Small sample stress prediction method and system based on fault activity

Abstract

The application discloses a small sample ground stress prediction method and system based on fault activity, and the method comprises the following steps: acquiring geological data of a plurality of wells randomly selected in a research area, and extracting ground stress sample points in a preset range of faults in the geological data of the wells; importing the geological data of the wells into Petrel software to establish a three-dimensional grid model and a fault model; establishing a ground stress fault function according to the ground stress sample points, the three-dimensional grid model and the fault model; calculating the curvature correction distance of each grid unit in the three-dimensional grid model to the nearest fault, and generating a ground stress three-dimensional space constraint trend body according to the curvature correction distance and the ground stress fault function; and performing ground stress random modeling on the ground stress three-dimensional space constraint trend body according to the ground stress sample points by using a sequential Gaussian simulation method. Based on the above data processing procedure, the application converts the relationship between fault activity and stress into a constraint trend body in ground stress random modeling, and realizes ground stress modeling under the control and constraint of faults.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration and development technology, and in particular to a method and system for predicting geostress based on fault activity using small samples. Background Technology

[0002] In-situ stress prediction is a core geomechanical problem for efficient oil and gas development, and is crucial for engineering decisions such as well drilling optimization and fracturing design. Existing prediction methods all have significant limitations: geological model methods have clear mechanisms, but rely on ideal assumptions and are difficult to characterize the heterogeneity of actual geology and faults; data-driven methods have limited multi-source data and sample sizes, and predictions are unstable under conditions with few wells; pure machine learning methods have strong fitting capabilities, but lack the conditional constraints of fault activity, and are prone to producing results that violate geological laws.

[0003] To improve prediction accuracy, researchers have incorporated geological knowledge related to stratigraphic distribution and structural characteristics into three-dimensional geostress modeling, constructing models by combining geological data with seismic attribute interpolation. However, these methods rely on a single data source related to the stratigraphy or the assumption of homogeneity, and lack the constraint of faults, making it difficult to truly reflect the heterogeneity and dynamic changes under complex geological conditions. They also have poor applicability in deep, unconventional reservoirs and areas with few wells.

[0004] In offshore, deep-sea, and new blocks, the problems of fewer wells, difficulty in coring, and sparse data are more prominent, leading to a significant decrease in the prediction accuracy of traditional methods. Therefore, considering fault constraints and compensating for the lack of parameters in small samples, there is an urgent need for a method to extract multivariate geostress-related data (well trajectory, logging curves, geostress, trend body, fault characteristics) from a single geological data source to achieve high-precision geostress prediction. Summary of the Invention

[0005] This invention provides a small-sample geostress prediction method and system based on fault activity. By quantifying fault knowledge, a functional relationship between the distance to the fault and the stress distribution is established. At the same time, the relationship between fault activity and stress is transformed into a constraint trend body in the stochastic modeling of geostress, realizing geostress modeling with fault control constraints and reducing inter-well uncertainty.

[0006] Firstly, a small-sample geostress prediction method based on fault activity is provided, including: Obtain geological data from multiple randomly selected wells in the research area, and extract geostress sample points within the predetermined fault range from the geological data of the wells; Import the well geological data into Petrel software to create a three-dimensional mesh model and fault model; Based on the geostress sample points, the three-dimensional mesh model, and the fault model, a geostress fault function is established. Calculate the curvature correction distance from each grid cell in the three-dimensional mesh model to the nearest fault in the fault model, and generate a three-dimensional spatial constraint trend body of geostress based on the curvature correction distance and the geostress fault function; Using the sequential Gaussian simulation method, the geostress is stochastically modeled on the three-dimensional spatial constraint trend body of the geostress based on the geostress sample points, and the final geostress model is obtained.

