Orthorhombic seismic prediction method based on model parameterization and step-by-step inversion strategy

Abstract

The application provides an orthogonal medium seismic prediction method based on a model parameterization and a step-by-step inversion strategy, relates to the technical field of seismic inversion, and comprises the following steps: constructing a first forward model and a second forward model based on a preset P-wave reflection coefficient model; solving the first forward model by using crack normal plane seismic data to obtain target crack normal plane S-wave modulus and target crack normal plane P-wave velocity; solving the second forward model by using crack plane seismic data to obtain target crack plane S-wave modulus and target crack plane P-wave velocity; and calculating target crack parameters and target P-wave impedance parameters based on the above parameters. The preset P-wave reflection coefficient model contains five to-be-inverted parameters, and compared with a conventional P-wave reflection coefficient model which needs to calculate nine parameters, the number of to-be-calculated parameters is greatly reduced, and the inversion efficiency is improved. The step-by-step inversion mode is adopted to calculate parameters in different directions and properties step by step, the multi-solution property of parameter inversion is reduced, the stability of parameter inversion is improved, and the seismic inversion efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of earthquake inversion technology, and in particular to an orthogonal medium earthquake prediction method based on model parameterization and step-by-step inversion strategies. Background Technology

[0002] Fractured reservoirs are the core target in the field of oil and gas exploration and development. Reliable prediction of fractured reservoirs can provide crucial quantitative basis for well location deployment, reserve assessment and development plan formulation, and is a key technical link to achieve accurate characterization and efficient development of oil and gas reservoirs.

[0003] Influenced by sedimentary compaction and tectonic activity, underground reservoirs typically develop layered structures and high-angle fractures. Under the long-wavelength assumption of seismic events, such reservoirs can be equivalent to orthorombic anisotropy (OA) media. OA media are a typical type of anisotropic media, whose physical properties (such as elasticity and wave velocity) are symmetrical in three mutually perpendicular orthogonal directions, but differ in other directions.

[0004] Seismic forward modeling equations serve as a bridge between hydrocarbon reservoir parameters and macroscopic seismic responses, while seismic inversion is an effective means of predicting reservoir parameters and describing subsurface spatial structures using seismic data. Model parameterization is fundamental to constructing seismic forward and inversion operators. However, existing forward modeling equations for OA media contain numerous parameters to be inverted, leading to multiple solutions and instability in multi-parameter seismic inversion of OA media, which hinders the efficient exploration and development of orthogonal fractured hydrocarbon reservoirs. Summary of the Invention

[0005] In view of this, embodiments of the present invention provide an orthogonal medium seismic prediction method based on model parameterization and step-by-step inversion strategy to improve seismic inversion efficiency.

[0006] According to one aspect of the present invention, an orthogonal medium seismic prediction method based on model parameterization and step-by-step inversion strategy is provided, the method comprising:

[0007] Acquire target seismic data, wherein the target seismic data includes at least fracture surface seismic data and fracture normal surface seismic data at three different incident angles;

[0008] A first forward model and a second forward model are constructed based on a preset P-wave reflection coefficient model. The preset P-wave reflection coefficient model includes P-wave impedance parameters, shear wave modulus parameters of the crack normal surface, shear wave modulus parameters of the crack surface, P-wave velocity parameters of the crack normal surface, and P-wave velocity parameters of the crack surface. The first forward model includes the P-wave impedance parameters, the shear wave modulus parameters of the crack normal surface, and the P-wave velocity parameters of the crack normal surface; the second forward model includes the P-wave impedance parameters, the shear wave modulus parameters of the crack surface, and the P-wave velocity parameters of the crack surface.

[0009] The first forward model is solved based on the seismic data of the fracture normal surface to obtain the shear wave modulus and the longitudinal wave velocity of the target fracture normal surface.

[0010] The second forward model is solved based on the seismic data of the fracture surface to obtain the shear wave modulus and the longitudinal wave velocity of the target fracture surface.

[0011] Based on the shear wave modulus of the target crack normal surface, the longitudinal wave velocity of the target crack normal surface, the shear wave modulus of the target crack surface, and the longitudinal wave velocity of the target crack surface, the preset longitudinal wave reflection coefficient model is solved to obtain the target longitudinal wave impedance parameters.

[0012] The target crack parameters are calculated based on the shear wave modulus of the target crack normal surface, the longitudinal wave velocity of the target crack normal surface, the shear wave modulus of the target crack surface, and the longitudinal wave velocity of the target crack surface.

[0013] According to another aspect of the present invention, an orthogonal medium seismic prediction device based on model parameterization and step-by-step inversion strategy is provided, the device comprising:

[0014] An acquisition module is used to acquire target seismic data, wherein the target seismic data includes at least fracture surface seismic data and fracture normal surface seismic data at three different incident angles;

[0015] A construction module is used to construct a first forward model and a second forward model based on a preset P-wave reflection coefficient model. The preset P-wave reflection coefficient model includes P-wave impedance parameters, shear wave modulus parameters of the crack normal surface, shear wave modulus parameters of the crack surface, P-wave velocity parameters of the crack normal surface, and P-wave velocity parameters of the crack surface. The first forward model includes the P-wave impedance parameters, the shear wave modulus parameters of the crack normal surface, and the P-wave velocity parameters of the crack normal surface; the second forward model includes the P-wave impedance parameters, the shear wave modulus parameters of the crack surface, and the P-wave velocity parameters of the crack surface.

[0016] The first solution module is used to solve the first forward model based on the seismic data of the fracture normal surface to obtain the shear wave modulus and the longitudinal wave velocity of the target fracture normal surface.

[0017] The second solution module is used to solve the second forward model based on the seismic data of the fracture surface to obtain the shear wave modulus and the longitudinal wave velocity of the target fracture surface.

[0018] The third solution module is used to solve the preset longitudinal wave reflection coefficient model based on the shear wave modulus of the target crack normal surface, the longitudinal wave velocity of the target crack normal surface, the shear wave modulus of the target crack surface, and the longitudinal wave velocity of the target crack surface, so as to obtain the target longitudinal wave impedance parameters.

[0019] The calculation module is used to calculate the target crack parameters based on the shear wave modulus of the target crack normal surface, the longitudinal wave velocity of the target crack normal surface, the shear wave modulus of the target crack surface, and the longitudinal wave velocity of the target crack surface.

[0020] According to another aspect of the present invention, an electronic device is provided, comprising:

[0021] Processor; and

[0022] Stored program memory,

[0023] The program includes instructions that, when executed by the processor, cause the processor to perform any of the above-described orthogonal medium seismic prediction methods based on model parameterization and step-by-step inversion strategies.

[0024] According to another aspect of the present invention, a non-transient computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause a computer to execute any of the above-described orthogonal medium seismic prediction methods based on model parameterization and step-by-step inversion strategies.

