A ground stress correction method and device, electronic equipment and storage medium

Abstract

The present application belongs to the technical field of geophysical technology, and particularly relates to a ground stress correction method, device, equipment and medium. The present application provides a ground stress correction method, device, electronic equipment and storage medium. The present application obtains a prediction result of ground stress, determines a ground stress attribute according to the prediction result, determines a pseudo-well curve according to the ground stress attribute, and corrects the prediction result according to the ground stress attribute and the pseudo-well curve. The correction of the prediction result of ground stress is realized, and the corrected prediction result of ground stress is more consistent with the geological law, especially in areas with dramatic structural changes.

Description

Technical Field

[0001] This invention belongs to the technical field of geophysics, specifically relating to a method, device, equipment, and medium for correcting geostress. Background Technology

[0002] In-situ stress is a commonly used geological parameter in geological studies. Currently, the most widely used method for obtaining in-situ stress is based on pre-stack elastic parameter inversion. This method is fundamentally based on rock physics modeling, establishing a relationship between in-situ stress and elastic parameters, and then obtaining the elastic parameters through pre-stack inversion, thereby obtaining the in-situ stress. The drawback of this method is that the seismic data on which the pre-stack inversion is based reflects information within the strata. The inversion method does not provide special processing for areas with large strata curvature or well-developed faults in the lateral direction, thus reducing the accuracy of in-situ stress prediction in these structural locations. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention proposes a method, apparatus, electronic device, and storage medium for correcting geostress. This application involves obtaining predicted geostress results; determining geostress attributes based on the predicted results; determining a pseudo-well curve based on the geostress attributes; and correcting the predicted results based on the geostress attributes and the pseudo-well curve. This achieves the correction of geostress prediction results, and the corrected geostress prediction results are particularly consistent with geological laws in areas with drastic tectonic changes.

[0004] To solve the above-mentioned technical problems, the technical solution adopted by the present invention includes four aspects.

[0005] In a first aspect, a method for correcting geostress is provided, comprising: obtaining a predicted result of geostress; the predicted result being determined by seismic trace data of a target area; determining geostress attributes based on the predicted result; determining a pseudo-well curve based on the geostress attributes; and correcting the predicted result based on the geostress attributes and the pseudo-well curve.

[0006] In some embodiments, the geostress attribute includes: a second dip angle enhancement attribute, a second curvature attribute, and a second ant-body attribute; determining the geostress attribute based on the prediction result includes: obtaining the target range for geostress correction; extracting the first dip angle enhancement attribute, the first curvature attribute, and the first ant-body attribute from the prediction result based on the target range; and normalizing the first dip angle enhancement attribute, the first curvature attribute, and the first ant-body attribute respectively to obtain the second dip angle enhancement attribute, the second curvature attribute, and the second ant-body attribute.

[0007] In some embodiments, determining the pseudo-well curve based on the geostress attribute includes: acquiring the well coordinate data of a sample well; the sample well is an actual drilled well at a location where a fracture has developed; and extracting the dip angle enhancement pseudo-well curve, curvature pseudo-well curve, and ant body pseudo-well curve from the second dip angle enhancement attribute, the second curvature attribute, and the second ant body attribute, respectively, based on the well coordinate data.

[0008] In some embodiments, the method further includes: extracting a three-dimensional pseudo-well curve from the prediction result based on the well coordinate data.

[0009] In some embodiments, correcting the prediction result based on the geostress attribute and the pseudo-well curve includes: obtaining the measured geostress curve of the sample well; and correcting the prediction result based on the measured geostress curve, the three-dimensional pseudo-well curve, the dip angle-enhanced pseudo-well curve, the curvature pseudo-well curve, the ant-body pseudo-well curve, and the geostress attribute.

[0010] In some embodiments, the prediction result is corrected based on the measured geostress curve, the three-dimensional pseudo-well curve, the dip angle enhancement pseudo-well curve, the curvature pseudo-well curve, the ant-body pseudo-well curve, and the geostress attribute, including: determining a correction coefficient based on the measured geostress curve, the three-dimensional pseudo-well curve, the dip angle enhancement pseudo-well curve, the curvature pseudo-well curve, and the ant-body pseudo-well curve; determining a correction value based on the correction coefficient, the second dip angle enhancement attribute, the second curvature attribute, and the second ant-body attribute; and correcting the prediction result based on the correction value.

