Three-dimensional crustal stress field inversion method, system and platform based on numerical simulation

Abstract

The invention relates to the technical field of crustal stress inversion, in particular to a three-dimensional crustal stress field inversion method, system and platform based on numerical simulation, and the method comprises the steps: analyzing the geodetic coordinates and three-dimensional crustal stress data of all crustal stress measuring points in a research area; and calculating the weight of a corresponding residual error in a least square method used by each crustal stress measuring point when the crustal stress regression equation is subjected to optimization solution so as to solve the optimized crustal stress regression equation, and completing three-dimensional crustal stress field inversion of the research area in the three-dimensional crustal stress field according to the crustal stress regression equation subjected to optimization solution. The invention aims to improve the authenticity and reliability of the inversion result of the three-dimensional crustal stress field.

Description

Technical Field

[0001] This application relates to the field of geostress inversion technology, specifically to a three-dimensional geostress field inversion method, system, and platform based on numerical simulation. Background Technology

[0002] Three-dimensional (3D) geostress field inversion is a mathematical model established using known field-measured geostress data to deduce the initial geostress distribution of underground rock masses in three-dimensional space. The results of 3D geostress inversion provide important data for geological engineering design and geological hazard assessment, and are widely used in geological engineering, oil and gas exploration, and geological hazard prevention. Current methods for 3D geostress field inversion typically involve constructing a 3D finite element model based on geological data of the study area, numerically simulating the geostress at geostress measuring points under different influencing factors to obtain calculated stress values, then using the least squares method to solve the geostress regression equation established from the calculated stress values ​​and 3D geostress data, and finally performing finite element calculations on the solved geostress regression equation to generate a 3D geostress field in a numerical simulation software platform.

[0003] However, this method ignores the fact that the three-dimensional geostress data collected from various geostress measurement points will have different degrees of data deviation due to different noise interferences (such as electromagnetic interference, mechanical vibration, instrument drift, etc.). The traditional least squares method uses the same weight for the residuals of all data. When solving the geostress regression equation constructed by using the least squares method on the three-dimensional geostress data of all geostress measurement points, the residuals calculated from the three-dimensional geostress data with less noise interference among all the collected three-dimensional geostress data will not be given higher weight in the least squares method. As a result, the geostress regression equation obtained in the final solution cannot reflect the true geostress distribution, thus affecting the inversion result of the three-dimensional geostress field. Summary of the Invention

[0004] To address the aforementioned technical problems, the purpose of this application is to provide a method, system, and platform for inverting three-dimensional geostress fields based on numerical simulation. The specific technical solution adopted is as follows: In a first aspect, embodiments of this application provide a three-dimensional geostress field inversion method based on numerical simulation, the method comprising the following steps: A three-dimensional finite element model of the study area is constructed to simulate the measured stress values ​​of stress measurement points in various locations under gravity field and a preset number of geological structural stress fields, so as to obtain the geostress regression equation, which is used to simulate the stress calculation values ​​at all locations in the three-dimensional finite element model and complete the inversion of the three-dimensional geostress field. The optimal solution method for the geostress regression equation is as follows: S1, collect three-dimensional geostress data and their geodetic coordinates of all preset geostress measurement points in the study area; S2, based on the difference in the linear distribution law of vertical stress with underground depth among all geostress measuring points before and after excluding any geostress measuring point in the study area, calculate the first data deviation coefficient of any geostress measuring point, which is used to evaluate the degree of data deviation of the vertical stress collected by any geostress measuring point due to noise interference. S3, based on the difference in the nonlinear distribution law of the difference between the maximum and minimum principal stresses of all geostress measuring points before and after excluding any geostress measuring point in the study area, calculate the second data deviation coefficient of any geostress measuring point, which is used to evaluate the degree of data deviation of the horizontal stress collected by any geostress measuring point due to noise interference. S4 integrates the first and second data deviation coefficients to determine the confidence level of any geostress measuring point, and normalizes and maps the confidence levels of all geostress measuring points to obtain weight values, which are used as the weights of the corresponding residuals in the least squares method used to optimize the geostress regression equation for each geostress measuring point, in order to solve the geostress regression equation.