[0025] One or more technical solutions provided in this embodiment of the invention construct a first forward model and a second forward model based on a preset P-wave reflection coefficient model. Both the first and second forward models include some parameters from the preset P-wave reflection coefficient model. The first forward model is solved using seismic data of the fracture normal surface to obtain the shear wave modulus and P-wave velocity of the target fracture normal surface. The second forward model is solved using seismic data of the fracture surface to obtain the shear wave modulus and P-wave velocity of the target fracture surface. The target fracture parameters are then calculated based on the obtained parameters, and the preset P-wave reflection coefficient model is solved based on the obtained parameters to obtain the target P-wave impedance parameters.

[0026] In this embodiment of the invention, the preset P-wave reflection coefficient model contains five parameters to be inverted. These five parameters are sufficient to meet the calculation requirements of fracture parameters. Compared to the conventional P-wave reflection coefficient model, which requires the calculation of nine parameters, the number of parameters to be calculated is significantly reduced, thus improving seismic inversion efficiency. Furthermore, when calculating the five parameters, a step-by-step inversion method is adopted, calculating parameters with different directions and properties step by step, reducing the ambiguity of parameter inversion, improving the stability of parameter inversion, and further enhancing seismic inversion efficiency. Attached Figure Description

[0027] Further details, features, and advantages of the invention are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which:

[0028] Figure 1 A schematic diagram of a method for seismic prediction in orthogonal media based on model parameterization and step-by-step inversion strategy provided in an embodiment of the present invention;

[0029] Figure 2 This is a schematic diagram of an orthogonal anisotropic medium model;

[0030] Figure 3(a) is a schematic diagram of different longitudinal wave reflection coefficient curves when the azimuth angle is 0 degrees;

[0031] Figure 3(b) is a schematic diagram of different longitudinal wave reflection coefficient curves at an azimuth angle of 45 degrees;

[0032] Figure 3(c) is a schematic diagram of different longitudinal wave reflection coefficient curves when the azimuth angle is 90 degrees;

[0033] Figure 4 This is another flowchart illustrating the orthogonal medium seismic prediction method based on model parameterization and step-by-step inversion strategy provided in this embodiment of the invention.

[0034] Figure 5(a) is a schematic diagram of an inversion result of parameter A in the longitudinal wave reflection coefficient model provided by the present invention;

[0035] Figure 5(b) is a schematic diagram of an inversion result of parameter B in the longitudinal wave reflection coefficient model provided by the present invention;

[0036] Figure 5(c) is a schematic diagram of an inversion result of parameter C in the longitudinal wave reflection coefficient model provided by the present invention;

[0037] Figure 5(d) is a schematic diagram of an inversion result of parameter D in the longitudinal wave reflection coefficient model provided by the present invention;

[0038] Figure 5(e) is a schematic diagram of an inversion result of parameter E in the longitudinal wave reflection coefficient model provided by the present invention;

[0039] Figure 6(a) is a schematic diagram of the crack parameters obtained by the orthogonal medium seismic prediction method based on model parameterization and step-by-step inversion strategy provided by the present invention;

[0040] Figure 6(b) is a schematic diagram of another result of the crack parameters obtained by the orthogonal medium seismic prediction method based on model parameterization and step-by-step inversion strategy provided by the present invention;

[0041] Figure 7 A schematic diagram of a process for an orthogonal medium seismic prediction device based on model parameterization and step-by-step inversion strategy provided in an embodiment of the present invention;

[0042] Figure 8 A structural block diagram of an exemplary electronic device that can be used to implement embodiments of the present invention is shown. Detailed Implementation

[0043] Embodiments of the present invention will now be described in more detail with reference to the accompanying drawings. While some embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the invention. It should be understood that the accompanying drawings and embodiments are for illustrative purposes only and are not intended to limit the scope of protection of the invention.

[0044] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0045] The term "comprising" and its variations as used herein are open-ended, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the following description. It should be noted that the concepts of "first", "second", etc., mentioned in this invention are used only to distinguish different devices, modules, or units, and are not intended to limit the order of functions performed by these devices, modules, or units or their interdependencies.

[0046] It should be noted that the terms "a" and "a plurality of" used in this invention are illustrative rather than restrictive. Those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0047] The names of the messages or information exchanged between the multiple devices in the embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of these messages or information.

[0048] Under the theory of equivalent seismic media and the assumption of weak anisotropy, existing seismic P-wave reflection coefficient equations for OA media are typically divided into three parts: an isotropic part (the basic background of the OA medium, corresponding to a medium without bedding or cracks, whose physical properties are independent of direction); a transversely isotropic part with a vertical axis of symmetry (corresponding to the horizontal bedding structure in the medium, whose anisotropy originates from the directional arrangement of bedding planes, with consistent properties within the bedding planes and different properties perpendicular to the bedding planes); and a transversely isotropic part with a horizontal axis of symmetry (corresponding to vertical or near-vertical cracks in the medium, whose anisotropy originates from the directional distribution of cracks, with consistent directional properties parallel to the crack direction and different directional properties perpendicular to the crack direction). During model parameterization, the isotropic part is characterized by three parameters: background P-wave velocity, background S-wave velocity, and density. The transversely isotropic parts with vertical and horizontal axes of symmetry are each characterized by three different anisotropic parameters. Therefore, the overall seismic reflection coefficient equation for OA media is characterized by nine parameters, leading to significant redundancy in the construction of seismic forward and inverse operators. Furthermore, the contribution of isotropic parameters to the reflection coefficient equation of OA medium is much higher than that of fracture parameters and anisotropic parameters. Moreover, existing seismic inversion prediction techniques for OA medium mostly adopt direct inversion strategies to predict isotropic parameters, fracture parameters and anisotropic parameters simultaneously, and the prediction methods have strong ambiguity and instability.

[0049] Based on this, the present invention provides an orthogonal medium seismic prediction method, device, electronic device, and storage medium based on a model parameterization and step-by-step inversion strategy. The orthogonal medium seismic prediction method provided by the present invention can be applied to any electronic device with seismic prediction capabilities, such as a computer, industrial control computer, or mobile terminal. The following describes the invention with reference to the accompanying drawings:

[0050] like Figure 1 As shown, Figure 1 A flowchart illustrating the orthogonal medium seismic prediction method based on model parameterization and step-by-step inversion strategy provided by this invention may include the following steps:

[0051] S101. Acquire target seismic data, wherein the target seismic data includes at least fracture surface seismic data and fracture normal surface seismic data at three different incident angles.

