A seismic wave impedance inversion method, device, medium and product

Abstract

The present application discloses a seismic wave impedance inversion method, equipment, medium and product, which relate to the field of seismic exploration technology. The method comprises obtaining observed seismic data and well logging data; constructing a total variation regularization term by minimizing the norm; establishing a seismic wave impedance inversion objective function based on the observed seismic data and well logging data in combination with the total variation regularization term; optimizing and solving the seismic wave impedance inversion objective function to obtain a seismic wave impedance inversion result. The present application can overcome the defects of easy generation of step artifacts and inability to clearly depict reservoir details, thereby improving the accuracy of the wave impedance inversion result.

Description

Technical Field

[0001] The present application relates to the field of seismic exploration technology, and in particular to a seismic wave impedance inversion method, equipment, medium and product. Background Art

[0002] Seismic impedance inversion is a technique for inferring the impedance of underground media from surface seismic data. Since the impedance has a good correspondence with oil and gas reservoirs, seismic impedance inversion is widely used in reservoir prediction and fluid identification. Among the many seismic impedance inversion methods, the seismic inversion method based on total variation regularization is very popular because the formation impedance is usually distributed in blocks, and the total variation regularization with edge protection characteristics has an inherent advantage in characterizing block models.

[0003] Although this technology is now very mature, the traditional total variation regularization has limited ability to represent the sparseness of the signal and is prone to produce step artifacts during the inversion process, resulting in poor accuracy of the wave impedance inversion results in describing reservoir details. In addition, with the increasing efforts in oil and gas exploration and development in recent years, the scale of exploration targets has become smaller and smaller. In this context, the seismic wave impedance inversion method based on traditional total variation regularization has gradually failed to meet the needs of fine reservoir prediction. It is necessary to further improve the accuracy of the wave impedance inversion results, thereby providing support for the improvement of oil and gas exploration accuracy.

[0004] In view of the above problems, it is urgent to improve the existing total variation regularization method and provide a seismic wave impedance inversion method that can effectively enhance the wave impedance inversion accuracy and significantly reduce the step artifacts, so as to achieve a fine characterization of reservoir details. Summary of the invention

[0005] The purpose of this application is to provide a seismic wave impedance inversion method, equipment, medium and product, which can overcome the defects of easy generation of step artifacts and inability to clearly depict reservoir details, thereby improving the accuracy of wave impedance inversion results.

[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, comprising:

[0008] Acquire observational seismic data and well logging data;

[0009] By minimizing The norm constructs the total variation regularization term;

[0010] According to the observed seismic data and the logging data, combined with the total variation regularization term, a seismic wave impedance inversion objective function is established;

[0011] The seismic wave impedance inversion objective function is optimized and solved to obtain a seismic wave impedance inversion result.

[0012] Optionally, according to the observed seismic data and the logging data, combined with the total variation regularization term, a seismic wave impedance inversion objective function is established, including:

[0013] Obtaining a seismic wavelet according to the observed seismic data;

[0014] Obtaining low-frequency wave impedance according to the logging data;

[0015] A seismic wave impedance inversion objective function is established based on the observed seismic data, the seismic wavelet and the low-frequency wave impedance in combination with the total variation regularization term.

[0016] Optionally, according to the observed seismic data, the seismic wavelet and the low-frequency wave impedance, in combination with the total variation regularization term, a seismic wave impedance inversion objective function is established, including:

[0017] Establishing an error fitting term and a low-frequency constraint term according to the observed seismic data, the seismic wavelet and the low-frequency wave impedance;

[0018] The seismic wave impedance inversion objective function is obtained according to the error fitting term, the low-frequency constraint term and the total variation regularization term.

[0019] Optionally, the seismic impedance inversion objective function is expressed as :

[0020] ;

[0021] In the formula, is the error fitting term, is the low frequency constraint term, is the total variation regularization term, β for The regularization parameter of μ for The regularization parameter of Represents the logarithm of the wave impedance to be inverted.

[0022] Optionally, the error fitting term can be expressed as:

[0023] ;

[0024] In the formula, is the error fitting term, To observe earthquake data, is the seismic wavelet, is the first-order downward difference matrix, Represents the logarithm of the wave impedance to be inverted;

[0025] The low frequency constraint term can be expressed as:

[0026] ;

[0027] In the formula, is the low frequency constraint term, represents the logarithm of the wave impedance to be inverted, Represents the natural logarithm of low-frequency wave impedance.

