Intelligent modeling method for rock mechanical properties of tight sandstone reservoir

Abstract

The application discloses a method for intelligently modeling rock mechanics properties of a tight sandstone reservoir, comprising the following steps: obtaining three-dimensional seismic interpretation data, well logging data and rock mechanics experiment data; establishing a three-dimensional geological structure model of the tight sandstone reservoir which fuses a fault structure; calculating a quantitative fitting relationship between static rock mechanics parameters and a sand ratio; extracting a lithology factor data body from the three-dimensional seismic interpretation data, discriminating the lithology factor data body, generating a three-dimensional spatial distribution of sandstone and mudstone, and thus obtaining a three-dimensional sand ratio distribution model; and based on the three-dimensional sand ratio distribution model and the quantitative fitting relationship, calculating initial values of rock mechanics parameters of each grid unit, and assigning values to the three-dimensional geological structure model and correcting the fault, so as to obtain a final three-dimensional rock mechanics property model. The application realizes multi-source data fusion and quantitative modeling of fault disturbance, and solves the problems of insufficient description of reservoir heterogeneity, lack of physical basis of the model and low reliability of modeling of a fracture zone in the prior art.

Description

Technical Field

[0001] This application relates to the field of oil and gas, and more specifically, to an intelligent modeling method for the rock mechanical properties of tight sandstone reservoirs. Background Technology

[0002] In the field of oil and gas exploration and development, the rock mechanical properties of tight sandstone reservoirs (such as Young's modulus, Poisson's ratio, and cohesion) are key parameters for evaluating reservoir fracturing potential, optimizing drilling design, and improving oil recovery. Currently, common rock mechanical property modeling methods mainly include the following: (1) Well logging interpolation-based method: Using limited well logging data, spatial interpolation is performed through geostatistical methods (such as Kriging interpolation) to construct a three-dimensional distribution model of rock mechanical parameters. This method is limited by well density and distribution uniformity, resulting in low prediction accuracy in inter-well regions and difficulty in reflecting reservoir heterogeneity.

[0003] (2) Method based on direct conversion of seismic attributes: Elastic parameters (such as P-wave / S-wave velocity ratio, wave impedance, etc.) obtained by seismic inversion are directly converted into rock mechanics parameters. Although this method can provide a continuous spatial distribution, the conversion relationship is usually based on empirical formulas, and its applicability is limited.

[0004] (3) Method based on geological modeling and attribute assignment: First, a three-dimensional geological structure model is established, and then corresponding rock mechanical parameters are assigned according to the distribution of lithofacies or sedimentary facies. The parameter assignment of this method mostly relies on statistical averages or simple proportional relationships, and fails to establish a quantitative physical relationship between parameters and lithology, resulting in insufficient model accuracy. Summary of the Invention

[0005] To address the aforementioned issues, this application provides an intelligent modeling method for the rock mechanical properties of tight sandstone reservoirs. This method aims to solve the technical problems in existing intelligent modeling methods for the rock mechanical properties of tight sandstone reservoirs, such as the inability to accurately characterize reservoir heterogeneity due to reliance on well point interpolation or low-resolution seismic data, insufficient physical basis of the model due to ineffective integration of multi-source data, and poor reliability of fault zone modeling due to the lack of quantitative consideration of fault disturbance.

[0006] The first aspect of this invention provides an intelligent modeling method for the rock mechanical properties of tight sandstone reservoirs, comprising: Acquire 3D seismic interpretation data, well logging data, and rock mechanics experimental data; Based on the aforementioned three-dimensional seismic interpretation data, a three-dimensional geological structure model of tight sandstone reservoirs incorporating fault structures was established. Based on the well logging data and rock mechanics experimental data, static rock mechanics parameters were calculated, and a quantitative fitting relationship between the static rock mechanics parameters and the sand-storage ratio was established; wherein, the sand-storage ratio is the ratio of the sandstone thickness in the reservoir to the total thickness of the section. The lithological factor data volume is extracted from the three-dimensional seismic interpretation data. Based on the critical values ​​of sandstone and mudstone calibrated by the well logging data, the lithological factor data volume is judged to generate the three-dimensional spatial distribution of sandstone and mudstone. Based on the three-dimensional spatial distribution, a three-dimensional sand-reservoir ratio distribution model is obtained. Based on the three-dimensional sand-storage ratio distribution model and quantitative fitting relationship, the initial values ​​of rock mechanical parameters of each grid cell are calculated, and the three-dimensional geological structure model is assigned values ​​based on the initial values ​​of the rock mechanical parameters; a quantitative attenuation relationship between the static rock mechanical parameters and fault distance is established, and the rock mechanical parameter values ​​of the grid cells within the fault disturbance range are corrected using the attenuation relationship to obtain the final three-dimensional rock mechanical property model.

