Fracture detection method and device based on tensor identification tracking

Abstract

The invention relates to the field of seismic data comprehensive interpretation, and particularly discloses a fracture detection method and device based on tensor identification tracking, and the method comprises the steps: solving a structure tensor of a seismic data volume; smoothing the structure tensor to obtain a smooth tensor; and carrying out ant tracking by taking the smooth tensor as a basic attribute, and adjusting a smooth parameter of the smooth tensor until an expected ant tracking effect is achieved. According to the fracture detection method based on tensor identification and tracking, the gradient structure tensor is used as a basic attribute to perform ant tracking, and the artificial interpretation fracture is introduced as a tensor smoothing parameter constraint term, so that the position of the fracture body of the fracture control fracture-cavity reservoir can be effectively and accurately identified; meanwhile, the fracture position is identified at the position passing through the middle of the cave broken body, and the fracture real space position under the specific geological condition is better met.

Description

Technical Field

[0001] The present invention relates to the field of comprehensive interpretation of seismic data, and particularly to a fracture detection method and device based on tensor recognition and tracking. Background Art

[0002] Currently, the commonly used fracture detection technologies mainly include extracting likelihood attributes, coherence attributes, AFE attributes, etc. from seismic data. Some scholars use multiple types of attributes, combine the algorithm characteristics and advantages of the attributes themselves, and comprehensively judge the fracture position and development intensity at different scales and categories, and have achieved good results. Generally speaking, the conventional fracture detection attributes all use the amplitude and phase differences caused by seismic axis offset and discontinuity as the recognition features to judge the fracture position.

[0003] However, the fracture structures of fault-controlled fracture-cavity reservoirs are diverse and the reservoir formation models are complex. Using conventional fracture detection means often identifies the fracture position at the place where the seismic in-phase axis is offset. This method purely starts from the geophysical exploration perspective and cannot uniformly describe the fracture distribution law. After comparing with actual logging data, in the reservoirs of such oil and gas reservoirs, especially when the fracture geological structure belongs to the strike-slip structure, the fracture position is located in the middle of the fractured body.

[0004] Based on this technical background and combined with the geological characteristics of fracture development in fault-controlled fracture-cavity reservoirs, the present invention studies a fracture detection method and device based on tensor recognition and tracking. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the present invention provides a fracture detection method and device based on tensor recognition and tracking. The method uses the gradient structure tensor as the basic attribute for ant tracking, and introduces the manually interpreted fracture as a tensor smoothing parameter constraint term, which can effectively and accurately identify the position of the fractured body in the fault-controlled fracture-cavity reservoir, and at the same time identify the fracture position in the middle of the fractured body passing through the cave, which is more in line with the true spatial position of the fracture under specific geological conditions.

[0006] To achieve the above object, the first aspect of the present invention provides a fracture detection method based on tensor recognition and tracking, including:

[0007] Obtaining the structure tensor of the seismic data volume;

[0008] Performing smoothing processing on the structure tensor to obtain a smoothed tensor;

[0009] Taking the smoothed tensor as the basic attribute for ant tracking, and adjusting the smoothing parameter of the smoothed tensor until the expected ant tracking effect is achieved.

[0010] The second aspect of the present invention provides a fracture detection device based on tensor recognition and tracking, including:

[0011] A tensor calculation module for calculating the structure tensor of a seismic data volume;

[0012] A smoothing processing module for smoothing the structure tensor to obtain a smoothed tensor;

[0013] A tracking module for performing ant tracking based on the smoothed tensor as a basic attribute, and adjusting the smoothing parameter of the smoothed tensor until the expected ant tracking effect is achieved.

[0014] A third aspect of the present invention provides an electronic device, which includes:

[0015] A memory storing executable instructions;

[0016] A processor that runs the executable instructions in the memory to implement the fracture detection method based on tensor recognition and tracking described in the first aspect.

[0017] A fourth aspect of the present invention provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, it implements the fracture detection method based on tensor recognition and tracking described in the first aspect.

[0018] The beneficial effects of the present invention include:

[0019] (1) The fracture detection method based on tensor recognition and tracking proposed by the present invention uses the gradient structure tensor as a basic attribute for ant tracking, and introduces artificial interpretation of fractures as a tensor smoothing parameter constraint term, which can effectively and accurately identify the position of the broken body of the fault-controlled fracture-vug reservoir. At the same time, the fracture position is identified at the position passing through the middle of the cave broken body, which is more in line with the true spatial position of the fracture under specific geological conditions.