[0052] S102. Construct a first forward model and a second forward model based on a preset P-wave reflection coefficient model; wherein, the preset P-wave reflection coefficient model includes P-wave impedance parameters, crack normal surface S-wave modulus parameters, crack surface S-wave modulus parameters, crack normal surface P-wave velocity parameters, and crack surface P-wave velocity parameters; the first forward model includes the P-wave impedance parameters, crack normal surface S-wave modulus parameters, and crack normal surface P-wave velocity parameters, and the second forward model includes the P-wave impedance parameters, crack surface S-wave modulus parameters, and crack surface P-wave velocity parameters;

[0053] S103. Solve the first forward model based on the seismic data of the crack normal surface to obtain the shear wave modulus of the target crack normal surface and the longitudinal wave velocity of the target crack normal surface.

[0054] S104. Solve the second forward model based on the seismic data of the fracture surface to obtain the shear wave modulus and the longitudinal wave velocity of the target fracture surface.

[0055] S105. Based on the shear wave modulus of the target crack normal surface, the longitudinal wave velocity of the target crack normal surface, the shear wave modulus of the target crack surface, and the longitudinal wave velocity of the target crack surface, the preset longitudinal wave reflection coefficient model is solved to obtain the target longitudinal wave impedance parameters.

[0056] S106. Calculate the target crack parameters based on the shear wave modulus of the target crack normal surface, the longitudinal wave velocity of the target crack normal surface, the shear wave modulus of the target crack surface, and the longitudinal wave velocity of the target crack surface.

[0057] In this embodiment of the invention, a first forward model and a second forward model are constructed based on a preset P-wave reflection coefficient model. Both the first and second forward models include some parameters from the preset P-wave reflection coefficient model. The first forward model is solved using seismic data of the fracture normal surface to obtain the shear wave modulus and P-wave velocity of the target fracture normal surface. The second forward model is solved using seismic data of the fracture surface to obtain the shear wave modulus and P-wave velocity of the target fracture surface. The target fracture parameters are then calculated based on the obtained parameters, and the preset P-wave reflection coefficient model is solved based on the obtained parameters to obtain the target P-wave impedance parameters.

[0058] In this embodiment of the invention, the preset P-wave reflection coefficient model contains five parameters to be inverted. These five parameters are sufficient to meet the calculation requirements of fracture parameters. Compared to the conventional P-wave reflection coefficient model, which requires the calculation of nine parameters, the number of parameters to be calculated is significantly reduced, thus improving seismic inversion efficiency. Furthermore, when calculating the five parameters, a step-by-step inversion method is adopted, calculating parameters with different directions and properties step by step, reducing the ambiguity of parameter inversion, improving the stability of parameter inversion, and further enhancing seismic inversion efficiency.

[0059] S101-S106 are illustrated below by example:

[0060] Seismic data, also known as seismic trace data, is the foundation of seismic inversion. It typically includes seismic wave data collected at multiple sampling points with different azimuths, incident angles, and reflection interfaces. In S101, target seismic data can be acquired in any feasible manner. As one possible implementation, the target seismic data can be obtained by transmitting P-wave signals into the ground and receiving reflected wave signals at various sampling points.

[0061] like Figure 2 As shown, Figure 2 This is a schematic diagram of an OA medium model. A stratigraphic background with layered structures can be equivalent to a transversely isotropic (VTI) medium with a vertical axis of symmetry. When high-angle fractures develop in a VTI medium, the medium as a whole can be equivalent to an OA medium.

[0062] Anisotropic parameters and isotropic parameters can be included in an OA medium. Isotropic parameters refer to physical properties of the medium (such as wave velocity and elastic modulus) that do not change with the measurement direction, meaning the properties are consistent in all directions. Anisotropic parameters refer to physical properties of the medium that change with the measurement direction, meaning the properties differ in different directions. Specifically, Figure 2The spatial orientation of the orthogonal anisotropic medium is defined using a rectangular coordinate system X1, X2, and X3, corresponding to the three orthogonal axes of symmetry of the medium. X1 is parallel to the extension direction of the bedding plane, X2 is parallel to another horizontal extension direction of the bedding plane and perpendicular to X1, and X3 is perpendicular to the normal to the bedding plane. The X1-X2 plane in the medium is the bedding plane, the X1-X3 plane is the fracture normal plane and perpendicular to the fracture plane; the X2-X3 plane is the fracture surface. The angle between the propagation direction of the incident P-wave and the interface normal (perpendicular to the X3 direction of the bedding plane in the figure) is the incident angle θ, and φ is the azimuth angle of the incident P-wave. Anisotropic parameters are usually caused by the presence of fractures in the geology; therefore, anisotropic parameters can be further divided into fracture surface parameters and fracture normal surface parameters. Correspondingly, the target seismic data acquired in S101 can include fracture surface seismic data and fracture normal surface seismic data. Specifically, the target seismic data may include seismic data of the fracture surface and seismic data of the fracture normal surface at at least three different incident angles. The specific value of the incident angle can be selected according to the actual measurement results, such as any angle less than 30°.

[0063] The preset P-wave reflection coefficient model can then be solved based on the target seismic data. In related technologies, the azimuth reflection coefficient of an OA medium typically includes nine parameters. Specifically, the equation for the azimuth reflection coefficient of a conventional OA medium is shown below:

[0064] Formula (1)

[0065] in, This represents the azimuth reflection coefficient of the longitudinal wave. Indicates the angle of incidence of the longitudinal wave. Indicates azimuth. This represents the difference in elastic parameters between upper and lower strata. This represents the mean value of the elastic parameters between the upper and lower strata. Indicates the density of the strata. Indicates the P-wave velocity of the strata. Indicates the shear wave velocity of the formation. , , and express Anisotropic parameters within the surface (crack normal surface), , , express Anisotropic parameters within the crack surface, for In-plane anisotropy parameters, This represents the anisotropic gradient within the crack normal plane. , This represents the anisotropic gradient within the crack surface. Among them, the anisotropy parameter can reflect the differences in longitudinal wave propagation in different directions.

[0066] The conventional OA medium azimuth reflection coefficient equation contains nine parameters. The large number of parameters to be inverted results in a large computational load and multiple solutions, leading to low stability, accuracy, and efficiency in seismic inversion. Therefore, the conventional OA medium azimuth reflection coefficient equation can be simplified to reduce the number of parameters to be solved.

[0067] Under the assumption that the interface between the upper and lower strata is a weak correlation interface (i.e., the model parameters between the upper and lower strata are relatively similar), a mathematical approximation can be obtained. , here x It can refer to any physical quantity; therefore, formula (1) can be rewritten as:

[0068] Formula (2)

[0069] in, Represents longitudinal wave impedance. ; Represents the transverse wave modulus. ; Represents the longitudinal wave velocity. .

[0070] Based on trigonometric function relationships , , Formula (2) can be further rewritten. Specifically, based on the model parameterization theory, formula (2) can be rearranged to obtain:

[0071] Formula (3)

[0072] in, Represents longitudinal wave impedance. ; This represents the transverse wave modulus within the crack normal plane. ; This represents the transverse wave modulus within the crack surface. ; This represents the longitudinal wave velocity within the crack normal plane. ; This represents the longitudinal wave velocity within the crack surface. ; .

