Automatic crack imaging enhancement and quantitative characterization method, system, medium and equipment

Abstract

The invention provides a crack automatic imaging enhancement and quantitative characterization method and system, a medium and equipment, and belongs to the technical field of oil and gas geological exploration. The method comprises the following steps: acquiring a post-stack three-dimensional seismic data volume of a target layer section of a target work area and carrying out filtering processing on the post-stack three-dimensional seismic data volume; performing variance operation on the seismic data volume after filtering processing to obtain a variance volume; performing angle domain control on the fracture development condition; carrying out attribute body analysis and ant body tracking operation on the variance body, and carrying out repeated iteration superposition operation on the ant body; performing boundary optimization construction smoothing processing on the ant body to obtain a crack continuity imaging enhancement attribute body; carrying out vectorization and denoising smoothing processing on crack imaging of the target horizon, and then carrying out center line contour tracing and target element separation processing; and carrying out quantitative characterization on the independently separated cracks of the target layer. According to the invention, the resolution of small-scale crack imaging is enhanced, and the signal-to-noise ratio and the crack imaging quality are improved; and quantitative characterization evaluation of the crack is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of oil and gas geological exploration, and in particular relates to a method, system, medium and equipment for automatic imaging enhancement and quantitative characterization of fractures. Background Art

[0002] Fractures are cracks caused by stress on rocks and are a very common geological structural phenomenon. In the field of oil and gas geological exploration, fractures are the bridge connecting the oil and gas geological sweet spot and the engineering sweet spot. They are important storage spaces and seepage channels of reservoirs (especially tight reservoirs), and also reflect the distribution of the geostress field. The degree of development of fractures affects the oil and gas enrichment and accumulation in the reservoir. Therefore, the effective identification and quantitative characterization of fractures are of great significance to the exploration and development of oil and gas fields.

[0003] Conventional methods for identifying fractures include curvature and ant tracking. Ant bodies obtained by ant tracking technology have good applicability for interpretation of fractures along layers and profiles, which greatly overcomes the subjectivity of manual interpretation and improves interpretation efficiency. However, there are still problems such as severe noise interference, low resolution, and inaccurate imaging, which lead to very noisy distribution and poor continuity of the identified fractures. At the same time, for fractures with unclear characteristics, conventional ant tracking imaging is poor or even impossible to image. In addition, the fractures identified by ant tracking are mostly described qualitatively, lacking automated quantitative characterization methods. These shortcomings seriously limit the identification of effective fractures and subsequent quantitative characterization and evaluation, which in turn affects the exploration process of oil and gas geology. With the continuous advancement of exploration and development, the automated imaging enhancement and quantitative characterization of small-scale fractures are one of the topics that urgently need to be tackled. Summary of the invention

[0004] The purpose of the present invention is to solve the difficulties existing in the above-mentioned prior art, and to provide a method, system, medium and equipment for automated crack imaging enhancement and quantitative characterization, which solves the problems of low resolution, noise interference and poor continuity of effective crack identification imaging in the prior art, and achieves the purpose of effective crack imaging re-enhancement and automated quantitative characterization.

[0005] The present invention is achieved through the following technical solutions:

[0006] A first aspect of the present invention provides a method for automated crack imaging enhancement and quantitative characterization, comprising the following steps:

[0007] Step S1, obtaining a post-stack 3D seismic data volume of a target layer section in a target work area and performing filtering processing;

[0008] Step S2, performing variance calculation on the seismic data volume after filtering to obtain a variance volume;

[0009] Step S3, controlling the crack development in the angle domain;

[0010] Step S4, performing attribute volume analysis and ant volume tracking operations on the variance volume, and performing multiple iterative superposition operations on the ant volume;

[0011] Step S5, performing boundary optimization and structural smoothing processing on the ant body to obtain a fracture continuity imaging enhancement attribute body;

[0012] Step S6, performing vectorization and denoising and smoothing processing on the fracture imaging of the target layer to obtain a processed fracture imaging;

[0013] Step S7, performing centerline contour tracing and target element separation processing on the processed crack imaging to obtain independently separated cracks;

[0014] Step S8, quantitatively characterizing the independently separated fractures in the target layer.

[0015] A further improvement of the present invention is:

[0016] In step S2, variance calculation is performed on the seismic data volume after filtering to obtain a variance volume, and the specific operations include:

[0017] The variance value of each sampling point in the three-dimensional seismic data volume after filtering is calculated using a seismic variance value calculation formula to obtain a three-dimensional variance volume;

[0018] The earthquake variance value calculation formula is:

[0019]

[0020] Among them, δ 2 is the variance value of a certain sampling point; j is the variance calculation time; i is the number of seismic traces, which is a real number; I is the number of adjacent traces selected when calculating the variance; L is the time window length under the variance calculation time; w ij is the weight factor function of the earthquake variance triangle, and its interval range is [0,1]; X ij is the seismic amplitude value of the jth sample point in the ith channel; is the average amplitude value of all i-channel data at time j.

[0021] A further improvement of the present invention is:

[0022] In step S3, the angle domain control of the crack development is performed, and the specific operations include:

[0023] First, determine the fracture dip and azimuth development of the target layer in the target work area, and then control the fracture development in the angle domain based on the determined fracture dip and azimuth development.

[0024] When the cracks developed in the target layer are identified as high-angle cracks, the crack angle range is controlled to be 60°-90°;

[0025] When the cracks developed in the target layer are judged to be low-angle cracks, the crack angle range is controlled to be 0°-30°;

[0026] When the fractures developed in the target layer are identified as medium-angle fractures, the fracture angle range is controlled to be 30°-60°;

[0027] When the fractures developed in the target layer are identified as medium-high angle fractures, the fracture angle domain is controlled within 45°-90°;

[0028] When the fractures developed in the target layer are determined to be medium-low angle fractures, the fracture angle domain is controlled within 0°-45°.

