Fault velocity inversion method and device based on amplitude coherent attribute positioning

Abstract

The invention relates to the technical field of seismic data processing, and particularly discloses a fault velocity inversion method and device based on amplitude coherence attribute localization, and the method comprises the steps: obtaining a fault development position through the amplitude coherence attribute of a migration imaging data body; the range distance influenced by the fault velocity model is judged based on the fault development position and the migration imaging profile; picking up the residual time difference of the common imaging point gather based on the range distance; and performing grid chromatography velocity iteration based on the residual time difference to obtain an optimized fault velocity model. The fault development position is identified through the amplitude coherence attribute, the speed range influenced by the fault is comprehensively judged according to the fault distribution characteristics and the common reflection point depth gather, the common reflection point remaining gather in the effective range is used for grid tomography inversion, the update quantity of the speed model near the fault can be obtained, and the fault development speed is improved. The method improves the speed model precision of a complex structure region with fault development, and is more beneficial to correct homing of the fault and accurate implementation of the structure form near the fault.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of seismic data processing, in particular to a fault velocity inversion method and device based on amplitude coherent attribute positioning. BACKGROUND

[0002] With the deepening of exploration, the exploration target is more and more complex, especially the widely developed fault structure, which needs high-precision imaging to accurately depict the subsurface structure model and provide reliable seismic imaging data for oil and gas reservoir interpretation. The factors affecting the high-precision imaging of the earthquake mainly include the accuracy of the velocity and the imaging algorithm. Due to the limitation of the method, the imaging algorithm in the time domain is prone to produce false structure in the area with large lateral variation of processing velocity. Due to the advancement of the algorithm, the depth migration can realize the spatial accurate positioning of the geological structure, and is more applicable in the field of complex structure imaging. The key factor of depth domain imaging to realize real seismic imaging is velocity, so the accuracy of the velocity model is the core of high-precision imaging of the fault structure.

[0003] In the process of velocity modeling in the depth domain, the existing technology generally adopts the grid tomography velocity inversion method, which relies on data driving, considers geological information by picking up residual curvature, and updates the velocity model. Generally, this velocity field is a relatively smooth velocity field, which has no obvious response to the fault structure characteristics. For small faults, the smooth velocity field has no effect on imaging, and for large faults, the velocity difference of the stratum caused by the fault is large, at this time the smooth velocity field is not suitable for the imaging of the large fault. It is necessary to introduce fault control to further improve the accuracy of the velocity model near the fault and realize the accurate positioning of the fault and the surrounding stratum.

[0004] Based on this technical background, the present application studies a fault velocity inversion method and device based on amplitude coherent attribute positioning. SUMMARY

[0005] In view of the shortcomings of the prior art, the present application provides a fault velocity inversion method and device based on amplitude coherent attribute positioning, which identifies the fault development position based on the amplitude coherent attribute, comprehensively judges the velocity range affected by the fault according to the fault distribution characteristics and common shot depth gather, and performs grid tomography inversion on the common reflection point residual gather in the effective range. The velocity model update near the fault can be obtained, the accuracy of the velocity model in the complex structure area with fault development is improved, and the correct positioning of the fault and the accurate implementation of the structure form near the fault are more beneficial.

[0006] In order to achieve the above purpose, the first aspect of the present application provides a fault velocity inversion method based on amplitude coherent attribute positioning, comprising:

[0007] acquiring a fault development position by amplitude coherent attribute of the migration data volume;

[0008] judging a range distance of fault velocity model influence based on the fault development position and the migration imaging profile;

[0009] picking up residual moveout of common imaging point gathers based on the range distance;

[0010] obtaining an optimized fault velocity model by grid tomography velocity iteration based on the residual moveout.

[0011] A second aspect of the present application provides a fault velocity inversion device based on amplitude coherent attribute positioning, comprising:

[0012] an attribute acquisition module, configured to acquire a fault development position by amplitude coherent attribute of the migration data volume;

[0013] a range distance judgment module, configured to judge a range distance of fault velocity model influence based on the fault development position and the migration imaging profile;

[0014] a residual moveout picking module, configured to pick up residual moveout of common imaging point gathers based on the range distance;

[0015] a velocity model optimization module, configured to obtain an optimized fault velocity model by grid tomography velocity iteration based on the residual moveout.

[0016] A third aspect of the present application provides an electronic device, comprising:

[0017] a memory, storing executable instructions;

[0018] a processor, configured to run the executable instructions in the memory, so as to realize the fault velocity inversion method based on amplitude coherent attribute positioning of the first aspect.