[0007] Secondly, a small-sample geostress prediction system based on fault activity is provided, including: The data acquisition module is used to acquire geological data from multiple randomly selected wells in the research area, and to extract geostress sample points within the preset fault range from the geological data of the wells. The model building module is communicatively connected to the data acquisition module and is used to import the well geological data into Petrel software to build a three-dimensional mesh model and a fault model. The function module is communicatively connected to the data acquisition module and the model building module, and is used to establish a geostress fault function based on the geostress sample points, the three-dimensional mesh model and the fault model. A correction module, communicatively connected to the function module, is used to calculate the curvature correction distance from each grid cell in the three-dimensional mesh model to the nearest fault in the fault model, and to generate a three-dimensional spatial constraint trend body for geostress based on the curvature correction distance and the geostress fault function; and, The simulation module, connected to the correction module, is used to perform stochastic modeling of the three-dimensional spatial constraint trend body of the ground stress using the sequential Gaussian simulation method based on the ground stress sample points, and to obtain the final ground stress model.

[0008] Thirdly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the small-sample geostress prediction method based on fault activity as described above.

[0009] Fourthly, embodiments of the present invention provide an electronic device, including a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, wherein the processor, when executing the computer program, implements the small-sample geostress prediction method based on fault activity as described above.

[0010] Compared with the prior art, the advantages of the present invention are as follows: By combining the method of extracting complex well profile grids with the numericalization of geological knowledge and stochastic modeling of geostress, this method solves the technical challenges of "limited sample size, poor model generalization, and low geological consistency" in geostress prediction in deep, ultra-deep, and low-well areas, achieving precise prediction of geostress under small sample conditions. Furthermore, all steps of this method can be implemented in Petrel software, and the standardized function calculation and modeling process reduces manual intervention, improves data processing efficiency, and possesses good engineering operability and scalability. It can be widely applied to geostress modeling of oil and gas reservoirs in offshore, deep, and new blocks with limited well conditions. Attached Figure Description

[0011] Figure 1 This is a flowchart illustrating a small-sample geostress prediction method based on fault activity according to the present invention. Figure 2 This is an analysis diagram of outcrops of fractures and stresses controlled by the fault according to the present invention; Figure 3 This is a schematic diagram of the geostress sample points of some wells in this invention; Figure 4 This is a schematic diagram of the fault model of the present invention; Figure 5 This is a schematic diagram of the geostress fault function of the present invention; Figure 6 This is a schematic diagram of the theoretical constraint values ​​of geostress of the present invention; Figure 7 This is a schematic diagram of the three-dimensional spatial constraint trend body of the present invention; Figure 8 This is a schematic diagram of the single initial geostress model obtained by sequential Gaussian simulation according to the present invention; Figure 9 This is a schematic diagram of the geostress model verification of the present invention. Detailed Implementation

[0012] Referring now to specific embodiments of the invention, examples of which are illustrated in the accompanying drawings. Although the invention will be described in conjunction with specific embodiments, it will be understood that it is not intended to limit the invention to the described embodiments. Rather, it is intended to cover variations, modifications, and equivalents included within the spirit and scope of the invention as defined by the appended claims. It should be noted that the method steps described herein can be implemented by any functional block or functional arrangement, and any functional block or functional arrangement can be implemented as a physical entity or a logical entity, or a combination of both.

[0013] To enable those skilled in the art to better understand the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0014] Note: The examples described below are merely specific examples and are not intended to limit the embodiments of the present invention to the specific steps, values, conditions, data, order, etc. Those skilled in the art can utilize the concept of the present invention to construct more embodiments not mentioned herein by reading this specification.

[0015] In oil and gas exploration and development in deep, ultra-deep, and complex fault-bounded areas, accurate prediction of in-situ stress is crucial. Traditional methods suffer from significant bottlenecks: limited sample sizes under conditions of few wells lead to poor model generalization and large errors; existing methods rely on a single data source related to the formation or the assumption of homogeneity, employing well-controlled stochastic simulation methods, but failing to establish a static three-dimensional model constrained by surface fault properties. Therefore, well control is poor in deep and ultra-deep areas and in areas with few wells, resulting in considerable uncertainty and failing to meet the accuracy requirements for reservoir development. To address these issues, the present invention provides the following solution.

