Crack quantitative interpretation method and device based on post-stack seismic data and medium

Abstract

The invention discloses a crack quantitative interpretation method and device based on post-stack seismic data and a medium, and the method comprises the steps: carrying out the normalization processing and denoising processing of a crack attribute slice obtained through the analysis and processing of the post-stack seismic data; then adopting a crack line tracking method based on water diffusion to extract crack lines and mark the crack lines, performing crack distribution parameter statistics on the extracted crack lines, and adopting a sliding window statistical method to realize quantitative interpretation of post-stack cracks; the device comprises a pre-processing module, a normalization module, a denoising processing module, a crack line extraction and marking module and an interpretation module. The computer readable storage medium stores a computer program, and the computer program can realize the crack quantitative interpretation method based on the post-stack seismic data.

Description

Technical Field

[0001] The present invention relates to the technical field of geophysical exploration data processing for unconventional oil and gas resources, and specifically to a method, device and medium for quantitative interpretation of fractures based on post-stack seismic data. Background Art

[0002] Fractured reservoirs are widely distributed in China. Especially today with the increasing exploration degree, fractured non-structural oil and gas reservoirs have gradually become the main exploration direction in the exploration field. However, due to the strong heterogeneity of fracture distribution and the complexity of distribution laws, the quantitative interpretation of fractures has always been the focus and difficulty of exploration. By accurately quantitatively interpreting fractures, the fracture characteristics and distribution laws of reservoirs can be better understood, providing a reliable basis for oil and gas exploration and development, improving exploration efficiency and the accuracy of reserve assessment. Therefore, the quantitative interpretation of fractures is of great significance.

[0003] At present, the methods for quantitative interpretation of fractures are mainly divided into two types: using logging data for statistics and seismic data for fracture parameter interpretation. Among them, using logging data can only obtain the fracture distribution parameters at points, while using seismic data for fracture interpretation has stronger practicability due to its transverse continuity, which makes the method of using seismic data for fracture interpretation more widely applied.

[0004] The methods for using seismic data to interpret fractures mainly include two types. One is to obtain fracture parameters by inverting azimuthal anisotropy parameters from pre-stack seismic data, and the other is to calculate fracture attributes using post-stack seismic data. In actual production work, the amount of pre-stack OVT gather data is huge and difficult to interpret and obtain. Moreover, pre-stack fracture parameter inversion is a method based on model-driven to extract fracture parameters, which has certain errors and can only obtain fracture density and azimuth, and cannot obtain the strength and length distribution of fractures. This makes the utilization rate of pre-stack fracture parameter inversion relatively low. At the same time, there are often situations where the existing seismic data do not meet the application requirements of pre-stack fracture prediction technology in some areas. Therefore, in actual production work, the method of calculating fracture attributes using post-stack seismic data is usually adopted.

[0005] However, in the current methods for calculating fracture attributes using post-stack seismic data, the calculated post-stack fracture attributes can only reflect the location and possibility of the existence of fractures, and cannot obtain accurate information on the quantitative distribution parameters of fractures. Summary of the Invention

[0006] To solve the above deficiencies in the prior art, the present invention aims to provide a method, device and medium for quantitative interpretation of fractures based on post-stack seismic data, so as to obtain accurate quantitative distribution parameters of fractures.

[0007] To achieve the above object, the technical solution adopted by the present invention is as follows: A method for quantitative interpretation of fractures based on post-stack seismic data, comprising the following steps:

[0008] Step 1: Obtain fracture attributes using post-stack seismic data;

[0009] Step 2: Extract fracture attribute slices of the reservoir according to reservoir horizon information and fracture attributes;

[0010] Step 3: Normalize the fracture attribute slices according to the type of fracture attributes. When the large value in the fracture attributes represents fractures, the first normalization formula is used to normalize the data. When the small value in the fracture attributes represents fractures, the second normalization formula is used to normalize the data;

[0011] Step 4: Use a multi-scale fracture attribute slice enhancement method based on the Hessian matrix to denoise the normalized fracture attribute slices to obtain enhanced and filtered fracture attribute slices;

[0012] Step 5: Use the enhanced and filtered fracture attribute slices and adopt a fracture line tracking method based on water flooding diffusion to extract and mark the fracture lines;

[0013] Step 6: Statistically analyze the fracture distribution parameters of the marked fracture lines to obtain quantitative fracture distribution parameters; among them, the fracture intensity and fracture density parameters in the quantitative fracture distribution parameters need to be obtained by using a sliding window statistical method.