[0020] (2) The fracture detection method based on tensor recognition and tracking proposed by the present invention designs a forward model according to the fault-controlled hydrocarbon accumulation mode, and uses the ant body tracking technology based on tensor attributes to identify the fracture position, providing a new idea for the fine characterization and description of fractures in this type of oil and gas reservoir.

[0021] (3) The fracture detection method based on tensor recognition and tracking proposed by the present invention combines the geological characteristics of fracture development in fault-controlled fracture-vug type oil reservoirs, and identifies the fracture at the position passing through the middle of the cave broken body, which is more in line with the geological understanding of the development of strike-slip fractures associated with caves in fault-controlled fracture-vug type oil and gas reservoirs.

[0022] (4) The fracture detection method based on tensor recognition and tracking proposed by the present invention can find the best balance point between identifying the details of fracture cracks and identifying the position of the main fracture by adjusting the value of the smoothing parameter.

[0023] Other features and advantages of the present invention will be described in detail in the following detailed implementation section. Description of the Drawings

[0024] By describing the exemplary embodiments of the present invention in more detail in conjunction with the accompanying drawings, the above and other objects, features, and advantages of the present invention will become more apparent.

[0025] Figure 1 It is a schematic flow diagram of the fracture detection method based on tensor recognition and tracking proposed by the present invention.

[0026] Figure 2 It is a schematic flow diagram of the tensor-antibody calculation technology in a specific implementation manner of the fracture detection method based on tensor recognition and tracking proposed by the present invention.

[0027] Figure 3 It is a schematic diagram of the forward model and imaging results of Model 1 - fracture-solution body in a specific implementation manner of the fracture detection method based on tensor recognition and tracking proposed by the present invention.

[0028] Figure 4 It is a schematic diagram for comparing the recognition effects of different fracture detection attributes of Model 1 in a specific implementation manner of the fracture detection method based on tensor recognition and tracking proposed by the present invention.

[0029] Figure 5 It is a schematic diagram of the forward model and imaging results of Model 2 - production well pattern in a specific implementation manner of the fracture detection method based on tensor recognition and tracking proposed by the present invention.

[0030] Figure 6 It is a schematic diagram for comparing the recognition effects of different fracture detection attributes of Model 2 in a specific implementation manner of the fracture detection method based on tensor recognition and tracking proposed by the present invention.

[0031] Figure 7 It is a schematic diagram for comparing the fracture detection effects of the seismic profile passing through Well S3 in a specific implementation manner of the fracture detection method based on tensor recognition and tracking proposed by the present invention.

[0032] Figure 8 It is a schematic diagram for comparing the electric image logging of Well B12 and the predicted fractures in a specific implementation manner of the fracture detection method based on tensor recognition and tracking proposed by the present invention. Detailed Implementation Manner

[0033] The following will describe the preferred embodiments of the present invention in more detail. Although the preferred embodiments of the present invention are described below, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein.

[0034] The present invention provides a fracture detection method based on tensor recognition and tracking, such asFigure 1 As shown in the figure, it includes:

[0035] Obtain the structure tensor of the seismic data volume;

[0036] Smooth the structure tensor to obtain a smoothed tensor;

[0037] Use the smoothed tensor as the basic attribute for ant tracking, and adjust the smoothing parameter of the smoothed tensor until the expected ant tracking effect is achieved.

[0038] In the present invention, using the gradient structure tensor as the basic attribute for ant tracking and introducing the manually interpreted fracture as the tensor smoothing parameter constraint term can effectively and accurately identify the position of the fractured body in the fault-controlled fracture-cave reservoir. At the same time, the fracture position is identified at the position passing through the middle of the cave fractured body, which is more in line with the true spatial position of the fracture under specific geological conditions.

[0039] According to the present invention, the formula for obtaining the structure tensor of the seismic data is:

[0040]

[0041] Wherein, are the derivatives of the seismic data volume in the x, y, and z directions, and D is the structure tensor.

[0042] According to the present invention, the formula for obtaining the derivatives of the seismic data volume in the x, y, and z directions is:

[0043]

[0044] Wherein, is the first derivative of the Gaussian kernel function, * is the convolution operation, and u is the seismic data volume.