[0005] Preferably, the first data deviation coefficient is positively correlated with the goodness of fit of the linear regression model constructed based on the linear distribution law after excluding any geostress measuring point, and negatively correlated with the goodness of fit of the linear regression model constructed based on the linear distribution law before excluding any geostress measuring point.

[0006] Preferably, the goodness of fit is determined by the coefficient of determination of a linear regression model constructed from the data of underground depth and vertical stress at all geostress measuring points.

[0007] Preferably, the second data deviation coefficient is positively correlated with the goodness of fit of the nonlinear regression model constructed based on the nonlinear distribution law after excluding any geostress measuring point, and negatively correlated with the goodness of fit of the nonlinear regression model constructed based on the nonlinear distribution law before excluding any geostress measuring point.

[0008] Preferably, the goodness of fit is determined by the coefficient of determination of the nonlinear regression model between the difference between the maximum and minimum principal stresses at all geostress measuring points and the subsurface depth.

[0009] Preferably, the confidence level is negatively correlated with both the first data deviation coefficient and the second data deviation coefficient.

[0010] Preferably, the sum of the weights of all geostress measurement points obtained after normalizing the confidence level is 1.

[0011] Preferably, the geological structural stress field includes: x-axis compression, y-axis compression, xy-plane shear, yz-plane shear, and zx-plane shear structural stress fields.

[0012] Secondly, embodiments of this application provide a three-dimensional geostress field inversion system based on numerical simulation, the system comprising: The three-dimensional finite element model construction module is used to construct a three-dimensional finite element model of the study area and transfer it to the geostress numerical simulation module and the three-dimensional geostress field inversion module. The three-dimensional geostress data acquisition module is used to implement step S1 in the above-mentioned three-dimensional geostress field inversion method, and transmit the data to the geostress numerical simulation module and the three-dimensional geostress data analysis module. The geostress numerical simulation module is used to perform numerical simulation of the measured stress values ​​at various stress measurement points under the self-gravity field and a preset number of geological structural stress fields using a three-dimensional finite element model, so as to obtain the geostress regression equation and transmit the geostress regression equation to the three-dimensional geostress data analysis module. The three-dimensional geostress data analysis module is used to implement steps S2-S4 in the above three-dimensional geostress field inversion method, and to transfer the optimized geostress regression equation to the three-dimensional geostress field inversion module. The 3D geostress field inversion module is used to perform inversion in a 3D finite element model based on the geostress regression equation obtained by optimization.

[0013] Thirdly, embodiments of this application also provide a three-dimensional geostress field inversion platform based on numerical simulation. The platform includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the three-dimensional geostress field inversion method based on numerical simulation described above.

[0014] As can be seen from the above embodiments, the three-dimensional geostress field inversion method, system, and platform based on numerical simulation provided in this application have at least the following beneficial effects: This application constructs first and second data deviation coefficients by analyzing the basic distribution law of geostress in the collected three-dimensional geostress data. This effectively assesses the degree of data deviation caused by noise interference in the three-dimensional geostress data at different geostress measuring points. Based on the first and second data deviation coefficients, a confidence level is constructed, which effectively assesses the authenticity of the three-dimensional geostress data at different geostress measuring points. Then, based on the weight value calculated by the confidence level, different weights are assigned to the corresponding residuals of each geostress measuring point in the least squares method used to solve the established geostress regression equation. Compared with the traditional least squares method using uniform weights, this effectively reduces the impact of three-dimensional geostress data with large data deviations due to significant noise interference on the authenticity of the subsequently solved geostress regression equation, thereby improving the authenticity and reliability of the final three-dimensional geostress field inversion results. Attached Figure Description

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art 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.

[0016] Figure 1 A flowchart illustrating the steps of an optimization method for solving the geostress regression equation provided in one embodiment of this application; Figure 2 A flowchart of a three-dimensional geostress field inversion system based on numerical simulation provided in one embodiment of this application. Detailed Implementation

[0017] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of the three-dimensional geostress field inversion method, system, and platform based on numerical simulation proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise specified and limited, terms such as “comprising,” “including,” or any other variations thereof are intended to cover a non-exclusive inclusion, such that a circuit structure, article, or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase “comprising one…” does not exclude the presence of other identical elements in the article or device that includes said element. Furthermore, the term “and / or” as used herein includes any and all combinations of one or more of the associated listed items. 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 pertains.

