The present invention relates to the technical field of geological modeling, and in particular to a three-dimensional geological modeling method, system and medium for natural fractures in reservoirs. Background Art

[0002] Natural fractures in reservoirs are rock fracture structures formed in the course of geological history, which are usually affected by factors such as ground stress, rock physical properties, and stratum movement. These natural fractures have an important impact on the storage and mobility of resources such as oil, gas, and groundwater. In geological reservoir research, the identification and characterization of natural fractures are key contents, especially under complex geological conditions, where their characteristics and impacts are more complex. Three-dimensional geological modeling technology plays a vital role in the evaluation and prediction of natural fractures in reservoirs. Through accurate three-dimensional models, the geometric morphology, spatial distribution, and influence of natural fractures in reservoirs on fluid penetration can be revealed.

[0003] Traditional reservoir geological modeling faces many challenges in the identification, characterization and seepage simulation of natural fractures, mainly including the following difficulties: Due to the multi-scale and irregular characteristics of natural fractures, it is difficult to fully and accurately identify fractures in the reservoir by relying on single logging or seismic data; the lack of complete fracture characteristic data will affect the accuracy of the model, resulting in deviations in the prediction of reservoir fluidity and reserves. The distribution and development of fractures are usually affected by the geostress field, especially in the context of multi-level structures. Traditional modeling methods are difficult to effectively combine the distribution of fracture density and stress field, resulting in a lack of clear expression of stress field information in the model, affecting the accurate prediction and zoning modeling of fractures. Summary of the invention

[0004] Based on this, it is necessary for the present invention to provide a three-dimensional geological modeling method, system and medium for natural fractures in reservoirs to solve at least one of the above-mentioned technical problems.

[0005] To achieve the above object, a three-dimensional geological modeling method for natural fractures in a reservoir comprises the following steps: Step S1: Acquire reservoir characteristic parameter data, including seismic, well logging, core and production dynamic information; perform fracture identification and characterization based on the reservoir characteristic parameter data, and perform digital processing to generate fracture characteristic data; Step S2: Perform multi-scale analysis of the geometric features, physical properties and genetic mechanism of the fracture feature data, and calculate the fracture density to generate fracture density distribution data; perform dynamic stress field analysis based on the fracture density distribution data to obtain stress field distribution data; perform structural partitioning processing based on the stress field distribution data to generate structural unit division data; Step S3: Performing zoning and layering modeling according to the structural unit division data to generate initial geological model data; performing development law analysis on the fracture density distribution data to generate fracture development law data; performing geological constraint optimization on the initial geological model data through the fracture development law data to generate constraint model data; Step S4: Perform multi-medium-discrete fracture hybrid modeling based on fracture characteristic data to obtain hybrid modeling data; perform dynamic response analysis on stress field distribution data and hybrid modeling data, and perform seepage parameter calculation to generate seepage parameter data; perform three-dimensional geological modeling on natural fractures in the reservoir based on the seepage parameter data and constraint model data to generate three-dimensional geological model data; perform historical fitting verification on the three-dimensional geological model data to obtain a corrected three-dimensional geological model.

[0006] The reservoir characteristic parameter data obtained by the present invention come from multiple sources (seismic, well logging, core, production dynamic data), covering the diversity and integrity of the reservoir. After processing, these data generate fracture characteristic data, which has high spatial accuracy and rich geological information, providing an accurate basis for subsequent fracture identification and characterization. Digital processing can unify the data format, facilitate subsequent analysis and calculation, and improve the integration of data. Multi-scale analysis of fracture characteristic data can provide an in-depth understanding of the geometric characteristics, physical properties and genetic mechanism of fractures, making fracture characterization more detailed and accurate. The calculation of fracture density generates density distribution data, reflecting the spatial distribution of fractures. Dynamic stress field analysis based on density distribution data helps to understand the impact of stress changes on fracture expansion and closure, thereby obtaining stress field distribution data that is more in line with actual geological conditions. The structural unit division data generated by structural zoning processing can help establish a more accurate zoning model and lay the foundation for the zoning structure for subsequent modeling. The initial geological model data is generated by zoning and layered modeling, so that the structure of the model is more in line with reservoir characteristics. The analysis of fracture development laws provides the spatial distribution laws of fractures in different regions and depths, making the model more realistic and refined. The constrained model data further optimizes the initial model, so that the generated geological model not only conforms to the law of fracture development, but also is consistent with the actual geological constraints, improving the reliability and accuracy of the model. Multi-media-discrete fracture hybrid modeling realizes the true reproduction of the complexity of natural fractures. The calculation of dynamic stress field and seepage parameters adds dynamic characteristics to the model by simulating the fracture response under actual stress changes. Finally, the historical fitting verification step of the three-dimensional geological model compares the actual production data with the modeling data, effectively improving the authenticity and prediction reliability of the model. Through correction, the final generated three-dimensional geological model data has high accuracy, which can provide strong support for the subsequent oil and gas exploration and production plan formulation.

[0007] The present invention also provides a three-dimensional geological modeling system for natural fractures in reservoirs, which is used to execute the above-mentioned three-dimensional geological modeling method for natural fractures in reservoirs. The three-dimensional geological modeling system for natural fractures in reservoirs comprises: The data acquisition and characterization module is used to obtain reservoir characteristic parameter data, including seismic, well logging, core and production dynamic information; identify and characterize fractures based on reservoir characteristic parameter data, and perform digital processing to generate fracture characteristic data; The multi-scale feature analysis module is used to perform multi-scale analysis of the geometric features, physical properties and genetic mechanism of the fracture feature data, and to calculate the fracture density to generate fracture density distribution data; perform dynamic stress field analysis based on the fracture density distribution data to obtain stress field distribution data; perform structural partitioning processing based on the stress field distribution data to generate structural unit division data; The geological constraint modeling module is used to perform zoning and layering modeling according to the structural unit division data to generate initial geological model data; to analyze the development law of fracture density distribution data to generate fracture development law data; to optimize the geological constraints of the initial geological model data through the fracture development law data to generate constraint model data; The three-dimensional modeling and verification module is used to perform multi-media-discrete fracture hybrid modeling based on fracture characteristic data to obtain hybrid modeling data; perform dynamic response analysis on stress field distribution data and hybrid modeling data, and calculate seepage parameters to generate seepage parameter data; perform three-dimensional geological modeling of natural fractures in the reservoir based on seepage parameter data and constraint model data to generate three-dimensional geological model data; perform historical fitting verification on the three-dimensional geological model data to obtain a corrected three-dimensional geological model.

[0008] The comprehensive data acquisition of the present invention can enhance the understanding of the physical properties of the reservoir and lay a solid foundation for fracture identification and characterization. Accurate fracture identification and characterization can help understand the seepage characteristics of the reservoir and its influence on fluid flow, provide a specific basis for subsequent analysis, and improve the reliability of the model. Multi-scale analysis helps to fully understand the geometry, physical properties and formation mechanism of the fracture, and provide detailed background information for subsequent fracture density calculation. Fracture density distribution data can reflect the seepage capacity of the reservoir, provide important parameters for dynamic stress field analysis, and help evaluate the overall performance of the reservoir. Stress field distribution data reveals the stress state of different regions in the reservoir, which is crucial to understanding the behavior of fractures under stress and their influence on fluid flow. The structural unit division data provides a clear spatial framework for subsequent hierarchical modeling, ensuring that the model better reflects the geological characteristics. The establishment of the initial geological model provides a basic framework for subsequent analysis, so that the spatial distribution characteristics of the reservoir can be reflected. Understanding the development law of fractures helps to identify their distribution characteristics in the reservoir and provide a basis for subsequent optimization modeling. Constrained model data ensures the geological rationality of the modeling process, so that the model can accurately reflect the actual situation of the reservoir and improve the effectiveness of the model. This hybrid modeling method combines the characteristics of different media, enhances the comprehensiveness and accuracy of the model, and is more in line with the actual reservoir conditions. Dynamic response analysis helps understand the flow characteristics of the reservoir under different stress conditions, and the seepage parameter data provides a basis for actual production. The three-dimensional geological model can fully reflect the spatial structure and characteristics of the reservoir, providing support for subsequent resource assessment and development plans. Historical matching verification ensures that the model is consistent with actual production data, improves the credibility and application value of the model, and provides a scientific basis for long-term reservoir management and optimization decisions.

[0009] The present invention also provides a computer-readable storage medium storing a computer program, wherein the computer program is executed to implement any one of the above-mentioned three-dimensional geological modeling methods for natural fractures in reservoirs. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings: Figure 1 A schematic diagram of the steps of the three-dimensional geological modeling method for natural fractures in a reservoir according to the present invention; Figure 2 for Figure 1 Detailed step flow diagram of step S1; Figure 3 for Figure 1 Detailed step flow chart of step S2 in FIG. DETAILED DESCRIPTION

[0011] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0012] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0013] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0014] To achieve this, please refer to Figures 1 to 3 The present invention provides a three-dimensional geological modeling method for natural fractures in a reservoir, the method comprising the following steps: Step S1: Acquire reservoir characteristic parameter data, including seismic, well logging, core and production dynamic information; perform fracture identification and characterization based on the reservoir characteristic parameter data, and perform digital processing to generate fracture characteristic data; Step S2: Perform multi-scale analysis of the geometric features, physical properties and genetic mechanism of the fracture feature data, and calculate the fracture density to generate fracture density distribution data; perform dynamic stress field analysis based on the fracture density distribution data to obtain stress field distribution data; perform structural partitioning processing based on the stress field distribution data to generate structural unit division data; Step S3: Performing zoning and layering modeling according to the structural unit division data to generate initial geological model data; performing development law analysis on the fracture density distribution data to generate fracture development law data; performing geological constraint optimization on the initial geological model data through the fracture development law data to generate constraint model data; Step S4: Perform multi-medium-discrete fracture hybrid modeling based on fracture characteristic data to obtain hybrid modeling data; perform dynamic response analysis on stress field distribution data and hybrid modeling data, and perform seepage parameter calculation to generate seepage parameter data; perform three-dimensional geological modeling on natural fractures in the reservoir based on the seepage parameter data and constraint model data to generate three-dimensional geological model data; perform historical fitting verification on the three-dimensional geological model data to obtain a corrected three-dimensional geological model.