[0028] Optionally, the total variation regularization term is expressed as:

[0029] ;

[0030] In the formula, is the total variation regularization term, represents the logarithm of the wave impedance to be inverted, for The first-order difference along the seismic trace direction, , for The first-order difference along the time direction, ; Indicated in i Time sampling point j +1 logarithm of the wave impedance to be inverted for the seismic trace, Indicated in i Time sampling point j The logarithm of the wave impedance to be inverted, Indicated in i +1 time sampling point j The logarithm of the wave impedance to be inverted for each seismic trace; , ; nt and nx Respectively represent the number of time sampling points and seismic traces; α is the weight parameter, .

[0031] Optionally, optimizing and solving the seismic wave impedance inversion objective function to obtain a seismic wave impedance inversion result includes:

[0032] The convex difference algorithm and the split Bregman algorithm are used to optimize and solve the seismic wave impedance inversion objective function to obtain the seismic wave impedance inversion result.

[0033] In a second 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 of the above-mentioned seismic wave impedance inversion methods.

[0034] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the above-described seismic wave impedance inversion methods.

[0035] In a fourth aspect, the present application provides a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of any of the above-mentioned seismic wave impedance inversion methods.

[0036] According to the specific embodiments provided in this application, this application has the following technical effects:

[0037] The present application provides a seismic wave impedance inversion method, device, medium and product, by minimizing The total variation regularization term is constructed based on the norm, and the seismic wave impedance inversion objective function is established in combination with the total variation regularization term. It is used for seismic wave impedance inversion, which can overcome the defects of the traditional seismic wave impedance inversion method based on total variation regularization, such as the easy generation of step artifacts and the inability to clearly characterize the reservoir details, and improve the accuracy of the wave impedance inversion results, thereby providing a high-resolution seismic wave impedance model for the fine prediction and quantitative evaluation of reservoirs. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] 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.

[0039] Figure 1 This is a flow chart of a seismic wave impedance inversion method in an embodiment of the present application;

[0040] Figure 2 A real wave impedance model diagram provided in Example 2 of the present application;

[0041] Figure 3 A schematic diagram of seismic wavelets provided in Example 2 of the present application;

[0042] Figure 4 A schematic diagram of observed seismic data provided in Example 2 of the present application;

[0043] Figure 5 A schematic diagram of a low-frequency wave impedance model provided in Example 2 of the present application;

[0044] Figure 6 A schematic diagram of the seismic wave impedance inversion results provided in Example 2 of the present application;

[0045] Figure 7 A schematic diagram of the residual between the real wave impedance model provided in Example 2 of the present application and the seismic wave impedance inversion result;

[0046] Figure 8 A comparison diagram of the inversion wave impedance model curve and the real wave impedance model curve provided in Example 2 of the present application;

[0047] Fig. 9 A schematic diagram of the structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0048] 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.

[0049] 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 with reference to the accompanying drawings and specific implementation methods.

[0050] Example 1

[0051] This embodiment provides a seismic wave impedance inversion method, which is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In this embodiment, the method is applied to seismic wave impedance inversion as an example for explanation. Figure 1 As shown, the method includes:

[0052] Step 1: Obtain observation seismic data and well logging data.

[0053] Step 2, by minimizing The norm constructs the total variation regularization term.

[0054] Step 3: Based on the observed seismic data and logging data, combined with the total variation regularization term, establish the seismic wave impedance inversion objective function.

[0055] Step 4: Optimize and solve the seismic wave impedance inversion objective function to obtain the seismic wave impedance inversion result.

[0056] As an optional implementation, in order to overcome the defect of easily generating staircase artifacts, the total variation regularization term constructed in step 2 can be expressed as:

[0057] .

[0058] In the formula, is the total variation regularization term, Represents the logarithm of the wave impedance to be inverted. for The first-order difference along the seismic trace direction, , for The first-order difference along the time direction, . Indicated in i Time sampling point j +1 logarithm of the wave impedance to be inverted for the seismic trace, Indicated in i Time sampling point j The logarithm of the wave impedance to be inverted, Indicated in i +1 time sampling point j The logarithm of the wave impedance to be inverted for each seismic trace. , . nt and nx Respectively represent the number of time sampling points and seismic traces; α is the weight parameter, , α Can be selected by trial and error.

[0059] As an optional implementation, in order to improve the effectiveness of the seismic wave impedance inversion results, the specific process of step 3 may include: obtaining a seismic wavelet based on the observed seismic data, and obtaining a low-frequency wave impedance based on the logging data; establishing an error fitting term and a low-frequency constraint term based on the observed seismic data, the seismic wavelet and the low-frequency wave impedance; and obtaining a seismic wave impedance inversion objective function based on the error fitting term, the low-frequency constraint term and the total variation regularization term.