[0019] The following, in conjunction with the accompanying drawings, details the specific scheme of the numerical simulation-based three-dimensional geostress field inversion method, system, and platform provided in this application.

[0020] Example 1 This embodiment provides a three-dimensional geostress field inversion method based on numerical simulation. This method can be implemented through a three-dimensional geostress field inversion system and a three-dimensional geostress field inversion platform, specifically as follows: This application constructs a three-dimensional finite element model of the study area to numerically simulate the measured stress values ​​at various stress measurement points under a self-gravity field and a preset number of geological structural stress fields, so as to obtain the geostress regression equation, which is used to simulate the stress calculation values ​​at all locations in the three-dimensional finite element model and complete the inversion of the three-dimensional geostress field.

[0021] For the optimization solution method of the geostress regression equation, please refer to [link / reference]. Figure 1 The method includes the following steps: S1 collects three-dimensional geostress data and their geodetic coordinates from all preset geostress measurement points in the study area.

[0022] This application uses the three-dimensional geostress data acquisition module in the three-dimensional geostress field inversion system to collect the three-dimensional geostress data and their geodetic coordinates of all preset geostress measuring points in the study area.

[0023] It should be noted that the three-dimensional geostress data and the subsurface depth in geodetic coordinates of all geostress measuring points in the study area were normalized to reduce the impact of data dimensions on subsequent data processing. For the convenience of subsequent processing, the vertical stress, maximum horizontal principal stress, minimum horizontal principal stress and subsurface depth of the geostress measuring points processed later are all normalized data.

[0024] In this embodiment, the Min-Max normalization method is used. The Min-Max normalization method is a well-known technique, and the specific process will not be described in detail.

[0025] S2, based on the difference in the linear distribution law of vertical stress with underground depth among all geostress measuring points before and after excluding any geostress measuring point in the study area, calculate the first data deviation coefficient of any geostress measuring point, which is used to evaluate the degree of data deviation of the vertical stress collected by any geostress measuring point due to noise interference.

[0026] Under normal circumstances, the measured vertical stress is basically equal to the weight of the overlying rock strata. This means that the magnitude of the vertical stress at the collected geostress measurement points usually increases linearly with the underground depth of the geostress measurement points. As a result, the vertical stress in the three-dimensional geostress data of the collected geostress measurement points exhibits an approximately linear data distribution characteristic with respect to the underground depth of the geostress measurement points. However, when the three-dimensional geostress data of the collected geostress measurement points is subject to large data deviations due to significant noise interference, this distribution characteristic of vertical stress will be significantly disrupted.

[0027] Based on the above analysis, this application calculates the first data deviation coefficient for any geostress measuring point based on the difference in the linear distribution law of vertical stress with underground depth among all geostress measuring points before and after excluding any geostress measuring point in the study area. This coefficient is used to assess the degree of data deviation caused by noise interference in the vertical stress collected by any geostress measuring point.

[0028] The first data deviation coefficient is positively correlated with the goodness of fit of the linear regression model constructed based on the linear distribution law after excluding any geostress measuring point, and negatively correlated with the goodness of fit of the linear regression model constructed based on the linear distribution law before excluding any geostress measuring point. That is, the greater the goodness of fit calculated after excluding any geostress measuring point compared with the goodness of fit calculated before excluding any geostress measuring point, the greater the degree to which the appearance of vertical stress data of any geostress measuring point disrupts the linear relationship between vertical stress and underground depth of geostress measuring points in the study area. In this case, the greater the degree of data deviation of the vertical stress collected by any geostress measuring point due to noise interference, that is, the larger the first data deviation coefficient.

[0029] It is understandable that a positive correlation means that the dependent variable increases as the independent variable increases, and decreases as the independent variable decreases; a negative correlation means that the dependent variable decreases as the independent variable increases, and increases as the independent variable decreases. This is determined by the actual application, and this application does not impose any special restrictions.

[0030] Specifically, in this embodiment, taking any geostress measuring point M in the study area as an example, the calculation method U1 of the first data deviation coefficient of geostress measuring point M is as follows: U1= In the formula, R1 represents the goodness of fit of the linear regression model between the underground depth and vertical stress established for all geostress measuring points in the study area except for geostress measuring point M, and R2 represents the goodness of fit of the linear regression model between the underground depth and vertical stress established for all geostress measuring points in the study area. To prevent the denominator from being 0, since the number is a very small positive number, in this embodiment... Set to 0.001.