[0007] In one optional implementation, establishing a three-dimensional geological structure model of the tight sandstone reservoir with integrated fault structures includes: Based on the structural interpretation results of the aforementioned three-dimensional seismic interpretation data, a three-dimensional structural model of the reservoir is established. Based on the fault interpretation results and fault disturbance range of the aforementioned 3D seismic interpretation data, a 3D fault model is constructed. The three-dimensional structural model is fused with the three-dimensional fault model to generate a three-dimensional geological structure model of a tight sandstone reservoir with fused fault structure.

[0008] In one alternative implementation, the fault disturbance range is determined by the following method: Obtain the minimum principal stress gradient data of multiple wells in the work area and the corresponding vertical distance from the well point to the fault; The correlation between the minimum principal stress gradient data and the vertical distance is analyzed to determine the effective range of fault disturbance.

[0009] In one optional implementation, establishing the quantitative fitting relationship between the rock mechanical static parameters and the sand-storage ratio includes: Based on the well logging data, the dynamic rock mechanics parameters of a single well are calculated using elasticity formulas; wherein the well logging data includes at least P-wave transit time, S-wave transit time, and density, and the rock mechanics parameters include at least Young's modulus and Poisson's ratio; Static rock mechanics parameters were obtained from the same well through core rock mechanics experiments. Based on the dynamic and static rock mechanics parameters, a conversion relationship between the dynamic and static parameters is established. Using this conversion relationship, the dynamic rock mechanics parameters are converted into static rock mechanics parameters, thereby obtaining a static rock mechanics parameter curve. Based on the static rock mechanics parameter curves, the average static rock mechanics parameters of each layer and the corresponding sand-storage ratio are calculated. The average value of the static rock mechanical parameters is fitted to the sand-storage ratio to obtain the quantitative fitting relationship.

[0010] In one alternative implementation, a lithological factor data volume is extracted from three-dimensional seismic interpretation data, including obtaining a lithological factor data volume capable of characterizing lithological differences through seismic inversion.

[0011] In one optional implementation, the step of judging the lithological factor data volume based on the critical values ​​of sandstone and mudstone calibrated by the well logging data to generate a three-dimensional spatial distribution of sandstone and mudstone includes: By comparing the sandstone and mudstone intervals interpreted from the well logging at the well point with the lithological factor values ​​from the seismic traces, the critical values ​​of the lithological factors that distinguish sandstone from mudstone are determined. The lithological factor critical value is used to distinguish the lithological factor data volume, and a three-dimensional spatial distribution data volume of sandstone and mudstone is generated.

[0012] In one optional implementation, the three-dimensional geological structure model is assigned values ​​and faults are corrected based on the initial values ​​of the rock mechanical parameters, including: Obtain the static rock mechanics parameters of multiple wells in the target layer near the fault, and obtain the normal distance between a single well and the fault; The static rock mechanical parameters are fitted with the corresponding normal distance to establish the attenuation relationship of static rock mechanical parameters with distance within the fault influence zone; During the assignment of rock mechanical parameters, grid cells located within the fault influence zone are identified, their normal distance from the fault is calculated, and the initial values ​​of the rock mechanical parameters are corrected by substituting them into the attenuation relationship.

[0013] A second aspect of this invention provides an intelligent modeling device for the mechanical properties of tight sandstone reservoirs, the device comprising: The multi-source data acquisition module is used to acquire 3D seismic interpretation data, well logging data, and rock mechanics experimental data; The fault fusion geological modeling module establishes a three-dimensional geological structure model of tight sandstone reservoirs with fused fault structures based on the three-dimensional seismic interpretation data. The mechanical parameter-sand-reservoir ratio fitting module calculates static rock mechanical parameters based on the well logging data and rock mechanics experimental data, and establishes a quantitative fitting relationship between the static rock mechanical parameters and the sand-reservoir ratio; wherein, the sand-reservoir ratio is the ratio of the sandstone thickness in the reservoir to the total thickness of the section. The three-dimensional sand-reservoir ratio modeling module extracts lithological factor data volumes from three-dimensional seismic interpretation data. Based on the critical values ​​of sandstone and mudstone calibrated by the well logging data, it discriminates the lithological factor data volumes, generates the three-dimensional spatial distribution of sandstone and mudstone, and obtains a three-dimensional sand-reservoir ratio distribution model based on the three-dimensional spatial distribution; and The fault-corrected mechanical property modeling module calculates the initial values ​​of rock mechanical parameters for each grid cell based on the three-dimensional sand-storage ratio distribution model and quantitative fitting relationship, and assigns values ​​to the three-dimensional geological structure model based on the initial values ​​of rock mechanical parameters; it establishes a quantitative attenuation relationship between the static rock mechanical parameters and the fault distance, and uses the attenuation relationship to correct the rock mechanical parameter values ​​of the grid cells within the fault disturbance range, thus obtaining the final three-dimensional rock mechanical property model.

[0014] A third aspect of the present invention provides an electronic device, characterized in that it includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement an intelligent modeling method for the rock mechanical properties of tight sandstone reservoirs.