[0016] Please see Figure 1 This invention provides a small-sample geostress prediction method based on fault activity. The method includes: Step S100: Obtain geological data from multiple wells randomly selected in the research area, and extract geostress sample points within the preset fault range from the geological data of the wells. Taking a low-well block of deep oil and gas reservoirs in an existing work area as an example, in the work area (attached) Figure 2 (A, 2B, 2C) Seven wells (well names TK441, TK442, TK478, TK482, TK602, TK610, TK619) were selected, representing a typical small-sample work area. The target formation is a deep carbonate reservoir with multiple fault structures. Traditional geostress prediction methods have significant errors. This method reduces the average prediction error to a controllable and accurate range, fully meeting the engineering requirements for well drilling and completion optimization and fracturing design in oil and gas reservoirs. To accurately locate and predict faults and sample points in the geological model, and addressing the inaccuracy of traditional methods in stress prediction for small-sample areas, fine calculations and modeling are required in Petrel software.

[0017] Based on geological understanding—that is, summarizing and generalizing from natural geological phenomena—outcrops indicate a high rate of fracture development near faults, and since fractures are related to fault attitude, in-situ stress is significantly affected by fault structures. Simultaneously, in-situ stress sample points near faults and their related parameters were statistically analyzed from well geological data; see [link to documentation]. Figure 3 As shown. The method for obtaining ground stress sample points is as follows: First, the fault influence zone is automatically determined based on the shortest distance from the well trajectory sampling points in multiple well geological data to the fault model described below, and the in-situ stress sample points within the fault influence zone are extracted. The fault influence zone is the spatial range in which the distance from the well trajectory sampling point to the nearest fault is less than a preset distance threshold. This spatial range corresponds to the aforementioned preset fault range. The preset distance threshold is determined based on the fault scale, fault displacement, well spacing, and distribution of in-situ stress sample points in the research area.

[0018] Step S200: Import the well geological data into Petrel software to establish a three-dimensional mesh model and fault model; Specifically, in this embodiment of the invention, geological data from seven wells selected in the work area are collected, including: Trajectory data: including measured depth and three-dimensional spatial coordinates (X / Y / Z) (taking TK441 as an example, the measured depth is 480.0m, and the three-dimensional coordinates are 15240308m / 4584246m / 480.0m); Well logging data: density, acoustic wave; Fault data interpreted by 3D seismic analysis: 3D coordinates and attitude of fault lines; Layer data: a set of three-dimensional coordinate points on the top and bottom surfaces of each target layer; The geological data was standardized according to the Petrel software data format to remove inconsistencies in coordinate systems and data units. A 3D grid and a fault model were then established; see [link to documentation] for details. Figure 4 As shown.

[0019] Step S300: Based on the ground stress sample points, the three-dimensional mesh model, and the fault model, establish the ground stress fault function, including: Step S310: Calculate the geostress parameters of the geostress sample points; The geostress parameters include: P-wave velocity, S-wave velocity, Poisson's ratio, shear modulus, Young's modulus, and compressive strength.

[0020] Longitudinal wave velocity Vp = 1 / (DT) 0.3048 / 1000000); Transverse wave velocity Vs = Vp Sqrt( (1 - 2 0.25) / (2 (1 - 0.25)) ); Poisson's ratio Nu = (Pow(Vp,2) - 2) Pow(Vs,2)) / (2 (Pow(Vp,2) - Pow(Vs,2))) represents the ratio of transverse strain to longitudinal strain in the rock; Shear modulus G = Rho Pow(Vs,2) represents the rock's ability to resist shear deformation; Young's modulus E = 2 G (1 + Nu) represents the rock's ability to resist elastic tensile and compressive deformation; Compressive strength UCS = E / (10 (1 - Nu) represents the uniaxial compressive failure strength of rock.

[0021] In the formula, DT is the acoustic time difference; Rho is the density; Pow(Vp,2) represents the square of Vp, Pow(Vs,2) represents the square of Vs, and Sqrt represents the square root.