[0031] The goodness of fit of the linear regression model can be calculated using the least squares method or the gradient descent method. The goodness of fit can be the coefficient of determination of the linear regression model, the reciprocal of the sum of squared residuals, or the reciprocal of the mean square error. In this embodiment, the coefficient of determination of the least squares method is selected as the goodness of fit of the linear regression model.

[0032] S3, based on the difference in the nonlinear distribution law of the difference between the maximum and minimum principal stresses of all geostress measuring points before and after excluding any geostress measuring point in the study area, calculate the second data deviation coefficient of any geostress measuring point, which is used to assess the degree of data deviation of the horizontal stress collected by any geostress measuring point due to noise interference.

[0033] Furthermore, in the shallow crust, the maximum and minimum horizontal principal stresses at the same depth typically exhibit significant differences, with the difference increasing closer to the surface. As the Poisson effect in rocks amplifies, the relative difference between the maximum and minimum horizontal principal stresses within a certain depth range decreases. This means that the difference between the maximum and minimum horizontal principal stresses at the geostress measuring points in the study area decreases with increasing underground depth, and the rate of decrease gradually increases with increasing underground depth. In other words, the difference between the maximum and minimum horizontal principal stresses at the collected geostress measuring points exhibits an approximately inverse proportional function trend with the underground depth of the geostress measuring points (in this embodiment...). (Regression analysis was performed using the inverse proportional function equation). However, when the three-dimensional geostress data collected from geostress measuring points exhibits significant data deviation due to large noise interference, it will significantly disrupt the distribution characteristics between the maximum and minimum horizontal principal stresses.

[0034] Based on the above analysis, this application calculates the second data deviation coefficient for any geostress measuring point based on the difference in the nonlinear distribution law of the difference between the maximum and minimum principal stresses of all geostress measuring points before and after excluding any geostress measuring point in the study area, which is used to assess the degree of data deviation of the horizontal stress collected by any geostress measuring point due to noise interference.

[0035] The second data deviation coefficient is positively correlated with the goodness of fit of the nonlinear regression model constructed based on the nonlinear distribution law after excluding any geostress measuring point, and negatively correlated with the goodness of fit of the nonlinear regression model constructed based on the nonlinear distribution law before excluding any geostress measuring point. That is, the greater the goodness of fit calculated after excluding any geostress measuring point compared to the goodness of fit calculated before excluding any geostress measuring point, the greater the degree to which the appearance of the horizontal stress data of geostress measuring point M disrupts the data change trend of the inverse proportional function between the difference between the horizontal maximum principal stress and the horizontal minimum principal stress of geostress measuring points in the study area and the underground depth of geostress measuring points. In other words, the greater the degree of data deviation of the three-dimensional geostress data collected by geostress measuring point M due to noise interference, that is, the larger the second data deviation coefficient.

[0036] Specifically, in this embodiment, taking the geostress measuring point M as an example, the calculation method of the second data deviation coefficient U2 of the geostress measuring point M is as follows: U2= In the formula, H1 represents the goodness of fit of the nonlinear regression model between the absolute value of the difference between the maximum and minimum horizontal principal stresses at all geostress measuring points in the study area (excluding geostress measuring point M) and the subsurface depth, and H2 represents the goodness of fit of the nonlinear regression model between the absolute value of the difference between the maximum and minimum horizontal principal stresses at all geostress measuring points in the study area and the subsurface depth, and H2 represents the goodness of fit of the nonlinear regression model between the two variables. To prevent the denominator from being 0, since the number is a very small positive number, in this embodiment... Set to 0.001.

[0037] In this case, the goodness of fit of the nonlinear regression model can be calculated using the least squares method or the gradient descent method. The goodness of fit can be the coefficient of determination of the nonlinear regression model, the reciprocal of the sum of squared residuals, or the reciprocal of the mean square error. In this embodiment, the coefficient of determination of the least squares method is selected as the goodness of fit of the nonlinear regression model.