[0015] A fourth aspect of the present invention provides a computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, a method for intelligent modeling of the rock mechanical properties of tight sandstone reservoirs is provided.

[0016] This application has at least the following advantages or beneficial effects: The intelligent modeling method for rock mechanical properties of tight sandstone reservoirs provided by this invention integrates multi-source data from 3D seismic, well logging, and rock mechanics experiments to construct a rock mechanical parameter conversion path with a clear physical mechanism. It also quantitatively incorporates fault disturbance into the modeling system. Compared with existing technologies, this method significantly improves modeling accuracy, accurately depicting the lateral heterogeneity of tight sandstone reservoirs and fundamentally solving the problem of prediction blind zones in inter-well regions. It achieves differentiated and refined modeling of fault disturbance, effectively restoring the true characteristics of rock mechanical properties in fracture-developed areas and improving the overall realism and reliability of the model. The modeling process is automated and integrates multiple software programs, greatly improving modeling efficiency. Furthermore, the entire process ensures lossless data transfer, standardized operation, and reproducibility. The method can also be extended to modeling various rock mechanical parameters such as cohesion, internal friction angle, and compressive strength, demonstrating good parameter scalability. This invention addresses the long-standing pain points of existing technologies from four dimensions: data fusion depth, spatial resolution, fault disturbance correction, and modeling automation. It realizes a paradigm shift in intelligent modeling of rock mechanical properties of tight sandstone reservoirs from "empirical statistical interpolation" to "physical mechanism driven" modeling. It provides high-precision and reliable rock mechanical parameter field support for evaluating the fracturing potential of tight sandstone reservoirs, optimizing drilling design, and improving recovery rates in the oil and gas field, and has significant technological advancement and industrial application value. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of an intelligent modeling method for the rock mechanical properties of tight sandstone reservoirs proposed in an embodiment of this application; Figure 2 This is a structural diagram of an intelligent modeling device for the rock mechanical properties of tight sandstone reservoirs proposed in one embodiment of this application; Figure 3 This is a three-dimensional distribution map of the top surface structure of a certain layer of a reservoir according to an embodiment of this application; Figure 4 This is a three-dimensional geological structure model of each sublayer of the reservoir proposed in one embodiment of this application; Figure 5 This is a statistical diagram of the minimum principal stress gradient perturbation of the fault pair proposed in one embodiment of this application; Figure 6 This is a detailed model of the reservoir fracture structure proposed in one embodiment of this application; Figure 7This is a reservoir three-dimensional geological structure model based on fault disturbance evaluation results proposed in one embodiment of this application; Figure 8 This is a fitting relationship of dynamic and static parameters in rock mechanics proposed in an embodiment of this application; Figure 9 This is a single-well rock mechanics dynamic and static parameter interpretation profile proposed in an embodiment of this application; Figure 10 This application proposes an embodiment that relates the Young's modulus, Poisson's ratio, and sand-storage ratio of the i-th layer in the research area. Figure 11 This is a three-dimensional seismic lithology factor profile of a reservoir proposed in an embodiment of this application; Figure 12 This is a three-dimensional distribution model of the i-th layer of sandstone and mudstone in the research area proposed in one embodiment of this application; Figure 13 This is a planar distribution of sand reservoir ratio in the i-th layer of the research area proposed in one embodiment of this application; Figure 14 This is the correlation between rock mechanical parameters and fault distance proposed in one embodiment of this application; Figure 15 This is a planar distribution map of Young's modulus of a small layer obtained by interpolation based on single-well logging interpretation, according to an embodiment of this application. Figure 16 This is a planar distribution map of the modulus of a small layer of rock after integrating well logging, 3D seismic data, and fault disturbance, according to an embodiment of this application. Figure 17 This is a schematic diagram of an electronic device according to this application. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating an intelligent modeling method for the rock mechanical properties of tight sandstone reservoirs, as proposed in an embodiment of this application. Figure 1 As shown, an intelligent modeling method for the rock mechanical properties of tight sandstone reservoirs includes: S100: Acquire 3D seismic interpretation data, well logging data, and rock mechanics experimental data; In this embodiment, the three-dimensional seismic interpretation data includes structural interpretation results, fault interpretation results, and other related data, which can be used for three-dimensional modeling of reservoir structures and faults; the well logging data includes at least P-wave transit time, S-wave transit time, and density data, as well as accurate interpretation data of sandstone and mudstone intervals at the well points, providing a basis for calculating rock mechanics parameters and calibrating critical values ​​of lithology factors; the rock mechanics experimental data are static rock mechanics parameter data measured by core experiments, used to establish the conversion relationship between dynamic and static rock mechanics parameters. The above-mentioned multi-source data provides data support for the entire subsequent modeling process.