[0014] As a limitation of the present invention, the first normalization formula is:

[0015]

[0016] The second normalization formula is:

[0017]

[0018] where S(x) = S(x, y) is the fracture attribute slice, S norm (x) is the data after normalization, max[S(x)] is the maximum value of the data in the fracture attributes, and min[S(x)] is the minimum value of the data in the fracture attributes.

[0019] As a limitation of the present invention, the preprocessing of denoising the fracture attributes by using a multi-scale fracture attribute slice enhancement method based on the Hessian matrix includes the following steps:

[0020] Step A: According to the linear scale space theory, obtain fracture attribute slices corresponding to different scale factors by using the following formula,

[0021] When σ ∈ [σ1, σ2] and σ1 < σ2, S(x; σ) = S norm (x) * G(x; σ)

[0022] where σ is the scale factor, "*" is the convolution operation, G(x; σ) is the Gaussian kernel function, S(x; σ) is the crack attribute slice corresponding to different scale factors;

[0023] Step B: Construct the Hessian matrix corresponding to S(x; σ) as follows:

[0024]

[0025] Step C: Construct the enhanced result of the crack according to the following formula

[0026]

[0027] E(x) = max[v(x; σ)] σ ∈ [σ1, σ2]

[0028] where λ1 > λ2, λ1 and λ2 are the eigenvalues of the Hessian matrix, v(x; σ) is the filtering result when the scale factor is σ, E(x) is the result of multi-scale crack attribute enhancement, β is the filtering sensitivity threshold parameter for controlling the linear structure, and c is the smoothing degree sensitivity threshold parameter.

[0029] As a limitation of the present invention, the crack line tracking method based on water flooding diffusion includes the following steps:

[0030] a. Normalize the enhanced filtered crack attribute slice E(x) using the following formula to obtain E norm (x),

[0031]

[0032] b. Binarize the normalized enhanced filtered crack attribute slice using the following formula. When binarizing, only a threshold needs to be given, and according to the threshold, the crack attribute value is set to a binary image B(x) composed of 0 and 1,

[0033]

[0034] where δ is the threshold;

[0035] c. Search for the initial seed point in the input binary image, then search for non-zero points within a window with the seed point as the midpoint and R as the radius, and mark the crack segment number. Take the searched non-zero points as new seed points and search within a window with R as the radius until no non-zero points can be searched, then the tracking and marking of one crack are completed. Similarly, loop through the points in the binary image that have not been searched until the tracking and marking of all cracks are completed.

[0036] As a limitation of the present invention, the statistical method of the sliding window includes statistically analyzing the crack distribution parameters of the extracted and marked crack lines by using the following formula to obtain the crack intensity and crack density distribution parameters.

[0037]

[0038]

[0039] Where P 21 (x,y) and P 20 (x,y) are the crack intensity and crack density at the position (x,y) respectively, S(x,y:r) is the area of a circle with (x,y) as the center and r as the radius, L f is the sum of the lengths of all cracks within the circle, and N f is the number of cracks within the circle.

[0040] The present invention also provides a device for quantitative interpretation of cracks based on post-stack seismic data, including: a preprocessing module for analyzing and processing post-stack seismic data to obtain crack attributes and reservoir crack attributes; a normalization module for analyzing and processing according to the type of crack attributes to obtain normalized data; a denoising processing module for analyzing and processing the normalized data to obtain enhanced and filtered crack attributes; an extracting and marking crack line module for analyzing and processing the enhanced and filtered crack attributes to extract and mark cracks; and an interpretation module for analyzing and processing the extracted and marked cracks to obtain quantitative parameters of crack distribution, so as to realize quantitative interpretation of cracks.