[0038] S4 integrates the first and second data deviation coefficients to determine the confidence level of any geostress measuring point, and normalizes and maps the confidence levels of all geostress measuring points to obtain weight values, which are used as the weights of the corresponding residuals in the least squares method used to optimize the geostress regression equation for each geostress measuring point, in order to solve the geostress regression equation.

[0039] Furthermore, the first and second data deviation coefficients of all geostress measuring points in the study area were normalized to obtain the normalized results of the first and second data deviation coefficients for each geostress measuring point in the study area. This embodiment uses the Min-Max normalization method, which is a well-known technique, and the specific process will not be described in detail.

[0040] Based on the above analysis, this application integrates the first and second data deviation coefficients to determine the confidence level of any geostress measuring point, which is used to evaluate the authenticity of the three-dimensional geostress data collected at any geostress measuring point.

[0041] The confidence level is negatively correlated with both the first data deviation coefficient and the second data deviation coefficient. That is, the smaller the first and second data deviation coefficients are, the less the three-dimensional geostress data deviates from its true data, and the greater the confidence level is.

[0042] Specifically, in this embodiment, the confidence level D of the geostress measuring point M is calculated as follows: D= In the formula, U represents the mean of the normalized results of the deviation coefficients of the first and second data of the geostress measuring point M; To prevent the denominator from being 0, since the number is a very small positive number, in this embodiment... Set to 0.001.

[0043] Furthermore, the confidence scores of all geostress measuring points in the study area are processed to map the values ​​of all geostress measuring points to the range (0,1) and make the sum of the mapping results of the confidence scores of all geostress measuring points equal to 1. The mapping result of the confidence score of each geostress measuring point in the study area is recorded as the weight value of each geostress measuring point, which is used as the weight of the corresponding residual in the least squares method used to optimize the geostress regression equation for each geostress measuring point, so as to solve the geostress regression equation.

[0044] The least squares method is used to find a set of undetermined regression coefficients that minimize the sum of squared residuals (the solution is unique). These undetermined regression coefficients are then substituted into the established geostress regression equation to complete the optimization solution of the established geostress regression equation. The method of using the least squares method to solve the undetermined regression coefficients of the multiple linear regression equation is a well-known technique, and the specific process will not be elaborated here.

[0045] In this embodiment, the confidence level is numerically mapped using the proportion method. The method of normalization using the proportion method is a well-known technique, and the specific process will not be described in detail.

[0046] Example 2 See appendix Figure 2Based on the same inventive concept as the above method, this application also provides a three-dimensional geostress field inversion system based on numerical simulation, including a three-dimensional finite element model construction module, a three-dimensional geostress data acquisition module, a geostress numerical simulation module, a three-dimensional geostress data analysis module, and a three-dimensional geostress field inversion module. This system is mainly applicable to the three-dimensional geostress field inversion in research areas located in the shallow crust.

[0047] In the three-dimensional finite element model construction module, based on geological data such as well logging, seismic and experimental data of the study area, a three-dimensional geological model of the study area is established using the three-dimensional geological structure modeling method. The established three-dimensional geological model is imported into the numerical simulation software platform for the construction of a three-dimensional finite element model. For the convenience of subsequent processing, it is denoted as three-dimensional finite element model V, and three-dimensional finite element model V is transferred to the geostress numerical simulation module and the three-dimensional geostress field inversion module.

[0048] In the construction of the three-dimensional finite element model, hexahedral elements were used for mesh generation. The rock mass mechanical parameters in the study area were used as the material property parameters of the mesh elements in the three-dimensional finite element model. The rock mass mechanical parameters include the lithology of the rock mass (such as granite, lamprophyre, etc.) and density (kg / m³). Poisson's ratio, elastic modulus (MPa) The numerical simulation software platform is one of ANSYS, ABAQUS or COMSOL. In this embodiment, ANSYS numerical simulation software platform is selected. The construction of the three-dimensional geological model and the construction of the three-dimensional finite element model are well known technologies, and the specific process will not be described in detail.

