Prestack crack prediction evaluation method and device, computer equipment and storage medium

Abstract

The invention provides a pre-stack crack prediction evaluation method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring a plurality of pre-stack azimuth gathers of different azimuth angles; performing dip angle filtering on each seismic offset data volume of different azimuth angles by using a frequency domain-based filtering algorithm to obtain a plurality of filtering data volumes of different azimuth angles; and performing ellipse phase fitting on each filtering data body of different azimuth angles to obtain a crack prediction result. A filtering algorithm based on a frequency domain is utilized to carry out frequency domain filtering on seismic migration data volumes with different azimuth angles, fracture seismic response characteristics can be highlighted, anisotropy characteristics caused by factors such as deposition can be effectively suppressed, anisotropy caused by fractures can be effectively identified, three-dimensional space distribution characteristics of fracture development can be provided, and the method is applicable to the field of seismic data processing. More bases are provided for fractured high-quality reservoir evaluation, and the prediction accuracy can be effectively improved.

Description

Technical Field

[0001] This invention relates to the technical field of fracture prediction in oil and gas exploration and development, and particularly to a method, apparatus, computer equipment, and storage medium for predicting and evaluating pre-stack fractures. Background Technology

[0002] Currently, reservoirs associated with fracture development are attracting increasing attention in oil and gas exploration. For example, the fault-dissolved reservoirs of the Tarim Basin in Xinjiang, the fault-fractured tight sandstone reservoirs of the Ordos Basin, and the tight limestone fractured reservoirs of central Sichuan have become exploration hotspots. Because fracture development is influenced by various factors, its distribution is complex, and prediction accuracy is poor. Conventional fracture prediction techniques are mainly divided into pre-stack and post-stack types. Post-stack fracture prediction techniques are more numerous and offer more stable prediction results, including coherence, enhanced coherence, curvature, and likelihood techniques used for fracture detection. For pre-stack fracture properties, anisotropy is primarily used to detect fractures.

[0003] Studies have shown that many factors contribute to reservoir anisotropy, including differences in reservoir grain size and bedding in different directions due to sedimentary causes, differences in tectonic stress, and fracture development. Because traditional pre-stack fracture prediction methods neglect these factors, the prediction results are inaccurate. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, computer equipment, and storage medium for predicting and evaluating pre-stack cracks to address the aforementioned technical problems.

[0005] In a first aspect, this disclosure provides a method for predicting and evaluating pre-stack cracks, including:

[0006] Obtain multiple front-stacked azimuth gathers at different azimuth angles;

[0007] Pre-stack time migration was performed on each of the aforementioned pre-stack azimuth gathers at different azimuth angles to obtain multiple seismic migration data volumes at different azimuth angles;

[0008] Using a frequency domain-based filtering algorithm, dip filtering is performed on each of the seismic migration data volumes at different azimuth angles to obtain multiple filtered data volumes at different azimuth angles.

[0009] Elliptical phase fitting is performed on each of the filtered data volumes at different azimuth angles to obtain crack prediction results.

[0010] In one embodiment, the step of using a frequency domain-based filtering algorithm to perform dip filtering on each of the seismic migration data volumes at different azimuth angles to obtain multiple filtered data volumes at different azimuth angles includes:

[0011] Detect the dip angle of each of the aforementioned seismic migration data volumes at different azimuth angles;

[0012] The filtering direction of the seismic migration data volume is determined based on the preset crack dip angle range and the dip angle of each seismic migration data volume;

[0013] Based on the filtering direction of the seismic migration data volume, a frequency domain-based filtering algorithm is used to perform dip filtering on each of the seismic migration data volumes at different azimuth angles, resulting in multiple filtered data volumes at different azimuth angles.

[0014] In one embodiment, the step of obtaining multiple front-stack azimuth gathers at different azimuth angles includes:

[0015] Obtain the pre-stack set;

[0016] Based on the preset crack orientation, the pre-stack gather is divided into multiple pre-stack gathers with different azimuth angles, wherein the center angle of one of the multiple different azimuth angles is consistent with the preset crack orientation.