[0041] The present invention also provides a computer-readable storage medium storing a computer program that can implement the method for quantitative interpretation of cracks based on post-stack seismic data described in any one of the above.

[0042] Due to the adoption of the above technical solutions, compared with the prior art, the beneficial effects achieved by the present invention are:

[0043] (1) The present invention first analyzes and processes post-stack seismic data to obtain crack attributes and crack attribute slices of the reservoir, then normalizes the data and uses a multi-scale crack attribute slice enhancement method based on the Hessian matrix to denoise the crack attribute slices, which can enhance and highlight the crack information, improve the signal-to-noise ratio of the crack attribute slices, and further improve the accuracy of subsequent processing results. The crack line tracking method of water flooding diffusion is used to extract and mark the crack lines, and finally the marked crack lines are statistically analyzed for crack distribution parameters (azimuth, length, density, and intensity), and accurate quantitative distribution parameters of cracks can be obtained, so as to realize quantitative interpretation of cracks.

[0044] (2) The present invention obtains the fracture distribution parameters by using the statistical method of sliding window, so that each point has the statistically obtained fracture distribution parameters, which reduces the phenomenon of "drawing circles in groups" and at the same time reduces the interpolation operation, making the statistical results more reasonable and accurate, and ensuring the reliability and accuracy of the implementation of the scheme.

[0045] (3) The fracture quantitative interpretation method provided by the present invention has good practicability and economy. Especially in areas where the existing seismic data do not meet the application requirements of prestack fracture prediction technology, this method can greatly save production costs.

[0046] In summary, the present invention uses the fracture attributes and fracture attribute slices obtained from the post-stack seismic data analysis and processing, normalizes and denoises the fracture attribute slices, then extracts the fracture lines and marks the fracture lines, and realizes the post-stack fracture quantitative interpretation by statistically analyzing the fracture distribution parameters (density, intensity, azimuth, length) of the extracted fracture lines. Through this method, the distribution of fractures can be understood more accurately, providing a reliable method for the quantitative analysis of fracture characteristics. The present invention is suitable for use in the quantitative interpretation of fractures. Brief Description of the Drawings

[0047] The following further describes the present invention in detail with reference to the drawings and specific embodiments.

[0048] Figure 1 It is a flow chart of the steps of the fracture quantitative interpretation method based on post-stack seismic data in Embodiment 1 of the present invention;

[0049] Figure 2 It is a schematic diagram of the post-stack seismic data volume of a certain work area in the Bohai Bay in Embodiment 1 of the present invention;

[0050] Figure 3 For Figure 2 It is a schematic diagram of the seismic amplitude corresponding to the horizon in the middle;

[0051] Figure 4 For Figure 3 It is a schematic diagram of the corresponding coherence attribute slice;

[0052] Figure 5 For Figure 4 It is a schematic diagram of the normalization result of the coherence attribute slice in;

[0053] Figure 6 For Figure 5 It is a schematic diagram of the coherence attribute slice after denoising the normalization result of the coherence attribute slice in;

[0054] Figure 7 For Figure 6 It is a schematic diagram of the binary result of the coherence attribute slice after denoising in;

[0055] Figure 8 The result of the crack line extracted and marked in Embodiment 1 of the present invention;

[0056] Figure 9 The result of the crack line after deleting points less than 10 in Embodiment 1 of the present invention;

[0057] Figure 10 Schematic diagram of the statistical method of the sliding window in Embodiment 1 of the present invention;

[0058] Figure 11 Schematic diagram of the statistical result of the crack intensity obtained by using the statistical method of the sliding window in Embodiment 1 of the present invention;

[0059] Figure 12 For Figure 9 Schematic diagram of the statistical result of the crack density obtained by using the statistical method of the sliding window for the crack line result in;

[0060] Figure 13 For Figure 9 Schematic diagram of the statistical result of the crack length obtained for the crack line result in;