[0073] In the field of seismic inversion, when the incident angle is less than 30 degrees, including The terms are usually negligible; furthermore, The value is always less than 0.25, therefore the final parameter coefficient terms The absolute value is always less than 0.0104. Therefore, under the premise of ensuring the accuracy of the reflection coefficient equation, the parameter... This can be ignored. The final equation is as follows:

[0074] Formula (4)

[0075] Formula (4) is the preset P-wave reflection coefficient model used in this invention. Compared with the conventional OA medium P-wave reflection coefficient equation (Formula (1)), it contains only 5 parameters to be inverted. The number of parameters is small, and each parameter has a clear physical meaning. At the same time, the coefficient values ​​of each parameter are relatively close, making the contribution of each parameter to the reflection coefficient more similar, which can alleviate the instability caused by the large difference in contribution between parameters during the inversion process. Although the preset P-wave reflection coefficient model has fewer parameters to be inverted compared with the conventional OA medium P-wave reflection coefficient equation, after testing, the preset P-wave reflection coefficient model can still guarantee high accuracy.

[0076] As shown in Figures 3(a), 3(b), and 3(c), the reflection coefficient equation curves for azimuth angles of 0 degrees, 45 degrees, and 90 degrees are respectively. The solid lines, dashed lines, and dotted lines in the figures represent the curves of the three approximate reflection coefficient equations derived by Zong and Ji in 2021: Formula (1) (conventional OA medium longitudinal wave reflection coefficient equation), Formula (4) (preset longitudinal wave reflection coefficient model used in this invention), and Formula (4). It can be seen that the curve of the preset longitudinal wave reflection coefficient model in this invention is closer to the curve of the conventional OA medium longitudinal wave reflection coefficient equation and has higher accuracy.

[0077] As can be seen from formula (4), in the preset longitudinal wave reflection coefficient model, the longitudinal wave impedance parameter A The coefficient is a constant, unaffected by azimuth and incident angles, and is an isotropic parameter; the transverse wave modulus of the crack normal surface. B and the longitudinal wave velocity of the crack normal surface D The coefficient is the cosine function of the azimuth angle φ; the transverse wave modulus of the crack surface C and the longitudinal wave velocity of the crack surface E The coefficient is a sine function of the azimuth angle φ. Therefore, when the azimuth angle sine function is 0, such as when the azimuth angle is 0 degrees or 180 degrees, only the parameter... A , B , D Contribution to the equation for the longitudinal wave reflection coefficient, parameters C , E It makes no contribution to the equation for the P-wave reflection coefficient. When the azimuth cosine function is 0, such as when the azimuth is 90 degrees or 270 degrees, only the parameters... , , Contribution to the reflection coefficient equation, parameters , It makes no contribution to the reflection coefficient equation.

[0078] Based on this, the present invention can construct a first forward model and a second forward model according to a preset P-wave reflection coefficient model, so as to perform step-by-step inversion of each parameter to be inverted, thereby reducing the number of parameters to be solved each time, reducing the multiple solutions of the equation, and improving the inversion stability. For example, when the azimuth sine function is 0, a first forward model can be constructed based on the preset P-wave reflection coefficient model, which includes parameters... A , B , D When the azimuth cosine function is 0, a second forward model is constructed based on the preset P-wave reflection coefficient model. This second forward model includes parameters. A , C , E The following section describes how to solve for the parameters using the first forward model. B , D For example, parameters B , C , D , E The solution process will be explained as follows:

[0079] In one possible embodiment, the parameters can be solved by the following steps. B , D :

[0080] S131. Based on the seismic data of the fracture normal surface and the first forward model, a first forward simulation model is constructed. The first forward simulation model includes seismic incident angle amplitude difference data of the fracture normal surface, a first forward modeling operator, and a first parameter to be inverted. The first forward modeling operator is constructed based on the wavelet matrix and the incident angle difference matrix corresponding to the seismic incident angle amplitude difference data of the fracture normal surface. The first parameter to be inverted includes the shear wave modulus parameter of the fracture normal surface and the P-wave velocity parameter of the fracture normal surface.

[0081] Seismic data for the crack normal surface is seismic data with azimuth angles of 0° and 180°. This seismic data for the crack normal surface can include data with... An incident angle ( ), Seismic data containing information about the reflecting interface can include incident angle, azimuth, number of sampling points, etc. Seismic incident angle amplitude difference data is obtained by subtracting the amplitude data corresponding to different incident angles. However, due to the isotropic parameter P-wave impedance... AThis is a constant term. When calculating the difference in earthquake incident angle amplitude data, this term can be eliminated, thereby removing the isotropic parameter. The influence of anisotropic parameters , Targeted inversion further improves the inversion stability of anisotropic parameters.

[0082] For example, the following first forward modeling simulation model can be constructed based on the first forward modeling model:

[0083] Formula (5)

[0084] in, This represents the difference in earthquake incident angle amplitude when the azimuth is 0 degrees. Represents the first forward operator, The parameters to be inverted represent the values ​​when the azimuth angle is 0 degrees. Specifically, the seismic incident angle amplitude difference data, the first forward modeling operator, and the parameters to be inverted when the azimuth angle is 0 degrees can be expressed by the following formula:

[0085] Formula (6)

[0086] in, This represents the incident angle amplitude data for seismic waves in OA media. The subscript PP represents the reflected P-wave, and the subscript n of θ within parentheses represents the nth incident angle. The subscript 0 degrees represents the azimuth angle data of 0 degrees. This represents the seismic incident angle amplitude data of the nth incident angle in OA medium when the azimuth angle is 0°, where k represents the number of sampling points. The wavelet matrix represents the core component of the seismic record convolution model, defined by the amplitude and phase spectra. Each element in the wavelet matrix represents the seismic wavelet acquired at each sampling point. A' represents the coefficient matrix for different parameters, and 'a' represents the calculation method for the coefficients of different parameters. To calculate the coefficients using method a μ The coefficient matrix between the first and nth incident angles is calculated. To calculate the coefficients using method a vp The coefficient matrix between the first and nth incident angles is calculated, and D' represents the difference matrix. Represents earthquake records. This represents the seismic record acquired at the k-th sampling point with an incident angle of θ and an azimuth angle of φ. Let m represent the identity matrix, and m represent the parameters to be inverted. lnB Indicates that the parameter to be inverted is ln B From (lnB)1 at sampling point 1 to (lnB) at sampling point k kComposition, m lnD Indicates that the parameter to be inverted is ln D From (lnD)1 at sampling point 1 to (lnD) at sampling point k k The structure is defined by i, which is the incident angle index, with a value range of [1,2]. Kron The superscript represents the Kronecker product, used to describe the coupling relationship between different directions or components. This represents the transpose of a matrix.

[0087] S 132. Solve the first forward modeling model to obtain the shear wave modulus and longitudinal wave velocity of the target crack normal surface.

