High-precision seismic imaging method and device for special geologic body

Abstract

The invention relates to the technical field of petroleum geophysical exploration, and particularly discloses a high-precision seismic imaging method and device for a special geologic body, and the method comprises the steps: carrying out the combined modeling of a plurality of logging wells in a work area, and fitting an imaging constraint factor; performing iterative optimization on the original imaging speed model by taking the imaging constraint factor as a constraint factor; if an iterative optimization result converges, taking the imaging speed model after iterative optimization as a final speed field, carrying out TTI depth domain migration imaging, and outputting imaging data; and if the iterative optimization result is not convergent, re-fitting the imaging constraint factor until the iterative optimization result is convergent. According to work area multi-well combined modeling, special geologic body imaging factors are fitted, imaging constraint factors serve as constraint factors, an original imaging speed model is optimized and adjusted, after iteration, a newest speed achievement model is output, the speed model serves as a final speed field, limitation of conventional depth imaging is avoided, the result is easy to converge, and the method is suitable for large-scale popularization and application. And the well determination accuracy is high.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of petroleum geophysical exploration technology, in particular to a high-precision seismic imaging method and device for special geological bodies. BACKGROUND

[0002] In conventional seismic imaging processing, seismic data imaging is carried out based on conventional seismic imaging theory. However, in actual production, the data obtained according to this method does not meet the premise assumptions of geophysical prospecting theory, so the target results processed are not completely true structures and imaging forms of the target geological bodies, and the contact relationship with the nearby faults is relatively complex. These special geological bodies are not clear and intuitive in conventional seismic data, and their ranges cannot be accurately determined and quantified. Although subsequent work has carried out various optimization processing methods, the actual seismic information has been affected by stacking factors, so that most of the effective information has been submerged and superimposed. It cannot be effectively extracted and utilized. This brings great technical pitfalls to subsequent geological exploration and well location.

[0003] In addition, the method of depth domain imaging is artificially assumed to adapt to the changes and structure of the measured underground strata, and cannot accurately constrain and correct the imaging of the data by known exploration multiple information to ensure the convergence and imaging of the reflection wave field of the special geological body. Therefore, even if multiple methods are used after imaging, the results obtained cannot truly reflect the seismic data of small geological bodies in deep and ultra-deep underground, and the reliability is uncertain. Especially some special geological bodies such as secondary faults, special lithological bodies, intrusive bodies and lens bodies, the contact relationship around them is relatively vague, and the breakpoint fault is not clear enough. This brings some false seismic geological phenomena and technical risks for subsequent exploration.

[0004] Based on this technical background, the present application studies a high-precision seismic imaging method and device for special geological bodies. SUMMARY

[0005] In view of the deficiencies of the prior art, the present application provides a high-precision seismic imaging method and device for special geological bodies. The method is based on multi-well joint modeling in the work area, fits the imaging factor of the special geological body, takes the imaging constraint factor as a constraint factor, optimizes and adjusts the original imaging velocity model, outputs the latest velocity result model after iteration, takes this velocity model as the final velocity field, and performs TTI depth domain migration imaging, which is suitable for large-scale regional development and well positioning, avoids the limitations of conventional depth imaging data, and has high calculation efficiency and high well positioning accuracy.

[0006] In order to achieve the above object, the first aspect of the present application provides a high-precision seismic imaging method for special geological bodies, comprising:

[0007] jointly modeling multiple well logs in a work area to fit imaging constraint factors;

[0008] iteratively optimizing the original imaging velocity model by taking the imaging constraint factors as constraint factors;

[0009] if the result of the iterative optimization converges, taking the imaging velocity model after the iterative optimization as a final velocity field, performing TTI depth domain migration imaging, and outputting imaging data;

[0010] if the result of the iterative optimization does not converge, re-fitting the imaging constraint factors until the result of the iterative optimization converges.

[0011] The second aspect of the present application provides a high-precision seismic imaging device for special geological bodies, comprising:

[0012] a fitting module configured to jointly model multiple well logs in a work area to fit imaging constraint factors;

[0013] an iterative optimization module configured to iteratively optimize the original imaging velocity model by taking the imaging constraint factors as constraint factors;

[0014] a migration imaging module configured to, if the result of the iterative optimization converges, take the imaging velocity model after the iterative optimization as a final velocity field, perform TTI depth domain migration imaging, and output imaging data;

[0015] a re-fitting module configured to, if the result of the iterative optimization does not converge, re-fit the imaging constraint factors until the result of the iterative optimization converges.

