Method, device, computer device and storage medium for seismic migration imaging

Abstract

The application provides a method and device for seismic migration imaging, computer equipment and storage medium, and belongs to the technical field of geophysical exploration. The method comprises the following steps: obtaining seismic wave data corresponding to an oil and gas reservoir in a to-be-measured work area; determining forward simulation parameter, conjugate transpose migration operator parameter and sensitive kernel function parameter corresponding to the oil and gas reservoir; determining reflectivity vector corresponding to the oil and gas reservoir based on the forward simulation parameter, the conjugate transpose migration operator parameter and the sensitive kernel function parameter; determining migration parameter between forward seismic wave data corresponding to the reflectivity vector of each iteration and the seismic wave data through iterative calculation of a preset number of iterations, and determining the reflectivity vector corresponding to the minimum migration parameter; and determining the transverse wave stacking profile and the longitudinal wave stacking profile corresponding to the reflectivity vector. Since the forward data closest to the actual seismic wave data is determined through the least square method, the accuracy of the determined stacking profile is improved.

Description

Technical Field

[0001] This application relates to the field of geophysical exploration technology, and in particular to a method, apparatus, computer equipment, and storage medium for seismic migration imaging. Background Technology

[0002] Currently, with conventional oil and gas reservoirs becoming increasingly scarce, unconventional oil and gas reservoirs such as deep-sea oil and gas reservoirs, coalbed methane reservoirs, and shale gas reservoirs have become the focus of exploration. When exploring unconventional oil and gas reservoirs, conventional migration imaging methods cannot meet the requirements for high-precision imaging due to their complex structures.

[0003] Among related technologies, the reverse time migration imaging method can image unconventional oil and gas reservoirs. The reverse time migration imaging method simulates the propagation process of P-waves and S-waves by solving the two-way wave equations in the oil and gas reservoir, and then applies imaging conditions to obtain elastic wave imaging profiles, thereby realizing the imaging of complex oil and gas reservoirs.

[0004] However, before applying imaging conditions, high-precision reverse time migration imaging methods require wavefield separation of P-waves and S-waves. This process is prone to phase distortion and inaccurate amplitude of P-waves and S-waves, which ultimately reduces imaging accuracy. Summary of the Invention

[0005] This application provides a method, apparatus, computer device, and storage medium for seismic migration imaging, which can improve the accuracy of imaging. The technical solution is as follows:

[0006] On the one hand, this application provides a method for seismic migration imaging, the method comprising:

[0007] Obtain seismic wave data corresponding to the oil and gas reservoirs in the area to be tested;

[0008] Determine the forward modeling parameters corresponding to the oil and gas reservoir, as well as the conjugate transpose offset operator parameters corresponding to the oil and gas reservoir, and the sensitive kernel function parameters corresponding to the oil and gas reservoir;

[0009] Based on the forward modeling operator parameters, the conjugate transpose offset operator parameters, and the sensitive kernel function parameters, the reflectivity vector corresponding to the oil and gas reservoir is determined;

[0010] The reflectivity vector is iteratively calculated for a preset number of iterations to determine the offset parameter between the forward modeling seismic wave data and the seismic wave data corresponding to the reflectivity vector in each iteration. The reflectivity vector corresponding to the minimum offset parameter is determined, and the reflectivity vector corresponding to the minimum offset parameter includes the transformed shear wave reflectivity parameter and the P-wave reflectivity parameter.

[0011] A shear wave stacking profile corresponding to the converted shear wave reflectivity parameter and a longitudinal wave stacking profile corresponding to the longitudinal wave reflectivity parameter are determined. The shear wave stacking profile and the longitudinal wave stacking profile are used for migration imaging of the oil and gas reservoir.

[0012] In one possible implementation, determining the reflectivity vector corresponding to the oil and gas reservoir based on the forward modeling operator parameters, the conjugate transpose offset operator parameters, and the sensitive kernel function parameters includes:

[0013] Based on the forward modeling operator parameters, determine the Born operator parameters; and based on the forward modeling operator parameters, the conjugate transpose offset operator parameters, and the sensitive kernel function parameters, determine the adjoint offset operator parameters;

[0014] Determine the initial forward modeling seismic wave data corresponding to the initial model reflectivity vector, and determine the initial variation gradient corresponding to the initial forward modeling seismic wave data based on the Born operator parameters, the accompanying migration operator parameters, the initial forward modeling seismic wave data, and the seismic wave data corresponding to the oil and gas reservoir.

[0015] Based on the initial model reflectivity vector and the initial change gradient, the updated reflectivity vector is determined, and the updated reflectivity vector is used as the reflectivity vector corresponding to the oil and gas reservoir.

[0016] In another possible implementation, determining the offset parameter between the forward-modeled seismic wave data and the seismic wave data corresponding to the reflectivity vector in each iteration includes:

[0017] Obtain the adjustment parameters corresponding to the oil and gas reservoir;

[0018] Based on the Born operator parameters, the adjustment parameters, the forward modeled seismic wave data corresponding to the reflectivity vector in each iteration, and the seismic wave data, the offset parameter between the forward modeled seismic wave data corresponding to the reflectivity vector in each iteration and the seismic wave data is determined by the following formula 1.

[0019] Formula 1: J(m)=||Lm-d obs || 2 +λ||m|| TV

[0020] Where J(m) represents the offset parameter, m represents the reflectivity vector, and d obs Let L represent the seismic wave data, L represent the Born operator parameters, and λ represent the adjustment parameters.

[0021] In another possible implementation, the seismic wave data includes shear wave data and p-wave data;

[0022] Determining the forward modeling parameters corresponding to the oil and gas reservoir includes:

[0023] The shear wave velocity corresponding to the shear wave data, the longitudinal wave velocity corresponding to the longitudinal wave data, the density of the oil and gas reservoir, the anisotropy parameter characterizing the difference between vertical and transverse longitudinal wave velocities, the anisotropy parameter characterizing the two guides of the vertical incident longitudinal wave velocity, and the theoretical source data are determined. The theoretical source data includes the source horizontal component and the source vertical component emitted by the theoretical source.

[0024] The stiffness matrix parameters are determined based on the shear wave velocity, the longitudinal wave velocity, the density, the anisotropic parameter characterizing the difference between the vertical and transverse longitudinal wave velocities, and the anisotropic parameter characterizing the second derivative of the vertical incident longitudinal wave velocity.

[0025] Based on the density, the stiffness matrix component parameters, and the theoretical source data, the horizontal velocity, vertical velocity, normal stress in the x-direction, normal stress in the z-direction, and shear stress corresponding to the oil and gas reservoir are determined, and the forward modeling parameters corresponding to the oil and gas reservoir are determined.

[0026] In another possible implementation, determining the conjugate transpose offset operator parameters corresponding to the oil and gas reservoir includes:

[0027] The horizontal and vertical velocities of the target point within the oil and gas reservoir, the density of the reservoir at the location of the target point, the anisotropy parameters characterizing the difference between vertical and transverse P-wave velocities, the anisotropy parameters characterizing the second derivative of the vertical incident P-wave velocity, the normal stress in the x-direction, the normal stress in the z-direction, and the shear stress are determined, wherein the normal stress in the x-direction and the normal stress in the z-direction are perpendicular to each other.

[0028] Determine the horizontal accompanying velocity corresponding to the horizontal velocity and the vertical accompanying velocity corresponding to the vertical velocity, and determine the accompanying normal stress in the x-direction corresponding to the normal stress in the x-direction, the accompanying normal stress in the z-direction corresponding to the normal stress in the z-direction, and the accompanying shear stress corresponding to the shear stress.

[0029] The conjugate transpose offset operator parameters corresponding to the oil and gas reservoir are determined based on the horizontal associated velocity, the vertical associated velocity, the associated normal stress in the x-direction, the associated normal stress in the z-direction, and the associated shear stress.

[0030] In another possible implementation, the seismic wave data includes shear wave data and p-wave data;

[0031] The step of determining the conjugate transpose offset operator parameters corresponding to the oil and gas reservoir based on the horizontal associated velocity, the vertical associated velocity, the associated normal stress in the x-direction, the associated normal stress in the z-direction, and the associated shear stress includes:

[0032] Based on the horizontal associated velocity, the vertical associated velocity, the associated normal stress in the x-direction, the associated normal stress in the z-direction, and the associated shear stress, the data residuals of the horizontal component and the vertical component are obtained through the following formula seven, and the data residuals of the horizontal component and the vertical component are determined as the conjugate transpose offset operator parameters corresponding to the oil and gas reservoir.