[0029] A further improvement of the present invention is:

[0030] In step S4, the attribute volume analysis ant volume tracking operation is performed on the variance volume, and multiple iterative superposition operations are performed on the ant volume, including the following steps:

[0031] Step S41: Select the obtained variance body, and use the probability formula to calculate the initial distribution of the ant body. The probability value calculated at the location of the discontinuity such as cracks is large;

[0032] The probability formula is:

[0033]

[0034] Among them, P x is the probability that the xth data point corresponding to a data block among several uniformly distributed small data blocks is selected; C x and C y represents the normalized coherence value of the xth and yth data points;

[0035] Step S42: Divide the initial activity range of the ant body into smaller n×n small blocks, and use the gradient direction formula to calculate the main direction of each small block, that is, the main direction of ant tracking. The gradient direction formula is:

[0036]

[0037] Among them, θ is the main direction of ant tracking; G i (x,y),G j (x, y) are the gradients of the corresponding data points in each data block; (x, y) are the position coordinates of the data point; w is the length of the data block;

[0038] Step S43: Determine the candidate search range P(x, y) of the ant according to the transition probability formula, the transition probability formula is:

[0039]

[0040] Where R is the set of points within the search range of the current data point; (x, y) is the candidate position; τ(x, y), η(x, y) and τ(ω,υ), η(ω,υ) represent the pheromone and heuristic information of points (x, y) and (ω,υ) respectively; α and β are the control factors of pheromone and heuristic information, respectively, controlling the relative weights of pheromone and heuristic information;

[0041] Step S44: Setting the positive feedback mechanism of the ant colony algorithm.

[0042] First, the heuristic information η(x, y) is defined as:

[0043] η(x,y)=1-D(x,y)

[0044] Where D(x,y) is the coherence value of the point (x,y); if the heuristic information is defined to be dominant, β takes a relatively high value; if the pheromone is defined to be dominant, α takes a relatively high value; when the initial pheromone is the same, the point with a smaller coherence value is more likely to be selected. If all ants have completed the tracking, proceed to the next step, otherwise continue tracking;

[0045] Step S45: After the ants complete a search process within the initial distribution range, the pheromone of each data point (x, y) is updated according to the update algorithm;

[0046] The update algorithm formula is:

[0047]

[0048] Where: τ(x,y) is the pheromone at the data point (x,y) in this cycle, τ max is the upper limit of pheromone; is the increment of pheromone at the data point (x, y) in this cycle; n is the number of ants passing through this path in this cycle; ξ'L(r) represents the amount of information left by the rth ant on this path in this cycle, and its value is proportional to the length of the path searched by the ant; ξ' is the pheromone update coefficient; L(r) is the length of the path searched by the rth ant in this cycle;

[0049] Step S46: After completing one iteration process in sequence, if the number of iterations reaches the set maximum number of iterations, go to the next step, otherwise jump back to the initial tracking direction, and calculate again in sequence to complete multiple iterations to form a positive feedback mechanism and finally generate an ant automatic tracking three-dimensional data volume.

[0050] A further improvement of the present invention is:

[0051] In step S5, the ant body is subjected to boundary optimization and structural smoothing processing to obtain a fracture continuity imaging enhanced attribute body, and the specific operations include:

[0052] The boundary is optimized by controlling the number of transverse seismic traces and longitudinal sampling points used to estimate structural smoothness. Gaussian smoothing is performed along the strike of the structure using a three-dimensional Gaussian weighted average function to highlight the structural boundary and obtain fracture continuity imaging enhancement attribute volume.

[0053] The formula for obtaining the mean value by the three-dimensional Gaussian weighted average is as follows:

[0054]

[0055] Among them, δ x ,δ y ,δ t are the scale parameters of x, y, and t respectively; x is the horizontal survey line; y is the vertical survey line; t is the time variable; is the mean of the three-dimensional Gaussian weighted average; ψ (x，y，t) It is the earthquake data in x, y, t scale.

[0056] A further improvement of the present invention is:

[0057] The step S7 is to perform centerline contour tracing and target element separation processing on the processed crack imaging to obtain independently separated cracks. The specific operations are as follows:

[0058] The processed fracture imaging is processed by automatic centerline contour tracing to thin the fracture lines, enhance the fracture details, obtain continuous and clear fracture tracks, and generate the final high-resolution fracture imaging;

[0059] The target element separation process is to automatically split the curve of the high-resolution fracture imaging fine line body to obtain the fractures that are separated and exist independently, so that each fracture has identifiable length and occurrence element information.

[0060] A further improvement of the present invention is:

[0061] In step S8, the independently separated fractures in the target layer are quantitatively characterized, and the specific operations include:

[0062] Matlab software is used to automatically calculate and characterize the crack trend, crack pixel length, crack density, crack development intensity, and crack tensor of the target layer, and the crack trend rose map, crack pixel length map, crack density map, crack intensity map, and crack tensor map are obtained to achieve quantitative evaluation of cracks.

[0063] The second aspect of the present invention provides a crack automated imaging enhancement and quantitative characterization system, comprising:

[0064] An acquisition module is used to acquire the post-stack 3D seismic data volume of the target layer section in the target work area and perform filtering processing;

[0065] A variance calculation module is used to perform variance calculation on the seismic data volume after filtering to obtain a variance volume;

[0066] The fracture angle domain control module is used to control the fracture development in the angle domain according to the determined fracture inclination and azimuth development conditions;

[0067] The ant body tracking operation module is used to perform attribute body analysis ant body tracking operation on the variance body and perform multiple iterative superposition operations on the ant body;

[0068] A structural smoothing processing module is used to perform boundary optimization and structural smoothing processing on the ant body to obtain a fracture continuity imaging enhancement attribute body;

[0069] A vectorization and denoising processing module is used to perform vectorization processing on the crack imaging of the target layer, and perform denoising and smoothing processing on the vectorized crack imaging to obtain the crack imaging after denoising and smoothing processing;

[0070] The target element separation module is used to perform centerline contour tracing and target element separation processing on the crack imaging after denoising and smoothing, so as to obtain independently separated cracks;

[0071] The quantitative characterization module is used to automatically and quantitatively characterize the orientation, length, density, strength, tensor, etc. of independently separated cracks in the target layer, and complete the evaluation and research of cracks in the target layer.

