Ground stress prediction method and device, electronic equipment and storage medium

Abstract

The invention provides a crustal stress prediction method and device, electronic equipment and a storage medium, and relates to the technical field of seismic survey. The method comprises the following steps: obtaining observation seismic data of seismic wavelets in a strong VTI medium, and constructing a to-be-inverted parameter matrix based on a PP wave reflection coefficient corresponding to the strong VTI medium; constructing a posterior probability function obeyed by an inversion parameter matrix corresponding to the to-be-inverted parameter matrix based on a Bayesian inversion theory and observation seismic data, and determining a target functional based on a prior probability function and a likelihood function corresponding to the posterior probability function; determining medium density and each stiffness matrix coefficient based on an inversion parameter matrix solving result of the target functional, and determining a flexibility matrix of the strong VTI medium based on each stiffness matrix coefficient; and predicting the ground stress distribution of the target profile in the strong VTI medium based on the medium density and the positive strain matrix and the flexibility matrix corresponding to the strong VTI. Therefore, the crustal stress prediction accuracy under the strong VTI medium is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of seismic exploration, and in particular to a geostress prediction method and device, an electronic device, and a storage medium. BACKGROUND

[0002] Geostress is a key factor determining the shape, orientation, and extension direction of cracks generated underground. Accurate prediction of geostress distribution is conducive to selecting areas that are easy to fracture into a network, so as to improve the recovery rate and yield of underground storage resources. The geostress includes three normal stresses (which can be denoted as σ x , σ y and σ z ) perpendicular to each other, and the directions of the three normal stresses are the same as the positive directions of the three coordinate axes of a space rectangular coordinate system constructed for a stratigraphic profile.

[0003] At present, a geostress prediction method based on weak anisotropy theory is usually used to predict geostress under strong vertical transverse isotropy (VTI) medium. However, the aforementioned geostress prediction method has limited applicability in strong VTI medium, that is, when the medium underground is a strong VTI medium, the prediction of geostress is not accurate enough.

[0004] Therefore, it is currently an urgent problem to be solved to improve the prediction accuracy of geostress under strong VTI medium. SUMMARY

[0005] The embodiments of the present application provide a geostress prediction method, device, electronic device, and storage medium to improve the prediction accuracy of geostress under strong VTI medium.

[0006] In a first aspect, the embodiments of the present application provide a geostress prediction method, which comprises: obtaining observed seismic data of a seismic wavelet under strong VTI medium, and constructing a parameter matrix to be inverted based on a PP wave reflection coefficient corresponding to the strong VTI medium; the parameter matrix to be inverted includes: parameters to be inverted respectively set for each non-zero stiffness matrix coefficient included in a stiffness matrix of the strong VTI medium and a medium density of the strong VTI medium, and each stiffness matrix coefficient is related to calculation of geostress of the strong VTI medium; based on the Bayesian inversion theory and the observed seismic data, constructing a posterior probability function to which an inversion parameter matrix corresponding to the parameter matrix to be inverted is subjected, and determining a target functional based on a prior probability function and a likelihood function corresponding to the posterior probability function; the target functional is used to solve an inversion parameter matrix corresponding to a maximum posterior probability; Determine the medium density and the coefficients of the stiffness matrix based on the inversion parameter matrix solution result of the target functional, and determine the compliance matrix of the strong VTI medium based on the coefficients of the stiffness matrix; Predict the ground stress distribution of the 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 ground stress distribution includes: the first normal stress, the second normal stress, and the third normal stress corresponding to each point included in the target profile respectively, wherein the first normal stress, the second normal stress, and the third normal stress are mutually perpendicular normal stresses in the space rectangular coordinate system constructed for the target profile, and the first normal stress or the second normal stress is perpendicular to the target profile.

[0007] In an optional embodiment, the observed seismic data of the seismic wave in the strong VTI medium is obtained, including: Obtain the phase and amplitude of the seismic wave in the strong VTI medium during propagation under different incident angles of the seismic wave, to obtain a plurality of observed seismic data; Take the seismic data vector composed of the plurality of observed seismic data as the observed seismic data.

[0008] In an optional embodiment, the to-be-inverted parameter matrix is constructed based on the PP wave reflection coefficient corresponding to the strong VTI medium, including: Determine a plurality of medium attribute parameters to be inverted based on the calculation formula of the PP wave reflection coefficient; the plurality of medium attribute parameters include: a plurality of stiffness matrix coefficients and a medium density; Select the stiffness matrix coefficients related to the ground stress calculation from the plurality of stiffness matrix coefficients, and construct the to-be-inverted parameter matrix based on the natural logarithm corresponding to each stiffness matrix coefficient and the medium density.

[0009] In an optional embodiment, the target functional is determined based on the prior probability function and the likelihood function corresponding to the posterior probability function, including: Determine the probability density distribution function of the posterior probability function based on the Gaussian distribution probability density functions corresponding to the prior probability function and the likelihood function respectively; Take the maximum value index function corresponding to the probability density distribution function as the target functional.

[0010] In an optional embodiment, the probability density distribution function of the posterior probability function is determined based on the Gaussian distribution probability density functions corresponding to the prior probability function and the likelihood function respectively, including: The forward factor is constructed based on a calculation formula of a PP wave reflection coefficient, a first-order difference factor corresponding to an incident angle-dependent seismic wave matrix and a parameter matrix to be inverted, wherein the first-order difference factor is used to compensate for a difference between each stiffness matrix coefficient in each stiffness matrix coefficient and a natural logarithm of each stiffness matrix coefficient, and a difference between a medium density and a natural logarithm of the medium density. The noise vector in the observed seismic data is determined based on a product result of the forward factor and the parameter matrix to be inverted, and the observed seismic data, wherein the product result represents theoretical seismic data of the seismic wave in the strong VTI medium. The Gaussian distribution probability density function of the noise vector is taken as a Gaussian distribution probability density function of a likelihood function, and the probability density distribution function of the posterior probability function is determined based on Gaussian distribution probability density functions corresponding to the prior probability function and the likelihood function, respectively.