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

[0017] a memory storing executable instructions;

[0018] a processor configured to execute the executable instructions in the memory to implement the high-precision seismic imaging method for special geological bodies according to the first aspect.

[0019] The fourth aspect of the present application provides a computer readable storage medium storing a computer program, which is executed by a processor to implement the high-precision seismic imaging method for special geological bodies according to the first aspect.

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

[0021] (1) The special geological body high-precision seismic imaging method provided by the application, according to the multi-well joint modeling of the work area, fitting of the special geological body imaging factor, taking the imaging constraint factor as a constraint factor, optimizing and adjusting the original imaging velocity model, outputting the latest velocity result model after iteration, taking the velocity model as the final velocity field, and performing TTI depth domain migration imaging, is suitable for large-scale regional development well fixing, avoids the limitation of conventional depth imaging data, the calculation result is easy to converge, the calculation efficiency is high, the result is easy to converge, and the well fixing accuracy is higher.

[0022] (2) The special geological body high-precision seismic imaging method provided by the application has clear ideas and strong pertinence, avoids the limitation of traditional methods, fully utilizes effective information, improves imaging precision, is suitable for high-precision imaging of special geological bodies under various data conditions, better eliminates the technical risks and technical pitfalls caused by the uncertainty and non-quantization of the special geological bodies, and improves the exploration well fixing success rate.

[0023] (3) The special geological body high-precision seismic imaging method provided by the application fully utilizes the existing well data information of the work area to optimize and correct the current seismic imaging method, and comprehensively solves the uncertainty of conventional seismic imaging.

[0024] Other features and advantages of the application will be described in detail in the following specific embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0025] The above and other objects, features and advantages of the present application will become more apparent from the following detailed description of exemplary embodiments of the present application taken in conjunction with the accompanying drawings.

[0026] Figure 1 A flowchart of the special geological body high-precision seismic imaging method provided by the application.

[0027] Figure 2 A flowchart of one specific embodiment of the special geological body high-precision seismic imaging method provided by the application.

[0028] Figure 3 A schematic diagram of the special geological body well distribution in one specific embodiment of the special geological body high-precision seismic imaging method provided by the application.

[0029] Figure 4 A schematic diagram of the multi-well modeling and imaging constraint factor in one specific embodiment of the special geological body high-precision seismic imaging method provided by the application.

[0030] Figure 5This is a schematic diagram comparing the cross-sectional results of multi-scale migration imaging and conventional imaging in a specific implementation of the high-precision seismic imaging method for special geological bodies proposed in this invention.

[0031] Figure 6 This is a schematic diagram comparing the cross-sectional results of multi-scale migration imaging and conventional imaging in a specific implementation of the high-precision seismic imaging method for special geological bodies proposed in this invention. Detailed Implementation

[0032] Preferred embodiments of the invention will now be described in more detail. While preferred embodiments of the invention are described below, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0033] This invention provides a high-precision seismic imaging method for special geological bodies, such as... Figure 1 As shown, it includes:

[0034] Joint modeling was performed on multiple well logs in the work area to fit the imaging constraint factor;

[0035] The original imaging velocity model is iteratively optimized by using the imaging constraint factor as a constraint.

[0036] If the result of the iterative optimization converges, the imaging velocity model after iterative optimization is used as the final velocity field to perform depth domain migration imaging of TTI and output imaging data.

[0037] If the result of iterative optimization does not converge, refit the imaging constraint factor until the result of iterative optimization converges.

[0038] In this invention, based on multi-well joint modeling of the work area, imaging factors of special geological bodies are fitted, and imaging constraint factors are used as constraint factors to optimize and adjust the original imaging velocity model. After iteration, the latest velocity result model is output. Using this velocity model as the final velocity field, depth domain migration imaging of TTI is performed. This method is suitable for large-scale regional development well determination, avoids the limitations of conventional depth imaging data, and the calculation results are easy to converge, with high calculation efficiency and higher well determination accuracy.