[0072] According to a third aspect of the present invention, a computer-readable medium is provided, wherein the computer-readable storage medium stores at least one computer-executable program, and when the at least one program is executed by the computer, the computer executes the steps in the method for automated imaging enhancement and quantitative characterization of cracks as described above.

[0073] A fourth aspect of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps in the method for automated imaging enhancement and quantitative characterization of cracks as described above.

[0074] Compared with the prior art, the present invention has the following beneficial effects:

[0075] The present invention can greatly enhance the resolution of small-scale fracture imaging, suppress the influence of noise and useless information, improve the signal-to-noise ratio and fracture imaging quality; and realize automated fracture quantitative characterization and evaluation, and realize an integrated operation process of fracture identification, imaging enhancement and quantitative evaluation, providing a complete set of technical methods for fracture identification, processing, interpretation and evaluation, improving the accuracy and efficiency of reservoir fracture research, and having further popularization and application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 A schematic diagram of a process of an automated crack imaging enhancement and quantitative characterization method provided in Example 1 of the present invention;

[0077] Figure 2 A core image showing the development of medium- and high-angle fractures in the target layer provided in the first embodiment of the present invention;

[0078] Figure 3 An imaging logging diagram showing the development of medium- and high-angle fractures in the target layer provided in the first embodiment of the present invention;

[0079] Figure 4 The ant body attribute volume slice diagram obtained by "active" ant tracking provided in the first embodiment of the present invention;

[0080] Figure 5 The ant tracking attribute volume slice diagram obtained by the "active-passive" ant superposition tracking provided in the first embodiment of the present invention;

[0081] Figure 6 The ant tracking attribute volume slice diagram obtained by the "active-passive-active" ant superposition tracking provided in the first embodiment of the present invention;

[0082] Figure 7a The ant tracking attribute volume slice diagram of the iterative superposition operation provided in the first embodiment of the present invention;

[0083] Figure 7b It is a fracture continuity imaging enhanced attribute volume slice image after boundary optimization and structural smoothing;

[0084] Figure 8 A crack continuity imaging enhanced attribute volume slice image after vectorization processing provided in the first embodiment of the present invention;

[0085] Fig. 9 A crack imaging image obtained by performing denoising and smoothing processing on the vectorized cracks provided in the first embodiment of the present invention;

[0086] Fig.10 A high-resolution crack image generated by performing automated centerline contour tracing, detail enhancement and target element separation processing on the crack image after denoising and smoothing processing provided in the first embodiment of the present invention;

[0087] Fig.11 A fracture direction rose diagram representing fracture orientation provided in the first embodiment of the present invention;

[0088] Fig.12 A crack pixel length map characterizing the scale of crack development provided in the first embodiment of the present invention;

[0089] Fig.13 A fracture density map characterizing fracture development provided in the first embodiment of the present invention;

[0090] Fig.14 A crack strength diagram characterizing the degree of crack development provided in the first embodiment of the present invention;

[0091] Fig.15 A fracture tensor diagram characterizing the effects of fractures of different levels on geological processes such as geological fluids and geostress distribution, provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0092] The present invention is further described in detail below in conjunction with the accompanying drawings:

[0093] Embodiment 1

[0094] This embodiment provides a method for automatic crack imaging enhancement and quantitative characterization, such as Figure 1 As shown, the following steps are included:

[0095] Step S1, obtaining a post-stack 3D seismic data volume of a target layer section in a target work area and performing filtering processing;

[0096] Seismic data of the target layer is obtained by using seismic acquisition equipment, and stacking is performed to obtain post-stack 3D seismic data. Median filtering is performed on the post-stack 3D seismic data to suppress irrelevant high-amplitude random noise while preserving the clear boundaries of the fractures, thus obtaining a filtered seismic data volume. Median filtering can effectively remove high-frequency noise in the data volume and improve the quality and accuracy of seismic data.

[0097] During the propagation process, seismic wave signals will be affected by factors such as the lithology and physical properties of the underground strata, and will therefore undergo corresponding changes. This change is a complex reflection of the comprehensive characteristics of the underground reservoir. Spatial changes in underground rock physics and other properties will lead to changes in the characteristics of seismic reflection waves, thereby affecting seismic attributes.

[0098] Here, since high-frequency noise often exists in seismic data, this noise will have a great impact on the interpretation and analysis of seismic data. Median filtering can effectively remove this noise and improve the credibility of seismic data. In addition, median filtering will not make the data blurred or distorted, but will protect the details of the data as much as possible and make the data more real and credible.

[0099] Step S2, performing variance calculation on the seismic data volume after filtering to obtain a variance volume;

[0100] The variance value of each sampling point in the three-dimensional seismic data volume after filtering is calculated using a seismic variance value calculation formula to obtain a three-dimensional variance volume;

[0101] Among them, the calculation formula of earthquake variance value is:

[0102]

[0103] Among them, δ 2 is the variance value of a certain sampling point; j is the variance calculation time; i is the number of seismic traces, which is a real number; I is the number of adjacent traces selected when calculating the variance; L is the time window length under the variance calculation time; w ij is the weight factor function of the earthquake variance triangle, and its interval range is [0,1]; X ij is the seismic amplitude value of the jth sample point in the ith channel; is the average amplitude value of all i-channel data at time j.

[0104] Among them, the variance volume is a volume data used for seismic data analysis and processing. It is obtained by calculating the seismic variance value of the time series of seismic data. Compared with seismic data, the seismic variance volume has better stability and readability, can more accurately reflect the fluctuation changes in seismic data, and has important earthquake prediction and analysis value. In seismic data processing and analysis, the seismic variance volume can be used for abnormal data point detection and seismic data feature extraction. Through the analysis of the seismic variance volume, outliers and noise in seismic data can be discovered and removed, thereby improving the accuracy and credibility of seismic data.