[0088] Based on the above formula (6), the solution can be obtained. The solution obtained The corresponding elements are the shear wave modulus and the longitudinal wave velocity at the normal surface of the target crack. As one possible implementation, the first forward simulation model can be solved according to the following steps:

[0089] S1321. Calculate the first posterior probability of the first parameter to be inverted based on the first forward modeling model;

[0090] S1322. Maximize the first posterior probability to obtain the first objective functional;

[0091] S1323. Minimize the first target functional to obtain the shear wave modulus and longitudinal wave velocity of the target crack normal surface.

[0092] Within the framework of Bayesian inversion theory, the prior probability density of the parameters to be inverted follows... , where, μ m Let be the prior mean of the parameters to be inverted. The prior variance of the parameters to be inverted is given by the seismic noise following the order of... Gaussian distribution, For the seismic noise variance, the posterior probability for the first forward simulation model (Equation (5)) is... It can be represented as:

[0093] Formula (7)

[0094] To maximize the posterior probability density function, the objective functional can be obtained:

[0095] Formula (8)

[0096] By minimizing the objective functional, the final inversion function of equation (5) can be obtained:

[0097] Formula (9)

[0098] The above minimization can be achieved by differentiating formula (8) to make the derivative equal to 0, thus obtaining formula (9). The parameters can then be obtained by solving formula (9). , The inversion results.

[0099] parameter C , E Solution process and B , D Similarly, the following is only a brief explanation:

[0100] Inversion parameters were obtained using seismic incident angle amplitude difference data at an azimuth angle of 90 degrees. , Seismic incident angle amplitude difference data can eliminate isotropic parameters. The influence of anisotropic parameters , Targeted inversion is performed to improve the inversion stability of anisotropic parameters. For seismic data with an azimuth angle of 90 degrees, forward modeling equations are constructed as follows:

[0101] Formula (10)

[0102] in, Data representing the difference in earthquake incident angle amplitude when the azimuth is 90 degrees. The representative operator can be the same as the first forward operator. The parameters to be inverted represent the azimuth angle of 90 degrees, specifically expressed as follows:

[0103] Formula (11)

[0104] in, This represents the incident angle amplitude data of the seismic wave in the OA medium. The subscript PP represents the reflected P-wave. subscript n Representing the n An angle of incidence, The subscript 90 degrees represents the 90-degree azimuth angle. k The number of sampling points represents m, and the parameter to be inverted is m. lnC Indicates that the parameter to be inverted is ln C From (lnC)1 at sampling point 1 to (lnC) at sampling point k k Composition, m lnE Indicates that the parameter to be inverted is ln E From (lnE)1 at sampling point 1 to (lnE) at sampling point k kComposition, superscript This represents the transpose of a matrix.

[0105] Based on the Bayesian inversion theory framework, the inversion of formula (10) can be performed using the same functional form as formula (9) to obtain the parameters. , The inversion results are as follows:

[0106] . Formula (12)

[0107] After inverting the parameters with anisotropic characteristics B , C , D , E Then, the responses of anisotropic parameters can be removed from the target seismic data, and the isotropic parameters can be inverted. In one possible embodiment, the isotropic parameters can be inverted through the following steps. A :

[0108] S 141. Construct a second forward modeling model based on the preset P-wave reflection coefficient model, wherein the second forward modeling model includes seismic incident angle amplitude difference data, a second forward modeling operator, and a second parameter to be inverted, wherein the second forward modeling operator is constructed based on the wavelet matrix and the incident angle difference matrix corresponding to the seismic incident angle amplitude difference data; the second parameter to be inverted includes P-wave impedance parameters;

[0109] S 142. Solve the second forward modeling model to obtain the target longitudinal wave impedance parameters.

[0110] For parameters A The process of inversion and B , C , D , E The inversion process is similar, and will be briefly explained below without further details.

[0111] Based on the preset P-wave reflection coefficient model, the second forward modeling equation can be constructed as follows:

[0112] Formula (13)

[0113] in, This represents seismic data from which anisotropic parameter responses have been removed. Represents the second orthogonal operator. Represents the isotropic parameters to be inverted Specifically, it is expressed as:

[0114] Formula (14)

[0115] This represents the seismic record at the nth incident angle when the azimuth is 90°, where k represents the number of sampling points. Denotes the wavelet matrix, A' Ip Indicates the method of coefficient calculation a A The calculated coefficient matrix, D', represents the difference matrix. Denotes the identity matrix, m iso Let A represent the parameter to be inverted, ranging from (lnA)1 at sampling point 1 to (lnA) at sampling point k. k Composition, superscript This represents the transpose of the matrix. B1' is the matrix calculated using the coefficient method a. B The calculated coefficient matrix, B2', is obtained through coefficient calculation method a. C The calculated coefficient matrix, B3', is obtained through coefficient calculation method a. D The calculated coefficient matrix, B4', is obtained through coefficient calculation method a. E The calculated coefficient matrix. Each coefficient matrix (A') Ip The θ in (B1', B2', B3', B4') can include [θ1, θ2, ..., θ3']. n ], φ can include φ 0° With φ 90° .

[0116] Based on the Bayesian inversion theory framework, the inversion of formula (13) can be performed using the same functional form as formula (9) to obtain the parameters. The inversion results are as follows:

[0117] . Formula (15)

[0118] After obtaining the above parameters, the target crack parameters can be obtained through the following steps:

[0119] S 161. Calculate the crack normal weakness parameter based on the longitudinal wave velocity of the target crack normal surface and the longitudinal wave velocity of the target crack surface;

[0120] S 162. Calculate the tangential weakness parameter of the crack based on the shear wave modulus of the target crack normal surface, the shear wave modulus of the target crack surface, and the crack normal weakness parameter.

[0121] In related technologies, the anisotropy parameters have the following relationship:

[0122] Formula (16)

[0123] Based on the definitions of each parameter in this invention, we can obtain:

[0124] Formula (17)

[0125] therefore,

[0126] Formula (18)

[0127] in, Here is the normal weakness parameter of the crack. The tangential weakness parameter of the crack, the normal weakness parameter of the crack, and the tangential weakness parameter together constitute the target crack parameter.

[0128] Figure 4 This is another flowchart illustrating the orthogonal medium seismic prediction method based on model parameterization and step-by-step inversion strategy provided in this embodiment of the invention, which may include the following steps:

[0129] Based on model parameterization, a system is constructed using five parameters ( A , B , C , D , E The equation for the longitudinal wave reflection coefficient under orthogonal media is described. Considering the direction dependence of orthogonal media, target functionals are constructed for two typical azimuth angles (0 degrees and 90 degrees). Using the target functionals at different azimuth angles, the corresponding anisotropy parameters are inverted: using the functional at 0 degrees azimuth angle: the anisotropy parameters are inverted. B , D Using a functional with an azimuth angle of 90 degrees, the anisotropy parameters are inverted. C , E After obtaining the anisotropic parameters, an objective functional with isotropic parameters is constructed separately, and the direction-independent isotropic parameters are derived from this functional. A Using the anisotropy parameters obtained from the inversion ( B , C , D , E By combining the physical mechanisms of orthogonal media, the crack weakness parameters are finally predicted.

