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

Abstract

The application provides a ground stress prediction method and device, electronic equipment and storage medium, and relates to the technical field of seismic exploration. In the application, the observed seismic data of the seismic wavelet under the strong VTI medium is obtained, and the to-be-inverted parameter matrix is constructed based on the PP wave reflection coefficient corresponding to the strong VTI medium; based on the Bayesian inversion theory and the observed seismic data, the posterior probability function to which the inversion parameter matrix corresponding to the to-be-inverted parameter matrix is subjected is constructed, and the target functional is determined based on the prior probability function and the likelihood function corresponding to the posterior probability function; the medium density and the coefficients of the stiffness matrix of each stiffness matrix are determined based on the inversion parameter matrix solving result of the target functional, and the compliance matrix of the strong VTI medium is determined based on the coefficients of the stiffness matrix of each stiffness matrix; and the ground stress distribution of the target profile in the strong VTI medium is predicted based on the medium density, the normal strain matrix corresponding to the strong VTI and the compliance matrix. In this way, the ground stress prediction accuracy under the strong VTI medium is improved.

Description

Technical Field

[0001] This application relates to the field of seismic survey technology, and in particular to a method, apparatus, electronic device and storage medium for predicting geostress. Background Technology

[0002] In-situ stress is a key factor determining the morphology, orientation, and extension direction of underground fractures. Accurate prediction of in-situ stress distribution is beneficial for selecting areas prone to fracturing and network formation, thereby improving the recovery rate and production of underground storage resources. In-situ stress includes three mutually perpendicular normal stresses (which can be denoted as: s x , s y and s z The directions of the three normal stresses are the same as the positive directions of the three coordinate axes of the spatial rectangular coordinate system constructed for the stratigraphic profile.

[0003] Currently, geostress prediction methods based on weak anisotropy theory are commonly used to predict geostress in strongly vertical transverse isotropy (VTI) media. However, the applicability of the aforementioned geostress prediction methods is limited in strongly VTI media; that is, when the underlying medium is a strongly VTI medium, the geostress prediction is not accurate enough.

[0004] Therefore, improving the accuracy of geostress prediction in strong VTI media is an urgent problem to be solved. Summary of the Invention

[0005] This application provides a geostress prediction method, apparatus, electronic device, and storage medium to improve the accuracy of geostress prediction under strong VTI media.

[0006] In a first aspect, embodiments of this application provide a method for predicting geostress, the method comprising:

[0007] Seismic wavelet observation data in strong VTI medium is obtained, and the parameter matrix to be inverted is constructed based on the reflection coefficient of PP wave corresponding to strong VTI medium. The parameter matrix to be inverted includes: the parameters to be inverted for the medium density of strong VTI medium and the non-zero stiffness matrix coefficients included in the stiffness matrix of strong VTI medium. Each stiffness matrix coefficient is related to the calculation of geostress in strong VTI medium.

[0008] Based on Bayesian inversion theory and observed seismic data, a posterior probability function is constructed to follow the inversion parameter matrix corresponding to the parameter matrix to be inverted. The objective functional is then determined based on the prior probability function and likelihood function corresponding to the posterior probability function. The objective functional is used to solve for the inversion parameter matrix corresponding to the maximum posterior probability.

[0009] Based on the solution of the inversion parameter matrix of the objective functional, the medium density and coefficients of each stiffness matrix are determined, and the compliance matrix of the strong VTI medium is determined based on the coefficients of each stiffness matrix.

[0010] Based on the medium density, the normal strain matrix and compliance matrix corresponding to strong VTI, the geostress distribution of the target profile in the strong VTI medium is predicted. The geostress distribution includes the first normal stress, the second normal stress and the third normal stress corresponding to each point in the target profile, wherein the first normal stress, the second normal stress and the third normal stress are mutually perpendicular normal stresses in a spatial rectangular coordinate system constructed for the target profile, and the first normal stress or the second normal stress is perpendicular to the target profile.

[0011] In one optional embodiment, acquiring observed seismic data of the seismic wavelet in a strongly VTI medium includes:

[0012] The phase and amplitude of the seismic wavelet were observed at different incident angles in the strong VTI medium, and multiple seismic data were obtained.

[0013] Seismic data vectors composed of multiple observed seismic sub-data are used as observed seismic data.

[0014] In one optional embodiment, the parameter matrix to be inverted is constructed based on the PP wave reflection coefficient corresponding to the strong VTI medium, including:

[0015] Based on the calculation formula of PP wave reflection coefficient, multiple medium property parameters to be inverted are determined; the multiple medium property parameters include: multiple stiffness matrix coefficients and medium density;

[0016] From multiple stiffness matrix coefficients, the stiffness matrix coefficients related to the calculation of geostress are selected, and the parameter matrix to be inverted is constructed based on the natural logarithm corresponding to each stiffness matrix coefficient and the medium density.

[0017] In one optional embodiment, determining the target functional based on the prior probability function and the likelihood function corresponding to the posterior probability function includes:

[0018] Based on the Gaussian probability density functions corresponding to the prior probability function and the likelihood function, respectively, the probability density distribution function of the posterior probability function is determined.

[0019] The maximum index function corresponding to the probability density distribution function is used as the objective functional.

[0020] In one optional embodiment, the probability density distribution function of the posterior probability function is determined based on the Gaussian probability density functions corresponding to the prior probability function and the likelihood function, respectively, including:

[0021] Based on the calculation formulas of the incident angle-dependent seismic wavelet matrix corresponding to the seismic wavelet, the first-order difference factor corresponding to the parameter matrix to be inverted, and the reflection coefficient of the PP wave, a forward modeling factor is constructed. Among them, the first-order difference factor is used to compensate for the difference between each stiffness matrix coefficient and the natural logarithm of each stiffness matrix coefficient, as well as the difference between the medium density and the natural logarithm of the medium density.

[0022] Based on the product of the forward modeling factor and the inversion parameter matrix, and the observed seismic data, the noise vector in the observed seismic data is determined; whereby the product result represents the theoretical seismic data of the seismic wavelet in the strong VTI medium.

[0023] The Gaussian probability density function that the noise vector follows is taken as the Gaussian probability density function of the likelihood function, and the probability density function of the posterior probability function is determined based on the Gaussian probability density functions corresponding to the prior probability function and the likelihood function, respectively.

[0024] In one optional embodiment, the medium density and coefficients of each stiffness matrix are determined based on the solution of the inversion parameter matrix of the objective functional, including:

[0025] A low-frequency model is constructed based on well logging data corresponding to strong VTI media, and a low-frequency constraint function is obtained based on the low-frequency prior vector and the exponential term of the probability density distribution function of the low-frequency model.

[0026] Input the minimum index function corresponding to the low-frequency constraint function into the preset Markov chain model to obtain the function solution result of the minimum index function;

[0027] The solution results of the function are used as the solution results of the inversion parameter matrix, and the medium density and coefficients of each stiffness matrix are determined based on the solution results of the inversion parameter matrix.

[0028] In one alternative embodiment, the geostress distribution of a target profile in a strong VTI medium is predicted based on the medium density, the normal strain matrix of the strong VTI, and the compliance matrix, including:

[0029] For each point included in the target profile, perform the following operations:

[0030] Based on the distance between the target point and the surface of the strong VTI medium, and the density of the medium, the point density corresponding to the target point is determined, and the third normal stress at the target point is determined based on the point density; where the target point is any one of the points.

