A data-driven speed modeling method and system for a discontinuity

Abstract

The application provides a data-driven velocity modeling method and system for a discontinuous body, and the method comprises the following steps: S1, performing OVT domain prestack depth migration on prestack traces to form OVG traces; S2, dividing the OVG traces into multiple groups of OVG sub-traces, and separately performing residual moveout picking and establishing tomographic equations on each group of OVG sub-traces; S3, obtaining high-precision model update quantities through the multiple groups of tomographic equations; and S4, updating the velocity model and performing depth domain migration imaging. The application is beneficial to improving the depth domain migration imaging precision.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of processing and interpretation of geophysical exploration seismic data, and in particular to a data-driven velocity modeling method and system for fault-controlled bodies. BACKGROUND

[0002] With the increasing complexity of geological targets in exploration and development, for example, the Shunbei oil and gas field in the Tarim Basin dominated by fault-controlled oil and gas reservoirs, a large number of strike-slip faults are developed, which have strong directionality, small fault throw, and low velocity in the fault fracture zone. This small-scale and directionally dependent low-velocity anomaly is difficult to obtain through conventional velocity inversion techniques. Even the existing high-precision modeling techniques cannot handle the directional changes in velocity, resulting in limited imaging accuracy of faulted karst bodies in the region, affecting the accuracy of subsequent fault interpretation, reservoir prediction, and trap evaluation, and restricting the solution of geological problems in the region.

[0003] Conventional depth domain velocity modeling techniques can only invert the velocity changes of medium and long wavelengths, and the inversion accuracy is very limited. Even high-precision multi-information constrained depth domain modeling techniques only increase certain constraints and controls, appropriately reduce the inversion scale under the guidance of geological understanding. Such techniques are not purely data-driven and are greatly influenced by human factors. The application effect is closely related to the understanding and knowledge of the modeling personnel about geological phenomena. The built model is difficult to have a unified standard, and the application process needs to be very careful and cautious.

[0004] Currently, when facing special abnormal geological bodies with velocity distributed along a certain direction, conventional velocity inversion techniques based on OFFSET gathers do not consider the directional velocity changes, and the inversion results reflect the average and smoothing effect of each direction. SUMMARY

[0005] The features and advantages of the present application are partially set forth in the following description, or can be learned by the description, or can be learned by practice of the present application.

[0006] To overcome the problems of the prior art, the present application provides a data-driven velocity modeling method for fault-controlled bodies, comprising:

[0007] S1, performing OVT domain prestack depth migration on prestack gathers to form OVG gathers;

[0008] S2, dividing the OVG gathers into multiple groups of OVG sub-gathers, and separately picking up residual moveout and establishing tomographic equations for each OVG sub-gather;

[0009] S3, obtaining high-precision model update quantities through multiple groups of tomographic equations;

[0010] S4, updating the velocity model and performing depth domain migration imaging.

[0011] Optionally, before the step S1, the method comprises:

[0012] The prestack gathers are subjected to optimization processing, and the optimization processing comprises denoising and / or multiple wave suppression.

[0013] Optionally, the step S1 comprises:

[0014] The OVT domain prestack depth migration is performed on the prestack gathers subjected to the optimization processing by using the best model obtained by the conventional velocity modeling, and the OVT domain imaging gathers are formed, and the OVG gathers are formed by sorting according to the offset and the azimuth.

[0015] Optionally, when the residual time difference is picked up in the step S2, the quality of the picked up residual time difference is controlled by superimposedly displaying the picked up residual time difference and the corresponding OVG subgathers, and the accuracy of the picked up residual time difference is ensured.

[0016] Optionally, in the step S2, the tomographic equations are respectively established based on the residual time differences of different azimuths.

[0017] The application provides a data-driven velocity modeling system for a faulted body, comprising:

[0018] The gather establishing module is used for performing the OVT domain prestack depth migration on the prestack gathers to form the OVG gathers.

[0019] The grouping tomographic equation establishing module is used for grouping the OVG gathers into a plurality of OVG subgathers, and picking up the residual time difference and establishing the tomographic equation for each OVG subgather.