[0011] In some embodiments, the correction coefficients include: tilt enhancement correction coefficient, curvature correction coefficient, and ant body correction coefficient; the correction values ​​include: tilt enhancement correction values, curvature correction values, and ant body correction values; the correction values ​​for the prediction result based on the correction coefficients, the second tilt enhancement attribute, the second curvature attribute, and the second ant body attribute include: determining the tilt enhancement correction value based on the tilt enhancement coefficient and the second tilt enhancement attribute; determining the curvature correction value based on the curvature coefficient and the second curvature attribute; and determining the ant body correction value based on the ant body number and the second ant body attribute.

[0012] Secondly, this application proposes a geostress correction device, comprising: a first acquisition module for acquiring a predicted geostress result; a first determination module for determining a geostress attribute based on the predicted result; a second determination module for determining a pseudo-well curve based on the geostress attribute; and a first execution module for correcting the predicted result based on the geostress attribute and the pseudo-well curve.

[0013] A third aspect provides an electronic device including a storage device and a processor, the storage device storing a computer program, the processor executing the computer program to implement the steps of a method for correcting ground stress.

[0014] The fourth aspect provides a storage medium storing a computer program that can be executed by one or more processors, the computer program being able to implement the steps of any of the stress correction methods in the first aspect.

[0015] The beneficial effects of this invention are as follows: This application obtains the predicted results of geostress; determines the geostress attributes based on the predicted results; determines the pseudo-well curve based on the geostress attributes; and corrects the predicted results based on the geostress attributes and the pseudo-well curve. This achieves the correction of geostress prediction results, and the corrected geostress prediction results are more consistent with geological laws, especially in areas with drastic tectonic changes. Attached Figure Description

[0016] The scope of this disclosure can be better understood by reading the following detailed description of exemplary embodiments in conjunction with the accompanying drawings. The accompanying drawings are:

[0017] Figure 1 A flowchart illustrating an overall method for correcting geostress provided in an embodiment of this application;

[0018] Figure 2 This is a schematic diagram of the structure of a ground stress correction device provided in an embodiment of this application. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limitations on 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] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0021] If the application documents contain similar descriptions such as "first, second, third", the following explanation shall be added: In the following description, the terms "first, second, third" are used only to distinguish similar objects and do not represent a specific order of objects. It is understood that "first, second, third" may be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0023] Example 1:

[0024] In-situ stress is a commonly used geological parameter in geological studies. Currently, the most widely used method for obtaining in-situ stress is based on pre-stack elastic parameter inversion. This method is fundamentally based on rock physics modeling, establishing a relationship between in-situ stress and elastic parameters, and then obtaining the elastic parameters through pre-stack inversion, thereby obtaining the in-situ stress. The drawback of this method is that the seismic data on which the pre-stack inversion is based reflects information within the strata. The inversion method does not provide special processing for areas with large strata curvature or well-developed faults in the lateral direction, thus reducing the accuracy of in-situ stress prediction in these structural locations.

[0025] To address the problems existing in the current technology, such as Figure 1 As shown, this application provides a method for correcting ground stress. This method is applied to an electronic device, such as a server, mobile terminal, computer, or cloud platform. The functions implemented by the device data processing provided in this application embodiment can be achieved by the processor of the electronic device calling program code. The program code can be stored in a computer storage medium. The ground stress correction method includes:

[0026] Step S1: Obtain the predicted results of geostress.

[0027] The predicted in-situ stress is a known data point obtained through inversion from pre-stack seismic trace data. Since this application requires correction of the predicted gravity, the predicted in-situ stress needs to be obtained.

[0028] Step S2: Determine the geostress properties based on the prediction results.

[0029] The prediction results contain various geostress-related attributes, among which AFE dip enhancement, curvature enhancement, and ant-body enhancement attributes can be directly extracted. However, these directly extracted attributes cannot be directly used as constant gravity attributes to correct the prediction results.

