A deep domain modeling method based on deep complex structures

Abstract

The present invention provides a depth domain modeling method based on deep complex structures. First, the elevation data of the shot and detection points are used to establish an approximate true surface undulation, and a shallow high-precision velocity model is established through the first arrival information. The velocity model is optimized iteratively through layered inversion based on data-driven. An accurate velocity model is obtained through layered iterative inversion of high signal-to-noise ratio data, and layer velocity scanning and migration are performed layer by layer to determine the layer velocity range between different marker layers. The velocity models of high signal-to-noise ratio data and low signal-to-noise ratio data are integrated, and the model is manually optimized for the local position in combination with the migration results. At the same time, the velocity model is laterally smoothed along each marker layer to ensure that the velocity model is consistent with the structural characteristics. In order to eliminate the high-frequency changes between velocities, a small-scale global inversion is performed as a whole to further optimize the depth domain velocity model, and finally a complete set of depth domain modeling methods for deep complex structures is formed.

Description

Technical Field

[0001] The present invention relates to the field of oil and gas geophysical seismic data processing, and in particular to a depth domain modeling method based on deep complex structures. Background Art

[0002] As oil and gas exploration and development enter the refined potential phase, the requirements for imaging deep, complex structures are increasing, and the establishment of detailed depth velocity models has become crucial. Complex structures are often composed of old strata with poor stratification and unclear wave group anti-interfaces. Their velocity characteristics are characterized by significant differences in velocity between the overlying strata and surrounding rock. Furthermore, faults are well-developed, resulting in multiple reflections and refractions of seismic rays between strata and faults during the excitation process, creating extremely complex ray paths. These factors contribute to the complexity of the seismic wavefield, weak effective information reflection energy, difficulty in analyzing vertical and horizontal velocities, and difficulties in performing migration imaging. Therefore, detailed velocity modeling of complex structures is a crucial step in seismic data imaging.

[0003] Current methods for deep migration velocity modeling suffer from two major shortcomings. First, existing well data and formation information are used to build depth-domain velocity models. This drawback is that deep, complex structures are poorly explored and well information is scarce, making it difficult to accurately build a depth-domain velocity model based on these well and formation information. Second, current velocity modeling primarily relies on data-driven tomographic inversion, which places certain demands on data quality. However, deep, complex structures often suffer from large formation dips, low data signal-to-noise ratios, inversion issues with interlayer velocities, and low velocity model accuracy, which directly reduce the quality of the migration imaging gathers and affect the accuracy of the residual curvature picked up by the post-migration gathers. Consequently, the reliability of data-driven tomographic inversion iterations is low, making it impossible to accurately generate high-precision velocity models. Summary of the Invention

[0004] In view of the above problems, the present invention is proposed to provide a deep domain modeling method based on deep complex structure, which overcomes the above problems or at least partially solves the above problems.

[0005] According to one aspect of the present invention, a deep domain modeling method based on deep complex structures is provided, the modeling method comprising:

[0006] Import the elevation data of the shot and detection points to establish the surface elevation ground surface database file;

[0007] Establish a shallow velocity model based on the first arrival information of single shots in the entire area, and determine the shallow model depth;

[0008] On the basis of the shallow velocity model, an initial depth domain velocity model is established;

[0009] Iteratively updating the initial depth-domain velocity model to obtain an updated depth-domain velocity model;

[0010] Converting time domain horizon data into depth domain horizon data using a map migration technique according to the updated depth domain velocity model;

[0011] Optimizing depth migration parameters, finely constraining the updated depth domain velocity model, and obtaining optimized information;

[0012] Based on the optimized information, a data-driven layer-by-layer stripping inversion is used to calibrate the layer velocity model layer by layer. The high signal-to-noise ratio data is improved by migration imaging after layer-by-layer tomography inversion, and an accurate depth-domain velocity model is obtained with the high signal-to-noise ratio data.

[0013] Based on the velocity scan migration of the low signal-to-noise ratio data layer and the information of the marker layer, a velocity model of the low signal-to-noise ratio area is established by gradient filling from shallow to deep between layers.

[0014] The velocity models of high and low signal-to-noise ratio areas are integrated for migration to obtain a complete depth-domain velocity model of deep and complex structures.

[0015] Optionally, the step of importing shot and detection point elevation data to establish a surface elevation ground surface library file specifically includes:

[0016] Import the elevation data of the shot and detection points to establish the surface elevation ground surface database file;

[0017] Applying a small smoothing radius to smooth the elevation surface library file;

[0018] The starting surface of the approximate true surface depth domain migration is established, and the depth migration and travel time calculation start from the approximate true surface undulating surface.

[0019] Optionally, establishing a shallow velocity model based on the first arrival information of a single shot in the entire area and determining the shallow model depth specifically includes:

[0020] A shallow velocity model was established based on the first arrival information of single shots in the entire area;

[0021] The shallow velocity model is updated by iterative inversion based on the shallow velocity model and the ray density, and the ray density bottom boundary envelope is selected to determine the shallow model depth.

[0022] Optionally, establishing an initial depth-domain velocity model based on the shallow velocity model specifically includes:

[0023] On the basis of shallow velocity model, the RMS velocity model of middle and deep layers is established according to the geological information of the work area;

[0024] The Dicks formula is used to convert the root mean square velocity into layer velocity to establish the shallow, medium and deep layer velocity model and the initial depth domain velocity model.

