Pre-stack seismic inversion method and device, storage medium and electronic equipment

Abstract

The invention provides a pre-stack seismic inversion method and device, a storage medium and electronic equipment, and belongs to the technical field of exploration and development. The method comprises the steps that incident angle seismic data and a low-frequency initial model are acquired, mixed phase wavelets are extracted from the incident angle seismic data, and the incident angle seismic data comprise a seismic wave incident angle and corresponding measurement seismic data; determining a coefficient matrix based on a three-item approximate expression of the seismic wave incident angle and a preset reflection coefficient; based on the coefficient matrix and the mixed phase wavelet, calculating by using a preset forward modeling matrix to obtain simulated seismic data and a forward operator; and based on the low-frequency initial model, the measured seismic data, the simulated seismic data and the forward operator, performing inversion on the incident angle seismic data to obtain an inversion result. And the pre-stack seismic inversion under the VTI medium assumption can be accurately realized.

Description

Technical Field

[0001] This invention relates to the field of exploration and development technology, specifically to a pre-stack seismic inversion method, a pre-stack seismic inversion device, a machine-readable storage medium, and an electronic device. Background Technology

[0002] Subsurface media generally exhibit elastic anisotropy, with Ti anisotropy being a typical form of anisotropy in sedimentary basins. When seismic waves propagate in anisotropic media, their amplitude and velocity are significantly affected by anisotropy. Therefore, obtaining reliable anisotropy parameters is of great significance for improving seismic imaging quality and detecting the distribution of reservoir heterogeneity.

[0003] Traditional pre-stack seismic inversion methods utilize P-waves to solve for elastic and anisotropic parameters under the assumption of transverse isotropy. Due to the easier acquisition of P-wave data, it has become the preferred seismic inversion method. However, the seismic response characteristics are highly complex due to the influence of vertical and transverse isotropy caused by factors such as strong reservoir heterogeneity and tuning effects. Directly applying existing techniques to interpret actual data to understand seismic response patterns can lead to significant errors, especially in S-wave, density, and anisotropic information, resulting in a mismatch between simulated and actual amplitudes at deeper depths.

[0004] Therefore, existing pre-stack seismic inversion methods suffer from low accuracy in retrieving parameters such as shear wave velocity, density, and anisotropy. Summary of the Invention

[0005] The purpose of this invention is to provide a pre-stack seismic inversion method, a pre-stack seismic inversion device, a machine-readable storage medium, and an electronic device. The pre-stack seismic inversion method realizes a pre-stack shear wave seismic inversion method based on vertically and laterally isotropic media. Using pure shear waves, shear wave impedance, shear wave velocity, and anisotropic parameters can be obtained, so that pre-stack seismic inversion under the VTI medium assumption can be accurately realized.

[0006] To achieve the above objectives, the first aspect of this application provides a pre-stack seismic inversion method, comprising:

[0007] Acquire incident angle seismic data and a low-frequency initial model, and extract a mixed phase wavelet from the incident angle seismic data, which includes the incident angle of the seismic wave and the corresponding measured seismic data;

[0008] Based on the incident angle of the seismic wave and the three approximate expressions of the preset reflection coefficient, the coefficient matrix is ​​determined.

[0009] Based on the coefficient matrix and the hybrid phase wavelet, simulated seismic data and forward modeling operators are calculated using a preset forward modeling matrix;

[0010] Based on the low-frequency initial model, measured seismic data, simulated seismic data, and forward modeling operator, the incident angle seismic data are inverted to obtain the inversion result.

[0011] The preset reflection coefficient three-term approximation expression is an approximation expression for the transverse wave reflection coefficient based on vertical and transverse isotropic properties. The first parameter in the preset reflection coefficient three-term approximation expression is the transverse wave impedance, the second parameter is the anisotropic transverse wave velocity, and the third parameter is the transverse wave velocity.

[0012] In this embodiment of the application, obtaining the low-frequency initial model includes:

[0013] Acquire the first logging data, which is logging data at the depth domain scale;

[0014] The first logging data is processed by time-depth conversion to obtain the second logging data, which is logging data at the time domain scale.

[0015] Under the constraint of seismic horizon data, the initial shear wave impedance, initial anisotropic shear wave velocity, and initial shear wave velocity are calculated by back-calculation using the second well logging data.

[0016] Based on the initial shear wave impedance, initial anisotropic shear wave velocity, and initial shear wave velocity, a low-frequency initial model is obtained.

[0017] In this embodiment of the application, the three approximate expressions for the preset reflection coefficient are:

[0018]

[0019] Where j represents the incident angle of the seismic wave, α is the first parameter, β is the second parameter, and ξ is the third parameter. It is the transverse wave reflection coefficient based on vertical and transverse isotropic properties.

[0020] In this embodiment of the application, the step of determining the coefficient matrix based on the incident angle of the seismic wave and a preset approximate expression for the reflection coefficient includes:

[0021] Substitute the incident angle of the seismic wave into the coefficients of each term in the three pre-set approximate expressions for the reflection coefficient to obtain the reflection coefficients.

[0022] Based on the aforementioned reflection coefficients, a coefficient matrix is ​​obtained.

[0023] In this embodiment of the application, the step of inverting the incident angle seismic data based on the low-frequency initial model, measured seismic data, simulated seismic data, and forward modeling operator to obtain the inversion result includes:

[0024] Based on the simulated seismic data and the measured seismic data, the first coefficient is calculated;

[0025] Based on the inversion results of the previous inversion and the low-frequency initial model, the second coefficients are calculated;

[0026] Based on the first coefficient and the second coefficient, the regularization coefficient is calculated;

[0027] The forward modeling operator, regularization coefficient, low-frequency initial model, and simulated seismic data are substituted into the preset Bayesian inversion solution expression to solve for the inversion result.

[0028] In this embodiment of the application, the preset Bayesian inversion solution expression is:

[0029]

[0030] in, Let μ be the mean of the model parameters, and μ be the regularization coefficient. As the first coefficient, is the second coefficient, and d is the simulated earthquake data.

[0031] In this embodiment of the application, the step of inverting the incident angle seismic data based on the low-frequency initial model, measured seismic data, simulated seismic data, and forward modeling operator to obtain the inversion result includes:

[0032] Based on the low-frequency initial model, measured seismic data, simulated seismic data, and forward modeling operator, the incident angle seismic data are inverted to obtain the inverted shear wave impedance, inverted anisotropic shear wave velocity, and inverted shear wave velocity.

[0033] Based on the inverted anisotropic shear wave velocity and the inverted shear wave velocity, the inverted anisotropic parameters are calculated according to the preset anisotropic parameter calculation formula.

[0034] Based on the inversion anisotropy parameters, inversion shear wave impedance, inversion anisotropic shear wave velocity, and inversion shear wave velocity, the inversion results are obtained.

