A seismic wave impedance inversion method, system and device based on edge enhancement

Abstract

The present application discloses a seismic wave impedance inversion method, system and equipment based on edge enhancement, which relates to the field of seismic survey. The method comprises acquiring seismic data, seismic wavelets and an initial wave impedance model, and then establishing a seismic wave impedance inversion objective function under a quadratic L2 norm regularization constraint based on the convolution principle, and adopting a conjugate gradient algorithm and an edge enhancement filtering algorithm to obtain a seismic wave impedance inversion result. The present application can effectively characterize the edge of geological structures, improve the resolution of wave impedance inversion results, and enhance the ability to identify oil and gas reservoirs.

Description

Technical Field

[0001] The present application relates to the field of seismic surveying, and in particular to a seismic wave impedance inversion method, system and equipment based on edge enhancement. Background Art

[0002] As an important rock property, wave impedance has a good indication of the oil and gas content of the reservoir. Since wave impedance and seismic signals are approximately linearly related, the wave impedance parameters of the underground medium can be calculated through seismic data. This process is called seismic wave impedance inversion. Seismic wave impedance inversion is a key technology for oil and gas reservoir prediction and identification. Its basic principle is to establish an inversion objective function by minimizing the error between synthetic seismic data and observed seismic data, and then use an optimization algorithm to iteratively solve the inversion objective function, thereby achieving quantitative estimation of formation wave impedance.

[0003] Seismic impedance inversion is highly ill-posed, and regularization constraints are needed to alleviate the ill-posedness of seismic impedance inversion to obtain stable impedance inversion results. In seismic impedance inversion, the most commonly used regularization constraint is quadratic L2 norm regularization, which has the advantages of simple implementation and high computational efficiency. However, quadratic L2 norm regularization cannot fully protect the sharp edge information of geological structures, and will produce smooth inversion results with blurred edges. This low-resolution inversion result can no longer meet the current needs of high-precision reservoir prediction and fine structural interpretation.

[0004] In response to the above problems, there is an urgent need to provide a seismic wave impedance inversion method or system based on edge enhancement, which can effectively characterize the edge of geological structure, improve the resolution of wave impedance inversion results, enhance the ability to identify oil and gas reservoirs, and lay the foundation for subsequent high-precision reservoir prediction and fine structural interpretation. Summary of the invention

[0005] The purpose of this application is to provide a seismic wave impedance inversion method, system and equipment based on edge enhancement, which can effectively characterize the edge of geological structure, improve the resolution of wave impedance inversion results, and enhance the ability to identify oil and gas reservoirs.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In a first aspect, the present application provides a seismic wave impedance inversion method based on edge enhancement, comprising:

[0008] Obtain seismic data, seismic wavelets and initial wave impedance model;

[0009] According to the seismic data, the seismic wavelet and the initial wave impedance model, based on the convolution principle, a seismic wave impedance inversion objective function under a quadratic L2 norm regularization constraint is established;

[0010] According to the seismic wave impedance inversion objective function, a conjugate gradient algorithm and an edge enhancement filtering algorithm are used to obtain a seismic wave impedance inversion result.

[0011] Optionally, the obtaining of seismic data, seismic wavelets and an initial wave impedance model specifically includes:

[0012] Acquisition of seismic data;

[0013] According to the seismic data, seismic wavelets are obtained.

[0014] Optionally, the establishing of a seismic wave impedance inversion objective function under a quadratic L2 norm regularization constraint based on the seismic data, the seismic wavelet and the initial wave impedance model based on the convolution principle specifically includes:

[0015] Using the formula Establishing the objective function of seismic impedance inversion under the constraint of quadratic L2 norm regularization ;

[0016] in, For earthquake data, is the seismic wavelet matrix, is the difference matrix, is the logarithm of the wave impedance, is the logarithm of the initial wave impedance, is the regularization parameter, is the quadratic L2 norm.

[0017] Optionally, the method of obtaining a seismic wave impedance inversion result by using a conjugate gradient algorithm and an edge enhancement filtering algorithm according to the seismic wave impedance inversion objective function specifically includes:

[0018] Initializing parameters and determining the initialized parameters; the parameters include: regularization parameters, initial error, iteration initial value, initial search direction, maximum number of iterations and iteration error limit;

[0019] According to the initialized parameters, the conjugate gradient algorithm and the edge enhancement filtering algorithm are used to iterate the seismic wave impedance inversion objective function. When the error is less than the iteration error limit or the iteration reaches the maximum number of iterations, the iteration stops and the seismic wave impedance inversion result is obtained.