[0045] Preferably, the structure tensor is a diagonal matrix;

[0046] The eigenvalues of the diagonal matrix are respectively λ 1 , λ 2 , λ 3 ;

[0047] The conditions satisfied by the eigenvalues are: λ 1 ≥λ 2 ≥λ 3 > 0.

[0048] According to the present invention, adjusting the smoothing parameter of the smoothed tensor until the expected ant tracking effect is achieved includes:

[0049] Introduce the regional manually interpreted fault as the comparison fitting term. When the fitting deviation is greater than the expected value, continuously adjust the smoothing parameter until the fitting condition is satisfied;

[0050] Meeting the fitting conditions is to achieve the expected ant tracking effect.

[0051] Preferably, when the smoothing parameter decreases, the ant tracking effect shows that it is easy to identify the details of fracture cracks;

[0052] When the smoothing parameter increases, the ant tracking effect shows a tendency to identify the positions of main fractures.

[0053] In the present invention, by adjusting the value of the smoothing parameter, an optimal balance point can be found between identifying the details of fracture cracks and identifying the positions of main fractures.

[0054] In the present invention, the gradient tensor attribute after structural smoothing is used as the ant tracking data volume. After multiple tests, it is found that the smoothing degree of the gradient tensor has a great influence on the morphology of the identified ant body. When the smoothing parameter is small, the details of fracture recognition are more obvious, and the tiny fractures outside the main fracture zone are also identified, interfering with the judgment of the position of the main fracture. When the smoothing parameter increases, it is more inclined to depict fractures with larger scale and intensity while ignoring tiny fractures. Therefore, an optimal smoothing parameter needs to be selected to clearly identify the positions of main fractures without losing too many details.

[0055] According to the present invention, ant tracking is to place multiple electronic ants at the initial positions on the seismic data volume, release pheromones when capturing fault information, and finally generate the optimal forward route of the electronic ant colony to represent the spatial position of fractures;

[0056] The parameters set in the ant tracking algorithm include:

[0057] Ant boundary, which is used to control the total number and initial distribution form of the electronic ant colony, and serves as the control radius for each electronic ant;

[0058] Step size and tracking deviation, which are used to limit the single-step length and the maximum allowable deviation of the electronic ant when searching for local maxima;

[0059] Termination condition, which is used to determine the search termination condition based on the percentage of the actual tracking steps of the electronic ant to the total number of steps. When the percentage reaches the limit condition, the ant tracking stops;

[0060] Tracking azimuth, which is used to control the dip angle and azimuth angle of the electronic ant search according to the fracture occurrence characteristics of the research area.

[0061] In the present invention, a forward model is designed according to the fault-controlled hydrocarbon accumulation mode, and the ant body tracking technology based on tensor attributes is used to identify the fracture positions, providing a new idea for the fine characterization and depiction of fractures in this type of hydrocarbon reservoir.

[0062] The method in the present invention combines the geological characteristics of fracture development in fault-controlled fracture-vuggy reservoirs, and identifies the fractures at the positions in the middle of the cave fractured bodies, which is more in line with the geological understanding of the development of strike-slip fractures associated with caves inside fault-controlled fracture-vuggy oil and gas reservoirs.

[0063] The ant algorithm adopted in the present invention is a bionic algorithm for path optimization first proposed by Colorni, Dorigo, etc. Its core idea is to simulate the process of ants foraging and continuously optimize the path until it is determined that all ants can reach the position of the food on the shortest-distance path. Later, Schlumberger introduced the algorithm into fracture interpretation. A large number of "electronic ants" were placed at the initial positions on the seismic data volume to capture fault information and release pheromones at the same time. Finally, the optimal advancing route of the "ant colony" was generated to represent the spatial position of the fracture.

[0064] The ant tracking algorithm needs to set the ant boundary as the control radius of each "ant", and this parameter defines the total number of "ants" and the initial distribution form. At the same time, the ant step length and tracking deviation need to be set to limit the single-step length and the maximum allowable deviation of the ants when searching for local maxima. The number of allowed illegal steps is how many ant step lengths within which no maximum value can be searched. The search termination condition is determined by setting the percentage of this parameter in the total number of steps. When the percentage reaches the limit condition, the ant tracking stops.