[0022] Step S320: Call the Geometrical modeling tool to calculate the initial distance to fault from each mesh cell in the three-dimensional mesh model to the nearest fault in the fault model; Specifically, Geometrical modeling is a tool in Petrel used to generate geometric property volumes in batches. It does not directly create construction models, but automatically calculates various geometrically related property volumes, such as the distance to the fault, on an existing 3D construction framework (fault).

[0023] Step S330: Invoke the Function Window tool to establish the geostress fault function between the geostress parameters and the initial distance.

[0024] Specifically, the Function Window is a graphical tool designed to create, edit, and visualize mathematical function relationships. Using extracted geostress sample points, with Distance to faults on the x-axis and these geostress parameters on the y-axis, a weighted linear regression method is employed to establish a quantitative functional relationship between geostress parameters and the initial distance—the geostress-fault function. The weights are taken as the reciprocal of the distance from the sample point to the fault; that is, sample points closer to the fault are assigned higher weights to highlight key information about stress changes near the fault.

[0025] See also for details. Figure 5 As shown, the final geostress fault function is as follows: Stress_Constraint = a Distance_to_Fault + b; In the formula, Stress_Constraint represents the ground stress distribution at the sample point; Distance_to_Fault represents the initial distance from each grid cell to the nearest fault; and a and b are regression coefficients.

[0026] Therefore, the stress-fault function realizes the transformation of fault activity knowledge into a numerical model. The weighted regression makes the fitting results more focused on the stress distribution near the fault, avoiding the excessive pulling of the regression curve by distant sample points, and is more in line with the geological law that the fault dominates the stress distribution.

[0027] Step S400: Calculate the curvature correction distance from each grid cell in the three-dimensional mesh model to the nearest fault in the fault model, and generate a three-dimensional spatial constraint trend body of geostress based on the curvature correction distance and the geostress fault function; The Distance To Object function under the Geometry category is called, taking the 3D seismic interpretation fault data of the work area as the object. Step S410, the method for calculating the curvature correction distance from each grid cell in the 3D mesh model to the nearest fault in the fault model is as follows: First, calculate the average curvature [C=(C] for each fault plane grid point. min-local +C max-local ) / 2], where C min-local and C max-local These represent the minimum and maximum principal curvatures in the two principal directions at the grid points on the fault plane, respectively. Then, the total cross-sectional curvature C is normalized to the interval [0,1] to obtain the normalized average curvature C0. norm The normalization formula is C norm =(CC min-all ) / (C max-all -C min-all ), where C min-all C is the minimum average curvature of the entire fault plane. max-all This represents the maximum average curvature of the entire cross-section. Then, the curvature weighting factor W is obtained. C =1+k·C norm Where k is the curvature enhancement coefficient (ranging from 0.2 to 0.5, obtained by inverse calculation using well point stress data near the fault). Finally, the curvature correction distance from each grid cell to the nearest fault is shown in the following formula: D curved =D straight ×exp( W C ·C norm / C max ); In the formula, D curvedW represents the curvature correction distance from each mesh cell in the 3D mesh model to the nearest fault in the fault model. C D is the curvature weighting factor; straight C is the Euclidean linear distance from each mesh cell in the 3D mesh model to the nearest fault; norm C represents the normalized average curvature of the grid points at the nearest fault plane of this grid cell; max This is a reference value for curvature normalization. After normalization, it is usually taken as 1, but it can also be the maximum value of the normalized curvature of the entire fault plane.

[0028] Therefore, the more severe the fault curvature, the smaller the equivalent distance, thus imparting a higher stress value in the subsequent constraint body. This correction avoids neglecting the fault geometry by using conventional straight-line distances.

[0029] The step of generating a three-dimensional spatial constraint trend body for geostress based on the curvature correction distance and the geostress fault function includes: Step S420: Call the Distance To Object tool to correct the distance according to the curvature and generate the distance to the fault attribute body; Step S430: Substitute the distance to fault attribute volume into the geostress fault function, and call the Petrel attribute calculator to generate the geostress theoretical constraint value volume; this geostress theoretical constraint value volume assigns a geostress theoretical value based on geological knowledge to each grid cell, see details. Figure 6 As shown, this reflects the macroscopic control law of fault structure on the geostress of the entire work area.

