A seismic identification method and system for the internal structure of small-scale low-order fault zones

Abstract

The present invention belongs to the technical field of oil and gas field exploration and development, and discloses a seismic identification method and system for the internal structure of a small-scale, low-order fault zone, wherein the method includes: determining a seismic frequency fraction of a target layer segment based on geological data; determining a tuning frequency fraction of a main component of a small fault and a background frequency fraction of a small fault based on the seismic frequency fraction of the target layer segment; performing breakpoint sharpening on the tuning frequency fraction of the main component of the small fault to obtain seismic amplitude data after breakpoint sharpening; merging the small fault background frequency fraction and the seismic amplitude data after breakpoint sharpening to obtain a fused data volume; and determining the internal structure of the fault zone based on the fused data volume. The present invention takes into account the internal structure of a small fault zone and its seismic reflection characteristics, introduces breakpoint sharpening for the first time, and strengthens the longitudinal continuity characteristics of the small fault. The present invention can improve the recognition accuracy of small faults on seismic data, and the reflected small fault structure is more certain.

Description

Technical Field

[0001] The present invention belongs to the technical field of oil and gas field exploration and development, and in particular relates to a seismic identification method and system for the internal structure of a small-scale low-sequence fault zone. Background Art

[0002] At present, the oil reservoir types in some exploration areas are mainly block oil and gas reservoirs. The oil and gas exploration in the fault lake basin has reached the middle and late stages of exploration. The large-scale structural oil and gas reservoirs have been basically drilled out, and the exploration of the remaining small fault block oil and gas reservoirs is becoming more and more difficult. The identification of low-order small faults and their internal structures has become a major challenge.

[0003] Existing technologies use features such as amplitude anomalies and morphological misalignment of travel-time events in seismic data to identify small faults, but do not pay attention to the breakpoint sharpening characteristics based on the frequency response of small faults. Summary of the Invention

[0004] To address the above problems, the present invention provides a seismic identification method and system for the internal structure of a small-scale, low-order fault zone, which adopts the following technical solutions:

[0005] The present invention provides a seismic identification method for the internal structure of a small-scale low-order fault zone, comprising the following steps: determining a seismic frequency fraction of a target layer segment based on geological data; determining a tuning frequency fraction of a main component of a small fault and a background frequency fraction of a small fault based on the seismic frequency fraction of the target layer segment; performing breakpoint sharpening processing on the tuning frequency fraction of the main component of the small fault to obtain seismic amplitude data after the breakpoint sharpening processing; merging the small fault background frequency fraction and the seismic amplitude data after the breakpoint sharpening processing to obtain a fused data volume; and determining the internal structure of the fault zone based on the fused data volume.

[0006] Furthermore, determining the seismic frequency distribution of the target layer segment based on the geological data includes the following steps:

[0007] The seismic frequency volume of the target layer is calculated based on the geological data, and the seismic frequency volume is obtained by Fourier transform.

[0008] Furthermore, according to the seismic frequency fraction of the target layer segment, the tuning frequency fraction of the main component of the small fault and the background frequency fraction of the small fault are determined, which includes the following steps:

[0009] Compare the coherent response characteristics of small faults in each main component tuning frequency division body under the same coherence parameters, and take the frequency range with the best performance of small fault characteristics as the main component tuning frequency division body of small fault;

[0010] The seismic data volume of the tuning frequency volume of the minor fault main component is screened out from the seismic amplitude data of the seismic frequency volume corresponding to the target layer segment to obtain the minor fault background frequency volume.

[0011] Furthermore, the breakpoint sharpening process is performed on the tuned frequency fraction of the main component of the small fault to obtain the seismic amplitude data after the breakpoint sharpening process, which includes the following steps:

[0012] The seismic attributes transformed by Laplacian operator template are used to process the tuning frequency volume of the main component of the small fault to obtain the seismic amplitude data after Laplacian sharpening.

[0013] According to the longitudinal sharpening factor and the seismic amplitude data after Laplacian sharpening, the seismic amplitude data after breakpoint sharpening is determined.

[0014] Furthermore, the small fault background frequency volume and the seismic amplitude data after breakpoint sharpening are merged to obtain a fused data volume, which includes the following steps:

[0015] The small fault background frequency division volume and the seismic amplitude data after breakpoint sharpening are weighted to obtain a fused data volume, wherein the weight coefficient of the seismic amplitude data after breakpoint sharpening is not less than 60%.