[0011] In an optional embodiment, the medium density and the stiffness matrix coefficients are determined based on a solution result of the parameter matrix to be inverted of the objective function, including: A low-frequency model is constructed based on logging data corresponding to the strong VTI medium, and a low-frequency constraint function is obtained based on a low-frequency prior vector of the low-frequency model and an 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 a function solution result of the minimum value index function; The function solution result is taken as a solution result of the parameter matrix to be inverted, and the medium density and the stiffness matrix coefficients are determined based on the solution result of the parameter matrix to be inverted.

[0012] In an optional embodiment, the stress distribution of a target profile in the strong VTI medium is predicted based on the medium density, a normal strain matrix and a compliance matrix of the strong VTI, including: For each point included in the target profile, the following operations are performed respectively: A point density corresponding to a target point is determined based on a distance between the target point and a surface of the strong VTI medium and the medium density, and a third normal stress at the target point is determined based on the point density; wherein the target point is any one of the points. A first normal stress and a second normal stress at the target point are determined based on stress expressions respectively corresponding to a first normal strain and a second normal strain included in the normal strain matrix, and a plurality of compliance matrix coefficients related to the two stress expressions included in the compliance matrix; wherein the first normal strain is in the same direction as the first normal stress, and the second normal strain is in the same direction as the second normal stress.

[0013] In a second aspect, the embodiments of the present application also provide a stress prediction device, and the device includes: The matrix construction module is configured to obtain observed seismic data of a seismic wave in a strong VTI medium, and construct a parameter matrix to be inverted based on a PP wave reflection coefficient corresponding to the strong VTI medium. The parameter matrix to be inverted includes parameters to be inverted respectively set for each non-zero stiffness matrix coefficient included in a stiffness matrix of the strong VTI medium and a medium density of the strong VTI medium, and each stiffness matrix coefficient is related to the calculation of the ground stress of the strong VTI medium. The function construction module is configured to construct a posterior probability function to which an inversion parameter matrix corresponding to the parameter matrix to be inverted is subjected based on a Bayesian inversion theory and the observed seismic data, and determine a target functional based on a prior probability function and a likelihood function corresponding to the posterior probability function. The target functional is used to solve an inversion parameter matrix corresponding to a maximum posterior probability. The matrix processing module is configured to determine the medium density and each stiffness matrix coefficient based on a solution result of the inversion parameter matrix of the target functional, and determine a compliance matrix of the strong VTI medium based on each stiffness matrix coefficient. The stress prediction module is configured to predict a ground stress distribution of a target profile in the strong VTI medium based on the medium density, a normal strain matrix corresponding to the strong VTI, and the compliance matrix. The ground stress distribution includes a first normal stress, a second normal stress, and a third normal stress respectively corresponding to each point included in the target profile. The first normal stress, the second normal stress, and the third normal stress are mutually perpendicular normal stresses in a space rectangular coordinate system constructed for the target profile, and the first normal stress or the second normal stress is perpendicular to the target profile.

[0014] In an optional embodiment, when obtaining the observed seismic data of the seismic wave in the strong VTI medium, the matrix construction module is specifically configured to: Obtain a phase and an amplitude of the seismic wave in the strong VTI medium during propagation of the seismic wave in the strong VTI medium under different incidence angles of the seismic wave, and obtain a plurality of observed seismic data. Take a seismic data vector composed of the plurality of observed seismic data as the observed seismic data.

[0015] In an optional embodiment, when constructing the parameter matrix to be inverted based on the PP wave reflection coefficient corresponding to the strong VTI medium, the matrix construction module is specifically configured to: Determine a plurality of medium attribute parameters to be inverted based on a calculation formula of the PP wave reflection coefficient. The plurality of medium attribute parameters include a plurality of stiffness matrix coefficients and a medium density. Select each stiffness matrix coefficient related to the calculation of the ground stress from the plurality of stiffness matrix coefficients, and construct the parameter matrix to be inverted based on a natural logarithm respectively corresponding to each stiffness matrix coefficient and the medium density.

[0016] In an optional embodiment, when the target functional is determined based on the prior probability function and the likelihood function corresponding to the posterior probability function, the function construction module is specifically configured to: determine the probability density distribution function of the posterior probability function based on the Gaussian distribution probability density functions corresponding to the prior probability function and the likelihood function, respectively; take the maximum value index function corresponding to the probability density distribution function as the target functional.

[0017] In an optional embodiment, when the probability density distribution function of the posterior probability function is determined based on the Gaussian distribution probability density functions corresponding to the prior probability function and the likelihood function, respectively, the function construction module is specifically configured to: construct the forward factor based on the incidence angle-dependent seismic wave matrix corresponding to the seismic wavelet, the first-order difference factor corresponding to the matrix of the to-be-inverted parameters, and the calculation formula of the PP wave reflection coefficient; wherein the first-order difference factor is used to compensate the difference between each stiffness matrix coefficient and the natural logarithm of each stiffness matrix coefficient, and the difference between the medium density and the natural logarithm of the medium density; determine the noise vector in the observed seismic data based on the product result of the forward factor and the inversion parameter matrix, and the observed seismic data; wherein the product result represents the theoretical seismic data of the seismic wavelet in the strong VTI medium; take the Gaussian distribution probability density function to which the noise vector is subjected as the Gaussian distribution probability density function of the likelihood function, and determine the probability density distribution function of the posterior probability function based on the Gaussian distribution probability density functions corresponding to the prior probability function and the likelihood function, respectively.