[0061] Figure 14 For Figure 9 Schematic diagram of the statistical result of the crack direction obtained for the crack line result in;

[0062] Figure 15 Schematic diagram of the statistical method of the bounding box algorithm in Embodiment 1 of the present invention;

[0063] Figure 16 For Figure 9 Schematic diagram of the statistical result of the crack intensity obtained by using the statistical method of the bounding box algorithm for the crack line result in;

[0064] Figure 17 For Figure 9 Schematic diagram of the statistical result of the crack density obtained by using the statistical method of the bounding box algorithm for the crack line result in;

[0065] Figure 18 Block diagram of a crack quantitative interpretation device based on post-stack seismic data in Embodiment 2 of the present invention.

[0066] In the figure: 201, preprocessing module; 202, normalization module; 203, denoising processing module; 204, extracting and marking crack line module; 205, interpretation module. Detailed implementation manners

[0067] The following describes the preferred embodiments of the present invention with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only for explaining and understanding the present invention, and are not used to limit the present invention.

[0068] Embodiment 1: A method for quantitative interpretation of fractures based on post-stack seismic data

[0069] This Embodiment 1 includes the following steps:

[0070] Step 1: Obtain fracture attributes using post-stack seismic data;

[0071] Step 2: Extract fracture attribute slices of the reservoir according to reservoir horizon information and fracture attributes;

[0072] Step 3: Normalize the fracture attribute slices according to the type of fracture attributes. When large values in the fracture attributes represent fractures, select the first normalization formula to normalize the data. When small values in the fracture attributes represent fractures, select the second normalization formula to normalize the data;

[0073] Step 4: Use a multi-scale fracture attribute slice enhancement method based on the Hessian matrix to denoise the normalized fracture attribute slices to obtain enhanced and filtered fracture attribute slices;

[0074] Step 5: Use the enhanced and filtered fracture attribute slices and adopt a fracture line tracking method based on water flooding diffusion to extract fracture lines and mark the fracture lines;

[0075] Step 6: Statistically analyze the fracture distribution parameters of the marked fracture lines to obtain quantitative fracture distribution parameters; among them, the fracture intensity and fracture density parameters in the quantitative fracture distribution parameters need to be obtained by using a sliding window statistical method.

[0076] Specifically, the first normalization formula in Step 3 is:

[0077]

[0078] The second normalization formula in Step 3 is:

[0079]

[0080] Where S(x) = S(x, y) is the fracture attribute slice, S norm (x) is the data after normalization, max[S(x)] is the maximum value of the data in the fracture attributes, and min[S(x)] is the minimum value of the data in the fracture attributes.

[0081] The pre-denoising process of the fracture attributes using a multi-scale fracture attribute slice enhancement method based on the Hessian matrix in Step 4 includes the following steps:

[0082] Step A: According to the linear scale space theory and using the following formula, obtain fracture attribute slices corresponding to different scale factors,

[0083] When σ ∈ [σ1, σ2] and σ1 < σ2, S(x; σ) = S norm (x) * G(x; σ)

[0084] where σ is the scale factor, "*" is the convolution operation, G(x; σ) is the Gaussian kernel function, S(x; σ) is the crack attribute slice corresponding to different scale factors;

[0085] Step B: Construct the Hessian matrix corresponding to S(x; σ) as follows:

[0086]

[0087] Step C: Construct the enhanced result of the crack according to the following formula,

[0088]

[0089] E(x) = max[v(x; σ)] σ ∈ [σ1, σ2]

[0090] where λ1 > λ2, λ1 and λ2 are the eigenvalues of the Hessian matrix, v(x; σ) is the filtering result when the scale factor is σ, E(x) is the result of multi-scale crack attribute enhancement, β is the filtering sensitivity threshold parameter for controlling the linear structure, and c is the smoothing degree sensitivity threshold parameter. It should be noted that the larger β is, the more the nonlinear structure in the image will be suppressed, and the larger c is, the smoother the result will be.

