Multi-wave joint inversion method and device, electronic equipment and storage medium

Abstract

The invention provides a multi-wave joint inversion method and device, electronic equipment and a storage medium. The multi-wave joint inversion method comprises the steps of determining a longitudinal wave-longitudinal wave angle gather and a longitudinal wave-transverse wave angle gather in a longitudinal wave-longitudinal wave angle gather time domain in seismic data; determining formation parameters of a wellhead of the receiving well, and determining an initial inversion model according to the formation parameters of the wellhead; according to the initial inversion model, determining a longitudinal wave-longitudinal wave synthesis record and a longitudinal wave-transverse wave synthesis record by using an accurate vertical transverse isotropic medium reflection coefficient equation; determining a real stratum model, determining relationships between the longitudinal wave-longitudinal wave synthesis record and the longitudinal wave-longitudinal wave angle gather and between the longitudinal wave-transverse wave synthesis record and the longitudinal wave-transverse wave angle gather by using a Bayesian framework, and determining an optimal model of the real stratum model by using a maximum posterior probability; and linearizing the accurate vertical and transverse isotropic medium reflection coefficient equation by using a Gaussian Newton method, determining the update quantity of the initial inversion model, and updating the initial inversion model by using the update quantity.

Description

Technical Field

[0001] This application relates to the field of stratigraphic exploration technology, and in particular to a multi-wave joint inversion method, apparatus, electronic device and storage medium. Background Technology

[0002] Amplitude Versus Offset Inversion (AVO) is an important technique in seismic exploration. It is primarily used to invert the physical and fluid parameters of a target reservoir or underlying medium by analyzing the variation of seismic wave amplitude with offset (shot-receiver distance). Compared to P-wave inversion alone, combined P-wave-P-wave and P-wave-S-wave AVO inversion can provide more accurate information on formation elastic parameters.

[0003] Currently, exploration is gradually shifting from the development of conventional oil and gas to the development of unconventional oil and gas with more complex geological features. This places higher demands on the accuracy of inversion methods. At the same time, for seismic exploration, it is equally important to achieve the inversion of complex strata. Summary of the Invention

[0004] In view of this, the purpose of this application is to provide a multi-wave joint inversion method, apparatus, electronic device and storage medium that overcomes or at least partially solves the above problems.

[0005] To achieve the above objectives, this application provides a first aspect of a multi-wave joint inversion method, comprising:

[0006] Identify the P-wave-P-wave angle gathers and P-wave-Sh-wave angle gathers located in the time domain of the P-wave-P-wave angle gathers in the seismic data;

[0007] Determine the elastic parameters and anisotropic parameters of the wellhead located at the receiving well, and determine the initial inversion model based on the elastic parameters and anisotropic parameters of the wellhead;

[0008] Based on the initial inversion model, the longitudinal wave-longitudinal wave composite record and the longitudinal wave-transverse wave composite record are determined using the precise vertical and transverse isotropic medium reflection coefficient equation.

[0009] The true stratigraphic model is determined, and the relationship between the P-wave-P-wave composite record and the P-wave-S-wave composite record and the P-wave-P-wave angle gather and the P-wave-S-wave angle gather is determined using a Bayesian framework. The optimal model of the true stratigraphic model is determined using the maximum a posteriori probability.

[0010] The exact vertical and horizontal isotropic medium reflection coefficient equation is linearized using the Gauss-Newton method to determine the update amount of the initial inversion model, and the initial inversion model is updated using the update amount.

[0011] In response to the updated initial inversion model, if the errors between the second P-wave composite record and the second P-wave composite record and the actual P-wave angle gather and the actual P-wave angle gather are less than a preset threshold, the parameters of the currently updated initial inversion model are output.

[0012] Optionally, the elastic parameters of the wellhead include: P-wave velocity, S-wave velocity, and formation density at the wellhead location; the anisotropic parameters of the wellhead include: anisotropic parameters describing the difference between horizontal and vertical propagation S-wave velocities and anisotropic parameters describing the difference between vertical and horizontal P-wave velocities.

[0013] The process of determining the elastic parameters and anisotropy parameters at the wellhead of the receiving well, and determining the initial inversion model based on the elastic parameters and anisotropy parameters at the wellhead, includes:

[0014] Determine the P-wave velocity, S-wave velocity, formation density, anisotropy parameters describing the difference between horizontal and vertical propagation S-wave velocities, and anisotropy parameters describing the difference between vertical and horizontal P-wave velocities at the wellhead location of the receiving well.

[0015] Identify a formation that is far from the wellhead and in the same formation as the formation at the wellhead, and synchronize the parameters of this formation to the same time domain as the formation at the wellhead, including the P-wave velocity, S-wave velocity, formation density, anisotropic parameters describing the difference between horizontal and vertical propagation S-wave velocity, and anisotropic parameters describing the difference between vertical and horizontal P-wave velocity, to determine the initial inversion model.

[0016] Optionally, based on the initial inversion model, and using the precise vertical-transverse isotropic medium reflection coefficient equation, the P-wave composite record and the P-wave composite record are determined, including:

[0017] The reflection coefficients of the initial inversion model are determined using the precise vertical and horizontal isotropic medium coefficient reflection equation;

[0018] Extract the P-wave-P-wave angle gather and the P-wave-S-wave angle gather respectively, and determine the P-wave-P-wave wavelet matrix and the P-wave-S-wave wavelet matrix;

[0019] The P-wave-P-wave wavelet matrix and the P-wave-S-wave wavelet matrix are convolved with the reflection coefficients respectively to determine the P-wave-P-wave composite record and the P-wave-S-wave composite record.

[0020] Optionally, a true stratigraphic model is determined, and the relationships between the P-wave-P-wave composite records and the P-wave-S-wave composite records and the P-wave-P-wave angle gathers and the P-wave-S-wave angle gathers are determined using a Bayesian framework. The optimal model of the initial inversion model is then determined using the maximum a posteriori probability, including:

[0021] Determine the true stratigraphic model and determine the prior distribution satisfied by the true stratigraphic model;

[0022] Determine the likelihood function of the parameters of the real stratigraphic model under the P-P wave composite record and the P-P wave composite record;

[0023] Based on the prior distribution and the likelihood function, determine the posterior distribution probability of the real stratigraphic model under the P-wave-P-wave composite record and the P-wave-S-wave composite record;

[0024] The optimal model of the real stratigraphic model is determined using the posterior probability distribution.