[0105] Step S3, controlling the crack development in the angle domain;

[0106] First, the fracture dip and azimuth development of the target layer in the target work area are determined by using imaging logging data, core observation, fracturing monitoring methods, physical simulation experiments and geological knowledge. Then, the fracture development is controlled in the angle domain based on the determined fracture dip and azimuth development.

[0107] When the cracks developed in the target layer are identified as high-angle cracks, the crack angle range is controlled to be 60°-90°;

[0108] When the cracks developed in the target layer are judged to be low-angle cracks, the crack angle range is controlled to be 0°-30°;

[0109] When the fractures developed in the target layer are identified as medium-angle fractures, the fracture angle range is controlled to be 30°-60°;

[0110] When the fractures developed in the target layer are identified as medium-high angle fractures, the fracture angle domain is controlled within 45°-90°;

[0111] When the cracks developed in the target layer are identified as medium-low angle cracks, the crack angle range is controlled to be 0°-45°;

[0112] Among them, the cracks that are not within the angle domain are hidden and the crack bodies within the angle domain are retained.

[0113] Imaging logging data, core observation, fracturing monitoring methods, physical simulation experiments and geological knowledge are all existing technical methods and will not be described in detail here.

[0114] The purpose of angle domain control of crack development is to identify more accurate cracks according to the actual situation of the work area; the subsequent step S4 needs to use the angle domain range of the work area cracks obtained by angle domain control of crack development in step S3 during multiple iterative calculations of the ant body.

[0115] like Figure 2 As shown, by using lithology observation, it is determined that medium-high angle fractures are developed in the target layer section of the target work area; therefore, the fracture angle range is controlled within 45°-90°.

[0116] like Figure 3 As shown, the imaging logging data in the logging data is used to determine that medium-high angle fractures are developed in the target layer of the target work area; therefore, the fracture angle domain is controlled within 45°-90°.

[0117] Step S4, performing attribute volume analysis and ant volume tracking operations on the variance volume, and performing multiple iterative superposition operations on the ant volume;

[0118] The steps include:

[0119] Step S41: Select the variance body obtained in step S2, and use the probability formula to calculate the initial distribution of the ant body, and the probability value calculated at the location of the discontinuity such as cracks is large;

[0120] The probability formula is:

[0121]

[0122] Among them, P xis the probability that the xth data point corresponding to a data block among several uniformly distributed small data blocks is selected; C x and C y represents the normalized coherence value of the xth and yth data points (the coherence value is obtained by calculating the coherence body, and calculating the coherence body is a prior art and will not be repeated here); the data block refers to the divided three-dimensional interval, and the data point is a certain position point in the data block;

[0123] Step S42: Divide the initial activity range of the ant body into smaller n×n small blocks (that is, divide the data block in S41 into smaller n×n small blocks), and use the gradient direction formula to calculate the main direction of each small block, which is the main direction of ant tracking, where the gradient direction formula is:

[0124]

[0125] Among them, θ is the main direction of ant tracking; G i (x,y),G j (x, y) are the gradients of the corresponding data points in each data block (the gradient value can be obtained by calculating the gradient attribute, which is a prior art and will not be described here); (x, y) are the position coordinates of the data point; w is the length of the data block.

[0126] Step S43: Determine the candidate search range P(x, y) of the ant according to the transition probability formula, where the transition probability formula is:

[0127]

[0128] Where R is the set of points within the search range of the current data point; (x, y) is the candidate position; τ(x, y), η(x, y) and τ(ω,υ), η(ω,υ) represent the pheromone and heuristic information of points (x, y) and (ω,υ), respectively; α and β are the control factors of pheromone and heuristic information, respectively, controlling the relative weights of pheromone and heuristic information.

[0129] Here, the transition probability formula refers to the probability calculation formula for the ants to determine the next move based on the mechanism of pheromone concentration and heuristic information during the search process. The pheromone concentration and heuristic information in the transition probability formula are important bases for ants to choose the next move. Pheromone concentration refers to the amount of pheromone released by ants on the path from node x to node y in the past search process, reflecting the quality of this path. Heuristic information refers to the heuristic evaluation value from node x to node y calculated through some heuristic strategies based on the characteristics of the problem, reflecting the feasibility and expected benefits of this path. The calculation process of the transition probability formula is based on the weighted average of pheromones and heuristic information, in which the importance of pheromones and heuristic information can be adjusted according to the needs of the actual problem. The transition probability formula can help ants make more intelligent decisions in the search space, thereby improving search efficiency and search quality.

[0130] Step S44: Setting the positive feedback mechanism of the ant colony algorithm.

[0131] First, the heuristic information η(x, y) is defined as:

[0132] η(x,y)=1-D(x,y)

[0133] Where D(x,y) is the coherence value of point (x,y); if the heuristic information is defined as dominant, β takes a relatively high value; if the pheromone is defined as dominant, α takes a relatively high value. When the initial pheromone is the same, the point with a smaller coherence value is more likely to be selected. If all ants have completed the tracking, proceed to the next step, otherwise continue tracking.

[0134] Step S45: After the ants complete a search process within the initial distribution range, the pheromone of each data point (x, y) is updated according to the update algorithm;

[0135] The update algorithm formula is:

[0136]

[0137] Where: τ(x,y) is the pheromone at the data point (x,y) in this cycle, τ max is the upper limit of pheromone; is the increment of pheromone at the data point (x, y) in this cycle; n is the number of ants passing through this path in this cycle; ξ'L(r) represents the amount of information left by the r-th ant on this path in this cycle, and its value is proportional to the length of the path searched by the ant; ξ' is the pheromone update coefficient; L(r) is the length of the path searched by the r-th ant in this cycle.