[0019] A fourth aspect of the present application provides a computer readable storage medium, storing a computer program, which is executed by a processor to realize the fault velocity inversion method based on amplitude coherent attribute positioning of the first aspect.

[0020] The present application has the following beneficial effects:

[0021] (1) The fault velocity inversion method based on amplitude coherent attribute positioning provided by the application can identify the fault development position through amplitude coherent attribute, judge the velocity range affected by the fault according to the fault distribution characteristics and common shot depth gather, perform grid tomography inversion on the common reflection point residual gather in the effective range, can obtain the velocity model update amount near the fault, improve the velocity model precision of the complex structure area with fault development, and is more beneficial to correct positioning of the fault and accurate implementation of the structure form near the fault.

[0022] (2) The fault velocity inversion method based on amplitude coherent attribute positioning provided by the application can improve the precision of the fault velocity model and the imaging quality of the fault and the stratum near the fault by introducing the fault constraint, positioning the fault position according to the amplitude coherent attribute, judging the structural abnormal range caused by the fault as the boundary, and selecting the seismic data in the effective range to perform grid tomography velocity inversion.

[0023] Other features and advantages of the application will be described in detail in the following specific embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0024] The above and other objects, features and advantages of the present application will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings in which:

[0025] Figure 1 The flowchart of the fault velocity inversion method based on amplitude coherent attribute positioning provided by the application.

[0026] Figure 2 The flowchart of one specific embodiment of the fault velocity inversion method based on amplitude coherent attribute positioning provided by the application.

[0027] Figure 3A The left far-from-fault gather analysis schematic diagram in one specific embodiment of the fault velocity inversion method based on amplitude coherent attribute positioning provided by the application.

[0028] Figure 3B The right near-fault gather analysis schematic diagram in one specific embodiment of the fault velocity inversion method based on amplitude coherent attribute positioning provided by the application.

[0029] Figure 4 The along-layer amplitude coherent attribute graph schematic diagram in one specific embodiment of the fault velocity inversion method based on amplitude coherent attribute positioning provided by the application.

[0030] Figure 5The velocity updating amount schematic diagram in one specific embodiment of the fault velocity inversion method based on amplitude coherent attribute positioning provided in the application.

[0031] Figure 6 The left front and right rear imaging point gather contrast schematic diagram before and after the fault velocity optimization in one specific embodiment of the fault velocity inversion method based on amplitude coherent attribute positioning provided in the application. DETAILED DESCRIPTION

[0032] The preferred embodiments of the application will be described in more detail below. Although the preferred embodiments of the application are described below, it should be understood that the application can be implemented in various forms and should not be limited by the embodiments set forth herein.

[0033] The application provides a fault velocity inversion method based on amplitude coherent attribute positioning, as shown in the formula: Figure 1 The method comprises the following steps of:

[0034] acquiring the position of the fault development through the amplitude coherent attribute of the migration imaging data volume;

[0035] judging the range distance of the fault velocity model influence based on the fault development position and the migration imaging profile;

[0036] picking up the residual moveout of the common imaging point gather based on the range distance;

[0037] performing grid tomographic velocity iteration based on the residual moveout to obtain the optimized fault velocity model.

[0038] In the application, the fault development position is recognized through the amplitude coherent attribute, the velocity range of the fault influence is comprehensively judged according to the fault distribution characteristics and the common shot point depth gather, the grid tomographic inversion is performed on the common reflection point residual gather in the effective range, the velocity model updating amount near the fault can be obtained, the velocity model precision in the complex structure area of the fault development is improved, and it is more beneficial to correctly position the fault and accurately implement the structure form near the fault.

[0039] According to the application, the position of the fault development is acquired through the amplitude coherent attribute of the migration imaging data volume, which comprises the following steps of:

[0040] the amplitude coherent attribute obtained by performing the amplitude coherent attribute extraction on the migration imaging data volume;

[0041] performing the along-layer attribute extraction on the amplitude coherent attribute along the layer position of the fault development to obtain the along-layer amplitude attribute plane distribution graph;

[0042] determining the position of the fault development based on the along-layer amplitude attribute plane distribution graph.

[0043] According to the application, the range distance of the influence of the fault velocity model is determined based on the fault development position and the migration imaging profile.

[0044] The depth gather of the common imaging point is analyzed based on the fault development position and the migration imaging profile.

[0045] The range distance of the influence of the fault velocity model is determined based on the depth gather of the common imaging point.

[0046] According to the application, the residual moveout of the depth gather of the common imaging point is picked up based on the range distance.

[0047] The boundary position of the range distance is picked up along the fault development position on the amplitude attribute plane profile distribution map based on the range distance.