[0015] In the embodiment of the present invention, reference Figure 1 The above is a schematic diagram of the steps of a three-dimensional geological modeling method for natural fractures in a reservoir according to the present invention. In this example, the three-dimensional geological modeling method for natural fractures in a reservoir comprises the following steps: Step S1: Acquire reservoir characteristic parameter data, including seismic, well logging, core and production dynamic information; perform fracture identification and characterization based on the reservoir characteristic parameter data, and perform digital processing to generate fracture characteristic data; The embodiment of the present invention collects seismic wave data of the reservoir based on seismic data, processes the seismic data into a high-resolution seismic reflection profile through full waveform inversion technology, and identifies the fault and fracture characteristics of the reservoir structure. Subsequently, the physical properties of the rock formation are analyzed in combination with the logging data, and the distribution and density of the fractures are refined through methods such as acoustic wave, density and resistivity logging. The core data is used to verify the type, morphology and scale of the fractures, and the filling composition and structural characteristics of the fractures are obtained through microscopic image analysis. The collection of production dynamic information focuses on changes in production and pressure, and the conductivity of the fractures is evaluated through historical dynamic data. Finally, the collected seismic, logging, core and production data are digitized and uniformly output as standardized fracture characteristic data to ensure that different data types are consistent in terms of spatial resolution, time span and accuracy, providing basic data support for subsequent analysis.

[0016] Step S2: Perform multi-scale analysis of the geometric features, physical properties and genetic mechanism of the fracture feature data, and calculate the fracture density to generate fracture density distribution data; perform dynamic stress field analysis based on the fracture density distribution data to obtain stress field distribution data; perform structural partitioning processing based on the stress field distribution data to generate structural unit division data; The embodiment of the present invention uses fracture characteristic data to first extract the geometric characteristics of the fracture, including the direction, inclination, length and opening of the fracture, and statistically calculate the probability distribution of these geometric parameters to obtain the fracture geometric distribution law. Then, the physical properties of the fracture are analyzed, and the physical property data such as fracture conductivity and permeability are calculated. At the same time, the mineral composition and particle size of the filling are considered, and the influence of the filling is determined by lithofacies image analysis and lithology sensitivity experiments. The genetic mechanism analysis is based on the tectonic stress field data to identify the tectonic action, lithology control and multi-stage transformation of the fracture, and to evaluate the stress sensitivity to obtain the fracture stress response characteristics. The fracture density is calculated using the geometric parameter probability distribution data and the fracture genetic data, and the fracture density data is interpolated to obtain a continuous density field, and then the dynamic stress field analysis is completed based on the fracture density field and stress response data to obtain the stress field distribution data. Finally, the structural unit division is performed based on the stress field characteristics such as stress intensity, direction and gradient, and the structural unit division data is output.

[0017] Step S3: Performing zoning and layering modeling according to the structural unit division data to generate initial geological model data; performing development law analysis on the fracture density distribution data to generate fracture development law data; performing geological constraint optimization on the initial geological model data through the fracture development law data to generate constraint model data; The embodiment of the present invention performs partition and hierarchical modeling of three-dimensional space according to the structural unit division data obtained in step S2. First, a three-dimensional grid is divided in each structural unit, and the key layers and interfaces are determined using geological information and rock formation data to generate a preliminary hierarchical framework. On this basis, the development law of the cracks is analyzed based on the crack density distribution data, and the spatial variation characteristics of the cracks are analyzed using geological statistical methods, and the directional and anisotropic characteristics are identified to generate crack development law data. Then, the crack development law data is associated with the regional stress field information, constraint conditions are constructed, and constraint rule data is generated. According to the constraint rules, the crack parameters and spatial distribution in the initial geological model are optimized and adjusted to ensure that the model is consistent with the actual geological conditions, thereby generating constraint model data, which lays the foundation for subsequent precise modeling.

[0018] Step S4: Perform multi-medium-discrete fracture hybrid modeling based on fracture characteristic data to obtain hybrid modeling data; perform dynamic response analysis on stress field distribution data and hybrid modeling data, and perform seepage parameter calculation to generate seepage parameter data; perform three-dimensional geological modeling on natural fractures in the reservoir based on the seepage parameter data and constraint model data to generate three-dimensional geological model data; perform historical fitting verification on the three-dimensional geological model data to obtain a corrected three-dimensional geological model.

[0019] The embodiment of the present invention uses fracture characteristic data to establish a multi-medium model, takes fractures and matrix as different media, establishes a multi-medium model by coupling the seepage characteristics of fracture-matrix, and derives the fracture-matrix seepage control equation according to the multi-medium model data. The discrete fracture network is modeled by a random simulation method to generate a discrete fracture model that meets the statistical characteristics, and a connectivity analysis is performed to obtain fracture network connectivity data. Then, the multi-medium model and the discrete fracture model are mixed modeled, and the model parameters are optimized to generate mixed modeling data. Combined with the stress field distribution data of step S2, the influence of the stress field on the fracture conductivity is analyzed, and the initial seepage parameter data is calculated by the seepage equation, and a dynamic response analysis is performed. Subsequently, a dynamic prediction model of the seepage field is constructed based on the mixed modeling data and the initial seepage parameter data to obtain the seepage parameter data. Three-dimensional geological modeling is performed according to the seepage parameter data and the constraint model data, and finally the three-dimensional geological model data is generated. The three-dimensional geological model is historically fitted using production dynamic data, and the model parameters are corrected to improve the reliability of the model, so as to obtain a corrected three-dimensional geological model that meets the actual production situation.

[0020] The reservoir characteristic parameter data obtained by the present invention come from multiple sources (seismic, well logging, core, production dynamic data), covering the diversity and integrity of the reservoir. After processing, these data generate fracture characteristic data, which has high spatial accuracy and rich geological information, providing an accurate basis for subsequent fracture identification and characterization. Digital processing can unify the data format, facilitate subsequent analysis and calculation, and improve the integration of data. Multi-scale analysis of fracture characteristic data can provide an in-depth understanding of the geometric characteristics, physical properties and genetic mechanism of fractures, making fracture characterization more detailed and accurate. The calculation of fracture density generates density distribution data, reflecting the spatial distribution of fractures. Dynamic stress field analysis based on density distribution data helps to understand the impact of stress changes on fracture expansion and closure, thereby obtaining stress field distribution data that is more in line with actual geological conditions. The structural unit division data generated by structural zoning processing can help establish a more accurate zoning model and lay the foundation for the zoning structure for subsequent modeling. The initial geological model data is generated by zoning and layered modeling, so that the structure of the model is more in line with reservoir characteristics. The analysis of fracture development laws provides the spatial distribution laws of fractures in different regions and depths, making the model more realistic and refined. The constrained model data further optimizes the initial model, so that the generated geological model not only conforms to the law of fracture development, but also is consistent with the actual geological constraints, improving the reliability and accuracy of the model. Multi-media-discrete fracture hybrid modeling realizes the true reproduction of the complexity of natural fractures. The calculation of dynamic stress field and seepage parameters adds dynamic characteristics to the model by simulating the fracture response under actual stress changes. Finally, the historical fitting verification step of the three-dimensional geological model compares the actual production data with the modeling data, effectively improving the authenticity and prediction reliability of the model. Through correction, the final generated three-dimensional geological model data has high accuracy, which can provide strong support for the subsequent oil and gas exploration and production plan formulation.

[0021] Preferably, step S1 comprises the following steps: Step S11: Acquire reservoir characteristic parameter data, including seismic acquisition data, well logging measurement data, core analysis data, and production dynamic monitoring data; Step S12: preprocessing and standardizing the reservoir characteristic parameter data to generate standardized characteristic parameter data; Step S13: extracting the characteristics of the fracture based on the occurrence, scale, density and filling of the fracture from the standardized characteristic parameter data to generate fracture basic characteristic data; Step S14: performing classification and identification based on a preset crack classification standard according to the crack basic feature data, thereby obtaining crack classification data; Step S15: Identify the spatial distribution characteristics of different types of cracks on the crack classification data, and perform digital conversion to obtain crack characteristic data.

[0022] As an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 Detailed step flow diagram of step S1 in the embodiment of the present invention, step S1 includes the following steps: Step S11: Acquire reservoir characteristic parameter data, including seismic acquisition data, well logging measurement data, core analysis data, and production dynamic monitoring data; The embodiment of the present invention plans the acquisition process of reservoir seismic data and selects high-precision seismic exploration technology, such as three-dimensional seismic reflection wave imaging technology, to ensure that high-resolution reservoir structure images can be obtained. The location and structural details of the fractures in the reservoir are identified through velocity analysis and reflection wave feature analysis of seismic data. Well logging measurements include density logging, acoustic logging, and resistivity logging to obtain parameters such as reservoir lithology, porosity, and permeability, providing physical support for fracture feature identification. Core analysis is used to verify the mechanism of fracture genesis and its internal filling conditions. Microscope scanning, X-ray diffraction, and other methods are usually used to obtain rock microstructure and mineral composition. Production dynamic monitoring data is obtained from the actual production process. The conductivity and fluid utilization of the fractures are analyzed through real-time monitoring of oil and gas flow and pressure changes. All collected data are stored uniformly to provide original reservoir characteristic parameter data for subsequent analysis and processing.

[0023] Step S12: preprocessing and standardizing the reservoir characteristic parameter data to generate standardized characteristic parameter data; The embodiment of the present invention preprocesses the collected reservoir characteristic parameter data to remove noise and outliers. For seismic data, filtering is used to eliminate low-frequency noise, and dynamic range compression is performed to ensure the clarity of the seismic image. The preprocessing of logging and core data includes data denoising and interpolation to fill in missing values ​​caused by sampling intervals. The preprocessing of production dynamic data is based on time series data smoothing methods, such as moving average or Kalman filtering, to remove abnormal fluctuations in the production process. Subsequently, all data are standardized, and the minimum-maximum scaling method is used to normalize the numerical data to the same magnitude range (such as between 0 and 1), thereby eliminating the dimensional differences of the data to ensure that different types of data have consistent contributions in the analysis. Finally, these processed data are uniformly formatted into standardized characteristic parameter data to facilitate subsequent feature extraction and analysis.

[0024] Step S13: extracting the characteristics of the fracture based on the occurrence, scale, density and filling of the fracture from the standardized characteristic parameter data to generate fracture basic characteristic data; The embodiment of the present invention extracts the basic characteristics of the fracture from the standardized characteristic parameter data, including the occurrence (such as strike and dip), scale (length and width), density (number of fractures / unit area) and filling characteristics (filling type and composition) of the fracture. The extraction of occurrence is mainly based on seismic data and well logging data, and the direction and dip of the fracture are identified by differential analysis of seismic reflection characteristics and well logging curves. The scale characteristics are calculated in combination with well logging and core analysis, and the length and width of the fracture are determined by the spacing and extension range of the fracture. The extraction of density characteristics can be obtained by counting the number of fractures per unit area, and the fracture density is calculated using core slicing or image recognition technology. The identification of filling characteristics is based on the results of core analysis, and the type and composition of the mineral filling inside the fracture are obtained by X-ray or microscope scanning. These basic characteristic data of fractures provide necessary support for subsequent classification and spatial distribution analysis.