[0020] The update amount obtaining module is used for obtaining the high-precision model update amount through the plurality of tomographic equations.

[0021] The imaging module is used for updating the velocity model and performing the depth domain migration imaging.

[0022] Optionally, the method further comprises an optimization processing module, which is used for performing the optimization processing on the prestack gathers, and the optimization processing comprises denoising and / or multiple wave suppression.

[0023] Optionally, the gather establishing module is used for performing the OVT domain prestack depth migration on the prestack gathers subjected to the optimization processing by using the best model obtained by the conventional velocity modeling, and forming the OVG gathers by sorting the OVT domain imaging gathers according to the offset and the azimuth.

[0024] Optionally, the grouping tomographic equation establishing module is used for:

[0025] The quality of the picked up residual time difference is controlled by superimposedly displaying the picked up residual time difference and the corresponding OVG subgathers, and the accuracy of the picked up residual time difference is ensured.

[0026] The tomographic equations are respectively established based on the residual time difference of different azimuths.

[0027] The application provides a computer readable storage medium, which stores at least one computer executable program, and the at least one program enables a computer to execute the steps in the method when the computer executes the at least one program.

[0028] Based on the targeted gather optimization processing, the application combines the existing best velocity model of the conventional pre-stack depth migration in the early stage, completes the OVT domain pre-stack depth migration, divides the imaging gathers by azimuth, retains the azimuth information in the gathers, picks up the residual curvature by azimuth, obtains different residual time differences of different azimuth gathers, establishes the tomographic equation groups of different azimuths, and then performs joint tomographic inversion to obtain the velocity model update related to the velocity variation of the geological anomaly, which is beneficial to improving the imaging precision of the depth domain migration.

[0029] The application is the high-precision velocity inversion technology for the region, is helpful to improving the velocity model inversion precision, more finely inverts the directional velocity variation of the faulted zone, improves the faulted body imaging precision of the Ordovician target layer, and provides more accurate basic data for the fine reservoir interpretation.

[0030] Those skilled in the art will better understand the features and contents of the technical solutions by reading the specification. BRIEF DESCRIPTION OF DRAWINGS

[0031] The application will be described in detail below by referring to the drawings and combining with examples, and the advantages and implementation modes of the application will be more obvious, wherein the contents shown in the drawings are only used for explaining and describing the application, and do not constitute any limitation on the application in the sense, and in the drawings:

[0032] Figure 1 It is a flowchart of the data-driven velocity modeling method of the embodiment of the application for the faulted and controlled body.

[0033] Figure 2 It is a CMP gather comparison chart before optimization, after five-dimensional denoising and after multiple wave suppression.

[0034] Figure 3 It is a superimposed comparison chart of the velocity model update and the seismic profile of the conventional velocity inversion and the data-driven velocity inversion for the faulted and controlled body.

[0035] Figure 4 It is an imaging effect comparison chart.

[0036] Figure 5 It is an imaging gather comparison chart.

[0037] Figure 6 The structural diagram of a data-driven velocity modeling system for faulted bodies according to an embodiment of the present application. DETAILED DESCRIPTION

[0038] It is necessary to point out that the following embodiments are only used for further illustration of the present application and cannot be understood as limitation on the protection scope of the present application, and some non-essential improvements and adjustments of the present application by those skilled in the art according to the content of the present application still belong to the protection scope of the present application.

[0039] The present application adopts pre-stack five-dimensional OVT domain gathers, which can retain azimuth information in the imaging gathers due to the limited offset and azimuth characteristics, and is beneficial to inverting the azimuth-dependent velocity variation, such as the directional velocity anomaly of the faulted zone, from the pure data-driven perspective.

[0040] The present application will be further described in detail below with reference to the drawings:

[0041] As shown in Figure 1 The present application provides a data-driven velocity modeling method for faulted bodies, comprising the following steps:

[0042] S10, performing denoising, multiple wave suppression and other optimization processing on the pre-stack gathers;

[0043] The velocity modeling technology for faulted body imaging needs to be based on the pre-stack gathers highlighting weak effective signals, and therefore the gathers need to be reasonably optimized. Firstly, the five-dimensional denoising technology can greatly improve the data signal-to-noise ratio, enhance the effective signals, and also highlight the interference of multiple wave classes, and then the related multiple wave removal technology is further adopted to suppress the multiple wave energy, so as to achieve the optimization processing purpose of enhancing the effective signals and provide good basic data for the establishment of the next step velocity model.