[0138] Here, the pheromone update coefficient refers to the coefficient used to calculate the update amount of pheromone concentration in the ant tracking algorithm. After each ant completes an iteration, the pheromone concentration needs to be updated according to the current iteration result to reflect the quality of the path and the ant's search experience. The calculation process of the pheromone update coefficient is based on the mechanism of pheromone concentration and pheromone volatilization, reflecting the quality of the path and the ant's search experience. Specifically, the higher the pheromone concentration, the better the quality of the path, and the pheromone concentration needs to be increased to attract more ants to walk through this path. The higher the pheromone volatilization coefficient, the faster the pheromone volatilizes, and the pheromone concentration needs to be increased to maintain the continuity and stability of the pheromone.

[0139] Step S46: After completing one iteration process in sequence, if the number of iterations reaches the set maximum number of iterations, go to the next step, otherwise jump back to the initial tracking direction, and calculate again in sequence to complete multiple iterations to form a positive feedback mechanism and finally generate an ant automatic tracking three-dimensional data volume.

[0140] Figure 4 The ant body attribute body slice diagram obtained by "active" ant tracking provided by the first embodiment of the present invention is shown; Figure 4 It can be seen that after an “active” ant body tracking, the resolution of crack identification on the ant body attribute slice map is very low, the imaging is very weak, the noise is obvious, and the crack continuity is poor.

[0141] Figure 5 The ant tracking attribute volume slice diagram obtained by the "active-passive" ant superposition tracking provided by the first embodiment of the present invention is shown; Figure 5 It can be seen that after the “active-passive” ant body tracking, the imaging of crack recognition on the ant body attribute slice map is enhanced, but the noise is still obvious, the continuity of the identified cracks is very poor, the crack distribution is very noisy, and effective cracks cannot be identified.

[0142] Figure 6 The ant tracking attribute volume slice diagram obtained by the "active-passive-active" ant superposition tracking provided by the first embodiment of the present invention is shown; Figure 6 It can be seen that after the "active-passive-active" ant body superposition tracking, the resolution of crack identification on the ant body attribute slice map is enhanced, the noise is effectively suppressed, the crack continuity is enhanced, the crack noise level is reduced, and the effective crack identification level is enhanced.

[0143] Here, the difference between the "passive" and "active" ant tracking methods is quite large. The active ant tracking algorithm is similar to "hard-working ants" and is better at digging cracks, but because of its strong "initiative", the noise is clearer. The passive ant tracking algorithm, i.e., the "lazy ant", tends to track extremely strong signals and abandon weaker signals, so it helps to suppress noise and reflect the trend of large cracks, but it is easy to cause local low-level small cracks to be intermittent, thereby destroying their continuity and causing noisy crack imaging. Since both tracking strategies have drawbacks and cannot meet the needs of fault interpretation and combination when used alone, the present invention performs ant tracking superposition calculations. It has been proven in practice that the combination of two parameters is cross-superimposed (active-passive-active superposition), the effect is better, the continuity of the cracks is significantly enhanced, and the noise is better suppressed (Figure 6).

[0144] Step S5, performing boundary optimization and structural smoothing processing on the ant body to obtain a fracture continuity imaging enhancement attribute body;

[0145] The boundary is optimized by controlling the number of transverse seismic traces and longitudinal sampling points used to estimate structural smoothness. Gaussian smoothing is performed along the strike of the structure using a three-dimensional Gaussian weighted average function to highlight the structural boundary and obtain fracture continuity imaging enhancement attribute volume.

[0146] The formula for obtaining the mean value of three-dimensional Gaussian weighted average is as follows:

[0147]

[0148] Among them, δ x ,δ y ,δ t are the scale parameters of x, y, and t, which determine the degree of smoothing. The larger the value, the wider the frequency band of the Gaussian filter and the better the smoothing degree. x is the horizontal survey line; y is the vertical survey line; t is the time variable; is the mean of the three-dimensional Gaussian weighted average; ψ (x，y，t) It is the earthquake data in x, y, t scale.

[0149] Here, the purpose of Gaussian smoothing is to smooth the noise and irregular signals in the seismic data to improve the clarity and readability of the data. Specifically, Gaussian smoothing can be achieved by weighted averaging each data point in the seismic data. The closer the data point is, the larger the Gaussian function value is, and the farther the distance is, the smaller the Gaussian function value is, thus achieving smoothing around the data point.

[0150] Figure 7a The ant tracking attribute volume slice diagram of the iterative superposition operation provided by the first embodiment of the present invention is shown. Figure 7bThe following is a slice diagram of the fracture continuity imaging enhancement attribute volume after the boundary optimization structure smoothing process according to the first embodiment of the present invention. Figure 7a and Figure 7b It can be seen that the ant body made by variance body attributes has a strong ability to identify small-scale cracks, but its resolution is relatively low, resulting in a very noisy crack distribution and noise interference, which is not conducive to the identification of effective cracks in the study area (medium-high angle cracks 45°-90°); the crack imaging after boundary optimization structural smoothing processing shows clearer crack edges on the attribute body slices, more obvious noise suppression, smoother, clearer structures, and better continuity; it reduces structural illusions and multiple solutions of interpretation and improves the accuracy of interpretation.

[0151] Step S6, performing vectorization and denoising and smoothing processing on the fracture imaging of the target layer to obtain a processed fracture imaging;

[0152] Here, the crack imaging of the target layer needs to be vectorized using the image processing software coredraw to generate vectorized crack imaging;

[0153] It is also necessary to perform tracking control processing such as detail optimization correction, smoothing, and removal of object overlap on the vectorized crack imaging to retain and optimize the required crack information, reduce information irrelevant to the cracks, and obtain crack imaging after denoising and smoothing.

[0154] Figure 8 A volume slice diagram of crack continuity imaging enhancement attribute after vectorization processing provided by the first embodiment of the present invention is shown.

[0155] Here, vector graphics are constructed based on mathematical formulas and can be scaled and enlarged without distortion. They can be easily edited and modified because each element is a separate object, making it easier to identify and quantify the image in the subsequent system program.