[0018] In an optional embodiment, when the medium density and the stiffness matrix coefficients are determined based on the inversion parameter matrix solution result of the target functional, the function construction module is specifically configured to: construct a low-frequency model based on the logging data corresponding to the strong VTI medium, and obtain a low-frequency constraint function based on the low-frequency prior vector of the low-frequency model and the exponential term of the probability density distribution function; input the minimum value index function corresponding to the low-frequency constraint function into the preset Markov chain model to obtain a function solution result of the minimum value index function; take the function solution result as the inversion parameter matrix solution result, and determine the medium density and the stiffness matrix coefficients based on the inversion parameter matrix solution result.

[0019] In an optional embodiment, when the stress distribution of a target profile in the strong VTI medium is predicted based on the medium density, the normal strain matrix and the compliance matrix of the strong VTI, the stress prediction module is specifically configured to: for each point included in the target profile, the following operations are performed respectively: Determine the point density corresponding to the target point based on the distance between the target point and the surface of the strong VTI medium and the medium density, and determine the third normal stress at the target point based on the point density; wherein the target point is any one of the points; Determine the first normal stress and the second normal stress at the target point based on the stress expressions corresponding to the first normal strain and the second normal strain included in the normal strain matrix, and the plurality of compliance matrix coefficients related to the two stress expressions included in the compliance matrix; 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.

[0020] In a third aspect, the embodiments of the present application further provide an electronic device, comprising: a processor; and a memory storing a program, wherein the program includes instructions that, when executed by the processor, cause the processor to perform the geostress prediction method of the first aspect.

[0021] In a fourth aspect, the embodiments of the present application further provide a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to perform the geostress prediction method of the first aspect.

[0022] In a fifth aspect, the present application provides a computer program product, which, when invoked by a computer, causes the computer to perform the geostress prediction method steps of the first aspect.

[0023] The present application has the following beneficial effects: In the geostress prediction method provided in the embodiments of the present application, the observed seismic data of the seismic wavelet in the strong VTI medium is obtained, and the parameter matrix to be inverted is constructed based on the PP wave reflection coefficient corresponding to the strong VTI medium; then, based on the Bayesian inversion theory and the observed seismic data, the posterior probability function to which the inversion parameter matrix corresponding to the parameter matrix to be inverted 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; further, based on the inversion parameter matrix solving result of the target functional, the medium density and each stiffness matrix coefficient are determined, and the compliance matrix of the strong VTI medium is determined based on each stiffness matrix coefficient; 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, the medium density and each stiffness matrix coefficient of the strong VTI are accurately determined through the constructed target functional, and the geostress prediction accuracy in the strong VTI medium is improved.

[0024] Additionally, other features and advantages of the present application will be set forth in the description that follows, and in part will be apparent from the description, or can be learned by practice of the application. The objectives and other advantages of the application will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings. BRIEF DESCRIPTION OF DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments description. Obviously, the drawings described here are used to provide further understanding of the present application and form a part of the present application. However, the present application is not limited by the drawings shown herein. Figure 1 An optional system architecture schematic diagram applicable to the embodiments of the present application; Figure 2 An implementation flow schematic diagram of a crustal stress prediction method provided by the embodiments of the present application; Figure 3 An implementation flow schematic diagram of a method for determining a probability density distribution function of a posterior probability function provided by the embodiments of the present application; Figure 4 A structure schematic diagram of a crustal stress prediction device provided by the embodiments of the present application; Figure 5 A structure schematic diagram of an electronic device provided by the embodiments of the present application. DETAILED DESCRIPTION

[0026] The embodiments of the present application will be described in detail with reference to the drawings. Although some embodiments of the present application are shown in the drawings, it should be understood that the present application can be implemented in various forms, and should not be interpreted as being limited to the embodiments set forth herein, but rather these embodiments are provided to more thoroughly and completely understand the present application. It should be understood that the drawings and embodiments of the present application are only for exemplary purposes, and are not intended to limit the scope of protection of the present application.

[0027] It should be understood that each step recorded in the method implementation of the present application can be executed in different order, and / or executed in parallel. In addition, the method implementation can include additional steps and / or omit the execution of the steps shown. The scope of the present application is not limited in this respect.

[0028] As used herein, the term "includes" and its variants are to be read to be analogous to "comprises" or "comprising." The term "based on" is to be read as "based, at least in part, 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." Related terms have analogous meanings. It is noted that the use of "first," "second," etc., in the present application is merely to distinguish one concept from another, and does not imply order or sequence unless explicitly stated otherwise.

[0029] It is noted that the use of "one," "plurality," or "a number" of something in the present application is illustrative and non-limiting, and one of skill in the art will understand that "one," "plurality," or "a number" of something should be interpreted as "one or more" unless the context clearly indicates otherwise.

[0030] The names of the messages or information exchanged between the plurality of apparatuses in the embodiments of the present application are merely for illustrative purposes, and are not intended to limit the scope of the messages or information.

[0031] First, the design idea of the embodiments of the present application is briefly introduced as follows: Seismic prediction of earth stress is an important part of seismic exploration. Shale gas reservoirs have the characteristics of low porosity and low permeability. Hydraulic fracturing is generally required to extract shale gas reservoirs, which forms a large number of fractures in shale. Earth stress is a key factor that determines the shape, orientation, and extension direction of the generated fractures. Accurate prediction of the earth stress distribution of shale reservoirs is beneficial to selecting areas that are easy to fracture into a network, improving the recovery rate and yield of shale reservoirs, and is one of the necessary steps for shale gas reservoir exploration and development.

[0032] At present, the earth stress prediction method for shale, a strong VTI medium, is mainly based on weak anisotropy theory, which limits the applicability of existing earth stress prediction methods in strong VTI media, resulting in inaccurate earth stress prediction in strong VTI media. Therefore, improving the accuracy of earth stress prediction in strong VTI media is a problem that needs to be solved at present.