[0031] Based on the stress expressions corresponding to the first and second normal strains included in the normal strain matrix, and the multiple compliance matrix coefficients related to the two stress expressions included in the compliance matrix, the first normal stress and the second normal stress at the target point are determined; wherein, the first normal strain and the first normal stress are in the same direction, and the second normal strain and the second normal stress are in the same direction.

[0032] Secondly, embodiments of this application also provide a geostress prediction device, the device comprising:

[0033] The matrix construction module is used to acquire observed seismic data of seismic wavelets in strong VTI medium and construct the parameter matrix to be inverted based on the reflection coefficient of PP wave corresponding to the strong VTI medium. The parameter matrix to be inverted includes: the parameters to be inverted set for the medium density of the strong VTI medium and the non-zero stiffness matrix coefficients included in the stiffness matrix of the strong VTI medium. Each stiffness matrix coefficient is related to the calculation of the geostress of the strong VTI medium.

[0034] The function construction module is used to construct the posterior probability function that the inversion parameter matrix corresponding to the parameter matrix to be inverted follows, based on Bayesian inversion theory and observed seismic data, and to determine the objective functional based on the prior probability function and likelihood function corresponding to the posterior probability function; the objective functional is used to solve for the inversion parameter matrix corresponding to the maximum posterior probability.

[0035] The matrix processing module is used to determine the medium density and coefficients of each stiffness matrix based on the solution results of the inversion parameter matrix of the objective functional, and to determine the compliance matrix of the strong VTI medium based on the coefficients of each stiffness matrix.

[0036] The stress prediction module is used to predict the geostress distribution of a target profile in a strong VTI medium based on the medium density, the normal strain matrix and the compliance matrix corresponding to the strong VTI. The geostress distribution includes the first normal stress, the second normal stress and the third normal stress corresponding to each point in the target profile, wherein the first normal stress, the second normal stress and the third normal stress are mutually perpendicular normal stresses in a spatial rectangular coordinate system constructed for the target profile, and the first normal stress or the second normal stress is perpendicular to the target profile.

[0037] In one optional embodiment, when acquiring observed seismic data of the seismic wavelet in a strong VTI medium, the matrix construction module is specifically used for:

[0038] The phase and amplitude of the seismic wavelet were observed at different incident angles in the strong VTI medium, and multiple seismic data were obtained.

[0039] Seismic data vectors composed of multiple observed seismic sub-data are used as observed seismic data.

[0040] In an optional embodiment, when constructing the parameter matrix to be inverted based on the PP wave reflection coefficients corresponding to a strong VTI medium, the matrix construction module is specifically used for:

[0041] Based on the calculation formula of PP wave reflection coefficient, multiple medium property parameters to be inverted are determined; the multiple medium property parameters include: multiple stiffness matrix coefficients and medium density;

[0042] From multiple stiffness matrix coefficients, the stiffness matrix coefficients related to the calculation of geostress are selected, and the parameter matrix to be inverted is constructed based on the natural logarithm corresponding to each stiffness matrix coefficient and the medium density.

[0043] In an optional embodiment, when determining the target functional based on the prior probability function and likelihood function corresponding to the posterior probability function, the function construction module is specifically used for:

[0044] Based on the Gaussian probability density functions corresponding to the prior probability function and the likelihood function, respectively, the probability density distribution function of the posterior probability function is determined.

[0045] The maximum index function corresponding to the probability density distribution function is used as the objective functional.

[0046] In an optional embodiment, when determining the probability density distribution function of the posterior probability function based on the Gaussian probability density functions corresponding to the prior probability function and the likelihood function, the function construction module is specifically used for:

[0047] Based on the calculation formulas of the incident angle-dependent seismic wavelet matrix corresponding to the seismic wavelet, the first-order difference factor corresponding to the parameter matrix to be inverted, and the reflection coefficient of the PP wave, a forward modeling factor is constructed. Among them, the first-order difference factor is used to compensate for the difference between each stiffness matrix coefficient and the natural logarithm of each stiffness matrix coefficient, as well as the difference between the medium density and the natural logarithm of the medium density.

[0048] Based on the product of the forward modeling factor and the inversion parameter matrix, and the observed seismic data, the noise vector in the observed seismic data is determined; whereby the product result represents the theoretical seismic data of the seismic wavelet in the strong VTI medium.

[0049] The Gaussian probability density function that the noise vector follows is taken as the Gaussian probability density function of the likelihood function, and the probability density function of the posterior probability function is determined based on the Gaussian probability density functions corresponding to the prior probability function and the likelihood function, respectively.

[0050] In an optional embodiment, when determining the medium density and coefficients of each stiffness matrix based on the solution results of the inversion parameter matrix of the objective functional, the function construction module is specifically used for:

[0051] A low-frequency model is constructed based on well logging data corresponding to strong VTI media, and a low-frequency constraint function is obtained based on the low-frequency prior vector and the exponential term of the probability density distribution function of the low-frequency model.

[0052] Input the minimum index function corresponding to the low-frequency constraint function into the preset Markov chain model to obtain the function solution result of the minimum index function;

[0053] The solution results of the function are used as the solution results of the inversion parameter matrix, and the medium density and coefficients of each stiffness matrix are determined based on the solution results of the inversion parameter matrix.

[0054] In an optional embodiment, when predicting the geostress distribution of a target profile in a strong VTI medium based on the medium density, the normal strain matrix of the strong VTI, and the compliance matrix, the stress prediction module is specifically used for:

[0055] For each point included in the target profile, perform the following operations:

[0056] Based on the distance between the target point and the surface of the strong VTI medium, and the density of the medium, the point density corresponding to the target point is determined, and the third normal stress at the target point is determined based on the point density; where the target point is any one of the points.

[0057] Based on the stress expressions corresponding to the first and second normal strains included in the normal strain matrix, and the multiple compliance matrix coefficients related to the two stress expressions included in the compliance matrix, the first normal stress and the second normal stress at the target point are determined; wherein, the first normal strain and the first normal stress are in the same direction, and the second normal strain and the second normal stress are in the same direction.

[0058] Thirdly, embodiments of this application also provide an electronic device, including:

[0059] Processor; and

[0060] Stored program memory,

[0061] The program includes instructions that, when executed by a processor, cause the processor to perform the geostress prediction method as described in the first aspect.

[0062] Fourthly, embodiments of this application also provide a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to execute the geostress prediction method as described in the first aspect.

[0063] Fifthly, this application provides a computer program product that, when invoked by a computer, causes the computer to execute the steps of the geostress prediction method as described in the first aspect.

[0064] The beneficial effects of this application are as follows:

[0065] In the geostress prediction method provided in this application embodiment, observed seismic data of seismic wavelets in a strong VTI medium are acquired, and a parameter matrix to be inverted is constructed based on the reflection coefficient of PP waves corresponding to the strong VTI medium. Next, based on Bayesian inversion theory and observed seismic data, a posterior probability function is constructed to which the inversion parameter matrix corresponding to the parameter matrix to be inverted follows, and a target functional is determined based on the prior probability function and likelihood function corresponding to the posterior probability function. Further, based on the solution results of the inversion parameter matrix of the target functional, the medium density and coefficients of each stiffness matrix are determined, and the compliance matrix of the strong VTI medium is determined based on the coefficients of each stiffness matrix. Finally, based on the medium density, the normal strain matrix corresponding to the strong VTI, and the compliance matrix, the geostress distribution of the target profile in the strong VTI medium is predicted. Thus, by accurately determining the medium density and coefficients of each stiffness matrix of the strong VTI through the constructed target functional, the accuracy of geostress prediction in strong VTI media is improved.