[0021] S200: Based on the aforementioned three-dimensional seismic interpretation data, establish a three-dimensional geological structure model of tight sandstone reservoirs that incorporates fault structures; In this embodiment, based on the structural interpretation results of the three-dimensional seismic interpretation data, the three-dimensional spatial distribution of the top and bottom structures of the target layer and each sub-layer in the study area is first obtained, such as... Figure 3 As shown. Based on the required vertical resolution, the thickness division scheme for each sublayer was determined. The structural layer data was imported into Rhino software using the geological modeling software Petrel and converted into a 3D mesh model to establish a 3D structural model of the reservoir, as shown. Figure 4 As shown; then, based on the fault interpretation results of the aforementioned 3D seismic interpretation data, the spatial location, dip angle, strike, and other geometric parameters of the fault are obtained, and the fault disturbance range is determined. Combining this disturbance range, the built-in plugin griddle of Rhino software is used to employ local mesh adaptive refinement technology on the basis of the established structural model to finely characterize the 3D spatial morphology of faults of different orders, thus constructing a 3D fault model, as shown. Figure 6 As shown; the fault disturbance range is determined by collecting minimum principal stress gradient data from multiple wells within the work area and the vertical distance from the corresponding well points to the fault. Correlation fitting analysis is performed on these two sets of data to establish a quantitative relationship model of the minimum principal stress gradient changing with the vertical distance from the fault. Based on the numerical variation characteristics and correlation significance of the model, the effective threshold for fault disturbance to the geostress field is determined, thus obtaining the effective range of fault disturbance. Figure 5 As shown, in this embodiment, the fault disturbance range is determined to be 400 meters based on the quantitative fitting relationship; finally, the three-dimensional structural model and the three-dimensional fault model are fused to generate a three-dimensional geological structure model of a tight sandstone reservoir that integrates the matrix strata and fault system and incorporates the fault structure, as shown. Figure 7 As shown, this model provides accurate grid cells for the subsequent spatial assignment of rock mechanical properties.

[0022] S300: Based on the well logging data and rock mechanics experimental data, static rock mechanics parameters are calculated, and a quantitative fitting relationship between the static rock mechanics parameters and the sand-storage ratio is established; wherein, the sand-storage ratio is the ratio of the sandstone thickness in the reservoir to the total thickness of the section. In this embodiment, based on the P-wave transit time, S-wave transit time, and density data in the well logging data, the dynamic rock mechanics parameters of a single well are calculated using elasticity formulas. These rock mechanics parameters include at least Young's modulus and Poisson's ratio. The calculation formulas are as follows: Dynamic Poisson's ratio: ; Dynamic Young's modulus: ; in, For transverse wave time difference, For P-wave time difference, This refers to the logging density.

[0023] Based on the same well, static rock mechanics parameters were obtained through core rock mechanics experiments. Subsequently, based on the dynamic and static rock mechanics parameters, multiple sets of correlation data of dynamic and static rock mechanics parameters were collected for fitting analysis to establish a quantitative conversion relationship model between dynamic and static parameters, such as... Figure 8 As shown, using the aforementioned conversion relationship, the dynamic rock mechanics parameters of the entire well section are corrected to static rock mechanics parameters, thereby obtaining a single-well static rock mechanics parameter curve with high vertical resolution, as shown below. Figure 9 As shown; then, based on the static rock mechanics parameter curve, the average static rock mechanics parameter value and the corresponding sand-reservoir ratio of each layer are calculated; finally, the correlation data between the average static rock mechanics parameter value and the corresponding sand-reservoir ratio of each layer are collected, and correlation analysis and fitting are performed to establish a quantitative fitting relationship model between the sand-reservoir ratio and rock mechanics parameters of different layers, as shown. Figure 10 As shown, the quantitative fitting relationship formula obtained in this embodiment is: E = 10.639 × Si + 34.155 μ = -0.0323 × Si + 0.1796 Where Si is the sand-storage ratio of the i-th layer, E is Young's modulus, and μ is Poisson's ratio.

[0024] S400: Extract lithological factor data volume from 3D seismic interpretation data, discriminate the lithological factor data volume based on the critical values ​​of sandstone and mudstone calibrated by the well logging data, generate the 3D spatial distribution of sandstone and mudstone, and obtain the 3D sand-reservoir ratio distribution model based on the 3D spatial distribution. In this embodiment, lithological factor data volumes that can effectively characterize lithological differences are extracted from 3D seismic interpretation data through seismic inversion; lithological factor values ​​corresponding to sandstone and mudstone intervals interpreted from well logging at well points are collected, and critical values ​​for distinguishing sandstone and mudstone are determined through comparative analysis; in this embodiment, lithological factors greater than 0.2 are identified as sandstone, and vice versa for mudstone. Figure 11 As shown; the lithological factor critical values ​​are used to discriminate the lithological factor data volume, generating a three-dimensional spatial distribution data volume of sandstone and mudstone within each sublayer, as shown. Figure 12 As shown; finally, based on the three-dimensional spatial distribution data of sandstone and mudstone, the thickness of sandstone and mudstone in each sub-layer was statistically obtained, according to the definition formula of sand-reservoir ratio: S i =H i砂 / (H i砂 +H i泥 ) Among them, S i Let H be the sand reservoir ratio of the i-th layer. i砂 H represents the thickness of the i-th sandstone layer. i泥 Let be the thickness of the mudstone in the i-th layer.