[0020] Optionally, the initializing the parameters and determining the initialized parameters specifically includes:

[0021] Use trial and error to determine the regularization parameter;

[0022] Using the formula Determine the initial error ;in, For earthquake data, is the seismic wavelet matrix, is the difference matrix, is the identity matrix, is the regularization parameter, and the superscript T is the transpose;

[0023] Using the formula Determine the initial value of the iteration ,in is the logarithm of initial wave impedance;

[0024] Using the formula Determine the initial search direction ;

[0025] Determine the maximum number of iterations and the iteration error limit.

[0026] Optionally, the seismic wave impedance inversion objective function is iterated using a conjugate gradient algorithm and an edge enhancement filtering algorithm according to the initialized parameters, and when the error is less than an iteration error limit or the iteration reaches a maximum number of iterations, the iteration stops to obtain a seismic wave impedance inversion result, which specifically includes:

[0027] Using the formula Determine the search step size; where, is the search step length calculated in the kth iteration, is the search direction calculated in the k-1th iteration, is the error calculated in the k-1th iteration, is the seismic wavelet matrix, is the difference matrix, is the regularization parameter, is the identity matrix, and the superscript T is the transpose;

[0028] Using the formula Determine the auxiliary variables; among them, is the auxiliary variable calculated in the kth iteration, is the logarithm of the wave impedance calculated in the k-1th iteration;

[0029] Using the formula Determine the logarithm of the wave impedance; where, is the logarithm of the wave impedance calculated in the kth iteration, is the filter weight parameter, is the Kuwahara filter operator;

[0030] Using the formula Determine the error; where, is the error calculated in the kth iteration;

[0031] Using the formula Coefficient of determination; where is the coefficient calculated in the kth iteration;

[0032] Using the formula Determine the search direction; where, is the search direction calculated in the kth iteration.

[0033] In a second aspect, the present application provides a seismic wave impedance inversion system based on edge enhancement, comprising:

[0034] A data acquisition module, used to acquire seismic data, seismic wavelets and an initial wave impedance model;

[0035] An objective function establishment module is used to establish a seismic wave impedance inversion objective function under a quadratic L2 norm regularization constraint based on the seismic data, the seismic wavelet and the initial wave impedance model and based on the convolution principle;

[0036] The result determination module is used to obtain the seismic wave impedance inversion result by using the conjugate gradient algorithm and the edge enhancement filtering algorithm according to the seismic wave impedance inversion objective function.

[0037] In a third aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any one of the above-mentioned seismic wave impedance inversion methods.

[0038] According to the specific embodiments provided in this application, this application has the following technical effects:

[0039] The present application provides a method, system and device for seismic wave impedance inversion based on edge enhancement. By acquiring seismic wavelets, seismic data and an initial wave impedance model, and based on the convolution principle, a seismic wave impedance inversion objective function under a quadratic L2 norm regularization constraint is established, and a conjugate gradient algorithm and an edge enhancement filtering algorithm are used to obtain seismic wave impedance inversion results. The present application can effectively characterize the edges of geological structures, improve the resolution of wave impedance inversion results, enhance the ability to identify oil and gas reservoirs, and provide high-resolution wave impedance inversion results with clear edges for subsequent high-precision reservoir prediction and fine structural interpretation. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0041] Figure 1 This is a flow chart of a seismic wave impedance inversion method in one embodiment of the present application;

[0042] Figure 2 A schematic diagram of a reference wave impedance model provided in an embodiment of the present application;

[0043] Figure 3 A schematic diagram of a seismic wavelet provided in an embodiment of the present application;

[0044] Figure 4 A schematic diagram of seismic data provided by an embodiment of the present application;

[0045] Figure 5 A schematic diagram of an initial wave impedance model provided in an embodiment of the present application;

[0046] Figure 6 A schematic diagram of seismic wave impedance inversion results based on quadratic L2 norm regularization provided in one embodiment of the present application;

[0047] Figure 7 A schematic diagram of seismic wave impedance inversion results based on edge enhancement provided in one embodiment of the present application;

[0048] Figure 8 A one-dimensional effect comparison diagram of seismic wave impedance inversion results based on quadratic L2 norm regularization provided in one embodiment of the present application;

[0049] Fig. 9 A one-dimensional effect comparison diagram of the seismic wave impedance inversion result based on edge enhancement provided in one embodiment of the present application;

[0050] Fig.10 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0052] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0053] In an exemplary embodiment, Figure 1 As shown, a seismic wave impedance inversion method based on edge enhancement is provided, which specifically includes the following S1 to S3. Among them:

[0054] S1: Obtain seismic data, seismic wavelets and initial wave impedance model.