[0035] In this embodiment of the application, the formula for calculating the preset anisotropy parameters is as follows:

[0036]

[0037] Where β is the inverted anisotropic shear wave velocity, ξ is the inverted shear wave velocity, and σ is the inverted anisotropic parameter.

[0038] A second aspect of this application provides a pre-stack seismic inversion device, comprising:

[0039] The acquisition module is used to acquire incident angle seismic data and a low-frequency initial model, and extract the mixed phase wavelet from the incident angle seismic data, wherein the incident angle seismic data includes the incident angle of the seismic wave and the corresponding measured seismic data;

[0040] The coefficient module is used to determine the coefficient matrix based on the incident angle of the seismic wave and a preset three-term approximation expression for the reflection coefficient. The preset three-term approximation expression for the reflection coefficient is an approximation expression for the reflection coefficient of a transverse wave that is isotropic in both the vertical and transverse directions. The first parameter in the preset three-term approximation expression for the reflection coefficient is the transverse wave impedance, the second parameter is the anisotropic transverse wave velocity, and the third parameter is the transverse wave velocity.

[0041] The forward modeling module is used to calculate simulated seismic data and forward modeling operators based on the coefficient matrix and the mixed phase wavelet using a preset forward modeling matrix;

[0042] The inversion module is used to invert the incident angle seismic data based on the low-frequency initial model, measured seismic data, simulated seismic data, and forward modeling operators to obtain the inversion results.

[0043] In this embodiment of the application, the acquisition module includes:

[0044] The first acquisition unit is used to acquire the first logging data, which is logging data at the depth domain scale.

[0045] The conversion unit is used to perform time-depth conversion processing on the first logging data to obtain the second logging data, wherein the second logging data is logging data at the time domain scale;

[0046] The first calculation unit is used to back-calculate the initial shear wave impedance, initial anisotropic shear wave velocity, and initial shear wave velocity using the second well logging data under the constraint of seismic horizon data.

[0047] The confirmation unit is used to obtain a low-frequency initial model based on the initial shear wave impedance, initial anisotropic shear wave velocity, and initial shear wave velocity.

[0048] In this embodiment of the application, the three approximate expressions for the preset reflection coefficient are:

[0049]

[0050] Where j represents the incident angle of the seismic wave, α is the first parameter, β is the second parameter, and ξ is the third parameter. It is the transverse wave reflection coefficient based on vertical and transverse isotropic properties.

[0051] In this embodiment of the application, the coefficient module includes:

[0052] The coefficient unit is used to substitute the incident angle of the seismic wave into the coefficients of each term in the three-term approximate expression of the reflection coefficient to obtain the reflection coefficients.

[0053] The matrix unit is used to obtain the coefficient matrix based on the aforementioned reflection coefficients.

[0054] In this embodiment of the application, the inversion module includes:

[0055] The second calculation unit is used to calculate the first coefficient based on the simulated seismic data and the measured seismic data;

[0056] The third calculation unit is used to calculate the second coefficient based on the inversion result of the previous inversion and the low-frequency initial model;

[0057] The third calculation unit is used to calculate the regularization coefficient based on the first coefficient and the second coefficient;

[0058] The solution unit is used to substitute the forward modeling operator, regularization coefficient, low-frequency initial model, and simulated seismic data into a preset Bayesian inversion solution expression to obtain the inversion result.

[0059] In this embodiment of the application, the preset Bayesian inversion solution expression is:

[0060]

[0061] in, Let μ be the mean of the model parameters, and μ be the regularization coefficient. As the first coefficient, is the second coefficient, and d is the simulated earthquake data.

[0062] In this embodiment of the application, the inversion module includes:

[0063] The initial inversion unit is used to invert the incident angle seismic data based on the low-frequency initial model, measured seismic data, simulated seismic data and forward modeling operator to obtain the inverted shear wave impedance, inverted anisotropic shear wave velocity and inverted shear wave velocity.

[0064] The fourth calculation unit is used to calculate the inversion anisotropic parameters based on the inversion anisotropic shear wave velocity and the inversion shear wave velocity, according to the preset anisotropic parameter calculation formula.

[0065] The result unit is used to obtain the inversion result based on the inversion anisotropy parameters, inversion shear wave impedance, inversion anisotropic shear wave velocity, and inversion shear wave velocity.

[0066] In this embodiment of the application, the formula for calculating the preset anisotropy parameters is as follows:

[0067]

[0068] Where β is the inverted anisotropic shear wave velocity, ξ is the inverted shear wave velocity, and σ is the inverted anisotropic parameter.

[0069] A third aspect of this application provides an electronic device, the electronic device comprising:

[0070] At least one processor;

[0071] A memory connected to the at least one processor;

[0072] The memory stores instructions that can be executed by the at least one processor, and the at least one processor implements the pre-stack seismic inversion method described above by executing the instructions stored in the memory.

[0073] A fourth aspect of this application provides a machine-readable storage medium storing instructions that, when executed by a processor, configure the processor to perform the pre-stack seismic inversion method described above.

[0074] The above technical solution involves acquiring incident angle seismic data and a low-frequency initial model, and extracting a mixed-phase wavelet from the incident angle seismic data, which includes the incident angle of the seismic wave and the corresponding measured seismic data. A coefficient matrix is ​​determined based on the incident angle of the seismic wave and a preset three-term approximation expression for the reflection coefficient. Based on the coefficient matrix and the mixed-phase wavelet, simulated seismic data and a forward modeling operator are calculated using a preset forward modeling matrix. The incident angle seismic data is then inverted based on the low-frequency initial model, measured seismic data, simulated seismic data, and the forward modeling operator to obtain the inversion result. The preset three-term approximation expression for the reflection coefficient is an approximation expression based on vertically and laterally isotropic shear wave reflection coefficients. The first parameter in the preset three-term approximation expression for the reflection coefficient is the shear wave impedance, the second parameter is the anisotropic shear wave velocity, and the third parameter is the shear wave velocity. By improving the approximate expression of the shear wave reflection coefficient of VTI, a new three-term approximate formula for the shear wave reflection coefficient under transversely isotropic media is obtained. The inversion results are obtained by combining this formula with the inversion algorithm, realizing a pre-stack shear wave seismic inversion method based on vertically transversely isotropic media. Using pure shear waves, shear wave impedance, shear wave velocity and anisotropy parameters can be obtained, making the pre-stack seismic inversion under the VTI medium assumption accurate. This allows the influence of VTI characteristics to be fully considered in the seismic inversion process, solves the problem of inaccurate prediction using single P-wave seismic data, and improves the prediction accuracy of reservoir parameters.

[0075] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0076] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings:

[0077] Figure 1 The schematic diagram illustrates a pre-stack seismic inversion method according to an embodiment of this application;

[0078] Figure 2 An improved SV-SV reflection coefficient accuracy analysis diagram according to an embodiment of this application is schematically shown;

[0079] Figure 3 A schematic diagram of a seismic wavelet according to an embodiment of this application is shown.