[0017] In one embodiment, the step of obtaining multiple front-stack azimuth gathers at different azimuth angles includes:

[0018] Obtain the pre-stack set;

[0019] Based on the preset crack orientation and the signal-to-noise ratio of each pre-stack gather, the pre-stack gathers are divided according to different azimuth angles to obtain multiple pre-stack azimuth gathers with different azimuth angles. Among these multiple different azimuth angles, the center angle of one of the azimuth angles is consistent with the preset crack orientation.

[0020] In one embodiment, the step of obtaining the pre-stack gather includes:

[0021] Acquire 3D seismic data;

[0022] The pre-stack gathers are obtained by performing at least one of the following processing steps on the three-dimensional seismic data:

[0023] The three-dimensional seismic data is then denoised.

[0024] The three-dimensional seismic data are subjected to residual time difference correction processing.

[0025] In one embodiment, the frequency-domain based filtering algorithm is a frequency-domain Gabor directional filtering algorithm.

[0026] Secondly, this disclosure provides a pre-stack crack prediction and evaluation device, comprising:

[0027] The stack front azimuth gather acquisition module is used to acquire multiple stack front azimuth gathers at different azimuth angles;

[0028] The seismic migration data volume acquisition module is used to perform pre-stack time migration on each of the pre-stack azimuth gathers at different azimuth angles to obtain multiple seismic migration data volumes at different azimuth angles.

[0029] The filtering module is used to perform dip filtering on the seismic migration data volumes at different azimuth angles using a frequency domain-based filtering algorithm to obtain multiple filtered data volumes at different azimuth angles.

[0030] The fitting and prediction module is used to perform elliptical phase fitting on each of the filtered data volumes at different azimuth angles to obtain crack prediction results.

[0031] Thirdly, this disclosure provides a computer device including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in the foregoing aspects.

[0032] Fourthly, this disclosure provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the methods described in the above aspects.

[0033] Fifthly, this disclosure provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the methods described in the above aspects.

[0034] The aforementioned pre-stack fracture prediction and evaluation method, device, computer equipment, and storage medium utilize a frequency domain-based filtering algorithm to perform frequency domain filtering on seismic migration data volumes at different azimuth angles. This can highlight the seismic response characteristics of fractures, effectively suppress anisotropic characteristics caused by factors such as deposition, effectively identify anisotropy caused by fractures, provide three-dimensional spatial distribution characteristics of fracture development, provide more evidence for the evaluation of high-quality fractured reservoirs, and effectively improve the accuracy of prediction. Attached Figure Description

[0035] Figure 1 This is a flowchart illustrating a pre-stack crack prediction and evaluation method in one embodiment.

[0036] Figure 2 This is a flowchart illustrating the pre-stack crack prediction and evaluation method in another embodiment;

[0037] Figure 3 This is a comparison chart of the prediction results obtained using a conventional prediction method and the pre-stack crack prediction and evaluation method in one embodiment of this application. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0039] Example 1

[0040] In this embodiment, as Figure 1 As shown, a method for predicting and evaluating pre-stack cracks is provided, which includes:

[0041] Step 110: Obtain multiple front-stack azimuth gathers at different azimuth angles.

[0042] In this embodiment, the pre-stack azimuth gather belongs to the pre-stack gather category, which includes common shot point gather, common receiver point gather, and common reflection point gather. This pre-stack azimuth gather is divided into different pre-stack gathers at different azimuth angles, with each azimuth angle corresponding to one pre-stack gather.

[0043] Step 120: Perform pre-stack time migration on each of the pre-stack azimuth gathers at different azimuth angles to obtain multiple seismic migration data volumes at different azimuth angles.

[0044] In this embodiment, pre-stack time migration is used for imaging complex structures, providing accurate imaging even with significant variations in longitudinal and lateral velocities. Its basic principle is to utilize spatiotemporal Kirchhoff integral migration, first imaging each common shot-receiver profile separately, and then superimposing all results to form a migration profile. This method iteratively corrects the root mean square velocity to obtain common imaging point gathers (CIGs), which are then stacked to ultimately obtain the pre-stack time migration profile. In this embodiment, velocity analysis and pre-stack time migration are performed on pre-stack azimuth gathers at different azimuth angles to obtain seismic migration data volumes at different azimuth angles.