[0130] Figures 5(a)-5(e) and 6(a) and 6(b) show the parameters. A , B , C , D , EThe fracture parameter inversion results were used to test the method proposed in this invention in a fractured shale reservoir. Geological and well logging interpretation data from the study area indicate that the target layer has numerous vertical and near-vertical directional fractures. Therefore, the target reservoir in this study area can be considered equivalent to an OA medium. To fully utilize the anisotropic information in the azimuth seismic data, based on geological data and prior information from imaging logging, seismic data for the fracture normal plane azimuth (0 degrees) and fracture plane azimuth (90 degrees) of the study area were selected, with incident angles of 8°, 16°, and 24° corresponding to each azimuth seismic data point. The step-by-step inversion strategy proposed in this patent was adopted, and the inversion parameters... A , B , C , D , E With vertical crack parameters.

[0131] Figures 5(a), 5(b), 5(c), 5(d), and 5(e) show the parameters respectively. A , B , C , D , E A schematic diagram of the inversion results is shown in Figures 5(a), 5(b), 5(c), 5(d), and 5(e). The curves in the figures are high-shear filtered displays of the measured well curves for each parameter. It can be seen that the seismic inversion results have good consistency with the well curves, which proves the reliability and stability of the parameter inversion results in the preset P-wave reflection coefficient model used in this invention.

[0132] Figures 6(a) and 6(b) are schematic diagrams of the fracture parameter calculation results obtained by using the orthogonal medium seismic prediction method based on model parameterization and step-by-step inversion strategy provided by the present invention. As shown in Figures 6(a) and 6(b), the curves in the figures are high-shear filtering displays of the true values ​​of the well logging curves of fracture parameters. The seismic inversion results of fracture parameters have good consistency with the wellbore values, which proves the reliability of the fracture parameter inversion results.

[0133] Compared with existing orthogonal medium longitudinal wave reflection coefficient equations, this invention has fewer parameters to be inverted and significantly reduces the difference in contribution between inversion parameters; the step-by-step inversion strategy proposed in this invention improves the stability of the inversion; and the vertical crack parameter prediction method proposed in this invention reduces the ill-conditioned nature of the inversion.

[0134] Based on the same inventive concept, this invention also provides an orthogonal medium seismic prediction device based on model parameterization and a step-by-step inversion strategy, such as... Figure 7 As shown, the device 700 may include:

[0135] The acquisition module 701 is used to acquire target seismic data, wherein the target seismic data includes at least fracture surface seismic data and fracture normal surface seismic data at three different incident angles.

[0136] The construction module 702 is used to construct a first forward model and a second forward model based on a preset P-wave reflection coefficient model; wherein, the preset P-wave reflection coefficient model includes P-wave impedance parameters, crack normal surface S-wave modulus parameters, crack surface S-wave modulus parameters, crack normal surface P-wave velocity parameters, and crack surface P-wave velocity parameters; the first forward model includes the P-wave impedance parameters, crack normal surface S-wave modulus parameters, and crack normal surface P-wave velocity parameters, and the second forward model includes the P-wave impedance parameters, crack surface S-wave modulus parameters, and crack surface P-wave velocity parameters;

[0137] The first solution module 703 is used to solve the first forward model based on the seismic data of the fracture normal surface to obtain the shear wave modulus and the longitudinal wave velocity of the target fracture normal surface.

[0138] The second solution module 704 is used to solve the second forward model based on the seismic data of the fracture surface to obtain the shear wave modulus and the longitudinal wave velocity of the target fracture surface.

[0139] The third solving module 705 is used to solve the preset longitudinal wave reflection coefficient model based on the shear wave modulus of the target crack normal surface, the longitudinal wave velocity of the target crack normal surface, the shear wave modulus of the target crack surface, and the longitudinal wave velocity of the target crack surface, to obtain the target longitudinal wave impedance parameters.

[0140] The calculation module 706 is used to calculate the target crack parameters based on the shear wave modulus of the target crack normal surface, the longitudinal wave velocity of the target crack normal surface, the shear wave modulus of the target crack surface, and the longitudinal wave velocity of the target crack surface.

[0141] In one possible embodiment, in the preset P-wave reflection coefficient model, the coefficients of the P-wave impedance parameter are constants, and the coefficients of the shear wave modulus parameter and the P-wave velocity parameter at the crack normal surface are azimuth cosine functions; the coefficients of the shear wave modulus parameter and the P-wave velocity parameter at the crack surface are azimuth sine functions; the construction of the first forward model and the second forward model based on the preset P-wave reflection coefficient model includes:

[0142] When the azimuth sine function is 0, a first forward model is constructed based on the preset longitudinal wave reflection coefficient model;

[0143] When the azimuth cosine function is 0, a second forward model is constructed based on the preset longitudinal wave reflection coefficient model;

[0144] The step of solving the first forward model based on the seismic data of the fracture normal surface to obtain the shear wave modulus and the p-wave velocity of the target fracture normal surface includes:

[0145] A first forward modeling simulation model is constructed based on the seismic data of the fracture normal surface and the first forward modeling model. The first forward modeling simulation model includes seismic incident angle amplitude difference data of the fracture normal surface, a first forward modeling operator, and a first parameter to be inverted. The first forward modeling operator is constructed based on the wavelet matrix and the incident angle difference matrix corresponding to the seismic incident angle amplitude difference data of the fracture normal surface. The first parameter to be inverted includes the shear wave modulus parameter of the fracture normal surface and the p-wave velocity parameter of the fracture normal surface.

[0146] Solve the first forward modeling model to obtain the shear wave modulus and longitudinal wave velocity of the target crack normal surface;

[0147] The first inversion parameter and the seismic noise both follow a Gaussian distribution. Solving the first forward simulation model to obtain the shear wave modulus and P-wave velocity at the normal surface of the target fracture includes:

[0148] Calculate the first posterior probability of the first parameter to be inverted based on the first forward model;

[0149] Maximize the first posterior probability to obtain the first objective functional;

[0150] Minimize the first objective functional to obtain the shear wave modulus and longitudinal wave velocity of the target crack normal surface;

[0151] The target longitudinal wave impedance parameters are obtained by solving the preset longitudinal wave reflection coefficient model based on the shear wave modulus of the target crack normal surface, the longitudinal wave velocity of the target crack normal surface, the shear wave modulus of the target crack surface, and the longitudinal wave velocity of the target crack surface, including:

[0152] A second forward modeling model is constructed based on the preset P-wave reflection coefficient model. The second forward modeling model includes seismic incident angle amplitude difference data, a second forward modeling operator, and a second parameter to be inverted. The second forward modeling operator is constructed based on the wavelet matrix and the incident angle difference matrix corresponding to the seismic incident angle amplitude difference data. The second parameter to be inverted includes P-wave impedance parameters.