[0044] Please refer to Figure 2 , Figure 2 is the CMP gather comparison before optimization, after five-dimensional denoising and after multiple wave suppression. It can be seen that the five-dimensional denoising can greatly improve the data signal-to-noise ratio, and in combination with the multiple wave suppression, the influence of the multiple wave on the effective energy can be reduced, and the gather optimization processing of enhancing the pre-stack data effective signals is realized.

[0045] S20, performing pre-stack depth migration in the OVT domain on the pre-stack gathers to form OVG gathers;

[0046] The best model obtained by using the conventional velocity modeling is used to perform pre-stack depth migration in the OVT domain on the pre-stack gathers after optimization processing, and the OVG gathers are formed by sorting the OVT domain imaging gathers according to the offset and azimuth.

[0047] S30, the OVG gather is divided into multiple groups of OVG sub-gathers, and residual moveout is picked up and tomographic equations are established for each of the OVG sub-gathers separately;

[0048] According to the azimuth information in the OVG gather, the OVG gather is divided into multiple groups of OVG sub-gathers with different dominant azimuths, and then the residual moveout of the OVG sub-gathers with different azimuths is picked up separately, and the picking range, picking density and curvature range are controlled respectively. After the division of the azimuth, the regularity of the gather is better, and it is easier to pick up.

[0049] In specific implementation, the picked residual moveout can be superimposed and displayed with the corresponding OVG sub-gather to quality control the quality of the picking, and ensure the accuracy of the picked residual moveout, which is the basis for the next fine inversion.

[0050] According to the residual moveout picked in different azimuths, multiple tomographic inversion equations with different azimuths are constructed respectively:

[0051]

[0052] Wherein, A is the ray path length related to the azimuth angle, is the azimuth angle, Δt is the residual moveout of different azimuths extracted from the gather, and Δm is the inversion model update to be solved.

[0053] S40, jointly solve the multi-azimuth tomographic inversion equation set to obtain a high-precision model update;

[0054] The tomographic inversion equations with different azimuths constitute a multi-azimuth tomographic inversion equation set. The multi-azimuth tomographic inversion equation set is jointly solved, and the update includes the velocity residual.

[0055] The conventional tomographic inversion does not consider the azimuth information of the data, and the result can be regarded as a smoothing effect on each azimuth. The multi-information constraint inversion is limited by human factors, and a good result depends on geological understanding to a large extent. The present application reacts different residual moveouts in different azimuths in the tomographic inversion equation set from the pure data point of view, and the inversion result comprehensively reflects the velocity change in different azimuths, so that the velocity model update related to the velocity change of the geological anomaly body can be obtained, high-precision depth domain migration imaging is realized, and it is more objective and effective.

[0056] S50, update the velocity model, that is, add the velocity model update to the previous best velocity model for depth domain migration imaging.

[0057] Please refer to Figure 3 , Figure 3 is the superimposed comparison of the velocity model update of the conventional velocity inversion and the data-driven velocity inversion for the fault-controlled body and the seismic profile. The present application can better invert the directional velocity change of the strike-slip fault.

[0058] Please refer to Figure 4 , Figure 4 is the imaging effect contrast, through the data-driven velocity modeling technology for faulted and controlled body, the fault imaging precision is obviously improved, the bead imaging is more focused, and the characteristics of the faulted and dissolved body are clearer, which is very helpful for improving the imaging precision of the inside of the faulted and controlled body reservoir.

[0059] Please refer to Figure 5 , Figure 5 is the imaging trace set contrast, from the imaging trace set contrast after the conventional inversion best and the data-driven velocity inversion for the faulted and controlled body, it can be seen that after the conventional trace set has been flattened and cannot be iterated, the imaging trace set quality can be further improved through the data-driven velocity modeling technology for the faulted and controlled body, the phase axis continuity is better, the tiny details are improved, and it is beneficial to obtain fine imaging effect.