[0055] In an exemplary embodiment, Figure 2 As shown in the figure, the reference wave impedance model has 600 seismic traces in the horizontal direction and 200 sampling points in the vertical direction, with a sampling interval of 1 ms. The wave impedance model is mainly composed of block strata, and there is an inclined fault between traces 200 and 300. Figure 3 As shown in the figure, the seismic wavelet with a main frequency of 40 Hz is convolved with the reference wave impedance model and random noise is added to obtain noisy seismic data with a signal-to-noise ratio of 10. The schematic diagram of seismic data is shown in Figure 4 shown.

[0056] In an exemplary embodiment, Figure 5 As shown in the figure, the initial wave impedance model is usually obtained by interpolating the logging data along the geological interpretation layer and then low-pass filtering. The initial wave impedance model is consistent with the reference wave impedance model in size, but its resolution is low and it cannot accurately identify the formation edge and fault position.

[0057] S2: According to the seismic data, the seismic wavelet and the initial wave impedance model, based on the convolution principle, a seismic wave impedance inversion objective function under the quadratic L2 norm regularization constraint is established.

[0058] The calculation formula of the seismic wave impedance inversion objective function under the quadratic L2 norm regularization constraint is as follows:

[0059] .

[0060] in, For earthquake data, is the seismic wavelet matrix, is the difference matrix, is the logarithm of the wave impedance, is the logarithm of the initial wave impedance, is the regularization parameter, is the quadratic L2 norm.

[0061] S3: According to the seismic wave impedance inversion objective function, a conjugate gradient algorithm and an edge enhancement filtering algorithm are used to obtain a seismic wave impedance inversion result.

[0062] S3 specifically includes:

[0063] S31: Based on the conjugate gradient algorithm, an iterative solution framework for the seismic impedance inversion objective function is constructed.

[0064] This application uses two calculation methods to calculate and compare the seismic wave impedance inversion results. The first is the seismic wave impedance inversion result based on quadratic L2 norm regularization.

[0065] Parameter initialization: Use trial and error to determine the regularization parameters , the given iteration initial value is the logarithm of the initial wave impedance , given the initial search direction , given the maximum number of iterations is , given the iterative error limit is , given the initial error , the calculation formula of the initial error is:

[0066] .

[0067] in, is the identity matrix.

[0068] Iterative solution: Perform the following operations until the iteration error limit tol is met or the maximum number of iterations niter is reached:

[0069] The search step length calculation formula is:

[0070] .

[0071] The calculation formula of wave impedance logarithm is:

[0072] .

[0073] The error calculation formula is:

[0074] .

[0075] Make a judgment: If , then stop the iteration and output .

[0076] The coefficient calculation formula is:

[0077] .

[0078] The search direction calculation formula is:

[0079] .

[0080] in, is the search step size calculated in the kth iteration, is the search direction calculated in the kth iteration, is the error calculated in the kth iteration, is the logarithm of the wave impedance calculated in the kth iteration, is the logarithm of the wave impedance calculated in the k-1th iteration, is the error calculated in the k-1th iteration, express The L2 norm of is the coefficient calculated in the kth iteration, is the search direction calculated in the k-1th iteration.

[0081] In an exemplary embodiment, Figure 6 As shown in the figure, although the seismic wave impedance inversion results based on quadratic L2 norm regularization can roughly reflect the spatial distribution of underground structures, they cannot clearly depict the stratum edge (the area indicated by the arrow) and fault information (the area indicated by the ellipse). Such wave impedance inversion results are not conducive to the fine identification of reservoirs.

[0082] Secondly, the edge enhancement filtering process is integrated into the iterative solution framework of the seismic wave impedance inversion objective function based on the conjugate gradient algorithm. The edge enhancement filtering algorithm used in this application is Kuwahara filtering.

[0083] Parameter initialization: Use trial and error to determine the regularization parameters , the given iteration initial value is the logarithm of the initial wave impedance , given the Kuwahara filter window size is , given the filter weight parameter , given the initial error , given the initial search direction , given the maximum number of iterations is , given the iterative error limit is , where the calculation formula for the initial error is:

[0084] .

[0085] in, is the identity matrix.

[0086] Iterative solution: Perform the following operations until Satisfy the iteration error limit tol or reach the maximum number of iterations niter:

[0087] The search step length calculation formula is:

[0088] .