[0025] The planar distribution of sand-storage ratio in different sub-layers was calculated, and then a three-dimensional sand-storage ratio distribution model was constructed, such as... Figure 13 As shown, due to the continuous vertical and horizontal distribution and high resolution of 3D seismic lithology factors, the accuracy limitations of traditional modeling methods are overcome. Therefore, different grid step sizes, such as 10×10m, 20×20m, and 50×50m, can be flexibly set according to different research accuracy requirements to obtain sand-reservoir ratio distribution fields with different interpretation accuracies. The sand-reservoir ratio distribution obtained in this way is far more accurate than that of traditional wellpoint interpolation methods, and can effectively characterize the strong heterogeneity of tight sandstone reservoirs, providing an accurate lithological parameter basis for subsequent high-precision rock mechanics parameter modeling.

[0026] S500: Based on the three-dimensional sand-storage ratio distribution model and quantitative fitting relationship, the initial values ​​of rock mechanical parameters of each grid cell are calculated, and the three-dimensional geological structure model is assigned values ​​based on the initial values ​​of the rock mechanical parameters; a quantitative attenuation relationship between the static rock mechanical parameters and fault mechanical properties is established, and the rock mechanical parameter values ​​of the grid cells within the fault disturbance range are corrected using the attenuation relationship to obtain the final three-dimensional rock mechanical property model.

[0027] In this embodiment, the sand-storage ratio data of the three-dimensional sand-storage ratio distribution model is substituted into the quantitative fitting relationship to directly calculate the initial values ​​of rock mechanical parameters corresponding to each grid unit in the three-dimensional geological structure model of the tight sandstone reservoir with integrated fault structure. By writing an automatic assignment script with embedded programming languages ​​such as FISH, the calculated initial values ​​of rock mechanical parameters are batch assigned to the corresponding grid units of the three-dimensional geological structure model to complete the attribute assignment of the reservoir matrix.

[0028] Building upon the assignment of matrix properties, this invention quantitatively corrects the grid cells within the fault influence zone to further improve the accuracy of rock mechanical property modeling near the fault zone. First, measured in-situ stress data from multiple wells near the fault for different target layers are acquired. The measured shear stress intensity of each stratigraphic unit is calculated using the shear stress intensity calculation formula, and the theoretical shear stress intensity under undisturbed conditions is obtained. Then, the comprehensive shear stress index of each stratigraphic unit is calculated using the comprehensive shear stress index calculation model for the fault. This index quantitatively characterizes the degree of disturbance of the fault to the in-situ stress field; a larger T value indicates a stronger disturbance. Simultaneously, the normal distance between each stratigraphic unit and the fault is obtained. For cases where only the horizontal distance is known, it is converted to normal distance based on the fault dip angle. Geometric corrections are then performed based on the well location on the hanging wall or footwall of the fault and the positional relationship of different depth layers to accurately calculate the normal distance of each layer. Based on this, the comprehensive shear index of geostress faults in different single wells and different target sections was fitted with the corresponding normal distance to establish an exponential attenuation function relationship. This fitting model quantitatively describes the law that the fault disturbance intensity decreases with the increase of normal distance.

[0029] In the process of assigning rock mechanical parameters, grid cells located within the fault influence zone are identified, their normal distance from the fault is calculated, and the result is substituted into the aforementioned fitting model to obtain the geostress-fault comprehensive shear index at that grid cell location, i.e., the fault disturbance intensity at that location. Then, the initial values ​​of rock mechanical parameters directly calculated from the sand-storage ratio are corrected based on this disturbance intensity. The correction relationship can be established according to the actual statistical laws of the work area. For example, based on the comparative analysis of measured rock mechanical parameters near the fault and matrix background values, a correspondence between disturbance intensity and correction coefficient is established to achieve differentiated correction of the initial values. Through the above correction, the correction amplitude is larger for grid cells closer to the fault and smaller for grid cells farther from the fault, until it approaches the matrix background value.