[0033] Formula 7:

[0034] Where, Δd x The data residual Δd represents the horizontal component. z C represents the data residual of the vertical component. 33 C represents the first stiffness matrix component. 55 C represents the components of the second stiffness matrix. 11 C represents the third stiffness matrix component. 13 This represents the fourth stiffness matrix component. Indicates the accompanying horizontal velocity, The vertical accompanying velocity is indicated. This represents the accompanying normal stress in the x-direction. This represents the accompanying normal stress in the z-direction. ρ represents the accompanying shear stress, and ρ represents the density.

[0035] In another possible implementation, determining the sensitive kernel function parameters corresponding to the oil and gas reservoir includes:

[0036] Based on the anisotropic parameters representing the difference between vertical and transverse P-wave velocities corresponding to the target point and the anisotropic parameters representing the second derivative of the vertical incident P-wave velocity, the transverse wave sensitive core parameters and P-wave sensitive core parameters corresponding to the oil and gas reservoir are determined by the following formula eight, and the transverse wave sensitive core parameters and the P-wave sensitive core parameters are determined as sensitive core function parameters.

[0037] Formula 8:

[0038]

[0039] in, This represents the parameters of the shear wave sensitive core. C represents the longitudinal wave sensitive core parameter. 33 C represents the first stiffness matrix component. 55 C represents the components of the second stiffness matrix. 11 C represents the third stiffness matrix component. 13 This represents the fourth stiffness matrix component. This represents the accompanying normal stress in the x-direction. This represents the accompanying normal stress in the z-direction. ν represents the accompanying shear stress, ε represents the anisotropy parameter characterizing the difference between vertical and transverse P-wave velocities, δ represents the anisotropy parameter characterizing the quadratic derivative of the vertical incident P-wave velocity, and ν represents the anisotropy parameter characterizing the quadratic derivative of the vertical incident P-wave velocity. x The horizontal velocity, ν z This indicates the vertical velocity.

[0040] On the other hand, this application provides a seismic migration imaging apparatus, the apparatus comprising:

[0041] The acquisition module is used to acquire seismic wave data corresponding to the oil and gas reservoirs in the test area;

[0042] The first determining module is used to determine the forward modeling parameters corresponding to the oil and gas reservoir, as well as the conjugate transpose offset operator parameters corresponding to the oil and gas reservoir and the sensitive kernel function parameters corresponding to the oil and gas reservoir.

[0043] The second determining module determines the reflectivity vector corresponding to the oil and gas reservoir based on the forward modeling operator parameters, the conjugate transpose offset operator parameters, and the sensitive kernel function parameters.

[0044] The third determining module is used to perform iterative calculations on the reflectivity vector for a preset number of iterations, determine the offset parameter between the forward modeling seismic wave data and the seismic wave data corresponding to the reflectivity vector in each iteration, and determine the reflectivity vector corresponding to the minimum offset parameter. The reflectivity vector corresponding to the minimum offset parameter includes the transformed shear wave reflectivity parameter and the P-wave reflectivity parameter.

[0045] The fourth determining module determines the shear wave superposition profile corresponding to the converted shear wave reflectivity parameter and the longitudinal wave superposition profile corresponding to the longitudinal wave reflectivity parameter. The shear wave superposition profile and the longitudinal wave superposition profile are used to perform migration imaging on the oil and gas reservoir.

[0046] In one possible implementation, the second determining module is configured to: determine Born operator parameters based on the forward modeling operator parameters; and determine adjoint migration operator parameters based on the forward modeling operator parameters, the conjugate transpose migration operator parameters, and the sensitive kernel function parameters; determine initial forward modeling seismic data corresponding to the initial model reflectivity vector; determine the initial variation gradient corresponding to the initial forward modeling seismic data based on the Born operator parameters, the adjoint migration operator parameters, the initial forward modeling seismic data, and the seismic data corresponding to the oil and gas reservoir; determine an updated reflectivity vector based on the initial model reflectivity vector and the initial variation gradient; and use the updated reflectivity vector as the reflectivity vector corresponding to the oil and gas reservoir.

[0047] In another possible implementation, the second determining module is used to obtain the adjustment parameters corresponding to the oil and gas reservoir; and to determine the offset parameters between the forward seismic data corresponding to the reflectivity vector and the seismic data based on the Born operator parameters, the adjustment parameters, the forward seismic data corresponding to the reflectivity vector, and the seismic data using the following formula 1.

[0048] Formula 1: J(m)=||Lm-d obs || 2 +λ||m|| TV

[0049] Where J(m) represents the offset parameter, m represents the reflectivity vector, and d obs Let L represent the seismic wave data, L represent the Born operator parameters, and λ represent the adjustment parameters.

[0050] In another possible implementation, the seismic wave data includes shear wave data and p-wave data;

[0051] The first determining module is used to determine the shear wave velocity corresponding to the shear wave data, the P-wave velocity corresponding to the P-wave data, the density of the oil and gas reservoir, the anisotropy parameter characterizing the difference between vertical and transverse P-wave velocities, the anisotropy parameter characterizing the second derivative of the vertical incident P-wave velocity, and theoretical source data, wherein the theoretical source data includes the source horizontal component and source vertical component emitted by the theoretical source; determine the stiffness matrix parameters based on the shear wave velocity, the P-wave velocity, the density, the anisotropy parameter characterizing the difference between vertical and transverse P-wave velocities, and the anisotropy parameter characterizing the second derivative of the vertical incident P-wave velocity; and determine the forward modeling parameters corresponding to the oil and gas reservoir based on the density, the stiffness matrix component parameters, and the theoretical source data.

[0052] In another possible implementation, the first determining module is configured to determine the horizontal and vertical velocities of a target point within the oil and gas reservoir, as well as the reservoir density at the location of the target point, anisotropy parameters characterizing the difference between vertical and transverse P-wave velocities, anisotropy parameters characterizing the second derivative of the vertical incident P-wave velocity, normal stress in the x-direction, normal stress in the z-direction, and shear stress, wherein the normal stress in the x-direction and the normal stress in the z-direction are perpendicular to each other; determine the horizontal accompanying velocity corresponding to the horizontal velocity and the vertical accompanying velocity corresponding to the vertical velocity; and determine the accompanying normal stress in the x-direction corresponding to the normal stress in the x-direction, the accompanying normal stress in the z-direction corresponding to the normal stress in the z-direction, and the accompanying shear stress corresponding to the shear stress; and determine the conjugate transpose offset operator parameters corresponding to the oil and gas reservoir based on the horizontal accompanying velocity, the vertical accompanying velocity, the accompanying normal stress in the x-direction, the accompanying normal stress in the z-direction, and the accompanying shear stress.

[0053] In another possible implementation, the seismic wave data includes shear wave data and p-wave data;

[0054] The first determining module is used to obtain the data residuals of the horizontal component and the vertical component according to the horizontal accompanying velocity, the vertical accompanying velocity, the accompanying normal stress in the x direction, the accompanying normal stress in the z direction and the accompanying shear stress, using the following formula seven, and to determine the data residuals of the horizontal component and the vertical component as the conjugate transpose offset operator parameters corresponding to the oil and gas reservoir.

[0055] Formula 7:

[0056] Where, Δd x The data residual Δd represents the horizontal component. z C represents the data residual of the vertical component. 33 C represents the first stiffness matrix component. 55 C represents the components of the second stiffness matrix. 11 C represents the third stiffness matrix component. 13 This represents the fourth stiffness matrix component. The atmosphere indicates the accompanying horizontal velocity. This indicates the vertical accompanying velocity. This represents the accompanying normal stress in the x-direction. This represents the accompanying normal stress in the z-direction. ρ represents the accompanying shear stress, and ρ represents the density.