[0048] The depth gather of the common imaging point in the effective range is selected according to the boundary position.

[0049] The residual moveout of the depth gather of the common imaging point is picked up.

[0050] Preferably, the optimized fault velocity model is obtained by grid tomography velocity iteration based on the residual moveout.

[0051] The velocity update is obtained by grid tomography velocity inversion based on the residual moveout.

[0052] The optimized velocity update is obtained by optimizing the velocity update based on the depth gather flattening condition and the range distance.

[0053] The optimized velocity update is added to the fault velocity model before inversion to obtain the optimized fault velocity model.

[0054] According to the application, the grid tomography velocity inversion is performed based on the residual moveout.

[0055] The matrix equation is established based on the residual moveout to perform the grid tomography velocity inversion.

[0056] According to the application, after the optimized velocity update is obtained by optimizing the velocity update, the application further comprises:

[0057] The optimized velocity update is sequentially subjected to depth migration and gather flattening.

[0058] If the gather flattening result meets the requirement, subsequent operation is performed, otherwise, the depth gather flattening condition analysis of the common imaging point is performed again.

[0059] In this invention, by introducing fault constraints, the fault location is located based on amplitude coherence attributes. Using the fault as a boundary, the range of structural anomalies caused by the fault is determined. Seismic data within the effective range is selected for grid tomography velocity inversion, thereby improving the accuracy of the fault velocity model and the imaging quality of the fault and its surrounding strata. This solves the problem that the global grid tomography velocity inversion method cannot address the velocity differences caused by faults.

[0060] The present invention will be described in more detail below through embodiments.

[0061] Example 1:

[0062] like Figure 2 As shown in the figure, this embodiment proposes a fault velocity inversion method based on amplitude coherence attribute localization. The specific steps are as follows:

[0063] Step 1: Extract amplitude coherence attributes from the offset imaging data volume;

[0064] Step 2: Extract the along-layer attributes based on the amplitude coherence attributes obtained in Step 1 along the fault development horizon;

[0065] Step 3: Determine the location of fault development based on the planar distribution map of the bedding properties;

[0066] Step 4: Combine the offset imaging profile and the fault development location obtained in Step 3 to analyze the flattening of the common imaging point depth gather.

[0067] Step 5: Based on the analysis in Step 4, determine the range of influence of the fault velocity model.

[0068] Step 6: Based on the range distance in Step 5, pick the boundary position of the influence range along the fault development location on the along-layer amplitude attribute plane map;

[0069] Step 7: Based on the boundary positions in Step 6, select the common imaging point depth gathers within the effective range;

[0070] Step 8: Based on the common imaging point depth gathers selected in Step 7, pick the remaining time difference of the gathers;

[0071] Step 9: Based on the remaining time difference picked up in Step 8, perform grid tomography velocity inversion to obtain the velocity update amount;

[0072] Step 10: Combining the results of Steps 4, 5, and 8, analyze the speed update amount obtained in Step 9 and optimize the update amount.

[0073] The velocity update near the fault obtained in steps 11 and 10 is added to the velocity model before inversion to obtain the optimized velocity model.

[0074] In this embodiment, as shown in Figure 3A and Figure 3B , different position gathers (left far from the fault and right close to the fault) of Xinjiang seismic data are selected to obtain the along-layer amplitude coherence attribute map as shown in Figure 4 and the velocity update amount as shown in Figure 5 . Through seismic profile and gather analysis, it can be known from the comparison of imaging point gathers before and after fault velocity optimization (left front and right rear) as shown in Figure 6 that the events close to the fault position are uneven, and the events far from the fault position are relatively even; it can be seen that the fault velocity inversion technology based on amplitude coherence attribute positioning can invert the high-frequency velocity update amount of the fault velocity and improve the precision of the velocity model through actual data.

[0075] Embodiment two

[0076] The embodiment provides a fault velocity inversion method based on amplitude coherence attribute positioning, as shown in Figure 1 , comprising the following steps:

[0077] acquiring the position of fault development through the amplitude coherence attribute of the migration imaging data volume;

[0078] judging the range distance of the influence of the fault velocity model based on the position of fault development and the migration imaging profile;

[0079] picking up the residual moveout of the common imaging point gather based on the range distance;

[0080] obtaining the optimized fault velocity model through grid tomography velocity iteration based on the residual moveout;

[0081] acquiring the position of fault development through the amplitude coherence attribute of the migration imaging data volume comprises the following steps:

[0082] extracting the amplitude coherence attribute from the migration imaging data volume to obtain the amplitude coherence attribute;

[0083] extracting the along-layer attribute from the amplitude coherence attribute along the layer position of fault development to obtain the along-layer amplitude attribute plane distribution map;