[0030] The specific process takes the i-th layer of the research area as an example: Sand-reservoir ratio data points were collected according to a grid step size of 1500m×1500m, resulting in 17605 sand-reservoir ratio data points for this layer. These data points were then divided into a C1 table containing the horizontal axis, a C2 table containing the vertical axis, and a C3 table containing the sand-reservoir ratio. The three-dimensional geological structure model was then imported into FLAC3D software, and the quantitative fitting formula was programmed into the programming language to assign rock mechanics parameters to each unit grid. Simultaneously, well logging rock mechanics parameters and measured geostress data from different sections of multiple wells near the fault zone in this research area were obtained. The normal distance from each well's corresponding section to the fault was accurately calculated, forming multiple sets of correlated data samples of "rock mechanics parameters - geostress fault comprehensive shear index - normal distance". Correlation analysis and fitting were performed on the sample data to establish a quantitative relationship model of the variation of rock mechanics parameters within the fault influence zone with the normal distance from the fault, as shown in Figure 14. As shown, in the process of assigning rock mechanical parameters, all grid cells located within the fault influence zone are first identified through a three-dimensional geological structure model. The normal distance from each target grid cell to the fault is accurately calculated. The correction coefficients of the corresponding grid cells are obtained by substituting them into the quantitative relationship model. Then, the initial values ​​of the rock mechanical parameters are differentially corrected using these correction coefficients. This achieves a refined modeling effect where the correction amplitude is larger the closer to the fault and smaller the correction amplitude the farther away, until it approaches the matrix background value. Finally, a high-precision three-dimensional rock mechanical property model that considers both lithological changes and lateral disturbances of the fault is obtained. The principle and process of this model are also applicable to constructing three-dimensional models of other rock mechanical parameters such as cohesion, internal friction angle, compressive strength, and tensile strength. It can provide a reliable mechanical parameter field for reservoir fracturing evaluation, fracturing simulation, and wellbore stability analysis.

[0031] Please refer to Figure 2 , Figure 2 This is a structural diagram of an intelligent modeling device for the rock mechanical properties of tight sandstone reservoirs, as proposed in one embodiment of this application. Figure 2 As shown in the figure, this disclosure also provides an intelligent modeling device for the mechanical properties of tight sandstone reservoirs. The device includes: a multi-source data acquisition module 201, a fault fusion geological modeling module 202, a mechanical parameter-sand-reservoir ratio fitting module 203, a three-dimensional sand-reservoir ratio modeling module 204, and a fault-corrected mechanical property modeling module 205; wherein, The multi-source data acquisition module 201 is used to acquire three-dimensional seismic interpretation data, well logging data, and rock mechanics experimental data. The fault fusion geological modeling module 202 establishes a three-dimensional geological structure model of tight sandstone reservoirs with fused fault structures based on the three-dimensional seismic interpretation data. The mechanical parameter-sand-reservoir ratio fitting module 203 calculates static rock mechanical parameters based on the well logging data and rock mechanical experimental data, and establishes a quantitative fitting relationship between the static rock mechanical parameters and the sand-reservoir ratio; wherein, the sand-reservoir ratio is the ratio of the sandstone thickness in the reservoir to the total thickness of the section. The three-dimensional sand-reservoir ratio modeling module 204 extracts lithological factor data volumes from three-dimensional seismic interpretation data, discriminates the lithological factor data volumes based on the critical values ​​of sandstone and mudstone calibrated by the well logging data, generates the three-dimensional spatial distribution of sandstone and mudstone, and obtains a three-dimensional sand-reservoir ratio distribution model based on the three-dimensional spatial distribution; and The fault-corrected mechanical property modeling module 205 calculates the initial values ​​of rock mechanical parameters for each grid cell based on the three-dimensional sand-storage ratio distribution model and quantitative fitting relationship, and assigns the three-dimensional geological structure model based on the initial values ​​of the rock mechanical parameters; establishes a quantitative attenuation relationship between the static rock mechanical parameters and the fault distance, and uses the attenuation relationship to correct the rock mechanical parameter values ​​of the grid cells within the fault disturbance range, thereby obtaining the final three-dimensional rock mechanical property model.

[0032] This disclosure also provides an electronic device, please refer to... Figure 17 , Figure 17 This is a schematic diagram of an electronic device illustrated in an embodiment of this disclosure. For example... Figure 17 As shown, the electronic device 100 includes a memory 110 and a processor 120. The memory 110 and the processor 120 are connected via a bus for communication. The memory 110 stores a computer program that can run on the processor 120 to implement the steps in the intelligent modeling method for the rock mechanical properties of tight sandstone reservoirs disclosed in this embodiment.

[0033] The disclosed embodiments also provide a computer-readable storage medium that, when the instructions in the computer-readable storage medium are executed by a processor of a computer device, enables the computer device to perform the steps in the intelligent modeling method for the rock mechanical properties of tight sandstone reservoirs as described in the embodiments of this disclosure.