[0091] The crack line tracking method based on water flooding diffusion in Step Five includes the following steps:

[0092] a. Since the value range of the crack attribute slice after enhanced filtering has changed, in order to facilitate binarization of the crack attribute slice after enhanced filtering with a given threshold, the crack attribute slice E(x) after enhanced filtering is normalized using the following formula to obtain E norm (x),

[0093]

[0094] b. The normalized enhanced filtering crack attribute slice is binarized using the following formula. When binarizing, only a given threshold is required, and the crack attribute value is set to a binary image B(x) composed of 0 and 1 according to the threshold,

[0095]

[0096] where δ is the threshold;

[0097] c. Search for the initial seed points in the input binary image. Then, taking the seed points as the midpoints, search for non-zero points within a window with a radius of R, and mark the crack line numbers. Take the non-zero points found as new seed points and search within a window with a radius of R until no non-zero points can be found, thus completing the tracking and marking of one crack. Similarly, loop through the un-searched points in the binary image until all cracks are tracked and marked. Specifically, the algorithm flow in this Embodiment 1 is as follows:

[0098] Step b1: Input the binary image B composed of 0s and 1s, the search radius R, the crack line number k = 0, and the queue Q;

[0099] Step b2: Loop through and search the value B corresponding to the (i, j) grid point in the binary image i,j , if B i,j is 1 and the current position has not been marked with a crack line number, then add the (i, j) grid position to the queue Q, and mark the crack line number corresponding to the current grid position (i, j) as k = k + 1;

[0100] Step b3: Taking the grid position (i0, j0) of the first element in the queue Q as the center, search for non-zero points B i1,j1 within a region with a search radius of R. Similarly, add the corresponding grid position (i1, j1) to the queue Q;

[0101] Step b4: Mark the crack line number k for the crack corresponding to the position (i1, j1). If Q is not empty, repeat step 3; otherwise, complete the search for the current crack, and then repeat step b2 to search for the next grid point.

[0102] The statistical method of the sliding window in Step 6 includes using the following formula to statistically analyze the crack distribution parameters of the extracted and marked crack lines to obtain the crack intensity and crack density distribution parameters,

[0103]

[0104]

[0105] where P 21 (x, y) and P 20 (x, y) are the crack intensity and crack density at the position (x, y) respectively, S(x, y:r) is the area of a circle with (x, y) as the center and r as the radius, L f is the sum of the lengths of all cracks within the circle, and N f is the number of cracks within the circle.

[0106] Application Example

[0107] To facilitate the understanding of the solution and its effects of Embodiment 1 of the present invention, a specific application example is given below. Those skilled in the art should understand that this example is only for facilitating the understanding of the present invention, and any specific details are not intended to limit the present invention in any way.

[0108] In this application example, post-stack seismic data of a certain work area in the Bohai Bay is processed and tested, and the method for quantitatively interpreting fractures in post-stack seismic data is described with reference to the accompanying drawings. This application example only illustrates the coherence attribute, specifically as follows:

[0109] I. The post-stack seismic data volume of a certain work area in the Bohai Bay is as Figure 2 shown. The GeoEast software is used to analyze and process the post-stack seismic data to obtain the coherence attribute.

[0110] II. Figure 2 The seismic amplitudes corresponding to the middle horizons are as Figure 3 shown. The GeoEast software is used to analyze and process the reservoir horizons and the coherence attribute to extract the coherence attribute slices corresponding to Figure 3 , as Figure 4 shown.

[0111] III. Small values in the coherence attribute indicate fractures. Therefore, the second normalization formula is selected to normalize the data, and the normalization result of the coherence attribute slice is obtained, as Figure 5 shown.

[0112] IV. The multi-scale fracture attribute slice enhancement method based on the Hessian matrix is used to denoise the normalization result of the coherence attribute slice, and the enhanced and filtered coherence attribute slice is obtained, as Figure 6 shown.