[0057] In another possible implementation, the first determining module is used to determine the shear wave sensitive core parameter and the P-wave sensitive core parameter corresponding to the oil and gas reservoir according to the anisotropic parameter representing the difference between vertical and transverse P-wave velocities and the anisotropic parameter representing the second derivative of the vertical incident P-wave velocity, using the following formula eight, and to determine the shear wave sensitive core parameter and the P-wave sensitive core parameter as sensitive core function parameters;

[0058] Formula 8:

[0059]

[0060] in, This represents the parameters of the shear wave sensitive core. C represents the longitudinal wave sensitive core parameter. 33 C represents the first stiffness matrix component. 55 C represents the components of the second stiffness matrix. 11 C represents the third stiffness matrix component. 13 This represents the fourth stiffness matrix component. The accompanying normal stress in the x-direction is indicated by the atmosphere. This represents the accompanying normal stress in the z-direction. ν represents the accompanying shear stress, ε represents the anisotropy parameter characterizing the difference between vertical and transverse P-wave velocities, δ represents the anisotropy parameter characterizing the quadratic derivative of the vertical incident P-wave velocity, and ν represents the anisotropy parameter characterizing the quadratic derivative of the vertical incident P-wave velocity. x The horizontal velocity, v z This indicates the vertical velocity.

[0061] On the other hand, embodiments of this application provide a computer device, the computer device including: a processor and a memory, the memory storing at least one piece of program code, the at least one piece of program code being loaded and executed by the processor to implement the operations performed in the seismic migration imaging method described in any of the above possible implementations.

[0062] On the other hand, embodiments of this application provide a computer-readable storage medium storing at least one piece of program code, which is loaded and executed by a processor to implement the operations performed in the seismic migration imaging method described in any of the above possible implementations.

[0063] The beneficial effects of the technical solutions provided in this application include at least the following:

[0064] This application provides a method for seismic migration imaging. This method obtains reflectivity vectors with different iteration numbers through forward modeling operator parameters, conjugate transpose migration operator parameters, and sensitive kernel function parameters. From these, the model reflectivity vector with the smallest migration parameters is determined. That is, the forward modeling seismic wave data determined by the reflectivity vector that is closest to the actual seismic wave data is determined by the least squares method, thereby improving the accuracy of the stacked profile determined based on least squares inverse time migration. Attached Figure Description

[0065] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0066] Figure 1 This is a flowchart illustrating a method for seismic migration imaging according to an exemplary embodiment;

[0067] Figure 2 This is a schematic diagram of a shear wave stacking profile determined by a conventional reverse time migration method according to an exemplary embodiment;

[0068] Figure 3 This is a schematic diagram illustrating a shear wave superposition profile determined in this application according to an exemplary embodiment;

[0069] Figure 4 This is a schematic diagram of a longitudinal wave stacking profile determined by a conventional reverse time migration method according to an exemplary embodiment;

[0070] Figure 5 This is a schematic diagram illustrating a longitudinal wave superposition profile determined in this application according to an exemplary embodiment;

[0071] Figure 6 This is a block diagram illustrating a seismic migration imaging apparatus according to an exemplary embodiment;

[0072] Figure 7 This is a structural block diagram of a computer device according to an exemplary embodiment. Detailed Implementation

[0073] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0074] Figure 1 This is a flowchart illustrating a method for seismic migration imaging according to an exemplary embodiment. See also... Figure 1 The method includes:

[0075] 101. Computer equipment acquires seismic wave data corresponding to oil and gas reservoirs in the area to be tested.

[0076] In this step, the area to be measured includes multiple geophones. The target geophone is the one that receives the seismic wave signals emitted by the seismic source. Accordingly, this step involves: the target geophone receiving seismic wave data of the oil and gas reservoir within the area to be measured, transmitting the received seismic wave data to a computer, and the computer receiving the seismic wave data of the oil and gas reservoir within the area to be measured. The seismic wave data can be the seismic wave signals received by the target geophone within a preset time period.

[0077] In this embodiment of the application, the value of the preset duration is not specifically limited, and can be set and modified as needed.

[0078] It should be noted that the seismic wave signals emitted by the earthquake source include both shear waves and longitudinal waves, and correspondingly, the seismic wave data received by the target detector includes both shear wave data and longitudinal wave data.

[0079] 102. The computer equipment determines the forward simulation parameters corresponding to the oil and gas reservoir, as well as the conjugate transpose offset operator parameters corresponding to the oil and gas reservoir and the sensitive kernel function parameters corresponding to the oil and gas reservoir.

[0080] In one possible implementation, the seismic wave data includes both shear wave and p-wave data. Accordingly, the computer equipment determines the forward modeling parameters corresponding to the oil and gas reservoir through the following steps: the computer equipment determines the shear wave velocity corresponding to the shear wave data, the p-wave velocity corresponding to the p-wave data, the density of the oil and gas reservoir, the anisotropy parameters characterizing the difference between vertical and transverse p-wave velocities, the anisotropy parameters characterizing the second derivative of the vertical incident p-wave velocity, and the theoretical source data, which includes the horizontal and vertical components of the source excited by the theoretical source; based on the shear wave velocity, p-wave velocity, density, the anisotropy parameters characterizing the difference between vertical and transverse p-wave velocities, and the anisotropy parameters characterizing the second derivative of the vertical incident p-wave velocity, the stiffness matrix parameters are determined; based on the density, stiffness matrix component parameters, and theoretical source data, the horizontal velocity, vertical velocity, normal stress in the x-direction, normal stress in the z-direction, and shear stress corresponding to the oil and gas reservoir are calculated to determine the forward modeling parameters corresponding to the oil and gas reservoir.

[0081] In one possible implementation, the stiffness matrix parameters include a first stiffness matrix component, a second stiffness matrix component, a third stiffness matrix component, and a fourth stiffness matrix component. Correspondingly, the computer device determines the stiffness matrix parameters based on the transverse wave velocity, longitudinal wave velocity, density, anisotropy parameters characterizing the difference between vertical and transverse longitudinal wave velocities, and anisotropy parameters characterizing the second derivative of the vertical incident longitudinal wave velocity. The steps are as follows: the computer device determines that the product of the square of the longitudinal wave velocity and the density is the first stiffness matrix component, that is, The product of the square of the shear wave velocity and the density is determined to be a component of the second stiffness matrix; that is, Based on the anisotropic parameters characterizing the difference between vertical and transverse P-wave velocities and the first stiffness matrix component, the third stiffness matrix component is determined using the following formula 2. Furthermore, based on the anisotropic parameters characterizing the difference between vertical incident P-wave velocities and the first and second stiffness matrix components, the fourth stiffness matrix component is determined using the following formula 3.

[0082] Formula 2: C 11 = (1+2ε)C 33

[0083] Formula 3:

[0084] Among them, C 33 C represents the first stiffness matrix component. 55 C represents the second stiffness matrix component. 11 C represents the third stiffness matrix component. 13 The fourth stiffness matrix component is represented by ε, the anisotropy parameter characterizing the difference between vertical and transverse P-wave velocities is represented by δ, the anisotropy parameter characterizing the second derivative of the vertical incident P-wave velocity is represented by ρ, and v represents density. p V represents the longitudinal wave velocity. s This indicates the velocity of the transverse wave.

[0085] In one possible implementation, the computer equipment calculates the horizontal velocity, vertical velocity, normal stress in the x-direction, normal stress in the z-direction, and shear stress based on the density, stiffness matrix component parameters, and theoretical source data, and determines the forward modeling parameters corresponding to the oil and gas reservoir as follows: The computer equipment inputs the source horizontal component and source vertical component, density, and stiffness matrix component parameters excited by the theoretical source into the following formula four, calculates the horizontal velocity, vertical velocity, normal stress in the x-direction, normal stress in the z-direction, and shear stress, and obtains the forward modeling parameters;

[0086] Formula 4:

[0087] Among them, C 33 C represents the first stiffness matrix component. 55 C represents the second stiffness matrix component. 11 C represents the third stiffness matrix component. 13 V represents the fourth stiffness matrix component. x ν represents horizontal velocity. z σ represents vertical velocity. xx σ represents the normal stress in the x-direction. zz σ represents the normal stress in the z-direction. xz f represents shear stress. xf represents the horizontal component of the earthquake source. z This represents the vertical component of the earthquake source.

[0088] In one possible implementation, the horizontal and vertical components of the earthquake excited by the theoretical source are reflected by the oil and gas reservoir and received by the target detector to obtain the seismic wave data corresponding to the oil and gas reservoir.

[0089] In one possible implementation, the forward modeling parameters include background forward modeling parameters and disturbance forward modeling parameters; the stiffness matrix parameters include a first stiffness matrix component, a second stiffness matrix component, a third stiffness matrix component, and a fourth stiffness matrix component.