[0080] Figure 4 The inversion results and anisotropy parameter σ results according to embodiments of this application are illustrated schematically.

[0081] Figure 5 The schematic diagram illustrates the structure of a pre-stack seismic inversion device according to an embodiment of this application;

[0082] Figure 6 The diagram illustrates the internal structure of a computer device according to an embodiment of this application.

[0083] Explanation of reference numerals in the attached figures

[0084] 410 - Acquisition Module; 420 - Coefficient Module; 430 - Forward Modeling Module; 440 - Inversion Module; A01 - Processor; A02 - Network Interface; A03 - Internal Memory; A04 - Display Screen; A05 - Input Device; A06 - Non-volatile Storage Medium; B01 - Operating System; B02 - Computer Program. Detailed Implementation

[0085] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the scope of the present invention.

[0086] It should be noted that the acquisition, transmission, storage, use, and processing of data in the technical solution of this application all comply with the relevant provisions of national laws and regulations. In the embodiments of this application, certain existing industry solutions such as software, components, and models may be mentioned. These should be considered exemplary, intended only to illustrate the feasibility of implementing the technical solution of this application, and do not imply that the applicant has already used or necessarily used such solutions.

[0087] Furthermore, if the embodiments of this application involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0088] The abbreviations and key terms mentioned in this embodiment are defined as follows:

[0089] Isotropy: Isotropy refers to the property that the physical, chemical and other properties of an object (rock) do not change with different directions. That is, the property values ​​(elastic properties or physical properties) of a certain object (rock) are exactly the same in different directions.

[0090] Anisotropy: Anisotropy refers to the change in all or part of the chemical, physical, and other properties of a substance (rock) as its orientation changes, exhibiting different properties in different directions. Rocks exhibit anisotropy at the microscopic, mesoscopic, and macroscopic scales. The mineral crystals of certain rocks possess anisotropic structural characteristics; for example, kaolinite and muscovite crystals have platy structures, and rocks composed of these minerals exhibit a certain degree of initial anisotropy at the microscopic level.

[0091] Vertical-lateral isotropy (VTI): This is a special case of anisotropic bodies. When an object (rock) has a vertical axis of symmetry inside, the object's properties are the same in all directions within a plane perpendicular to the axis of symmetry (lateral direction), meaning it has no directionality. This property is called lateral isotropy.

[0092] SV waves: SV waves are a type of seismic wave in which the direction of particle vibration is perpendicular to the direction of wave propagation, and they belong to transverse waves or shear waves.

[0093] Seismic response: Seismic response refers to the study of the influence of traveling wave effect on structural seismic response using the virtual excitation method.

[0094] Thomsen anisotropy parameter ε: Longitudinal wave anisotropy parameter.

[0095] Thomsen anisotropy parameter δ: defines the second derivative of the P-wave phase velocity function at perpendicular incidence, characterizing the angular dependence of the P-wave velocity near the vertical (symmetric) axis.

[0096] Anisotropy parameter σ: This parameter can characterize the degree of anisotropy and plays a key role in the migration velocity analysis of anisotropic media.

[0097] It should be noted that the transverse waves mentioned in this embodiment include SV-SV waves and SH-SH waves. For the sake of explaining the scheme, this embodiment mainly uses SV-SV waves as an example for explanation.

[0098] Please refer to Figure 1 , Figure 1 The schematic diagram illustrates a pre-stack seismic inversion method according to an embodiment of this application. This embodiment provides a pre-stack seismic inversion method, including the following steps:

[0099] Step 210: Acquire incident angle seismic data and low-frequency initial model, and extract mixed phase wavelet from the incident angle seismic data, wherein the incident angle seismic data includes the incident angle of seismic waves and the corresponding measured seismic data;

[0100] In this embodiment, the aforementioned low-frequency initial model refers to the data such as shear wave impedance, anisotropic shear wave velocity, and shear wave velocity obtained by extrapolation calculation using well logging data under the constraint of seismic horizon data. Based on these data, a low-frequency initial model required for inversion can be constructed. This low-frequency initial model is a physical model, a matrix, in which the elements include the extrapolated shear wave impedance, anisotropic shear wave velocity, and shear wave velocity. Incident angle seismic data refers to the seismic signals recorded when seismic waves are incident on the subsurface medium from a well logging bypass at different angles. These signals can be received by deploying a seismic detector array on the surface or in the well. The extraction of the mixed phase wavelet from the incident angle seismic data can be achieved using autocorrelation methods, statistical methods, etc. It should be noted that the extrapolation calculation and the extraction of the mixed phase wavelet mentioned above are existing technologies and will not be elaborated further here.

[0101] In some embodiments, obtaining a low-frequency initial model includes the following steps:

[0102] First, acquire the first logging data, which is logging data at the depth domain scale;

[0103] In this embodiment, the depth domain scale refers to the measurement results of logging data expressed in units of vertical depth. The first logging data can usually be collected during the drilling process by means of a cable or a measurement while drilling tool.

[0104] Then, the first logging data is converted to time depth to obtain the second logging data, which is logging data at the time domain scale.

[0105] In this embodiment, well logging data at the depth domain scale needs to be converted to time-domain scale through time-depth conversion processing. This time-depth data is then used for well-seismic calibration with time-domain seismic records to obtain updated time-depth relationships and depth-domain scale well logging data, specifically including P-wave velocity, S-wave velocity, density, Thomsen anisotropy parameters, and other parameters. The aforementioned time-depth conversion processing can employ methods such as velocity modeling or synthetic seismic recording, which are existing technologies and will not be elaborated upon here.

[0106] Then, under the constraint of seismic horizon data, the initial shear wave impedance, initial anisotropic shear wave velocity, and initial shear wave velocity are calculated by back-calculation using the second well logging data.

[0107] In this embodiment, seismic stratigraphic data provides macroscopic structural information about subsurface strata, including depth, thickness, and morphology. This data can serve as constraints to help determine the scope and initial model for back-calculation from well logging data. Under the constraints of the seismic stratigraphic data, the low-frequency initial model required for inversion can be obtained by extrapolating from coarsened second well logging data. Coarsening well logging data is typically done to adapt to the needs of specific geological models or numerical simulations. In some cases, the resolution of the original well logging data is too high, which may lead to excessive computation or make it difficult to process in certain models. Coarsening can reduce the resolution of the data while retaining key geological information.

[0108] Finally, based on the initial shear wave impedance, initial anisotropic shear wave velocity, and initial shear wave velocity, the low-frequency initial model is obtained.

[0109] In this embodiment, the low-frequency initial model is a physical model, which is a matrix. The elements in the matrix include extrapolated shear wave impedance, anisotropic shear wave velocity, shear wave velocity, etc.

[0110] Well logging data at the depth domain scale needs to be processed by time-depth conversion to obtain well logging data at the time domain scale. Under the constraint of seismic horizon data, the low-frequency initial model required for inversion is obtained by extrapolation using the second well logging data, thereby obtaining more detailed subsurface geological information.