[0113] V. The enhanced and filtered coherence attribute slice is binarized to obtain the binarization result, as Figure 7 shown. The fracture lines are extracted by using the fracture line tracking method based on water flooding diffusion and the fracture lines are marked to obtain the result of Figure 8 . The fracture lines with the number of points greater than 10 in the result of Figure 8 are screened to obtain the fracture line result of Figure 9 .

[0114] VI. Finally, according to the fracture lines, the fracture quantification parameters are statistically calculated. Among them, the fracture intensity and fracture density parameters in the fracture distribution quantification parameters need to be obtained by using the statistical method of the sliding window. The statistical method of the sliding window is as Figure 10 shown. The result of statistically calculating the fracture quantification parameters is as Figure 11 , Figure 12 , Figure 13 , Figure 14As shown. At the same time, this application example uses the statistical method of the bounding box algorithm to obtain the results of crack strength and crack density. The statistical method of the bounding box algorithm is as Figure 15 shown, and the results of statistically quantifying crack parameters are respectively as Figure 16 , Figure 17 shown. Comparing with the results obtained by the statistical method of the sliding window in the present invention, the results show that compared with the statistical method of the traditional bounding box algorithm, the results of the crack statistical method of the present invention are more reasonable and accurate.

[0115] Embodiment 2 A crack quantitative interpretation device based on post-stack seismic data

[0116] As Figure 17 This Embodiment 2 includes a preprocessing module 201, a normalization module 202, a denoising processing module 203, a crack line extraction and marking module 204, and an interpretation module 205. Among them, the preprocessing module 201 includes the existing GeoEast software, which can analyze and process post-stack seismic data to obtain crack attributes and reservoir crack attributes. The normalization module 202 can analyze and process according to the type of crack attributes to obtain the data after normalization processing. The denoising processing module 203 can analyze and process the data after normalization processing to obtain the crack attributes after enhanced filtering. The crack line extraction and marking module 204 can analyze and process the crack attributes after enhanced filtering to extract and mark cracks. The interpretation module 205 can analyze and process the extracted and marked cracks to obtain the quantitative parameters of crack distribution, so as to realize the quantitative interpretation of cracks. It should be added that the preprocessing module 201, the normalization module 202, the denoising processing module 203, the crack line extraction and marking module 204, and the interpretation module 205 all adopt the methods in Embodiment 1 to realize the corresponding functions.

[0117] Embodiment 3 A computer-readable storage medium

[0118] In this Embodiment 3, the computer-readable storage medium stores a computer program, and this computer program can implement the above-mentioned crack quantitative interpretation method based on post-stack seismic data. Specifically, non-transitory computer-readable instructions are stored on the computer-readable storage medium. When the non-transitory computer-readable instructions are run by a processor, all or part of the steps of the methods of the various embodiments of the present disclosure described above are executed. Computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROMs and DVDs), magneto-optical storage media (e.g., MOs), magnetic storage media (e.g., magnetic tapes or external hard drives), media with built-in rewritable non-volatile memory (e.g., memory cards), and media with built-in ROMs (e.g., ROM cartridges).

[0119] It should be noted that the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art can still modify the technical solutions recorded in the above embodiments or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for quantitative interpretation of fractures based on post-stack seismic data, characterized in that: It includes the following steps: Step 1: Obtain fracture attributes using post-stack seismic data; Step 2: Extract fracture attribute slices of the reservoir according to reservoir horizon information and fracture attributes; Step 3: Normalize the fracture attribute slices according to the type of fracture attributes. When large values in the fracture attributes represent fractures, use the first normalization formula to normalize the data. When small values in the fracture attributes represent fractures, use the second normalization formula to normalize the data; Step 4: Denoise the normalized fracture attribute slices using a multi-scale fracture attribute slice enhancement method based on the Hessian matrix to obtain enhanced and filtered fracture attribute slices; Step 5: Use the enhanced and filtered fracture attribute slices and adopt a fracture line tracking method based on water flooding diffusion to extract and mark fracture lines; Step 6: Statistically analyze the fracture distribution parameters of the marked fracture lines to obtain quantitative fracture distribution parameters; among them, the fracture intensity and fracture density parameters in the quantitative fracture distribution parameters need to be obtained using a sliding window statistical method.