[0090] It should be noted that the stress and velocity fields can be expressed as the sum of the background field and the disturbance field. For stress, we have: That is, in, Δσ represents the background field normal stress in the x-direction. xx This represents the normal stress of the perturbation field in the x-direction; Δσ represents the background field normal stress in the z-direction. zz This represents the normal stress of the perturbation field in the z-direction; The background field shear stress, Δσ xz This represents the shear stress in the perturbation field. For a velocity field, we have: That is, in, Δv represents the horizontal velocity of the background field. x This represents the horizontal velocity of the perturbation field. Δv represents the vertical velocity of the background field. z This represents the vertical velocity of the perturbation field.

[0091] Accordingly, the steps for the computer equipment to determine the forward modeling parameters are as follows: The computer equipment determines the background field normal stress in the x-direction, the background field normal stress in the z-direction, and the background field shear stress. The stiffness matrix parameters, background field horizontal velocity, background field vertical velocity, theoretical source horizontal component, and theoretical source vertical component are input into the forward modeling parameters to obtain Formula 5. Formula 5 is determined as the background forward modeling parameter. Furthermore, the difference between the first stiffness matrix component and the background first stiffness matrix component is determined as the P-wave reflectivity parameter, that is... The difference between the second stiffness matrix component and the background second stiffness matrix component is determined as the converted transverse wave reflectivity parameter, that is, By inputting the stiffness matrix parameters, the normal stress of the perturbation field in the x-direction, the normal stress of the perturbation field in the z-direction, the horizontal velocity of the perturbation field, the vertical velocity of the perturbation field, the horizontal velocity of the background field, the vertical velocity of the background field, the anisotropy parameter representing the difference between the vertical and transverse P-wave velocities, the anisotropy parameter representing the two guides of the vertical incident P-wave velocity, the P-wave reflectivity parameter, and the converted S-wave reflectivity parameter into the forward modeling parameters, the following formula 6 is obtained, and formula 6 is determined as the perturbation forward modeling parameters;

[0092] Formula 5:

[0093] Formula Six:

[0094] Among them, I S I represents the longitudinal wave reflectivity parameter. P C represents the converted transverse wave reflectivity parameter. 33 C represents the first stiffness matrix component. 55 C represents the second stiffness matrix component. 11 C represents the third stiffness matrix component. 13 ν represents the fourth stiffness matrix component. x ν represents horizontal velocity. z σ represents vertical velocity. xx σ represents the normal stress in the x-direction. zz σ represents the normal stress in the z-direction. xz ε represents shear stress, δ represents the anisotropy parameter characterizing the difference between vertical and transverse P-wave velocities, ρ represents density, and f represents the anisotropy parameter characterizing the second guide of the vertical incident P-wave velocity. x f represents the horizontal component of the earthquake source. z This represents the vertical component of the earthquake source.

[0095] In one possible implementation, the steps for the computer equipment to determine the conjugate transpose offset operator parameters corresponding to the oil and gas reservoir are as follows: The computer equipment determines the horizontal and vertical velocities of the target point within the oil and gas reservoir, the reservoir density at the location of the target point, anisotropy parameters characterizing the difference between vertical and transverse P-wave velocities, anisotropy parameters characterizing the second derivative of the vertical incident P-wave velocity, the normal stress in the x-direction, the normal stress in the z-direction, and the shear stress, wherein the normal stress in the x-direction and the normal stress in the z-direction are perpendicular to each other; it determines the horizontal accompanying velocity corresponding to the horizontal velocity and the vertical accompanying velocity corresponding to the vertical velocity, and determines the accompanying normal stress in the x-direction corresponding to the normal stress in the x-direction, the accompanying normal stress in the z-direction corresponding to the normal stress in the z-direction, and the accompanying shear stress corresponding to the shear stress; based on the horizontal accompanying velocity, the vertical accompanying velocity, the accompanying normal stress in the x-direction, the accompanying normal stress in the z-direction, and the accompanying shear stress, it determines the conjugate transpose offset operator parameters corresponding to the oil and gas reservoir. The target point within the oil and gas reservoir can be the location point to be tested within the oil and gas reservoir.

[0096] In one possible implementation, the seismic wave data includes shear wave data and p-wave data; correspondingly, the computer equipment determines the conjugate transpose migration operator parameters corresponding to the oil and gas reservoir based on the horizontal accompanying velocity, vertical accompanying velocity, accompanying normal stress in the x-direction, accompanying normal stress in the z-direction, and accompanying shear stress as follows: the computer equipment obtains the data residuals of the horizontal component and the vertical component using the following formula seven based on the horizontal accompanying velocity, vertical accompanying velocity, accompanying normal stress in the x-direction, accompanying normal stress in the z-direction, and accompanying shear stress, and determines the data residuals of the horizontal component and the vertical component as the conjugate transpose migration operator parameters corresponding to the oil and gas reservoir;

[0097] Formula 7:

[0098] Where, Δd x The data residual Δd represents the horizontal component. z C represents the data residual of the vertical component. 33 C represents the first stiffness matrix component. 55 C represents the second stiffness matrix component. 11 C represents the third stiffness matrix component. 13 Represents the fourth stiffness matrix component. Indicates the horizontal accompanying velocity, Indicates the vertical accompanying velocity, This represents the accompanying normal stress in the x-direction. This represents the accompanying normal stress in the z-direction. ρ represents the accompanying shear stress, and ρ represents the density.

[0099] In one possible implementation, the computer device determines the sensitive kernel function parameters corresponding to the oil and gas reservoir by the following steps: the computer device determines the shear wave sensitive kernel parameters and the P-wave sensitive kernel parameters corresponding to the oil and gas reservoir by the following formula 8, based on the anisotropic parameters representing the difference between vertical and transverse P-wave velocities and the anisotropic parameters representing the two derivatives of the vertical incident P-wave velocity at the target point.

[0100] Formula 8:

[0101]

[0102] in, Indicates the parameters of the longitudinal wave sensitive core. C represents the parameters of the shear wave sensitive core. 33 C represents the first stiffness matrix component. 55 C represents the second stiffness matrix component. 11 C represents the third stiffness matrix component. 13 Represents the fourth stiffness matrix component. This represents the accompanying normal stress in the x-direction. This represents the accompanying normal stress in the z-direction. ν represents the accompanying shear stress, ε represents the anisotropy parameter characterizing the difference between vertical and transverse P-wave velocities, δ represents the anisotropy parameter characterizing the secondary guide of the vertical incident P-wave velocity, and ν represents the anisotropy parameter characterizing the secondary guide of the vertical incident P-wave velocity. x ν represents horizontal velocity. z This indicates vertical velocity.

[0103] 103. The computer equipment determines the reflectivity vector corresponding to the oil and gas reservoir based on the forward modeling operator parameters, the conjugate transpose offset operator parameters, and the sensitive kernel function parameters.

[0104] In one possible implementation, this step involves: the computer equipment determining the Born operator parameters based on the forward modeling operator parameters; and determining the adjoint migration operator parameters based on the forward modeling operator parameters, the conjugate transpose migration operator parameters, and the sensitive kernel function parameters. The computer equipment determines the initial forward modeling seismic data corresponding to the initial reflectivity vector; and determines the initial gradient corresponding to the initial reflectivity vector based on the Born operator parameters, the adjoint migration operator parameters, the initial forward modeling seismic data corresponding to the initial reflectivity vector, and the seismic data corresponding to the oil and gas reservoir; and determines the updated reflectivity vector based on the initial reflectivity vector and the initial gradient.

[0105] Within a preset number of iterations, the reflectivity vector of a particular iteration is determined to be the model reflectivity vector corresponding to the oil and gas reservoir. In this embodiment, the value of the preset number of iterations is not specifically limited and can be set and modified as needed. For example, the preset number of iterations could be 40, 100, or 1000.

[0106] In one possible implementation, the reflectivity vector input in the first iteration is determined by the transverse wave velocity, the longitudinal wave velocity, the density, the anisotropy representing the difference between the vertical and transverse longitudinal wave velocities, and the anisotropy parameter representing the second derivative of the vertical incident longitudinal wave velocity. This can be expressed as m. k This means that the reflectivity vector of the second iteration is the sum of the reflectivity vector input in the first iteration and the gradient of the first iteration, which can be expressed as m. k+1 This means that the gradient of change in the first iteration is the initial gradient of change, which can be represented by αh. k This indicates that m... k+1 =m k +αh k Where k ranges from 1 to n, and n is the preset number of iterations.