[0039] According to the present invention, joint modeling of multiple well logs in the work area is performed, and the fitting imaging constraint factors include:

[0040] The well data from multiple logging wells in the work area were analyzed and optimized, and special geological bodies were inverted, quantified, and their ranges were determined.

[0041] Establish a seismic model for special geological bodies and calculate the imaging constraint factor for special geological bodies in this work area.

[0042] According to the present invention, iterative optimization of the original imaging velocity model by using the imaging constraint factor as a constraint factor includes:

[0043] Input seismic data from the work area to determine the conventional velocity model for pre-stack data;

[0044] The original imaging velocity model is iteratively optimized by taking the imaging constraint factor and the pre-stack data conventional velocity model as input.

[0045] According to the present invention, the result of iterative optimization converges to the point that all CIP gathers output by iterative optimization are flattened.

[0046] According to the present invention, the result of the iterative optimization is that the CIP gather output by the iterative optimization is not fully flattened.

[0047] The method of this invention has a clear concept and strong targeting, avoiding the limitations of traditional methods, making full use of effective information, improving imaging accuracy, adapting to high-precision imaging of special geological bodies under various data conditions, and better eliminating the technical risks and traps caused by insufficient clarity and quantification of special geological bodies, thereby improving the success rate of exploration well placement.

[0048] Preferably, depth-domain migration imaging is performed using TTI, and the output imaging data includes:

[0049] Anisotropic depth-domain migration imaging (TTI) is performed, and after comparing the new and old imaging data, the final result data is output for application.

[0050] According to the present invention, during the iterative optimization of the original imaging velocity model, the offset imaging of the output target line of the original imaging velocity model is not iteratively optimized.

[0051] This invention, based on conventional methods, makes full use of existing well data in the work area to optimize and correct current seismic imaging methods, and comprehensively addresses the uncertainties of conventional seismic imaging.

[0052] The present invention will be described in more detail below through embodiments.

[0053] Example 1:

[0054] like Figure 2As shown, this embodiment proposes a high-precision seismic imaging method for special geological bodies. First, well data from multiple wells in the work area are analyzed and optimized to invert the special geological body and quantify its extent. A seismic model of the special geological body is established, and the fitting imaging factor for the special geological body in this work area is calculated. Second, the fitting imaging factor of the special geological body, the input data of the work area, and the determined velocity model are input, and the model is optimized and iteratively updated. If the CIP gathers in the output results are not fully flattened, the fitting imaging factor is returned for optimization and correction, and the iteration is repeated. Third, if all the CIP gathers in the previous step are flattened and the gathers are in an ideal state, the model is output, and TTI anisotropic depth migration processing is performed. When the migration result meets the accuracy requirements, the final invention result is output. This achieves accurate migration imaging of special geological bodies.

[0055] The specific steps of this method are as follows:

[0056] (1) Multi-well joint modeling of the work area, outputting imaging fitting factors:

[0057] Well data from multiple wells in the work area were analyzed and optimized. Special geological bodies were inverted and their extent quantified. Seismic models of these special geological bodies were established, and the fitting imaging factors for these bodies in the work area were calculated.

[0058] (2) Optimize the model using input data and update iteratively:

[0059] The model is optimized and iteratively updated by inputting imaging factors for specific geological bodies, inputting work area data, and determining a velocity model. If the CIP gather in the output results is not fully flattened, the fitted imaging factors are returned for optimization and correction, and the iteration is repeated.

[0060] (3) Multiscale migration imaging:

[0061] The CIP gathers from the previous step are all flattened. If the gathers are in an ideal state, the model is output and then subjected to TTI anisotropic depth migration processing.

[0062] (4) Output the final imaging data:

[0063] Once the offset results meet the accuracy requirements, the final invention result is output.

[0064] In this embodiment, a 3D seismic survey area with complex surface and subsurface was selected for the experiment; the terrain was mountainous with significant variations; the elevation of the survey area ranged from 600 to 800 meters; 3D seismic acquisition was used in the survey area; the design area was 25m × 25m, the design coverage was 18 (vertical) × 12 (horizontal) = 216 times, and the aspect ratio was 0.66; after high-precision pre-stack preprocessing and conventional depth migration modeling and imaging, joint modeling was performed on multiple well logs in the survey area, such as... Figure 3As shown, initial imaging constraint factors are determined based on the modeling results, such as... Figure 4 As shown; the fitted imaging constraint factor and pre-stack conventional velocity model are used as inputs to optimize and correct the model; then, the migration imaging of the target line is iterated. If the data imaging accuracy does not meet the requirements, the process returns to multi-well joint modeling and re-optimizes the fitted imaging factor, continuously optimizing the data and performing migration imaging quality control; if the quality control results reach the design accuracy, the results are output, and TTI anisotropic migration imaging is performed. After comparing the old and new data, the final result data is output for application, such as... Figure 5 , Figure 6 As shown.

