A method and device for high-resolution seismic characterization of thin-layer structures

Abstract

The present invention provides a method and device for high-resolution seismic characterization of thin-layer structures, which relates to the technical field of oil and gas exploration. The method and device specifically include the following steps: obtaining seismic data S to obtain seismic wavelet w(t), obtaining a well logging reflection coefficient sequence ξ(t) to obtain a well logging reflection coefficient subsegment ξ i (t), and generate a well logging reflection coefficient dictionary matrix F; construct a seismic inversion objective function based on the well logging reflection coefficient dictionary matrix F, seismic data S, and seismic data S, and solve the seismic inversion objective function using a nonlinear staggered iterative method to obtain a reflection coefficient vector R; obtain a desired seismic wavelet #imgabs0#, and perform a convolution operation on the desired seismic wavelet #imgabs1# and the reflection coefficient vector R to obtain thin-layer structure representation seismic data G. The method and apparatus provided by the present invention can alleviate the technical problem in existing seismic data processing methods that, in order to improve resolution, the effective frequency band is too narrow, resulting in an inability to effectively represent thin-layer structure seismic data.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas exploration, and in particular to a method and device for high-resolution seismic characterization of thin-layer structures. Background Art

[0002] Using reflected seismic waves to detect and characterize the spatial position and geometric structure of subsurface formations is a major area of ​​research in seismic exploration and plays a crucial role in oil and gas exploration and development. Due to the viscoelastic absorption effect of seismic waves in underground media, the high-frequency components of seismic waves are continuously absorbed and attenuated as the propagation distance increases. This viscoelastic absorption effect significantly reduces the resolution of seismic data and impairs its ability to characterize thin-layer structures. Therefore, improving the resolution of seismic data has long been a key research topic in oil and gas seismic exploration.

[0003] Deconvolution and inverse Q filtering are currently the most widely used methods for improving seismic data resolution. Deconvolution employs Wiener filtering for wavelet compression, implicitly imposing two fundamental assumptions: minimum-phase wavelets and white-noise reflection coefficients. It is a data-driven, high-resolution processing method. Inverse Q filtering, based on a formation absorption model, employs wavefield backpropagation to eliminate the effects of formation absorption on resolution, making it a model-driven, high-resolution processing method. Although their implementations differ, both methods operate within a linear theory framework for high-resolution processing. Their processing capabilities are limited by the effective frequency band of the seismic data, making them inadequate for characterizing thin-layer structures. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a method and device for high-resolution seismic characterization of thin-layer structures, so as to alleviate the technical problem in the existing seismic data processing method that the effective frequency band is too narrow in order to improve the resolution, and the thin-layer structure earthquake cannot be well characterized, broaden the effective frequency band in the high-resolution seismic data processing method, and improve the ability of seismic data to characterize thin-layer structures.

[0005] The present invention provides a method for high-resolution seismic characterization of thin-layer structures, which specifically includes the following steps:

[0006] Get seismic data S to get seismic wavelet w(t), get logging reflection coefficient sequence ξ(t) to get logging reflection coefficient subsegment ξ i (t), and generate the well logging reflection coefficient dictionary matrix F;

[0007] Constructing a seismic inversion objective function based on the well logging reflection coefficient dictionary matrix F, seismic data S, and seismic data S, and solving the seismic inversion objective function using a nonlinear staggered iterative method to obtain a reflection coefficient vector R;

[0008] Get expected seismic wavelet Based on the expected seismic wavelet A convolution operation is performed with the reflection coefficient vector R to obtain thin layer structure characterization seismic data G.

[0009] Preferably, the step of acquiring seismic data S to obtain seismic wavelet w(t) includes:

[0010] Acquire seismic data S, the seismic data S being used for information about the propagation of seismic waves underground, and perform Fourier transform on the seismic data S;

[0011] The spectral simulation method is used to obtain the seismic wavelet w(t) from the Fourier transformed seismic data S.