[0065] In addition to the setting of the above basic parameters, the dip angle and azimuth angle of ant search can also be controlled according to the fracture occurrence characteristics of the research area. Using different seismic attributes for ant volume tracking will also produce quite different fracture identification results.

[0066] In order to obtain the optimized smoothing parameter, the regional manually interpreted faults are introduced as the contrast fitting term. When the fitting deviation is greater than the expected value, the smoothing parameter will be continuously adjusted until the fitting condition is met. Then, the tensor smoothing parameter is introduced into the calculation process of the whole area to obtain the ant tracking volume of the whole work area.

[0067] The present invention will be described in more detail below through embodiments.

[0068] Embodiment 1:

[0069] In this embodiment, a series of deep and large strike-slip fractures developed in the Shunbei area of the Tarim Basin are selected as the research object. The Ordovician carbonate reservoirs around the fracture zone are rich in oil and gas, and it has currently become an important position for oil and gas production in the Tarim Basin. The fault-controlled reservoir bodies developed in the Shunbei area are mainly characterized by fracture-associated cave reservoirs. Therefore, it is of great significance to clarify the position, spatial structure and development intensity of the fractures for the analysis of regional fracture evolution and hydrocarbon accumulation patterns.

[0070] This embodiment adopts as Figure 2The technical process of tensor-ant body calculation shown;

[0071] In order to compare and analyze the application effects and accuracies of different fracture properties, combined with the geological understanding of the fault-karst bodies in the Shunbei area, a forward theoretical model of fault-karst bodies (Model 1) was designed for forward simulation; as Figure 3 shown, under the bedrock background with a formation velocity of 6000 m / s, small-scale fracture zones are developed. Inside the fracture zones, circular caves with a diameter of about 50 m are developed. The seismic wave velocity inside the caves is set to 3600 m / s, the wave velocity inside the fractures is set to 3800 m / s, and the overlying formation velocity is 5000 m / s. A Ricker wavelet with a main frequency of 25 Hz is used as the seismic source for excitation. Seismic waves are received on both sides centered on the seismic source. A total of 201 shots are excited, and the shot interval and geophone acquisition interval are both 50 m. Migration imaging is performed on the seismic trace gather. It can be seen from the imaging profile that obvious "bead string" response characteristics exist at the positions of the two fracture zones;

[0072] Analyze the effects of different fracture detection methods on detecting the seismic data of this model, and extract similarity attributes, Likelihood attributes, and the tensor-ant body attributes of the present invention from the seismic data; as Figure 4 shown, the dark lines represent the fracture positions identified by the attributes, Figure 4 (a) and (b) are the identification results of the Likelihood attribute and the similarity attribute respectively. Both identify the fractures on the two sides of the "bead string". The similarity attribute depicts a relatively broad fracture range. The Likelihood attribute depicts the fracture more continuously and clearly than the similarity attribute, Figure 4 (c) is the identification effect of the tensor-ant body attribute of the present invention, which identifies the fracture at the position passing through the "bead string", is more consistent with the fracture position of the designed model, and depicts the fracture relatively continuously with a clear boundary;

[0073] To better understand the underground fault-reservoir structure in the work area and verify the fracture development pattern in the Shunbei area, according to the seismic and logging data of the production wells in the studied work area and combined with relevant geological understandings, a geological model (Model 2) as Figure 5 shown was designed for forward simulation. Fracture bodies + caves are developed in the formation, forming a fault-karst reservoir pattern. After migration imaging processing, the obtained seismic profile is basically consistent with the morphological characteristics of the seismic profile passing through the production wells;

[0074] Based on the seismic data obtained from the forward simulation, a comparative study of various fracture detection attributes was carried out, and three attribute bodies of coherence attribute, AFE attribute, and the tensor-ant of the present invention were extracted in total; as Figure 6(a) and (b) respectively show the fracture recognition effects of the coherence attribute and the variance-ant body attribute. The main fracture positions recognized by both are at the places where the bead string is on both sides and the seismic event axis is offset. In addition, the fractures recognized by the coherence attribute are more continuous and clear, while the variance-ant body attribute is more disordered. Figure 6 (c) shows the fracture recognition effect of the tensor-ant body. The tensor-ant body recognizes the fracture at the place passing through the center of the bead string, and the fracture surface is continuous and clear, which can more effectively describe the position of the fracture zone in the model.