[0111] Step 220: Based on the seismic wave incident angle and the preset three-term approximation expression for the reflection coefficient, determine the coefficient matrix; wherein, the preset three-term approximation expression for the reflection coefficient is an approximation expression based on the vertical and transverse isotropic shear wave reflection coefficient, and the first parameter in the preset three-term approximation expression for the reflection coefficient is the shear wave impedance, the second parameter is the anisotropic shear wave velocity, and the third parameter is the shear wave velocity;

[0112] In this embodiment, the preset three approximate expressions for the reflection coefficient are approximate expressions for the reflection coefficient of transverse waves that are isotropic in both the vertical and transverse directions. For SV-SV waves, they can be obtained by parameterizing the approximate expressions for the reflection coefficient of SV-SV waves in vertically and transversely isotropic (VTI) media derived from the Rüger formula.

[0113] The approximate formula for the SV-SV wave reflection coefficient based on VTI is expressed as follows:

[0114]

[0115] In the formula, Z S V represents the transverse wave impedance. p V represents the longitudinal wave velocity. S ρ represents the shear wave velocity, ρ represents the density, j represents the seismic wave incident angle, and ε and δ represent anisotropy parameters. It is the transverse wave reflection coefficient based on vertical and transverse isotropic properties.

[0116] in, Then we can get:

[0117]

[0118] because The above equation can then be written as:

[0119]

[0120] Where α=Z S , ξ=V S ,

[0121] Based on this, a new three-term approximation formula for the reflection coefficient of SV-SV waves under VTI medium can be obtained. The first term α represents the transverse wave impedance, the second term β represents the anisotropic transverse wave velocity, the third term ξ represents the transverse wave velocity, and σ represents the anisotropic parameter.

[0122] That is, the three approximate expressions for the preset reflection coefficient are:

[0123]

[0124] Where j represents the incident angle of the seismic wave, α is the first parameter, β is the second parameter, and ξ is the third parameter. It is the transverse wave reflection coefficient based on vertical and transverse isotropic properties.

[0125] It should be noted that for SH-SH waves, the approximate expression for the reflection coefficient of SH-SH waves in vertically and laterally isotropic (VTI) media can also be parameterized according to the above steps to obtain the corresponding three-term approximate expression for the reflection coefficient, which will not be elaborated here.

[0126] In some embodiments, determining the coefficient matrix based on the seismic wave incident angle and a preset three-term approximate expression for the reflection coefficient includes the following steps:

[0127] First, the incident angle of the seismic wave is substituted into the coefficients of the three pre-set approximate expressions for the reflection coefficient to obtain the reflection coefficients.

[0128] In this embodiment, the coefficients of the three terms in the preset approximate expression for the reflection coefficient are as follows: The first term coefficient is: The second coefficient is: 2sin 2 j, the coefficient of the third term is: By substituting the incident angle of the seismic wave into the coefficient j, we can obtain the various reflection coefficients.

[0129] Then, based on the aforementioned reflection coefficients, the coefficient matrix is ​​obtained.

[0130] In this embodiment, the reflection coefficients are elements of the coefficient matrix. Assume the number of samples of the seismic wave incident angle is M, denoted as θ1...θ M The number of time samples is N, and the coefficient matrix K can be represented as:

[0131]

[0132] Where A is the first reflection coefficient, B is the second reflection coefficient, C is the third reflection coefficient, and each reflection coefficient forms a matrix, including the reflection coefficients corresponding to multiple time downsamplings at that incident angle, i.e.:

[0133]

[0134] Where, A(θ) k ,t1) can be the incident angle θ of the seismic wave at time t1. k Substituting these values ​​into the first coefficient, we can obtain the first reflection coefficient at other times in the same way.

[0135] Similarly, we can obtain B(θ) k ) and C(θ) k ), and then the coefficient matrix is ​​obtained.

[0136] Step 230: Based on the coefficient matrix and the mixed phase wavelet, simulated seismic data and forward modeling operators are calculated using a preset forward modeling matrix;

[0137] In this embodiment, the model is considered to be a function that varies with time (i.e., along the depth direction). Assuming the gradient change can be viewed as the derivative of the natural logarithm, a forward modeling matrix can be obtained using a convolution model. The convolution model, based on the physical process of seismic wave propagation, represents the seismic record as a convolution of the seismic wavelet and the sequence of subsurface reflection coefficients. Assuming the number of samples at the seismic wave incidence angle is M, denoted as θ1...θ M The number of time samples is N, which can be represented in matrix form as:

[0138] K·D·M=R,

[0139] Where K is the coefficient matrix, D is the difference matrix, M is the data logarithm matrix, and R is the reflection coefficient matrix.

[0140] Given a coefficient matrix K, a difference matrix D, a data logarithm matrix M, and multiplied by a wavelet convolution matrix S (where the wavelet convolution matrix is ​​constructed based on the convolution operation between the mixed phase wavelet and the reflection coefficient matrix), we can express this in forward form, thus obtaining the forward matrix formula:

[0141] S·K·D·M=d,

[0142] Where d represents simulated earthquake data.

[0143] The difference matrix D is:

[0144]

[0145] The wavelet convolution matrix S is:

[0146]

[0147] The model data matrix M is:

[0148] M = [lnA(t1)...lnA(t)] N ),lnB(t1)…lnB(t N ),lnC(t1)…lnC(t N )] T .

[0149] Let G = S·K·D, then the forward operator G can be calculated.

[0150] Step 240: Based on the low-frequency initial model, measured seismic data, simulated seismic data, and forward modeling operator, invert the incident angle seismic data to obtain the inversion result;

[0151] In this embodiment, the above inversion can be performed using a generalized linear inversion algorithm. For example, it can be performed using regularization methods within a Bayesian framework to obtain elastic parameters and anisotropy parameters. For the inversion of incident angle seismic data, assuming d obs Given the observational data, i.e., measured seismic data, and m as the Bayesian equation corresponding to the subsurface medium model, then:

[0152]

[0153] The subsurface medium model m is considered as a function following a certain probability density distribution, with the observed data being d. obs The posterior distribution is obtained, and then the model data m corresponding to the point of maximum probability is found. This is the maximum a posteriori probability estimation. There is a certain linear relationship between seismic data and subsurface medium models:

[0154] d obs =G·m+n,

[0155] Where G is the forward modeling operator, and n is the seismic data noise that follows a Gaussian distribution and is independent (correlation coefficient is 0). Then we have:

[0156]

[0157] It is generally believed that the observed data d obs The marginal distribution probability is a constant. Assuming the prior information of the model parameters follows a Gaussian distribution, then:

[0158]

[0159] Furthermore, the estimated subsurface medium model m under the observed data conditions can be derived:

[0160]

[0161] Based on the maximum a posteriori probability estimation, the objective function is established as follows:

[0162]

[0163] Taking the derivative of this function, we get:

[0164]

[0165] This establishes the Bayesian inversion solution, i.e., the expression for the Bayesian inversion solution, where, The mean of the model parameters represents the low-frequency initial model, and μ is the regularization coefficient. The first coefficient is the weighted average of the errors. The second coefficient is a weighted average for the subsurface medium model. d represents simulated earthquake data.