[0049] In the three-dimensional geostress data acquisition module, multiple geostress measuring points are arranged in the study area. In this embodiment, the number of geostress measuring points is 136, which can be set by the implementer. The geodetic coordinates of each geostress measuring point in the geodetic coordinate system (three-dimensional coordinates formed by longitude, latitude, and underground depth (the opposite of altitude)) are obtained. The three-dimensional geostress data of each geostress measuring point (including the values ​​of vertical stress, horizontal maximum principal stress, and horizontal minimum principal stress (MPa), direction angle (°), and dip angle (°)) are measured by the stress relief method (borehole core stress relief method). The geodetic coordinates and three-dimensional geostress data of all geostress measuring points in the study area are transmitted to the geostress numerical simulation module, and the geodetic coordinates and three-dimensional geostress data of all geostress measuring points are transmitted to the three-dimensional geostress data analysis module.

[0050] In the geostress numerical simulation module, based on the conversion formulas between the geodetic coordinate system and the spatial rectangular coordinate system, and the conversion formulas between principal stresses and total stress components, the geodetic coordinates and three-dimensional geostress data of all geostress measuring points in the study area are converted into spatial coordinates in the rectangular coordinate system of the three-dimensional finite element model V, as well as all stress components (including normal stress components acting in the x, y, and z directions). , , and the shear stress components acting on the xy plane, yz plane, and zx plane. , , These six stress components are used to obtain the spatial coordinates and all stress components of each geostress measuring point in the study area. The conversion between the geodetic coordinate system and the spatial rectangular coordinate system, as well as the conversion between stress and total stress components, are well-known techniques, and the specific process will not be described in detail.

[0051] Secondly, in the ANSYS numerical simulation software platform, the self-gravity field of the three-dimensional finite element model V and the stress fields of a preset number of geological structures are numerically simulated and calculated based on the measured stress values ​​at various stress measurement points to obtain the geostress regression equation, and the geostress regression equation is transmitted to the three-dimensional geostress data analysis module.

[0052] In this embodiment, the self-weight stress field is calculated using the measured density of the rock mass to determine the self-weight stress field formed under its own weight. The geological tectonic stress field includes the stress fields generated by five sub-geological tectonic actions in the coordinate system of the three-dimensional finite element model V: x-axis compressional tectonic movement, y-axis compressional tectonic movement, xy-plane shear tectonic movement, yz-plane shear tectonic movement, and zx-plane shear tectonic movement. The spatial coordinates of each geostress measuring point in the study area are obtained, and the corresponding stress calculation values ​​in the self-weight stress field and each geological tectonic stress field are obtained. The stress calculation values ​​of all geostress measuring points in the study area under the action of self-weight and the five sub-geological tectonic actions are transmitted to the three-dimensional geostress field inversion module. The finite element simulation calculation of the self-weight field and the geological tectonic stress field is a well-known technique, and the specific process will not be described in detail.

[0053] In the three-dimensional geostress data analysis module, the geodetic coordinates and three-dimensional geostress data of all geostress measuring points in the study area transmitted by the three-dimensional geostress data acquisition module are analyzed to calculate the weight of the corresponding residual in the least squares method used by each geostress measuring point when optimizing the geostress regression equation, so as to solve the optimized geostress regression equation, and then transmit the optimized geostress regression equation to the three-dimensional geostress field inversion module.

[0054] In the three-dimensional geostress field inversion module, all measured stress values ​​at any location in the three-dimensional finite element model V under the adjusted geostress influencing factors are calculated based on the geostress regression equation obtained by the optimization solution. The three-dimensional geostress field is then generated in the ANSYS numerical simulation software platform to complete the three-dimensional geostress field inversion of the study area.

[0055] Example 3 Based on the same inventive concept as the above method, this application also provides a three-dimensional geostress field inversion platform based on numerical simulation. The platform includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the three-dimensional geostress field inversion method based on numerical simulation described above.

[0056] The various embodiments in this application are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0057] It should be noted that, unless otherwise specified and limited, terms such as “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a circuit structure, article, or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such article or device. Without further limitations, an element defined by the phrase “comprising one…” does not exclude the presence of other identical elements in the article or device that includes said element. Furthermore, the term “and / or” as used herein includes any and all combinations of one or more of the associated listed items.

[0058] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not invented in this application.

[0059] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope.