[0033] To solve or improve the above problems, the application provides a method for predicting the ground stress of a strong VTI medium, which can specifically include the following steps: obtaining observed seismic data of a seismic wave under the strong VTI medium, and constructing 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 the parameter to be inverted respectively set for each stiffness matrix coefficient in the stiffness matrix of the strong VTI medium and the medium density of the strong VTI medium, and each stiffness matrix coefficient is related to the ground stress calculation of the strong VTI medium; then, based on the Bayesian inversion theory and the observed seismic data, a posterior probability function to which an inversion parameter matrix corresponding to the parameter matrix to be inverted is subjected is constructed, and a target functional is determined based on a prior probability function and a 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; further, the medium density and each stiffness matrix coefficient are determined based on the inversion parameter matrix solving result of the target functional, and a compliance matrix of the strong VTI medium is determined based on each stiffness matrix coefficient; finally, the ground stress distribution of a 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; the ground stress distribution includes the first normal stress, the second normal stress, and the third normal stress respectively corresponding to each point included in the target profile, wherein the first normal stress, the second normal stress, and the third normal stress are normal stresses perpendicular to each other in a space right-angle coordinate system constructed for the target profile, and the first normal stress or the second normal stress is perpendicular to the target profile.

[0034] In this way, the problem that the applicability of the ground stress prediction based on the weak anisotropy theory is limited in the strong VTI medium is solved, the introduction of the integration of the prior and boundary constraint algorithm improves the stability of the parameter inversion, the sequential simulation algorithm and the stochastic inversion strategy improve the resolution capability of the stiffness matrix coefficient prediction result, the inversion stiffness matrix can directly construct the constitutive relation with the ground stress, thereby avoiding the cumulative error of the conventional ground stress step-by-step inversion, the medium density and each stiffness matrix coefficient of the strong VTI are accurately determined through the constructed target functional, and thus the ground stress prediction accuracy in the strong VTI medium is improved.

[0035] Particularly, the preferred embodiments of the application are described below in conjunction with the drawings of the specification, and it should be understood that the preferred embodiments described herein are only used to illustrate and explain the application, and are not used to limit the application, and the embodiments of the application and the features in the embodiments can be combined with each other without conflict.

[0036] Reference is made to Figure 1As shown, it is an optional system architecture diagram applicable to the embodiments of the present application, which can include: terminal devices (101a, 101b) and a server 102. The terminal devices (101a, 101b) and the server 102 can interact information through a communication network. Among them, the communication mode of the aforementioned communication network can include: wireless communication mode and wired communication mode. Exemplarily, the terminal devices (101a, 101b) can access the network through cellular mobile communication technology and communicate with the server 102. Among them, the aforementioned cellular mobile communication technology, for example, includes the fifth generation mobile communication (5th generation mobile networks, 5G) technology or the next generation mobile communication technology. Alternatively, the terminal devices (101a, 101b) can access the network through short-range wireless communication mode and communicate with the server 102. Among them, the aforementioned short-range wireless communication mode, for example, includes wireless fidelity (wireless fidelity, Wi-Fi) technology.

[0037] The embodiments of the present application do not make any limitation on the number of communication devices involved in the above-mentioned system architecture, for example, the above-mentioned system architecture can include more terminal devices, or include fewer terminal devices, or also include other network devices. For example, Figure 1 As shown, only the terminal devices (101a, 101b) and the server 102 are taken as examples for description, and the following briefly introduces each communication device and its respective function.

[0038] The terminal devices (101a, 101b) are devices that can provide voice and / or data connectivity to users, which can be devices supporting wired and / or wireless connection modes.

[0039] Exemplarily, the terminal devices (101a, 101b) can include but are not limited to: mobile phones, tablet computers, notebook computers, palm computers, mobile internet devices (mobile internet device, MID), wearable devices, virtual reality (virtual reality, VR) devices, augmented reality (augmented reality, AR) devices, wireless terminal devices in industrial control, wireless terminal devices in unmanned driving, wireless terminal devices in smart grid, wireless terminal devices in transportation safety, wireless terminal devices in smart city, or wireless terminal devices in smart home, etc.

[0040] In addition, the terminal devices (101a, 101b) can be installed with related clients, which can be software, for example, application programs (application, APP), browsers, short video software, etc., or web pages, applets, etc.

[0041] It should be noted that the terminal device (101a, 101b) in the embodiment of the present application can make the above-mentioned client related to the ground stress prediction send the observed seismic data of the seismic wavelet (such as the Ricker wavelet) under the strong VTI medium (such as shale) to the server 102, so as to subsequently perform the method steps of the ground stress prediction for the strong VTI medium.

[0042] The server 102 can be a stand-alone physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing 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 basic cloud computing services such as big data and artificial intelligence platforms.

[0043] It is worth mentioning that in the embodiment of the present application, the server 102 can be used to obtain the observed seismic data of the seismic wavelet under the strong VTI medium, and construct a to-be-inverted parameter matrix based on the PP wave reflection coefficient corresponding to the strong VTI medium; then, based on the Bayesian inversion theory and the observed seismic data, a posterior probability function to which the inversion parameter matrix corresponding to the to-be-inverted parameter matrix is subjected is constructed, and a target functional is determined based on the prior probability function and the likelihood function corresponding to the posterior probability function; further, based on the inversion parameter matrix solving result of the target functional, the medium density and each stiffness matrix coefficient are determined, and the compliance matrix of the strong VTI medium is determined based on each stiffness matrix coefficient; finally, based on the medium density, the normal strain matrix corresponding to the strong VTI, and the compliance matrix, the ground stress distribution of the target profile in the strong VTI medium is predicted.