[0166] It should be noted that the above inversion can also be performed using other inversion methods, such as least squares inversion, genetic algorithm inversion, etc., and this implementation is not limited to these methods. The inversion performed using regularization methods within the Bayesian framework is more stable and has better universality.

[0167] In some embodiments, the inversion of the incident angle seismic data based on the low-frequency initial model, measured seismic data, simulated seismic data, and forward modeling operators to obtain inversion results includes:

[0168] First, based on the simulated seismic data and the measured seismic data, the first coefficient is calculated;

[0169] In this embodiment, the first coefficient is the error between the earthquake data obtained by forward modeling and the actual earthquake data, that is, the difference between the simulated earthquake data and the measured earthquake data.

[0170] Then, based on the inversion results of the previous inversion and the low-frequency initial model, the second coefficients are calculated;

[0171] In this embodiment, the second coefficient is the difference between the previous inversion result and the low-frequency initial model. It should be noted that in the first inversion, the second coefficient is the low-frequency initial model.

[0172] Then, based on the first coefficient and the second coefficient, the regularization coefficient is calculated;

[0173] In this embodiment, the ratio of the second coefficient to the first coefficient is calculated to obtain the regularization coefficient.

[0174] Finally, the forward modeling operator, regularization coefficient, low-frequency initial model, and simulated seismic data are substituted into the preset Bayesian inversion solution expression to solve for the inversion result.

[0175] In this embodiment, the inversion result can be calculated by substituting the forward modeling operator, regularization coefficient, low-frequency initial model, and simulated seismic data into the preset Bayesian inversion solution expression.

[0176] By calculating the first and second coefficients respectively, and then calculating the regularization coefficient based on the first and second coefficients, and finally substituting the forward modeling operator, regularization coefficient, low-frequency initial model, and simulated seismic data into the preset Bayesian inversion solution expression for solving, the inversion result can be quickly calculated.

[0177] In some embodiments, the inversion of the incident angle seismic data based on the low-frequency initial model, measured seismic data, simulated seismic data, and forward modeling operators to obtain inversion results includes:

[0178] First, based on the low-frequency initial model, measured seismic data, simulated seismic data, and forward modeling operator, the incident angle seismic data is inverted to obtain the inverted shear wave impedance, inverted anisotropic shear wave velocity, and inverted shear wave velocity.

[0179] In this embodiment, the parameters obtained by inversion first include: inverted shear wave impedance, inverted anisotropic shear wave velocity, and inverted shear wave velocity.

[0180] Then, based on the inverted anisotropic shear wave velocity and the inverted shear wave velocity, the inverted anisotropic parameters are calculated according to the preset anisotropic parameter calculation formula.

[0181] In this embodiment, the second parameter of the three-term approximation expression for the reflection coefficient includes the non-elliptic coefficient σ proposed by Tsvankin. Therefore, the anisotropy parameters can be obtained using the second and third terms of the shear wave velocity obtained from anisotropy inversion. The above-mentioned preset formula for calculating the anisotropy parameters is as follows:

[0182]

[0183] Substituting the inverted anisotropic shear wave velocity into the above equation with β and the inverted shear wave velocity into the above equation, the inverted anisotropic parameters are calculated.

[0184] Finally, based on the inverted anisotropic parameters, inverted shear wave impedance, inverted anisotropic shear wave velocity, and inverted shear wave velocity, the inversion results are obtained.

[0185] In this embodiment, the inversion anisotropy parameters, inversion shear wave impedance, inversion anisotropic shear wave velocity, and inversion shear wave velocity constitute the final inversion result.

[0186] By first calculating the inverted shear wave impedance, inverted anisotropic shear wave velocity, and inverted shear wave velocity, and then using the inverted anisotropic shear wave velocity and inverted shear wave velocity, the anisotropic parameters can be calculated quickly and accurately, and finally the inversion results are obtained, which improves the calculation efficiency and makes the inversion results more comprehensive.

[0187] In the above implementation process, incident angle seismic data and a low-frequency initial model are acquired, and a mixed phase wavelet is extracted from the incident angle seismic data. The incident angle seismic data includes the incident angle of the seismic wave and the corresponding measured seismic data. Based on the incident angle of the seismic wave and a preset three-term approximation expression for the reflection coefficient, a coefficient matrix is ​​determined. Based on the coefficient matrix and the mixed phase wavelet, simulated seismic data and a forward modeling operator are calculated using a preset forward modeling matrix. Based on the low-frequency initial model, measured seismic data, simulated seismic data, and forward modeling operator, the incident angle seismic data is inverted to obtain the inversion result. Among them, the preset three-term approximation expression for the reflection coefficient is an approximation expression based on the vertical and transverse isotropic shear wave reflection coefficient. The first parameter in the preset three-term approximation expression for the reflection coefficient is the shear wave impedance, the second parameter is the anisotropic shear wave velocity, and the third parameter is the shear wave velocity. By improving the approximate expression of the shear wave reflection coefficient of VTI, a new three-term approximate formula for the shear wave reflection coefficient under transversely isotropic media is obtained. The inversion results are obtained by combining this formula with the inversion algorithm, realizing a pre-stack shear wave seismic inversion method based on vertically transversely isotropic media. Using pure shear waves, shear wave impedance, shear wave velocity and anisotropy parameters can be obtained, making the pre-stack seismic inversion under the VTI medium assumption accurate. This allows the influence of VTI characteristics to be fully considered in the seismic inversion process, solves the problem of inaccurate prediction using single P-wave seismic data, and improves the prediction accuracy of reservoir parameters.

[0188] The following section explains this scheme based on actual well logging data from the work area. The inversion process includes:

[0189] 1) Data collection and analysis

[0190] Based on actual well logging data from the work area, and using the SV-SV wave reflection coefficient formula derived from the improved VTI (Volume Injection Theorem) combined with actual seismic data, forward modeling was performed. The extracted seismic wavelet is as follows: Figure 3 As shown, Figure 3 A schematic diagram of a seismic wavelet according to an embodiment of this application is shown. Noise is added to obtain seismic records of different angle traces, which are then used as the inversion and synthesis records.

[0191] 2) Improved inversion of the SV-SV reflection coefficient equation for VTI media

[0192] Using well logging data and synthetic seismic record data, and based on the improved VTI reflection coefficient equation, the generalized linear inversion method was used to obtain the shear wave impedance, the shear wave velocity term with anisotropic parameters, and the shear wave velocity term.