[0045] Pre-stack time migration of pre-stack lookouts can effectively eliminate the influence of tectonic factors, resulting in profiles with high signal-to-noise ratios and good imaging. Furthermore, it is suitable for non-common reflection point stacking problems at interfaces with large dip angles, providing more accurate images of subsurface geological bodies. Compared to pre-stack depth migration, pre-stack time migration is less sensitive to velocity fields and exhibits better structural imaging results under conditions of complex structures and small lateral velocity variations.

[0046] In one embodiment, during pre-stack time migration of the pre-stack azimuth gathers at different azimuth angles, each pre-stack azimuth gather at its own migration rate is migrated separately. It is worth noting that pre-stack time migration is highly sensitive to migration rate; even small rate errors can affect the migration imaging results. In practical applications, the optimal migration rate is typically determined iteratively.

[0047] Step 130: Using a frequency domain-based filtering algorithm, dip filtering is performed on each of the seismic migration data volumes at different azimuth angles to obtain multiple filtered data volumes at different azimuth angles.

[0048] In this embodiment, a frequency domain filtering algorithm is used to perform dip filtering on seismic migration data volumes at different azimuth angles. This can highlight the seismic response characteristics of fractures and effectively suppress anisotropic characteristics caused by factors such as deposition.

[0049] Step 140: Perform elliptic phase fitting on each of the filtered data volumes at different azimuth angles to obtain crack prediction results.

[0050] In this embodiment, elliptic anisotropic fitting is used to perform pre-stack crack prediction on the filtered data volume at different azimuth angles, thereby obtaining the crack prediction result.

[0051] In the above embodiments, frequency domain filtering algorithms are used to perform frequency domain filtering on seismic migration data volumes at different azimuth angles. This can highlight the seismic response characteristics of fractures, effectively suppress anisotropic characteristics caused by factors such as deposition, effectively identify anisotropy caused by fractures, provide three-dimensional spatial distribution characteristics of fracture development, provide more evidence for the evaluation of high-quality fractured reservoirs, and effectively improve the accuracy of prediction.

[0052] Example 2

[0053] Based on the above embodiments, in this embodiment, the step of using a frequency domain-based filtering algorithm to perform dip filtering on each of the seismic migration data volumes at different azimuth angles to obtain multiple filtered data volumes at different azimuth angles includes:

[0054] Detect the dip angle of each of the aforementioned seismic migration data volumes at different azimuth angles;

[0055] The filtering direction of the seismic migration data volume is determined based on the preset crack dip angle range and the dip angle of each seismic migration data volume;

[0056] Based on the filtering direction of the seismic migration data volume, a frequency domain-based filtering algorithm is used to perform dip filtering on each of the seismic migration data volumes at different azimuth angles, resulting in multiple filtered data volumes at different azimuth angles.

[0057] In this embodiment, the preset crack dip angle range is the statistical range of crack dip angles within the study area. This study area is the work area under study, i.e., the target area. Therefore, the study area can also be called the target area. In this embodiment, the dip angle of the seismic migration data volume at each azimuth is detected, and the filtering direction θ of the seismic migration data volume is determined by combining it with the preset crack dip angle range angle. Then, a frequency domain-based filtering algorithm is used to perform dip angle filtering on each seismic migration data volume at different azimuths to obtain the filtered data volume.

[0058] Example 3

[0059] Based on the above embodiments, in this embodiment, the step of obtaining multiple pre-stack azimuth gathers with different azimuth angles includes: obtaining pre-stack gathers; dividing the pre-stack gathers according to different azimuth angles based on a preset crack orientation to obtain multiple pre-stack azimuth gathers with different azimuth angles, wherein the center angle of one of the multiple different azimuth angles is consistent with the preset crack orientation.

[0060] In this embodiment, the preset crack orientation is the crack orientation of the target area, which can also be called the study area. Using the crack orientation of the target area as a reference, one azimuth angle consistent with the crack orientation of the target area is selected as the reference azimuth angle. Multiple azimuth angles are selected within a preset range around the reference azimuth angle. The reference azimuth angle and the selected azimuth angles are used as the basis for dividing the pre-stack gathers. Based on the reference azimuth angle and the selected azimuth angles, the pre-stack gathers are divided into multiple pre-stack azimuth gathers with different azimuth angles. In this embodiment, the preset range is 4-6.