[0075] For the comparative analysis of the fracture attributes in the actual work area, the research work area is located on the fracture zone in the Shunbei area, which has experienced multiple tectonic activities and mainly developed deep and large strike-slip fractures. A typical well S3 in the work area was selected for the comparative analysis of fracture attributes.

[0076] There is a leakage phenomenon in the logging section from 8251 meters to 8522 meters at the bottom of this production well, with a total leakage of 575 cubic meters. Figure 7 (a) is the seismic profile passing through Well S3. Figure 7 (b), (c), and (d) respectively show the fracture detection effects of the coherence attribute, the likelihood attribute, and the tensor-ant attribute of the present invention. The likelihood attribute recognizes fractures more continuously than the coherence attribute, but both recognize the fracture positions on both sides of the bead string, which does not match the actual leakage point position. However, the fractures recognized by the tensor-ant body of the present invention are continuous and clear, and can better match the logging leakage point position.

[0077] To more intuitively judge the accuracy of the fracture recognition by the tensor-ant body, as Figure 8 shown, the resistivity image logging data of Well B12 in the work area is introduced. The data in the well section shows that this well encounters high-dip fractures, and the fracture strike is nearly north-south and the dip direction is due west. The three-dimensional characterization result of the fracture predicted by the tensor-ant of the present invention shows that fractures are relatively developed in the resistivity logging section of this well, and the occurrence is nearly vertical fractures, and the dip angle and azimuth information can better correspond to the logging data.

[0078] In summary, compared with other fracture detection attributes, the tensor-ant body of the present invention has a higher well-seismic matching degree. It can not only effectively characterize the overall structure of the fracture, but also effectively represent the differences in the longitudinal and transverse development intensities of the fractures, providing a reliable reference basis for the evaluation of the development scale and connectivity relationship of the fault-controlled reservoirs.

[0079] Example 2:

[0080] This example provides a fracture detection method based on tensor recognition and tracking. As Figure 1 shown, it includes:

[0081] Obtain the structure tensor of the seismic data volume.

[0082] Smoothing the structure tensor to obtain a smoothed tensor;

[0083] Using the smoothed tensor as the basic attribute for ant tracking, adjusting the smoothing parameter of the smoothed tensor until the expected ant tracking effect is achieved;

[0084] The formula for calculating the structure tensor of seismic data is:

[0085]

[0086] Where, are the derivatives of the seismic data volume in the x, y, and z directions, and D is the structure tensor;

[0087] The formula for calculating the derivatives of the seismic data volume in the x, y, and z directions is:

[0088]

[0089] Where, is the first derivative of the Gaussian kernel function, * is the convolution operation, and u is the seismic data volume;

[0090] The structure tensor is a diagonal matrix;

[0091] The eigenvalues of the diagonal matrix are λ 1 , λ 2 , λ 3 ;

[0092] The conditions satisfied by the eigenvalues are: λ 1 ≥λ 2 ≥λ 3 > 0;

[0093] Adjusting the smoothing parameter of the smoothed tensor until the expected ant tracking effect is achieved includes:

[0094] Introducing the regional manual interpretation fault as a contrast fitting term. When the fitting deviation is greater than the expected value, continuously adjust the smoothing parameter until the fitting condition is satisfied;

[0095] Satisfying the fitting condition means achieving the expected ant tracking effect;

[0096] When the smoothing parameter decreases, the ant tracking effect is manifested as being easy to identify the details of fracture cracks;

[0097] When the smoothing parameter increases, the ant tracking effect is manifested as being inclined to identify the position of the main fracture;

[0098] Ant tracking is to place multiple electronic ants at the initial position on the seismic data volume, release pheromones when capturing fault information, and finally generate the optimal forward route of the electronic ant colony to represent the fracture spatial position;

[0099] The parameters set in the ant tracking algorithm include:

[0100] The ant boundary is used to control the total number and initial distribution pattern of the electronic ant colony, and serves as the control radius for each electronic ant.

[0101] The step size and tracking deviation are used to limit the single-step length and maximum allowable deviation of the electronic ant when searching for the local maximum.

[0102] The termination condition is used to determine the search termination condition based on the percentage of the actual tracking steps of the electronic ant to the total number of steps. When the percentage reaches the limit condition, the ant tracking stops.