[0084] determining the position of fault development based on the along-layer amplitude attribute plane distribution map;

[0085] judging the range distance of the influence of the fault velocity model based on the position of fault development and the migration imaging profile comprises the following steps:

[0086] analyzing the common imaging point depth gather flattening based on the position of fault development and the migration imaging profile;

[0087] judging the range distance of the influence of the fault velocity model based on the depth gather flattening;

[0088] The remaining time difference based on range distance-based common imaging point gather picking includes:

[0089] Based on the range distance, the boundary position of the range distance is picked along the fault development location on the plane distribution map of the amplitude attribute along the layer;

[0090] Based on the boundary location, select the common imaging point depth gather within the effective range;

[0091] Extract the remaining time difference of the gather from the common imaging point depth gather;

[0092] The optimized fault velocity model obtained by iterative mesh tomography based on residual time difference includes:

[0093] Based on the remaining time difference, grid tomography velocity inversion is performed to obtain the velocity update amount;

[0094] Based on the flattening of the depth gather and the range distance, the speed update amount is optimized to obtain the optimized speed update amount;

[0095] The optimized velocity update is added to the fault velocity model before inversion to obtain the optimized fault velocity model.

[0096] Based on the remaining time difference, the mesh tomography velocity inversion includes:

[0097] Based on the remaining time difference, a matrix equation is established for grid tomography velocity inversion;

[0098] After optimizing the speed update amount to obtain the optimized speed update amount, the following is also included:

[0099] The optimized speed update amount is then subjected to depth offset and gather flattening in sequence;

[0100] If the gather flattening results meet the requirements, proceed with subsequent operations; otherwise, re-analyze the common imaging point depth gather flattening situation.

[0101] Example 3:

[0102] This embodiment provides a fault velocity inversion device based on amplitude coherence attribute localization, including:

[0103] The attribute acquisition module is used to obtain the location of fault development by using the amplitude coherence properties of the offset imaging data volume;

[0104] The range distance determination module is used to determine the range distance of the influence of the fault velocity model based on the fault development location and the offset imaging profile.

[0105] The remaining time difference picking module is used to pick the remaining time difference of the common imaging point gather based on the range distance;

[0106] The velocity model optimization module is used to obtain an optimized fault velocity model by iterating the grid tomography velocity based on the remaining time difference;

[0107] Determining the location of fault development by analyzing the amplitude coherence properties of offset imaging data includes:

[0108] Amplitude coherence attributes obtained by extracting amplitude coherence attributes from offset imaging data volume;

[0109] The plane distribution map of amplitude coherence attributes along the fault is obtained by extracting the attributes along the fault.

[0110] The location of fault development is determined based on the planar distribution map of amplitude properties along the bedding plane;

[0111] The range of influence of the fault velocity model, determined based on fault development location and migration imaging profiles, includes:

[0112] Analysis of common imaging point depth gather flattening based on fault development location and offset imaging profile;

[0113] Determine the range and distance of the influence of the fault velocity model based on the flattening of deep gathers;

[0114] The remaining time difference based on range distance-based common imaging point gather picking includes:

[0115] Based on the range distance, the boundary position of the range distance is picked along the fault development location on the plane distribution map of the amplitude attribute along the layer;

[0116] Based on the boundary location, select the common imaging point depth gather within the effective range;

[0117] Extract the remaining time difference of the gather from the common imaging point depth gather;

[0118] The optimized fault velocity model obtained by iterative mesh tomography based on residual time difference includes:

[0119] Based on the remaining time difference, grid tomography velocity inversion is performed to obtain the velocity update amount;

[0120] Based on the flattening of the depth gather and the range distance, the speed update amount is optimized to obtain the optimized speed update amount;

[0121] The optimized velocity update is added to the fault velocity model before inversion to obtain the optimized fault velocity model.

[0122] Based on the remaining time difference, the mesh tomography velocity inversion includes:

[0123] Based on the remaining time difference, a matrix equation is established for grid tomography velocity inversion;

[0124] After optimizing the speed update amount to obtain the optimized speed update amount, the following is also included:

[0125] The optimized speed update amount is then subjected to depth offset and gather flattening in sequence;

[0126] If the gather flattening results meet the requirements, proceed with subsequent operations; otherwise, re-analyze the common imaging point depth gather flattening situation.

[0127] Example 4:

[0128] This invention provides an electronic device including a memory and a processor.

[0129] Memory, which stores executable instructions;

[0130] The processor runs executable instructions in memory to implement a fault velocity inversion method based on amplitude coherence properties.

[0131] This memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.