[0025] Optionally, before determining the posterior distribution probability of the real stratigraphic model under the P-wave-P-wave composite record and the P-wave-S-wave composite record based on the prior distribution and the likelihood function, the method further includes:

[0026] The Cauchy distribution is used to describe the parameter distribution of the optimal model.

[0027] Optionally, the exact vertical-to-lateral isotropic medium reflection coefficient equation is linearized using the Gauss-Newton method to determine the update amount of the initial inversion model, and the initial inversion model is updated using the update amount, including:

[0028] The exact vertical and transverse isotropic medium reflection coefficient equation is expanded using Taylor expansion at the initial inversion model;

[0029] Determine the logarithm of the posterior distribution probability of the real stratigraphic model under the P-P wave composite record and the P-S wave composite record;

[0030] Substituting the Taylor expansion into the logarithm, the maximum value of the posterior distribution probability of the optimal model under the P-wave-P-wave composite record and the P-wave-S-wave composite record is determined;

[0031] Determine the error weights of the P-wave-P-wave angle gather and the P-wave-S-wave angle gather for the initial inversion model and the optimal model;

[0032] The update amount of the initial inversion model is determined based on the error weights and the maximum value.

[0033] Optionally, the error includes a first error and a second error;

[0034] In response to the updated initial inversion model determining that the errors between the second P-P composite record and the second P-S composite record and the actual P-P angle gather and the actual P-S angle gather are less than a preset threshold, the parameters of the currently updated initial inversion model are output, and the model further includes:

[0035] The first error between the second P-wave composite record and the actual P-wave angle gather was determined by forward modeling using the updated initial inversion model.

[0036] The second error between the second P-wave-S-wave composite record and the actual P-wave-S-wave angle gather was determined by forward modeling using the updated initial inversion model.

[0037] In response to the sum of the first error and the second error being greater than the preset threshold, the second P-wave-P-wave composite record and the second P-wave-S-wave composite record are used as the P-wave-P-wave composite record and the P-wave-S-wave composite record to repeatedly determine the updated initial inversion model until the sum of the first error and the second error is less than the preset threshold.

[0038] A second aspect of this application provides a multi-wave joint inversion device, comprising:

[0039] The determination module is used to determine the P-wave-P-wave angle gathers and P-wave-S-wave angle gathers located in the time domain of the P-wave-P-wave angle gathers in the seismic data;

[0040] The initial inversion module is used to determine the elastic parameters and anisotropy parameters at the wellhead of the receiving well, and to determine the initial inversion model based on the elastic parameters and anisotropy parameters at the wellhead.

[0041] The synthetic recording module is used to determine the P-wave-P-wave synthetic recording and the P-wave-S-wave synthetic recording based on the initial inversion model and using the precise vertical and transverse isotropic medium reflection coefficient equation.

[0042] The target module is used to determine the true stratigraphic model. It uses a Bayesian framework to determine the relationship between the P-wave-P-wave composite record and the P-wave-S-wave composite record and the P-wave-P-wave angle gather and the P-wave-S-wave angle gather, respectively. It uses the maximum a posteriori probability to determine the optimal model of the true stratigraphic model.

[0043] The update module is used to linearize the exact vertical and horizontal isotropic medium reflection coefficient equation using the Gauss-Newton method, determine the update amount of the initial inversion model, and update the initial inversion model using the update amount.

[0044] The output module is used to determine whether the error between the updated initial inversion model and the second P-wave composite record and the second P-wave composite record determined by forward modeling is less than a preset threshold, and outputs the parameters of the currently updated initial inversion model.

[0045] A third aspect of this application provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method described in the first aspect.

[0046] A fourth aspect of this application provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the method described in the first aspect.

[0047] As can be seen from the above, the multi-wave joint inversion method, apparatus, electronic equipment, and storage medium provided in this application, based on the accurate vertical and transverse isotropic medium reflection coefficient equation, determine the optimal model of the real stratigraphic model through Cauchy constraints within a Bayesian framework, and solve the generalized nonlinear inversion problem using the Gauss-Newton method to determine the update amount of the initial inversion model, thereby achieving iterative processing of the initial inversion model. This enables accurate inversion of P-wave-P-wave gathers and P-wave-S-wave gathers.

[0048] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in this application or related technologies, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 This is a flowchart of the multi-wave joint inversion method 100 according to an embodiment of this application;

[0051] Figure 2 This is a flowchart illustrating the process of determining the P-wave-P-wave composite record and the P-wave-S-wave composite record in an embodiment of this application.

[0052] Figure 3 This is a flowchart illustrating the process of determining the optimal model in an embodiment of this application.

[0053] Figure 4 A flowchart illustrating the determination of the update amount of the initial inversion model in an embodiment of this application;

[0054] Figure 5 This is a flowchart illustrating how to determine whether the initial inversion model has been updated, as described in this application embodiment.

[0055] Figure 6 This is a schematic diagram of a longitudinal wave-longitudinal wave synthesis record and a longitudinal wave-transverse wave synthesis record according to an embodiment of this application;

[0056] Figure 7 This is the noiseless model inversion result of an embodiment of this application;

[0057] Figure 8 This is a schematic diagram of a longitudinal wave-longitudinal wave synthesis record and a longitudinal wave-transverse wave synthesis record with a signal-to-noise ratio of 30dB, according to an embodiment of this application.

[0058] Figure 9 This is a schematic diagram of the signal-to-noise ratio (SNR) inversion result of an embodiment of this application;

[0059] Figure 10 This is a schematic diagram of a longitudinal wave-longitudinal wave synthesized record and a longitudinal wave-transverse wave synthesized record with a signal-to-noise ratio of 23dB, according to an embodiment of this application.