[0066] Furthermore, other features and advantages of this application will be set forth in the following description and will be apparent in part from the description, or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description

[0067] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described herein are used to provide a further understanding of this application, constitute a part of this application, and do not constitute an improper limitation of this application. In the accompanying drawings:

[0068] Figure 1 This is a schematic diagram of an optional system architecture applicable to the embodiments of this application;

[0069] Figure 2 A schematic diagram illustrating the implementation process of a geostress prediction method provided in this application embodiment;

[0070] Figure 3 A schematic diagram illustrating the implementation process of a method for determining the probability density distribution function of a posterior probability function, provided in an embodiment of this application;

[0071] Figure 4 This is a schematic diagram of the structure of a geostress prediction device provided in an embodiment of this application;

[0072] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0073] Embodiments of this application will now be described in more detail with reference to the accompanying drawings. While some embodiments of this application are shown in the drawings, it should be understood that this application can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this application. It should be understood that the drawings and embodiments of this application are for illustrative purposes only and are not intended to limit the scope of protection of this application.

[0074] It should be understood that the steps described in the method embodiments of this application may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this application is not limited in this respect.

[0075] The term "comprising" and its variations as used herein are open-ended, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the following description. It should be noted that the concepts of "first", "second", etc., mentioned in this application are used only to distinguish different devices, modules, or units, and are not intended to limit the order of functions performed by these devices, modules, or units or their interdependencies.

[0076] It should be noted that the terms "a" and "a plurality of" used in this application are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0077] The names of the messages or information exchanged between multiple devices in the embodiments of this application are for illustrative purposes only and are not intended to limit the scope of these messages or information.

[0078] First, the design concept of the embodiments of this application will be briefly introduced below:

[0079] Seismic prediction of geostress is an important aspect of seismic exploration. Shale gas reservoirs are characterized by low porosity and low permeability, and hydraulic fracturing is generally required for their exploitation to create numerous fractures in the shale. Geostress is a key factor determining the morphology, orientation, and extension direction of these fractures. Accurately predicting the geostress distribution in shale reservoirs is beneficial for selecting areas that are easy to fracture and form a network, thereby improving the recovery rate and production of shale reservoirs. It is an essential step in the exploration and development of shale gas reservoirs.

[0080] Currently, in-situ stress prediction methods for strong VTI media such as shale are mainly based on weak anisotropy theory. This limits the applicability of existing in-situ stress prediction methods in strong VTI media, resulting in inaccurate in-situ stress prediction under these conditions. Therefore, improving the accuracy of in-situ stress prediction in strong VTI media is an urgent problem to be solved.

[0081] To address or improve the aforementioned problems, this application provides a method for predicting geostress in strong VTI media. Specifically, it includes: acquiring observed seismic data of a seismic wavelet in a strong VTI medium, and constructing a parameter matrix to be inverted based on the reflection coefficient of the PP wave corresponding to the strong VTI medium; the parameter matrix to be inverted includes: parameters to be inverted for the medium density of the strong VTI medium and the non-zero stiffness matrix coefficients included in the stiffness matrix of the strong VTI medium, each stiffness matrix coefficient being related to the geostress calculation of the strong VTI medium; then, based on Bayesian inversion theory and observed seismic data, constructing a posterior probability function that the inversion parameter matrix corresponding to the parameter matrix to be inverted follows, and based on the prior probability function and likelihood function corresponding to the posterior probability function... The objective functional is determined; the objective functional is used to solve the inversion parameter matrix corresponding to the maximum a posteriori probability; further, based on the solution of the inversion parameter matrix of the objective functional, the medium density and coefficients of each stiffness matrix are determined, and the compliance matrix of the strong VTI medium is determined based on the coefficients of each stiffness matrix; finally, based on the medium density, the normal strain matrix and compliance matrix corresponding to the strong VTI, the geostress distribution of the target profile in the strong VTI medium is predicted; the geostress distribution includes: the first normal stress, the second normal stress and the third normal stress corresponding to each point of the target profile, wherein the first normal stress, the second normal stress and the third normal stress are mutually perpendicular normal stresses in a spatial rectangular coordinate system constructed for the target profile, and the first normal stress or the second normal stress is perpendicular to the target profile.

[0082] This approach addresses the limitation of in-situ stress prediction based on weak anisotropy theory in strong VTI media. The integration of prior and boundary constraint algorithms improves the stability of parameter inversion. Sequential simulation algorithms and stochastic inversion strategies enhance the resolution of stiffness matrix coefficient prediction results. The inverted stiffness matrix can be directly used to construct a constitutive relationship with in-situ stress, avoiding the accumulated errors of conventional step-by-step inversion of in-situ stress. The constructed objective functional accurately determines the medium density and various stiffness matrix coefficients in strong VTI media, thereby improving the accuracy of in-situ stress prediction in strong VTI media.

[0083] In particular, the preferred embodiments of this application will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit this application. Furthermore, the embodiments of this application and the features in the embodiments can be combined with each other unless otherwise specified.

[0084] See Figure 1 The diagram illustrates an optional system architecture applicable to an embodiment of this application. This system architecture may include: terminal devices (101a, 101b) and server 102. The terminal devices (101a, 101b) and server 102 can interact via a communication network. The communication network may employ wireless communication or wired communication methods. For example, the terminal devices (101a, 101b) can access the network and communicate with server 102 via cellular mobile communication technology. The aforementioned cellular mobile communication technology may include, for example, 5th generation mobile networks (5G) technology or next-generation mobile communication technology. Optionally, the terminal devices (101a, 101b) can access the network and communicate with server 102 via short-range wireless communication. The aforementioned short-range wireless communication method may include, for example, wireless fidelity (Wi-Fi) technology.

[0085] This application embodiment does not impose any limitation on the number of communication devices involved in the above system architecture. For example, the above system architecture may include more terminal devices, or fewer terminal devices, or other network devices. Figure 1 As shown, only terminal devices (101a, 101b) and server 102 are described as examples. The following is a brief introduction to each of the above communication devices and their respective functions.

[0086] A terminal device (101a, 101b) is a device that can provide voice and / or data connectivity to a user, and may be a device that supports wired and / or wireless connections.

[0087] For example, terminal devices (101a, 101b) may include, but are not limited to: mobile phones, tablets, laptops, handheld computers, mobile internet devices (MID), wearable devices, virtual reality (VR) devices, augmented reality (AR) devices, wireless terminal devices in industrial control, wireless terminal devices in autonomous driving, wireless terminal devices in smart grids, wireless terminal devices in transportation safety, wireless terminal devices in smart cities, or wireless terminal devices in smart homes, etc.

[0088] In addition, the terminal devices (101a, 101b) can have related clients installed. These clients can be software, such as applications (APPs), browsers, short video software, web pages, mini programs, etc.

[0089] It should be noted that the terminal devices (101a, 101b) in this embodiment can enable the aforementioned client related to geostress prediction to send the observed seismic data of seismic wavelets (e.g., Ricker wavelets) in strong VTI media (e.g., shale) to the server 102, so as to carry out subsequent methods and steps such as geostress prediction for strong VTI media.