[0030] Therefore, in some embodiments, step S2, "determining the geostress properties based on the prediction results," includes:

[0031] Step S21: Obtain the target range for the required correction of geostress.

[0032] Seismic trace data has three dimensions: trace number, line number, and time. Because existing technologies have low accuracy in predicting in-situ stress at fault locations, this application aims to correct the predictions for fault-prone areas. Therefore, it requires the trace number range, line number range, and time range for fault-prone areas, i.e., the target range.

[0033] Step S22: Extract the first tilt angle enhancement attribute, the first curvature attribute, and the first ant body attribute from the prediction result according to the target range.

[0034] Step S23: Normalize the first tilt angle enhancement attribute, the first curvature attribute, and the first ant body attribute respectively to obtain the second tilt angle enhancement attribute, the second curvature attribute, and the second ant body attribute.

[0035] Therefore, in order to correct the prediction results located at fault development areas, this application needs to extract the first dip angle enhancement attribute, the first curvature attribute, and the first ant-body attribute from the prediction results according to the target range. Then, the first dip angle enhancement attribute, the first curvature attribute, and the first ant-body attribute are normalized to obtain the second dip angle enhancement attribute, the second curvature attribute, and the second ant-body attribute. The gravitational attributes ultimately used to correct the prediction results include: the second dip angle enhancement attribute, the second curvature attribute, and the second ant-body attribute. The terms "first" and "second" are used here for distinction, as the data before and after normalization will have numerical differences. The dip angle enhancement attribute refers to the AFE dip angle enhancement attribute. Furthermore, the dip angle enhancement attribute represents large-scale faults, the curvature attribute represents mesoscale faults, and the ant-body attribute represents small-scale faults.

[0036] Step S3: Determine the pseudo-well curve based on the aforementioned geostress properties.

[0037] When correcting the prediction results, a sample well needs to be found to determine the region, and then the pseudo-well curve for that region is obtained from the geostress properties based on the region where the sample well is located. Therefore, in some embodiments, step S3, "determining the pseudo-well curve based on the geostress properties," includes:

[0038] Step S31: Obtain the well coordinate data of the sample well.

[0039] Step S32: Extract the dip angle enhancement pseudo-well curve, curvature pseudo-well curve, and ant body pseudo-well curve from the second dip angle enhancement attribute, the second curvature attribute, and the second ant body attribute, respectively, based on the well coordinate data.

[0040] Therefore, we need to select a real-world drilled well as a sample well. Furthermore, this sample well must have encountered a fractured area. After determining the sample well, we can obtain its well coordinate data. Based on the well coordinate data, we can extract the dip enhancement pseudo-well curve, curvature pseudo-well curve, and ant-body pseudo-well curve for the region where the sample well is located from the second dip enhancement attribute, the second curvature attribute, and the second ant-body attribute.

[0041] Of course, simply having dip angle-enhanced pseudo-well curves, curvature pseudo-well curves, and ant-body pseudo-well curves is insufficient to make the corrected prediction results closely resemble reality. Therefore, in some embodiments, the geostress correction method further includes:

[0042] Step S33: Extract the three-dimensional pseudo-well curve from the prediction result based on the well coordinate data.

[0043] By extracting the three-dimensional pseudo-well curve as another basic data, the acquisition and determination of the pseudo-well curve were completed.

[0044] Step S4: Correct the prediction results based on the geostress properties and the pseudo-well curve.

[0045] After completing the collection of pseudo-well curves and geostress properties, the prediction results can be corrected based on the pseudo-well curves and geostress properties.

[0046] Therefore, this application obtains the predicted results of geostress; determines the geostress attributes based on the predicted results; determines the pseudo-well curve based on the geostress attributes; and corrects the predicted results based on the geostress attributes and the pseudo-well curve. This achieves the correction of geostress prediction results, and the corrected geostress prediction results are more consistent with geological laws, especially in areas with drastic tectonic changes.