[0060] Figure 11 This is a schematic diagram of the signal-to-noise ratio (SNR) inversion result of an embodiment of this application;

[0061] Figure 12 This is a schematic diagram illustrating the calibration of the longitudinal wave-longitudinal wave composite record and the longitudinal wave-semi-transverse wave composite record in an embodiment of this application.

[0062] Figure 13 This is a schematic diagram of the well bypass inversion results according to an embodiment of this application;

[0063] Figure 14 This is a schematic diagram of the initial inversion model of an embodiment of this application;

[0064] Figure 15 This is a schematic diagram of the inversion result of the initial inversion model in an embodiment of this application;

[0065] Figure 16 This is a schematic diagram of a multi-wave joint inversion device according to an embodiment of this application;

[0066] Figure 17 This is a schematic diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0067] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with specific embodiments and the accompanying drawings.

[0068] It should be noted that, unless otherwise defined, the technical or scientific terms used in the embodiments of this application should have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms "first," "second," and similar terms used in the embodiments of this application do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are only used to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0069] Primary (P) wave: Seismic waves are triggered from the source in the form of primary (P) waves, and after being reflected from the subsurface interface, they return to the detector in the form of primary waves.

[0070] P-wave-S-wave (PS wave): Seismic waves are triggered from the source in the form of primary (P) waves, and after being reflected by the subsurface interface, they are converted into secondary (S) waves and return to the detector.

[0071] As described in the background section, current seismic and oil and gas exploration demands higher accuracy in inversion methods. Vertical Transverse Isotropy (VTI) media are widely present in the Earth's crust, especially in reservoirs; when the layer thickness is much smaller than the wavelength of seismic waves, these layered reservoirs exhibit significant VTI characteristics. To quantify the intensity of formation anisotropy, two dimensionless Thomsen parameters, δ and ε, are typically used to describe it. The Greabner exact reflection coefficient equation for VTI media is complex and highly nonlinear, making it difficult to apply directly to inversion. Therefore, most current inversion methods are based on less accurate approximations. Inversion methods based on the exact reflection coefficient equation, such as stepwise inversion and generalized nonlinear inversion methods, suffer from problems such as ambiguous stepwise determinations and slow convergence speeds.

[0072] Based on this, refer to Figure 1 As shown in the embodiment of this application, a multi-wave joint inversion method 100 is provided to achieve direct inversion of P-wave velocity, S-wave velocity, formation density, and anisotropy parameters δ and ε for P-wave-P-wave and P-wave-S-wave AVO (Amplitude Variation with Offset) gathers.

[0073] Method 100 begins with step S101, identifying the P-wave-P-wave angle gathers and P-wave-S-wave angle gathers in the seismic data that are located in the time domain of the P-wave-P-wave angle gathers.

[0074] In the embodiments of this application, the main objective is to perform joint inversion of pre-stack imaging gathers of seismic waves, that is, to invert the P-wave-P-wave and P-wave-S-wave angle gathers in the seismic waves, so as to achieve direct inversion of P-wave velocity, S-wave velocity, formation density, and anisotropy parameters δ and ε.

[0075] Then, in step S102, the elastic parameters and anisotropy parameters at the inlet of the receiving well are determined, and the initial inversion model is determined based on the elastic parameters and anisotropy parameters at the wellhead.

[0076] In some implementations, the elastic parameters of the wellhead include: P-wave velocity, S-wave velocity, and formation density at the wellhead location; the anisotropic parameters of the wellhead include: anisotropic parameter δ describing the difference between horizontal and vertical propagation S-wave velocities and anisotropic parameter ε describing the difference between vertical and horizontal P-wave velocities.

[0077] The initial model is determined by using the P-wave velocity, S-wave velocity, formation density, anisotropic parameter δ describing the difference between horizontal and vertical propagation of S-wave velocity, and anisotropic parameter ε describing the difference between vertical and horizontal P-wave velocity at the wellhead location of the receiving well as constraints.

[0078] Specifically, the P-wave velocity, S-wave velocity, formation density, anisotropic parameters describing the difference between horizontal and vertical propagation S-wave velocities, and anisotropic parameters describing the difference between vertical and horizontal P-wave velocities at the wellhead location of the receiving well are determined. Then, a formation located away from the wellhead but in the same formation as the formation at the wellhead is determined, and the parameters of this formation are synchronized to the same time domain as the P-wave velocity, S-wave velocity, formation density, anisotropic parameters describing the difference between horizontal and vertical propagation S-wave velocities, and anisotropic parameters describing the difference between vertical and horizontal P-wave velocities of the formation at the wellhead, thus determining the initial inversion model.

[0079] For example, if there is a formation at 1s from the wellhead, determine its location and elastic parameters. At the same time, determine that the formation at 1.1s away from the wellhead is the same as the formation at the wellhead. By synchronizing the formation at 1.1s to 1s, determine the elastic parameters of the formation at 1.1s. And so on, determine the initial inversion model.

[0080] In step S103, based on the initial inversion model, the longitudinal wave-longitudinal wave composite record and the longitudinal wave-transverse wave composite record are determined using the precise vertical and transverse isotropic medium reflection coefficient equation.

[0081] Specifically, such as Figure 2 As shown, step S103 includes:

[0082] S201. The reflection coefficient of the initial inversion model is determined using the reflection equation of the precise vertical and horizontal isotropic medium coefficient.

[0083] S202. Extract the P-wave wavelets and P-wave wavelets from the P-wave angle gather and the P-wave angle gather, respectively, and determine the P-wave wavelet matrix and the P-wave wavelet matrix.

[0084] By extracting the P-P wavelet and P-S wavelet sub-wave from the P-P wave angle gather and the P-S wave angle gather respectively, the P-P wavelet matrix and the P-S wavelet matrix are determined. This facilitates the convolution with the reflection coefficient, directly transforming it into a multiplication operation, which helps in the subsequent determination of the P-P wave composite record and the P-S wave composite record.

[0085] It should be noted that the P-wave-P-wave sub-matrix and the P-wave-S-wave sub-matrix can be determined directly in step S101 by extracting the P-wave-P-wave sub-matrix and the P-wave-S-wave sub-matrix from the P-wave-P-wave angle gather and the P-wave-S-wave angle gather.