[0156] Fig. 9 The crack imaging image obtained by performing denoising and smoothing processing on the vectorized cracks provided in the first embodiment of the present invention is shown. Fig. 9 It can be seen that performing tracking control processing such as detail optimization correction, smoothing, and removing object overlap on the vectorized cracks can remove the noise generated during the vectorization process, making the imaging smoother and the details clearer.

[0157] Step S7, performing centerline contour tracing and target element separation processing on the processed crack imaging to obtain independently separated cracks;

[0158] The processed crack imaging is automatically processed by centerline contour tracing to thin the cracks and enhance the crack details to obtain continuous and clear crack trajectories and generate the final high-resolution crack imaging.

[0159] Here, the thinning process can eliminate many crack-like point trajectories that are irrelevant to cracks recorded in the ant body attributes, making the crack information more focused and eliminating the multi-solution of the crack information of the ant body itself, so that continuous and clear crack point trajectories can be obtained.

[0160] Among them, the target element separation processing is to perform automated curve splitting processing on the high-resolution fracture imaging fine line body to obtain independently separated fractures, so that each fracture has identifiable length and occurrence element information.

[0161] Here, the automated curve splitting process can separate the overlapping fractures and effectively define the cross-overlapping relationship between the fractures, thereby clearly distinguishing the distribution morphology and development direction of multiple sets of fractures, and improving the accuracy of the subsequent automated quantitative characterization of the fractures.

[0162] Fig.10 The high-resolution crack image generated by the crack image after denoising and smoothing processing provided by the first embodiment of the present invention is shown, and the centerline contour is automatically traced, the detail is enhanced, and the target element is separated. Fig.10 It can be seen that after automated centerline contour tracing and fine line processing and detail enhancement, the crack trajectories and cross-overlapping matching relationships are clearer.

[0163] Step S8, quantitatively characterizing the independently separated fractures in the target layer;

[0164] The orientation, length, density, strength, tensor and other automatic quantitative characterizations of the independently separated cracks in the target layer are carried out to complete the evaluation and research of the cracks in the target layer, including:

[0165] Matlab software is used to automatically calculate and characterize the crack direction, crack pixel length, crack density, crack development intensity, crack tensor, etc. of the target layer, and the crack direction rose diagram, crack pixel length diagram, crack density diagram, crack intensity diagram, crack tensor diagram, etc. are obtained to achieve quantitative evaluation of cracks.

[0166] Fig.11 The fracture direction rose diagram for characterizing the fracture orientation provided by the first embodiment of the present invention is shown. Fig.11 The dominant orientation of crack development can be accurately determined.

[0167] Here, the fracture orientation rose diagram is a type of chart used to show the distribution of fracture orientation in rocks or minerals. Usually, the fracture orientation rose diagram consists of a circular chart divided into several sectors, each of which represents the distribution of fracture density within a certain orientation range. The center of the fracture orientation rose diagram usually represents the crack-free state, and the outermost circle represents the maximum fracture density. The angles of each sector area are usually equal, but the specific angles can be adjusted according to actual needs. The sector areas can be divided according to different orientations, such as due north, northeast, due east, etc., or according to the range of fracture orientation, such as within 5 degrees, within 10 degrees, etc. The fracture orientation rose diagram can intuitively show the orientation and density distribution of fractures, helping people better understand the geological characteristics of the material. It can be used to study the structure, mechanical properties, stability, etc. of rocks or minerals.

[0168] Fig.12 FIG. 1 shows a crack pixel length diagram for characterizing the scale of crack development provided by the first embodiment of the present invention. Fig.12 The relative length of crack development and the frequency of occurrence of the corresponding length can be intuitively judged.

[0169] Here, the crack pixel length map is a type of chart that shows the distribution of crack length. The horizontal axis usually represents the pixel length range, and the vertical axis represents the number of cracks. It is used to show the distribution of the number of cracks in different pixel length ranges, helping researchers better understand the characteristics and distribution patterns of cracks.

[0170] Fig.13 FIG. 1 shows a fracture density map characterizing fracture development provided by the first embodiment of the present invention. Fig.12 The distribution of crack density can be intuitively judged in the figure. The denser the cracks, the larger the color code value and the darker the color.

[0171] Here, fracture density refers to the number of fractures per unit area in rocks or other materials. It is usually used to describe the density and characteristics of fracture distribution and is an important parameter for studying the geological characteristics, structure, mechanical properties, etc. of materials.

[0172] Fig.14 The fracture intensity diagram representing the degree of fracture development provided by the first embodiment of the present invention is shown. Fig.13 The intensity characteristics of the crack development can be intuitively judged. In the legend, the darker the color, the higher the crack intensity, and the lighter the color, the lower the crack intensity. By observing this picture, we can understand the distribution and intensity changes of the cracks, and thus better understand the geological structure and geological process of the region.

[0173] Here, crack strength is one of the physical properties of cracks, and is usually used to describe indicators such as the strength, density or length of cracks in fault zones. Specifically, crack strength can be expressed as a comprehensive function of physical quantities such as the area, length, width, depth, and density of the cracks.

[0174] Fig.15 A fracture tensor diagram characterizing the effects of fractures of different levels on geological processes such as geological fluids and geostress distribution is shown according to an embodiment of the present invention.

[0175] Here, the fracture tensor diagram can understand the influence of fracture tensors of different levels on geological processes such as fluid movement and ground stress distribution in geology, and further understand the distribution and migration laws of groundwater or hydrocarbons in geology. In the figure, 0th, 2nd, 4th, and 8th represent fracture tensors of different levels, and are distributed in the image in the form of concentric circles. The edge of the circle indicates the distribution in different directions, and the value in the radius direction indicates the relative strength of fracture tensors of different levels in different directions. The fracture tensor diagram can be used to simulate and predict the movement of underground fluids and ground stress distribution to better guide the development and utilization of geological resources.