[0090] Server 102 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0091] It is worth mentioning that, in this embodiment of the application, server 102 can be used to acquire observed seismic data of seismic wavelets in strong VTI medium, and construct the parameter matrix to be inverted based on the reflection coefficient of PP waves corresponding to the strong VTI medium; then, based on Bayesian inversion theory and observed seismic data, a posterior probability function of the inversion parameter matrix corresponding to the parameter matrix to be inverted is constructed, and the target functional is determined based on the prior probability function and likelihood function corresponding to the posterior probability function; further, based on the solution results of the inversion parameter matrix of the target functional, the medium density and coefficients of each stiffness matrix are determined, and the compliance matrix of the strong VTI medium is determined based on the coefficients of each stiffness matrix; finally, based on the medium density, the normal strain matrix and compliance matrix corresponding to the strong VTI, the geostress distribution of the target profile in the strong VTI medium is predicted.

[0092] The following describes the geostress prediction method provided by the exemplary embodiments of this application in conjunction with the above-described system architecture and with reference to the accompanying drawings. It should be noted that the above-described system architecture is only shown to facilitate understanding of the spirit and principles of this application, and the embodiments of this application are not limited in any way in this respect.

[0093] See Figure 2 The diagram shown illustrates the implementation flow of a geostress prediction method provided in this application embodiment. Taking a server as an example, the specific implementation flow of this method is as follows:

[0094] S201: Obtain observed seismic data of the seismic wavelet in a strong VTI medium, and construct the parameter matrix to be inverted based on the reflection coefficient of the PP wave corresponding to the strong VTI medium.

[0095] The aforementioned inversion parameter matrix may include: inversion parameters set separately for the non-zero stiffness matrix coefficients included in the density and stiffness matrix of the strong VTI medium. These stiffness matrix coefficients are related to the calculation of in-situ stress in the strong VTI medium.

[0096] Optionally, the aforementioned geostress may include: a first normal stress, a second normal stress, and a third normal stress. The first normal stress, the second normal stress, and the third normal stress are mutually perpendicular normal stresses in a spatial rectangular coordinate system constructed for the target profile, and the first normal stress or the second normal stress is perpendicular to the target profile.

[0097] Specifically, the plane formed by the X-axis (or Y-axis) and Z-axis of the spatial rectangular coordinate system is parallel to the target profile, while the Y-axis (or Z-axis) of the spatial rectangular coordinate system is perpendicular to the target profile. For example, the first normal stress is a normal stress in the same direction as the positive X-axis, and the first normal stress can be expressed as... s x The second normal stress is a normal stress in the same direction as the positive Y-axis, and the second normal stress can be expressed as: s y The third normal stress is a normal stress in the same direction as the positive Z-axis, and can be expressed as: s z .

[0098] Therefore, the stiffness matrix coefficients mentioned above can include: the first normal stress of the strong VTI medium. s x Second normal stress s y and the third normal stress s z The calculation of the stiffness matrix coefficients is related to this.

[0099] To improve the accuracy of ground stress prediction, the aforementioned observed seismic data may include observed seismic sub-data of the seismic wavelet at different incident angles. Therefore, in one optional implementation, when executing step S201, the server, while acquiring observed seismic data of the seismic wavelet in a strong VTI medium, can use the seismic data observation module to observe the phase and amplitude of the seismic wavelet during its propagation in the strong VTI medium at different incident angles, obtaining multiple observed seismic sub-data. The resulting seismic data vector, composed of these multiple observed seismic sub-data, is then used as the observed seismic data.

[0100] In other words, the aforementioned observed seismic data can be an observed data vector composed of partially superimposed angular seismic data. For example, observed seismic data... Specifically, it can be expressed as: That is, observing earthquake data Depend on h The data consists of observational seismic data at each incident angle.

[0101] In one optional implementation, during step S201, when constructing the parameter matrix to be inverted based on the PP wave reflection coefficient corresponding to the strong VTI medium, the server can determine multiple medium property parameters to be inverted based on the calculation formula of the PP wave reflection coefficient. These multiple medium property parameters include multiple stiffness matrix coefficients and the medium density. Thus, stiffness matrix coefficients related to the geostress calculation are selected from the multiple stiffness matrix coefficients, and the parameter matrix to be inverted is constructed based on the natural logarithms corresponding to each stiffness matrix coefficient and the medium density. It is understood that the aforementioned multiple stiffness matrix coefficients are elastic parameters, while the medium density is an inelastic parameter.

[0102] For example, the formula for calculating the PP wave reflection coefficient mentioned above can be an equation for the PP wave reflection coefficient of a strong VTI medium based on the elastic impedance tensor, which can be specifically expressed as follows:

[0103]

[0104] in, and These are horizontal slowness and vertical slowness, respectively.

[0105]

[0106]

[0107] in,

[0108]

[0109]

[0110] in,

[0111]

[0112]

[0113] , and For Thomsen anisotropy parameters, and This describes the degree of variation in the propagation speed of P-waves and SH-waves in VTI media. This reflects the nonlinear relationship between the propagation velocities of longitudinal and transverse waves. Furthermore, l 2 In this context, l represents the ratio of transverse to longitudinal wave velocities. For P-wave phase velocity, The incident angle of the seismic wavelet.

[0114] It should be noted that, , , and The stiffness matrix of a strong VTI medium includes non-zero values ​​that are related to the first normal stress of the strong VTI medium. s x Second normal stress s y and the third normal stress s z The calculation of the relevant stiffness matrix coefficients, and C 66 Although it is not 0, it is related to the first normal stress. s x Second normal stress s y and the third normal stress s z The calculations are independent of each other, meaning the coefficients of the above stiffness matrices are: , , and The density of the aforementioned medium can be: r .

[0115] At this point, the stiffness matrix of the VTI medium can be a strip matrix C as follows:

[0116]

[0117] Optionally, the process by which the server constructs the parameter matrix to be inverted based on the natural logarithms corresponding to the coefficients of each stiffness matrix and the medium density can be as follows: According to the above equation for the reflection coefficient of PP waves in a strong VTI medium based on the elastic impedance tensor, the disturbance term can be characterized in the form of a natural logarithm:

[0118]

[0119] in, Let represent the natural logarithm of each of the stiffness matrix coefficients and the medium density mentioned above. Then, the parameter matrix to be inverted can be expressed as: .

[0120] S202: Based on Bayesian inversion theory and observed seismic data, construct the posterior probability function that the inversion parameter matrix corresponding to the parameter matrix to be inverted follows, and determine the target functional based on the prior probability function and likelihood function corresponding to the posterior probability function.

[0121] In this way, by defining prior information as the probability density function of all random unknown parameters through Bayesian theory, unknown probabilities can be obtained from known probabilities using Bayesian theory. That is, by transforming prior information into posterior information, we can accurately invert each parameter included in the parameter matrix to be inverted.

[0122] For example, during step S202, the posterior probability function of the inversion parameter matrix corresponding to the parameter matrix to be inverted, constructed by the server based on Bayesian inversion theory and observed seismic data, can be specifically expressed as follows:

[0123]

[0124] in, This indicates observed earthquake data. This represents the inversion parameter matrix corresponding to the parameter matrix X to be inverted. Represents the inversion parameter matrix It follows the posterior probability density function. This represents the prior probability density function. Represents the inversion parameter matrix With observed earthquake data The degree of fit between them is the likelihood function.