[0086] S203. Convolve the P-wave wavelet matrix and the P-wave wavelet matrix with the reflection coefficient respectively to determine the P-wave composite record and the P-wave wavelet composite record.

[0087] In some implementations, the P-wave composite record and the P-wave composite record can be represented as:

[0088]

[0089] Among them, S PP and S PS These represent the P-P combined record and the P-S combined record, respectively. m0 represents the model parameters of the initial inversion model, and R... PP and R PS The reflection coefficient is determined by the equation for the reflection coefficient of an isotropic medium in both the exact vertical and transverse directions, where n represents random noise in the data; W PP and W PS Both the P-wave and P-wave wavelet matrices of length l can be represented as:

[0090]

[0091] Where 0, 1, 2...M, L are all data of the longitudinal wave-long wavelet matrix or the longitudinal wave-transverse wavelet matrix.

[0092] In step S104, the true stratigraphic model is determined, and the relationship between the P-wave-P-wave composite record and the P-wave-S-wave composite record and the P-wave-P-wave angle gather and the P-wave-S-wave angle gather is determined using the Bayesian framework. The optimal model of the true stratigraphic model is determined using the maximum a posteriori probability.

[0093] It is understandable that in the process of determining the P-wave angle gathers and P-wave angle gathers, the actual strata are determined directly through seismic data, and the actual strata model is determined.

[0094] Specifically, such as Figure 3 As shown, step S104 includes:

[0095] S301. Determine the true stratigraphic model and the prior distribution satisfied by the true stratigraphic model;

[0096] S302. Determine the likelihood function of the actual stratigraphic model parameters under the P-wave-P-wave composite record and the P-wave-S-wave composite record.

[0097] S303. Based on the prior distribution and likelihood function, determine the posterior distribution probability of the real stratigraphic model under the P-wave-P-wave composite record and the P-wave-S-wave composite record.

[0098] S304. Use the posterior probability distribution to determine the optimal model of the real stratigraphic model.

[0099] In some embodiments, the posterior probability distribution of the real stratigraphic model under P-P wave composite records and P-P wave composite records can be expressed as:

[0100]

[0101] Where X represents P or S, i.e. S PX It can be S PP It can also be S PS p(m) represents the prior distribution satisfied by the real stratigraphic model. Its probability distribution is independent of the seismic data, and different probability distributions need to be selected according to different inversion requirements.

[0102] p(m|S PX ) indicates that given data S PX Given the condition that the likelihood function of parameter m represents the probability distribution of seismic data obtained under the condition of the real stratigraphic model m, and its probability distribution is only related to its own noise; that is, S PX For S PP In the case of p(m|S) PX ) represents the likelihood function of the actual stratigraphic model parameters recorded in the P-wave-P-wave composite record. PX For S PS In the case of p(m|S) PXp(S) represents the likelihood function of the actual stratigraphic model parameters recorded by the P-wave-S-wave composite record. PX p(m|S) represents a normalized constant independent of m. PX ) represents the seismic data S that needs to be obtained. PX The posterior distribution probability of the m-parameter of the real stratigraphic model under the given conditions, i.e., the posterior distribution probability of the real stratigraphic model under P-P wave composite records and P-P wave composite records.

[0103] Furthermore, equation (3) can be simplified to:

[0104] p(m|S PX )∝p(S PX |m)p(m), (4)

[0105] In some embodiments, the initial inversion model is used to determine the computational data, and the error between the trace collections of the computational data and the trace collections of the real data conforms to a normal distribution. The likelihood function can be written as follows:

[0106]

[0107] Similarly, W PX It can be W PP It can also be W PS R PX For R PP It can also be R PS Seismic records are obtained through the seismic wavelet matrix W. PX (including the P-wave wavelet matrix W) PP and the P-wave / S-wave sub-wavelet matrix W PS ) and reflection coefficient R PX (including R) PP and R PS The fold is determined. (D) PX Represents actual data, δ err M represents the standard deviation of the error, and M+N represents the total number of P-wave-P-wave and P-wave-S-wave gathers.

[0108] It is understandable that both the calculated data and the actual data are P-wave-P-wave wavelet matrices and P-wave-S-wave wavelet matrices, where the calculated data is determined by the initial inversion model, while the actual data is determined by step S202.

[0109] In some embodiments, to improve the sparsity of the inversion results and increase the probability of maxima and minima, the Cauchy distribution is used to describe the parameter distribution of the optimal model of the real stratigraphic model, i.e.

[0110]

[0111] Where, δ mC represents the standard deviation of the optimal model. st U is a constant, n represents the number of sampling points, u i writing:

[0112]

[0113] δ1, δ2, δ3, δ4, and δ5 represent the actual longitudinal wave velocities V. P Shear wave velocity V S The formation density ρ, the anisotropy parameter δ describing the difference in shear wave velocity between horizontal and vertical propagation, and the standard deviation of the anisotropy parameter ε describing the difference in P-wave velocity between the vertical and horizontal directions are given. Δm represents the perturbation of the optimal model. The actual P-wave velocity V is also mentioned above. P Shear wave velocity V S The standard deviations of formation density ρ, anisotropy parameter δ describing the difference in horizontal and vertical propagation shear wave velocity, and anisotropy parameter ε describing the difference in vertical and horizontal P-wave velocity can be determined using a real wellhead model.

[0114] Based on the Cauchy distribution, the parameter distribution and likelihood function of the optimal model are described, and the posterior distribution probability of the real stratigraphic model is further determined.

[0115] Substitute formulas (5) and (6) into formula (4) and determine:

[0116]

[0117] Taking its logarithm gives us:

[0118]

[0119] In step S105, the exact vertical and horizontal isotropic medium reflection coefficient equation is linearized using the Gauss-Newton method to determine the update amount of the initial inversion model, and the initial inversion model is updated using the update amount.

[0120] Specifically, such as Figure 4 As shown, step S105 includes:

[0121] S501. Perform Taylor expansion on the equation for the reflection coefficient of the precise vertical and horizontal isotropic medium at the initial inversion model.