[0025] Step S14: performing classification and identification based on a preset crack classification standard according to the crack basic feature data, thereby obtaining crack classification data; The embodiment of the present invention applies preset fracture classification standards based on fracture basic feature data to automatically classify fractures. The classification standards are usually based on the causes and occurrence of fractures, for example, fractures are divided into structural fractures, interlayer fractures and microcracks. First, preliminary screening is performed based on the occurrence characteristics of the fractures (such as strike and dip) to identify the main structural fractures. Then, based on the filling characteristics and scale parameters, the types of fractures are further distinguished, and fractures with significant filling characteristics are classified as interlayer fractures, while cracks lacking filling are classified as microcracks. Classification algorithms such as decision trees or support vector machines are used to complete automated fracture classification, and fractures of different categories are stored in the form of data labels to generate fracture classification data, which is convenient for subsequent spatial distribution analysis.

[0026] Step S15: Identify the spatial distribution characteristics of different types of cracks on the crack classification data, and perform digital conversion to obtain crack characteristic data.

[0027] The embodiment of the present invention identifies the spatial distribution characteristics of different types of fractures based on fracture classification data. The spatial distribution of structural fractures is identified by fracture geometry and directional distribution based on seismic data, and a three-dimensional fracture network model is generated in combination with seismic layer data. The distribution of interlayer fractures uses logging and core data and interpolation technology to obtain the distribution of interlayer fractures in each layer of the reservoir. For micro-cracks, density contour maps are generated by counting the fracture densities at different locations to show the spatial distribution characteristics of micro-cracks. After completing the identification of spatial distribution characteristics, digital conversion technology is used to convert the spatial distribution data into a standardized three-dimensional spatial data format to generate fracture characteristic data for subsequent analysis of the impact of fractures on reservoir seepage characteristics.

[0028] The present invention can collect multi-dimensional information of the reservoir by acquiring seismic data, logging data, core data and production monitoring data of the reservoir. These data come from a wide range of sources, covering the geological characteristics, lithology, physical properties, etc. of the reservoir, making the data content more complete. This process provides an accurate basis for subsequent fracture identification and characterization, and ensures the comprehensiveness of the analysis and the diversity of the data. Preprocessing and standardizing the reservoir characteristic parameter data can remove noise and deviation in the data and improve the quality of the data. Through standardization, the data is converted into a unified format, which is convenient for analysis and comparison, and avoids the influence of format differences of data from different sources on the analysis results, thereby improving the accuracy and consistency of the fracture characteristic data. Feature extraction is performed on the standardized data, so that the occurrence, scale, density and filling of the fracture can be quantified, thereby generating fracture basic feature data. This process helps the subsequent classification and distribution feature identification by clarifying the basic attributes of the fracture. The extracted feature data enriches the information of fracture characterization and ensures that the model can better reflect the actual situation of the fracture. Classifying and identifying fractures based on preset classification standards helps to group and manage fracture data by type and generate fracture classification data. The classification and recognition process can help identify the properties of different types of fractures, ensure that the characteristics and behavior of fractures can be distinguished in subsequent analysis, and lay a solid foundation for the analysis of fracture spatial distribution. Finally, fracture characteristic data can be generated by identifying and digitally converting the spatial distribution characteristics of the classified data. This process can digitally express the location, direction, connectivity and other characteristics of fractures in the reservoir, thereby achieving a more accurate description of fracture distribution. Digital fracture characteristic data provides necessary support for the construction of high-precision three-dimensional models, ensuring that the model has good spatial resolution and structural rationality.

[0029] Preferably, step S2 comprises the following steps: Step S21: extracting geometric parameters from the crack feature data to generate crack geometric parameter data, wherein the geometric parameters include the direction, inclination, length and opening of the crack; performing probability distribution statistical analysis on the crack geometric parameter data to obtain geometric parameter probability distribution data; Step S22: performing physical property analysis on the fracture characteristic data based on fracture conductivity, permeability and filling properties to generate fracture physical property data; Step S23: Analyze the genetic mechanism of the fracture characteristic data and perform stress sensitivity assessment to generate fracture stress response data, wherein the genetic mechanism analysis includes tectonic stress action, lithology control and multi-stage transformation; Step S24: Calculate the crack density according to the geometric parameter probability distribution data and the crack cause data to generate crack density distribution data; Step S25: performing spatial interpolation analysis on the crack density distribution data to obtain continuous density field data; Step S26: Perform dynamic stress field analysis based on the continuous density field data and the fracture stress response data to generate stress field distribution data; and perform tectonic stress partitioning processing based on the stress field distribution data to obtain tectonic unit partitioning data.

[0030] As an embodiment of the present invention, refer to Figure 3 As shown, Figure 1 Detailed step flow diagram of step S2 in the embodiment of the present invention, step S2 includes the following steps: Step S21: extracting geometric parameters from the crack feature data to generate crack geometric parameter data, wherein the geometric parameters include the direction, inclination, length and opening of the crack; performing probability distribution statistical analysis on the crack geometric parameter data to obtain geometric parameter probability distribution data; The embodiment of the present invention extracts the geometric parameters of the fracture, including the strike, dip, length and opening, from the fracture characteristic data. Through the joint analysis of seismic images and logging data, the spatial strike and dip of the fracture are identified and calculated using the three-dimensional directional analysis method; the precise size and width of the fracture are obtained using accurate measurement tools for the length and opening of the fracture (such as laser scanning measurement or CT imaging technology). Then, statistical analysis is performed based on the extracted fracture geometric parameters to generate probability distribution data of the fracture geometric parameters. This analysis can use the quantile method or the maximum likelihood estimation method to generate the distribution characteristics of the fracture strike, dip, etc., thereby forming the probability distribution data of the fracture geometric parameters, which provides a basis for subsequent density analysis.

[0031] Step S22: performing physical property analysis on the fracture characteristic data based on fracture conductivity, permeability and filling properties to generate fracture physical property data; The embodiment of the present invention analyzes the physical property parameters of the fracture characteristic data. First, according to the differences in fracture conductivity, permeability and filling properties, the fracture conductivity is modeled and calculated by adopting the method of fracture classification and segmented evaluation. Using the permeability data of the well logging test, the correlation between the fracture conductivity and permeability is established through polynomial fitting. For the analysis of fracture filling properties, based on core analysis and mineral composition testing, the types of fracture fillings are identified and classified through microscopy or X-ray diffraction analysis. After integrating the above analysis results, fracture physical property data is generated for subsequent simulation analysis of fracture conductivity and seepage characteristics.

[0032] Step S23: Analyze the genetic mechanism of the fracture characteristic data and perform stress sensitivity assessment to generate fracture stress response data, wherein the genetic mechanism analysis includes tectonic stress action, lithology control and multi-stage transformation; The embodiment of the present invention analyzes the causal mechanism through the fracture characteristic data, and adopts a comprehensive analysis method of tectonic stress, lithology control and multi-stage transformation to identify the main causes of fracture formation. First, the influence of tectonic stress on fracture generation is evaluated through tectonic stress field reconstruction and geological history evolution analysis; then, according to the differences in reservoir lithology, the model of lithology control on fracture morphology is applied to analyze the influence of lithology on fracture characteristics; finally, the influence of multi-stage tectonic activities is identified by using stratum distribution and diagenetic evolution analysis, and the causes are classified. At the same time, the stress sensitivity of the fracture is evaluated through the fracture conductivity and rock mechanics experimental data, and the response data of the fracture under different stress conditions are generated, and finally the fracture stress response data is obtained, which provides a basis for the evaluation of the stability and seepage performance of the fracture.

[0033] Step S24: Calculate the crack density according to the geometric parameter probability distribution data and the crack cause data to generate crack density distribution data; The embodiment of the present invention calculates the crack density based on the probability distribution data of geometric parameters and the genetic mechanism data, and adopts a crack density estimation model based on probability statistics. First, the probability distribution data of geometric parameters is used to simulate the distribution probability of cracks, and regional crack distribution density data is generated according to the statistical results of crack length, density and inclination. Then, the genetic mechanism data of the cracks is used as a constraint condition to control the spatial distribution characteristics of the cracks to improve the accuracy of density calculation. Finally, various types of geometric and genetic data are integrated to generate crack density distribution data to provide a basis for subsequent stress field analysis.

[0034] Step S25: performing spatial interpolation analysis on the crack density distribution data to obtain continuous density field data; The embodiment of the present invention uses fracture density distribution data for spatial interpolation analysis and generates continuous density field data through an interpolation algorithm. The fracture density data is smoothed using the Kriging interpolation method to eliminate the discreteness and noise between the data and generate a continuous distribution of fracture density in the region. In practical applications, the parameters of the interpolation algorithm, such as the selection of the semivariogram function, are adjusted according to different reservoir geological conditions to improve the accuracy and reliability of the interpolation. The generated continuous density field data is used to identify the clustering and sparse areas of fractures and provide density field information for stress field analysis.

[0035] Step S26: Perform dynamic stress field analysis based on the continuous density field data and the fracture stress response data to generate stress field distribution data; and perform tectonic stress partitioning processing based on the stress field distribution data to obtain tectonic unit partitioning data.

[0036] The embodiment of the present invention analyzes the dynamic stress field in the reservoir based on continuous density field data and fracture stress response data. First, the spatial distribution model of the fracture is constructed using the continuous density field data, and the dynamic change trend of the stress in the reservoir is analyzed by combining the stress response characteristics of the fracture through geomechanical simulation methods (such as the finite element method). Then, according to the distribution characteristics of the stress field, the reservoir is divided into different structural units, each unit having different stress characteristics and fracture development degrees. Finally, the data of each structural unit is integrated to generate structural unit division data, which provides a geomechanical basis for subsequent reservoir seepage simulation and fracture network optimization.