[0012] Preferably, the acquisition of the logging reflection coefficient sequence ξ(t) to obtain the logging reflection coefficient subsegment ξ i (t), and the steps of generating the well logging reflection coefficient dictionary matrix F include:

[0013] Obtain density logging and acoustic logging data to obtain the logging reflection coefficient series ξ(t);

[0014] The well logging reflection coefficient sequence ξ(t) is used to obtain the well logging reflection coefficient sub-segment ξ using the sliding window algorithm. i (t);

[0015] Based on the logging reflection coefficient sub-segment ξ i (t) Obtain logging reflection coefficient unit

[0016] Based on the logging reflection coefficient unit Generate the well logging reflection coefficient dictionary matrix F.

[0017] Preferably, the well logging reflection coefficient unit The steps of generating the well logging reflection coefficient dictionary matrix F include:

[0018] Simulated reflection coefficient subsegment ξ i (t) All combination results are stored in the reflection coefficient dictionary matrix F, where each column in F represents a combination result.

[0019] Preferably, based on the logging reflection coefficient sub-segment ξ i (t) Obtain logging reflection coefficient unit The steps include:

[0020] Given a scaling factor α and a scaling step size Δα, α min ≤α≤α max , where α min is the minimum scaling factor, α max is the maximum scaling factor,

[0021]

[0022] Preferably, the seismic inversion objective function is constructed using the following formula:

[0023]

[0024] W—seismic wavelet convolution matrix

[0025] R—reflection coefficient vector;

[0026] E k —Reflection coefficient segmentation matrix;

[0027] β k —Contribution of different dictionary elements in the matrix F to the reflection coefficient;

[0028] λ—Regularization factor.

[0029] Preferably, the acquisition of the expected seismic wavelet Based on the expected seismic wavelet The steps of performing convolution operation with the reflection coefficient vector R to obtain thin layer structure characterization seismic data G include:

[0030] The thin layer structure characterization seismic data G is obtained using the following formula:

[0031] in for The wavelet convolution matrix of .

[0032] In another aspect, the present invention embodies a thin-layer structure high-resolution seismic characterization apparatus comprising:

[0033] The first acquisition module is used to acquire seismic data S to obtain seismic wavelet w(t), and to acquire the well logging reflection coefficient sequence ξ(t) to obtain the well logging reflection coefficient subsegment ξ i (t), and generate the well logging reflection coefficient dictionary matrix F;

[0034] The second acquisition module is used to construct a seismic inversion objective function based on the well logging reflection coefficient dictionary matrix F, seismic data S, and seismic data S, and solve the seismic inversion objective function using a nonlinear staggered iterative method to obtain a reflection coefficient vector R;

[0035] The third acquisition module: used to obtain the expected seismic wavelet Based on the expected seismic wavelet A convolution operation is performed with the reflection coefficient vector R to obtain thin layer structure characterization seismic data G.

[0036] The embodiments of the present invention bring the following beneficial effects: The present invention provides a method and apparatus for high-resolution seismic characterization of thin-layer structures, which relates to the technical field of oil and gas exploration, and specifically comprises the following steps: obtaining seismic data S to obtain seismic wavelet w(t), obtaining a well logging reflection coefficient sequence ξ(t) to obtain a well logging reflection coefficient subsegment ξ i (t), and generate the well logging reflection coefficient dictionary matrix F; construct the seismic inversion objective function based on the well logging reflection coefficient dictionary matrix F, seismic data S, and seismic data S, and use the nonlinear staggered iterative method to solve the seismic inversion objective function to obtain the reflection coefficient vector R; obtain the expected seismic wavelet Based on expected seismic wavelet A convolution operation is performed with the reflection coefficient vector R to obtain thin-layer structure characterization seismic data G. The method and apparatus provided by the present invention can alleviate the technical problem in existing seismic data processing methods that the effective frequency band is too narrow to improve resolution, making it difficult to well characterize thin-layer structure earthquakes. This can broaden the effective frequency band in high-resolution seismic data processing methods and improve the ability of seismic data to characterize thin-layer structures.