[0060] The application provides a computer readable storage medium, the computer readable storage medium stores at least one computer executable program, when the at least one program is executed by the computer, the computer executes the steps in the above method.

[0061] As Figure 6 shown, the application provides a data-driven velocity modeling system for faulted and controlled body, which comprises an optimization processing module 11, a trace set establishing module 12, a grouping tomography equation establishing module 13, an update quantity obtaining module 14 and an imaging module 15.

[0062] The optimization processing module 11 is used for optimizing the pre-stack trace set, and the optimization processing comprises denoising and / or multiple wave suppression. More specifically, first, the five-dimensional denoising technology is used to greatly improve the data signal-to-noise ratio, enhance the effective signal, and highlight the interference of multiple waves, and then the related multiple wave removal technology is further used to suppress the multiple wave energy, so as to achieve the optimization processing purpose of enhancing the effective signal.

[0063] The trace set establishing module 12 is connected with the optimization processing module 11 and is used for carrying out OVT domain pre-stack depth migration on the pre-stack trace set to form an OVG trace set; more specifically, the best model obtained by using the conventional velocity modeling is used to carry out OVT domain pre-stack depth migration on the pre-stack trace set after the optimization processing, and the OVT domain imaging trace set is sorted according to the offset and the azimuth to form the OVG trace set.

[0064] The group chromatographic equation establishing module 13 is connected with the gather establishing module 12, and is used for dividing the OVG gather into a plurality of OVG sub-gathers and performing residual moveout picking and establishing chromatographic equations on each of the OVG sub-gathers. More specifically, the OVG gather is divided into a plurality of OVG sub-gathers with different dominant azimuths according to the azimuth information in the OVG gather, and then the OVG sub-gathers with different azimuths are separately picked up residual moveout, and the picking range, picking density and curvature range are controlled respectively. After the division of azimuths, the regularity of the gather is better, and it is easier to pick up.

[0065] In the specific implementation, the picked residual moveout can be superimposed and displayed with the corresponding OVG sub-gather to control the quality of the picking and ensure the accuracy of the picked residual moveout, which is the basis for the next step of fine inversion.

[0066] According to the residual moveout picked in the different azimuths, a plurality of chromatographic inversion equations with different azimuths are constructed:

[0067]

[0068] Wherein, A is the ray path length related to the azimuth angle, is the azimuth angle, Δτ is the residual moveout of different azimuths extracted from the gather, and Δm is the inversion model update amount to be solved.

[0069] The update amount obtaining module 14 is connected with the group chromatographic equation establishing module 13, and is used for obtaining a high-precision model update amount through a plurality of chromatographic equations. More specifically, the plurality of chromatographic inversion equations with different azimuths are jointly solved, and the update amount includes a velocity residual. The conventional chromatographic inversion does not consider the azimuth information of the data, and the result can be regarded as a smoothing effect on each azimuth. The multi-information constraint inversion is limited by human factors, and a good result depends on geological understanding to a large extent. The present application starts from the pure data, and reacts the residual moveout of different azimuths in the chromatographic inversion equation set. The inversion result comprehensively reflects the velocity change of different azimuths, and can obtain the velocity model update amount related to the velocity change of the geological anomaly body, realize high-precision depth domain migration imaging, and is more objective and effective.

[0070] The imaging module 15 is connected with the update amount obtaining module 14, and is used for updating the velocity model, that is, adding the velocity model update amount to the previous best velocity model to perform depth domain migration imaging.

[0071] The application provides a data-driven velocity modeling method and system for faulted bodies, fully utilizes azimuth information of OVT domain data, brings velocity variation of different azimuths into overall tomographic inversion through a mode of separately picking up residual moveout of different azimuths, and further inverts directional velocity variation characteristics. From the perspective of pure data driving, the method inverts directional velocity variation of the faulted zone, effectively improves velocity model precision, and the achievement can provide effective support data for fine reservoir description, trap implementation and well location deployment.