[0044] The ground stress prediction method provided by the exemplary embodiments of the present application will be described below in combination with the above-mentioned system architecture and with reference to the accompanying drawings. It should be noted that the above-mentioned system architecture is only shown for the purpose of facilitating the understanding of the spirit and principles of the present application, and the embodiments of the present application are not limited in this respect.

[0045] Referring to Figure 2 As shown in FIG. 2, which is an implementation flowchart of a ground stress prediction method provided by an embodiment of the present application, the execution subject is taken as an example of a server, and the specific implementation flow of the method is as follows: S201: Obtain the observed seismic data of the seismic wavelet under the strong VTI medium, and construct a to-be-inverted parameter matrix based on the PP wave reflection coefficient corresponding to the strong VTI medium.

[0046] The to-be-inverted parameter matrix can include: to-be-inverted parameters respectively set for each stiffness matrix coefficient included in a stiffness matrix of the strong VTI medium and a medium density of the strong VTI medium. The stiffness matrix coefficient is related to the ground stress calculation of the strong VTI medium.

[0047] Optionally, the ground stress can 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 space rectangular coordinate system constructed for the target profile, and the first normal stress or the second normal stress is perpendicular to the target profile.

[0048] Specifically, a plane formed by the X axis (or the Y axis) and the Z axis of the space rectangular coordinate system is parallel to the target profile, and the Y axis (or the Z axis) of the space 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 direction of the X axis, and the first normal stress can be represented as σ x The second normal stress is a normal stress in the same direction as the positive direction of the Y axis, and the second normal stress can be represented as σ y The third normal stress is a normal stress in the same direction as the positive direction of the Z axis, and the third normal stress can be represented as σ z .

[0049] Therefore, each stiffness matrix coefficient can include: a stiffness matrix coefficient related to the calculation of the first normal stress σ x , the second normal stress σ y , and the third normal stress σ z of the strong VTI medium.

[0050] In order to improve the accuracy of the ground stress prediction, the observed seismic data can include observed seismic data of the seismic wavelet at different incidence angles. Therefore, in an optional implementation, when performing step S201, the server can obtain the observed seismic data of the seismic wavelet in the strong VTI medium by observing the phase and amplitude of the seismic wavelet in the strong VTI medium at different incidence angles of the seismic wavelet through the seismic data observation module, obtaining multiple observed seismic data, and taking the seismic data vector composed of the multiple observed seismic data as the observed seismic data.

[0051] In other words, the observed seismic data can be an observation data vector composed of a partial angle stack seismic data. For example, the observed seismic data Specifically, the observed seismic data can be represented as: The observed seismic data hThe observed seismic sub-data sets of different incidence angles are combined.

[0052] In an optional implementation, when performing step S201, the server can determine the multiple medium attribute parameters to be inverted based on a calculation formula of the PP wave reflection coefficient when constructing the parameter matrix to be inverted based on the PP wave reflection coefficient corresponding to the strong VTI medium, wherein the multiple medium attribute parameters include multiple stiffness matrix coefficients and a medium density, so as to filter out each stiffness matrix coefficient related to the ground stress calculation from the multiple stiffness matrix coefficients, and construct the parameter matrix to be inverted based on the natural logarithm corresponding to each stiffness matrix coefficient and the medium density. It can be understood that the multiple stiffness matrix coefficients are elastic parameters, and the medium density is a non-elastic parameter.

[0053] For example, the calculation formula of the PP wave reflection coefficient can be a PP wave reflection coefficient equation of the strong VTI medium based on the elastic impedance tensor, and can be specifically represented as follows:

[0054] wherein, and are horizontal slowness and vertical slowness respectively,

[0055]

[0056] wherein,

[0057]

[0058] wherein,

[0059]

[0060] , and are Thomsen anisotropy parameters, and describe the degree of change of the propagation speed of P wave and SH wave in VTI medium, reflects the nonlinear relationship between the propagation speed of longitudinal wave and transverse wave. In addition, l 2 in l is the ratio of transverse wave velocity to longitudinal wave velocity, is the phase velocity of P wave, is the incidence angle of the seismic wavelet. It should be noted that, , , and non-zero and related to the calculation of the first normal stress σ x , the second normal stress σ y and the third normal stress σ z of the strong VTI medium, while C 66 non-zero but irrelevant to the calculation of the first normal stress σ x , the second normal stress σ y and the third normal stress σ z , i.e. each of the above stiffness matrix coefficients is: , , and , and the above medium density can be: ρ .

[0061] At this time, the stiffness matrix of the VTI medium can be a strip matrix C as follows:

[0062] Optionally, the server can construct the parameter matrix to be inverted based on the natural logarithm of each stiffness matrix coefficient and the medium density corresponding to the natural logarithm. The process can be as follows: according to the strong VTI medium PP wave reflection coefficient equation based on the elastic impedance tensor, the perturbation term can be represented in the form of natural logarithm:

[0063] wherein represents the natural logarithm of each of the above stiffness matrix coefficients and the medium density. Then, the parameter matrix to be inverted can be represented as: .

[0064] S202: Based on the Bayesian inversion theory and the observed seismic data, a posterior probability function to which an inversion parameter matrix corresponding to the parameter matrix to be inverted is subjected is constructed, and a target functional is determined based on a prior probability function and a likelihood function corresponding to the posterior probability function.

[0065] In this way, the Bayesian theory defines the prior information as the probability density function to which all random unknown parameters are subjected. The Bayesian theory can obtain the unknown probability from the known probability, i.e. the prior information is transformed into the posterior information, and each of the parameters included in the parameter matrix to be inverted can be accurately inverted.

[0066] 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:

[0067] 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.

[0068] 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.

[0069] 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.