[0037] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or understood by practicing the present invention. The purposes and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0038] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0040] Figure 1 A flow chart of a method for high-resolution seismic characterization of thin-layer structures provided by an embodiment of the present invention;

[0041] Figure 2 A seismic data cross-section diagram of a method for high-resolution seismic characterization of thin-layer structures provided by an embodiment of the present invention;

[0042] Figure 3 A seismic wavelet of a thin-layer structure high-resolution seismic characterization method provided by an embodiment of the present invention;

[0043] Figure 4 A well logging reflection coefficient sequence of a thin layer structure high-resolution seismic characterization method provided by an embodiment of the present invention;

[0044] Figure 5 The embodiment of the present invention provides a method for high-resolution seismic characterization of thin-layer structures, and an expected output wavelet;

[0045] Figure 6 A thin-layer structure high-resolution seismic characterization method provided by an embodiment of the present invention characterizes high-resolution seismic data of thin-layer structure. DETAILED DESCRIPTION

[0046] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0047] Currently, deconvolution and inverse Q filtering are the most widely used methods for improving seismic data resolution. Deconvolution uses Wiener filtering for wavelet compression, implicitly embodying two basic assumptions: minimum phase wavelet and white noise reflection coefficient. It is a data-driven high-resolution processing method. Inverse Q filtering, based on a formation absorption model, uses wavefield backpropagation to eliminate the impact of formation absorption on resolution. It is a model-driven high-resolution processing method. Although their implementation methods differ, both methods are high-resolution processing methods within a linear theory framework. Their processing capabilities are limited by the effective frequency band of seismic data and cannot effectively meet the practical needs of seismic characterization of thin-layer structures. Based on this, the embodiment of the present invention provides a method for high-resolution seismic characterization of thin-layer structures. This method can alleviate the technical problem of existing seismic data processing methods in which the effective frequency band is too narrow and cannot effectively characterize thin-layer structures. It can also broaden the effective frequency band in high-resolution seismic data processing methods and improve the ability of seismic data to characterize thin-layer structures.

[0048] To facilitate understanding of this embodiment, a high-resolution seismic characterization method for thin-layer structures disclosed in an embodiment of the present invention is first introduced in detail.

[0049] Example 1:

[0050] Combine Figures 1 to 6 The first embodiment of the present invention provides a method for high-resolution seismic characterization of thin-layer structures, which specifically includes the following steps:

[0051] 1. A method for high-resolution seismic characterization of thin-layer structures, comprising the following steps:

[0052] Get seismic data S to get seismic wavelet w(t), get logging reflection coefficient sequence ξ(t) to get logging reflection coefficient subsegment ξ i (t), and generate the well logging reflection coefficient dictionary matrix F;

[0053] Constructing a seismic inversion objective function based on the well logging reflection coefficient dictionary matrix F, seismic data S, and seismic data S, and solving the seismic inversion objective function using a nonlinear staggered iterative method to obtain a reflection coefficient vector R;

[0054] Get expected seismic wavelet Based on the expected seismic wavelet A convolution operation is performed with the reflection coefficient vector R to obtain thin layer structure characterization seismic data G.

[0055] Preferably, the step of acquiring seismic data S to obtain seismic wavelet w(t) includes:

[0056] Acquire seismic data S, the seismic data S being used for information about the propagation of seismic waves underground, and perform Fourier transform on the seismic data S;

[0057] The spectral simulation method is used to obtain the seismic wavelet w(t) from the Fourier transformed seismic data S.

[0058] Preferably, the acquisition of the logging reflection coefficient sequence ξ(t) to obtain the logging reflection coefficient subsegment ξ i (t), and the steps of generating the well logging reflection coefficient dictionary matrix F include:

[0059] Obtain density logging and acoustic logging data to obtain the logging reflection coefficient series ξ(t);

[0060] The well logging reflection coefficient sequence ξ(t) is used to obtain the well logging reflection coefficient sub-segment ξ using the sliding window algorithm. i (t);

[0061] Based on the logging reflection coefficient sub-segment ξ i (t) Obtain logging reflection coefficient unit

[0062] Based on the logging reflection coefficient unit Generate the well logging reflection coefficient dictionary matrix F.