[0030] Step S440: Using the well geological data (such as fault attitude, fracture development degree, and lithological distribution), the theoretical constraint value of the geostress is trend-corrected to ultimately generate a three-dimensional spatial constraint trend body of geostress under fault control. See details below. Figure 7 As shown, this realizes the transformation of abstract fault knowledge into quantitative modeling constraints.

[0031] Step S500: Using the sequential Gaussian simulation method, based on the stress sample points, perform stochastic modeling of the three-dimensional spatial constraint trend volume of stress to obtain the final stress model, including: Step S510: Based on the ground stress sample points and the geological characteristics of the study area, perform a variation function analysis of the ground stress. Specifically, based on the geostress sample points and combined with the geological characteristics and lithological distribution of the work area, the spatial variation function of geostress properties is analyzed to determine parameters such as range, sill value, and nugget value, reflecting the spatial continuity characteristics of geostress and ensuring that the variation function conforms to geological laws (e.g., the range is greater along the fault strike and smaller perpendicular to the fault strike).

[0032] Step S520: Using the sequential Gaussian simulation method, multiple model simulations are performed based on the analysis results, the geostress parameters and well logging data of the geostress sample points, and the three-dimensional spatial constraint trend body of the geostress, to obtain multiple initial geostress models. Specifically, the sequential Gaussian simulation method is used for multiple simulations (≥20 simulations). During the simulation, the simulated geostress values ​​of each grid cell are constrained by a three-dimensional spatial constraint trend body: Open the Sequential Gaussian Simulation tool in the Modeling menu, use the 3D mesh as the input mesh, select the geostress parameters and well logging data containing geostress sample points as hard data in the Primary property, select the previously generated three-dimensional spatial constraint trend body as the global trend constraint in the Secondary property, and finally click Run to generate an initial geostress model constrained by geological knowledge. See details... Figure 8 As shown. Finally, a well is randomly selected from the Well Section (taking TK441 as an example) (see appendix). Figure 9 The results show that the model fit is verified by comparing the original measured curve with the predicted curve.

[0033] Step S530: Screen and merge all the initial geostress models to generate the final geostress model.

[0034] Specifically, the initial geostress model of multiple simulation results is evaluated for quality. Simulation results that obviously contradict geological understanding or have large fitting deviations of sample points are eliminated. The optimized simulation results are then fused using the arithmetic mean method to generate the final geostress model. This model includes the P-wave and S-wave velocities, Poisson's ratio, Young's modulus, and compressive strength of each grid cell in the entire work area, and its spatial resolution is consistent with the geological model of the work area.

[0035] Finally, the validated final geostress model will be output in the Petrel software standard format, and can also be exported in various forms such as numerical tables, profile maps, and planar contour maps, providing multi-dimensional geostress data support for oil and gas field exploration and development projects. (i) Drilling and completion engineering: Provide data on the distribution of geostress along the well trajectory, optimize the well trajectory design, determine a reasonable drilling fluid density, and avoid risks such as wellbore collapse and leakage; (ii) Fracturing design: Provide data on the magnitude, direction, and spatial distribution of geostress in the target formation, determine the direction and extension range of the fracturing fractures, optimize parameters such as fracturing fluid dosage and construction pressure, and improve the fracturing effect; (III) Reservoir Development: Combine geostress model analysis to analyze reservoir fracture connectivity and oil and gas migration patterns, optimize well location deployment and development methods, and improve oil and gas reservoir recovery rate.

[0036] Therefore, this implementation method combines the method of extracting complex well profile grids with the numericalization of geological knowledge and stochastic modeling of geostress, solving the technical problems of "limited sample size, poor model generalization, and low geological consistency" in geostress prediction in deep, ultra-deep, and low-well areas. It achieves accurate prediction of geostress under small sample conditions. At the same time, all steps of this method can be implemented in Petrel software. The standardized function calculation and modeling process reduces manual intervention, improves data processing efficiency, and has good engineering operability and scalability. It can be widely applied to geostress modeling of oil and gas reservoirs with few wells in offshore, deep, and new blocks.