[0016] Furthermore, the internal structure of the fault zone is determined based on the fused data volume, including the following steps:

[0017] The third generation coherence calculation is performed on the fused data volume, in which the aperture in the coherence parameter takes a 9-point operator, and the internal structure of the fault zone is determined based on the planar combination characteristics of small faults on the coherence results.

[0018] Furthermore, geological data includes petroleum seismic data, oil and gas well logging data, and small fault interpretation data.

[0019] The present invention also provides a seismic identification system for the internal structure of a small-scale low-sequence fault zone, comprising:

[0020] The first calculation module is used to determine the seismic frequency division volume of the target layer segment according to the geological data;

[0021] The second calculation module is used to determine the tuning frequency fraction of the main component of the small fault and the background frequency fraction of the small fault according to the seismic frequency fraction of the target layer segment;

[0022] The third calculation module is used to perform breakpoint sharpening processing on the tuned frequency fraction of the main component of the small fault to obtain seismic amplitude data after breakpoint sharpening processing;

[0023] The fourth calculation module is used to merge the small fault background frequency volume and the seismic amplitude data after breakpoint sharpening to obtain a fused data volume;

[0024] The fifth calculation module is used to determine the internal structure of the fault zone based on the fused data volume.

[0025] Furthermore, the second calculation module is specifically configured to:

[0026] Compare the coherent response characteristics of small faults in each main component tuning frequency division body under the same coherence parameters, and take the frequency range with the best performance of small fault characteristics as the main component tuning frequency division body of small fault;

[0027] The seismic data volume of the tuning frequency volume of the small fault main component is screened out from the seismic amplitude data of the seismic frequency volume corresponding to the target layer segment to obtain the small fault background frequency volume.

[0028] Furthermore, the third calculation module is specifically used for:

[0029] The seismic attributes transformed by Laplacian operator template are used to process the tuning frequency volume of the main component of the small fault to obtain the seismic amplitude data after Laplacian sharpening.

[0030] According to the longitudinal sharpening factor and the seismic amplitude data after Laplacian sharpening, the seismic amplitude data after breakpoint sharpening is determined.

[0031] Furthermore, the fourth calculation module is specifically configured to:

[0032] The small fault background frequency division volume and the seismic amplitude data after breakpoint sharpening are weighted to obtain a fused data volume, wherein the weight coefficient of the seismic amplitude data after breakpoint sharpening is not less than 60%.

[0033] Beneficial effects of the present invention:

[0034] 1. This invention takes into account the internal structure of small fault zones and their seismic reflection characteristics, introduces breakpoint sharpening processing for the first time, strengthens the vertical continuity characteristics of small faults, and fuses them with background data in a weighted manner to form a thematic data body reflecting the characteristics of small faults.

[0035] 2. The present invention can improve the accuracy of identifying small faults in seismic data, and the reflected small fault structures are more certain, providing a new method for interpreting small faults. The evaluation effect brought by this method is more in line with actual geological laws, so as to reduce the risk of fault trap drilling and provide a geological evaluation reference for optimizing the development plan of block oil and gas reservoirs in the development stage.

[0036] 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 will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the 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.

[0038] Figure 1 A schematic flow chart of a method for seismic identification of the internal structure of a small-scale low-sequence fault zone according to an embodiment of the present invention is shown;

[0039] Figure 2 A schematic structural diagram of a seismic identification system for the internal structure of a small-scale low-sequence fault zone according to an embodiment of the present invention is shown;

[0040] Figure 3a A schematic diagram of the coherence results of the original processed data of a certain exploration area is shown;

[0041] Figure 3b A schematic diagram shows the coherence results of applying the seismic identification method and system for the internal structure of a small-scale low-order fault zone according to an embodiment of the present invention to a certain exploration area. DETAILED DESCRIPTION

[0042] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0043] The present invention provides a seismic identification method and system for the internal structure of small-scale low-order fault zones, which utilizes the frequency response characteristics and breakpoint sharpening characteristics of small faults to solve the problem of identifying small-scale low-order faults in seismic data.

[0044] like Figure 1 As shown, a seismic identification method for the internal structure of a small-scale low-order fault zone includes the following steps:

[0045] S1. Acquire geological data, including petroleum seismic data, oil and gas well logging data, and fault interpretation data.