[0193] 3) Calculation of anisotropy parameters

[0194] The anisotropic parameter σ is calculated from the second and third shear wave velocity terms with anisotropic parameters obtained by inversion from the SV-SV reflection coefficient equation. Figure 4 As shown, Figure 4 The inversion results and anisotropy parameter σ results according to embodiments of this application are illustrated schematically.

[0195] Please refer to Figure 2 , Figure 2 The diagram schematically illustrates the improved SV-SV reflection coefficient accuracy analysis according to an embodiment of this application. The solid black line represents the amplitude variation curve calculated using the precise formula, the dashed black line represents the curve calculated using this method, and the gray line represents the curve commonly used at present without considering anisotropy. As can be seen from the diagram, the accuracy of this method is higher than that of the commonly used formula.

[0196] Figure 1 This is a flowchart illustrating the pre-stack seismic inversion method in one embodiment. It should be understood that, although... Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0197] Please refer to Figure 5 , Figure 5 A schematic diagram of a pre-stack seismic inversion device according to an embodiment of this application is shown. This embodiment provides a pre-stack seismic inversion device, including an acquisition module 410, a coefficient module 420, a forward modeling module 430, and an inversion module 440, wherein:

[0198] The acquisition module 410 is used to acquire incident angle seismic data and a low-frequency initial model, and extract a mixed phase wavelet from the incident angle seismic data, wherein the incident angle seismic data includes the incident angle of the seismic wave and the corresponding measured seismic data;

[0199] The coefficient module 420 is used to determine the coefficient matrix based on the incident angle of the seismic wave and a preset three-term approximate expression for the reflection coefficient. The preset three-term approximate expression for the reflection coefficient is an approximate expression based on the vertical and transverse isotropic shear wave reflection coefficient. The first parameter in the preset three-term approximate expression for the reflection coefficient is the shear wave impedance, the second parameter is the anisotropic shear wave velocity, and the third parameter is the shear wave velocity.

[0200] The forward modeling module 430 is used to calculate simulated seismic data and forward modeling operators based on the coefficient matrix and the hybrid phase wavelet using a preset forward modeling matrix.

[0201] The inversion module 440 is used to invert the incident angle seismic data based on the low-frequency initial model, measured seismic data, simulated seismic data and forward modeling operator to obtain the inversion result.

[0202] The acquisition module 410 includes:

[0203] The first acquisition unit is used to acquire the first logging data, which is logging data at the depth domain scale.

[0204] The conversion unit is used to perform time-depth conversion processing on the first logging data to obtain the second logging data, wherein the second logging data is logging data at the time domain scale;

[0205] The first calculation unit is used to back-calculate the initial shear wave impedance, initial anisotropic shear wave velocity, and initial shear wave velocity using the second well logging data under the constraint of seismic horizon data.

[0206] The confirmation unit is used to obtain a low-frequency initial model based on the initial shear wave impedance, initial anisotropic shear wave velocity, and initial shear wave velocity.

[0207] In this embodiment of the application, the three approximate expressions for the preset reflection coefficient are:

[0208]

[0209] Where j represents the incident angle of the seismic wave, α is the first parameter, β is the second parameter, and ξ is the third parameter. It is the transverse wave reflection coefficient based on vertical and transverse isotropic properties.

[0210] In this embodiment of the application, the coefficient module 420 includes:

[0211] The coefficient unit is used to substitute the incident angle of the seismic wave into the coefficients of each term in the three-term approximate expression of the reflection coefficient to obtain the reflection coefficients.

[0212] The matrix unit is used to obtain the coefficient matrix based on the aforementioned reflection coefficients.

[0213] In this embodiment of the application, the inversion module 440 includes:

[0214] The second calculation unit is used to calculate the first coefficient based on the simulated seismic data and the measured seismic data;

[0215] The third calculation unit is used to calculate the second coefficient based on the inversion result of the previous inversion and the low-frequency initial model;

[0216] The third calculation unit is used to calculate the regularization coefficient based on the first coefficient and the second coefficient;

[0217] The solution unit is used to substitute the forward modeling operator, regularization coefficient, low-frequency initial model, and simulated seismic data into a preset Bayesian inversion solution expression to obtain the inversion result.

[0218] In this embodiment of the application, the preset Bayesian inversion solution expression is:

[0219]

[0220] in, Let μ be the mean of the model parameters, and μ be the regularization coefficient. As the first coefficient, is the second coefficient, and d is the simulated earthquake data.

[0221] In this embodiment of the application, the inversion module 440 includes:

[0222] The initial inversion unit is used to invert the incident angle seismic data based on the low-frequency initial model, measured seismic data, simulated seismic data and forward modeling operator to obtain the inverted shear wave impedance, inverted anisotropic shear wave velocity and inverted shear wave velocity.

[0223] The fourth calculation unit is used to calculate the inversion anisotropic parameters based on the inversion anisotropic shear wave velocity and the inversion shear wave velocity, according to the preset anisotropic parameter calculation formula.

[0224] The result unit is used to obtain the inversion result based on the inversion anisotropy parameters, inversion shear wave impedance, inversion anisotropic shear wave velocity, and inversion shear wave velocity.

[0225] In this embodiment of the application, the formula for calculating the preset anisotropy parameters is as follows:

[0226]

[0227] Where β is the inverted anisotropic shear wave velocity, ξ is the inverted shear wave velocity, and σ is the inverted anisotropic parameter.

[0228] The pre-stack seismic inversion device includes a processor and a memory. The acquisition module 410, coefficient module 420, forward modeling module 430 and inversion module 440 are all stored in the memory as program units. The processor executes the program units stored in the memory to realize the corresponding functions.

[0229] The processor contains a kernel, which retrieves the corresponding program unit from memory. One or more kernels can be configured, and the inversion accuracy can be improved by adjusting kernel parameters.

[0230] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0231] This invention provides a machine-readable storage medium storing a program that, when executed by a processor, implements the pre-stack seismic inversion method.

[0232] This invention provides a processor for running a program, wherein the program executes the pre-stack seismic inversion method during runtime.

[0233] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 6 As shown in the figure, the computer device includes a processor A01, a network interface A02, a display screen A04, an input device A05, and a memory (not shown) connected via a system bus. The processor A01 provides computing and control capabilities. The memory includes internal memory A03 and a non-volatile storage medium A06. The non-volatile storage medium A06 stores an operating system B01 and a computer program B02. The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 stored in the non-volatile storage medium A06. The network interface A02 is used for communication with external terminals via a network connection. When the computer program is executed by the processor A01, it implements a pre-stack seismic inversion method. The display screen A04 can be a liquid crystal display (LCD) or an e-ink display. The input device A05 can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse.