[0122] That is, for the reflection coefficient equation R PX(m) Performing a Taylor expansion at m = m0 yields:

[0123] R PX (θ i ,m0+Δm)=R PX0 (θ i ,m0)+GPX (θ i ,m)Δm, (10)

[0124] Where m0 is the parameter of the initial inversion model, Δm = [ΔV P , ΔV S , Δρ, Δδ, Δε], indicating the update amount, G PX (m) represents the Jacobian matrix, expressed as:

[0125]

[0126] in,

[0127]

[0128]

[0129] Among them, A PX (θ i ,m), B PX (θ i ,m), C PX (θ i ,m),D PX (θ i ,m) and E PX (θ i ,m) are the gradient matrices reflecting the reflection coefficient values ​​as a function of P-wave velocity in the initial model, the gradient matrices reflecting the reflection coefficient values ​​as a function of S-wave velocity in the initial model, the gradient matrices reflecting the reflection coefficient values ​​as a function of formation density in the initial model, the gradient matrices reflecting the reflection coefficient values ​​as a function of anisotropic parameters describing the difference between horizontal and vertical propagation S-wave velocities in the initial model, and the gradient matrices reflecting the reflection coefficient values ​​as a function of anisotropic parameters describing the difference between vertical and horizontal P-wave velocities in the initial model.

[0130] To obtain the partial derivatives of the reflection coefficient with respect to each parameter, i.e. the actual P-wave velocity V P Shear wave velocity V S The partial derivatives of the formation density ρ, the anisotropy parameter δ describing the difference in shear wave velocity between horizontal and vertical propagation, and the anisotropy parameter ε describing the difference in P-wave velocity between the vertical and horizontal directions are solved using the perturbation method. For the optimal model with n sampling points, there are n-1 reflecting masks. Taking the derivative of the j-th reflection coefficient with respect to the P-wave velocity as an example, the partial derivative can be expressed as:

[0131]

[0132] Among them, I (1) and I (2)These represent disturbances to the upper and lower layers of the j-th reflective interface, respectively; m (1) and m (2) dV represents the parameters of the optimal model at the upper and lower layers of the interface, respectively. P This represents the perturbation amount of the optimal model, and is a small value.

[0133] S502. Determine the logarithm of the posterior probability distribution of the optimal model under both P-wave-P-wave composite records and P-wave-S-wave composite records.

[0134] Substituting formula (10) into formula (9), the logarithmic representation of the posterior probability distribution of the optimal model under the P-wave-P-wave composite record and the P-wave-S-wave composite record is obtained as follows:

[0135]

[0136] S503. Substitute the Taylor expansion into the logarithm to determine the maximum value of the posterior distribution probability of the optimal model under the P-wave-P-wave composite record and the P-wave-S-wave composite record.

[0137] To solve for the optimal model posterior distribution P(m|S) PX To find the maximum value of ), take the derivative of the model parameter m in formula (18) with respect to the derivative and set the derivative to 0, we can obtain:

[0138] [(W PX *G PX ) T (W PX *G PX )+λQ]Δm=(W PX *G PX ) T (D PX -W PX *R PX ), (19)

[0139] Where λ is a constant, T denotes the transpose of the matrix, and Q denotes a diagonal weight matrix of dimension (5n×5n) with diagonal elements as follows:

[0140]

[0141] S504. Determine the error weights ω for the P-wave angle gathers and P-wave angle gathers of the initial inversion model and the optimal model.

[0142] S505. Determine the update amount of the initial inversion model based on the error weights and the maximum value.

[0143]

[0144] The updated initial inversion model is then expressed as:

[0145] m k+1 =m k +η k Δm k , (twenty two)

[0146] Where k represents the k-th update, η k This represents the step size of the k-th update.

[0147] Then, in step S106, in response to the forward modeling determination of the updated initial inversion model, if the error between the second P-wave composite record and the second P-wave composite record and the actual P-wave angle gather and the actual P-wave angle gather is less than a preset threshold, the parameters of the currently updated initial inversion model are output.

[0148] The purpose of this application embodiment is to improve the inversion accuracy through multi-wave joint inversion method. Therefore, after obtaining the updated initial inversion model, the second P-wave composite record and the second P-wave-semi-wave composite record obtained by forward modeling must both satisfy the error between the actual P-wave angle gather and the actual P-wave angle gather and the actual P-wave angle gather is less than a preset threshold, indicating that the current updated initial inversion model satisfies the conditions of multi-wave joint inversion.

[0149] In some embodiments, such as Figure 5 As shown, step S106 further includes:

[0150] S601. Using the updated initial inversion model, determine the first error between the second P-wave composite record and the actual P-wave angle gather by forward modeling.

[0151] S602. Using the updated initial inversion model, determine the second error between the second P-wave-S-wave composite record and the actual P-wave-S-wave angle gather by forward modeling.

[0152] S603. In response to the sum of the first error and the second error being greater than the preset threshold, the updated initial inversion model is repeatedly determined using the second longitudinal wave-longitudinal wave composite record and the second longitudinal wave-transverse wave composite record as longitudinal wave-longitudinal wave composite record and longitudinal wave-transverse wave composite record, until the sum of the first error and the second error is less than the preset threshold.

[0153] It should be noted that, when the sum of the first error and the second error is used, the second longitudinal wave-longitudinal wave composite record and the second longitudinal wave-semi-transverse wave composite record are used as the longitudinal wave-longitudinal wave composite record and the longitudinal wave-semi-transverse wave composite record, that is, return to step S103 to update the updated initial inversion model again.

[0154] Table 1 below shows the parameter table for the four-layer model.

[0155]

[0156] Among them, V P V S ρ, δ, and ε represent the P-wave velocity, S-wave velocity, formation density, anisotropy parameter describing the difference in S-wave velocity between horizontal and vertical propagation, and anisotropy parameter describing the difference in P-wave velocity between the vertical and horizontal directions, respectively, of the actual formation model. The actual formation model and the wellhead model are the same model. Since it is a real formation model, the above parameters V... P V S ρ, δ, and ε are to be inverted, but are known. The accuracy of the inversion model is determined by comparing the inversion results with the known parameters.