[0176] Embodiment 2

[0177] The embodiment of the present invention further provides a crack automated imaging enhancement and quantitative characterization system, the system comprising:

[0178] An acquisition module is used to acquire the post-stack 3D seismic data volume of the target layer section in the target work area and perform filtering processing;

[0179] A variance calculation module is used to perform variance calculation on the seismic data volume after filtering to obtain a variance volume;

[0180] The fracture angle domain control module is used to control the fracture development in the angle domain according to the determined fracture inclination and azimuth development conditions;

[0181] The ant body tracking operation module is used to perform attribute body analysis ant body tracking operation on the variance body and perform multiple iterative superposition operations on the ant body;

[0182] A structural smoothing processing module is used to perform boundary optimization and structural smoothing processing on the ant body to obtain a fracture continuity imaging enhancement attribute body;

[0183] A vectorization and denoising processing module is used to perform vectorization processing on the crack imaging of the target layer, and perform denoising and smoothing processing on the vectorized crack imaging to obtain the crack imaging after denoising and smoothing processing;

[0184] The target element separation module is used to perform centerline contour tracing and target element separation processing on the crack imaging after denoising and smoothing, so as to obtain independently separated cracks;

[0185] The quantitative characterization module is used to automatically and quantitatively characterize the orientation, length, density, strength, tensor, etc. of independently separated cracks in the target layer, and complete the evaluation and research of cracks in the target layer.

[0186] Embodiment 3

[0187] According to an embodiment of the present invention, a computer-readable storage medium is further provided, on which a program code is stored. When the program code is executed by a processor, the method for automated imaging enhancement and quantitative characterization of cracks as described in any of the above embodiments is implemented.

[0188] Among them, the storage medium can be, for example, flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, server, App application store, etc.

[0189] Embodiment 4

[0190] According to an embodiment of the present invention, an electronic device is also provided, which includes a memory and a processor, wherein the memory stores program code that can be run on the processor, and when the program code is executed by the processor, the method for automated imaging enhancement and quantitative characterization of cracks as described in any of the above embodiments is implemented.

[0191] It can be understood that the electronic device may also include an input / output (I / O) interface and a communication component.

[0192] The processor is used to execute all or part of the steps in the method for automated crack imaging enhancement and quantitative characterization as described in any one of the embodiments. The memory is used to store various types of data, which may include instructions of any application or method in the electronic device, as well as data related to the application.

[0193] The processor can be an application specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a controller, a microcontroller, a microprocessor or other electronic components, and is used to execute the method for automated crack imaging enhancement and quantitative characterization described in any of the above embodiments.

[0194] The memory can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0195] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0196] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present invention.

[0197] 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. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0198] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for enabling an electronic device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0199] Although the embodiments disclosed in the present invention are as above, the contents described are only embodiments adopted for facilitating the understanding of the present invention and are not intended to limit the present invention. Any technician in the technical field to which the present invention belongs can make any modifications and changes in the form and details of the implementation without departing from the spirit and scope disclosed in the present invention, but the protection scope of the present invention shall still be subject to the scope defined in the attached claims.

Claims

1. A method for automated crack imaging enhancement and quantitative characterization, characterized in that: The following steps are involved: Step S1, obtaining a post-stack 3D seismic data volume of a target layer section in a target work area and performing filtering processing; Step S2, performing variance calculation on the seismic data volume after filtering to obtain a variance volume; Step S3, controlling the crack development in the angle domain; Step S4, performing attribute volume analysis and ant volume tracking operations on the variance volume, and performing multiple iterative superposition operations on the ant volume; Step S5, performing boundary optimization and structural smoothing processing on the ant body to obtain a fracture continuity imaging enhancement attribute body; Step S6, performing vectorization and denoising and smoothing processing on the fracture imaging of the target layer to obtain a processed fracture imaging; Step S7, performing centerline contour tracing and target element separation processing on the processed crack imaging to obtain independently separated cracks; Step S8, quantitatively characterizing the independently separated fractures in the target layer.

2. The method for automated crack imaging enhancement and quantitative characterization according to claim 1, characterized in that: In step S2, variance calculation is performed on the seismic data volume after filtering to obtain a variance volume, and the specific operations include: The variance value of each sampling point in the three-dimensional seismic data volume after filtering is calculated using a seismic variance value calculation formula to obtain a three-dimensional variance volume; The earthquake variance value calculation formula is: Among them, δ 2 is the variance value of a certain sampling point; j is the variance calculation time; i is the number of seismic traces, which is a real number; I is the number of adjacent traces selected when calculating the variance; L is the time window length under the variance calculation time; w ij is the weight factor function of the earthquake variance triangle, and its interval range is [0,1]; X ij is the seismic amplitude value of the jth sample point in the ith channel; is the average amplitude value of all i-channel data at time j.

3. The method for automated crack imaging enhancement and quantitative characterization according to claim 1, characterized in that: In step S3, the angle domain control of the crack development is performed, and the specific operations include: First, determine the fracture dip and azimuth development of the target layer in the target work area, and then control the fracture development in the angle domain based on the determined fracture dip and azimuth development. When the cracks developed in the target layer are identified as high-angle cracks, the crack angle range is controlled to be 60°-90°; When the cracks developed in the target layer are judged to be low-angle cracks, the crack angle range is controlled to be 0°-30°; When the fractures developed in the target layer are identified as medium-angle fractures, the fracture angle range is controlled to be 30°-60°; When the fractures developed in the target layer are identified as medium-high angle fractures, the fracture angle domain is controlled within 45°-90°; When the fractures developed in the target layer are determined to be medium-low angle fractures, the fracture angle domain is controlled within 0°-45°.