[0234] Those skilled in the art will understand that Figure 6The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0235] In one embodiment, the pre-stack seismic inversion device provided in this application can be implemented as a computer program, which can be implemented in the form of, for example... Figure 6 The computer device shown runs on this system. The computer device's memory can store the various program modules that make up the pre-stack seismic inversion device, for example... Figure 5 The diagram shows the acquisition module 410, coefficient module 420, forward modeling module 430, and inversion module 440. The computer program comprised of these modules causes the processor to execute the steps in the pre-stack seismic inversion methods of the various embodiments of this application described in this specification.

[0236] Figure 6 The computer device shown can be used as follows Figure 5 The acquisition module 410 in the pre-stack seismic inversion device shown executes step 210. The computer device can execute step 220 via the coefficient module 420. The computer device can execute step 230 via the forward modeling module 430. The computer device can execute step 240 via the inversion module 440.

[0237] This application provides an electronic device comprising: at least one processor; and a memory connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the at least one processor implements the pre-stack seismic inversion method described above by executing the instructions stored in the memory. When the processor executes the instructions, it performs the following steps:

[0238] Acquire incident angle seismic data and a low-frequency initial model, and extract a mixed phase wavelet from the incident angle seismic data, which includes the incident angle of the seismic wave and the corresponding measured seismic data;

[0239] Based on the incident angle of the seismic wave and the three approximate expressions of the preset reflection coefficient, the coefficient matrix is ​​determined.

[0240] Based on the coefficient matrix and the hybrid phase wavelet, simulated seismic data and forward modeling operators are calculated using a preset forward modeling matrix;

[0241] Based on the low-frequency initial model, measured seismic data, simulated seismic data, and forward modeling operator, the incident angle seismic data are inverted to obtain the inversion result.

[0242] The preset reflection coefficient three-term approximation expression is an approximation expression for the transverse wave reflection coefficient based on vertical and transverse isotropic properties. The first parameter in the preset reflection coefficient three-term approximation expression is the transverse wave impedance, the second parameter is the anisotropic transverse wave velocity, and the third parameter is the transverse wave velocity.

[0243] In one embodiment, obtaining a low-frequency initial model includes:

[0244] Acquire the first logging data, which is logging data at the depth domain scale;

[0245] The first logging data is processed by time-depth conversion to obtain the second logging data, which is logging data at the time domain scale.

[0246] Under the constraint of seismic horizon data, the initial shear wave impedance, initial anisotropic shear wave velocity, and initial shear wave velocity are calculated by back-calculation using the second well logging data.

[0247] Based on the initial shear wave impedance, initial anisotropic shear wave velocity, and initial shear wave velocity, a low-frequency initial model is obtained.

[0248] In one embodiment, the preset reflection coefficient is expressed as a three-term approximation:

[0249]

[0250] Where j represents the incident angle of the seismic wave, α is the first parameter, β is the second parameter, and ξ is the third parameter. It is the transverse wave reflection coefficient based on vertical and transverse isotropic properties.

[0251] In one embodiment, the coefficient matrix determined based on the seismic wave incident angle and a preset three-term approximate expression for the reflection coefficient includes:

[0252] Substitute the incident angle of the seismic wave into the coefficients of each term in the three pre-set approximate expressions for the reflection coefficient to obtain the reflection coefficients.

[0253] Based on the aforementioned reflection coefficients, a coefficient matrix is ​​obtained.

[0254] In one embodiment, the inversion of the incident angle seismic data based on the low-frequency initial model, measured seismic data, simulated seismic data, and forward modeling operator to obtain the inversion result includes:

[0255] Based on the simulated seismic data and the measured seismic data, the first coefficient is calculated;

[0256] Based on the inversion results of the previous inversion and the low-frequency initial model, the second coefficients are calculated;

[0257] Based on the first coefficient and the second coefficient, the regularization coefficient is calculated;

[0258] The forward modeling operator, regularization coefficient, low-frequency initial model, and simulated seismic data are substituted into the preset Bayesian inversion solution expression to solve for the inversion result.

[0259] In one embodiment, the preset Bayesian inversion solution expression is:

[0260]

[0261] in, Let μ be the mean of the model parameters, and μ be the regularization coefficient. As the first coefficient, is the second coefficient, and d is the simulated earthquake data.

[0262] In one embodiment, the inversion of the incident angle seismic data based on the low-frequency initial model, measured seismic data, simulated seismic data, and forward modeling operator to obtain the inversion result includes:

[0263] Based on the low-frequency initial model, measured seismic data, simulated seismic data, and forward modeling operator, the incident angle seismic data are inverted to obtain the inverted shear wave impedance, inverted anisotropic shear wave velocity, and inverted shear wave velocity.

[0264] Based on the inverted anisotropic shear wave velocity and the inverted shear wave velocity, the inverted anisotropic parameters are calculated according to the preset anisotropic parameter calculation formula.

[0265] Based on the inversion anisotropy parameters, inversion shear wave impedance, inversion anisotropic shear wave velocity, and inversion shear wave velocity, the inversion results are obtained.

[0266] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0267] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0268] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0269] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0270] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0271] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0272] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0273] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0274] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A pre-stack seismic inversion method, characterized in that, include: Acquire incident angle seismic data and a low-frequency initial model, and extract a mixed phase wavelet from the incident angle seismic data, which includes the incident angle of the seismic wave and the corresponding measured seismic data; Based on the incident angle of the seismic wave and the three approximate expressions of the preset reflection coefficient, the coefficient matrix is ​​determined. Based on the coefficient matrix and the hybrid phase wavelet, simulated seismic data and forward modeling operators are calculated using a preset forward modeling matrix; Based on the low-frequency initial model, measured seismic data, simulated seismic data, and forward modeling operator, the incident angle seismic data are inverted to obtain the inversion result. The preset reflection coefficient three-term approximation expression is an approximation expression for the transverse wave reflection coefficient based on vertical and transverse isotropic properties. The first parameter in the preset reflection coefficient three-term approximation expression is the transverse wave impedance, the second parameter is the anisotropic transverse wave velocity, and the third parameter is the transverse wave velocity.

2. The pre-stack seismic inversion method according to claim 1, characterized in that, Obtain the low-frequency initial model, including: Acquire the first logging data, which is logging data at the depth domain scale; The first logging data is processed by time-depth conversion to obtain the second logging data, which is logging data at the time domain scale. Under the constraint of seismic horizon data, the initial shear wave impedance, initial anisotropic shear wave velocity, and initial shear wave velocity are calculated by back-calculation using the second well logging data. Based on the initial shear wave impedance, initial anisotropic shear wave velocity, and initial shear wave velocity, a low-frequency initial model is obtained.

3. The pre-stack seismic inversion method according to claim 1, characterized in that, The three approximate expressions for the preset reflection coefficient are as follows: Where j represents the incident angle of the seismic wave, α is the first parameter, β is the second parameter, and ξ is the third parameter. It is the transverse wave reflection coefficient based on vertical and transverse isotropic properties.