[0061] Example 4

[0062] Based on the above embodiments, in this embodiment, the step of obtaining multiple pre-stack azimuth gathers with different azimuth angles includes: obtaining pre-stack gathers; dividing the pre-stack gathers according to different azimuth angles based on the preset crack orientation and the signal-to-noise ratio of each pre-stack gather, to obtain multiple pre-stack azimuth gathers with different azimuth angles, wherein the center angle of the azimuth angle of one of the multiple different azimuth angles is consistent with the preset crack orientation.

[0063] In this embodiment, the division of pre-stack gathers into different azimuth angles is mainly based on the fracture orientation and signal-to-noise ratio (SNR) of the target area, generally divided into 4-6 azimuth angles, one of which has a central angle consistent with the fracture orientation. Specifically, a reference azimuth angle is first determined based on the fracture orientation (preset fracture orientation) of the target area. The direction of this reference azimuth angle is consistent with the fracture orientation of the target area. Multiple azimuth angles are selected within a preset range around this reference azimuth angle. The reference azimuth angle and the selected azimuth angles are used as the basis for dividing pre-stack gathers. Based on the reference azimuth angle, the selected azimuth angles, and the SNR of each pre-stack gather, the pre-stack gathers are divided into multiple pre-stack gathers with different azimuth angles. Specifically, pre-stack gathers with the same SNR within the same range and belonging to the same azimuth angle are divided into the same pre-stack gather.

[0064] Example 5

[0065] Based on the above embodiments, in this embodiment, the step of obtaining the pre-stack gather includes:

[0066] Acquire three-dimensional seismic data; perform at least one of the following processing steps on the three-dimensional seismic data to obtain the pre-stack gather: perform denoising processing on the three-dimensional seismic data; perform residual time difference correction processing on the three-dimensional seismic data.

[0067] In this embodiment, fine-grained gather processing is performed on the pre-stack gathers to form accurate pre-stack gathers. This fine-grained gather processing includes denoising and residual time difference correction. In this embodiment, denoising and / or residual time difference correction processing is performed on the 3D seismic data to obtain accurate pre-stack gathers.

[0068] Example 6

[0069] Based on the above embodiments, in this embodiment, the frequency domain-based filtering algorithm is a frequency domain Gabor directional filtering algorithm.

[0070] In this embodiment, the frequency domain Gabor directional filtering algorithm is implemented using a Gabor filter. Specifically, firstly, a Gabor filter is pre-constructed, and then the Gabor filter is used to perform dip filtering on each of the seismic migration data volumes at different azimuth angles. This Gabor filter extracts correlation features at different scales and directions in the frequency domain. r The filter is as follows:

[0071]

[0072] x θ =xcosθ+ysinθ (2)

[0073] x θ = -xcosθ + ysinθ (3)

[0074] Where λ is the scale of the Gabor filter and θ is the filtering direction. The direction change of the filter is achieved by formulas (2) and (3). In this embodiment, the dip angle of the seismic migration data volume at different azimuth angles is detected, and the filtering direction θ of the seismic migration data is determined by combining the fracture dip angle range (preset fracture dip angle range) statistically analyzed in the study area. The optimal values ​​of wavelength λ and Gaussian envelope standard deviation σ can be determined experimentally. In this embodiment, by introducing the fracture dip angle range statistically analyzed in the study area, the seismic response characteristics of fractures can be highlighted, and the anisotropic characteristics caused by factors such as deposition can be effectively suppressed.

[0075] It should be understood that, although Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but may be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but may be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0076] Example 7

[0077] Based on the above embodiments, this embodiment provides an application example.

[0078] Many factors influence reservoir anisotropy. For pre-stack fracture anisotropy identification, frequency domain filtering is used to effectively identify fractures and evaluate the reservoir. For example... Figure 2 As shown, implementing this method includes the following steps:

[0079] Step 1: For actual field 3D seismic data, perform detailed gather processing on pre-stack gathers, including denoising and residual time difference correction;

[0080] Step 2: For pre-stack gathers, based on the fracture orientation in the study area, different azimuth angles are used to divide them into pre-stack gathers with different azimuth angles.