[0065] In this embodiment, the results of this method show that the application effect is quite ideal; this method is not only effective for the target processing area, but also very effective for imaging targets in large desert areas and piedmont zones, and has a better effect on the reuse of old data and the application effect of profiles is good; seismic profiles are also better than other imaging methods; the drilling success rate is increased by about 5-8%, which verifies the feasibility of this method.

[0066] Example 2:

[0067] This embodiment provides a high-precision seismic imaging method for special geological bodies, such as... Figure 1 As shown, it includes:

[0068] Joint modeling was performed on multiple well logs in the work area to fit the imaging constraint factor;

[0069] The original imaging velocity model is iteratively optimized by using the imaging constraint factor as a constraint.

[0070] If the result of the iterative optimization converges, the imaging velocity model after iterative optimization is used as the final velocity field to perform depth domain migration imaging of TTI and output imaging data.

[0071] If the result of iterative optimization does not converge, refit the imaging constraint factor until the result of iterative optimization converges.

[0072] In this embodiment, joint modeling is performed on multiple well logs in the work area, and the fitting imaging constraint factors include:

[0073] The well data from multiple logging wells in the work area were analyzed and optimized, and special geological bodies were inverted, quantified, and their ranges were determined.

[0074] Establish a seismic model for special geological bodies and calculate the imaging constraint factor for special geological bodies in this work area;

[0075] In this embodiment, the imaging constraint factor is used as a constraint factor, and the iterative optimization of the original imaging velocity model includes:

[0076] Input seismic data from the work area to determine the conventional velocity model for pre-stack data;

[0077] The original imaging velocity model is iteratively optimized by taking the imaging constraint factor and the pre-stack data conventional velocity model as input.

[0078] In this embodiment, the convergence of the iterative optimization result is that all CIP gathers output by the iterative optimization are flattened;

[0079] In this embodiment, the result of the iterative optimization does not converge because the CIP gather output by the iterative optimization is not fully flattened.

[0080] In this embodiment, depth-domain offset imaging (TTI) is performed, and the output imaging data includes:

[0081] Anisotropic depth-domain migration imaging (TTI) is performed, and after comparing the new and old imaging data, the final result data is output for application.

[0082] In this embodiment, during the iterative optimization of the original imaging velocity model, the offset imaging of the output target line of the original imaging velocity model is not iteratively optimized.

[0083] Example 3:

[0084] This embodiment provides a high-precision seismic imaging device for special geological bodies, including:

[0085] The fitting module is used to perform joint modeling of multiple well logs in the work area and fit imaging constraint factors.

[0086] The iterative optimization module is used to iteratively optimize the original imaging velocity model by taking the imaging constraint factor as a constraint factor.

[0087] The migration imaging module is used to perform depth-domain migration imaging of TTI (Temperature Transmission Imaging) and output imaging data if the results of iterative optimization converge, using the iteratively optimized imaging velocity model as the final velocity field.

[0088] The refit module is used to refit the imaging constraint factor if the result of iterative optimization does not converge, until the result of iterative optimization converges.

[0089] In this embodiment, joint modeling is performed on multiple well logs in the work area, and the fitting imaging constraint factors include:

[0090] The well data from multiple logging wells in the work area were analyzed and optimized, and special geological bodies were inverted, quantified, and their ranges were determined.

[0091] Establish a seismic model for special geological bodies and calculate the imaging constraint factor for special geological bodies in this work area;

[0092] In this embodiment, the imaging constraint factor is used as a constraint factor, and the iterative optimization of the original imaging velocity model includes:

[0093] Input seismic data from the work area to determine the conventional velocity model for pre-stack data;

[0094] The original imaging velocity model is iteratively optimized by taking the imaging constraint factor and the pre-stack data conventional velocity model as input.