[0034] Example 1 This invention conducted modeling verification experiments in a tight sandstone reservoir research area. The rock mechanical properties of the i-th layer in the area were modeled using both existing traditional modeling methods and the method of this invention. The modeling effect of fractured sections and the overall modeling efficiency were compared and verified. The existing Kriging interpolation method based on well logging data, relying only on interpolation data from a few to dozens of wells with well spacing of 1km to 10km, generates a planar distribution model of Young's modulus in a small layer, as shown below. Figure 15As shown. In the inter-well region, the mechanical parameter field is a smooth, gradually changing field, which cannot characterize the lateral heterogeneity of the reservoir. Furthermore, the modeling process of manual interpolation and assignment for a single layer takes several days. Simultaneously, existing methods ignore the influence of fault disturbances, homogenizing the fault zone with the surrounding rock, leading to significant deviations between the fault development zone model and the actual formation mechanical characteristics, resulting in model distortion. However, when modeling using the method of this invention, three-dimensional seismic lithology factors, well logging elastic parameters, and rock mechanics experimental data are integrated to construct a physical-driven modeling chain of "well logging interpretation → seismic lithology → sand-reservoir ratio → mechanical parameters." Using three-dimensional seismic lithology factors as the sand-reservoir ratio interpretation data source, fine interpretation grids such as 10×10m, 20×20m, and 50×50m can be set. In the i-th layer of the study area, 17605 sand-reservoir ratio data points were obtained, generating a Young's modulus plane distribution model, as shown... Figure 16 As shown, the model can clearly delineate the boundaries between high-value stripes and low-value blocks in the inter-well region, accurately reproducing the heterogeneous distribution characteristics of the reservoir. Simultaneously, statistical analysis determined the effective range of fault disturbance in the work area to be 400 meters, establishing a quantitative attenuation relationship between rock mechanical parameters within the fault influence zone and the distance from the fault. Differential corrections were made to the mechanical parameters of the fault zone grid units, achieving quantitative modeling of fault disturbance. The model parameters highly match the actual formation logging and experimental data. Furthermore, relying on the FISH embedded programming language, an automatic assignment script was written, integrating Petrel+FLAC3D and Rhino+Griddle to form a multi-software collaborative workflow. This enabled automatic traversal of grid units, parameter calculation, and attribute assignment, with lossless data transfer throughout the entire process. The single-layer modeling processing time in the work area was reduced from several days to several minutes, completely replacing traditional manual operations. Experimental verification shows that the rock mechanical property model constructed by the method of this invention is far superior to existing technologies in terms of spatial resolution, data density, and heterogeneity characterization. It also effectively solves the problem of model distortion in fracture development zones, while achieving an order-of-magnitude improvement in modeling efficiency. The entire process is standardized and reproducible, making it suitable for industrial application.

[0035] This application describes embodiments with reference to flowchart illustrations and / or block diagrams of methods, apparatuses, electronic devices, and computer program products according to embodiments of this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0036] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0037] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0038] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0039] Although preferred embodiments of the present application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present application.

[0040] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0041] The above provides a detailed description of the intelligent modeling method for the rock mechanical properties of tight sandstone reservoirs provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the method and its core ideas. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for intelligent modeling of the mechanical properties of tight sandstone reservoirs, characterized in that, include: Acquire 3D seismic interpretation data, well logging data, and rock mechanics experimental data; Based on the aforementioned three-dimensional seismic interpretation data, a three-dimensional geological structure model of tight sandstone reservoirs incorporating fault structures was established. Based on the well logging data and rock mechanics experimental data, static rock mechanics parameters were calculated, and a quantitative fitting relationship between the static rock mechanics parameters and the sand-storage ratio was established; wherein, the sand-storage ratio is the ratio of the sandstone thickness in the reservoir to the total thickness of the section. The lithological factor data volume is extracted from the three-dimensional seismic interpretation data. Based on the critical values ​​of sandstone and mudstone calibrated by the well logging data, the lithological factor data volume is judged to generate the three-dimensional spatial distribution of sandstone and mudstone. Based on the three-dimensional spatial distribution, a three-dimensional sand-reservoir ratio distribution model is obtained. Based on the three-dimensional sand-storage ratio distribution model and quantitative fitting relationship, the initial values ​​of rock mechanical parameters of each grid cell are calculated, and the three-dimensional geological structure model is assigned values ​​based on the initial values ​​of rock mechanical parameters. A quantitative attenuation relationship between the static rock mechanical parameters and the fault distance is established, and the rock mechanical parameter values ​​of the grid cells within the fault disturbance range are corrected using the attenuation relationship to obtain the final three-dimensional rock mechanical property model.

2. The intelligent modeling method for the rock mechanical properties of tight sandstone reservoirs according to claim 1, characterized in that, The establishment of a three-dimensional geological structure model for tight sandstone reservoirs with integrated fault structures includes: Based on the structural interpretation results of the aforementioned three-dimensional seismic interpretation data, a three-dimensional structural model of the reservoir is established. Based on the fault interpretation results and fault disturbance range of the aforementioned 3D seismic interpretation data, a 3D fault model is constructed. The three-dimensional structural model is fused with the three-dimensional fault model to generate a three-dimensional geological structure model of a tight sandstone reservoir with fused fault structure.