[0081] Step 3: Perform velocity analysis and pre-stack time migration on gathers of different sub-azimuth angles;

[0082] Step 4: Using the frequency domain Gabor directional filtering algorithm, dip filtering is performed on seismic migration data volumes at different azimuth angles for the study area, highlighting the seismic response characteristics of the fractures and effectively suppressing the anisotropic characteristics caused by factors such as deposition.

[0083] Step 5: Pre-stack crack prediction is performed by elliptic anisotropic fitting on the filtered data volumes at different azimuth angles.

[0084] The invention is described in detail below. First, in step one, fine preprocessing is carried out on the field seismic data, mainly using predictive denoising and moving integral gather flattening methods. In step two, the gathers are divided into different azimuth angles based on the fracture orientation and signal-to-noise ratio of the target area, generally into 4-6 azimuth angles, with one central angle consistent with the fracture orientation. In step three, the pre-stack time migration is carried out at their respective migration velocities. In step four, directional filtering is first carried out on the seismic migration data of different azimuth angles. Formula (1) is the constructed Gabor filter, which can extract the correlation features of different scales and directions in the frequency domain, where λ is the scale of the Gabor filter and θ is the filtering direction. The direction change of the filter is achieved by formulas (2) and (3).

[0085]

[0086] x θ =xcosθ+ysinθ (2)

[0087] x θ = -xcosθ + ysinθ (3)

[0088] The dip angle of seismic migration data at different azimuth angles is detected, and the filtering direction θ of the seismic migration data is determined by combining the fracture dip angle range statistically obtained in the study area. The optimal values ​​of wavelength λ and Gaussian envelope standard deviation σ can be determined experimentally. By introducing the fracture dip angle in the study area, the seismic response characteristics of fractures can be highlighted, and the anisotropic characteristics caused by factors such as deposition can be effectively suppressed.

[0089] Step five involves using elliptic anisotropic fitting to perform pre-stack fracture prediction on the filtered data volumes at different azimuth angles. Finally, an evaluation of the fractured reservoir is given based on the geological characteristics of the study area. Figure 2 This is a flowchart illustrating the implementation of the technical method in this embodiment.

[0090] In reservoir prediction related to fractures, this invention utilizes statistical fracture development angles from the study area to achieve pre-stack fracture prediction based on frequency domain filtering. This invention is simple and easy to implement. Compared with traditional pre-stack detection methods, it highlights the seismic response characteristics of fractures and effectively suppresses anisotropic characteristics caused by depositional factors, thus better aligning with the understanding of fracture development in the actual study area. These features demonstrate the strong practicality of this invention.

[0091] To verify the effectiveness of the technical method of the present invention, such as Figure 3 As shown, a comparative analysis of the results of conventional crack detection methods and pre-stack crack prediction based on frequency domain filtering is presented. Figure 3 In the diagram, a) represents the prediction result obtained using conventional prediction methods, and b) represents the pre-stack crack prediction result based on frequency domain directional filtering in this embodiment. (Comparison) Figure 3 In a) and b), it can be found that the latter is more effective in detecting the longitudinal development characteristics of the fractures, while the transverse characteristics caused by deposition are effectively suppressed, which is more consistent with the fracture development characteristics of the study area.

[0092] This invention belongs to the field of oil and gas exploration and development technology, and is mainly applied to reservoir prediction and identification research related to fractures. Based on a frequency domain filtering algorithm and combined with the statistical fracture characteristics of the study area, frequency domain filtering is performed on seismic migration data volumes at different azimuth angles to highlight the seismic response characteristics of fractures and effectively suppress anisotropic characteristics caused by deposition and other factors. The method first performs detailed gather preprocessing and angle division on actual field seismic data, then performs pre-stack time migration on data at different azimuth angles to obtain seismic migration data volumes at different azimuth angles. Finally, by introducing the statistical fracture characteristics of the study area, frequency domain filtering is performed to achieve pre-stack fracture prediction. Compared with traditional pre-stack detection methods, it highlights the seismic response characteristics of fractures, effectively suppresses anisotropic characteristics caused by deposition and other factors, and is more consistent with the understanding of fracture development in the actual study area, thus possessing strong practicality.