[0037] This invention provides a small-sample geostress prediction system based on fault activity, comprising: The data acquisition module is used to acquire geological data from multiple randomly selected wells in the research area, and to extract geostress sample points within the preset fault range from the geological data of the wells. The model building module is communicatively connected to the data acquisition module and is used to import the well geological data into Petrel software to build a three-dimensional mesh model and a fault model. The function module is communicatively connected to the data acquisition module and the model building module, and is used to establish a geostress fault function based on the geostress sample points, the three-dimensional mesh model and the fault model. A correction module, communicatively connected to the function module, is used to calculate the curvature correction distance from each grid cell in the three-dimensional mesh model to the nearest fault in the fault model, and to generate a three-dimensional spatial constraint trend body for geostress based on the curvature correction distance and the geostress fault function; and, The simulation module, connected to the correction module, is used to perform stochastic modeling of the three-dimensional spatial constraint trend body of the ground stress using the sequential Gaussian simulation method based on the ground stress sample points, and to obtain the final ground stress model.

[0038] Specifically, this embodiment corresponds one-to-one with the above method embodiments. The functions of each module have been described in detail in the corresponding method embodiments, so they will not be repeated here.

[0039] In summary, the beneficial effects of this invention are that it controls the average error of stress prediction in low-well blocks within the most accurate range, improving prediction certainty; the prediction results conform to geological and physical laws, eliminating unreasonable phenomena and enhancing engineering credibility; and the function of stress and distance to fault realizes the transformation of abstract geological knowledge into digital form.

[0040] Based on the same inventive concept, embodiments of this application also provide a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements all or part of the method steps of the above method.

[0041] The present invention can implement all or part of the processes in the above methods, or it can be accomplished by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content contained in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0042] Based on the same inventive concept, embodiments of this application also provide an electronic device, including a memory and a processor. The memory stores a computer program that runs on the processor. When the processor executes the computer program, it implements all or part of the method steps described above.

[0043] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the computer device, connecting all parts of the computer device through various interfaces and lines.

[0044] Memory can be used to store computer programs and / or modules. The processor performs various functions of the computer device by running or executing the computer programs and / or modules stored in the memory, and by accessing data stored in the memory. Memory can primarily include a program storage area and a data storage area. The program storage area can store the operating system and application programs required for at least one function (e.g., sound playback, image playback, etc.); the data storage area can store data created based on mobile usage (e.g., audio data, video data, etc.). Furthermore, memory can include high-speed random access memory, and can also include non-volatile memory, such as hard disks, RAM, plug-in hard disks, smart media cards (SMC), secure digital cards (SD cards), flash cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0045] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, servers, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0046] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), servers, and computer program products according to embodiments of the invention. It will 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 apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, 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.

[0047] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing 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.

[0048] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable 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.

[0049] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for predicting geostress using a small sample based on fault activity, characterized in that, include: Obtain geological data from multiple randomly selected wells in the research area, and extract geostress sample points within the predetermined fault range from the geological data of the wells; Import the well geological data into Petrel software to create a three-dimensional mesh model and fault model; Based on the geostress sample points, the three-dimensional mesh model, and the fault model, a geostress fault function is established. Calculate the curvature correction distance from each grid cell in the three-dimensional mesh model to the nearest fault in the fault model, and generate a three-dimensional spatial constraint trend body of geostress based on the curvature correction distance and the geostress fault function; Using the sequential Gaussian simulation method, the geostress is stochastically modeled on the three-dimensional spatial constraint trend body of the geostress based on the geostress sample points, and the final geostress model is obtained.