[0157] To test the adaptability of Method 100 to P-P wave composite records and P-S wave composite records, this embodiment establishes a one-dimensional strong impedance and strong anisotropy model. The lithological parameters and anisotropy parameters are shown in Table 1, where L1 is a mudstone stratum, L2 and L4 are limestone strata, and L3 is a sandstone stratum; all four strata exhibit strong anisotropy. 30Hz and 25Hz Ricker wavelets were selected as wavelets for the PP and PS composite records, respectively. The accurate reflection coefficient of the VTI medium was obtained using the exact Greabner formula. The sampling rate was 1ms, and the angle ranged from 0° to 42°, with 3° intervals, for a total of 15 channels. Noise and interlayer multiple interferences were not considered in the forward modeling. The resulting composite record is shown below. Figure 6 As shown, where, Figure 6 (a) shows the P-wave composite record. Figure 6 (b) shows the P-wave-S-wave composite record in the P-wave time domain. An actual stratigraphic model was used as prior information for inversion. In the actual stratigraphic model test, a weighting factor ω of 0.5 was selected.

[0158] The inversion results of the noise-free model are as follows Figure 7 As shown, the blue, red, and black lines represent the initial inversion model, the inversion result, and the true inversion model, respectively. After four iterations, the errors between the four parameters and the true stratigraphic model are close to zero, and subsequent iterations can hardly further correct the inversion results. The model inversion test demonstrates that the inversion method is relatively accurate for strongly anisotropic layered models.

[0159] In the noise-added test, the synthesized record with a signal-to-noise ratio of 30dB was first used as the target gather for inversion. Figure 8 ), Figure 8 In the diagram, (a) and (b) represent the inverted P-P wave composite record and the P-P wave composite record in the P-P wave time domain, respectively. The inversion results are as follows: Figure 9As shown, the blue, black, and red lines represent the initial inversion model, the actual stratigraphic model, and the inversion results, respectively. The inversion results for P-wave velocity, S-wave velocity, and stratigraphic density remain consistent with the actual model even with added noise from the angle gather. However, the anisotropy parameters show a more pronounced response to noise; δ exhibits more noticeable spikes in the inversion of gather data with a 30 dB signal-to-noise ratio; ε shows greater stability compared to δ, with only minor fluctuations at the interfaces, and the parameter values ​​at each layer still reflect the actual stratigraphic model.

[0160] In a noise test where the signal-to-noise ratio dropped to 23 dB, the synthesized record and inversion results after adding noise are as follows: Figure 10 and Figure 11 As shown. Figure 10 In the figure, (a) and (b) represent the P-P wave composite record and the P-P wave composite record in the P-P wave time domain, respectively. Figure 11 The blue, red, and black lines in the diagram represent the initial inversion model, the inversion results, and the actual stratigraphic model, respectively. In the low signal-to-noise ratio (SNR) inversion results, the inversion results for P-wave velocity, S-wave velocity, and stratigraphic density remain relatively stable, and the stratigraphic division is consistent with the actual model. The parameter values ​​of each stratigraphic layer have only a small error compared to the actual stratigraphic model. In the low SNR data inversion, δ shows increased spikes, but it can still invert the variation trend of each stratigraphic layer. ε is more stable than δ, and although it fluctuates at stratigraphic interfaces, it still clearly characterizes the stratigraphic layers.

[0161] In actual data testing, well logging data was used as the real model, smoothed well data as the initial model, and well data was used as a constraint for prior information. The calibration of P-wave-P-wave composite records and P-wave-S-wave composite records are as follows: Figure 12 As shown in (a) and (b) in the figure. The well bypass inversion results are as follows. Figure 13 As shown, lines 13 represent the inverted P-wave velocity, S-wave velocity, anisotropic parameters describing the difference between horizontal and vertical propagation S-wave velocity, and anisotropic parameters describing the difference between vertical and horizontal P-wave velocity, respectively. The blue line represents the initial inversion model, the red line represents the inversion result, and the black line represents the actual formation model. The inversion results show high consistency with the wellhead model and can accurately reproduce the actual formation characteristics.

[0162] The formation parameters at the wellhead are smoothed, and key stratigraphic levels are used as constraints to establish an initial model of the profile. Figure 14 ),in Figure 14 (a), (b), (c), (d), and (e) represent the P-wave velocity, S-wave velocity, anisotropy parameter describing the difference between horizontal and vertical propagation S-wave velocities, and anisotropy parameter describing the difference between vertical and horizontal P-wave velocities in the initial inversion model, respectively; the inversion results are as follows: Figure 15 As shown, Figure 15 (a), (b), (c), (d), and (e) represent the results of the P-wave velocity, S-wave velocity, anisotropy parameter describing the difference between horizontal and vertical propagation S-wave velocity, and anisotropy parameter describing the difference between vertical and horizontal P-wave velocity obtained from the initial inversion model, respectively. Under the constraints of the Bayesian framework, the anisotropy parameter values ​​are within a reasonable range. The inversion results of Thomsen anisotropy parameters δ and ε are more sensitive and have higher resolution than those of impedance. In this work area, the lithology is relatively homogeneous within the target time window of 1200ms-1500ms, mainly consisting of limestone with well-developed fractures. Within the gas-bearing target layer time range of 1300ms-1500ms, the inversion results of both anisotropy parameters clearly depict the reservoir location with higher anisotropy than the surrounding rock. The stratigraphic correlation is consistent with the range of gas-bearing layers in actual mining. It is believed that this method has achieved good results in practical applications to formations with strong impedance and strong anisotropy parameters in this area.

[0163] It should be noted that the method in this embodiment can be executed by a single device, such as a computer or server. The method can also be applied in a distributed scenario, where multiple devices cooperate to complete the task. In such a distributed scenario, one of these devices may execute only one or more steps of the method in this embodiment, and the multiple devices will interact with each other to complete the method described.

[0164] It should be noted that the above description describes some embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in a different order than that shown in the above embodiments and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0165] Based on the same technical concept, corresponding to any of the above embodiments, this application also provides a multi-wave joint inversion device.