[0089] The auxiliary variable calculation formula is:

[0090] .

[0091] The calculation formula of wave impedance logarithm is:

[0092] .

[0093] The error calculation formula is:

[0094] .

[0095] Make a judgment: If , then stop the iteration and output .

[0096] The coefficient calculation formula is:

[0097] .

[0098] The search direction calculation formula is:

[0099] .

[0100] in is the search step length calculated in the kth iteration, is the search direction calculated in the kth iteration, is the error calculated in the kth iteration, is the logarithm of the wave impedance calculated in the kth iteration, is the auxiliary variable calculated in the kth iteration, is the Kuwahara filter operator, The number of wave impedance logarithms calculated in the k-1th iteration, is the error calculated in the k-1th iteration, express The L2 norm of is the coefficient calculated in the kth iteration, is the search direction calculated in the k-1th iteration.

[0101] S32: Based on the conjugate gradient algorithm framework including the edge enhancement filtering process, the seismic wave impedance inversion objective function is iteratively solved and the iteration is stopped when the iteration termination condition is met.

[0102] Output the seismic wave impedance inversion results based on edge enhancement and compare them with the seismic wave impedance inversion results based on quadratic L2 norm regularization. It can be seen that Figure 7 As shown in the figure, the seismic wave impedance inversion results based on edge enhancement are more consistent with the reference wave impedance model, and the detailed depiction of geological structures is richer, such as the stratum edge (the area indicated by the arrow) and the fault (the area indicated by the ellipse), indicating that the seismic wave impedance inversion results based on edge enhancement have higher resolution.

[0103] In order to more intuitively reflect the superiority of the seismic wave impedance inversion results based on edge enhancement, the extraction Figure 6 and Figure 7 The inversion results of the 10th track are shown separately, such as Figure 8As shown in Figure 1, the one-dimensional inversion results of seismic wave impedance based on quadratic L2 norm regularization deviate far from the reference wave impedance model, and the inversion results are relatively "smooth", indicating that the inversion error is large. Fig. 9 As shown, the one-dimensional inversion result of seismic wave impedance based on edge enhancement is extremely close to the reference wave impedance model, and clearly depicts the "blocky" characteristics of the wave impedance model, indicating that the present application has higher inversion accuracy.

[0104] This application proposes a seismic wave impedance inversion method based on edge enhancement by integrating the edge enhancement filtering process into the iterative solution framework of the seismic wave impedance inversion objective function based on the conjugate gradient algorithm. This technical solution overcomes the defect that the traditional quadratic L2 norm regularization easily blurs the edge information of the inversion result, realizes the fine characterization of the geological structure edge, and can provide high-resolution wave impedance inversion results for subsequent high-precision reservoir prediction and fine structure interpretation.

[0105] In an exemplary embodiment, a seismic wave impedance inversion system based on edge enhancement is provided, comprising:

[0106] The data acquisition module is used to obtain seismic wavelets, seismic data and initial wave impedance models.

[0107] The objective function establishment module is used to establish a seismic wave impedance inversion objective function under a quadratic L2 norm regularization constraint based on the seismic wavelet, the seismic data and the initial wave impedance model and based on the convolution principle.

[0108] The result determination module is used to obtain the seismic wave impedance inversion result by using the conjugate gradient algorithm and the edge enhancement filtering algorithm according to the seismic wave impedance inversion objective function.

[0109] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Fig.10As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the seismic wave impedance inversion objective function, the reference wave impedance model and the initial wave impedance model. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a seismic wave impedance inversion method is implemented.

[0110] Those skilled in the art will understand that Fig.10 The structure shown in the figure is only a block diagram of a part of the structure related to the present application scheme, and does not constitute a limitation on the computer device to which the present application scheme is applied. The specific computer device may include Fig.10 More or fewer components may be shown, or certain components may be combined, or may have a different arrangement of components.