[0107] In one possible implementation, the Born operator parameters are denoted by L, and the associated offset operator parameters are denoted by L. This indicates the gradient change during the first iteration. hk =βq k +h k-1 ;in, in, Here, g represents the Hessian matrix parameters, which can be stored in the computer beforehand. From the above relationship, it can be seen that g is determined using the Born operator, the associated migration operator parameters, the initial forward modeling seismic wave data, and the seismic wave data. k This leads to the determination of the gradient change in the first iteration.

[0108] It should be noted that the iterative gradient is determined by the Born operator parameters, the associated migration operator parameters, the initial forward seismic data corresponding to the initial reflectivity vector, and the seismic data corresponding to the oil and gas reservoir. When the initial reflectivity vector is different, that is, when the initial forward seismic data is different, the iterative gradient will also be different.

[0109] The iterative reflectivity vector can be used as a new initial reflectivity vector. The gradient of change in the second iteration is determined by the new initial reflectivity vector. The new iterative reflectivity vector is determined by the sum of the initial reflectivity vector and the gradient of change in the second iteration. This process is repeated to obtain the final updated reflectivity vector.

[0110] 104. The computer equipment determines the offset parameter between the forward modeling seismic wave data and the seismic wave data corresponding to the reflectivity vector in each iteration through iterative calculation with a preset number of iterations, and determines the reflectivity vector corresponding to the minimum offset parameter. The reflectivity vector includes the P-wave reflectivity parameter and the converted S-wave reflectivity parameter.

[0111] In one possible implementation, the step of the computer device determining the offset parameter between the forward modeled seismic data and the seismic data corresponding to the reflectivity vector in each iteration is as follows: the computer device obtains the adjustment parameter corresponding to the oil and gas reservoir; based on the Born operator parameter, the adjustment parameter, the forward modeled seismic data and the seismic data corresponding to the reflectivity vector in each iteration, the offset parameter between the forward modeled seismic data and the seismic data corresponding to the reflectivity vector in each iteration is determined by the following formula 1.

[0112] Formula 1: J(m)=||Lm-d obs || 2 +λ||m|| TV

[0113] Where J(m) represents the offset parameter, m represents the reflectivity vector, and d obs λ represents seismic wave data, L represents the Born operator parameter, and λ represents the adjustment parameter.

[0114] Optionally, the computer device stores a correspondence between reservoir identifiers and adjustment parameters. Based on the reservoir identifier of the target oil and gas reservoir, the computer device determines the adjustment parameters corresponding to the target oil and gas reservoir from the stored correspondence between reservoir identifiers and adjustment parameters. The reservoir identifier is used to distinguish different reservoirs and can be letters, numbers, or codes.

[0115] It should be noted that Formula 1 is the least-squares fitting function between the forward-modeled seismic wave data and the seismic wave data. Through multiple iterations, the forward-modeled seismic wave data is updated, and the forward-modeled seismic wave data corresponding to the minimum value of the fitting function is the optimal forward-modeled seismic wave data.

[0116] In one possible implementation, the reflectivity vector includes transformed transverse wave reflectivity parameters and longitudinal wave reflectivity parameters. Optionally, the reflectivity vector m is [I PP I PS ] T Among them, I PP I represents the longitudinal wave reflectivity parameter. PS This represents the converted transverse wave reflectivity parameter.

[0117] In the embodiments of this application, the influence of the offset parameter on the amplitude can be gradually eliminated by least squares inversion imaging, thereby improving the amplitude and spatial resolution of the imaging.

[0118] 105. The computer equipment determines and converts the shear wave superposition profile corresponding to the shear wave reflectivity parameter and the longitudinal wave superposition profile corresponding to the longitudinal wave reflectivity parameter. The shear wave superposition profile and the longitudinal wave superposition profile are used for migration imaging of oil and gas reservoirs.

[0119] Optionally, the converted shear wave reflectivity parameters include the amplitude and time of the shear wave. Correspondingly, the step for the computer equipment to determine the shear wave superposition profile corresponding to the converted shear wave reflectivity parameters is as follows: the computer equipment superimposes the amplitudes of the shear wave migration profiles according to the time of the shear wave to obtain the shear wave superposition profile. For example, the shear wave superposition profile determined by traditional reverse time migration is as follows: Figure 2 As shown. The shear wave stacking profile determined by the reverse time-shifting imaging technique in this application is as follows: Figure 3 As shown. Compared with traditional reverse-time offset shear wave stacking profiles, the shear wave stacking profile determined in this application has a larger amplitude in the deep reflecting layer, resulting in clearer imaging.

[0120] Optionally, the P-wave reflectivity parameters include the amplitude and time of the P-wave. Correspondingly, the step for the computer equipment to determine the P-wave stacking profile corresponding to the P-wave reflectivity parameters is as follows: the computer equipment superimposes the amplitudes of the P-wave migration profiles based on the time of the P-waves to obtain the P-wave stacking profile. For example, the P-wave stacking profile determined by conventional reverse time migration is as follows: Figure 4As shown. The P-wave stacking profile determined by the reverse-time migration imaging technique in this application is as follows: Figure 5 As shown. Compared with traditional reverse-time offset P-wave stacking profiles, the P-wave stacking profile determined in this application has a larger amplitude in the deep reflecting layer, resulting in clearer imaging.

[0121] This application provides a method for seismic migration imaging. This method obtains reflectivity vectors with different iteration numbers through forward modeling operator parameters, conjugate transpose migration operator parameters, and sensitive kernel function parameters. From these, the model reflectivity vector with the smallest migration parameters is determined. That is, the forward modeling seismic wave data determined by the reflectivity vector that is closest to the actual seismic wave data is determined by the least squares method, thereby improving the accuracy of the stacked profile determined based on least squares inverse time migration.

[0122] Figure 6 This is a block diagram illustrating a seismic migration imaging apparatus according to an exemplary embodiment. See also Figure 6 The device includes:

[0123] The acquisition module 601 is used to acquire seismic wave data corresponding to the oil and gas reservoirs in the test area;

[0124] The first determining module 602 is used to determine the forward modeling parameters corresponding to the oil and gas reservoir, as well as the conjugate transpose offset operator parameters corresponding to the oil and gas reservoir and the sensitive kernel function parameters corresponding to the oil and gas reservoir.

[0125] The second determining module 603 determines the reflectivity vector corresponding to the oil and gas reservoir based on the forward modeling operator parameters, the conjugate transpose offset operator parameters, and the sensitive kernel function parameters.

[0126] The third determining module 604 is used to perform iterative calculations on the reflectivity vector for a preset number of iterations, determine the offset parameter between the forward modeling seismic wave data and the seismic wave data corresponding to the reflectivity vector in each iteration, and determine the reflectivity vector corresponding to the minimum offset parameter. The reflectivity vector corresponding to the minimum offset parameter includes the transformed shear wave reflectivity parameter and the P-wave reflectivity parameter.

[0127] The fourth determining module 605 determines the shear wave superposition profile corresponding to the converted shear wave reflectivity parameter and the longitudinal wave superposition profile corresponding to the longitudinal wave reflectivity parameter. The shear wave superposition profile and the longitudinal wave superposition profile are used for migration imaging of oil and gas reservoirs.

[0128] In one possible implementation, the second determining module 603 is used to determine the Born operator parameters based on the forward modeling operator parameters; and to determine the adjoint migration operator parameters based on the forward modeling operator parameters, the conjugate transpose migration operator parameters, and the sensitive kernel function parameters; to determine the initial forward modeling seismic data corresponding to the initial model reflectivity vector; and to determine the initial variation gradient corresponding to the initial forward modeling seismic data based on the Born operator parameters, the adjoint migration operator parameters, the initial forward modeling seismic data, and the seismic data corresponding to the oil and gas reservoir; to determine the updated reflectivity vector based on the initial model reflectivity vector and the initial variation gradient; and to use the updated reflectivity vector as the reflectivity vector corresponding to the oil and gas reservoir.

[0129] In another possible implementation, the second determining module 603 is used to obtain the adjustment parameters corresponding to the oil and gas reservoir; based on the Born operator parameters, the adjustment parameters, the forward seismic wave data and seismic wave data corresponding to the reflectivity vector of each iteration, the offset parameters between the forward seismic wave data and seismic wave data corresponding to the reflectivity vector of each iteration are determined by the following formula 1.