2. The small-sample geostress prediction method based on fault activity as described in claim 1, characterized in that, The step of establishing a geostress fault function based on the geostress sample points, the three-dimensional mesh model, and the fault model includes: Calculate the geostress parameters at the geostress sample points; The Geometrical modeling tool is used to calculate the initial distance from each grid cell in the 3D mesh model to the nearest fault in the fault model. The Function Window tool is invoked to establish the geostress fault function between the geostress parameters and the initial distance.

3. The small-sample geostress prediction method based on fault activity as described in claim 2, characterized in that, The geostress parameters include: P-wave velocity, S-wave velocity, Poisson's ratio, shear modulus, Young's modulus, and compressive strength.

4. The small-sample geostress prediction method based on fault activity as described in claim 2, characterized in that, The geostress fault function is shown below: Stress_Constraint = a Distance_to_Fault + b; In the formula, Stress_Constraint represents the ground stress distribution at the sample point; Distance_to_Fault represents the initial distance from each grid cell to the nearest fault; and a and b are regression coefficients.

5. The small-sample geostress prediction method based on fault activity as described in claim 1, characterized in that, The method for calculating the curvature correction distance from each mesh element in the three-dimensional mesh model to the nearest fault in the fault model is shown in the following formula: D curved =D straight ×exp( W C ·C norm / C max ); In the formula, D curved W represents the curvature correction distance from each mesh cell in the 3D mesh model to the nearest fault in the fault model. C Curvature weighting factor; D straight C is the Euclidean linear distance from each mesh cell in the 3D mesh model to the nearest fault; norm The normalized average curvature corresponding to the grid points of the nearest fault plane; C max This is the reference value for curvature normalization.

6. The small-sample geostress prediction method based on fault activity as described in claim 1, characterized in that, The step of generating a three-dimensional spatial constraint trend body for geostress based on the curvature correction distance and the geostress fault function includes: The Distance To Object tool is invoked to correct the distance based on the curvature and generate a distance-to-fault attribute body. Substitute the fault distance attribute into the geostress fault function, and call the Petrel attribute calculator to generate the geostress theoretical constraint value body; The geological data from the well is used to perform trend correction on the theoretical constraint value of geostress, generating a three-dimensional spatial constraint trend body for geostress.

7. The small-sample geostress prediction method based on fault activity as described in claim 1, characterized in that, The method of using sequential Gaussian simulation to perform stochastic modeling of the three-dimensional spatial constraint trend volume of ground stress based on the ground stress sample points, to obtain the final ground stress model, includes: Based on the aforementioned geostress sample points and the geological characteristics of the study area, a variation function analysis of geostress was conducted. Using the sequential Gaussian simulation method, multiple model simulations are performed based on the analysis results, the geostress parameters and well logging data of the geostress sample points, and the three-dimensional spatial constraint trend of geostress, resulting in multiple initial geostress models. All the initial geostress models are screened and merged to generate the final geostress model.

8. A small-sample geostress prediction system based on fault activity, characterized in that, include: The data acquisition module is used to acquire geological data from multiple randomly selected wells in the research area, and to extract geostress sample points near the fault lines in the geological data of the wells. The model building module is communicatively connected to the data acquisition module and is used to import the well geological data into Petrel software to build a three-dimensional mesh model and a fault model. The function module is communicatively connected to the data acquisition module and the model building module, and is used to establish a geostress fault function based on the geostress sample points, the three-dimensional mesh model and the fault model. The correction module, which is communicatively connected to the function module, is used to calculate the curvature correction distance from each grid cell in the three-dimensional mesh model to the nearest fault in the fault model, and to generate a three-dimensional spatial constraint trend body of geostress based on the curvature correction distance and the geostress fault function. as well as, The simulation module, connected to the correction module, is used to perform stochastic modeling of the three-dimensional spatial constraint trend body of the ground stress using the sequential Gaussian simulation method based on the ground stress sample points, and to obtain the final ground stress model.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the small-sample geostress prediction method based on fault activity as described in any one of claims 1 to 7.

10. An electronic device comprising a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, characterized in that, When the processor runs the computer program, it implements the small-sample geostress prediction method based on fault activity as described in any one of claims 1 to 7.

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