4. The method for automated crack imaging enhancement and quantitative characterization according to claim 2, characterized in that: In step S4, the attribute volume analysis ant volume tracking operation is performed on the variance volume, and multiple iterative superposition operations are performed on the ant volume, including the following steps: Step S41: Select the obtained variance body, and use the probability formula to calculate the initial distribution of the ant body, and the probability value calculated at the location where the crack discontinuity is located is large; The probability formula is: Among them, P x is the probability of selecting the xth data point corresponding to a data block among several uniformly distributed small data blocks; C x and C y represents the normalized coherence value of the xth and yth data points; Step S42: Divide the initial activity range of the ant body into smaller n×n small blocks, and use the gradient direction formula to calculate the main direction of each small block, that is, the main direction of ant tracking. The gradient direction formula is: Among them, θ is the main direction of ant tracking; G i (x,y),G j (x, y) are the gradients of the corresponding data points in each data block; (x, y) are the position coordinates of the data point; w is the length of the data block; Step S43: Determine the candidate search range P(x, y) of the ant according to the transition probability formula, the transition probability formula is: Where R is the set of points within the search range of the current data point; (x, y) is the candidate position; τ(x, y), η(x, y) and τ(ω,υ), η(ω,υ) represent the pheromone and heuristic information of points (x, y) and (ω,υ) respectively; α and β are the control factors of pheromone and heuristic information, respectively, controlling the relative weights of pheromone and heuristic information; Step S44: Setting the positive feedback mechanism of the ant colony algorithm. First, the heuristic information η(x, y) is defined as: η(x,y)=1-D(x,y) Where D(x,y) is the coherence value of the point (x,y); if the heuristic information is defined to be dominant, β takes a relatively high value; if the pheromone is defined to be dominant, α takes a relatively high value; when the initial pheromone is the same, the point with a smaller coherence value is more likely to be selected. If all ants have completed the tracking, proceed to the next step, otherwise continue tracking; Step S45: After the ants complete a search process within the initial distribution range, the pheromone of each data point (x, y) is updated according to the update algorithm; The update algorithm formula is: Where: τ(x,y) is the pheromone at the data point (x,y) in this cycle, τ max is the upper limit of pheromone; is the increment of pheromone at the data point (x, y) in this cycle; n is the number of ants passing through this path in this cycle; ξ'L(r) represents the amount of information left by the rth ant on this path in this cycle, and its value is proportional to the length of the path searched by the ant; ξ' is the pheromone update coefficient; L(r) is the length of the path searched by the rth ant in this cycle; Step S46: After completing one iteration process in sequence, if the number of iterations reaches the set maximum number of iterations, go to the next step, otherwise jump back to the initial tracking direction, and calculate again in sequence to complete multiple iterations to form a positive feedback mechanism and finally generate an ant automatic tracking three-dimensional data volume.

5. The method for automated crack imaging enhancement and quantitative characterization according to claim 4, characterized in that: In step S5, the ant body is subjected to boundary optimization and structural smoothing processing to obtain a fracture continuity imaging enhanced attribute body, and the specific operations include: The boundary is optimized by controlling the number of transverse seismic traces and longitudinal sampling points used to estimate structural smoothness. Gaussian smoothing is performed along the strike of the structure using a three-dimensional Gaussian weighted average function to highlight the structural boundary and obtain fracture continuity imaging enhancement attribute volume. The formula for obtaining the mean value by the three-dimensional Gaussian weighted average is as follows: Among them, δ x ,δ y ,δ t are the scale parameters of x, y, and t respectively; x is the horizontal survey line; y is the vertical survey line; t is the time variable; is the mean of the three-dimensional Gaussian weighted average; ψ (x，y，t) It is the earthquake data in x, y, t scale.

6. The method for automated crack imaging enhancement and quantitative characterization according to claim 5, characterized in that: The step S7 is to perform centerline contour tracing and target element separation processing on the processed crack imaging to obtain independently separated cracks. The specific operations are as follows: The processed fracture imaging is processed by automatic centerline contour tracing to thin the fracture lines, enhance the fracture details, obtain continuous and clear fracture tracks, and generate the final high-resolution fracture imaging; The target element separation process is to automatically split the curve of the high-resolution fracture imaging fine line body to obtain the fractures that are separated and exist independently, so that each fracture has identifiable length and occurrence element information.

7. The method for automated crack imaging enhancement and quantitative characterization according to claim 6, characterized in that: In step S8, the independently separated fractures in the target layer are quantitatively characterized, and the specific operations include: Matlab software is used to automatically calculate and characterize the crack trend, crack pixel length, crack density, crack development intensity, and crack tensor of the target layer, and the crack trend rose map, crack pixel length map, crack density map, crack intensity map, and crack tensor map are obtained to achieve quantitative evaluation of cracks.

8. A crack automated imaging enhancement and quantitative characterization system, characterized in that: include: An acquisition module is used to acquire the post-stack 3D seismic data volume of the target layer section in the target work area and perform filtering processing; A variance calculation module is used to perform variance calculation on the seismic data volume after filtering to obtain a variance volume; The fracture angle domain control module is used to control the fracture development in the angle domain according to the determined fracture inclination and azimuth development conditions; The ant body tracking operation module is used to perform attribute body analysis ant body tracking operation on the variance body and perform multiple iterative superposition operations on the ant body; A structural smoothing processing module is used to perform boundary optimization and structural smoothing processing on the ant body to obtain a fracture continuity imaging enhancement attribute body; A vectorization and denoising processing module is used to perform vectorization processing on the crack imaging of the target layer, and perform denoising and smoothing processing on the vectorized crack imaging to obtain the crack imaging after denoising and smoothing processing; The target element separation module is used to perform centerline contour tracing and target element separation processing on the crack imaging after denoising and smoothing, so as to obtain independently separated cracks; The quantitative characterization module is used to automatically and quantitatively characterize the orientation, length, density, strength, tensor, etc. of independently separated cracks in the target layer, and complete the evaluation and research of cracks in the target layer.

9. A computer-readable medium, characterized in that The computer-readable storage medium stores at least one computer-executable program, and when the at least one program is executed by the computer, the computer executes the steps in the method for automated fracture imaging enhancement and quantitative characterization according to any one of claims 1 to 7.

10. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps in the method for automated imaging enhancement and quantitative characterization of cracks as described in any one of claims 1 to 7.

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