[0130] Formula 1: J(m)=||Lm-d obs || 2 +λ||m|| TV

[0131] Where J(m) represents the offset parameter, m represents the reflectivity vector, and d obs λ represents seismic wave data, L represents the Born operator parameter, and λ represents the adjustment parameter.

[0132] In another possible implementation, the seismic wave data includes both shear wave and p-wave data;

[0133] The first determining module 602 is used to determine the shear wave velocity corresponding to the shear wave data, the P-wave velocity corresponding to the P-wave data, the density of the oil and gas reservoir, the anisotropy parameter characterizing the difference between vertical and transverse P-wave velocities, the anisotropy parameter characterizing the second derivative of the vertical incident P-wave velocity, and the theoretical source data, which includes the source horizontal component and source vertical component emitted by the theoretical source; based on the shear wave velocity, P-wave velocity, density, the anisotropy parameter characterizing the difference between vertical and transverse P-wave velocities, and the anisotropy parameter characterizing the second derivative of the vertical incident P-wave velocity, the module determines the horizontal velocity, vertical velocity, normal stress in the x-direction, normal stress in the z-direction, and shear stress corresponding to the oil and gas reservoir, and determines the forward modeling parameters corresponding to the oil and gas reservoir.

[0134] In another possible implementation, the first determining module 602 is used to determine the horizontal and vertical velocities of the target point within the oil and gas reservoir, as well as the reservoir density at the location of the target point, anisotropy parameters characterizing the difference between vertical and transverse P-wave velocities, anisotropy parameters characterizing the second derivative of the vertical incident P-wave velocity, normal stress in the x-direction, normal stress in the z-direction, and shear stress, wherein the normal stress in the x-direction and normal stress in the z-direction are perpendicular to each other; determine the horizontal accompanying velocity corresponding to the horizontal velocity and the vertical accompanying velocity corresponding to the vertical velocity, and determine the accompanying normal stress in the x-direction corresponding to the normal stress in the x-direction, the accompanying normal stress in the z-direction corresponding to the normal stress in the z-direction, and the accompanying shear stress corresponding to the shear stress; and determine the conjugate transpose offset operator parameters corresponding to the oil and gas reservoir based on the horizontal accompanying velocity, vertical accompanying velocity, accompanying normal stress in the x-direction, accompanying normal stress in the z-direction, and accompanying shear stress.

[0135] In another possible implementation, the seismic wave data includes both shear wave and p-wave data;

[0136] The first determining module 602 is used to obtain the data residuals of the horizontal component and the data residuals of the vertical component based on the horizontal accompanying velocity, the vertical accompanying velocity, the accompanying normal stress in the x direction, the accompanying normal stress in the z direction and the accompanying shear stress, and to determine the data residuals of the horizontal component and the data residuals of the vertical component as the conjugate transpose offset operator parameters corresponding to the oil and gas reservoir.

[0137] Formula 7:

[0138] Where, Δd x The data residual Δd represents the horizontal component. z C represents the data residual of the vertical component. 33 C represents the first stiffness matrix component. 55 C represents the second stiffness matrix component. 11 C represents the third stiffness matrix component. 13 Represents the fourth stiffness matrix component. Indicates the horizontal accompanying velocity, Indicates the vertical accompanying velocity, This represents the accompanying normal stress in the x-direction. This represents the accompanying normal stress in the z-direction. ρ represents the accompanying shear stress, and ρ represents the density.

[0139] In another possible implementation, the first determining module 602 is used to determine the shear wave sensitive core parameters and the P-wave sensitive core parameters corresponding to the oil and gas reservoir based on the anisotropic parameters representing the difference between vertical and transverse P-wave velocities and the anisotropic parameters representing the two derivatives of the vertical incident P-wave velocity, using the following formula 8. The shear wave sensitive core parameters and the P-wave sensitive core parameters are determined as sensitive core function parameters.

[0140] Formula 8:

[0141]

[0142] in, Indicates the parameters of the shear wave sensitive core. C represents the longitudinal wave sensitive core parameter. 33 C represents the first stiffness matrix component. 55 C represents the second stiffness matrix component. 11 C represents the third stiffness matrix component. 13 Represents the fourth stiffness matrix component. This represents the accompanying normal stress in the x-direction. The accompanying normal stress in the z-direction is indicated. ν represents the accompanying shear stress, ε represents the anisotropy parameter characterizing the difference between vertical and transverse P-wave velocities, δ represents the anisotropy parameter characterizing the secondary guide of the vertical incident P-wave velocity, and ν represents the anisotropy parameter characterizing the secondary guide of the vertical incident P-wave velocity. x ν represents horizontal velocity. z This indicates vertical velocity.

[0143] This application provides a seismic migration imaging apparatus. The apparatus obtains reflectivity vectors with different iteration numbers through forward modeling operator parameters, conjugate transpose migration operator parameters, and sensitive kernel function parameters. From these, it determines the model reflectivity vector with the smallest migration parameters. That is, it determines the forward modeled seismic wave data determined by the reflectivity vector that is closest to the actual seismic wave data through the least squares method, thereby improving the accuracy of the stacked profile determined based on least squares inverse time migration.

[0144] Figure 7This diagram illustrates a structural block diagram of a computer device 700 provided in an exemplary embodiment of the present invention. The computer device 700 may be a smartphone, tablet computer, MP3 player (Moving Picture Experts Group Audio Layer III), MP4 player (Moving Picture Experts Group Audio Layer IV), laptop computer, or desktop computer. The computer device 700 may also be referred to as a user device, portable computer device, laptop computer device, desktop computer device, or other names.

[0145] Typically, computer device 700 includes a processor 701 and a memory 702.

[0146] Processor 701 may include one or more processing cores, such as a quad-core processor, an octa-core processor, etc. Processor 701 may be implemented using at least one hardware form selected from DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). Processor 701 may also include a main processor and a coprocessor. The main processor, also known as a CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, processor 701 may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, processor 701 may also include an AI (Artificial Intelligence) processor, which is used to handle computational operations related to machine learning.

[0147] The memory 702 may include one or more computer-readable storage media, which may be non-transitory. The memory 702 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments, the non-transitory computer-readable storage media in the memory 702 are used to store at least one instruction, which is executed by the processor 701 to implement the seismic migration imaging method provided in the method embodiments of this application.

[0148] In some embodiments, the computer device 700 may also optionally include a peripheral device interface 703 and at least one peripheral device. The processor 701, memory 702, and peripheral device interface 703 can be connected via a bus or signal line. Each peripheral device can be connected to the peripheral device interface 703 via a bus, signal line, or circuit board. Specifically, the peripheral device includes at least one of the following: a radio frequency circuit 704, a display screen 705, a camera 706, an audio circuit 707, a positioning component 708, and a power supply 709.

[0149] Peripheral device interface 703 can be used to connect at least one I / O (Input / Output) related peripheral device to processor 701 and memory 702. In some embodiments, processor 701, memory 702 and peripheral device interface 703 are integrated on the same chip or circuit board; in some other embodiments, any one or two of processor 701, memory 702 and peripheral device interface 703 can be implemented on separate chips or circuit boards, which is not limited in this embodiment.

[0150] The radio frequency (RF) circuit 704 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The RF circuit 704 communicates with communication networks and other communication devices via electromagnetic signals. The RF circuit 704 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals back into electrical signals. Optionally, the RF circuit 704 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, etc. The RF circuit 704 can communicate with other computer devices via at least one wireless communication protocol. This wireless communication protocol includes, but is not limited to: metropolitan area networks (MANs), various generations of mobile communication networks (2G, 3G, 4G, and 5G), wireless local area networks (WLANs), and / or WiFi (Wireless Fidelity) networks. In some embodiments, the RF circuit 704 may also include circuitry related to NFC (Near Field Communication), which is not limited in this application.