[0060] Among them, the error fitting term can be expressed as: In the formula, is the error fitting term, To observe earthquake data, is the seismic wavelet, is the first-order downward difference matrix, represents the logarithm of the wave impedance to be inverted. The low-frequency constraint term can be expressed as: In the formula, is the low frequency constraint term, represents the logarithm of the wave impedance to be inverted (i.e. the natural logarithm of the real wave impedance model), Represents the natural logarithm of low-frequency wave impedance.

[0061] Seismic impedance inversion objective function It can be expressed as:

[0062] In the formula, is the error fitting term, is the low frequency constraint term, is the total variation regularization term, represents the logarithm of the wave impedance to be inverted, β for The regularization parameter of μ for The regularization parameter of . β and μ Can be selected by trial and error.

[0063] As an optional implementation, in order to improve the accuracy of the seismic wave impedance inversion results, the specific process of step 4 can be: using the convex difference algorithm and the split Bregman algorithm to optimize and solve the seismic wave impedance inversion objective function to obtain the seismic wave impedance inversion results.

[0064] For example, given the seismic impedance inversion objective function In the form of , the convex difference algorithm and the split Bregman algorithm can be combined to optimize the seismic impedance inversion objective function and obtain the logarithm of the impedance to be inverted. .right The final inversion wave impedance model (i.e., seismic wave impedance inversion result) can be obtained by performing exponential operation.

[0065] Example 2

[0066] According to the seismic wave impedance inversion method described in Example 1, an inversion effect test is performed based on the real wave impedance model. It should be noted that this embodiment provides a real wave impedance model, and the relevant data in the above seismic wave impedance inversion method are all obtained through the real wave impedance model. The order of obtaining the relevant data of the seismic wave impedance inversion method in this embodiment is not exactly the same as that in actual applications.

[0067] Step 1: Obtain observation seismic data and well logging data.

[0068] For example, Figure 2 As shown in the figure, a real wave impedance model is provided. The real wave impedance model contains 681 seismic traces in the horizontal direction and 321 time sampling points in the vertical direction. The sampling interval is 2ms. Figure 2 It can be seen that the real wave impedance model has obvious block features, in which the white part is a high-impedance salt body extending from left to right to the entire model.

[0069] like Figure 3 As shown, the main frequency of the seismic wavelet is 30 Hz. By convolving the seismic wavelet with the real wave impedance model and adding 20% ​​Gaussian random noise, we can get the following Figure 4 Observed earthquake data shown.

[0070] The real wave impedance model is low-pass filtered to obtain the low-frequency wave impedance model, such as Figure 5 In practical applications, the logging data is interpolated and extrapolated along the geological layer, and then low-pass filtered to obtain the low-frequency wave impedance model. Figure 2 and Figure 5 By comparison, it can be seen that although the low-frequency wave impedance model is consistent with the real wave impedance model in size, the resolution is low and only the outline of the high-impedance salt body can be roughly seen.

[0071] Step 2, by minimizing The norm constructs the total variation regularization term.

[0072] For example, the total variation regularization term is constructed according to the steps in Example 1. In this example, nt =321, nx = 681. The total variation regularization term can be viewed as based on The anisotropic total variation regularization term (i.e., the first term on the right side of the equation) that minimizes the norm is consistent with the The difference between the isotropic total variation regularization term (i.e. the second term on the right side of the equation) whose norm is minimized.

[0073] Step 3: Based on the observed seismic data and logging data, combined with the total variation regularization term, establish the seismic wave impedance inversion objective function.

[0074] For example, error fitting terms and low-frequency constraint terms are established based on observed seismic data, seismic wavelets, and low-frequency wave impedance models. Then, based on the error fitting terms, low-frequency constraint terms, and total variation regularization terms, the seismic wave impedance inversion objective function is obtained. For specific formulas, see Example 1.

[0075] Step 4: Optimize and solve the seismic wave impedance inversion objective function to obtain the seismic wave impedance inversion result.

[0076] For example, given the seismic impedance inversion objective function In the form of , the convex difference algorithm and the split Bregman algorithm can be combined to optimize the seismic impedance inversion objective function and obtain the logarithm of the impedance to be inverted. .right The final inversion wave impedance model (i.e., seismic wave impedance inversion result) can be obtained by performing exponential operation. The inversion wave impedance model is as follows: Figure 6 shown.