[0166] refer to Figure 16 The multi-wave joint inversion device includes:

[0167] The determination module 1601 is used to determine the P-wave-P-wave angle gathers and P-wave-S-wave angle gathers located in the time domain of the P-wave-P-wave angle gathers in the seismic data.

[0168] The initial inversion module 1602 is used to determine the elastic parameters and anisotropy parameters of the wellhead located at the receiving well, and to determine the initial inversion model based on the elastic parameters and anisotropy parameters of the wellhead.

[0169] The synthetic recording module 1603 is used to determine the longitudinal wave-longitudinal wave synthetic record and the longitudinal wave-transverse wave synthetic record based on the initial inversion model and the precise vertical and transverse isotropic medium reflection coefficient equation.

[0170] The target module 1604 is used to determine the true stratigraphic model. It uses a Bayesian framework to determine the relationship between the P-wave-P-wave composite record and the P-wave-S-wave composite record and the P-wave-P-wave angle gather and the P-wave-S-wave angle gather, respectively. It uses the maximum a posteriori probability to determine the optimal model of the true stratigraphic model.

[0171] The update module 1605 is used to linearize the exact vertical and horizontal isotropic medium reflection coefficient equation using the Gauss-Newton method, determine the update amount of the initial inversion model, and update the initial inversion model using the update amount.

[0172] The output module 1606 is used to determine whether the error between the updated initial inversion model and the second P-wave composite record and the second P-wave composite record and the actual P-wave angle gather and the actual P-wave angle gather is less than a preset threshold, and outputs the parameters of the currently updated initial inversion model.

[0173] For ease of description, the above devices are described in terms of function, divided into various modules. Of course, in implementing this application, the functions of each module can be implemented in one or more software and / or hardware.

[0174] The apparatus of the above embodiments is used to implement the corresponding multi-wave joint inversion method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0175] Based on the same technical concept, corresponding to the methods of any of the above embodiments, this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the multi-wave joint inversion method described in any of the above embodiments.

[0176] Figure 17This embodiment illustrates a more specific hardware structure of an electronic device, which may include a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, memory 1020, input / output interface 1030, and communication interface 1040 are interconnected internally via the bus 1050.

[0177] The processor 1010 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0178] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0179] The input / output interface 1030 is used to connect input / output modules to realize information input and output. Input / output modules can be configured as components within the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touchscreens, microphones, various sensors, etc., while output devices may include displays, speakers, vibrators, indicator lights, etc.

[0180] The communication interface 1040 is used to connect a communication module (not shown in the figure) to enable communication between this device and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0181] Bus 1050 includes a pathway for transmitting information between various components of the device, such as processor 1010, memory 1020, input / output interface 1030, and communication interface 1040.

[0182] It should be noted that although the above-described device only shows the processor 1010, memory 1020, input / output interface 1030, communication interface 1040, and bus 1050, in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the embodiments of this specification, and not necessarily all the components shown in the figures.

[0183] The electronic devices described above are used to implement the corresponding multi-wave joint inversion method in any of the foregoing embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0184] Based on the same technical concept, corresponding to the methods of any of the above embodiments, this application also provides a non-transitory computer-readable storage medium that stores computer instructions for causing the computer to execute the multi-wave joint inversion method as described in any of the above embodiments.

[0185] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules, 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 magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0186] The computer instructions stored in the storage medium of the above embodiments are used to cause the computer to execute the multi-wave joint inversion method as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0187] Based on the same inventive concept, corresponding to the multi-wave joint inversion method described in any of the above embodiments, this disclosure also provides a computer program product, which includes computer program instructions. In some embodiments, the computer program instructions can be executed by one or more processors of a computer to cause the computer and / or the processor to perform the multi-wave joint inversion method. Corresponding to the execution entity for each step in each embodiment of the multi-wave joint inversion method, the processor executing the corresponding step can belong to the corresponding execution entity.

[0188] The computer program product of the above embodiments is used to cause the computer and / or the processor to execute the multi-wave joint inversion method as described in any of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0189] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of this application (including the claims) is limited to these examples; within the framework of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the embodiments of this application as described above, which are not provided in the details for the sake of brevity.

[0190] Additionally, to simplify the description and discussion, and to avoid obscuring the embodiments of this application, the well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided drawings. Furthermore, the apparatus may be shown in block diagram form to avoid obscuring the embodiments of this application, and this also takes into account the fact that the details of the implementation of these block diagram apparatuses are highly dependent on the platform on which the embodiments of this application will be implemented (i.e., these details should be fully understood by those skilled in the art). While specific details (e.g., circuits) have been set forth to describe exemplary embodiments of this application, it will be apparent to those skilled in the art that the embodiments of this application can be implemented without these specific details or with variations thereof. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0191] Although this application has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0192] The embodiments of this application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of this application should be included within the protection scope of this application.

Claims

1. A multi-wave joint inversion method, characterized in that, include: Identify the P-wave-P-wave angle gathers and P-wave-Sh-wave angle gathers located in the time domain of the P-wave-P-wave angle gathers in the seismic data; Determine the elastic parameters and anisotropy parameters of the wellhead located at the receiving well, and determine the initial inversion model based on the elastic parameters and anisotropy parameters of the wellhead; Based on the initial inversion model, the longitudinal wave-longitudinal wave composite record and the longitudinal wave-transverse wave composite record are determined using the precise vertical and transverse isotropic medium reflection coefficient equation. The true stratigraphic model is determined, and the relationship between the P-wave-P-wave composite record and the P-wave-S-wave composite record and the P-wave-P-wave angle gather and the P-wave-S-wave angle gather is determined using a Bayesian framework. The optimal model of the true stratigraphic model is determined using the maximum a posteriori probability. The exact vertical and horizontal isotropic medium reflection coefficient equation is linearized using the Gauss-Newton method to determine the update amount of the initial inversion model, and the initial inversion model is updated using the update amount. In response to the updated initial inversion model, if the errors between the second P-wave composite record and the second P-wave composite record and the actual P-wave angle gather and the actual P-wave angle gather are less than a preset threshold, the parameters of the currently updated initial inversion model are output.