3. The intelligent modeling method for the rock mechanical properties of tight sandstone reservoirs according to claim 2, characterized in that, The fault disturbance range is determined in the following way: Obtain the minimum principal stress gradient data of multiple wells in the work area and the corresponding vertical distance from the well point to the fault; The correlation between the minimum principal stress gradient data and the vertical distance is analyzed to determine the effective range of fault disturbance.

4. The intelligent modeling method for the rock mechanical properties of tight sandstone reservoirs according to claim 1, characterized in that, The establishment of a quantitative fitting relationship between the static rock mechanical parameters and the sand-storage ratio includes: Based on the well logging data, the dynamic rock mechanics parameters of a single well are calculated using elasticity formulas; wherein the well logging data includes at least P-wave transit time, S-wave transit time, and density, and the rock mechanics parameters include at least Young's modulus and Poisson's ratio; Static rock mechanics parameters were obtained from the same well through core rock mechanics experiments. Based on the dynamic and static rock mechanics parameters, a conversion relationship between the dynamic and static parameters is established. Using this conversion relationship, the dynamic rock mechanics parameters are converted into static rock mechanics parameters, thereby obtaining a static rock mechanics parameter curve. Based on the static rock mechanics parameter curves, the average static rock mechanics parameters of each layer and the corresponding sand-storage ratio are calculated. The average value of the static rock mechanical parameters is fitted to the sand-storage ratio to obtain the quantitative fitting relationship.

5. The intelligent modeling method for the rock mechanical properties of tight sandstone reservoirs according to claim 1, characterized in that, Extracting lithological factor data volumes from 3D seismic interpretation data, including obtaining lithological factor data volumes that can characterize lithological differences through seismic inversion.

6. The intelligent modeling method for the rock mechanical properties of tight sandstone reservoirs according to claim 1, characterized in that, The critical values ​​for sandstone and mudstone calibrated based on the well logging data are used to discriminate the lithological factor data volume, generating a three-dimensional spatial distribution of sandstone and mudstone, including: By comparing the sandstone and mudstone intervals interpreted from the well logging at the well point with the lithological factor values ​​from the seismic traces, the critical values ​​of the lithological factors that distinguish sandstone from mudstone are determined. The lithological factor critical value is used to distinguish the lithological factor data volume, and a three-dimensional spatial distribution data volume of sandstone and mudstone is generated.

7. The intelligent modeling method for the rock mechanical properties of tight sandstone reservoirs according to claim 1, characterized in that, The three-dimensional geological structure model is assigned values ​​and faults are corrected based on the initial values ​​of the rock mechanics parameters, including: Obtain the static rock mechanics parameters of multiple wells in the target layer near the fault, and obtain the normal distance between a single well and the fault; The static rock mechanical parameters are fitted with the corresponding normal distance to establish the attenuation relationship of static rock mechanical parameters with distance within the fault influence zone; During the assignment of rock mechanical parameters, grid cells located within the fault influence zone are identified, their normal distance from the fault is calculated, and the initial values ​​of the rock mechanical parameters are corrected by substituting them into the attenuation relationship.

8. The intelligent modeling device for the rock mechanical properties of tight sandstone reservoirs according to any one of claims 1-7, characterized in that, The device includes: The multi-source data acquisition module is used to acquire 3D seismic interpretation data, well logging data, and rock mechanics experimental data; The fault fusion geological modeling module establishes a three-dimensional geological structure model of tight sandstone reservoirs with fused fault structures based on the three-dimensional seismic interpretation data. The mechanical parameter-sand-reservoir ratio fitting module calculates static rock mechanical parameters based on the well logging data and rock mechanics experimental data, and establishes a quantitative fitting relationship between the static rock mechanical parameters and the sand-reservoir ratio; wherein, the sand-reservoir ratio is the ratio of the sandstone thickness in the reservoir to the total thickness of the section. The three-dimensional sand-reservoir ratio modeling module extracts lithological factor data volumes from three-dimensional seismic interpretation data. Based on the critical values ​​of sandstone and mudstone calibrated by the well logging data, it discriminates the lithological factor data volumes, generates the three-dimensional spatial distribution of sandstone and mudstone, and obtains a three-dimensional sand-reservoir ratio distribution model based on the three-dimensional spatial distribution; and The fault-corrected mechanical property modeling module calculates the initial values ​​of rock mechanical parameters for each grid cell based on the three-dimensional sand-storage ratio distribution model and quantitative fitting relationship, and assigns values ​​to the three-dimensional geological structure model based on the initial values ​​of rock mechanical parameters; it establishes a quantitative attenuation relationship between the static rock mechanical parameters and fault mechanical properties, and uses the attenuation relationship to correct the rock mechanical parameter values ​​of grid cells within the fault disturbance range, thus obtaining the final three-dimensional rock mechanical property model.

9. An electronic device, characterized in that, include: A processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the intelligent modeling method for the rock mechanical properties of tight sandstone reservoirs as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, which, when executed by a processor, implements the intelligent modeling method for the rock mechanical properties of tight sandstone reservoirs as described in any one of claims 1 to 7.

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