[0111] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0112] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A seismic wave impedance inversion method based on edge enhancement, characterized in that: The edge-enhancement-based seismic wave impedance inversion method comprises: Obtain seismic data, seismic wavelets and initial wave impedance model; According to the seismic data, the seismic wavelet and the initial wave impedance model, based on the convolution principle, a seismic wave impedance inversion objective function under a quadratic L2 norm regularization constraint is established; According to the seismic wave impedance inversion objective function, a conjugate gradient algorithm and an edge enhancement filtering algorithm are used to obtain a seismic wave impedance inversion result; The process of determining the seismic wave impedance inversion results is as follows: Using the formula Determine the search step size; where, is the search step length calculated in the kth iteration, is the search direction calculated in the k-1th iteration, is the error calculated in the k-1th iteration, is the seismic wavelet matrix, is the difference matrix, is the regularization parameter, is the identity matrix, and the superscript T is the transpose; Using the formula Determine the auxiliary variables; among them, is the auxiliary variable calculated in the kth iteration, is the logarithm of the wave impedance calculated in the k-1th iteration; Using the formula Determine the logarithm of the wave impedance; where, is the logarithm of the wave impedance calculated in the kth iteration, is the filter weight parameter, is the Kuwahara filter operator; Using the formula Determine the error; where, is the error calculated in the kth iteration; Using the formula Coefficient of determination; where is the coefficient calculated in the kth iteration; Using the formula Determine the search direction; where, is the search direction calculated in the kth iteration.

2. The edge-enhanced seismic impedance inversion method according to claim 1, characterized in that: The obtaining of seismic data, seismic wavelets and an initial wave impedance model specifically includes: Acquisition of seismic data; According to the seismic data, seismic wavelets are obtained.

3. The edge-enhanced seismic impedance inversion method according to claim 1, characterized in that: The method of establishing a seismic wave impedance inversion objective function under a quadratic L2 norm regularization constraint based on the seismic data, the seismic wavelet and the initial wave impedance model based on the convolution principle specifically includes: Using the formula Establishing the objective function of seismic impedance inversion under the constraint of quadratic L2 norm regularization ; in, For earthquake data, is the seismic wavelet matrix, is the difference matrix, is the logarithm of the wave impedance, is the logarithm of the initial wave impedance, is the regularization parameter, is the quadratic L2 norm.

4. The edge-enhanced seismic impedance inversion method according to claim 1, characterized in that: The seismic wave impedance inversion result is obtained by using a conjugate gradient algorithm and an edge enhancement filtering algorithm according to the seismic wave impedance inversion objective function, specifically including: Initializing parameters and determining the initialized parameters; the parameters include: regularization parameters, initial error, iteration initial value, initial search direction, maximum number of iterations and iteration error limit; According to the initialized parameters, the conjugate gradient algorithm and the edge enhancement filtering algorithm are used to iterate the seismic wave impedance inversion objective function. When the error is less than the iteration error limit or the iteration reaches the maximum number of iterations, the iteration stops to obtain the seismic wave impedance inversion result.

5. The edge-enhanced seismic impedance inversion method according to claim 4, characterized in that: Initializing the parameters and determining the initialized parameters specifically include: Use trial and error to determine the regularization parameter; Using the formula Determine the initial error ;in, For earthquake data, is the seismic wavelet matrix, is the difference matrix, is the identity matrix, is the regularization parameter, and the superscript T is the transpose; Using the formula Determine the initial value of the iteration ,in is the logarithm of initial wave impedance; Using the formula Determine the initial search direction ; Determine the maximum number of iterations and the iteration error limit.

6. A seismic wave impedance inversion system based on edge enhancement, characterized in that: The edge-enhancement-based seismic wave impedance inversion system comprises: A data acquisition module, used to acquire seismic data, seismic wavelets and an initial wave impedance model; An objective function establishment module is used to establish a seismic wave impedance inversion objective function under a quadratic L2 norm regularization constraint based on the seismic data, the seismic wavelet and the initial wave impedance model and based on the convolution principle; A result determination module is used to obtain a seismic wave impedance inversion result by using a conjugate gradient algorithm and an edge enhancement filtering algorithm according to the seismic wave impedance inversion objective function; The process of determining the seismic wave impedance inversion results is as follows: Using the formula Determine the search step size; where, is the search step length calculated in the kth iteration, is the search direction calculated in the k-1th iteration, is the error calculated in the k-1th iteration, is the seismic wavelet matrix, is the difference matrix, is the regularization parameter, is the identity matrix, and the superscript T is the transpose; Using the formula Determine the auxiliary variables; among them, is the auxiliary variable calculated in the kth iteration, is the logarithm of the wave impedance calculated in the k-1th iteration; Using the formula Determine the logarithm of the wave impedance; where, is the logarithm of the wave impedance calculated in the kth iteration, is the filter weight parameter, is the Kuwahara filter operator; Using the formula Determine the error; where, is the error calculated in the kth iteration; Using the formula Coefficient of determination; where is the coefficient calculated in the kth iteration; Using the formula Determine the search direction; where, is the search direction calculated in the kth iteration.

7. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the edge-enhanced seismic wave impedance inversion method described in any one of claims 1 to 5.

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