[0070] 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 3 The 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: 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.

[0071] 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.

[0072] 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:

[0073] in,

[0074]

[0075]

[0076] 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.

[0077] 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.

[0078] 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.

[0079] For example, the likelihood function The Gaussian distribution probability density function of the prior probability function p(x) can be specifically represented as follows:

[0080] wherein, represents a vector obeys a Gaussian probability distribution function with a mean of 0 and a covariance of , and is a sampling point number of the observed seismic data . Optionally, the covariance is determined according to multiple experiments.

[0081] In addition, in the Bayesian inversion framework, the prior information of the to-be-inverted parameter is beneficial to reduce the solution space of the inversion parameter matrix . In order to improve the stability of the seismic inversion, the inversion parameter matrix also obeys a Gaussian-type probability density distribution. Then, the Gaussian distribution probability density function of the prior probability function p(x) can be specifically represented as follows:

[0082] wherein, is a covariance between each element included in the inversion parameter matrix , and M is a sampling point number of the inversion parameter matrix . Similarly, the covariance may also be determined according to multiple experiments, which is not limited by the embodiments of the present application.

[0083] Further, the server can determine the probability density distribution function of the posterior probability function p(x|y) after determining the Gaussian distribution probability density functions corresponding to the prior probability function p(x) and the likelihood function p(y|x) respectively, and the probability density distribution function of the posterior probability function p(x|y) can be specifically represented as follows:

[0084] S203: Determine the medium density and each stiffness matrix coefficient based on the inversion parameter matrix solving result of the target functional, and determine the compliance matrix of the strong VTI medium based on each stiffness matrix coefficient.

[0085] Exemplarily, the inversion parameter matrix solving result of the target functional can be an inversion parameter matrix when an exponential term of the probability density distribution function of the posterior probability function p(x|y) is a minimum value. Therefore, the inversion parameter matrix solving result of the target functional can be specifically represented as follows:

[0086] wherein, argmax​​​​​m {} is the maximum index function, and argmin m {} is the minimum index function.

[0087] Therefore, the server can determine the medium density and the stiffness matrix coefficients according to the inversion parameter matrix solving result of the target functional O m ), and determine the compliance matrix of the strong VTI medium based on the stiffness matrix coefficients.

[0088] In order to further improve the stability of seismic inversion, the server constructs a low-frequency model based on the logging data (or the stratigraphic framework information) corresponding to the strong VTI medium, and obtains a low-frequency constraint function based on the low-frequency prior vector of the low-frequency model and the exponential term of the probability density distribution function; then, inputs the minimum index function corresponding to the low-frequency constraint function into the preset Markov chain model to obtain a function solving result of the minimum index function; finally, takes the function solving result as the inversion parameter matrix solving result, and determines the medium density and the stiffness matrix coefficients based on the inversion parameter matrix solving result.

[0089] Optionally, the server can measure the low-frequency prior vector (i.e., the low-frequency model prior information) of the low-frequency model by the L2 norm, so as to introduce the L2 norm measurement result of the low-frequency prior vector into the exponential term of the probability density distribution function of the posterior probability function Therefore, the low-frequency constraint function can be specifically represented as follows:

[0090] At this time, O m ) is a new target functional, i.e., the low-frequency constraint function obtained based on the low-frequency prior vector of the low-frequency model and the exponential term of the probability density distribution function, is the L2 norm measurement result of the low-frequency prior vector.

[0091] In addition, the preset Markov chain model can have 50 chains, and the iteration number of each chain can be 2500 times. In this way, through the preset Markov chain model, the function solving result of the minimum index function can be quickly and accurately obtained, the resolution capability of the stiffness matrix coefficient prediction result is improved, and the inversion stiffness matrix can directly construct the constitutive relationship with the stress tensor, avoiding the cumulative error of the conventional stress seismic step-by-step inversion.

[0092] In other words, for the optimization problem of the target functional, the Markov chain model is used to approximate the posterior probability distribution, the inversion parameter matrix sample points subject to the distribution are obtained, the idea of Monte Carlo is combined, and the statistical characteristics of the random sample points are used to approximate the statistical characteristics of the inversion parameter matrix, which can ensure that the subsequent each stiffness matrix coefficient and medium density are accurate.

[0093] Further, the server can further convert the strip matrix into the following form when determining the medium density and the coefficients of the stiffness matrix of each medium based on the inversion parameter matrix solution result.

[0094] wherein,

[0095] The compliance matrix S of the strong VTI medium can be obtained by inverting the stiffness matrix of the strong VTI medium. Therefore, the compliance matrix S of the strong VTI medium can be specifically represented as follows:

[0096] wherein,

[0097]

[0098] wherein,

[0099] S204: predicting the ground stress distribution of the 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.

[0100] wherein, the ground stress distribution can include: the first normal stress σ x , the second normal stress σ y , and the third normal stress σ z corresponding to each point included in the target profile respectively. That is, the server can predict the first normal stress σ x , the second normal stress σ y , and the third normal stress σ z corresponding to all positions in the target profile based on the medium density, the normal strain matrix corresponding to the strong VTI, and the compliance matrix.

[0101] It should be understood that the strain-stress relationship of the compliance matrix of the strong VTI medium can be represented as follows:

[0102] In an alternative implementation, when performing step S204, the server can perform the following operations for each point included in the target profile, such as the target point: determining a point density corresponding to the target point based on a distance between the target point and a surface of the strong VTI medium and a medium density, and determining the first normal stress at the target point based on the point density σ z , the second normal stress at the target point based on the point density σ x , and the third normal stress at the target point based on the point density σ y . Wherein the first normal stress and the first normal strain are in the same direction, i.e., the first normal strain is σ x , and the second normal stress and the second normal strain are in the same direction, i.e., the second normal strain is ε x . σ y . ε y .