2. The method according to claim 1, characterized in that, The elastic parameters of the wellhead include: P-wave velocity, S-wave velocity, and formation density at the wellhead location; the anisotropic parameters of the wellhead include: anisotropic parameters describing the difference between horizontal and vertical propagation S-wave velocities and anisotropic parameters describing the difference between vertical and horizontal P-wave velocities. The process of determining the elastic parameters and anisotropy parameters at the wellhead of the receiving well, and determining the initial inversion model based on the elastic parameters and anisotropy parameters at the wellhead, includes: Determine the P-wave velocity, S-wave velocity, formation density, anisotropy parameters describing the difference between horizontal and vertical propagation S-wave velocities, and anisotropy parameters describing the difference between vertical and horizontal P-wave velocities at the wellhead location of the receiving well. Identify a formation that is far from the wellhead and in the same formation as the formation at the wellhead, and synchronize the parameters of this formation to the same time domain as the formation at the wellhead, including the P-wave velocity, S-wave velocity, formation density, anisotropic parameters describing the difference between horizontal and vertical propagation S-wave velocity, and anisotropic parameters describing the difference between vertical and horizontal P-wave velocity, to determine the initial inversion model.

3. The method according to claim 1, characterized in that, Based on the initial inversion model, and using the precise vertical-transverse isotropic medium reflection coefficient equation, the P-wave composite record and the P-wave composite record are determined, including: The reflection coefficients of the initial inversion model are determined using the precise vertical and horizontal isotropic medium coefficient reflection equation; Extract the P-wave-P-wave angle gather and the P-wave-S-wave angle gather respectively, and determine the P-wave-P-wave wavelet matrix and the P-wave-S-wave wavelet matrix; The P-wave-P-wave wavelet matrix and the P-wave-S-wave wavelet matrix are convolved with the reflection coefficients respectively to determine the P-wave-P-wave composite record and the P-wave-S-wave composite record.

4. The method according to claim 1, characterized in that, A true stratigraphic model is determined, and the relationships between the P-wave-P-wave composite records and the P-wave-S-wave composite records and the P-wave-P-wave angle gathers and the P-wave-S-wave angle gathers are determined using a Bayesian framework. The optimal model of the initial inversion model is then determined using maximum a posteriori probability, including: Determine the true stratigraphic model and determine the prior distribution satisfied by the true stratigraphic model; Determine the likelihood function of the parameters of the real stratigraphic model under the P-P wave composite record and the P-P wave composite record; Based on the prior distribution and the likelihood function, determine the posterior distribution probability of the real stratigraphic model under the P-wave-P-wave composite record and the P-wave-S-wave composite record; The optimal model of the real stratigraphic model is determined using the posterior probability distribution.

5. The method according to claim 4, characterized in that, Before determining the posterior distribution probability of the real stratigraphic model under the P-wave-P-wave composite record and the P-wave-S-wave composite record based on the prior distribution and the likelihood function, the method further includes: The Cauchy distribution is used to describe the parameter distribution of the optimal model.

6. The method according to claim 1, characterized in that, The exact vertical-to-lateral isotropic medium reflection coefficient equation is linearized using the Gauss-Newton method to determine the update amount of the initial inversion model, and the initial inversion model is updated using the update amount, including: The exact vertical and transverse isotropic medium reflection coefficient equation is expanded using Taylor expansion at the initial inversion model; Determine the logarithm of the posterior distribution probability of the real stratigraphic model under the P-P wave composite record and the P-S wave composite record; Substituting the Taylor expansion into the logarithm, the maximum value of the posterior distribution probability of the optimal model under the P-wave-P-wave composite record and the P-wave-S-wave composite record is determined; Determine the error weights of the P-wave-P-wave angle gather and the P-wave-S-wave angle gather for the initial inversion model and the optimal model; The update amount of the initial inversion model is determined based on the error weights and the maximum value.

7. The method according to claim 6, characterized in that, The error includes a first error and a second error; In response to the updated initial inversion model determining that the errors between the second P-P composite record and the second P-S composite record and the actual P-P angle gather and the actual P-S angle gather are less than a preset threshold, the parameters of the currently updated initial inversion model are output, and the model further includes: The first error between the second P-wave composite record and the actual P-wave angle gather was determined by forward modeling using the updated initial inversion model. The second error between the second P-wave-S-wave composite record and the actual P-wave-S-wave angle gather was determined by forward modeling using the updated initial inversion model. In response to the sum of the first error and the second error being greater than the preset threshold, the second P-wave-P-wave composite record and the second P-wave-S-wave composite record are used as the P-wave-P-wave composite record and the P-wave-S-wave composite record to repeatedly determine the updated initial inversion model until the sum of the first error and the second error is less than the preset threshold.

8. A multi-wave joint inversion device, characterized in that, include: The determination module is used to determine the P-wave-P-wave angle gathers and P-wave-Sh-wave angle gathers located in the time domain of the P-wave-P-wave angle gathers in the seismic data; The initial inversion module is used to determine the elastic parameters and anisotropy parameters at the wellhead of the receiving well, and to determine the initial inversion model based on the elastic parameters and anisotropy parameters at the wellhead. The synthetic recording module is used to determine the P-wave-P-wave synthetic recording and the P-wave-S-wave synthetic recording based on the initial inversion model and using the precise vertical and transverse isotropic medium reflection coefficient equation. The target module is used to determine the true stratigraphic model. It uses a Bayesian framework to determine the relationship between the P-wave-P-wave composite record and the P-wave-S-wave composite record and the P-wave-P-wave angle gather and the P-wave-S-wave angle gather, respectively. It uses the maximum a posteriori probability to determine the optimal model of the true stratigraphic model. The update module is used to linearize the exact vertical and horizontal isotropic medium reflection coefficient equation using the Gauss-Newton method, determine the update amount of the initial inversion model, and update the initial inversion model using the update amount. The output module is used to determine whether the error between the updated initial inversion model and the second P-wave composite record and the second P-wave composite record determined by forward modeling is less than a preset threshold, and outputs the parameters of the currently updated initial inversion model.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1 to 7.

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

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