[0047] In some embodiments, step S4, "correcting the prediction result based on the geostress properties and the pseudo-well curve," includes:

[0048] Step S41: Obtain the measured geostress curve of the sample well.

[0049] Step S42: Correct the prediction results based on the measured geostress curve, three-dimensional pseudo-well curve, dip angle enhanced pseudo-well curve, curvature pseudo-well curve, ant body pseudo-well curve and the geostress properties.

[0050] Because the correction needs to be based on the actual situation, this application also requires obtaining the measured in-situ stress curves of the sample wells. Then, the prediction results are corrected based on the measured in-situ stress curves, three-dimensional pseudo-well curves, dip angle-enhanced pseudo-well curves, curvature pseudo-well curves, ant-body pseudo-well curves, and in-situ stress properties.

[0051] In some embodiments, step S42, "correcting the prediction result based on the measured geostress curve, the three-dimensional pseudo-well curve, the dip angle-enhanced pseudo-well curve, the curvature pseudo-well curve, the ant-body pseudo-well curve, and the geostress attribute," includes:

[0052] Step S421: Determine the correction coefficients based on the measured geostress curve, three-dimensional pseudo-well curve, dip angle enhanced pseudo-well curve, curvature pseudo-well curve, and ant body pseudo-well curve.

[0053] Step S422: Determine the correction value based on the correction coefficient, the second tilt angle enhancement attribute, the second curvature attribute, and the second ant body attribute.

[0054] Step S423: Correct the prediction result according to the correction value.

[0055] When correcting the prediction results, it is necessary to first determine the correction coefficients, then determine the correction values, and finally correct the prediction results based on the correction data. The correction coefficients are determined based on the measured geostress curves, three-dimensional pseudo-well curves, dip angle-enhanced pseudo-well curves, curvature pseudo-well curves, and ant-body pseudo-well curves.

[0056] When determining the correction coefficients, it is first necessary to establish a gravity correction formula. This gravity correction formula is as follows:

[0057] well_P-well_actual measurement=a*well_AFE+b*well_curvature+c*well_ANT.

[0058] In the formula, well_P represents the three-dimensional pseudo-well curve, well_measured represents the experimental geostress curve, well_AFE represents the dip angle-enhanced pseudo-well curve, well_curvature represents the curvature pseudo-well curve, and well_ANT represents the ant-body pseudo-well curve. a, b, and c represent correction coefficients for various types of fracture development, corresponding to the dip angle enhancement correction coefficient, curvature correction coefficient, and ant-body correction coefficient, respectively.

[0059] The support vector machine algorithm is used to perform regression analysis on the three correction coefficients a, b, and c in the formula to obtain the specific values ​​of the three contribution coefficients a, b, and c.

[0060] Support Vector Machine (SVM) is a pattern recognition method based on statistical learning theory, first proposed by Vapnik et al. in 1995. It is built upon the VC dimension theory and the principle of structural risk minimization in statistical learning theory. It seeks the optimal trade-off between model complexity and learning ability based on limited sample information to achieve the best generalization capability. The SVM regression algorithm maps data to a high-dimensional feature space through a kernel function and performs linear regression in this space. The linear regression in the high-dimensional feature space corresponds to the nonlinear regression in the low-dimensional input space.

[0061] Let the training sample set be {(x i ,y i ), i = 1, 2, ..., l}, where x i ∈R N For the N-dimensional measurement model sample input value, y i ∈R N Let be the sample output value, and l be the number of samples. For the above training sample set, a nonlinear mapping is used... The sample data in the training set is mapped to a high-dimensional linear feature space, and a linear regression estimation function is constructed in this linear space, which may have infinite dimensions. Let the estimation function have the following form:

[0062]

[0063] where w∈R NH ,b∈R, It is a nonlinear mapping that maps the input space to a high-dimensional feature space; b is the bias. The goal of the solution is to find the parameter w. T b, such that for input x outside the sample, we have

[0064]

[0065] The optimization problem corresponding to equation (1) is:

[0066] Constraints:

[0067]

[0068] Introducing slack variables ε, ε * The purpose is to ensure that the above equation has a solution, so (2) is transformed into

[0069]

[0070] Constraints:

[0071]

[0072] Here, C>0 represents the penalty coefficient. A larger C indicates a greater penalty for data points exceeding the error ε. ε is the insensitive loss function, which takes the form:

[0073]

[0074] Equation (4) is clearly a constrained quadratic programming problem. The Lagrange multiplier method is used to solve this quadratic programming problem with linear inequality constraints, i.e.