[0103] For example, assuming that the strong VTI medium is bounded and immovable, the strain in the horizontal direction (i.e., the first normal strain is ε x and the second normal strain is ε y ) is 0. At this time, the relationship between the strain in the horizontal direction (i.e., the first normal strain is ε x and the second normal strain is ε y and the three normal stresses (i.e., the first normal stress σ x , the second normal stress σ y , and the third normal stress σ z ) (i.e., the two stress expressions described above) and the calculation formula of the three normal stresses are as follows:

[0104] Wherein g is the acceleration of gravity, ρ ( h ) is the density ρ distribution function corresponding to the medium density, H is the distance between the target point and the surface of the strong VTI medium, or the depth of the target point.

[0105] In summary, in the in-situ stress prediction method provided in the embodiments of the present application, the observed seismic data of the seismic wavelet in the strong VTI medium is obtained, and the parameter matrix to be inverted is constructed based on the PP wave reflection coefficient corresponding to the strong VTI medium. Then, based on the Bayesian inversion theory and the observed seismic data, the posterior probability function to which the inversion parameter matrix corresponding to the parameter matrix to be inverted is subjected is constructed, and the objective function is determined based on the prior probability function and the likelihood function corresponding to the posterior probability function. Further, based on the inversion parameter matrix solving result of the objective function, the medium density and each stiffness matrix coefficient are determined, and the compliance matrix of the strong VTI medium is determined based on each stiffness matrix coefficient. Finally, based on the medium density, the normal strain matrix corresponding to the strong VTI, and the compliance matrix, the in-situ stress distribution of the target profile in the strong VTI medium is predicted. In this way, the medium density of the strong VTI and each stiffness matrix coefficient are determined through the constructed objective function, which improves the problem that the applicability of the in-situ stress prediction based on the weak anisotropy theory is limited in the strong VTI medium. Moreover, the in-situ stress in the strong VTI medium can be more accurately and stably predicted regardless of the observed seismic data with different signal-to-noise ratios or in actual complex work areas.

[0106] Further, based on the same technical concept, the embodiments of the present application provide an in-situ stress prediction device for implementing the above method flow of the embodiments of the present application. As shown in Figure 4 The in-situ stress 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, where: The matrix construction module 401 is configured to obtain the observed seismic data of the seismic wavelet in the strong VTI medium, and construct the 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 the parameters to be inverted respectively set for the medium density of the strong VTI medium and each non-zero stiffness matrix coefficient included in the stiffness matrix of the strong VTI medium, and each stiffness matrix coefficient is related to the in-situ stress calculation of the strong VTI medium. The function construction module 402 is configured to construct the posterior probability function to which the inversion parameter matrix corresponding to the parameter matrix to be inverted is subjected based on the Bayesian inversion theory and the observed seismic data, and determine the objective function based on the prior probability function and the likelihood function corresponding to the posterior probability function. The objective function is used to solve the inversion parameter matrix corresponding to the maximum posterior probability. The matrix processing module 403 is configured to determine the medium density and each stiffness matrix coefficient based on the inversion parameter matrix solving result of the objective function, and determine the compliance matrix of the strong VTI medium based on each stiffness matrix coefficient. The stress prediction module 404 is configured to predict the ground stress distribution of the 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 ground stress distribution includes first normal stress, second normal stress, and third normal stress corresponding to each point included in the target profile, wherein the first normal stress, the second normal stress, and the third normal stress are mutually perpendicular normal stresses in the space rectangular coordinate system constructed for the target profile, and the first normal stress or the second normal stress is perpendicular to the target profile.

[0107] In an optional embodiment, when the observed seismic data of the seismic wave in the strong VTI medium is obtained, the matrix construction module 401 is specifically configured to: Obtain the phase and amplitude of the seismic wave in the strong VTI medium during propagation under different incident angles of the seismic wave, and obtain a plurality of observed seismic data; Take the seismic data vector composed of the plurality of observed seismic data as the observed seismic data.

[0108] In an optional embodiment, when the parameter matrix to be inverted is constructed based on the PP wave reflection coefficient corresponding to the strong VTI medium, the matrix construction module 401 is specifically configured to: Determine a plurality of medium attribute parameters to be inverted based on the calculation formula of the PP wave reflection coefficient; the plurality of medium attribute parameters include a plurality of stiffness matrix coefficients and a medium density; Select each stiffness matrix coefficient related to the ground stress calculation from the plurality of stiffness matrix coefficients, and construct the parameter matrix to be inverted based on the natural logarithm corresponding to each stiffness matrix coefficient and the medium density.

[0109] In an optional embodiment, when the target functional is determined based on the prior probability function and the likelihood function corresponding to the posterior probability function, the function construction module 402 is specifically configured to: Determine the probability density distribution function of the posterior probability function based on the Gaussian distribution probability density functions corresponding to the prior probability function and the likelihood function, respectively; Take the maximum value index function corresponding to the probability density distribution function as the target functional.

[0110] In an optional embodiment, when the probability density distribution function of the posterior probability function is determined based on the Gaussian distribution probability density functions corresponding to the prior probability function and the likelihood function, respectively, the function construction module 402 is specifically configured to: The forward factor is constructed based on a calculation formula of a PP wave reflection coefficient, a first-order difference factor corresponding to an incident angle-dependent seismic wave matrix and a parameter matrix to be inverted, wherein the first-order difference factor is used to compensate for a difference between each stiffness matrix coefficient in each stiffness matrix coefficient and a natural logarithm of each stiffness matrix coefficient, and a difference between a medium density and a natural logarithm of the medium density. The noise vector in the observed seismic data is determined based on a product result of the forward factor and the inversion parameter matrix, and the observed seismic data, wherein the product result represents theoretical seismic data of the seismic wave under the strong VTI medium. The Gaussian distribution probability density function of the noise vector is taken as a Gaussian distribution probability density function of a likelihood function, and the probability density distribution function of the posterior probability function is determined based on Gaussian distribution probability density functions corresponding to the prior probability function and the likelihood function, respectively.