[0125] Optionally, the above inversion parameter matrix It can be represented as: It should be understood that the inversion parameter matrix The specific values ​​of the parameter matrix X to be inverted, i.e., the inversion parameter matrix. Each inversion parameter included in the matrix corresponds one-to-one with the inversion parameter matrix X.

[0126] The aforementioned objective functional can be used to solve for the inversion parameter matrix corresponding to the maximum posterior probability, i.e., the posterior probability density function. The inversion parameter matrix at the maximum value is taken as the solution of the objective functional.

[0127] In one optional implementation, when determining the target functional based on the prior probability function and likelihood function corresponding to the posterior probability function, the server can determine the probability density distribution function of the posterior probability function based on the Gaussian probability density functions corresponding to the prior probability function and the likelihood function, respectively. The maximum value index function corresponding to this probability density distribution function is then used as the target functional. For example, see [link to relevant documentation]. Figure 3The diagram illustrates a method for determining the probability density distribution function of a posterior probability function, as provided in this application. The specific implementation process of this method is as follows:

[0128] S301: Construct forward modeling factors based on the calculation formulas of the incident angle-dependent seismic wavelet matrix corresponding to the seismic wavelet, the first-order difference factor corresponding to the parameter matrix to be inverted, and the reflection coefficient of the PP wave.

[0129] The first-order difference factor mentioned above can be used to compensate for the difference between each stiffness matrix coefficient and the natural logarithm of each stiffness matrix coefficient, as well as the difference between the natural logarithm of the medium density and the medium density. In other words, the first-order difference factor mentioned above is used to compensate for the error caused by the approximate equality when taking the natural logarithm of the above disturbance terms.

[0130] Optionally, it is assumed that the above incident angle-dependent seismic wavelet matrix can be expressed as: The first-order difference factor mentioned above can be expressed as: Then the above-mentioned forward modeling factor can be expressed as: And the orthogonal factor This can be specifically expressed as follows:

[0131]

[0132] in,

[0133]

[0134]

[0135]

[0136] S302: Based on the product of the forward modeling factor and the inversion parameter matrix, and the observed seismic data, determine the noise vector in the observed seismic data.

[0137] The product result described above can characterize the theoretical seismic data of the seismic wavelet in a strong VTI medium. For example, the aforementioned theoretical seismic data or product result can be expressed as: Gm So, observing earthquake data noise vector in n It can be represented as: - Gm It should be noted that, due to the observation of earthquake data... Due to the influence of random noise, the observed seismic data can be considered... It follows a Gaussian likelihood probability distribution. Therefore, the prior probability function... The Gaussian probability density function can be a noise vector n (or - Gm The probability density function of the Gaussian distribution follows.

[0138] S303: Take the Gaussian probability density function that the noise vector follows as the Gaussian probability density function of the likelihood function, and determine the probability density function of the posterior probability function based on the Gaussian probability density functions corresponding to the prior probability function and the likelihood function, respectively.

[0139] For example, the likelihood function The Gaussian probability density function can be specifically expressed as follows:

[0140]

[0141] in, Representing vectors It follows a mean of 0 and a covariance of Gaussian probability distribution function, To observe earthquake data The number of sampling points. Optional, covariance. It was determined based on multiple experiments.

[0142] Furthermore, within the Bayesian inversion framework, prior information about the parameters to be inverted is beneficial for reducing the inversion parameter matrix. The solution space. To improve the stability of seismic inversion, the inversion parameter matrix... It also follows a Gaussian probability density distribution. Therefore, the prior probability function... The Gaussian probability density function can be specifically expressed as follows:

[0143]

[0144] in, Inversion parameter matrix The covariance between the elements included in it. M Inversion parameter matrix The number of sampling points; similarly, the covariance. It can also be determined based on multiple experiments, but this application does not limit this.

[0145] Furthermore, the server determines the prior probability function. and likelihood function By determining the corresponding Gaussian probability density function, the posterior probability function can be identified. The probability density distribution function is specifically expressed as follows:

[0146]

[0147] S203: Based on the solution results of the inversion parameter matrix of the objective functional, determine the medium density and coefficients of each stiffness matrix, and determine the compliance matrix of the strong VTI medium based on the coefficients of each stiffness matrix.

[0148] For example, the solution to the inversion parameter matrix of the above objective functional can be the posterior probability function. The inversion parameter matrix is ​​the inversion parameter matrix when the exponential term of the probability density distribution function is at its minimum. Therefore, the solution result of the inversion parameter matrix of the above objective functional can be specifically expressed as follows:

[0149]

[0150] Where, argmax m {} represents the maximum index function, argmin m {} is the minimum value index function.

[0151] Therefore, the server can determine the target functional O( m The inversion parameter matrix solution results are used to determine the medium density and various stiffness matrix coefficients, and the compliance matrix of the strong VTI medium is determined based on the various stiffness matrix coefficients.

[0152] To further improve the stability of seismic inversion, the server constructs a low-frequency model based on well logging data (or stratigraphic framework information) corresponding to the strong VTI medium. Based on the low-frequency prior vector and the exponential term of the probability density distribution function of the low-frequency model, a low-frequency constraint function is obtained. Then, the minimum index function corresponding to the low-frequency constraint function is input into a preset Markov chain model to obtain the function solution result of the minimum index function. Finally, the function solution result is used as the solution result of the inversion parameter matrix, and based on the solution result of the inversion parameter matrix, the medium density and coefficients of each stiffness matrix are determined.

[0153] Optionally, the server can use the L2 norm to measure the low-frequency prior vectors of the low-frequency model (i.e., the prior information of the low-frequency model), thereby incorporating the L2 norm measurement result of the low-frequency prior vectors into the posterior probability function. The low-frequency constraint function is formed from the exponential term of the probability density distribution function. Therefore, the low-frequency constraint function can be specifically expressed as follows:

[0154]

[0155] At this time, O( m The new objective functional is the low-frequency constraint function obtained from the low-frequency prior vector and the exponential term of the probability density distribution function of the low-frequency model. This is the L2 norm measure of the low-frequency prior vector.

[0156] Furthermore, the pre-defined Markov chain model can have 50 chains, with each chain capable of 2500 iterations. This allows for the rapid and accurate determination of the minimum index function using the pre-defined Markov chain model, improving the resolution of stiffness matrix coefficient predictions. The inverted stiffness matrix can be directly correlated with the geostress tensor to establish a constitutive relationship, avoiding the accumulated errors of conventional step-by-step geostress seismic inversion.

[0157] In other words, for the optimization problem of the objective functional, we can use the Markov chain model to approximate the posterior probability distribution, obtain the inversion parameter matrix sample points that follow this distribution, and combine the Monte Carlo method to use the statistical characteristics of random sample points to approximate the statistical characteristics of the inversion parameter matrix. This can ensure that the accurate coefficients of each stiffness matrix and the medium density are obtained subsequently.

[0158] Furthermore, when determining the medium density and coefficients of each stiffness matrix based on the solution results of the inversion parameter matrix, the server can further transform the above-mentioned strip matrix into the following form:

[0159]

[0160] in,

[0161]

[0162] The compliance matrix S of a strong VTI medium can be obtained by inverting the stiffness matrix of the strong VTI medium. Therefore, the compliance matrix S of a strong VTI medium can be specifically expressed as follows:

[0163]

[0164] in,

[0165]

[0166]

[0167] in,

[0168]

[0169] S204: Based on the medium density, the normal strain matrix and compliance matrix corresponding to strong VTI, predict the geostress distribution of the target profile in a strong VTI medium.