[0075]

[0076] Where a i ,a i * ≥0, i=1,2,…,l, are Lagrange multipliers.

[0077] Introducing the kernel function K(x) i ,x j (replacing nonlinear mapping) The kernel function is any symmetric function that satisfies the Mercer condition. Therefore, the dual optimization problem of equation (5) can be obtained:

[0078]

[0079] Constraints:

[0080]

[0081] Solving equation (6) yields the final estimated function:

[0082]

[0083] When using support vector machines for linear regression, the learning speed is relatively slow when the number of samples is large, but the generalization ability is very good when the number of samples is small. In other words, support vector machines have a strong ability to learn from small samples.

[0084] Conventional methods for predicting geostress are based on pre-stack seismic inversion, which do not fully consider the impact of fault development on geostress. This invention uses AFE dip enhancement attributes to represent large-scale faults, curvature attributes to represent mesoscale faults, and ant-like structures to represent small-scale faults. By using a support vector machine algorithm, the relationship between fault attributes at different scales and geostress prediction errors is established, thereby determining the correction coefficients for various types of faults.

[0085] After determining the correction coefficients for various types of fractures, the correction value can be determined using the correction coefficients, the second dip angle enhancement attribute, the second curvature attribute, and the second ant-body attribute. Therefore, in some embodiments, step S422, "correction value of the prediction result based on the correction coefficients, the second dip angle enhancement attribute, the second curvature attribute, and the second ant-body attribute," includes:

[0086] Step S4221: Determine the tilt enhancement correction value based on the tilt enhancement coefficient and the second tilt enhancement attribute.

[0087] Step S4222: Determine the curvature correction value based on the curvature coefficient and the second curvature attribute.

[0088] Step S4223: Determine the ant body correction value based on the ant body number and the second ant body attribute.

[0089] The correction values ​​in this application include: tilt angle enhancement correction value, curvature correction value, and ant body correction value. Each correction value can be obtained by multiplying the corresponding correction coefficient by the corresponding attribute.

[0090] Therefore, in step S423, the formula for correcting the prediction result based on the correction value is as follows:

[0091] P_correction=Pa*seis_AFE-b*seis_curvature-c*seis_ANT.

[0092] In the formula, P_correction represents the corrected prediction result, P is the directly obtained prediction result, seis_AFE represents the second dip angle enhancement attribute, which is the result after normalization of the first dip angle enhancement attribute; seis_curvature represents the second curvature attribute, which is the result after normalization of the first curvature attribute; seis_ANT represents the second ant body attribute, which is the result after normalization of the first ant body attribute. a represents the dip angle enhancement correction coefficient, b represents the curvature correction coefficient, and c represents the ant body correction coefficient. Therefore, a*seis_AFE represents the dip angle enhancement correction value, b*seis_curvature represents the curvature correction value, and c*seis_ANT represents the ant body correction value. Thus, the corrected prediction result can be obtained by removing the dip angle enhancement correction value, curvature correction value, and ant body correction value from the original prediction result.

[0093] Therefore, this application analyzes factors such as stress changes near faults and corrects previously known stress prediction results based on fault properties and measured stress data, aiming to obtain stress data that better reflects actual geological conditions. The corrected stress prediction results are more consistent with geological laws, especially in areas with dramatic tectonic changes.

[0094] Example 2:

[0095] Based on the foregoing embodiments, this application provides a ground stress correction device. The various modules and units included in the device can be implemented by a processor in a computer device; of course, they can also be implemented by specific logic circuits. In the implementation process, the processor can be a central processing unit (CPU), a microprocessor unit (MPU), a digital signal processor (DSP), or a field programmable gate array (FPGA), etc.