[0037] The present invention extracts the geometric characteristics of the fracture (such as strike, dip, length, and opening), and performs statistical analysis on the probability distribution of these parameters, so as to generate the probability distribution data of the geometric parameters of the fracture. This step provides specific data for the morphological characterization of the fracture, helps to grasp the spatial distribution characteristics of the fracture at different scales, and enables the model to more accurately reflect the actual situation when simulating the fracture. At the same time, the probability distribution analysis of geometric parameters helps to identify the distribution trend of fracture characteristics under different geological environments, and provides statistical support for fracture prediction. By analyzing the physical properties of fracture conductivity, permeability, and filling properties, fracture physical property data are generated. These physical property data provide basic parameters for the fluid migration behavior of the fracture, so that the subsequent model can truly simulate the fluid conduction effect of the fracture in the reservoir. Physical property analysis can also help identify the behavioral characteristics of the fracture under specific pressure conditions, thereby providing a reference for the effectiveness evaluation of the fracture network and enhancing the accuracy and reliability of the model. The formation mechanism analysis combines tectonic stress, lithology control, and multi-stage transformation factors to explain the formation of the fracture, which restores the formation background and stress environment of the fracture and generates fracture stress response data. At the same time, the stress sensitivity assessment of fractures can reveal the deformation characteristics of fractures under different stress conditions, laying the foundation for subsequent dynamic stress field analysis. This analysis helps the model to more accurately capture the behavioral changes of fractures in actual pressure environments and improves the model's ability to simulate the dynamic response of fractures. Based on the probability distribution data of geometric parameters and the fracture genesis data, the fracture density is calculated and the density distribution data is generated, so that the spatial density characteristics of the fractures can be quantified. The density data can not only describe the distribution range of the fractures, but also provide the model with specific values ​​of the degree of fracture development. The quantitative density data provides a data basis for subsequent spatial interpolation, helping the model to achieve fine spatial positioning and distribution description of fractures in the reservoir. By performing spatial interpolation analysis on the fracture density distribution data, continuous fracture density field data can be generated. In this step, the discrete density data of the fractures is smoothed by the interpolation algorithm, so that the density field is continuously distributed in three-dimensional space. This data can help the model construct a more detailed fracture network representation, provide continuity information of the fractures in the reservoir, and avoid unrealistic faults or mutations in the fractures in the model. Dynamic stress field analysis is performed based on continuous density field data and fracture stress response data to generate stress field distribution data, and tectonic stress zoning is performed based on the stress field distribution. This step ensures that the model can dynamically reflect stress changes in the reservoir and realize the true distribution of the fracture network under a complex stress environment. Tectonic stress zoning can help identify fracture stress characteristics in different regions, facilitating subsequent zoning modeling and fracture density control. In this way, the model can better adapt to the heterogeneity within the reservoir and improve the realism of reservoir fracture simulation.

[0038] Preferably, step S23 includes the following steps: Step S231: Analyze the tectonic stress effect on the fracture characteristic data to generate tectonic stress data; establish a tectonic kinematics model based on the tectonic stress data to obtain stress field evolution data; The embodiment of the present invention analyzes the tectonic stress effect through the fracture characteristic data, and uses the geostress field recovery algorithm (such as elastic inversion or fault slip analysis) to infer the tectonic stress data in the reservoir. In the specific operation, based on the seismic profile and logging mechanical data, the distribution of the tectonic stress field at different depths is calculated; through the analysis of the tectonic stress direction and the direction data of the fracture surface, the reservoir tectonic stress distribution map is generated. Then, based on the tectonic stress data, a tectonic kinematic model is constructed, and through dynamic simulation, the stress field evolution analysis is combined with the stress history data to generate stress field evolution data, so as to further study the cause and evolution of the cracks.

[0039] Step S232: performing lithology control analysis on the fracture characteristic data based on rock brittleness, mechanical strength and mineral component influence, thereby obtaining lithology control data; The embodiment of the present invention performs lithology control analysis on the fracture characteristic data, focusing on the influence of rock brittleness, mechanical strength and mineral composition on fracture development. First, the content of different mineral components in the rock is obtained through core analysis and quantitative testing of mineral components; then, the brittleness and mechanical strength indicators of the rock are measured through rock mechanics testing (such as uniaxial compression test). Combined with the above analysis data, a multivariate regression model is used to analyze the influence of lithology on fracture development, generate lithology control data, and provide a basis for the analysis of fracture distribution and expansion laws.

[0040] Step S233: performing multi-period structural transformation analysis on the fracture characteristic data, thereby generating structural transformation data, wherein the multi-period structural transformation includes the superposition characteristics, transformation intensity and transformation direction of multi-period structural activities; The embodiment of the present invention uses fracture characteristic data to perform multi-period structural transformation analysis, focusing on identifying the superposition characteristics, transformation intensity and direction of multi-period structural activities. First, based on geological historical profiles and fault activity records, the specific characteristics of each period of structural transformation are identified by using geological time series and fault superposition characteristic analysis. Then, the intensity of each period of transformation is quantified by measuring the intensity of structural activity (such as slip rate and displacement); the structural strike data is compared to determine the transformation direction of different periods. Finally, the structural transformation data of each period are integrated to generate structural transformation data to provide support for the study of the fracture evolution process.

[0041] Step S234: establishing a multi-period structural evolution sequence according to the structural transformation data, thereby obtaining structural sequence data; The embodiment of the present invention establishes a multi-period structural evolution sequence based on structural transformation data, and reconstructs the structural evolution sequence in the reservoir through the stratigraphic age and structural activity records. The specific steps are: first, the structural events of different periods are arranged in chronological order, and the structural evolution model is applied to simulate the duration and impact range of each structural action; combined with geological profile data, the structural sequence data is established. This structural evolution sequence clearly shows the superposition effect of each period of structural activity, and provides basic data support for the multi-period dynamic analysis of the cause of fractures.

[0042] Step S235: Analyze the causes of fractures using the stress field evolution data, lithology control data, and structural sequence data, thereby generating comprehensive genetic data; The embodiment of the present invention integrates stress field evolution data, lithology control data and structural sequence data to conduct a comprehensive analysis of the causes of fractures. The fracture distribution characteristic model is used to simulate the influence of lithology and structural stress, and evaluate the development degree and spatial distribution of fractures under different geological conditions. Through the superposition analysis of geomechanical simulation and evolution sequence, comprehensive genetic data is generated to characterize the complex genetic mechanism and influencing factors of fractures, providing a basis for fracture classification and prediction.

[0043] Step S236: classifying the causes of cracks according to the comprehensive genesis data to obtain fracture cause classification data; The embodiment of the present invention classifies fractures by their causes based on the comprehensive genetic data. Fractures are classified into structural fractures, tension fractures, and compression fractures using classification standards. Fractures are subdivided by their causes based on the geological environment, genetic mechanism, and stress characteristics of fracture development, and fracture genetic classification data is generated. This classification data provides basic information on the laws of fracture development, which helps to more accurately represent fracture types and distribution characteristics in reservoir modeling.

[0044] Step S237: performing a fracture development pattern analysis on the fracture cause classification data to generate fracture development pattern data; establishing a fracture development prediction model based on the fracture development pattern data to obtain fracture cause data.

[0045] The embodiment of the present invention performs fracture development pattern analysis on fracture genesis classification data to identify the formation patterns of various fractures in geological structures. Based on the structural stress direction, lithological characteristics and genesis classification data, the fracture development pattern is constructed, and its distribution characteristics in different structural units are analyzed. Then, according to the fracture development pattern, a fracture development prediction model is established using geostatistical methods to generate fracture development pattern data. Finally, by simulating the fracture development prediction model under different reservoir conditions, fracture genesis data is obtained, providing a basis for further reservoir fracture distribution prediction and development plan optimization.

[0046] The present invention generates stress field evolution data by analyzing the tectonic stress effect in the fracture characteristic data and establishing a tectonic kinematic model. This step reveals the generation and evolution characteristics of fractures under the action of tectonic stress, and provides detailed data on the change of stress field in the reservoir over time. The stress field evolution data helps the model to simulate the dynamic distribution and change process of fractures more realistically, thereby improving the temporal and spatial accuracy of reservoir fracture distribution. The lithology control analysis takes into account the influence of rock brittleness, mechanical strength and mineral components, thereby generating lithology control data. This step provides the development law of fractures under different rock conditions, helping the model to accurately predict fracture generation and distribution according to the lithology characteristics in the reservoir. These data can identify the limiting or promoting effect of lithology on fracture formation, so that the model can better reflect the real situation of reservoir geological conditions. By performing multi-phase tectonic transformation analysis on fracture characteristic data, tectonic transformation data are obtained, including the superposition characteristics, transformation intensity and transformation direction of multi-phase tectonic activities. Multi-phase tectonic transformation data reveals the transformation and superposition effects of tectonic activities on fractures at different geological historical stages, so that the model can capture the evolution trajectory of fracture networks in different periods. These data provide support for the model in dealing with complex geological history, making the fracture distribution prediction more historically consistent. The tectonic sequence data are generated based on the multi-stage tectonic evolution sequence established by the tectonic transformation data, providing information on the development stages of fractures in geological history. This step shows the formation and development process of the fracture network in stages, which can help the model better reflect the temporal characteristics of fracture formation and capture the correlation between fracture development and tectonic activity. This time series data is extremely critical in establishing a historically consistent fracture distribution model. The stress field evolution data, lithology control data and tectonic sequence data are used to analyze the causes of fractures and generate comprehensive genetic data. The comprehensive genetic analysis combines a variety of geological factors to make the causes and development patterns of fracture generation more comprehensive, thus providing a high-precision data basis for the model. The comprehensive description of the causes of fractures by these data can support the classification of different types of fractures and help improve the accuracy of fracture prediction. Fracture causes are classified according to the comprehensive genetic data to obtain fracture cause classification data. The genetic classification data distinguishes fractures of different genetic types, helping the model to distinguish the characteristics of fractures of different causes when predicting fractures, avoiding overly single assumptions about fractures, and making the fracture generation mechanism of the model more consistent with geological reality. The fracture development pattern analysis is performed on the fracture genesis classification data to generate fracture development pattern data, and a fracture development prediction model is established based on this data. The fracture development pattern reveals the law and trend of fracture distribution in the reservoir, enabling the model to make inferences based on different development patterns when predicting fractures. The fracture development prediction model finally established can provide an estimate of the future fracture distribution of the reservoir, which is of great significance for optimizing reservoir development strategies and improving recovery.