[0093] Example 8

[0094] In this embodiment, a pre-stack crack prediction and evaluation device is provided, comprising:

[0095] The stack front azimuth gather acquisition module is used to acquire multiple stack front azimuth gathers at different azimuth angles;

[0096] The seismic migration data volume acquisition module is used to perform pre-stack time migration on each of the pre-stack azimuth gathers at different azimuth angles to obtain multiple seismic migration data volumes at different azimuth angles.

[0097] The filtering module is used to perform dip filtering on the seismic migration data volumes at different azimuth angles using a frequency domain-based filtering algorithm to obtain multiple filtered data volumes at different azimuth angles.

[0098] The fitting and prediction module is used to perform elliptical phase fitting on each of the filtered data volumes at different azimuth angles to obtain crack prediction results.

[0099] In one embodiment, the filtering module includes:

[0100] The tilt detection unit is used to detect the tilt angle of each of the seismic migration data volumes at different azimuth angles;

[0101] A filtering direction determination unit is used to determine the filtering direction of the seismic migration data volume based on a preset crack dip angle range and the dip angle of each seismic migration data volume.

[0102] The filtering unit is used to perform dip filtering on each of the seismic migration data volumes at different azimuth angles according to the filtering direction of the seismic migration data volume and using a frequency domain-based filtering algorithm to obtain multiple filtered data volumes at different azimuth angles.

[0103] In one embodiment, the pre-stack geometry acquisition module includes:

[0104] Pre-stack gather acquisition unit, used to acquire pre-stack gathers;

[0105] The pre-stack gather division unit is used to divide the pre-stack gather according to different azimuth angles based on the preset crack direction, so as to obtain multiple pre-stack azimuth gathers with different azimuth angles, wherein the center angle of one of the multiple different azimuth angles is consistent with the preset crack direction.

[0106] In one embodiment, the step of obtaining multiple front-stack azimuth gathers at different azimuth angles includes:

[0107] Pre-stack gather acquisition unit, used to acquire pre-stack gathers;

[0108] The pre-stack gather division unit is used to divide the pre-stack gathers according to different azimuth angles based on the preset crack orientation and the signal-to-noise ratio of each pre-stack gather, to obtain multiple pre-stack azimuth gathers with different azimuth angles, wherein the center angle of the azimuth angle of one of the multiple different azimuth angles is consistent with the preset crack orientation.

[0109] In one embodiment, the pre-stack gather acquisition unit is further configured to acquire three-dimensional seismic data; and to perform at least one of the following processing steps on the three-dimensional seismic data to obtain the pre-stack gather: denoising the three-dimensional seismic data; and performing residual time difference correction processing on the three-dimensional seismic data.

[0110] In one embodiment, the frequency-domain-based filtering algorithm is a frequency-domain Gabor directional filtering algorithm.

[0111] Specific limitations regarding the pre-stack crack prediction and evaluation device can be found in the limitations of the pre-stack crack prediction and evaluation method described above, and will not be repeated here. Each unit in the aforementioned pre-stack crack prediction and evaluation device can be implemented entirely or partially through software, hardware, or a combination thereof. These units can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each unit.

[0112] Example 9

[0113] Based on the above embodiments, this embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in the above embodiments.

[0114] In some embodiments of this example, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the method described in the above embodiments.

[0115] In some embodiments of this example, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the method described in the above embodiments.

[0116] The processor may include, but is not limited to, one or more processors or microprocessors. Each processor may be implemented as an Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), controller, microcontroller, microprocessor, or other electronic component, for executing the methods described in the above embodiments.

[0117] Computer-readable storage media can be implemented by any type of volatile or non-volatile storage device or a combination thereof. Computer-readable storage media may include, but are not limited to, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, and computer storage media (e.g., hard disks, floppy disks, solid-state drives, removable disks, Blu-ray discs, etc.).

[0118] Computer-readable storage media may also store at least one computer-executable program, such as computer-readable instructions. Computer-readable storage media include, but are not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Computer-readable storage media may include, for example, read-only memory (ROM), hard disk, flash memory, etc. For example, a non-transitory computer-readable storage medium may be connected to a computing device such as a computer, and then, when the computing device executes the computer-readable instructions stored on the computer-readable storage medium, the various methods described above can be performed.