[0072] The application realizes multi-azimuth high-precision velocity inversion from the perspective of pure data driving based on OVT domain data, obtains velocity model update amount with azimuth variation, the new velocity model has higher precision, and velocity variation trend is more consistent with geological understanding. The velocity model obtained by using the technology is used for prestack depth migration, imaging precision is improved, fault description is clearer, fracture-vug energy is better focused, and structural form is more reasonable, so that the depth migration effect of the conventional inversion velocity model is greatly improved, and good application effect is achieved.

[0073] The preferred embodiments of the application are described above with reference to the drawings, and those skilled in the art can realize the application in various modification schemes without departing from the scope and essence of the application. For example, the features shown or described as part of an embodiment can be used in another embodiment to obtain another embodiment. The above is only a preferred and feasible embodiment of the application, and does not limit the scope of the application, and equivalent changes made by applying the content of the specification and drawings are included in the scope of the application.

Claims

1. A data-driven speed modeling method for a fault control body, characterized in that: include: S1. Perform OVT domain prestack depth migration on the prestack gather to form an OVG gather; The step S1 comprises: Using the best model obtained by conventional velocity modeling, the optimized pre-stack gathers are subjected to OVT domain pre-stack depth migration, and the OVT domain imaging gathers are sorted by offset and azimuth to form OVG gathers. S2, dividing the OVG gather into a plurality of OVG sub-gathers, and performing residual moveout picking on each of the OVG sub-gathers separately and establishing a tomographic equation; The residual time difference picking process includes: controlling the picking quality by superimposing the picked residual time difference with the corresponding OVG sub-channel set to ensure the accuracy of the picked residual time difference; establishing tomographic equations based on the residual time differences at different directions, using the formula: where A is the ray path length associated with the azimuth angle, is the azimuth, Δt is the residual time difference at different azimuths extracted from the gather, and Δm is the update amount of the inversion model to be solved; S3, obtaining a high-precision model update through multiple sets of the tomographic equations; S4, update the velocity model and perform depth domain migration imaging; The depth domain migration imaging is performed by adding the velocity model update amount to the previous optimal velocity model.

2. The data-driven speed modeling method for a fault control body according to claim 1 is characterized in that: Before step S1, the process includes: The pre-stack gathers are optimized, wherein the optimization includes denoising and / or multiple wave suppression.

3. A data-driven speed modeling system for fault control bodies, characterized in that: include: The gather building module is used to perform OVT domain prestack depth migration on prestack gathers to form OVG gathers; The gather building module is used to: perform OVT domain prestack depth migration on the optimized prestack gathers using the best model obtained by conventional velocity modeling, and sort the OVT domain imaging gathers according to offset and azimuth to form OVG gathers; a grouping tomography equation establishing module, for dividing the OVG gather into a plurality of OVG sub-gathers, and performing residual moveout picking on each of the OVG sub-gathers separately and establishing a tomography equation; The residual time difference picking process includes: controlling the picking quality by superimposing the picked residual time difference with the corresponding OVG sub-channel set to ensure the accuracy of the picked residual time difference; establishing tomographic equations based on the residual time differences at different directions, using the formula: where A is the ray path length associated with the azimuth angle, is the azimuth, Δt is the residual time difference at different azimuths extracted from the gather, and Δm is the update amount of the inversion model to be solved; The grouped tomography equation establishment module is used to: control the quality of the picking by superimposing the picked residual time difference with the corresponding OVG sub-channel set to ensure the accuracy of the picked residual time difference, and establish tomography equations based on the residual time difference in different directions; An update amount acquisition module, used to obtain high-precision model update amounts through multiple groups of tomographic equations; The imaging module is used to update the velocity model and perform depth domain migration imaging.

4. The data-driven velocity modeling system for a fault control body according to claim 3, characterized in that: It further includes an optimization processing module for performing optimization processing on the pre-stack gathers, wherein the optimization processing includes denoising and / or multiple wave suppression.

5. A computer-readable storage medium storing at least one computer-executable program, characterized in that: When the at least one program is executed by the computer, the computer executes the steps of the method according to any one of claims 1 to 2.

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