[0046] S2. Determining the seismic frequency fraction of the target layer segment according to the geological data includes: calculating the seismic frequency fraction of the target layer segment according to the geological data, and obtaining the seismic frequency fraction by using Fourier transform.

[0047] S3, according to the seismic frequency fraction of the target layer segment, determining the tuning frequency fraction of the main component of the small fault and the background frequency fraction of the small fault, including the following steps:

[0048] S31. Compare the coherent response characteristics of the small fault in each main component tuning frequency division body under the same coherence parameters, and take the frequency range with the best performance of the small fault characteristics as the small fault main component tuning frequency division body, denoted as A(x,y).

[0049] S32. Filter out the seismic data volume of the tuning frequency volume of the small fault main component from the seismic amplitude data of the seismic frequency volume corresponding to the target layer segment to obtain the small fault background frequency volume, recorded as A1(x,y), as the background.

[0050] S4, performing breakpoint sharpening processing on the tuned frequency fraction of the main component of the small fault to obtain seismic amplitude data after breakpoint sharpening processing, including the following steps:

[0051] S41. Process the tuned frequency-fractional volume of the main component of the small fault using the seismic attributes transformed by the Laplacian operator template to obtain seismic amplitude data after Laplacian sharpening, as follows:

[0052]

[0053] Where A E (x, y) represents the seismic amplitude data after Laplacian sharpening, A(x, y) is the tuning frequency-dividing volume of the main component of the small fault, which is dimensionless; Represents the seismic attributes transformed by the Laplacian operator template.

[0054] In this step, the image contrast is enhanced by sharpening, making the edges, contours and details clearer. Usually, the corresponding sharpening operation is performed according to the cause of image blur, which belongs to the content of image restoration. The essence of image blur is that the image is caused by averaging or integration operations. Therefore, the image can be restored by performing restoration operations such as differential operations to make the image clear. From a spectral perspective, the essence of image blur is that its high-frequency components are attenuated. Therefore, the image can be clarified by high-pass filtering. However, it should be noted that the image that can be sharpened must have a high signal-to-noise ratio. Otherwise, the signal-to-noise ratio of the image after sharpening will be lower, so that the noise increases more than the signal. Therefore, it is generally necessary to remove or reduce the noise before performing sharpening.

[0055] The basic idea of ​​sharpening by the Laplacian algorithm in this step is that when the grayscale of the central pixel of the neighborhood is lower than the average grayscale of other pixels in its neighborhood, the grayscale of this central pixel should be further reduced, and when it is higher, the grayscale of the central pixel should be further increased, thereby achieving image sharpening.

[0056] The Laplacian algorithm works by subtracting the grayscale values ​​of surrounding pixels from 9 times its own grayscale value to create its new grayscale value. If a bright spot appears in a dark area, sharpening will brighten it, increasing the image noise. This is because edges in an image are areas where grayscale transitions occur.

[0057] S42. Determine the seismic amplitude data after breakpoint sharpening based on the longitudinal sharpening factor and the seismic amplitude data after Laplacian sharpening, as follows:

[0058] A F (x,y)=K*A E (x, y) (2)

[0059] Where A F (x, y) is the seismic amplitude data after breakpoint sharpening, dimensionless; A E (x, y) is the dimensionless seismic amplitude data after the commonly used Laplacian sharpening process; k is the longitudinal sharpening factor, which is a transformation template related to the sharpening diffusion effect. This parameter aims to enhance the vertical continuity of small faults.

[0060] Among them, the vertical sharpening factor k is as follows:

[0061]

[0062] S5, merging the small fault background frequency volume and the seismic amplitude data after breakpoint sharpening to obtain a fused data volume, including the following steps:

[0063] The background frequency volume of the small fault is weighted with the seismic amplitude data after breakpoint sharpening to obtain a fused data volume, wherein the weight coefficient of the seismic amplitude data after breakpoint sharpening is at least 60%, and a fused data volume M(x,y) with the small fault as the theme feature is obtained;

[0064] M(x,y)=w1*A F (x,y)+w2*A1(x,y) (4)

[0065] Among them, W1+W2=1, where W1 represents the weight coefficient of the seismic amplitude data after breakpoint sharpening processing; W2 represents the weight coefficient of the small fault background frequency partition body.