[0047] Preferably, step S24 comprises the following steps: Step S241: using the geometric parameter probability distribution data to classify the fracture system based on the primary and secondary fractures of the strike and dip angle, thereby obtaining fracture system classification data; The embodiment of the present invention performs statistical analysis on the fracture strike and dip data based on the probability distribution data of geometric parameters, and divides the fractures into primary fractures and secondary fracture systems by cluster analysis methods (such as K-means or hierarchical clustering). Primary fracture systems usually refer to fractures with concentrated strikes and dips and large scale, while secondary fracture systems include fractures with smaller scale and more dispersed development. Fracture system classification data is generated based on the clustering results, providing a basis for subsequent fracture development feature analysis.

[0048] Step S242: extracting development characteristics according to the fracture system classification data, thereby obtaining development characteristic data of different fracture systems; According to the fracture system classification data, the embodiment of the present invention extracts the development characteristics of the primary and secondary fracture systems respectively. For each fracture system, the length, density, connectivity and other development characteristic data of the fractures are extracted through spatial distribution statistics and morphological characteristics analysis. The development characteristics of the fractures are visualized and data characterized using statistical analysis software (such as SPSS or Python's Pandas library) to generate the development characteristic data of each fracture system, providing a basis for building a fracture development strength evaluation system.

[0049] Step S243: establishing a fracture development intensity index evaluation system including a tectonic stress intensity index, a lithology sensitivity index, and a transformation degree index according to the fracture genesis data, and performing normalization processing to generate fracture development intensity data; The embodiment of the present invention establishes a fracture development strength evaluation system based on fracture genesis data, and defines three evaluation indicators: tectonic stress intensity index, lithology sensitivity index, and transformation degree index. The specific method is: first, the tectonic stress intensity index is calculated through the stress field around the fracture; then, the lithology sensitivity index is calculated based on the mechanical properties of the rock (such as elastic modulus, brittleness index); finally, the transformation degree index is calculated through the superposition effect of the fracture in multiple phases of tectonic activities. After normalizing the data of these three indicators, the fracture development strength data is obtained, which is used to characterize the overall development degree of the fracture.

[0050] Step S244: performing weighted superposition analysis on the fracture system classification data and the fracture development intensity data, establishing a fracture density calculation model, and thereby generating initial density calculation data; The embodiment of the present invention uses fracture system classification data and fracture development intensity data for weighted superposition analysis. The analytic hierarchy process (AHP) is used to assign different weights to each fracture system, with the weight of the primary fracture system set to 0.7 and the weight of the secondary fracture system set to 0.3. The fracture development intensity data is weighted to generate a comprehensive fracture density distribution index, establish a fracture density calculation model, and obtain initial density calculation data for preliminary evaluation of fracture density distribution in the reservoir.

[0051] Step S245: performing parameter sensitivity analysis on the initial density calculation data, and identifying key control factors according to a preset sensitivity threshold, thereby obtaining density control factor data; The embodiment of the present invention performs parameter sensitivity analysis on the initial density calculation data to identify key control factors. A local sensitivity analysis method (such as the Sobol method or the Morris method) is used to analyze the impact of changes in various parameters in the density calculation model on the crack density results. According to the analysis results, the control factors with a greater impact on the density are compared with the preset sensitivity threshold, and the density control factor data is generated to provide a reference for subsequent model correction.

[0052] Step S246: performing crack density calculation model correction processing on the initial density calculation data according to the density control factor data, and performing accuracy evaluation based on cross-validation, thereby obtaining density verification data; The embodiment of the present invention uses density control factor data to perform model correction on the initial density calculation data. The accuracy of the model is improved by adjusting the key parameter values ​​and recalculating the crack density. Then, a cross-validation method (such as K-fold cross-validation) is used to evaluate the accuracy of the corrected crack density model, and density verification data is generated through the verification results, laying the foundation for further calculation and analysis of the crack density.

[0053] Step S247: establishing a multi-scale density conversion relationship based on a unified representation of fracture density at different scales according to the density verification data, and performing spatial distribution calculation to obtain fracture density distribution data.

[0054] The embodiment of the present invention establishes a unified characterization relationship of fracture density at different scales based on density verification data, and associates small-scale density data with large-scale density data through a multi-scale density conversion model. The specific operation is to establish a conversion coefficient between the microscale and the macroscale, and use a spatial interpolation method (such as Kriging interpolation) to perform spatial distribution calculation to generate the final fracture density distribution data for unified characterization of fracture density at different geological scales.

[0055] The present invention classifies fractures into primary and secondary systems based on strike and dip angle through geometric parameter probability distribution data, thereby obtaining fracture system classification data. This step divides fractures into different systems according to spatial distribution characteristics and directionality, so that the model can effectively distinguish primary and secondary fractures in the fracture network, thereby accurately reproducing the fracture structural system in the actual reservoir and improving the spatial resolution of fracture prediction. According to the fracture system classification data, the development characteristics of different fracture systems are extracted to generate development characteristic data. This step helps to understand the differences in the causes, extension directions, and development laws of different fracture systems. The extracted characteristic data provides a more detailed description of the fracture type for the model, so that the model can more accurately reflect the unique development characteristics of each fracture system when performing density and distribution calculations. By constructing a fracture development intensity index evaluation system, the structural stress intensity, lithology sensitivity and transformation degree are evaluated and normalized to generate fracture development intensity data. This step provides a set of quantitative evaluation systems for the development intensity of fractures, so that the model can scientifically measure the generation and expansion degree of fractures. The generated development intensity data has a strong guiding role in predicting fracture density, distribution and development trend, and further optimizes the fracture prediction accuracy of the model. The fracture system classification data and fracture development intensity data are weighted and superimposed to establish a fracture density calculation model and generate initial density calculation data. This step comprehensively considers the importance and development intensity of the fracture system, so that the density model can more realistically reproduce the spatial density distribution of fractures. The initial density calculation data provides a basis for subsequent sensitivity analysis and model correction, and improves the adaptability and accuracy of the model. Parameter sensitivity analysis is performed on the initial density calculation data, and key control factors are identified according to preset thresholds to generate density control factor data. This step can identify the key variables affecting fracture density, remove parameters with little influence on density calculation, and improve the calculation efficiency and accuracy of the model. The density control factor data provides a direction for the correction of the density calculation model, ensuring that the model has a stronger responsiveness to the key driving factors of fracture development. Based on the density control factor data, the initial density calculation data is corrected, and the accuracy is evaluated through cross-validation to obtain density verification data. This step ensures that the density calculation model can adapt to the fracture distribution characteristics under different geological conditions and provide more accurate fracture density prediction. At the same time, the reliability and robustness of the model are further verified through cross-validation accuracy evaluation, making the model more scientifically based when predicting reservoir fracture density. Based on the density verification data, a multi-scale conversion relationship of fracture density is established and spatial distribution calculation is performed to obtain fracture density distribution data. This step provides a unified representation of fracture density at different scales, enabling the model to adapt to the scale requirements of different geological studies or production management. At the same time, the spatial distribution calculation of fracture density reflects the spatial variation trend of reservoir fractures and provides high-precision input data for geological modeling of fracture networks.

[0056] Preferably, step S26 comprises the following steps: Step S261: Calculating reservoir stress distribution based on formation pressure, tectonic stress and rock mechanics parameters according to the continuous density field data, and performing tensile and shear stress component analysis to obtain stress component data; The embodiment of the present invention uses continuous density field data, combined with formation pressure, tectonic stress and rock mechanics parameters, to calculate the stress distribution of the reservoir. The finite element method (FEM) is used to simulate the stress state in the reservoir, and various parameters are input into the model. The formation pressure can be obtained through logging data, the tectonic stress is extracted using the geological structure model, and the rock mechanics parameters need to be obtained through laboratory tests (such as compression strength and shear modulus). After completing the stress distribution calculation, the components of tensile stress and shear stress are further analyzed, and the stress component data is obtained through the stress tensor analysis formula, which provides a basis for subsequent stress analysis.

[0057] Step S262: Calculate the dynamic response relationship between fracture conductivity and stress field according to fracture physical property data to generate stress-seepage response data; The embodiment of the present invention analyzes the dynamic response relationship between the conductivity of the fracture and the stress field based on the fracture physical property data. First, the physical property parameters of the fracture, including permeability, porosity, and fracture width, are collected, and the conductivity of the fracture under different stress states is calculated using a fluid dynamics model (such as Darcy's law). Through experimental data and numerical simulation, a relationship model between stress and seepage is established. These data are combined to form stress-seepage response data, thereby providing support for the subsequent prediction of the stress field.

[0058] Step S263: performing dynamic stress field prediction according to the stress-seepage response data and the stress component data to generate stress field evolution data; performing spatiotemporal evolution characteristic analysis on the stress field evolution data to obtain stress field distribution data; The embodiment of the present invention uses stress-seepage response data and stress component data to predict dynamic stress field. First, the stress component and stress-seepage response data are input into a time series model (such as an ARIMA model or an LSTM network) to predict the future evolution of the stress field. The stress field distribution characteristics at different time points are analyzed using spatiotemporal analysis technology to generate stress field evolution data. The spatiotemporal evolution characteristics of the stress field are displayed using visualization tools (such as Matlab or the Matplotlib library in Python) to obtain stress field distribution data.

[0059] Step S264: performing tectonic stress zoning processing based on stress intensity, stress direction and stress gradient indicators according to the stress field distribution data, thereby obtaining tectonic stress zoning data; The embodiment of the present invention performs tectonic stress zoning processing based on stress field distribution data. First, indicators such as stress intensity, stress direction and stress gradient are set, and these indicators are used to classify data. Clustering algorithms (such as K-means or DBSCAN) are used to partition stress field data and analyze stress characteristics in different regions. By visualizing the partition results, tectonic stress zoning data is generated to provide basic information for boundary identification of tectonic units.

[0060] Step S265: performing boundary feature recognition of structural units on the structural stress partition data, thereby obtaining final structural unit division data.

[0061] The embodiment of the present invention identifies the boundary features of the tectonic unit based on the tectonic stress partition data. The boundary features in the partition data are identified by using image processing technology (such as edge detection algorithm) and spatial analysis tools. The boundaries of each tectonic unit, including its shape and size, are identified through geometric feature analysis and attribute extraction. Finally, the tectonic unit partition data is generated to provide a clear basis for the tectonic unit partition for subsequent geological modeling and reservoir evaluation.