[0151] Display screen 705 is used to display a UI (User Interface). This UI may include graphics, text, icons, videos, and any combination thereof. When display screen 705 is a touch display screen, it also has the ability to collect touch signals on or above its surface. These touch signals can be input as control signals to processor 701 for processing. In this case, display screen 705 can also be used to provide virtual buttons and / or a virtual keyboard, also known as soft buttons and / or a soft keyboard. In some embodiments, there may be one display screen 705, which is disposed on the front panel of the computer device 700; in other embodiments, there may be at least two display screens 705, respectively disposed on different surfaces of the computer device 700 or in a folded design; in still other embodiments, display screen 705 may be a flexible display screen, disposed on a curved surface or folded surface of the computer device 700. Furthermore, display screen 705 may be configured as a non-rectangular irregular shape, i.e., a non-rectangular screen. Display screen 705 may be made of materials such as LCD (Liquid Crystal Display) or OLED (Organic Light-Emitting Diode).

[0152] The camera assembly 706 is used to acquire images or videos. Optionally, the camera assembly 706 includes a front-facing camera and a rear-facing camera. Typically, the front-facing camera is located on the front panel of the computer device, and the rear-facing camera is located on the back of the computer device. In some embodiments, there are at least two rear-facing cameras, which are any one of a main camera, a depth-sensing camera, a wide-angle camera, and a telephoto camera, to achieve background blurring by fusion of the main camera and the depth-sensing camera, panoramic shooting by fusion of the main camera and the wide-angle camera, VR (Virtual Reality) shooting, or other fusion shooting functions. In some embodiments, the camera assembly 706 may also include a flash. The flash can be a single-color temperature flash or a dual-color temperature flash. A dual-color temperature flash refers to a combination of a warm-light flash and a cool-light flash, which can be used for light compensation at different color temperatures.

[0153] The audio circuit 707 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, converting the sound waves into electrical signals that are input to the processor 701 for processing, or input to the radio frequency circuit 704 for voice communication. For stereo sound acquisition or noise reduction purposes, multiple microphones may be used, each located in a different part of the computer device 700. The microphone may also be an array microphone or an omnidirectional microphone. The speaker is used to convert electrical signals from the processor 701 or the radio frequency circuit 704 into sound waves. The speaker may be a conventional diaphragm speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can convert electrical signals not only into audible sound waves but also into inaudible sound waves for purposes such as distance measurement. In some embodiments, the audio circuit 707 may also include a headphone jack.

[0154] The positioning component 708 is used to locate the current geographical location of the computer device 700 in order to enable navigation or LBS (Location Based Service). The positioning component 708 can be a positioning component based on the US GPS (Global Positioning System), China's BeiDou system, Russia's Granas system, or the European Union's Galileo system.

[0155] Power supply 709 is used to supply power to the various components in computer device 700. Power supply 709 can be AC ​​power, DC power, a disposable battery, or a rechargeable battery. When power supply 709 includes a rechargeable battery, the rechargeable battery can support wired or wireless charging. The rechargeable battery can also be used to support fast charging technology.

[0156] In some embodiments, the computer device 700 further includes one or more sensors 710. The one or more sensors 710 include, but are not limited to: an accelerometer 711, a gyroscope 712, a pressure sensor 713, a fingerprint sensor 714, an optical sensor 715, and a proximity sensor 716.

[0157] Accelerometer 711 can detect the magnitude of acceleration along the three coordinate axes of a coordinate system established by computer device 700. For example, accelerometer 711 can be used to detect the components of gravitational acceleration along the three coordinate axes. Processor 701 can control display screen 705 to display the user interface in either a landscape or portrait view based on the gravitational acceleration signal acquired by accelerometer 711. Accelerometer 711 can also be used for games or for acquiring user motion data.

[0158] The gyroscope sensor 712 can detect the orientation and rotation angle of the computer device 700. The gyroscope sensor 712, in conjunction with the accelerometer sensor 711, can collect 3D motion data from the user on the computer device 700. Based on the data collected by the gyroscope sensor 712, the processor 701 can perform the following functions: motion sensing (e.g., changing the UI based on the user's tilt), image stabilization during shooting, game control, and inertial navigation.

[0159] The pressure sensor 713 can be disposed on the side bezel of the computer device 700 and / or on the lower layer of the display screen 705. When the pressure sensor 713 is disposed on the side bezel of the computer device 700, it can detect the user's grip signal on the computer device 700, and the processor 701 can perform left / right hand recognition or quick operation based on the grip signal collected by the pressure sensor 713. When the pressure sensor 713 is disposed on the lower layer of the display screen 705, the processor 701 can control the operable controls on the UI interface based on the user's pressure operation on the display screen 705. The operable controls include at least one of button controls, scroll bar controls, icon controls, and menu controls.

[0160] The fingerprint sensor 714 is used to collect a user's fingerprint. The processor 701 identifies the user based on the fingerprint collected by the fingerprint sensor 714, or vice versa. When the user's identity is verified as trusted, the processor 701 authorizes the user to perform relevant sensitive operations, including unlocking the screen, viewing encrypted information, downloading software, making payments, and changing settings. The fingerprint sensor 714 can be located on the front, back, or side of the computer device 700. When the computer device 700 has physical buttons or a manufacturer's logo, the fingerprint sensor 714 can be integrated with the physical buttons or the manufacturer's logo.

[0161] An optical sensor 715 is used to collect ambient light intensity. In one embodiment, the processor 701 can control the display brightness of the display screen 705 based on the ambient light intensity collected by the optical sensor 715. Specifically, when the ambient light intensity is high, the display brightness of the display screen 705 is increased; when the ambient light intensity is low, the display brightness of the display screen 705 is decreased. In another embodiment, the processor 701 can also dynamically adjust the shooting parameters of the camera assembly 706 based on the ambient light intensity collected by the optical sensor 715.

[0162] A proximity sensor 716, also known as a distance sensor, is typically mounted on the front panel of a computer device 700. The proximity sensor 716 is used to detect the distance between the user and the front of the computer device 700. In one embodiment, when the proximity sensor 716 detects that the distance between the user and the front of the computer device 700 is gradually decreasing, the processor 701 controls the display screen 705 to switch from a screen-on state to a screen-off state; when the proximity sensor 716 detects that the distance between the user and the front of the computer device 700 is gradually increasing, the processor 701 controls the display screen 705 to switch from a screen-off state to a screen-on state.

[0163] Those skilled in the art will understand that Figure 7 The structure shown does not constitute a limitation on the computer device 700, and may include more or fewer components than shown, or combine certain components, or use different component arrangements.

[0164] In an exemplary embodiment, a storage medium including program code is also provided, such as a memory 804 including program code, which can be executed by a processor 820 of the device 800 to perform the above-described method. Optionally, the storage medium may be a non-transitory computer-readable storage medium, such as a ROM (Read-Only Memory), RAM (Random Access Memory), CD-ROM (Compact Disc Read-Only Memory), magnetic tape, floppy disk, and optical data storage device.

[0165] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0166] The above description is merely an optional embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method of seismic migration imaging, characterized by, The method comprises: acquiring seismic wave data corresponding to an oil and gas reservoir in a work area to be measured; determining forward simulation operator parameters corresponding to the oil and gas reservoir, determining conjugate transpose migration operator parameters corresponding to the oil and gas reservoir, and determining sensitive kernel function parameters corresponding to the oil and gas reservoir; based on the forward simulation operator parameters, the conjugate transpose migration operator parameters and the sensitive kernel function parameters, determining reflectivity vectors corresponding to the oil and gas reservoir; performing iterative calculation on the reflectivity vectors for a preset number of iterations, determining migration parameters between forward seismic wave data corresponding to the reflectivity vectors of each iteration and the seismic wave data corresponding to the oil and gas reservoir, determining the reflectivity vectors corresponding to the minimum migration parameters, and the reflectivity vectors corresponding to the minimum migration parameters including converted shear wave reflectivity parameters and longitudinal wave reflectivity parameters; determining a shear wave stack profile corresponding to the converted shear wave reflectivity parameters and a longitudinal wave stack profile corresponding to the longitudinal wave reflectivity parameters, and the shear wave stack profile and the longitudinal wave stack profile are used for migration imaging of the oil and gas reservoir; wherein the determination of the sensitive kernel function parameters corresponding to the oil and gas reservoir comprises: according to the anisotropy parameters representing the vertical and lateral longitudinal wave velocity difference and the anisotropy parameters representing the vertical incident longitudinal wave velocity twice derivative corresponding to a target point in the oil and gas reservoir, determining the shear wave sensitive kernel parameters and the longitudinal wave sensitive kernel parameters corresponding to the oil and gas reservoir by formula eight, and determining the shear wave sensitive kernel parameters and the longitudinal wave sensitive kernel parameters as the sensitive kernel function parameters; formula eight: wherein denotes the transverse wave sensitive kernel parameter, denotes the longitudinal wave sensitive kernel parameter, denotes a first stiffness matrix component, denotes a second stiffness matrix component, denotes a third stiffness matrix component, denotes a fourth stiffness matrix component, denotes the accompanying normal stress in x direction, denotes the accompanying normal stress in z direction, denotes the accompanying shear stress, denotes the anisotropy parameter characterizing the difference between vertical and transverse longitudinal wave velocities, denotes the anisotropy parameter characterizing the second derivative of the vertical incident longitudinal wave velocity, denotes the horizontal velocity, denotes the vertical velocity.