[0063] Preferably, the well logging reflection coefficient unit The steps of generating the well logging reflection coefficient dictionary matrix F include:

[0064] Simulated reflection coefficient subsegment ξ i(t) All combination results are stored in the reflection coefficient dictionary matrix F, where each column in F represents a combination result.

[0065] Preferably, based on the logging reflection coefficient sub-segment ξ i (t) Obtain logging reflection coefficient unit The steps include:

[0066] Given a scaling factor α and a scaling step size Δα, α min ≤α≤α max , where α min is the minimum scaling factor, α max is the maximum scaling factor,

[0067]

[0068] Preferably, the seismic inversion objective function is constructed using the following formula:

[0069]

[0070] W—seismic wavelet convolution matrix

[0071] R—reflection coefficient vector;

[0072] E k —Reflection coefficient segmentation matrix;

[0073] β k —Contribution of different dictionary elements in the matrix F to the reflection coefficient;

[0074] λ—Regularization factor.

[0075] Preferably, the acquisition of the expected seismic wavelet Based on the expected seismic wavelet The steps of performing convolution operation with the reflection coefficient vector R to obtain thin layer structure characterization seismic data G include:

[0076] The thin layer structure characterization seismic data G is obtained using the following formula:

[0077] in for The wavelet convolution matrix of .

[0078] Example 2:

[0079] A second embodiment of the present invention provides a thin-layer structure high-resolution seismic characterization device, comprising:

[0080] The first acquisition module is used to acquire seismic data S to obtain seismic wavelet w(t), and to acquire the well logging reflection coefficient sequence ξ(t) to obtain the well logging reflection coefficient subsegment ξi (t), and generate the well logging reflection coefficient dictionary matrix F;

[0081] The second acquisition module is used to construct a seismic inversion objective function based on the well logging reflection coefficient dictionary matrix F, seismic data S, and seismic data S, and solve the seismic inversion objective function using a nonlinear staggered iterative method to obtain a reflection coefficient vector R;

[0082] The third acquisition module: used to obtain the expected seismic wavelet Based on the expected seismic wavelet A convolution operation is performed with the reflection coefficient vector R to obtain thin layer structure characterization seismic data G.

[0083] Unless otherwise specifically stated, the relative steps, numerical expressions and values ​​of the components and steps set forth in these embodiments do not limit the scope of the present invention.

[0084] The device provided in the embodiment of the present invention has the same implementation principle and technical effects as those in the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the device embodiment, reference can be made to the corresponding content in the aforementioned method embodiment.

[0085] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings.

[0086] The flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to multiple embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, program segment or part of code, and the module, program segment or part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0087] In addition, in the description of the embodiments of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; they may refer to mechanical connections or electrical connections; they may refer to direct connections or indirect connections through an intermediate medium; and they may refer to internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0088] In the description of the present invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0089] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0090] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. There may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed may be through some communication interface, indirect coupling or communication connection of devices or units, which may be electrical, mechanical or other forms.

[0091] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0092] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0093] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the above-described embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above-described embodiments within the technical scope disclosed by the present invention, or replace some of the technical features therein with equivalents. Such modifications, changes, or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of protection of the claims.