[0153] The target longitudinal wave impedance parameters are obtained by solving the second forward simulation model;

[0154] The calculation of target crack parameters based on the shear wave modulus of the target crack normal surface, the longitudinal wave velocity of the target crack normal surface, the shear wave modulus of the target crack surface, and the longitudinal wave velocity of the target crack surface includes:

[0155] Based on the longitudinal wave velocity of the target crack normal surface and the longitudinal wave velocity of the target crack surface, calculate the crack normal weakness parameter;

[0156] Based on the shear wave modulus of the target crack normal surface, the shear wave modulus of the target crack surface, and the crack normal weakness parameter, the crack tangential weakness parameter is calculated.

[0157] An exemplary embodiment of the present invention also provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to cause the electronic device to perform a method according to an embodiment of the present invention.

[0158] An exemplary embodiment of the present invention also provides a non-transitory computer-readable storage medium storing a computer program, wherein the computer program, when executed by a computer's processor, is used to cause the computer to perform a method according to an embodiment of the present invention.

[0159] An exemplary embodiment of the present invention also provides a computer program product, including a computer program, wherein, when executed by a computer's processor, the computer program is used to cause the computer to perform a method according to an embodiment of the present invention.

[0160] refer to Figure 8 The present invention will now be described in the form of a structural block diagram of an electronic device 800 that can serve as a server or client of the present invention, which is an example of a hardware device that can be applied to various aspects of the present invention. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0161] like Figure 8As shown, the electronic device 800 includes a computing unit 801, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 into a random access memory (RAM) 803. The RAM 803 may also store various programs and data required for the operation of the electronic device 800. The computing unit 801, ROM 802, and RAM 803 are interconnected via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0162] Multiple components in electronic device 800 are connected to I / O interface 805, including: input unit 806, output unit 807, storage unit 808, and communication unit 809. Input unit 806 can be any type of device capable of inputting information to electronic device 800. Input unit 806 can receive input digital or character information and generate key signal inputs related to user settings and / or function control of electronic device. Output unit 807 can be any type of device capable of presenting information and may include, but is not limited to, a display, speaker, video / audio output terminal, vibrator, and / or printer. Storage unit 808 may include, but is not limited to, disks and optical discs. Communication unit 809 allows electronic device 800 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers, and / or chipsets, such as Bluetooth™ devices, WiFi devices, WiMax devices, cellular communication devices, and / or the like.

[0163] The computing unit 801 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 801 performs the various methods and processes described above. For example, in some embodiments, any of the above-described orthogonal medium seismic prediction methods based on model parameterization and step-by-step inversion strategies can be implemented as computer software programs tangibly contained in a machine-readable medium, such as storage unit 808. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 800 via ROM 802 and / or communication unit 809. In some embodiments, the computing unit 801 can be configured by any other suitable means (e.g., by means of firmware) to perform any of the above-described orthogonal medium seismic prediction methods based on model parameterization and step-by-step inversion strategies.

[0164] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0165] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0166] As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, device, and / or apparatus (e.g., disk, optical disk, memory, programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0167] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0168] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0169] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other.

Claims

1. A seismic prediction method for orthogonal media based on model parameterization and step-by-step inversion strategy, characterized in that, The method includes: Acquire target seismic data, wherein the target seismic data includes at least fracture surface seismic data and fracture normal surface seismic data at three different incident angles; A first forward model and a second forward model are constructed based on a preset P-wave reflection coefficient model. The preset P-wave reflection coefficient model includes P-wave impedance parameters, shear wave modulus parameters of the crack normal surface, shear wave modulus parameters of the crack surface, P-wave velocity parameters of the crack normal surface, and P-wave velocity parameters of the crack surface. The first forward model includes the P-wave impedance parameters, the shear wave modulus parameters of the crack normal surface, and the P-wave velocity parameters of the crack normal surface; the second forward model includes the P-wave impedance parameters, the shear wave modulus parameters of the crack surface, and the P-wave velocity parameters of the crack surface. The first forward model is solved based on the seismic data of the fracture normal surface to obtain the shear wave modulus and the longitudinal wave velocity of the target fracture normal surface. The second forward model is solved based on the seismic data of the fracture surface to obtain the shear wave modulus and the longitudinal wave velocity of the target fracture surface. Based on the shear wave modulus of the target crack normal surface, the longitudinal wave velocity of the target crack normal surface, the shear wave modulus of the target crack surface, and the longitudinal wave velocity of the target crack surface, the preset longitudinal wave reflection coefficient model is solved to obtain the target longitudinal wave impedance parameters. The target crack parameters are calculated based on the shear wave modulus of the target crack normal surface, the longitudinal wave velocity of the target crack normal surface, the shear wave modulus of the target crack surface, and the longitudinal wave velocity of the target crack surface.

2. The method according to claim 1, characterized in that, In the preset longitudinal wave reflection coefficient model, the coefficients of the longitudinal wave impedance parameter are constants, and the coefficients of the shear wave modulus parameter and the longitudinal wave velocity parameter of the crack normal surface are azimuth cosine functions; the coefficients of the shear wave modulus parameter and the longitudinal wave velocity parameter of the crack surface are azimuth sine functions. The construction of the first and second forward models based on the preset longitudinal wave reflection coefficient model includes: When the azimuth sine function is 0, a first forward model is constructed based on the preset longitudinal wave reflection coefficient model; When the azimuth cosine function is 0, a second forward model is constructed based on the preset longitudinal wave reflection coefficient model.

3. The method according to claim 2, characterized in that, The step of solving the first forward model based on the seismic data of the fracture normal surface to obtain the shear wave modulus and the p-wave velocity of the target fracture normal surface includes: A first forward modeling simulation model is constructed based on the seismic data of the fracture normal surface and the first forward modeling model. The first forward modeling simulation model includes seismic incident angle amplitude difference data of the fracture normal surface, a first forward modeling operator, and a first parameter to be inverted. The first forward modeling operator is constructed based on the wavelet matrix and the incident angle difference matrix corresponding to the seismic incident angle amplitude difference data of the fracture normal surface. The first parameter to be inverted includes the shear wave modulus parameter of the fracture normal surface and the p-wave velocity parameter of the fracture normal surface. Solving the first forward model yields the shear wave modulus and longitudinal wave velocity of the target crack normal surface.