[0066] Where A F (x, y) is the seismic amplitude data after breakpoint sharpening; A1(x, y) is the small fault background frequency body.

[0067] S6. Determine the internal structure of the fault zone based on the fused data volume, including the following steps:

[0068] The publicly available third-generation coherence calculation is performed on the fused data volume, in which the aperture in the coherence parameter is a 9-point operator. The internal structure of the fault zone is determined based on the planar combination characteristics of small faults on the coherence results.

[0069] The third-generation coherence calculation method was proposed by Gersztenkorn and Marfurt (1999) and applied to the eigenvalue method of seismic coherence analysis. The third-generation coherence calculation method calculates the eigenvalues ​​of the covariance matrix and takes the ratio of the maximum eigenvalue to the matrix trace as the coherence value.

[0070] Typically, for a covariance matrix, the eigenvectors and eigenvalues ​​are sorted in descending order based on their ability to express the energy of the data within the analysis aperture and the main variance of the matrix. The top eigenvalues ​​and eigenvectors contain the most information. Third-generation coherent volume technology only requires the trace and first eigenvalue of the covariance matrix to estimate the characteristic structure coherence.

[0071] In this step, the third-generation coherence volume algorithm is used to convert seismic data into coherence volume data that is more conducive to crack and fault detection. It has better detection capabilities for small cracks and makes the identification results more accurate.

[0072] Based on the above-mentioned seismic identification method of the internal structure of small-scale low-order fault zones, such as Figure 2 As shown, the present invention also provides a seismic identification system for the internal structure of a small-scale low-order fault zone, comprising a first calculation module, a second calculation module, a third calculation module, a fourth calculation module and a fifth calculation module.

[0073] Among them, the first calculation module is used to determine the seismic frequency division body of the target layer segment based on geological data; the second calculation module is used to determine the small fault main component tuning frequency division body and the small fault background frequency division body based on the seismic frequency division body of the target layer segment; the third calculation module is used to perform breakpoint sharpening processing on the small fault main component tuning frequency division body to obtain the seismic amplitude data after breakpoint sharpening processing; the fourth calculation module is used to merge the small fault background frequency division body and the seismic amplitude data after breakpoint sharpening processing to obtain a fused data body; the fifth calculation module is used to determine the internal structure of the fault zone based on the fused data body.

[0074] Taking a small fault in a certain exploration area as an example, the implementation process and effect of the present invention are described. Figure 3b The middle figure shows the distribution of small fault planes after applying the identification method of the present invention. It is found that there are obvious changes within the same fault zone. This change reflects the internal structural differences and the segmented growth of the fault. Figure 3b Within the range shown by the middle circle, Figure 3aCompared with the middle figure, the number of developed small fractures is greater, reflecting that more small fractures can be identified after being processed by the identification method of the present invention, reflecting the technical advantages of the present invention. Figure 3b The figure shows the characteristics of the widespread development of small faults in the study area, which is consistent with the geological fact that small faults are common in the Tongbo Temple Formation in the area, indicating that the application effect of the present invention conforms to geological laws and is reliable.

[0075] The present invention takes into account the internal structure of small fault zones and their seismic reflection characteristics, introduces breakpoint sharpening processing for the first time, strengthens the vertical continuity characteristics of small faults, and fuses them with background data in a weighted manner to form a thematic data body reflecting the characteristics of small faults.

[0076] The present invention can improve the recognition accuracy of small faults in seismic data, and the reflected small fault structure is more certain, providing a new method for the interpretation of small faults. The evaluation effect brought by this method is more in line with actual geological laws, so as to reduce the risk of fault trap drilling, and also provide a geological evaluation reference for optimizing the development plan of block oil and gas reservoirs in the development stage.

[0077] Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A seismic identification method for the internal structure of a small-scale low-order fault zone, characterized in that: The following steps are involved: Determine the seismic frequency distribution of the target layer based on geological data; Determining a small fault main component tuned frequency volume and a small fault background frequency volume based on the seismic frequency volume of the target layer segment includes the following steps: comparing the coherent response characteristics of the small fault in each main component tuned frequency volume under the same coherence parameter, and taking the frequency range with the best performance of the small fault characteristics as the small fault main component tuned frequency volume; filtering out the seismic data volume of the small fault main component tuned frequency volume from the seismic amplitude data of the seismic frequency volume corresponding to the target layer segment to obtain the small fault background frequency volume; Perform breakpoint sharpening on the tuned frequency fraction of the main component of the small fault to obtain seismic amplitude data after breakpoint sharpening; The small fault background frequency volume and the seismic amplitude data after breakpoint sharpening are merged to obtain a fused data volume; The internal structure of the fault zone is determined based on the fused data volume.