[0062] The present invention uses formation pressure, tectonic stress and rock mechanics parameters to calculate the stress distribution in the reservoir and analyze the tensile and shear stress components to generate stress component data. This step can capture the different stress characteristics of the reservoir, thereby providing basic data for the subsequent analysis of fracture development patterns and conductivity. Through stress component analysis, the model can identify potential tensile fractures and shear fracture areas in the reservoir, thereby laying the foundation for accurate modeling of reservoir mechanical behavior. Based on fracture physical property data, the dynamic response relationship between fracture conductivity and stress field is analyzed to generate stress-seepage response data. This step helps understand the influence of stress field on fracture permeability and fluid flow by capturing the dynamic characteristics of fracture conductivity changing with stress. The generated stress-seepage response data provides key parameters for the reservoir fluid transport model, thereby helping to improve the accuracy of predicting reservoir seepage characteristics. According to the stress-seepage response data and stress component data, dynamic stress field prediction is performed to generate stress field evolution data, and stress field distribution data is obtained by analyzing the spatiotemporal evolution characteristics. This step predicts the temporal and spatial variation characteristics of the stress field in the reservoir, so that the model can dynamically simulate the trend of stress change over time, thereby more realistically reflecting the evolution behavior of the fracture network under different production conditions. These data provide important dynamic information for further reservoir management and development strategies. Using the stress field distribution data, tectonic stress zoning is processed based on stress intensity, direction and gradient to obtain tectonic stress zoning data. This step divides the reservoir into different stress regions, providing a geological structure framework closely related to stress characteristics for fracture density distribution, fracture orientation, and conductivity. The zoning data provides an accurate guide for the distribution and development trend of fractures in the reservoir, making the model more scientific in the subsequent fracture unit division. The boundary characteristics of the tectonic unit are identified through the tectonic stress zoning data to generate the final tectonic unit division data. This step helps to clarify the boundaries of different tectonic units in the reservoir, so that the model can accurately distinguish the physical properties and fracture behaviors of different stress regions. The generated tectonic unit division data ensures that the reservoir geological model remains accurate at multiple scales, providing a more accurate input for fracture conductivity and reservoir development plans.

[0063] Preferably, step S3 comprises the following steps: Step S31: performing three-dimensional grid division based on the structural unit according to the structural unit division data to generate partition grid data; The embodiment of the present invention divides the three-dimensional grid according to the structural unit division data. First, the structural unit division data is input using a three-dimensional modeling software (such as Petrel or Gocad) to determine the dimension and resolution of the grid division. Select an appropriate grid type (such as a hexahedron or a tetrahedron), and automatically or manually generate a three-dimensional grid based on the geometric characteristics of the structural unit. In the grid division process, according to the complexity of the reservoir, ensure that the grid refinement area can accurately reflect the structural characteristics, generate partitioned grid data, and provide a basis for subsequent modeling.

[0064] Step S32: using the partitioned grid data to establish a hierarchical modeling framework, determine key layers and interfaces, and generate hierarchical framework data; The embodiment of the present invention uses partitioned grid data to establish a hierarchical modeling framework. First, based on the partitioned grid data, key layers and interfaces, such as different stratigraphic or lithological interfaces, are identified. By analyzing the layered data, the thickness and inclination of the layer are determined to form hierarchical framework data. The hierarchical function of the modeling software is used to establish a three-dimensional hierarchical framework and clarify the relationship between the layers. This process can ensure that the geometric relationship and physical properties of each layer are correctly reflected in the modeling framework by establishing an inter-layer connection relationship diagram.

[0065] Step S33: assigning reservoir attribute parameters according to the layered framework data, thereby obtaining initial geological model data; The embodiment of the present invention assigns reservoir attribute parameters to each layer based on the layered framework data, thereby obtaining initial geological model data. First, relevant geological data, including porosity, permeability, rock mechanics parameters, etc., are collected and combined with the layered framework. Interpolation methods (such as Kriging interpolation) are used to assign values ​​to each layer in the framework to generate initial geological model data. This process requires combining geological survey data and laboratory analysis results to ensure the accuracy and reliability of the assignments, and to form a preliminary geological model that can be used for subsequent analysis.

[0066] Step S34: identifying the spatial variation characteristics of the fracture density on the fracture density distribution data to generate fracture variation data; and using the fracture variation data to analyze the directionality and anisotropy of the fracture density to obtain fracture development law data; The embodiment of the present invention identifies the spatial variation characteristics of the fracture density distribution data and generates fracture variation data. The fracture density data is spatially analyzed using geostatistical methods (such as variogram analysis) to identify its spatial variation characteristics and extract key variation parameters (such as range, intensity and direction). Then, the directionality and anisotropy of the fracture density are analyzed by numerical models, the anisotropy ratio and directional index are calculated, and the fracture development law data are obtained. This provides important information for subsequent fracture prediction and model optimization.

[0067] Step S35: performing correlation analysis on the fracture development law data and the regional tectonic stress field to generate geological constraint condition data; establishing constraint criteria according to the geological constraint condition data to obtain model constraint rule data; The embodiment of the present invention associates the crack development law data with the regional tectonic stress field to generate geological constraint condition data. Regression analysis or correlation analysis methods are used to compare the crack development law with the tectonic stress field data to identify potential constraint relationships. After the geological constraint condition data is generated, constraint criteria are established to clarify the crack development conditions and stress influences. This process needs to be combined with the regional geological history and tectonic evolution background to ensure that the generated constraint criteria have scientific basis and practical application value.

[0068] Step S36: using the model constraint rule data to perform fracture parameter correction and spatial distribution correction on the initial geological model data to obtain preliminary constraint model data; verifying the geological rationality of the preliminary constraint model data to generate constraint model data.

[0069] The embodiment of the present invention uses the model constraint rule data to perform fracture parameter correction and spatial distribution correction on the initial geological model data. First, according to the geological constraint conditions, the fracture parameters in the initial geological model, such as density, direction and extension range, are adjusted, and the adaptive adjustment of the model is achieved through an optimization algorithm (such as a genetic algorithm or a particle swarm optimization). After the correction is completed, the geological rationality of the preliminary constraint model data is verified, and the cross-validation method and the historical fitting method are used to compare the consistency of the simulation results with the actual production data, and finally generate constraint model data that conforms to the actual situation.

[0070] The present invention can clearly represent the spatial structure of the reservoir through three-dimensional grid division, ensuring that the mutual relationship and geological characteristics between different structural units can be effectively captured. This partitioned grid data lays the foundation for subsequent geological modeling, so that the attributes and behaviors in different regions can be analyzed and compared under the same framework. By clarifying the boundaries and characteristics of different layers, the geological model is more in line with the actual geological conditions. The definition of key layers has an important impact on the production performance, fluid flow path and fracture development of the reservoir. The layered framework data provides clear guidance for the detailed parameterization and attribute assignment of the model to ensure the accuracy of the model. By assigning specific physical and chemical property parameters to each part of the model, an initial geological model reflecting the real reservoir characteristics is constructed. The accuracy of the initial model is directly related to the effectiveness of subsequent analysis, ensuring the reasonable prediction of reservoir performance and the scientific understanding of flow behavior. By analyzing the spatial variation characteristics of fracture density, the regional characteristics of fracture development and its impact on reservoir performance can be revealed. Fracture variation data provides a basis for understanding the development law of fractures, and also lays the foundation for subsequent directional and anisotropic analysis. By establishing the relationship between fracture development and tectonic stress field, it is helpful to identify and quantify the geological factors that affect fracture development. The generated geological constraint condition data provides a scientific basis for the constraint criteria of the model, ensuring that the model can better reflect the geological characteristics of the reservoir. The initial geological model data is corrected for fracture parameters and spatial distribution using the model constraint rule data to obtain preliminary constraint model data, which is then verified for geological rationality to generate constraint model data. This step ensures that the model is adjusted based on historical data and existing geological characteristics, making the model more in line with actual conditions. Through rationality verification, potential errors in the model can be identified, thereby enhancing the reliability of the model and providing effective support for subsequent reservoir development and management.

[0071] Preferably, step S4 comprises the following steps: Step S41: construct a multi-medium model for the fracture characteristic data, establish a coupling relationship between the matrix system and the fracture system, and generate multi-medium model data; establish a fracture-matrix seepage equation based on the multi-medium model data to obtain seepage control equation data; The embodiment of the present invention constructs a multi-medium model for fracture characteristic data. First, relevant data of the fracture and matrix system are collected, including fracture geometric characteristics, physical parameters and physical properties of the matrix. The coupling relationship between the matrix and the fracture is constructed through numerical simulation software (such as COMSOL Multiphysics or FLAC), and multi-medium model data is established. The model should reflect the interaction between the porosity and permeability of the matrix and the fracture characteristics. Next, based on the multi-medium model data, the fracture-matrix seepage equation is established using the principle of fluid mechanics. By setting boundary conditions and initial conditions, the seepage control equation data is obtained, which provides a theoretical basis for subsequent seepage analysis.

[0072] Step S42: using the fracture characteristic data to perform discrete fracture network modeling, generating a fracture network that meets the statistical characteristics based on a random simulation method, and obtaining discrete fracture model data; performing connectivity analysis on the discrete fracture model data to generate fracture network connectivity data; The embodiment of the present invention uses fracture characteristic data to model a discrete fracture network, and adopts a random simulation method (such as Monte Carlo simulation) to generate a fracture network that meets statistical characteristics. First, the statistical characteristics of the fracture distribution, direction, aspect ratio, etc. are defined, and then a programming language (such as Python) is used to implement an algorithm for randomly generating a fracture network to generate discrete fracture model data. On this basis, the discrete fracture model data is subjected to connectivity analysis, and the connection relationship between fractures is determined by using graph theory methods to generate fracture network connectivity data to evaluate the seepage capacity and impact of the fracture network.

[0073] Step S43: performing mixed modeling calculation on the multiple medium model data and the discrete fracture model data, and performing parameter optimization to obtain mixed modeling data; The embodiment of the present invention performs hybrid modeling calculation on the multi-medium model data and the discrete fracture model data. First, select a suitable numerical simulation tool (such as CMG or TOUGH2), input the data of the two models, and set the corresponding coupling parameters. In the hybrid model, it is necessary to optimize the physical parameters of the fluid and the geometric characteristics of the fracture to ensure the accuracy and stability of the model. Through iterative calculation and sensitivity analysis, the model parameters are adjusted to obtain optimized hybrid modeling data, thereby providing a reliable basis for subsequent seepage analysis.