[0170] The aforementioned stress distribution may include: the first normal stress corresponding to each point in the target profile. s x Second normal stress s y and the third normal stress sz That is, based on the normal strain matrix and compliance matrix corresponding to the medium density and strong VTI, the server can predict the first normal stress at all locations in the target profile. s x Second normal stress s y and the third normal stress s z .

[0171] It should be understood that the strain-stress relationship of the compliance matrix connection of a strong VTI medium can be expressed as follows:

[0172]

[0173] In an alternative implementation, when performing step S204, the server may perform the following operations for each point included in the target profile, such as the target point: based on the distance between the target point and the surface of the strong VTI medium, and the medium density, determine the point density corresponding to the target point, and determine the third normal stress at the target point based on the point density. s z Therefore, based on the stress expressions corresponding to the first and second normal strains included in the normal strain matrix, and the multiple compliance matrix coefficients related to the two stress expressions included in the compliance matrix, the first normal stress at the target point is determined. s x Second normal stress s y Among them, the first normal strain and the first normal stress s x In the same direction, that is, the first normal strain is e x Second normal strain and second normal stress s y In the same direction, i.e., the second normal strain e y .

[0174] For example, assuming the strong VTI medium is bounded and cannot move, the strain in the horizontal direction (i.e., the first normal strain) is e x The second normal strain is e y The strain in the horizontal direction is 0. At this time, the strain in the horizontal direction (i.e., the first normal strain) is 0. e x The second normal strain is e y With 3 normal stresses (i.e., the first normal stress) s x Second normal stress s yand the third normal stress s z The relationship between the stresses (i.e., the two stress expressions mentioned above) and the calculation formulas for the three normal stresses are as follows:

[0175]

[0176] Where g is the acceleration due to gravity. r ( h () represents the density of the medium. r The corresponding density distribution function, H The distance between the target point and the surface of the strong VTI medium, or the depth of the target point.

[0177] In summary, the geostress prediction method provided in this application involves acquiring observed seismic data of a seismic wavelet in a strong VTI medium, and constructing a parameter matrix to be inverted based on the reflection coefficient of the PP wave corresponding to the strong VTI medium. Next, based on Bayesian inversion theory and observed seismic data, a posterior probability function is constructed to which the inversion parameter matrix corresponding to the parameter matrix to be inverted follows. The target functional is then determined based on the prior probability function and likelihood function corresponding to the posterior probability function. Further, based on the solution results of the inversion parameter matrix of the target functional, the medium density and coefficients of each stiffness matrix are determined, and the compliance matrix of the strong VTI medium is determined based on the coefficients of each stiffness matrix. Finally, based on the medium density, the normal strain matrix corresponding to the strong VTI, and the compliance matrix, the geostress distribution of the target profile in the strong VTI medium is predicted. In this way, by constructing a target functional to determine the medium density and coefficients of each stiffness matrix in strong VTI, the problem of limited applicability of in-situ stress prediction based on weak anisotropy theory in strong VTI medium is improved; and, whether for seismic observation data with different signal-to-noise ratios or in actual complex work areas, the in-situ stress in strong VTI medium can be predicted more accurately and stably.

[0178] Furthermore, based on the same technical concept, embodiments of this application provide a geostress prediction device to implement the above-described method flow of embodiments of this application. See also... Figure 4 As shown, the geostress prediction device 400 includes: a matrix construction module 401, a function construction module 402, a matrix processing module 403, and a stress prediction module 404, wherein:

[0179] The matrix construction module 401 is used to acquire the observed seismic data of the seismic wavelet in the strong VTI medium, and construct the parameter matrix to be inverted based on the reflection coefficient of the PP wave corresponding to the strong VTI medium. The parameter matrix to be inverted includes: the parameters to be inverted set for the medium density of the strong VTI medium and the non-zero stiffness matrix coefficients included in the stiffness matrix of the strong VTI medium. Each stiffness matrix coefficient is related to the calculation of the geostress of the strong VTI medium.

[0180] The function construction module 402 is used to construct the posterior probability function of the inversion parameter matrix corresponding to the parameter matrix to be inverted based on Bayesian inversion theory and observed seismic data, and to determine the target functional based on the prior probability function and likelihood function corresponding to the posterior probability function; the target functional is used to solve the inversion parameter matrix corresponding to the maximum posterior probability.

[0181] The matrix processing module 403 is used to determine the medium density and coefficients of each stiffness matrix based on the solution results of the inversion parameter matrix of the objective functional, and to determine the compliance matrix of the strong VTI medium based on the coefficients of each stiffness matrix.

[0182] The stress prediction module 404 is used to predict the geostress distribution of a target profile in a strong VTI medium based on the medium density, the normal strain matrix and the compliance matrix corresponding to the strong VTI. The geostress distribution includes the first normal stress, the second normal stress and the third normal stress corresponding to each point in the target profile, wherein the first normal stress, the second normal stress and the third normal stress are mutually perpendicular normal stresses in a spatial rectangular coordinate system constructed for the target profile, and the first normal stress or the second normal stress is perpendicular to the target profile.

[0183] In an optional embodiment, when acquiring observed seismic data of the seismic wavelet in a strong VTI medium, the matrix construction module 401 is specifically used for:

[0184] The phase and amplitude of the seismic wavelet were observed at different incident angles in the strong VTI medium, and multiple seismic data were obtained.

[0185] Seismic data vectors composed of multiple observed seismic sub-data are used as observed seismic data.

[0186] In an optional embodiment, when constructing the parameter matrix to be inverted based on the PP wave reflection coefficients corresponding to a strong VTI medium, the matrix construction module 401 is specifically used for:

[0187] Based on the calculation formula of PP wave reflection coefficient, multiple medium property parameters to be inverted are determined; the multiple medium property parameters include: multiple stiffness matrix coefficients and medium density;

[0188] From multiple stiffness matrix coefficients, the stiffness matrix coefficients related to the calculation of geostress are selected, and the parameter matrix to be inverted is constructed based on the natural logarithm corresponding to each stiffness matrix coefficient and the medium density.

[0189] In an optional embodiment, when determining the target functional based on the prior probability function and likelihood function corresponding to the posterior probability function, the function construction module 402 is specifically used for:

[0190] Based on the Gaussian probability density functions corresponding to the prior probability function and the likelihood function, respectively, the probability density distribution function of the posterior probability function is determined.

[0191] The maximum index function corresponding to the probability density distribution function is used as the objective functional.

[0192] In an optional embodiment, when determining the probability density distribution function of the posterior probability function based on the Gaussian probability density functions corresponding to the prior probability function and the likelihood function, the function construction module 402 is specifically used for:

[0193] Based on the calculation formulas of the incident angle-dependent seismic wavelet matrix corresponding to the seismic wavelet, the first-order difference factor corresponding to the parameter matrix to be inverted, and the reflection coefficient of the PP wave, a forward modeling factor is constructed. Among them, the first-order difference factor is used to compensate for the difference between each stiffness matrix coefficient and the natural logarithm of each stiffness matrix coefficient, as well as the difference between the medium density and the natural logarithm of the medium density.

[0194] Based on the product of the forward modeling factor and the inversion parameter matrix, and the observed seismic data, the noise vector in the observed seismic data is determined; whereby the product result represents the theoretical seismic data of the seismic wavelet in the strong VTI medium.