[0103] The tracking azimuth is used to control the dip angle and azimuth angle of the electronic ant search according to the fracture occurrence characteristics of the research area.

[0104] Example 3:

[0105] This example provides a fracture detection device based on tensor recognition and tracking, including:

[0106] A tensor calculation module for calculating the structure tensor of the seismic data volume.

[0107] A smoothing processing module for smoothing the structure tensor to obtain a smoothed tensor.

[0108] A tracking module for performing ant tracking based on the smoothed tensor as the basic attribute, and adjusting the smoothing parameters of the smoothed tensor until the expected ant tracking effect is achieved.

[0109] The formula for calculating the structure tensor of the seismic data is:

[0110]

[0111] Among them, are the derivatives of the seismic data volume in the x, y, and z directions, and D is the structure tensor;

[0112] The formula for calculating the derivatives of the seismic data volume in the x, y, and z directions is:

[0113]

[0114] Among them, is the first derivative of the Gaussian kernel function, * is the convolution operation, and u is the seismic data volume;

[0115] The structure tensor is a diagonal matrix;

[0116] The eigenvalues of the diagonal matrix are λ 1 , λ 2 , λ 3 ;

[0117] The conditions satisfied by the eigenvalues are: λ 1 ≥λ 2 ≥λ 3 >0;

[0118] Adjusting the smoothing parameter of the smoothing tensor until the expected ant tracking effect is achieved includes:

[0119] Introducing a regional artificial interpretation fault as a contrast fitting term. When the fitting deviation is greater than the expected value, continuously adjust the smoothing parameter until the fitting condition is met;

[0120] Meeting the fitting condition means achieving the expected ant tracking effect;

[0121] When the smoothing parameter decreases, the ant tracking effect is manifested as being easy to identify the details of fracture cracks;

[0122] When the smoothing parameter increases, the ant tracking effect is manifested as tending to identify the position of the main fracture;

[0123] Ant tracking is to place multiple electronic ants at the initial position on the seismic data volume, release pheromones when capturing fault information, and finally generate the optimal forward route of the electronic ant colony to represent the spatial position of the fracture;

[0124] The parameters set by the ant tracking algorithm include:

[0125] Ant boundary, used to control the total number of the electronic ant colony and the initial distribution pattern, and as the control radius of each electronic ant;

[0126] Step size and tracking deviation, used to limit the single-step length and the maximum allowable deviation of the electronic ant when searching for the local maximum;

[0127] Termination condition, used to determine the search termination condition based on the percentage of the actual tracking steps of the electronic ant to the total number of steps. When the percentage reaches the limit condition, the ant tracking stops;

[0128] Tracking azimuth, used to control the dip angle and azimuth angle of the electronic ant search according to the fracture occurrence characteristics of the research area.

[0129] Example 4:

[0130] An embodiment of the present invention provides an electronic device including a memory and a processor,

[0131] Memory, storing executable instructions;

[0132] Processor, the processor runs the executable instructions in the memory to implement the fracture detection method based on tensor recognition and tracking.

[0133] The memory is used to store non - transitory computer - readable instructions. Specifically, the memory may include one or more computer program products, and the computer program products may include various forms of computer - readable storage media, such as volatile memory and / or non - volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory, etc. The non - volatile memory may include, for example, read - only memory (ROM), hard disk, flash memory, etc.

[0134] The processor may be a central processing unit (CPU) or other forms of processing units with data - processing capabilities and / or instruction - execution capabilities, and may control other components in the electronic device to perform desired functions. In an embodiment of the present invention, the processor is used to run the computer - readable instructions stored in the memory.

[0135] Those skilled in the art should understand that, in order to solve the technical problem of how to obtain good user - experience effects, this embodiment may also include well - known structures such as communication buses, interfaces, etc., and these well - known structures should also be included in the protection scope of the present invention.

[0136] For the detailed description of this embodiment, reference may be made to the corresponding descriptions in the foregoing embodiments, and details will not be repeated here.

[0137] Embodiment Five:

[0138] An embodiment of the present invention provides a computer - readable storage medium storing a computer program, and when the computer program is executed by a processor, a fracture detection method based on tensor recognition and tracking is implemented.

[0139] According to the computer - readable storage medium of the embodiment of the present invention, non - transitory computer - readable instructions are stored thereon. When the non - transitory computer - readable instructions are run by a processor, all or part of the steps of the methods of the foregoing embodiments of the present invention are executed.