[0074] Step S44: establishing a stress-seepage response equation according to the stress field distribution data, and performing a dynamic characteristic analysis of the fracture conductivity changing with the stress field to generate dynamic response data; using the dynamic response data to calculate the seepage parameters to obtain initial seepage parameter data; The embodiment of the present invention establishes a stress-seepage response equation based on stress field distribution data. First, the stress field distribution data is input into the fluid mechanics model, and the influence of stress on seepage is solved by finite element analysis (FEA). Next, a dynamic characteristic analysis of the change of fracture conductivity with the stress field is performed, and dynamic response analysis technology is used to generate dynamic response data. In this process, attention should be paid to the seepage characteristics under different stress states, and these dynamic response data should be used to calculate the seepage parameters to obtain the initial seepage parameter data to ensure that it meets the actual production needs.

[0075] Step S45: performing coupling calculation on the initial seepage parameter data according to the hybrid modeling data, establishing a dynamic prediction model of the seepage field, and thus obtaining the seepage parameter data; The embodiment of the present invention couples the initial seepage parameter data with the hybrid modeling data to establish a dynamic prediction model for the seepage field. First, the initial seepage parameters are combined with the hybrid modeling results, and a numerical model (such as a nonlinear regression model or a machine learning algorithm) is used for coupling calculation. Through time series analysis, the changes in the seepage parameters at different time steps are predicted to generate seepage parameter data. This data should be able to reflect the actual situation of fluid flow in the reservoir and provide support for subsequent production decisions.

[0076] Step S46: establishing a three-dimensional geological modeling framework according to the seepage parameter data, determining the modeling area and boundary conditions, and generating modeling framework data; using the constraint model data to geologically constrain the modeling framework data to obtain the constrained modeling data; The embodiment of the present invention establishes a three-dimensional geological modeling framework based on the seepage parameter data. First, select appropriate modeling software (such as ArcGIS or Petrel), input the seepage parameter data, and determine the modeling area and boundary conditions. According to the geological characteristics in the area, establish the modeling framework data to ensure that the framework can cover all important geological units. Next, use the constraint model data to geologically constrain the modeling framework, apply the constraint rules based on prior knowledge, ensure that the model conforms to the actual geological conditions, and obtain the constrained modeling data.

[0077] Step S46: performing three-dimensional spatial interpolation and attribute modeling on the constraint modeling data, establishing a spatial distribution model of fracture parameters, and generating three-dimensional geological model data; The embodiment of the present invention establishes a three-dimensional geological modeling framework based on the seepage parameter data. First, select appropriate modeling software (such as ArcGIS or Petrel), input the seepage parameter data, and determine the modeling area and boundary conditions. According to the geological characteristics in the area, establish the modeling framework data to ensure that the framework can cover all important geological units. Next, use the constraint model data to geologically constrain the modeling framework, apply the constraint rules based on prior knowledge, ensure that the model conforms to the actual geological conditions, and obtain the constrained modeling data.

[0078] Step S47: determining a historical matching evaluation standard based on pressure, output and water content according to the production dynamic data, and generating historical matching standard data; The embodiment of the present invention performs three-dimensional spatial interpolation and attribute modeling on the constrained modeling data. By selecting a suitable interpolation method (such as Kriging or inverse distance weighted method), a spatial distribution model of the constrained modeling data is established to generate a spatial distribution model of fracture parameters. Special attention is paid to the spatial variation characteristics of fractures to ensure that the model can effectively reflect the actual situation and generate three-dimensional geological model data for subsequent analysis and verification.

[0079] Step S48: Performing history matching verification on the three-dimensional geological model data according to the history matching standard data, thereby obtaining a corrected three-dimensional geological model.

[0080] The embodiment of the present invention determines the historical matching evaluation standard based on the production dynamic data. First, relevant production dynamic data are collected, including parameters such as pressure, output and water content. The historical matching standard data are established by using time series analysis and regression analysis methods to determine key evaluation indicators and goodness of fit. The three-dimensional geological model data is verified by historical matching based on the historical matching standard data. The accuracy of the model is evaluated by calculating the deviation between the model output and the actual production data, and finally a corrected three-dimensional geological model is obtained.

[0081] The present invention constructs a multi-medium model for fracture characteristic data, establishes a coupling relationship between a matrix system and a fracture system, and generates multi-medium model data. This process enables the mutual influence between different media (matrix and fracture) to be quantified, thereby more realistically simulating the seepage behavior of the reservoir. Subsequently, a fracture-matrix seepage equation is established based on the multi-medium model data to obtain seepage control equation data. This seepage control equation provides a basis for subsequent fluid dynamics analysis, ensuring that the transmission characteristics of the medium can be accurately described in fluid flow simulation. The modeling process enables the distribution and connectivity of fractures to be statistically described, thereby more realistically reflecting the actual distribution of fractures in the reservoir. Connectivity analysis is performed on discrete fracture model data to generate fracture network connectivity data, which helps to evaluate the connectivity of the fracture system during fluid flow and provide more accurate seepage path information. By combining the data of the two models, the accuracy and applicability of the model are enhanced, so that the multi-medium characteristics of the reservoir and the complexity of the fracture network can be reflected in the same model. At the same time, through parameter optimization, the effectiveness and accuracy of the model are ensured, providing a solid foundation for subsequent analysis. By analyzing the dynamic relationship between stress and seepage, the influence of stress changes on fracture conductivity can be identified, and the flow characteristics of the reservoir under different stress conditions can be revealed. This provides an important basis for the effective utilization of fluids and reservoir management. The dynamic response data is used to calculate the seepage parameters to obtain the initial seepage parameter data, laying the foundation for subsequent fluid flow prediction. By coupling the data of different models, the understanding and prediction capabilities of the seepage field are enhanced, ensuring that the dynamic characteristics of fluid flow can be better grasped in the actual production process, providing support for effective resource development. By clarifying the modeling area and boundary conditions, the necessary spatial basis is provided for the construction of the three-dimensional model. The constrained model data is used to geologically constrain the modeling framework data to obtain the constrained modeling data. Such constraints ensure that the model can reasonably reflect the actual geological conditions and improve the reliability and accuracy of the model. By establishing the historical matching standard, the degree of fit between the model and the actual production data can be objectively evaluated, thereby providing a clear basis for subsequent model verification and correction. By comparing and correcting with the actual production data, it is ensured that the model is more in line with the actual situation, and the practicality and reliability of the model in future production and management are improved.

[0082] The present invention also provides a three-dimensional geological modeling system for natural fractures in reservoirs, which is used to execute the above-mentioned three-dimensional geological modeling method for natural fractures in reservoirs. The three-dimensional geological modeling system for natural fractures in reservoirs comprises: The data acquisition and characterization module is used to obtain reservoir characteristic parameter data, including seismic, well logging, core and production dynamic information; identify and characterize fractures based on reservoir characteristic parameter data, and perform digital processing to generate fracture characteristic data; The multi-scale feature analysis module is used to perform multi-scale analysis of the geometric features, physical properties and genetic mechanism of the fracture feature data, and to calculate the fracture density to generate fracture density distribution data; perform dynamic stress field analysis based on the fracture density distribution data to obtain stress field distribution data; perform structural partitioning processing based on the stress field distribution data to generate structural unit division data; The geological constraint modeling module is used to perform zoning and layering modeling according to the structural unit division data to generate initial geological model data; to analyze the development law of fracture density distribution data to generate fracture development law data; to optimize the geological constraints of the initial geological model data through the fracture development law data to generate constraint model data; The three-dimensional modeling and verification module is used to perform multi-media-discrete fracture hybrid modeling based on fracture characteristic data to obtain hybrid modeling data; perform dynamic response analysis on stress field distribution data and hybrid modeling data, and calculate seepage parameters to generate seepage parameter data; perform three-dimensional geological modeling of natural fractures in the reservoir based on seepage parameter data and constraint model data to generate three-dimensional geological model data; perform historical fitting verification on the three-dimensional geological model data to obtain a corrected three-dimensional geological model.

[0083] The comprehensive data acquisition of the present invention can enhance the understanding of the physical properties of the reservoir and lay a solid foundation for fracture identification and characterization. Accurate fracture identification and characterization can help understand the seepage characteristics of the reservoir and its influence on fluid flow, provide a specific basis for subsequent analysis, and improve the reliability of the model. Multi-scale analysis helps to fully understand the geometry, physical properties and formation mechanism of the fracture, and provide detailed background information for subsequent fracture density calculation. Fracture density distribution data can reflect the seepage capacity of the reservoir, provide important parameters for dynamic stress field analysis, and help evaluate the overall performance of the reservoir. Stress field distribution data reveals the stress state of different regions in the reservoir, which is crucial to understanding the behavior of fractures under stress and their influence on fluid flow. The structural unit division data provides a clear spatial framework for subsequent hierarchical modeling, ensuring that the model better reflects the geological characteristics. The establishment of the initial geological model provides a basic framework for subsequent analysis, so that the spatial distribution characteristics of the reservoir can be reflected. Understanding the development law of fractures helps to identify their distribution characteristics in the reservoir and provide a basis for subsequent optimization modeling. Constrained model data ensures the geological rationality of the modeling process, so that the model can accurately reflect the actual situation of the reservoir and improve the effectiveness of the model. This hybrid modeling method combines the characteristics of different media, enhances the comprehensiveness and accuracy of the model, and is more in line with the actual reservoir conditions. Dynamic response analysis helps understand the flow characteristics of the reservoir under different stress conditions, and the seepage parameter data provides a basis for actual production. The three-dimensional geological model can fully reflect the spatial structure and characteristics of the reservoir, providing support for subsequent resource assessment and development plans. Historical matching verification ensures that the model is consistent with actual production data, improves the credibility and application value of the model, and provides a scientific basis for long-term reservoir management and optimization decisions.

[0084] The present invention also provides a computer-readable storage medium storing a computer program, wherein the computer program is executed to implement any one of the above-mentioned three-dimensional geological modeling methods for natural fractures in reservoirs.

[0085] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0086] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A three-dimensional geological modeling method for natural fractures in a reservoir, characterized in that: The following steps are involved: Step S1: Acquire reservoir characteristic parameter data, including seismic, well logging, core and production dynamic information; perform fracture identification and characterization based on the reservoir characteristic parameter data, and perform digital processing to generate fracture characteristic data; Step S2: Perform multi-scale analysis of the geometric features, physical properties and genetic mechanism of the fracture feature data, and calculate the fracture density to generate fracture density distribution data; perform dynamic stress field analysis based on the fracture density distribution data to obtain stress field distribution data; perform structural partitioning processing based on the stress field distribution data to generate structural unit division data; Step S3: Performing zoning and layering modeling according to the structural unit division data to generate initial geological model data; performing development law analysis on the fracture density distribution data to generate fracture development law data; performing geological constraint optimization on the initial geological model data through the fracture development law data to generate constraint model data; Step S4: Perform multi-medium-discrete fracture hybrid modeling based on fracture characteristic data to obtain hybrid modeling data; perform dynamic response analysis on stress field distribution data and hybrid modeling data, and perform seepage parameter calculation to generate seepage parameter data; perform three-dimensional geological modeling on natural fractures in the reservoir based on the seepage parameter data and constraint model data to generate three-dimensional geological model data; perform historical fitting verification on the three-dimensional geological model data to obtain a corrected three-dimensional geological model.