[0195] The Gaussian probability density function that the noise vector follows is taken as the Gaussian probability density function of the likelihood function, and the probability density function of the posterior probability function is determined based on the Gaussian probability density functions corresponding to the prior probability function and the likelihood function, respectively.

[0196] In an optional embodiment, when determining the medium density and coefficients of each stiffness matrix based on the solution results of the inversion parameter matrix of the objective functional, the function construction module 402 is specifically used for:

[0197] A low-frequency model is constructed based on well logging data corresponding to strong VTI media, and a low-frequency constraint function is obtained based on the low-frequency prior vector and the exponential term of the probability density distribution function of the low-frequency model.

[0198] Input the minimum index function corresponding to the low-frequency constraint function into the preset Markov chain model to obtain the function solution result of the minimum index function;

[0199] The solution results of the function are used as the solution results of the inversion parameter matrix, and the medium density and coefficients of each stiffness matrix are determined based on the solution results of the inversion parameter matrix.

[0200] In an optional embodiment, when predicting the geostress distribution of a target profile in a strong VTI medium based on the medium density, the normal strain matrix of the strong VTI, and the compliance matrix, the stress prediction module 404 is specifically used for:

[0201] For each point included in the target profile, perform the following operations:

[0202] Based on the distance between the target point and the surface of the strong VTI medium, and the density of the medium, the point density corresponding to the target point is determined, and the third normal stress at the target point is determined based on the point density; where the target point is any one of the points.

[0203] Based on the stress expressions corresponding to the first and second normal strains included in the normal strain matrix, and the multiple compliance matrix coefficients related to the two stress expressions included in the compliance matrix, the first normal stress and the second normal stress at the target point are determined; wherein, the first normal strain and the first normal stress are in the same direction, and the second normal strain and the second normal stress are in the same direction.

[0204] Based on the description of the method and apparatus embodiments above, an exemplary embodiment of the present invention also provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores a computer program executable by the at least one processor, which, when executed by the at least one processor, causes the electronic device to perform the method according to an embodiment of the present invention.

[0205] This application also provides a non-transitory computer-readable storage medium storing a computer program, wherein the computer program, when executed by a computer's processor, is used to cause the computer to perform a method according to an embodiment of this application.

[0206] This application also provides a computer program product, including a computer program, wherein the computer program, when executed by a computer's processor, is used to cause the computer to perform a method according to an embodiment of this application.

[0207] See Figure 5 The diagram shown below illustrates the structure of an electronic device 500 that can serve as a server or client in this application, and is an example of a hardware device that can be applied to various aspects of this application. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the application described and / or claimed herein.

[0208] like Figure 5As shown, the electronic device 500 includes a computing unit 501, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 502 or a computer program loaded from a storage unit 508 into a random access memory (RAM) 503. The RAM 503 may also store various programs and data required for the operation of the device 500. The computing unit 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0209] Multiple components in electronic device 500 are connected to I / O interface 505, including: input unit 506, output unit 507, storage unit 508, and communication unit 509. Input unit 506 can be any type of device capable of inputting information to electronic device 500. Input unit 506 can receive input digital or character information and generate key signal inputs related to user settings and / or function control of electronic device. Output unit 507 can be any type of device capable of presenting information and may include, but is not limited to, a display, speaker, video / audio output terminal, vibrator, and / or printer. Storage unit 508 may include, but is not limited to, disks and optical discs. Communication unit 509 allows electronic device 500 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers and / or chipsets, such as Bluetooth devices, WiFi devices, worldwide interoperability for microwave access (WiMax) devices, cellular communication devices, and / or the like.

[0210] The computing unit 501 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 501 performs the various methods and processes described above. For example, in some embodiments, the above-described geostress prediction method can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as storage unit 508.

[0211] In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 500 via ROM 502 and / or communication unit 509. In some embodiments, the computing unit 501 may be configured to perform the above-described geostress prediction method by any other suitable means (e.g., by means of firmware).

[0212] The program code used to implement the methods of this application may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0213] In the context of this application, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, RAM, ROM, erasable programmable read-only memory (EPROM) or flash memory, optical fibers, compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0214] As used in this application, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, device, and / or apparatus (e.g., disk, optical disk, memory, programmable logic device, PLD) used to provide machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. The term "machine-readable signal" refers to any signal used to provide machine instructions and / or data to a programmable processor.

[0215] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a cathode ray tube (CRT) or liquid crystal display (LCD) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0216] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0217] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other.

[0218] Furthermore, it should be understood that the above-disclosed embodiments are merely preferred embodiments of this application and should not be construed as limiting the scope of the invention. Therefore, any equivalent variations made in accordance with the claims of this invention are still within the scope of this application.

Claims

1. A method for predicting geostress, characterized in that, include: Seismic wavelet observation data in a strongly VTI medium are acquired, and a parameter matrix to be inverted is constructed based on the PP wave reflection coefficient corresponding to the strongly VTI medium. The parameter matrix to be inverted includes: inversion parameters set for the medium density of the strongly VTI medium and the non-zero stiffness matrix coefficients of each stiffness matrix, respectively. Each stiffness matrix coefficient is related to the in-situ stress calculation of the strongly VTI medium. The formula for calculating the PP wave reflection coefficient is the PP wave reflection coefficient equation for a strongly VTI medium based on the elastic impedance tensor, specifically expressed as follows: in, and These are horizontal slowness and vertical slowness, respectively. in, in, , and For Thomsen anisotropy parameters, and Describe the degree of variation in the propagation speed of P-waves and SH-waves in VTI media. Reflecting the nonlinear relationship between the propagation velocities of longitudinal and transverse waves, l 2 In this context, l represents the ratio of transverse to longitudinal wave velocities. The incident angle of the seismic wavelet. , , and These are the non-zero stiffness matrix coefficients included in the stiffness matrix of a strong VTI medium, which are related to the calculation of the first, second, and third normal stresses of the strong VTI medium. C 66 Although non-zero, the coefficients of each stiffness matrix are independent of the calculation of the first normal stress, the second normal stress, and the third normal stress. , , and The density of the medium is: ρ At this point, the stiffness matrix of the VTI medium is a strip matrix C as follows: The process of constructing the parameter matrix to be inverted based on the coefficients of each stiffness matrix and the natural logarithm corresponding to the medium density is as follows: Based on the equation for the reflection coefficient of PP waves in a strong VTI medium based on the elastic impedance tensor, the perturbation term is characterized in the form of a natural logarithm: in, The natural logarithm of each stiffness matrix coefficient and the density of the medium is represented as follows: The parameter matrix to be inverted is expressed as follows: ; Based on Bayesian inversion theory and the observed seismic data, a posterior probability function is constructed to follow the inversion parameter matrix corresponding to the parameter matrix to be inverted. The objective functional is then determined based on the prior probability function and likelihood function corresponding to the posterior probability function. The objective functional is used to solve for the inversion parameter matrix corresponding to the maximum posterior probability. Based on the solution results of the inversion parameter matrix of the target functional, the medium density and the coefficients of each stiffness matrix are determined, and the compliance matrix of the strong VTI medium is determined based on the coefficients of each stiffness matrix. Based on the medium density, the normal strain matrix corresponding to the strong VTI, and the compliance matrix, the geostress distribution of the target profile in the strong VTI medium is predicted; the geostress distribution includes: the first normal stress, the second normal stress, and the third normal stress corresponding to each point in the target profile, wherein the first normal stress, the second normal stress, and the third normal stress are mutually perpendicular normal stresses in a spatial rectangular coordinate system constructed for the target profile, and the first normal stress or the second normal stress is perpendicular to the target profile.