2. The fracture quantitative interpretation method based on post-stack seismic data according to claim 1, wherein: The first normalization formula is: The second normalization formula is: where S(x) = S(x, y) is the crack attribute slice, and S norm (x) is the data after normalization, max[S(x)] is the maximum value of the data in the crack attribute, and min[S(x)] is the minimum value of the data in the crack attribute.

3. The fracture quantitative interpretation method based on post-stack seismic data according to claim 1, characterized in that: The preprocessing for denoising the fracture attributes using a multi-scale fracture attribute slice enhancement method based on the Hessian matrix includes the following steps: Step A: According to the linear scale space theory and using the following formula, obtain fracture attribute slices corresponding to different scale factors, When σ ∈ [σ1, σ2] and σ1 < σ2, S(x; σ) = S norm (x) * G(x; σ) where σ is the scale factor, "*" represents the convolution operation, G(x; σ) is the Gaussian kernel function, S(x; σ) is the crack attribute slice corresponding to different scale factors; Step B: Construct the Hessian matrix corresponding to S(x; σ) as: Step C: Construct the enhancement result of the fracture according to the following formula, E(x) = max[v(x; σ)] σ∈[σ1,σ2] where λ1 > λ2, λ1 and λ2 are the eigenvalues of the Hessian matrix, v(x; σ) is the filtering result when the scale factor is σ, E(x) is the result of multi-scale fracture attribute enhancement, β is the filtering sensitivity threshold parameter for controlling linear structures, and c is the smoothing degree sensitivity threshold parameter.

4. The fracture quantitative interpretation method based on post-stack seismic data according to claim 1, characterized in that: The fracture line tracking method based on water flooding diffusion includes the following steps: a. Normalize the enhanced filtered fracture attribute slice E(x) using the following formula to obtain E norm (x). b. Binarize the normalized enhanced and filtered fracture attribute slices using the following formula. When binarizing, only a threshold needs to be given, and according to the threshold, set the fracture attribute values to a binary image B(x) composed of 0 and 1, where δ is the threshold; c. Search for initial seed points in the input binary image, then search for non-zero points within a window with the seed point as the midpoint and a radius of R, and mark the fracture segment numbers. Take the searched non-zero points as new seed points and search within a window with a radius of R until no non-zero points can be searched, then complete the tracking and marking of one fracture. Similarly, loop through the points in the binary image that have not been searched until all fractures are tracked and marked.

5. The fracture quantitative interpretation method based on post-stack seismic data according to claim 1, characterized in that: The sliding window statistical method includes statistically analyzing the fracture distribution parameters of the extracted and marked fracture lines using the following formula to obtain fracture intensity and fracture density distribution parameters, Among them, P 21 (x, y) and P 20 (x, y) are the crack intensity and crack density at the position (x, y) respectively, and S(x, y:r) is the area of a circle with (x, y) as the center and r as the radius ， L f is the sum of the lengths of all cracks within the circle, and N f is the number of cracks within the circle.

6. A device for quantitative interpretation of fractures based on post-stack seismic data, characterized in that: This fracture quantitative interpretation device can implement the fracture quantitative interpretation method based on post-stack seismic data described in any one of claims 1-5; This fracture quantitative interpretation device includes: A preprocessing module that analyzes and processes post-stack seismic data to obtain fracture attributes and reservoir fracture attributes; Normalization module, which analyzes and processes according to the type of crack attributes to obtain data after normalization processing; Denoising processing module, which analyzes and processes the data after normalization processing to obtain crack attributes after enhanced filtering; Crack line extraction and marking module, which analyzes and processes the crack attributes after enhanced filtering to extract and mark cracks; Interpretation module, which analyzes and processes the extracted and marked cracks to obtain quantitative parameters of crack distribution, so as to realize quantitative interpretation of cracks.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program can implement the crack quantitative interpretation method based on post-stack seismic data described in any one of claims 1-5.

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