[0077] By comparison Figure 2 and Figure 6It can be obtained that the inversion wave impedance model is almost completely consistent with the real wave impedance model, especially it can clearly depict the stratum boundary and the spatial distribution of high impedance salt bodies, and no obvious staircase artifacts are found, indicating that the seismic wave impedance inversion method proposed in this application can obtain high-precision inversion results. By comparing the residuals between the real wave impedance model and the inversion wave impedance model, as shown in Figure 7 As shown, it can be found that there is no obvious signal leakage in the residual profile and the residual is small, which further confirms the effectiveness of the seismic wave impedance inversion method proposed in this application.

[0078] In addition, in order to more clearly compare the differences between the seismic wave impedance inversion results and the real wave impedance model, the 200th data in the real wave impedance model, the seismic wave impedance inversion results and the low-frequency wave impedance model are selected for comparison, as shown in Figure 2. Figure 8 As shown, it can be concluded that the seismic wave impedance inversion result curve is very close to the real wave impedance model curve, and can accurately reflect the step-like change characteristics of the formation wave impedance, which shows that the seismic wave impedance inversion result obtained according to the seismic wave impedance inversion method proposed in this application has a higher resolution.

[0079] 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. 9 As 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 data related to the seismic wave impedance inversion method. 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.

[0080] Those skilled in the art will understand that Fig. 9The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0081] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0082] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0083] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0084] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0085] The technical features of the above embodiments may be combined arbitrarily. 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.

[0086] 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, characterized in that: The seismic wave impedance inversion method comprises: Acquire observational seismic data and well logging data; By minimizing The norm constructs a total variation regularization term; the total variation regularization term is expressed as: ； In the formula, is the total variation regularization term, Represents the logarithm of the wave impedance to be inverted; for The first-order difference along the seismic trace direction, ; for The first-order difference along the time direction, ; represents the logarithm of the wave impedance to be inverted of the j+1th seismic trace at the i-th time sampling point, represents the logarithm of the jth wave impedance to be inverted at the i-th time sampling point, It represents the logarithm of the wave impedance to be inverted of the jth seismic trace at the i+1th time sampling point; , ; nt and nx represent the number of time sampling points and the number of seismic traces respectively; α is the weight parameter, ; According to the observed seismic data and the logging data, combined with the total variation regularization term, a seismic wave impedance inversion objective function is established; The seismic wave impedance inversion objective function is optimized and solved to obtain a seismic wave impedance inversion result.

2. The seismic wave impedance inversion method according to claim 1, characterized in that: According to the observed seismic data and the logging data, combined with the total variation regularization term, a seismic wave impedance inversion objective function is established, including: Obtaining a seismic wavelet according to the observed seismic data; Obtaining low-frequency wave impedance according to the logging data; A seismic wave impedance inversion objective function is established based on the observed seismic data, the seismic wavelet and the low-frequency wave impedance in combination with the total variation regularization term.

3. The seismic wave impedance inversion method according to claim 2, characterized in that: According to the observed seismic data, the seismic wavelet and the low-frequency wave impedance, combined with the total variation regularization term, a seismic wave impedance inversion objective function is established, including: Establishing an error fitting term and a low-frequency constraint term according to the observed seismic data, the seismic wavelet and the low-frequency wave impedance; The seismic wave impedance inversion objective function is obtained according to the error fitting term, the low-frequency constraint term and the total variation regularization term.

4. The seismic wave impedance inversion method according to claim 1, characterized in that: The seismic impedance inversion objective function is expressed as : ； In the formula, is the error fitting term, is the low frequency constraint term, is the total variation regularization term, β for The regularization parameter of μ for The regularization parameter of Represents the logarithm of the wave impedance to be inverted.

5. The seismic wave impedance inversion method according to claim 3, characterized in that: The error fitting term is expressed as: ； In the formula, is the error fitting term, To observe earthquake data, is the seismic wavelet, is the first-order downward difference matrix, Represents the logarithm of the wave impedance to be inverted; The low frequency constraint term is expressed as: ； In the formula, is the low frequency constraint term, represents the logarithm of the wave impedance to be inverted, Represents the natural logarithm of low-frequency wave impedance.

6. The seismic wave impedance inversion method according to claim 1, characterized in that: Optimizing and solving the seismic wave impedance inversion objective function to obtain the seismic wave impedance inversion result includes: The convex difference algorithm and the split Bregman algorithm are used to optimize and solve the seismic wave impedance inversion objective function to obtain the seismic wave impedance inversion result.

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 seismic wave impedance inversion method described in any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the seismic wave impedance inversion method described in any one of claims 1 to 6 is implemented.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the seismic wave impedance inversion method described in any one of claims 1 to 6 is implemented.

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