2. The method as described in claim 1, characterized in that, The acquisition of seismic wavelet observation data in strong VTI medium includes: At different incident angles, the phase and amplitude of the seismic wavelet during its propagation in the strong VTI medium were observed, resulting in multiple seismic data points. The earthquake data vector consisting of multiple observed earthquake sub-data is used as the observed earthquake data.

3. The method as described in claim 1, characterized in that, The construction of the parameter matrix to be inverted based on the PP wave reflection coefficient corresponding to the strong VTI medium includes: Based on the calculation formula of the PP wave reflection coefficient, multiple medium property parameters to be inverted are determined; the multiple medium property parameters include: multiple stiffness matrix coefficients and the medium density; The stiffness matrix coefficients related to the geostress calculation are selected from the plurality of stiffness matrix coefficients, and the parameter matrix to be inverted is constructed based on the natural logarithm corresponding to each stiffness matrix coefficient and the medium density.

4. The method as described in claim 1, characterized in that, The determination of the target functional based on the prior probability function and likelihood function corresponding to the posterior probability function includes: Based on the Gaussian probability density functions corresponding to the prior probability function and the likelihood function, respectively, the probability density distribution function of the posterior probability function is determined. The maximum value index function corresponding to the probability density distribution function is used as the target functional.

5. The method as described in claim 4, characterized in that, The step of determining the probability density distribution function of the posterior probability function based on the Gaussian probability density functions corresponding to the prior probability function and the likelihood function, respectively, includes: Based on the incident angle-dependent seismic wavelet matrix corresponding to the seismic wavelet, the first-order difference factor corresponding to the parameter matrix to be inverted, and the calculation formula of the PP wave reflection coefficient, a forward modeling factor is constructed; wherein, the first-order difference factor is used to compensate for the difference between each stiffness matrix coefficient and the natural logarithm of each stiffness matrix coefficient, as well as the difference between the medium density and the natural logarithm of the medium density; Based on the product of the forward modeling factor and the inversion parameter matrix, and the observed seismic data, the noise vector in the observed seismic data is determined; wherein, the product result represents: the theoretical seismic data of the seismic wavelet in the strong VTI medium; The Gaussian probability density function that the noise vector follows is taken as the Gaussian probability density function of the likelihood function, and the probability density function of the posterior probability function is determined based on the Gaussian probability density functions corresponding to the prior probability function and the likelihood function, respectively.

6. The method as described in claim 4, characterized in that, The determination of the medium density and the coefficients of each stiffness matrix based on the inversion parameter matrix solution of the objective functional includes: A low-frequency model is constructed based on the logging data corresponding to the strong VTI medium, and a low-frequency constraint function is obtained based on the low-frequency prior vector of the low-frequency model and the exponential term of the probability density distribution function. The minimum value index function corresponding to the low-frequency constraint function is input into a preset Markov chain model to obtain the function solution result of the minimum value index function; The solution result of the function is used as the solution result of the inversion parameter matrix, and the medium density and the coefficients of each stiffness matrix are determined based on the solution result of the inversion parameter matrix.

7. The method according to any one of claims 1-6, characterized in that, The method of predicting the geostress distribution of a target profile in a strong VTI medium based on the medium density, the normal strain matrix corresponding to the strong VTI, and the compliance matrix includes: For each point included in the target profile, perform the following operations respectively: Based on the distance between the target point and the surface of the strong VTI medium, and the density of the medium, the point density corresponding to the target point is determined, and the third normal stress at the target point is determined based on the point density; wherein, the target point is any one of the points. Based on the stress expressions corresponding to the first and second normal strains included in the normal strain matrix, and the multiple compliance matrix coefficients related to the two stress expressions included in the compliance matrix, the first normal stress and the second normal stress at the target point are determined; wherein, the first normal strain and the first normal stress are in the same direction, and the second normal strain and the second normal stress are in the same direction.

8. A geostress prediction device, characterized in that, include: A matrix construction module is used to acquire observed seismic data of seismic wavelets in a strong VTI medium and construct a parameter matrix to be inverted based on the PP wave reflection coefficient corresponding to the strong VTI medium. The parameter matrix to be inverted includes: inversion parameters set for the medium density of the strong VTI medium and the non-zero stiffness matrix coefficients included in the stiffness matrix of the strong VTI medium, respectively. Each stiffness matrix coefficient is related to the in-situ stress calculation of the strong VTI medium. The calculation formula for the PP wave reflection coefficient is a strong VTI medium PP wave reflection coefficient equation based on the elastic impedance tensor, specifically expressed as follows: in, and These are horizontal slowness and vertical slowness, respectively. in, in, , and For Thomsen anisotropy parameters, and Describe the degree of variation in the propagation speed of P-waves and SH-waves in VTI media. Reflecting the nonlinear relationship between the propagation velocities of longitudinal and transverse waves, l 2 In this context, l represents the ratio of transverse to longitudinal wave velocities. The incident angle of the seismic wavelet. , , and These are the non-zero stiffness matrix coefficients included in the stiffness matrix of a strong VTI medium, which are related to the calculation of the first, second, and third normal stresses of the strong VTI medium. C 66 Although non-zero, the coefficients of each stiffness matrix are independent of the calculation of the first normal stress, the second normal stress, and the third normal stress. , , and The density of the medium is: ρ At this point, the stiffness matrix of the VTI medium is a strip matrix C as follows: The process of constructing the parameter matrix to be inverted based on the coefficients of each stiffness matrix and the natural logarithm corresponding to the medium density is as follows: Based on the equation for the reflection coefficient of PP waves in a strong VTI medium based on the elastic impedance tensor, the perturbation term is characterized in the form of a natural logarithm: in, The natural logarithm of each stiffness matrix coefficient and the density of the medium is represented as follows: The parameter matrix to be inverted is expressed as follows: ; The function construction module is used to construct the posterior probability function of the inversion parameter matrix corresponding to the parameter matrix to be inverted based on Bayesian inversion theory and the observed seismic data, and to determine the target functional based on the prior probability function and the likelihood function corresponding to the posterior probability function; the target functional is used to solve for the inversion parameter matrix corresponding to the maximum posterior probability. The matrix processing module is used to determine the medium density and the coefficients of each stiffness matrix based on the solution result of the inversion parameter matrix of the target functional, and to determine the compliance matrix of the strong VTI medium based on the coefficients of each stiffness matrix. The stress prediction module is used to predict the geostress distribution of a target profile in the strong VTI medium based on the medium density, the normal strain matrix corresponding to the strong VTI, and the compliance matrix; the geostress distribution includes: the first normal stress, the second normal stress, and the third normal stress corresponding to each point in the target profile, wherein the first normal stress, the second normal stress, and the third normal stress are mutually perpendicular normal stresses in a spatial rectangular coordinate system constructed for the target profile, and the first normal stress or the second normal stress is perpendicular to the target profile.

9. An electronic device, comprising: processor; as well as Stored program memory, The program is characterized in that it includes instructions that, when executed by the processor, cause the processor to perform the method as described in any one of claims 1-7.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method as described in any one of claims 1-7.

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