2. The method of claim 1, wherein, the determination of the reflectivity vectors corresponding to the oil and gas reservoir based on the forward simulation operator parameters, the conjugate transpose migration operator parameters and the sensitive kernel function parameters comprises: determining Born operator parameters based on the forward simulation operator parameters, and determining adjoint migration operator parameters based on the forward simulation operator parameters, the conjugate transpose migration operator parameters and the sensitive kernel function parameters; determining initial forward seismic wave data corresponding to an initial model reflectivity vector, determining an initial change gradient corresponding to the initial forward seismic wave data according to the Born operator parameters, the adjoint migration operator parameters, the initial forward seismic wave data and the seismic wave data corresponding to the oil and gas reservoir, and determining an updated iteration reflectivity vector according to the initial model reflectivity vector and the initial change gradient, and taking the updated iteration reflectivity vector as the reflectivity vector corresponding to the oil and gas reservoir. the determination of the migration parameters between the forward seismic wave data corresponding to the reflectivity vectors of each iteration and the seismic wave data corresponding to the oil and gas reservoir comprises:

3. The method of claim 2, wherein, acquiring mediation parameters corresponding to the oil and gas reservoir; determining the migration parameters between the forward seismic wave data corresponding to the reflectivity vectors and the seismic wave data according to the Born operator parameters, the mediation parameters, the forward seismic wave data corresponding to the reflectivity vectors of each iteration and the seismic wave data by formula one; the seismic wave data comprises shear wave data and longitudinal wave data; Equation One: wherein, denotes the offset parameter, denotes the reflectivity vector, denotes the seismic wave data, denotes the Born operator parameter, denotes the mediation parameter.

4. The method of claim 1, wherein, the determination of the forward simulation operator parameters corresponding to the oil and gas reservoir comprises: ​ determining a shear wave velocity corresponding to the shear wave data, a compressional wave velocity corresponding to the compressional wave data, a density of the hydrocarbon reservoir, an anisotropy parameter representing a difference between vertical and lateral compressional wave velocities, an anisotropy parameter representing a second derivative of a vertical incident compressional wave velocity, and theoretical source data including a horizontal component and a vertical component of a source emitted by a theoretical source; determining stiffness matrix parameters according to the shear wave velocity, the compressional wave velocity, the density, the anisotropy parameter representing the difference between vertical and lateral compressional wave velocities, and the anisotropy parameter representing the second derivative of the vertical incident compressional wave velocity; determining a horizontal velocity, a vertical velocity, a normal stress in an x direction, a normal stress in a z direction, and a shear stress of the hydrocarbon reservoir according to the density, the stiffness matrix parameters, and the theoretical source data, and determining forward modeling operator parameters of the hydrocarbon reservoir.

5. The method of claim 1, wherein, The determination of the conjugate transpose migration operator parameters corresponding to the hydrocarbon reservoir includes: determining a horizontal velocity and a vertical velocity of a target point in the hydrocarbon reservoir, and a density, an anisotropy parameter representing a difference between vertical and lateral compressional wave velocities, an anisotropy parameter representing a second derivative of a vertical incident compressional wave velocity, a normal stress in an x direction, a normal stress in a z direction, and a shear stress of a reservoir in which the target point is located; determining a horizontal adjoint velocity corresponding to the horizontal velocity, a vertical adjoint velocity corresponding to the vertical velocity, a normal stress in an x direction corresponding to the normal stress in the x direction, a normal stress in a z direction corresponding to the normal stress in the z direction, and an adjoint shear stress corresponding to the shear stress; determining the conjugate transpose migration operator parameters corresponding to the hydrocarbon reservoir according to the horizontal adjoint velocity, the vertical adjoint velocity, the normal stress in the x direction, the normal stress in the z direction, and the adjoint shear stress.

6. The method of claim 5, wherein, The determination of the conjugate transpose migration operator parameters corresponding to the hydrocarbon reservoir according to the horizontal adjoint velocity, the vertical adjoint velocity, the normal stress in the x direction, the normal stress in the z direction, and the adjoint shear stress includes: determining data residuals of a horizontal component and data residuals of a vertical component according to the horizontal adjoint velocity, the vertical adjoint velocity, the normal stress in the x direction, the normal stress in the z direction, and the adjoint shear stress by Formula Seven, so that the data residuals of the horizontal component and the data residuals of the vertical component are the conjugate transpose migration operator parameters corresponding to the hydrocarbon reservoir. Equation Seven: wherein, represents the data residual of the horizontal component, represents the data residual of the vertical component, represents the first stiffness matrix component, represents the second stiffness matrix component, represents the third stiffness matrix component, represents the fourth stiffness matrix component, represents the horizontal adjoint velocity, represents the vertical adjoint velocity, represents the adjoint normal stress in the x direction, represents the adjoint normal stress in the z direction, represents the adjoint shear stress, represents the density.

7. An apparatus for seismic migration imaging, characterized by The device includes: an acquisition module configured to acquire seismic wave data corresponding to a hydrocarbon reservoir in a work area to be measured; a first determination module configured to determine forward modeling operator parameters corresponding to the hydrocarbon reservoir, determine conjugate transpose migration operator parameters corresponding to the hydrocarbon reservoir, and determine a sensitivity kernel function parameter corresponding to the hydrocarbon reservoir; a second determination module configured to determine a reflectivity vector corresponding to the hydrocarbon reservoir based on the forward modeling operator parameters, the conjugate transpose migration operator parameters, and the sensitivity kernel function parameter. a third determining module configured to perform iterative calculation on the reflectivity vector for a preset number of iterations, determine a migration parameter between forward seismic wave data corresponding to the reflectivity vector of each iteration and seismic wave data corresponding to the oil and gas reservoir, and determine a reflectivity vector corresponding to a minimum migration parameter, wherein the reflectivity vector corresponding to the minimum migration parameter includes a converted shear wave reflectivity parameter and a P wave reflectivity parameter; a fourth determining module configured to determine a shear wave stack profile corresponding to the converted shear wave reflectivity parameter and a P wave stack profile corresponding to the P wave reflectivity parameter, and perform migration imaging on the oil and gas reservoir based on the shear wave stack profile and the P wave stack profile; wherein the first determining module is configured to determine the sensitive kernel function parameters corresponding to the oil and gas reservoir based on an anisotropy parameter representing a difference between vertical and lateral P wave velocities and an anisotropy parameter representing a second derivative of vertical incident P wave velocity corresponding to a target point in the oil and gas reservoir, determine a shear wave sensitive kernel parameter and a P wave sensitive kernel parameter corresponding to the oil and gas reservoir based on the following formula eight, and determine the shear wave sensitive kernel parameter and the P wave sensitive kernel parameter as the sensitive kernel function parameters; Formula eight: wherein denotes the transverse wave sensitive kernel parameter, denotes the longitudinal wave sensitive kernel parameter, denotes a first stiffness matrix component, denotes a second stiffness matrix component, denotes a third stiffness matrix component, denotes a fourth stiffness matrix component, denotes the accompanying normal stress in x direction, denotes the accompanying normal stress in z direction, denotes the accompanying shear stress, denotes the anisotropy parameter characterizing the difference between vertical and transverse longitudinal wave velocities, denotes the anisotropy parameter characterizing the second derivative of the vertical incident longitudinal wave velocity, denotes the horizontal velocity, denotes the vertical velocity.

8. A computer device, comprising: The computer device comprises: a processor and a memory, wherein the memory stores at least one program code, and the at least one program code is loaded and executed by the processor to implement the operations performed in the method for seismic migration imaging according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores at least one program code, and the at least one program code is loaded and executed by the processor to implement the operations performed in the method for seismic migration imaging according to any one of claims 1 to 6.

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