2. The seismic identification method for the internal structure of a small-scale low-order fault zone according to claim 1, characterized in that: Determining the seismic frequency distribution of the target layer segment based on geological data includes the following steps: The seismic frequency volume of the target layer is calculated based on the geological data, and the seismic frequency volume is obtained by Fourier transform.

3. The seismic identification method for the internal structure of a small-scale low-order fault zone according to claim 1, characterized in that: Performing breakpoint sharpening processing on the tuned frequency fraction of the main component of the small fault to obtain seismic amplitude data after breakpoint sharpening processing includes the following steps: The seismic attributes transformed by Laplacian operator template are used to process the tuning frequency volume of the main component of the small fault to obtain the seismic amplitude data after Laplacian sharpening. According to the longitudinal sharpening factor and the seismic amplitude data after Laplacian sharpening, the seismic amplitude data after breakpoint sharpening is determined.

4. The seismic identification method for the internal structure of a small-scale low-order fault zone according to claim 1, characterized in that: The small fault background frequency volume and the seismic amplitude data after breakpoint sharpening are merged to obtain a fused data volume, including the following steps: The small fault background frequency volume and the seismic amplitude data after breakpoint sharpening are weighted to obtain a fused data volume, wherein the weight coefficient of the seismic amplitude data after breakpoint sharpening is not less than 60%.

5. The seismic identification method for the internal structure of a small-scale low-order fault zone according to claim 1, characterized in that: Determining the internal structure of the fault zone based on the fused data volume includes the following steps: The third generation coherence calculation is performed on the fused data volume, in which the aperture in the coherence parameter takes a 9-point operator, and the internal structure of the fault zone is determined based on the planar combination characteristics of small faults on the coherence results.

6. The seismic identification method for the internal structure of a small-scale low-order fault zone according to any one of claims 1 to 5, characterized in that: Geological data include petroleum seismic data, oil and gas well logging data and small fault interpretation data.

7. A seismic identification system for the internal structure of a small-scale low-order fault zone, characterized in that: include: The first calculation module is used to determine the seismic frequency division volume of the target layer segment according to the geological data; The second calculation module is used to determine the small fault main component tuned frequency fraction and the small fault background frequency fraction based on the seismic frequency fraction of the target layer segment, including the following steps: comparing the coherent response characteristics of the small fault in each main component tuned frequency fraction under the same coherence parameter, and taking the frequency range with the best performance of the small fault characteristics as the small fault main component tuned frequency fraction; screening out the seismic data volume of the small fault main component tuned frequency fraction from the seismic amplitude data of the seismic frequency fraction corresponding to the target layer segment to obtain the small fault background frequency fraction; The third calculation module is used to perform breakpoint sharpening processing on the tuned frequency fraction of the main component of the small fault to obtain seismic amplitude data after breakpoint sharpening processing; The fourth calculation module is used to merge the small fault background frequency volume and the seismic amplitude data after breakpoint sharpening to obtain a fused data volume; The fifth calculation module is used to determine the internal structure of the fault zone based on the fused data volume.

8. The seismic identification system for the internal structure of a small-scale low-order fault zone according to claim 7, characterized in that: The third calculation module is specifically used for: The seismic attributes transformed by Laplacian operator template are used to process the tuning frequency volume of the main component of the small fault to obtain the seismic amplitude data after Laplacian sharpening. According to the longitudinal sharpening factor and the seismic amplitude data after Laplacian sharpening, the seismic amplitude data after breakpoint sharpening is determined.

9. The seismic identification system for the internal structure of a small-scale low-order fault zone according to claim 7, characterized in that: The fourth calculation module is specifically used for: The small fault background frequency volume and the seismic amplitude data after breakpoint sharpening are weighted to obtain a fused data volume, wherein the weight coefficient of the seismic amplitude data after breakpoint sharpening is not less than 60%.

Images