2. The three-dimensional geological modeling method of natural fractures in reservoirs according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Acquire reservoir characteristic parameter data, including seismic acquisition data, well logging measurement data, core analysis data, and production dynamic monitoring data; Step S12: preprocessing and standardizing the reservoir characteristic parameter data to generate standardized characteristic parameter data; Step S13: extracting the characteristics of the fracture based on the occurrence, scale, density and filling of the fracture from the standardized characteristic parameter data to generate fracture basic characteristic data; Step S14: performing classification and identification based on a preset crack classification standard according to the crack basic feature data, thereby obtaining crack classification data; Step S15: Identify the spatial distribution characteristics of different types of cracks on the crack classification data, and perform digital conversion to obtain crack characteristic data.

3. The three-dimensional geological modeling method of natural fractures in reservoirs according to claim 2, characterized in that: Step S2 includes the following steps: Step S21: extracting geometric parameters from the crack feature data to generate crack geometric parameter data, wherein the geometric parameters include the direction, inclination, length and opening of the crack; performing probability distribution statistical analysis on the crack geometric parameter data to obtain geometric parameter probability distribution data; Step S22: performing physical property analysis on the fracture characteristic data based on fracture conductivity, permeability and filling properties to generate fracture physical property data; Step S23: Analyze the genetic mechanism of the fracture characteristic data and perform stress sensitivity assessment to generate fracture stress response data, wherein the genetic mechanism analysis includes tectonic stress action, lithology control and multi-stage transformation; Step S24: Calculate the crack density according to the geometric parameter probability distribution data and the crack cause data to generate crack density distribution data; Step S25: performing spatial interpolation analysis on the crack density distribution data to obtain continuous density field data; Step S26: Perform dynamic stress field analysis based on the continuous density field data and the fracture stress response data to generate stress field distribution data; and perform tectonic stress partitioning processing based on the stress field distribution data to obtain tectonic unit partitioning data.

4. The three-dimensional geological modeling method of natural fractures in reservoirs according to claim 3, characterized in that: Step S23 includes the following steps: Step S231: Analyze the tectonic stress effect on the fracture characteristic data to generate tectonic stress data; establish a tectonic kinematics model based on the tectonic stress data to obtain stress field evolution data; Step S232: performing lithology control analysis on the fracture characteristic data based on rock brittleness, mechanical strength and mineral component influence, thereby obtaining lithology control data; Step S233: performing multi-period structural transformation analysis on the fracture characteristic data, thereby generating structural transformation data, wherein the multi-period structural transformation includes the superposition characteristics, transformation intensity and transformation direction of multi-period structural activities; Step S234: establishing a multi-period structural evolution sequence according to the structural transformation data, thereby obtaining structural sequence data; Step S235: Analyze the causes of fractures using the stress field evolution data, lithology control data, and structural sequence data, thereby generating comprehensive genetic data; Step S236: classifying the causes of cracks according to the comprehensive genesis data to obtain fracture cause classification data; Step S237: performing a fracture development pattern analysis on the fracture cause classification data to generate fracture development pattern data; establishing a fracture development prediction model based on the fracture development pattern data to obtain fracture cause data.

5. The three-dimensional geological modeling method of natural fractures in reservoirs according to claim 4, characterized in that: Step S24 includes the following steps: Step S241: using the geometric parameter probability distribution data to classify the fracture system based on the primary and secondary fractures of the strike and dip angle, thereby obtaining fracture system classification data; Step S242: extracting development characteristics according to the fracture system classification data, thereby obtaining development characteristic data of different fracture systems; Step S243: establishing a fracture development intensity index evaluation system including a tectonic stress intensity index, a lithology sensitivity index, and a transformation degree index according to the fracture genesis data, and performing normalization processing to generate fracture development intensity data; Step S244: performing weighted superposition analysis on the fracture system classification data and the fracture development intensity data, establishing a fracture density calculation model, and thereby generating initial density calculation data; Step S245: performing parameter sensitivity analysis on the initial density calculation data, and identifying key control factors according to a preset sensitivity threshold, thereby obtaining density control factor data; Step S246: performing crack density calculation model correction processing on the initial density calculation data according to the density control factor data, and performing accuracy evaluation based on cross-validation, thereby obtaining density verification data; Step S247: establishing a multi-scale density conversion relationship based on a unified representation of fracture density at different scales according to the density verification data, and performing spatial distribution calculation to obtain fracture density distribution data.

6. The three-dimensional geological modeling method of natural fractures in reservoirs according to claim 5, characterized in that: Step S26 includes the following steps: Step S261: Calculating reservoir stress distribution based on formation pressure, tectonic stress and rock mechanics parameters according to the continuous density field data, and performing tensile and shear stress component analysis to obtain stress component data; Step S262: Calculate the dynamic response relationship between fracture conductivity and stress field according to fracture physical property data to generate stress-seepage response data; Step S263: performing dynamic stress field prediction according to the stress-seepage response data and the stress component data to generate stress field evolution data; performing spatiotemporal evolution characteristic analysis on the stress field evolution data to obtain stress field distribution data; Step S264: performing tectonic stress zoning processing based on stress intensity, stress direction and stress gradient indicators according to the stress field distribution data, thereby obtaining tectonic stress zoning data; Step S265: performing boundary feature recognition of structural units on the structural stress partition data, thereby obtaining final structural unit division data.

7. The three-dimensional geological modeling method of natural fractures in reservoirs according to claim 6, characterized in that: Step S3 includes the following steps: Step S31: performing three-dimensional grid division based on the structural unit according to the structural unit division data to generate partition grid data; Step S32: using the partitioned grid data to establish a hierarchical modeling framework, determine key layers and interfaces, and generate hierarchical framework data; Step S33: assigning reservoir attribute parameters according to the layered framework data, thereby obtaining initial geological model data; Step S34: identifying the spatial variation characteristics of the fracture density on the fracture density distribution data to generate fracture variation data; and using the fracture variation data to analyze the directionality and anisotropy of the fracture density to obtain fracture development law data; Step S35: performing correlation analysis on the fracture development law data and the regional tectonic stress field to generate geological constraint condition data; establishing constraint criteria according to the geological constraint condition data to obtain model constraint rule data; Step S36: using the model constraint rule data to perform fracture parameter correction and spatial distribution correction on the initial geological model data to obtain preliminary constraint model data; verifying the geological rationality of the preliminary constraint model data to generate constraint model data.

8. The three-dimensional geological modeling method of natural fractures in reservoirs according to claim 7, characterized in that: Step S4 includes the following steps: Step S41: construct a multi-medium model for the fracture characteristic data, establish a coupling relationship between the matrix system and the fracture system, and generate multi-medium model data; establish a fracture-matrix seepage equation based on the multi-medium model data to obtain seepage control equation data; Step S42: using the fracture characteristic data to perform discrete fracture network modeling, generating a fracture network that meets the statistical characteristics based on a random simulation method, and obtaining discrete fracture model data; performing connectivity analysis on the discrete fracture model data to generate fracture network connectivity data; Step S43: performing mixed modeling calculation on the multiple medium model data and the discrete fracture model data, and performing parameter optimization to obtain mixed modeling data; Step S44: establishing a stress-seepage response equation according to the stress field distribution data, and performing a dynamic characteristic analysis of the change of fracture conductivity with the stress field to generate dynamic response data; using the dynamic response data to calculate the seepage parameters to obtain initial seepage parameter data; Step S45: performing coupling calculation on the initial seepage parameter data according to the hybrid modeling data, establishing a dynamic prediction model of the seepage field, and thus obtaining the seepage parameter data; Step S46: establishing a three-dimensional geological modeling framework according to the seepage parameter data, determining the modeling area and boundary conditions, and generating modeling framework data; using the constraint model data to geologically constrain the modeling framework data to obtain the constrained modeling data; Step S46: performing three-dimensional spatial interpolation and attribute modeling on the constraint modeling data, establishing a spatial distribution model of fracture parameters, and generating three-dimensional geological model data; Step S47: determining a historical matching evaluation standard based on pressure, output and water content according to the production dynamic data, and generating historical matching standard data; Step S48: Performing history matching verification on the three-dimensional geological model data according to the history matching standard data, thereby obtaining a corrected three-dimensional geological model.

9. A three-dimensional geological modeling system for natural fractures in reservoirs, characterized in that: Used to execute the three-dimensional geological modeling method for natural fractures in reservoirs according to claim 1, the three-dimensional geological modeling system for natural fractures in reservoirs comprises: The data acquisition and characterization module is used to obtain reservoir characteristic parameter data, including seismic, well logging, core and production dynamic information; identify and characterize fractures based on reservoir characteristic parameter data, and perform digital processing to generate fracture characteristic data; The multi-scale feature analysis module is used to perform multi-scale analysis of the geometric features, physical properties and genetic mechanism of the fracture feature data, and to calculate the fracture density to generate fracture density distribution data; perform dynamic stress field analysis based on the fracture density distribution data to obtain stress field distribution data; perform structural partitioning processing based on the stress field distribution data to generate structural unit division data; The geological constraint modeling module is used to perform zoning and layering modeling according to the structural unit division data to generate initial geological model data; to analyze the development law of fracture density distribution data to generate fracture development law data; to optimize the geological constraints of the initial geological model data through the fracture development law data to generate constraint model data; The three-dimensional modeling and verification module is used to perform multi-media-discrete fracture hybrid modeling based on fracture characteristic data to obtain hybrid modeling data; perform dynamic response analysis on stress field distribution data and hybrid modeling data, and calculate seepage parameters to generate seepage parameter data; perform three-dimensional geological modeling of natural fractures in the reservoir based on seepage parameter data and constraint model data to generate three-dimensional geological model data; perform historical fitting verification on the three-dimensional geological model data to obtain a corrected three-dimensional geological model.

10. A computer-readable storage medium storing a computer program, characterized in that: The computer program is executed to implement the three-dimensional geological modeling method for natural fractures in reservoirs as claimed in any one of claims 1 to 8.

Images