[0095] In this embodiment, the convergence of the iterative optimization result is that all CIP gathers output by the iterative optimization are flattened;

[0096] In this embodiment, the result of the iterative optimization does not converge because the CIP gather output by the iterative optimization is not fully flattened.

[0097] In this embodiment, depth-domain offset imaging (TTI) is performed, and the output imaging data includes:

[0098] Anisotropic depth-domain migration imaging (TTI) is performed, and after comparing the new and old imaging data, the final result data is output for application.

[0099] In this embodiment, during the iterative optimization of the original imaging velocity model, the offset imaging of the output target line of the original imaging velocity model is not iteratively optimized.

[0100] Example 4:

[0101] This invention provides an electronic device including a memory and a processor, comprising:

[0102] Memory, which stores executable instructions;

[0103] The processor runs executable instructions in memory to enable high-precision seismic imaging methods for specific geological bodies.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] For a detailed description of this embodiment, please refer to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.

[0108] Example 5:

[0109] This invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements a high-precision seismic imaging method for special geological bodies.

[0110] 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.

[0111] 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).

[0112] The high-precision seismic imaging method for special geological bodies proposed in the embodiments of the present invention is based on multi-well joint modeling of the work area, fitting imaging factors of special geological bodies, using imaging constraint factors as constraint factors, optimizing and adjusting the original imaging velocity model, and outputting the latest velocity result model after iteration. This velocity model is used as the final velocity field for depth domain migration imaging (TTI), which is suitable for large-scale regional development well location, avoids the limitations of conventional depth imaging data, and the calculation results are easy to converge, with high computational efficiency and higher well location accuracy.

[0113] 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 high-precision seismic imaging method for special geological bodies, characterized in that, include: Joint modeling was performed on multiple well logs in the work area to fit the imaging constraint factor; The imaging constraint factor is used as a constraint factor to iteratively optimize the original imaging velocity model. If the result of the iterative optimization converges, the iteratively optimized imaging velocity model is used as the final velocity field to perform depth domain migration imaging of TTI and output imaging data. If the result of the iterative optimization does not converge, the imaging constraint factor is refitted until the result of the iterative optimization converges.

2. The method according to claim 1, characterized in that, Joint modeling was performed on multiple wells in the work area, and the fitting imaging constraint factors included: The well data from multiple logging wells in the work area were analyzed and optimized, and special geological bodies were inverted, quantified, and their ranges were determined. Establish a seismic model for special geological bodies and calculate the imaging constraint factor for special geological bodies in this work area.

3. The method according to claim 1, characterized in that, Using the imaging constraint factor as a constraint, the original imaging velocity model is iteratively optimized by: Input seismic data from the work area to determine the conventional velocity model for pre-stack data; The imaging constraint factor and the pre-stack data conventional velocity model are used as inputs to iteratively optimize the original imaging velocity model.

4. The method according to claim 1, characterized in that, The convergence of the iterative optimization result is that all CIP gathers output by the iterative optimization are flattened.

5. The method according to claim 1, characterized in that, The result of the iterative optimization is that the CIP gather output by the iterative optimization is not fully flattened.

6. The method according to claim 1, characterized in that, The depth-domain migration imaging of TTI is performed, and the output imaging data includes: Anisotropic depth-domain migration imaging (TTI) is performed, and after comparing the new and old imaging data, the final result data is output for application.

7. The method according to claim 1, characterized in that, During the iterative optimization of the original imaging velocity model, the offset imaging of the output target line of the original imaging velocity model is not iteratively optimized.

8. A high-precision seismic imaging device for special geological bodies, characterized in that, include: The fitting module is used to perform joint modeling of multiple well logs in the work area and fit imaging constraint factors. The iterative optimization module is used to iteratively optimize the original imaging velocity model by taking the imaging constraint factor as a constraint factor. The offset imaging module is used to perform depth domain offset imaging of TTI with the iteratively optimized imaging velocity model as the final velocity field if the result of the iterative optimization converges, and output imaging data. The refitting module is used to refit the imaging constraint factor if the result of the iterative optimization does not converge, until the result of the iterative optimization converges.

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 a high-precision seismic imaging method for special geological bodies 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 high-precision seismic imaging method for special geological bodies as described in any one of claims 1-7.

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