[0140] The above - mentioned computer - readable storage media include, but are not limited to: optical storage media (such as CD - ROM and DVD), magneto - optical storage media (such as MO), magnetic storage media (such as magnetic tapes or external hard drives), media with built - in rewritable non - volatile memory (such as memory cards), and media with built - in ROM (such as ROM cartridges).

[0141] The fracture detection method based on tensor recognition and tracking proposed in the embodiments of the present invention uses the gradient structure tensor as a basic attribute for ant tracking, and introduces artificial interpretation of fractures as a tensor smoothing parameter constraint term, which can effectively and accurately identify the positions of broken bodies in fractured and controlled fracture-vug reservoirs. At the same time, the fracture position is identified at the position passing through the middle of the cave broken body, which is more in line with the true spatial position of fractures under specific geological conditions.

[0142] The embodiments of the present invention have been described above. The above description is exemplary and not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations are obvious to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A fracture detection method based on tensor recognition and tracking, characterized in that, it includes: Calculating the structure tensor of the seismic data volume; Performing smoothing processing on the structure tensor to obtain a smoothed tensor; Taking the smoothed tensor as the basic attribute for ant tracking, and adjusting the smoothing parameter of the smoothed tensor until the expected ant tracking effect is achieved.

2. The method according to claim 1, characterized in that, the formula for calculating the structure tensor of the seismic data is: Among them, are the derivatives of the seismic data volume in the x, y, and z directions, and D is the structure tensor.

3. The method according to claim 2, characterized in that, the formula for calculating the derivatives of the seismic data volume in the x, y, and z directions is: Among them, is the first derivative of the Gaussian kernel function, * represents the convolution operation, and u is the seismic data volume.

4. The method according to claim 3, characterized in that, the structure tensor is a diagonal matrix; The eigenvalues of the diagonal matrix are λ 1 , λ 2 , λ 3 ; The conditions satisfied by the eigenvalue are: λ 1 ≥ λ 2 ≥ λ 3 > 0.

5. The method according to claim 1, characterized in that, adjusting the smoothing parameter of the smoothed tensor until the expected ant tracking effect is achieved includes: Introducing the regional artificial interpretation fault as a contrast fitting term. When the fitting deviation is greater than the expected value, continuously adjust the smoothing parameter until the fitting condition is satisfied; The satisfaction of the fitting condition is to achieve the expected ant tracking effect.

6. The method according to claim 5, characterized in that, when the smoothing parameter decreases, the ant tracking effect is manifested as being easy to identify the details of fracture cracks; when the smoothing parameter increases, the ant tracking effect is manifested as being inclined to identify the position of the main fracture.

7. The method according to claim 1, characterized in that, the ant tracking is to place multiple electronic ants at the initial position on the seismic data volume, release pheromones when capturing fault information, and finally generate the optimal forward route of the electronic ant colony to represent the fracture spatial position; The parameters set by the ant tracking algorithm include: Ant boundary, used to control the total number and initial distribution form of the electronic ant colony, and as the control radius of each electronic ant; Step size and tracking deviation, used to limit the single-step length and maximum allowable deviation of the electronic ant when searching for local maxima; Termination condition, used to determine the search termination condition based on the percentage of the actual tracking steps of the electronic ant to the total number of steps. When the percentage reaches the limit condition, the ant tracking stops; Tracking azimuth, used to control the dip angle and azimuth angle of the electronic ant search according to the fracture occurrence characteristics of the research area.

8. A fracture detection device based on tensor recognition and tracking, characterized in that, it includes: A tensor calculation module, used to calculate the structure tensor of the seismic data volume; A smoothing processing module, used to perform smoothing processing on the structure tensor to obtain a smoothed tensor; A tracking module, used to perform ant tracking based on the smoothed tensor as the basic attribute, and adjust the smoothing parameter of the smoothed tensor until the expected ant tracking effect is achieved.

9. An electronic device, characterized in that, the electronic device includes: A memory, storing executable instructions; A processor, the processor runs the executable instructions in the memory to implement the fracture detection method based on tensor recognition and tracking according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program which, when executed by a processor, implements the fracture detection method based on tensor recognition and tracking described in any one of claims 1-7.

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