[0132] The processor may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment of the invention, the processor is used to execute computer-readable instructions stored in the memory.

[0133] Those skilled in the art should understand that, in order to solve the technical problem of how to achieve a good user experience, this embodiment may also include well-known structures such as communication buses and interfaces, and these well-known structures should also be included within the protection scope of this invention.

[0134] For a detailed description of this embodiment, please refer to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.

[0135] Example 5:

[0136] This invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements a fault velocity inversion method based on amplitude coherence attribute localization.

[0137] A computer-readable storage medium according to embodiments of the present invention stores non-transitory computer-readable instructions. When these non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the methods described in the foregoing embodiments of the present invention are performed.

[0138] The aforementioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or portable hard drive), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).

[0139] The fault velocity inversion method based on amplitude coherence attribute localization proposed in the embodiments of the present invention identifies the fault development location through amplitude coherence attribute, comprehensively judges the velocity range of fault influence based on fault distribution characteristics and common reflection point depth gathers, and performs grid tomography inversion using the remaining gathers of common reflection points within the effective range. This can obtain the velocity model update amount near the fault, improve the accuracy of the velocity model in complex tectonic areas with fault development, and is more conducive to the correct location of faults and accurate determination of the tectonic morphology near faults.

[0140] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A fault velocity inversion method based on amplitude coherence attribute localization, characterized in that, include: The location of fault development is obtained by using the amplitude coherence properties of the offset imaging data volume; The range of influence of the fault velocity model is determined based on the fault development location and the offset imaging profile. The remaining time difference for picking up common imaging point gathers based on the range distance; An optimized fault velocity model is obtained by iterative mesh tomography based on the remaining time difference.

2. The method according to claim 1, characterized in that, Determining the location of fault development by analyzing the amplitude coherence properties of offset imaging data includes: Amplitude coherence attributes obtained by extracting amplitude coherence attributes from the offset imaging data volume; The plane distribution map of amplitude coherence attributes along the fault is obtained by extracting the along-layer attributes of the layer where the amplitude coherence attributes develop; Based on the planar distribution map of the amplitude properties along the layer, the location of fault development is determined.

3. The method according to claim 2, characterized in that, Based on the fault development location and the migration imaging profile, the range of influence of the fault velocity model is determined to include: Based on the fault development location and offset imaging profile, the common imaging point depth gather flattening situation was analyzed; The range of influence of the fault velocity model is determined based on the flattening of the deep gather.

4. The method according to claim 3, characterized in that, The remaining time difference for picking up the common imaging point gather based on the range distance includes: Based on the range distance, the boundary positions affecting the range distance are picked along the fault development location on the plane distribution map of the along-layer amplitude attribute; Based on the boundary position, select the common imaging point depth gather within the effective range; The remaining time difference of the gather is picked from the depth gather of the common imaging point.

5. The method according to claim 4, characterized in that, The optimized fault velocity model obtained by iterative mesh tomography based on the remaining time difference includes: Based on the remaining time difference, perform grid tomography velocity inversion to obtain the velocity update amount; Based on the flattening of the depth gather and the range distance, the speed update amount is optimized to obtain the optimized speed update amount; The optimized velocity update is added to the fault velocity model before inversion to obtain the optimized fault velocity model.

6. The method according to claim 5, characterized in that, Based on the remaining time difference, the grid tomography velocity inversion includes: Based on the remaining time difference, a matrix equation is established to perform grid tomography velocity inversion.

7. The method according to claim 6, characterized in that, After optimizing the speed update amount to obtain the optimized speed update amount, the following steps are also included: The optimized velocity update amount is then subjected to depth offset and gather flattening in sequence; If the gather flattening result meets the requirements, proceed with subsequent operations; otherwise, re-analyze the common imaging point depth gather flattening situation.

8. A fault velocity inversion device based on amplitude coherence attribute localization, characterized in that, include: The attribute acquisition module is used to obtain the location of fault development by using the amplitude coherence properties of the offset imaging data volume; The range distance determination module is used to determine the range distance of the influence of the fault velocity model based on the fault development location and the offset imaging profile. The remaining time difference picking module is used to pick the remaining time difference of the common imaging point gather based on the range distance; The velocity model optimization module is used to obtain an optimized fault velocity model by performing grid tomography velocity iteration based on the remaining time difference.

9. An electronic device, characterized in that, The electronic device includes: Memory, which stores executable instructions; A processor that executes the executable instructions in the memory to implement the fault velocity inversion method based on amplitude coherence attribute localization 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 that, when executed by a processor, implements the fault velocity inversion method based on amplitude coherence attribute localization as described in any one of claims 1-7.

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