[0096] like Figure 2 As shown, the second aspect provides a ground stress correction device, including: a first acquisition module 1, a first determination module 2, a second determination module 3, and a first execution module 4.

[0097] The first acquisition module 1 is used to acquire the predicted results of in-situ stress. The first determination module 2 is used to determine the in-situ stress attributes based on the predicted results. The second determination module 3 is used to determine the pseudo-well curve based on the in-situ stress attributes. The first execution module 4 is used to correct the predicted results based on the in-situ stress attributes and the pseudo-well curve.

[0098] In some embodiments, the first determining module 2 includes: a second obtaining module, a second execution module, and a third execution module.

[0099] The second acquisition module is used to acquire the target range for the required correction of ground stress. The second execution module is used to extract the first dip angle enhancement attribute, the first curvature attribute, and the first ant-body attribute from the prediction result based on the target range. The third execution module is used to normalize the first dip angle enhancement attribute, the first curvature attribute, and the first ant-body attribute respectively to obtain the second dip angle enhancement attribute, the second curvature attribute, and the second ant-body attribute.

[0100] In some embodiments, the second determining module 3 includes a third acquisition module and a fourth execution module.

[0101] The third acquisition module is used to acquire the well coordinate data of the sample well. The fourth execution module is used to extract the dip angle enhancement pseudo-well curve, curvature pseudo-well curve, and ant body pseudo-well curve from the second dip angle enhancement attribute, the second curvature attribute, and the second ant body attribute, respectively, based on the well coordinate data.

[0102] In some embodiments, the geostress correction device further includes a fifth execution module.

[0103] The fifth execution module is used to extract a three-dimensional pseudo-well curve from the prediction results based on the well coordinate data.

[0104] In some embodiments, the first execution module 4 comprises: a fourth acquisition module and a sixth execution module.

[0105] The fourth acquisition module is used to acquire the measured in-situ stress curve of the sample well. The sixth execution module is used to correct the prediction results based on the measured in-situ stress curve, the three-dimensional pseudo-well curve, the dip angle enhanced pseudo-well curve, the curvature pseudo-well curve, the ant-body pseudo-well curve, and the in-situ stress properties.

[0106] In some embodiments, the sixth execution module includes: a third determining module, a fourth determining module, and a seventh execution module.

[0107] The third determining module is used to determine correction coefficients based on the measured geostress curve, the three-dimensional pseudo-well curve, the dip angle enhancement pseudo-well curve, the curvature pseudo-well curve, and the ant-body pseudo-well curve. The fourth determining module is used to determine correction values ​​based on the correction coefficients, the second dip angle enhancement attribute, the second curvature attribute, and the second ant-body attribute. The seventh execution module is used to correct the prediction results based on the correction values.

[0108] In some embodiments, the fourth determining module includes a fifth determining module, a sixth determining module, and a seventh determining module.

[0109] The fifth determining module is used to determine the tilt angle enhancement correction value based on the tilt angle enhancement coefficient and the second tilt angle enhancement attribute. The sixth determining module is used to determine the curvature correction value based on the curvature coefficient and the second curvature attribute. The seventh determining module is used to determine the ant body correction value based on the ant body number and the second ant body attribute.

[0110] The modules in the aforementioned geostress correction device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of the device in hardware form or independently of it, or stored in the memory of the processing device in software form, so that the processor can call and execute the operations corresponding to each module. It should be noted that the module division in this embodiment is illustrative and only represents a logical functional division; in actual implementation, there may be other division methods.

[0111] Example 3:

[0112] The third aspect provides an electronic device including a storage device and a processor, the storage device storing a computer program, the processor executing the computer program to implement the steps of a method for correcting ground stress.

[0113] Example 4:

[0114] The fourth aspect provides a storage medium storing a computer program that can be executed by one or more processors, the computer program being able to implement the steps of any of the stress correction methods in the first aspect.

[0115] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0116] It should be understood that the phrase "one embodiment" or "an embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of this application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It should be understood that in the various embodiments of this application, the sequence numbers of the above-described processes do not imply a sequential order of execution; the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application. The sequence numbers of the above-described embodiments are merely descriptive and do not represent the superiority or inferiority of the embodiments.