[0111] In an optional embodiment, when the medium density and the stiffness matrix coefficients are determined based on a solution result of the inversion parameter matrix of the target functional, the function construction module 402 is specifically configured to: A low-frequency model is constructed based on logging data corresponding to the strong VTI medium, and a low-frequency constraint function is obtained based on a low-frequency prior vector of the low-frequency model and an 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 a function solution result of the minimum value index function; The function solution result is taken as a solution result of the inversion parameter matrix, and the medium density and the stiffness matrix coefficients are determined based on the solution result of the inversion parameter matrix.

[0112] In an optional embodiment, when the stress distribution of the target profile in the strong VTI medium is predicted based on the medium density, the normal strain matrix and the compliance matrix of the strong VTI, the stress prediction module 404 is specifically configured to: For each point included in the target profile, the following operations are performed respectively: The point density corresponding to the target point is determined based on a distance between the target point and a surface of the strong VTI medium and the medium density, and a third normal stress at the target point is determined based on the point density; wherein the target point is any one of the points. The first normal stress and the second normal stress at the target point are determined based on stress expressions respectively corresponding to the first normal strain and the second normal strain included in the normal strain matrix, and a plurality of compliance matrix coefficients related to the two stress expressions included in the compliance matrix; wherein the first normal strain is in the same direction as the first normal stress, and the second normal strain is in the same direction as the second normal stress.

[0113] Based on the description of the method embodiments and the device embodiments, the exemplary embodiments of the present application further provide an electronic device, comprising: at least one processor; and a memory connected with the at least one processor in communication. The memory stores a computer program capable of being executed by the at least one processor, and the computer program, when executed by the at least one processor, is configured to cause the electronic device to perform the method according to the embodiments of the present application.

[0114] The embodiments of the present application further provide a non-transitory computer readable storage medium storing a computer program, wherein the computer program, when executed by a processor of a computer, is configured to cause the computer to perform the method according to the embodiments of the present application.

[0115] The embodiments of the present application further provide a computer program product comprising a computer program, wherein the computer program, when executed by a processor of a computer, is configured to cause the computer to perform the method according to the embodiments of the present application.

[0116] Referring to Figure 5 As shown in FIG. 5, a block diagram of the structure of an electronic device 500 that can be a server or a client of the present application will now be described, which is an example of a hardware device that can be applied to various aspects of the present application. The electronic device is intended to represent a wide variety of digital electronic computer devices, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent a wide variety of mobile devices, such as personal digital processors, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown here, their connections, and their functions, are meant to be examples only, and are not intended to limit implementations of the present application described and / or claimed in this document.

[0117] As Figure 5 shown, the electronic device 500 includes a computing unit 501 that can perform various appropriate actions and processes according to 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. In the RAM 503, various programs and data required for the operation of the device 500 can also be stored. The computing unit 501, the ROM 502, and the RAM 503 are connected to each other through a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0118] A plurality of components in the electronic device 500 are connected to the I / O interface 505, including: an input unit 506, an output unit 507, a storage unit 508, and a communication unit 509. The input unit 506 can be any type of device that can input information to the electronic device 500, and can receive inputted digital or character information, and generate key signal inputs related to user settings and / or function controls of the electronic device. The output unit 507 can be any type of device that can present information, and can include, but is not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 508 can include, but is not limited to, a magnetic disk, an optical disk. The communication unit 509 allows the electronic device 500 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks, and can include, but is not limited to, a modem, a network card, an infrared communication device, a wireless communication transceiver, and / or a chipset, such as a Bluetooth device, a WiFi device, a worldwide interoperability for microwave access (WiMax) device, a cellular communication device, and / or the like.

[0119] The computing unit 501 can be various general 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 appropriate processor, controller, microcontroller, etc. The computing unit 501 performs 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 embodied in a machine-readable medium, such as the storage unit 508.

[0120] In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 500 via the ROM 502 and / or the communication unit 509. In some embodiments, the computing unit 501 can be configured to perform the above-described geostress prediction method by any other appropriate means, for example, by means of firmware.

[0121] Program code for carrying out methods of the present application can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program code, when executed by the processor or controller, causes the functions / acts specified in the flowcharts and / or block diagrams to be implemented. The program code can be entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine or entirely on a remote machine or server.

[0122] In the context of the present application, a machine-readable medium can be a tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include but is not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the machine-readable storage medium would include one or more lines of electrical conductors, a portable computer disk, a hard disk, RAM, ROM, erasable programmable read-only memory (EPROM) or flash memory, an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0123] As used in the present application, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus and / or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and / or data to a programmable processor.

[0124] To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a display device (e.g., a cathode ray tube (CRT) or a liquid crystal display (LCD) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; 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 acoustic, speech, or tactile input.

[0125] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0126] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

[0127] And, it should be understood that all the disclosed herein are only preferred embodiments of the application, and cannot be used to limit the scope of the application, therefore, the equivalent changes made according to the claims of the application, still belong to the scope of the application.

Claims

1. A method for predicting geostress, characterized in that, include: Seismic wavelet observation data in a strong VTI medium are acquired, and a parameter matrix to be inverted is constructed 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, and the stiffness matrix coefficients are related to the geostress calculation of the strong VTI medium. 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: a first normal stress, a second normal stress, and a 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 of 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 to construct 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 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, and the stiffness matrix coefficients are related to the calculation of the geostress of the strong VTI medium. 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: a first normal stress, a second normal stress, and a 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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