Claims

1. A three-dimensional geostress field inversion method based on numerical simulation, characterized in that, The method includes the following steps: A three-dimensional finite element model of the study area is constructed to simulate the measured stress values ​​of stress measurement points in various locations under gravity field and a preset number of geological structural stress fields, so as to obtain the geostress regression equation, which is used to simulate the stress calculation values ​​at all locations in the three-dimensional finite element model and complete the inversion of the three-dimensional geostress field. The optimal solution method for the geostress regression equation is as follows: S1, collect three-dimensional geostress data and their geodetic coordinates of all preset geostress measurement points in the study area; S2, based on the difference in the linear distribution law of vertical stress with underground depth among all geostress measuring points before and after excluding any geostress measuring point in the study area, calculate the first data deviation coefficient of any geostress measuring point, which is used to evaluate the degree of data deviation of the vertical stress collected by any geostress measuring point due to noise interference. S3, based on the difference in the nonlinear distribution law of the difference between the maximum and minimum principal stresses of all geostress measuring points before and after excluding any geostress measuring point in the study area, calculate the second data deviation coefficient of any geostress measuring point, which is used to evaluate the degree of data deviation of the horizontal stress collected by any geostress measuring point due to noise interference. S4 integrates the first and second data deviation coefficients to determine the confidence level of any geostress measuring point, and normalizes and maps the confidence levels of all geostress measuring points to obtain weight values, which are used as the weights of the corresponding residuals in the least squares method used to optimize the geostress regression equation for each geostress measuring point, in order to solve the geostress regression equation.

2. The three-dimensional geostress field inversion method based on numerical simulation as described in claim 1, characterized in that, The first data deviation coefficient is positively correlated with the goodness of fit of the linear regression model constructed based on the linear distribution law after excluding any geostress measuring point, and negatively correlated with the goodness of fit of the linear regression model constructed based on the linear distribution law before excluding any geostress measuring point.

3. The three-dimensional geostress field inversion method based on numerical simulation as described in claim 2, characterized in that, The goodness of fit is determined by the coefficient of determination of the linear regression model constructed between the underground depth and vertical stress data of all geostress measuring points.

4. The three-dimensional geostress field inversion method based on numerical simulation as described in claim 1, characterized in that, The second data deviation coefficient is positively correlated with the goodness of fit of the nonlinear regression model constructed based on the nonlinear distribution law after excluding any geostress measuring point, and negatively correlated with the goodness of fit of the nonlinear regression model constructed based on the nonlinear distribution law before excluding any geostress measuring point.

5. The three-dimensional geostress field inversion method based on numerical simulation as described in claim 4, characterized in that, The goodness of fit is determined by the coefficient of determination of the nonlinear regression model between the difference between the maximum and minimum principal stresses at all geostress measurement points and the subsurface depth.

6. The three-dimensional geostress field inversion method based on numerical simulation as described in claim 1, characterized in that, The confidence level is negatively correlated with both the first data deviation coefficient and the second data deviation coefficient.

7. The three-dimensional geostress field inversion method based on numerical simulation as described in claim 6, characterized in that, The sum of the weights of all geostress measurement points obtained after normalizing the confidence level is 1.

8. The three-dimensional geostress field inversion method based on numerical simulation as described in any one of claims 1-7, characterized in that, The geological structural stress field includes: x-axis compression, y-axis compression, xy-plane shear, yz-plane shear, and zx-plane shear structural stress fields.

9. A three-dimensional geostress field inversion system based on numerical simulation, characterized in that, The system includes: The three-dimensional finite element model construction module is used to construct a three-dimensional finite element model of the study area and transfer it to the geostress numerical simulation module and the three-dimensional geostress field inversion module. A three-dimensional geostress data acquisition module is used to implement step S1 in claim 1 above, and to transmit the data to the geostress numerical simulation module and the three-dimensional geostress data analysis module; The geostress numerical simulation module is used to perform numerical simulation of the measured stress values ​​at various stress measurement points under the self-gravity field and a preset number of geological structural stress fields using a three-dimensional finite element model, so as to obtain the geostress regression equation and transmit the geostress regression equation to the three-dimensional geostress data analysis module. The three-dimensional geostress data analysis module is used to implement steps S2-S4 in claim 1 above, and to transmit the optimally solved geostress regression equation to the three-dimensional geostress field inversion module; The 3D geostress field inversion module is used to perform inversion in a 3D finite element model based on the geostress regression equation obtained by optimization.

10. A three-dimensional geostress field inversion platform based on numerical simulation, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the three-dimensional geostress field inversion method based on numerical simulation as described in any one of claims 1-7.

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