4. The pre-stack seismic inversion method according to claim 1, characterized in that, The coefficient matrix is ​​determined based on the seismic wave incident angle and a preset three-term approximate expression for the reflection coefficient, including: Substitute the incident angle of the seismic wave into the coefficients of each term in the three pre-set approximate expressions for the reflection coefficient to obtain the reflection coefficients. Based on the aforementioned reflection coefficients, a coefficient matrix is ​​obtained.

5. The pre-stack seismic inversion method according to claim 1, characterized in that, The inversion of the incident angle seismic data based on the low-frequency initial model, measured seismic data, simulated seismic data, and forward modeling operator yields the inversion results, including: Based on the simulated seismic data and the measured seismic data, the first coefficient is calculated; Based on the inversion results of the previous inversion and the low-frequency initial model, the second coefficients are calculated; Based on the first coefficient and the second coefficient, the regularization coefficient is calculated; The forward modeling operator, regularization coefficient, low-frequency initial model, and simulated seismic data are substituted into the preset Bayesian inversion solution expression to solve for the inversion result.

6. The pre-stack seismic inversion method according to claim 5, characterized in that, The preset Bayesian inversion solution expression is: in, Let μ be the mean of the model parameters, and μ be the regularization coefficient. As the first coefficient, is the second coefficient, and d is the simulated earthquake data.

7. The pre-stack seismic inversion method according to claim 1, characterized in that, The inversion of the incident angle seismic data based on the low-frequency initial model, measured seismic data, simulated seismic data, and forward modeling operator yields the inversion results, including: Based on the low-frequency initial model, measured seismic data, simulated seismic data, and forward modeling operator, the incident angle seismic data are inverted to obtain the inverted shear wave impedance, inverted anisotropic shear wave velocity, and inverted shear wave velocity. Based on the inverted anisotropic shear wave velocity and the inverted shear wave velocity, the inverted anisotropic parameters are calculated according to the preset anisotropic parameter calculation formula. Based on the inversion anisotropy parameters, inversion shear wave impedance, inversion anisotropic shear wave velocity, and inversion shear wave velocity, the inversion results are obtained.

8. The pre-stack seismic inversion method according to claim 7, characterized in that, The formula for calculating the preset anisotropy parameters is as follows: Where β is the inverted anisotropic shear wave velocity, ξ is the inverted shear wave velocity, and σ is the inverted anisotropic parameter.

9. A pre-stack seismic inversion device, characterized in that, include: The acquisition module is used to acquire incident angle seismic data and a low-frequency initial model, and extract the mixed phase wavelet from the incident angle seismic data, wherein the incident angle seismic data includes the incident angle of the seismic wave and the corresponding measured seismic data; The coefficient module is used to determine the coefficient matrix based on the incident angle of the seismic wave and a preset three-term approximation expression for the reflection coefficient. The preset three-term approximation expression for the reflection coefficient is an approximation expression for the reflection coefficient of a transverse wave that is isotropic in both the vertical and transverse directions. The first parameter in the preset three-term approximation expression for the reflection coefficient is the transverse wave impedance, the second parameter is the anisotropic transverse wave velocity, and the third parameter is the transverse wave velocity. The forward modeling module is used to calculate simulated seismic data and forward modeling operators based on the coefficient matrix and the mixed phase wavelet using a preset forward modeling matrix; The inversion module is used to invert the incident angle seismic data based on the low-frequency initial model, measured seismic data, simulated seismic data, and forward modeling operators to obtain the inversion results.

10. The pre-stack seismic inversion device according to claim 9, characterized in that, The acquisition module includes: The first acquisition unit is used to acquire the first logging data, which is logging data at the depth domain scale. The conversion unit is used to perform time-depth conversion processing on the first logging data to obtain the second logging data, wherein the second logging data is logging data at the time domain scale; The first calculation unit is used to back-calculate the initial shear wave impedance, initial anisotropic shear wave velocity, and initial shear wave velocity using the second well logging data under the constraint of seismic horizon data. The confirmation unit is used to obtain a low-frequency initial model based on the initial shear wave impedance, initial anisotropic shear wave velocity, and initial shear wave velocity.

11. The pre-stack seismic inversion device according to claim 9, characterized in that, The three approximate expressions for the preset reflection coefficient are as follows: Where j represents the incident angle of the seismic wave, α is the first parameter, β is the second parameter, and ξ is the third parameter. It is the transverse wave reflection coefficient based on vertical and transverse isotropic properties.

12. The pre-stack seismic inversion device according to claim 9, characterized in that, The coefficient module includes: The coefficient unit is used to substitute the incident angle of the seismic wave into the coefficients of each term in the three-term approximate expression of the reflection coefficient to obtain the reflection coefficients. The matrix unit is used to obtain the coefficient matrix based on the aforementioned reflection coefficients.

13. The pre-stack seismic inversion device according to claim 9, characterized in that, The inversion module includes: The second calculation unit is used to calculate the first coefficient based on the simulated seismic data and the measured seismic data; The third calculation unit is used to calculate the second coefficient based on the inversion result of the previous inversion and the low-frequency initial model; The third calculation unit is used to calculate the regularization coefficient based on the first coefficient and the second coefficient; The solution unit is used to substitute the forward modeling operator, regularization coefficient, low-frequency initial model, and simulated seismic data into a preset Bayesian inversion solution expression to obtain the inversion result.

14. The pre-stack seismic inversion device according to claim 13, characterized in that, The preset Bayesian inversion solution expression is: in, Let μ be the mean of the model parameters, and μ be the regularization coefficient. As the first coefficient, is the second coefficient, and d is the simulated earthquake data.

15. The pre-stack seismic inversion device according to claim 9, characterized in that, The inversion module includes: The initial inversion unit is used to invert the incident angle seismic data based on the low-frequency initial model, measured seismic data, simulated seismic data and forward modeling operator to obtain the inverted shear wave impedance, inverted anisotropic shear wave velocity and inverted shear wave velocity. The fourth calculation unit is used to calculate the inversion anisotropic parameters based on the inversion anisotropic shear wave velocity and the inversion shear wave velocity, according to the preset anisotropic parameter calculation formula. The result unit is used to obtain the inversion result based on the inversion anisotropy parameters, inversion shear wave impedance, inversion anisotropic shear wave velocity, and inversion shear wave velocity.

16. The pre-stack seismic inversion device according to claim 15, characterized in that, The formula for calculating the preset anisotropy parameters is as follows: Where β is the inverted anisotropic shear wave velocity, ξ is the inverted shear wave velocity, and σ is the inverted anisotropic parameter.

17. An electronic device, characterized in that, The electronic device includes: At least one processor; A memory connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the at least one processor implements the pre-stack seismic inversion method according to any one of claims 1 to 8 by executing the instructions stored in the memory.

18. A machine-readable storage medium storing instructions thereon, characterized in that, When executed by a processor, this instruction causes the processor to be configured to perform the pre-stack seismic inversion method according to any one of claims 1 to 8.

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