4. The method according to claim 3, characterized in that, The first inversion parameter and the seismic noise both follow a Gaussian distribution. Solving the first forward simulation model to obtain the shear wave modulus and P-wave velocity at the normal surface of the target fracture includes: Calculate the first posterior probability of the first parameter to be inverted based on the first forward model; Maximize the first posterior probability to obtain the first objective functional; Minimize the first objective functional to obtain the shear wave modulus and longitudinal wave velocity of the target crack normal surface.

5. The method according to claim 1, characterized in that, The target longitudinal wave impedance parameters are obtained by solving the preset longitudinal wave reflection coefficient model based on the shear wave modulus of the target crack normal surface, the longitudinal wave velocity of the target crack normal surface, the shear wave modulus of the target crack surface, and the longitudinal wave velocity of the target crack surface, including: A second forward modeling model is constructed based on the preset P-wave reflection coefficient model. The second forward modeling model includes seismic incident angle amplitude difference data, a second forward modeling operator, and a second parameter to be inverted. The second forward modeling operator is constructed based on the wavelet matrix and the incident angle difference matrix corresponding to the seismic incident angle amplitude difference data. The second parameter to be inverted includes P-wave impedance parameters. The target longitudinal wave impedance parameters are obtained by solving the second forward model.

6. The method according to claim 1, characterized in that, The calculation of target crack parameters based on the shear wave modulus of the target crack normal surface, the longitudinal wave velocity of the target crack normal surface, the shear wave modulus of the target crack surface, and the longitudinal wave velocity of the target crack surface includes: Based on the longitudinal wave velocity of the target crack normal surface and the longitudinal wave velocity of the target crack surface, calculate the crack normal weakness parameter; Based on the shear wave modulus of the target crack normal surface, the shear wave modulus of the target crack surface, and the crack normal weakness parameter, the crack tangential weakness parameter is calculated.

7. An orthogonal medium seismic prediction device based on model parameterization and step-by-step inversion strategy, characterized in that, The device includes: An acquisition module is used to acquire target seismic data, wherein the target seismic data includes at least fracture surface seismic data and fracture normal surface seismic data at three different incident angles; A construction module is used to construct a first forward model and a second forward model based on a preset P-wave reflection coefficient model. The preset P-wave reflection coefficient model includes P-wave impedance parameters, shear wave modulus parameters of the crack normal surface, shear wave modulus parameters of the crack surface, P-wave velocity parameters of the crack normal surface, and P-wave velocity parameters of the crack surface. The first forward model includes the P-wave impedance parameters, the shear wave modulus parameters of the crack normal surface, and the P-wave velocity parameters of the crack normal surface; the second forward model includes the P-wave impedance parameters, the shear wave modulus parameters of the crack surface, and the P-wave velocity parameters of the crack surface. The first solution module is used to solve the first forward model based on the seismic data of the fracture normal surface to obtain the shear wave modulus and the longitudinal wave velocity of the target fracture normal surface. The second solution module is used to solve the second forward model based on the seismic data of the fracture surface to obtain the shear wave modulus and the longitudinal wave velocity of the target fracture surface. The third solution module is used to solve the preset longitudinal wave reflection coefficient model based on the shear wave modulus of the target crack normal surface, the longitudinal wave velocity of the target crack normal surface, the shear wave modulus of the target crack surface, and the longitudinal wave velocity of the target crack surface, so as to obtain the target longitudinal wave impedance parameters. The calculation module is used to calculate the target crack parameters based on the shear wave modulus of the target crack normal surface, the longitudinal wave velocity of the target crack normal surface, the shear wave modulus of the target crack surface, and the longitudinal wave velocity of the target crack surface.

8. The apparatus according to claim 7, characterized in that, In the preset longitudinal wave reflection coefficient model, the coefficients of the longitudinal wave impedance parameter are constants, and the coefficients of the shear wave modulus parameter and the longitudinal wave velocity parameter of the crack normal surface are azimuth cosine functions; the coefficients of the shear wave modulus parameter and the longitudinal wave velocity parameter of the crack surface are azimuth sine functions. The construction of the first and second forward models based on the preset longitudinal wave reflection coefficient model includes: When the azimuth sine function is 0, a first forward model is constructed based on the preset longitudinal wave reflection coefficient model; When the azimuth cosine function is 0, a second forward model is constructed based on the preset longitudinal wave reflection coefficient model; The step of solving the first forward model based on the seismic data of the fracture normal surface to obtain the shear wave modulus and the p-wave velocity of the target fracture normal surface includes: A first forward modeling simulation model is constructed based on the seismic data of the fracture normal surface and the first forward modeling model. The first forward modeling simulation model includes seismic incident angle amplitude difference data of the fracture normal surface, a first forward modeling operator, and a first parameter to be inverted. The first forward modeling operator is constructed based on the wavelet matrix and the incident angle difference matrix corresponding to the seismic incident angle amplitude difference data of the fracture normal surface. The first parameter to be inverted includes the shear wave modulus parameter of the fracture normal surface and the p-wave velocity parameter of the fracture normal surface. Solve the first forward modeling model to obtain the shear wave modulus and longitudinal wave velocity of the target crack normal surface; The first inversion parameter and the seismic noise both follow a Gaussian distribution. Solving the first forward simulation model to obtain the shear wave modulus and P-wave velocity at the normal surface of the target fracture includes: Calculate the first posterior probability of the first parameter to be inverted based on the first forward model; Maximize the first posterior probability to obtain the first objective functional; Minimize the first objective functional to obtain the shear wave modulus and longitudinal wave velocity of the target crack normal surface; The target longitudinal wave impedance parameters are obtained by solving the preset longitudinal wave reflection coefficient model based on the shear wave modulus of the target crack normal surface, the longitudinal wave velocity of the target crack normal surface, the shear wave modulus of the target crack surface, and the longitudinal wave velocity of the target crack surface, including: A second forward modeling model is constructed based on the preset P-wave reflection coefficient model. The second forward modeling model includes seismic incident angle amplitude difference data, a second forward modeling operator, and a second parameter to be inverted. The second forward modeling operator is constructed based on the wavelet matrix and the incident angle difference matrix corresponding to the seismic incident angle amplitude difference data. The second parameter to be inverted includes P-wave impedance parameters. The target longitudinal wave impedance parameters are obtained by solving the second forward simulation model; The calculation of target crack parameters based on the shear wave modulus of the target crack normal surface, the longitudinal wave velocity of the target crack normal surface, the shear wave modulus of the target crack surface, and the longitudinal wave velocity of the target crack surface includes: Based on the longitudinal wave velocity of the target crack normal surface and the longitudinal wave velocity of the target crack surface, calculate the crack normal weakness parameter; Based on the shear wave modulus of the target crack normal surface, the shear wave modulus of the target crack surface, and the crack normal weakness parameter, the crack tangential weakness parameter is calculated.

9. An electronic device, characterized in that, include: processor; And the memory for storing programs, The program includes instructions that, when executed by the processor, cause the processor to perform the method according to any one of claims 1-6.

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 according to any one of claims 1-6.

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