[0119] In addition, the computer device may include (but is not limited to) a data bus, an input / output (I / O) bus, a display, and input / output devices (e.g., keyboard, mouse, speakers, etc.).

[0120] The processor can communicate with external devices via the I / O bus through wired or wireless networks.

[0121] In one embodiment, the at least one computer-executable instruction may also be compiled into or comprise a software product / computer program product, wherein one or more computer-executable instructions are executed by a processor to perform the steps of the various functions and / or methods in the embodiments described herein.

[0122] In the embodiments provided in this disclosure, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0123] It should be noted that, in this disclosure, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element limited by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0124] While the embodiments disclosed herein are as described above, the foregoing content is merely for the purpose of facilitating understanding of this disclosure and is not intended to limit this disclosure. Any person skilled in the art to which this disclosure pertains may make any modifications and changes in form and detail of the implementation without departing from the spirit and scope of this disclosure; however, the scope of patent protection of this disclosure shall still be determined by the scope defined in the appended claims.

Claims

1. A method for predicting and evaluating pre-stack cracks, characterized in that, include: Obtain multiple front-stacked azimuth gathers at different azimuth angles; Pre-stack time migration was performed on each of the aforementioned pre-stack azimuth gathers at different azimuth angles to obtain multiple seismic migration data volumes at different azimuth angles; Using a frequency domain-based filtering algorithm, dip filtering is performed on each of the seismic migration data volumes at different azimuth angles to obtain multiple filtered data volumes at different azimuth angles. Elliptical phase fitting is performed on each of the filtered data volumes at different azimuth angles to obtain crack prediction results.

2. The method according to claim 1, characterized in that, The step of using a frequency domain-based filtering algorithm to perform dip filtering on each of the seismic migration data volumes at different azimuth angles to obtain multiple filtered data volumes at different azimuth angles includes: Detect the dip angle of each of the aforementioned seismic migration data volumes at different azimuth angles; The filtering direction of the seismic migration data volume is determined based on the preset crack dip angle range and the dip angle of each seismic migration data volume; Based on the filtering direction of the seismic migration data volume, a frequency domain-based filtering algorithm is used to perform dip filtering on each of the seismic migration data volumes at different azimuth angles, resulting in multiple filtered data volumes at different azimuth angles.

3. The method according to claim 1, characterized in that, The step of obtaining multiple front-stack azimuth gathers at different azimuth angles includes: Obtain the pre-stack set; Based on the preset crack orientation, the pre-stack gather is divided into multiple pre-stack gathers with different azimuth angles, wherein the center angle of one of the multiple different azimuth angles is consistent with the preset crack orientation.

4. The method according to claim 1, characterized in that, The step of obtaining multiple front-stack azimuth gathers at different azimuth angles includes: Obtain the pre-stack set; Based on the preset crack orientation and the signal-to-noise ratio of each pre-stack gather, the pre-stack gathers are divided according to different azimuth angles to obtain multiple pre-stack azimuth gathers with different azimuth angles. Among these multiple different azimuth angles, the center angle of one of the azimuth angles is consistent with the preset crack orientation.

5. The method according to claim 3 or 4, characterized in that, The steps for obtaining the pre-stack gather include: Acquire 3D seismic data; The pre-stack gathers are obtained by performing at least one of the following processing steps on the three-dimensional seismic data: The three-dimensional seismic data is then denoised. The three-dimensional seismic data are subjected to residual time difference correction processing.

6. The method according to any one of claims 1-4, characterized in that, The frequency-domain based filtering algorithm is a frequency-domain Gabor directional filtering algorithm.

7. A pre-stack crack prediction and evaluation device, characterized in that, include: The stack front azimuth gather acquisition module is used to acquire multiple stack front azimuth gathers at different azimuth angles; The seismic migration data volume acquisition module is used to perform pre-stack time migration on each of the pre-stack azimuth gathers at different azimuth angles to obtain multiple seismic migration data volumes at different azimuth angles. The filtering module is used to perform dip filtering on the seismic migration data volumes at different azimuth angles using a frequency domain-based filtering algorithm to obtain multiple filtered data volumes at different azimuth angles. The fitting and prediction module is used to perform elliptical phase fitting on each of the filtered data volumes at different azimuth angles to obtain crack prediction results.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 6.

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