[0117] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus 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 apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0118] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0119] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0120] In addition, each functional unit in the various embodiments of this application can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0121] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as mobile storage devices, read-only memory (ROM), magnetic disks, or optical disks.

[0122] Alternatively, if the integrated units described above are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a controller to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0123] The above description is merely an embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for correcting ground stress, characterized in that, include: Obtain the prediction results of ground stress; The prediction results are determined by seismic trace data of the target area; Determine the geostress properties based on the prediction results; The geostress properties include: a second dip angle enhancement property, a second curvature property, and a second ant body property; determining the geostress properties based on the prediction results includes: To determine the target range for the correction required to obtain the geostress; Based on the target range, extract the first tilt angle enhancement attribute, the first curvature attribute, and the first ant body attribute from the prediction result; The first tilt angle enhancement attribute, the first curvature attribute, and the first ant body attribute are normalized respectively to obtain the second tilt angle enhancement attribute, the second curvature attribute, and the second ant body attribute. The pseudo-well curve is determined based on the aforementioned geostress properties; The step of determining the pseudo-well curve based on the geostress properties includes: Obtain the well coordinate data of the sample well; The sample wells were actual wells drilled where fractures were encountered. Based on the well coordinate data, extract the corresponding dip angle enhancement pseudo-well curve, curvature pseudo-well curve, and ant body pseudo-well curve from the second dip angle enhancement attribute, the second curvature attribute, and the second ant body attribute, respectively. The method further includes: Three-dimensional pseudo-well curves are extracted from the prediction results based on the well coordinate data. The prediction results are corrected based on the geostress properties and the pseudo-well curve; The step of correcting the prediction results based on the geostress properties and the pseudo-well curve includes: Obtain the measured in-situ stress curves of the sample wells; The prediction results are corrected based on the measured geostress curve, the three-dimensional pseudo-well curve, the dip angle enhanced pseudo-well curve, the curvature pseudo-well curve, the ant body pseudo-well curve, and the geostress properties. The predicted results are corrected based on the measured in-situ stress curve, the three-dimensional pseudo-well curve, the dip angle-enhanced pseudo-well curve, the curvature pseudo-well curve, the ant-body pseudo-well curve, and the in-situ stress properties, including: The correction coefficients are determined based on the measured geostress curve, the three-dimensional pseudo-well curve, the dip angle enhanced pseudo-well curve, the curvature pseudo-well curve, and the ant body pseudo-well curve. The correction value is determined based on the correction coefficient, the second tilt angle enhancement attribute, the second curvature attribute, and the second ant body attribute; The prediction results are corrected based on the correction value; The correction coefficients include: tilt angle enhancement correction coefficient, curvature correction coefficient, and ant body correction coefficient; the correction values ​​include: tilt angle enhancement correction values, curvature correction values, and ant body correction values; determining the correction values ​​based on the correction coefficients, the second tilt angle enhancement attribute, the second curvature attribute, and the second ant body attribute includes: The tilt enhancement correction value is determined based on the tilt enhancement correction coefficient and the second tilt enhancement attribute; The curvature correction value is determined based on the curvature correction coefficient and the second curvature attribute; The ant body correction value is determined based on the ant body correction coefficient and the second ant body attribute.

2. A ground stress correction device, applicable to the method as described in claim 1, characterized in that, include: The first acquisition module is used to acquire the predicted results of ground stress; The first determining module is used to determine the geostress properties based on the prediction results; The second determining module is used to determine the pseudo-well curve based on the geostress properties; The first execution module is used to correct the prediction results based on the geostress properties and the pseudo-well curve.

3. An electronic device, characterized in that, include: The system includes a memory and a processor, wherein the memory stores a computer program that, when executed by the processor, performs a method for correcting ground stress as described in claim 1.

4. A storage medium, characterized in that, The computer program stored in the storage medium can be executed by one or more processors, and the computer program can be used to implement the steps of the geostress correction method as described in claim 1.

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