Claims

1. A method for high-resolution seismic characterization of thin-layer structures, characterized in that: The specific steps include: Get seismic data S to get seismic wavelet w(t), get logging reflection coefficient sequence ξ(t) to get logging reflection coefficient subsegment ξ i (t), and generate the well logging reflection coefficient dictionary matrix F; Constructing a seismic inversion objective function based on the well logging reflection coefficient dictionary matrix F, seismic data S, and seismic wavelet w(t), and solving the seismic inversion objective function using a nonlinear staggered iterative method to obtain a reflection coefficient vector R; Get expected seismic wavelet Based on the expected seismic wavelet Performing a convolution operation with the reflection coefficient vector R to obtain thin layer structure characterization seismic data G; The well logging reflection coefficient sequence ξ(t) is obtained to obtain the well logging reflection coefficient subsegment ξ i (t), and the steps of generating the well logging reflection coefficient dictionary matrix F include: Obtain density logging and acoustic logging data to obtain the logging reflection coefficient series ξ(t); The well logging reflection coefficient sequence ξ(t) is used to obtain the well logging reflection coefficient sub-segment ξ using the sliding window algorithm. i (t); Based on the logging reflection coefficient sub-segment ξ i (t) Obtain logging reflection coefficient unit Based on the logging reflection coefficient unit Generate logging reflection coefficient dictionary matrix F; The well logging reflection coefficient unit The steps of generating the well logging reflection coefficient dictionary matrix F include: Simulated reflection coefficient subsegment ξ i (t) All combination results are stored in the reflection coefficient dictionary matrix F, where each column in F represents a combination result; Based on the logging reflection coefficient sub-segment ξ i (t) Obtain logging reflection coefficient unit The steps include: Given a scaling factor α and a scaling step size Δα, α min ≤α≤α max , where α min is the minimum scaling factor, α max is the maximum scaling factor, The seismic inversion objective function is constructed using the following formula: W—seismic wavelet convolution matrix R—reflection coefficient vector; E k —Reflection coefficient segmentation matrix; β k —Contribution of different dictionary elements in the matrix F to the reflection coefficient; λ—Regularization factor.

2. The method according to claim 1, characterized in that The step of acquiring seismic data S to obtain seismic wavelet w(t) includes: Acquire seismic data S, where the seismic data S is used to represent information about the propagation of seismic waves underground, and perform Fourier transform on the seismic data S; The spectral simulation method is used to obtain the seismic wavelet w(t) from the Fourier transformed seismic data S.

3. The method according to claim 1, characterized in that The acquisition of the expected seismic wavelet Based on the expected seismic wavelet The steps of performing convolution operation with the reflection coefficient vector R to obtain thin layer structure characterization seismic data G include: The thin layer structure characterization seismic data G is obtained using the following formula: in for The wavelet convolution matrix of .

4. A thin layer structure high-resolution seismic characterization device, characterized in that: include: The first acquisition module is used to acquire seismic data S to obtain seismic wavelet w(t), and to acquire the well logging reflection coefficient sequence ξ(t) to obtain the well logging reflection coefficient subsegment ξ i (t), and generate the well logging reflection coefficient dictionary matrix F; The second acquisition module is used to construct a seismic inversion objective function based on the well logging reflection coefficient dictionary matrix F, seismic data S, and seismic wavelet w(t), and solve the seismic inversion objective function using a nonlinear staggered iterative method to obtain a reflection coefficient vector R; The third acquisition module: used to obtain the expected seismic wavelet Based on the expected seismic wavelet Performing a convolution operation with the reflection coefficient vector R to obtain thin layer structure characterization seismic data G; The well logging reflection coefficient sequence ξ(t) is obtained to obtain the well logging reflection coefficient subsegment ξ i (t), and generate the well logging reflection coefficient dictionary matrix F including: Obtain density logging and acoustic logging data to obtain the logging reflection coefficient series ξ(t); The well logging reflection coefficient sequence ξ(t) is used to obtain the well logging reflection coefficient sub-segment ξ using the sliding window algorithm. i (t); Based on the logging reflection coefficient sub-segment ξ i (t) Obtain logging reflection coefficient unit Based on the logging reflection coefficient unit Generate logging reflection coefficient dictionary matrix F; The well logging reflection coefficient unit Generating the well logging reflection coefficient dictionary matrix F includes: Simulated reflection coefficient subsegment ξ i (t) All combination results are stored in the reflection coefficient dictionary matrix F, where each column in F represents a combination result; Based on the logging reflection coefficient sub-segment ξ i (t) Obtain logging reflection coefficient unit include: Given a scaling factor α and a scaling step size Δα, α min ≤α≤α max , where α min is the minimum scaling factor, α max is the maximum scaling factor, The seismic inversion objective function is constructed using the following formula: W—seismic wavelet convolution matrix R—reflection coefficient vector; E k —Reflection coefficient segmentation matrix; β k —Contribution of different dictionary elements in the matrix F to the reflection coefficient; λ—Regularization factor.

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