[0025] Optionally, the geological information of the work area specifically includes: well velocity in the work area, geological information, and manually picked velocity spectrum.

[0026] Optionally, the iteratively updating the initial depth-domain velocity model to obtain an updated depth-domain velocity model specifically includes:

[0027] Performing large-scale grid migration on the initial depth-domain velocity model;

[0028] Pick the residual curvature of the depth domain gather after pre-stack depth migration;

[0029] The depth domain velocity model is updated after 2-3 rounds of iterations based on the data-driven tomographic inversion model to obtain an updated depth domain velocity model.

[0030] Optionally, converting the time domain horizon to the depth domain horizon data using a graph migration technique according to the updated depth domain velocity model specifically includes:

[0031] Use the velocity model updated by the overall inversion to perform prestack depth migration of the monitoring line;

[0032] Convert the migrated data to time domain data through deep time conversion;

[0033] Combined with geological information, the horizons of the main marker layers are interpreted on the time domain migration data. In the subsequent layered inversion, the map migration technology is used to convert the time domain horizons into depth domain horizon data.

[0034] Optionally, optimizing the depth offset parameters and finely constraining the updating of the depth domain velocity model specifically includes:

[0035] Optimize depth migration parameters including anti-aliasing, migration frequency, and perform trimming, denoising, and filtering on the depth migration gathers to improve the signal-to-noise ratio of the migrated gathers.

[0036] Combined with the migration results, the formation dip field file is calculated to finely constrain the velocity model after inversion.

[0037] Optionally, the optimized information specifically includes: optimized gathers, geological dip field information, and depth domain marker layer information.

[0038] Optionally, the fusing of velocity models of high signal-to-noise ratio areas and low signal-to-noise ratio areas for migration to obtain a complete depth-domain velocity model of deep complex structures specifically includes:

[0039] Migrate by fusing the velocity models of high and low signal-to-noise ratio areas. Optimize the local velocity model and the overall velocity model by manually modifying the depth domain layer velocity.

[0040] The velocity model is further optimized by using large-scale lateral smoothing technology along each marker horizon, so that the velocity model is more consistent with the structural characteristics.

[0041] The established depth-domain velocity model is subjected to small-scale global inversion to further optimize the high-frequency variation of inter-layer velocity and obtain a complete depth-domain velocity model of deep complex structures.

[0042] The present invention provides a depth domain modeling method based on deep complex structures, the modeling method comprising: importing shot and receiver point elevation data to establish a surface elevation ground surface library file; establishing a shallow velocity model based on the first arrival information of a single shot in the entire area, and determining the shallow model depth; establishing an initial depth domain velocity model based on the shallow velocity model; updating and iteratively processing the initial depth domain velocity model to obtain an updated depth domain velocity model; using a graph migration technology to convert time domain layers into depth domain layer data based on the updated depth domain velocity model; optimizing depth migration parameters, and finely constraining the The depth-domain velocity model is updated to obtain optimized information. Based on this optimized information, a data-driven layer-by-layer stripping inversion is used to calibrate the layer velocity model layer by layer. High-SNR data are then migrated and improved through layer-by-layer tomography inversion, resulting in an accurate depth-domain velocity model for the high-SNR data. A velocity model for the low-SNR area is established by performing layer-by-layer gradient filling from shallow to deep between layers based on the low-SNR data layer velocity scanning migration combined with marker layer information. The velocity models for the high-SNR and low-SNR areas are then integrated and migrated to obtain a complete depth-domain velocity model for deep, complex structures. High-frequency variations in velocity are eliminated, and a small-scale global inversion is performed to further optimize the depth-domain velocity model, ultimately forming a complete depth-domain modeling method for deep, complex structures.

[0043] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are specifically listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0045] Figure 1 A flow chart of a method for depth domain modeling based on deep complex structures provided by an embodiment of the present invention.

[0046] Figure 2 The embodiment of the present invention provides an undulating surface that approximates the true surface.

[0047] Figure 3 This is the initial depth-domain velocity model provided by an embodiment of the present invention.

[0048] Figure 4 This is an explanation of the main marker layer positions provided in the embodiment of the present invention.

[0049] Figure 5 This is the high signal-to-noise ratio data offset superposition provided by an embodiment of the present invention.

[0050] Figure 6 This is a layer-by-layer gradient filling model provided by an embodiment of the present invention.

[0051] Figure 7 This is the optimized depth-domain velocity model provided by an embodiment of the present invention.

[0052] Figure 8 The final depth-domain velocity model (left) and prestack depth migration profile (right) provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0053] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0054] The terms "comprises" and "comprising" and any variations thereof in the description, embodiments, claims and drawings of the present invention are intended to cover non-exclusive inclusions, for example, including a series of steps or units.

[0055] The technical solution of the present invention is further described in detail below with reference to the accompanying drawings and embodiments.

[0056] The present invention proposes a depth domain modeling method based on deep complex structures. First, the elevation data of the shot and receiver points are used to establish an approximate true surface undulation, and a shallow high-precision velocity model is established through the first arrival information. Secondly, on the basis of the shallow high-precision velocity model, the optimized track gather, geological information, inclination field, marker layer information, etc. are combined to adopt data-driven layered inversion iterative optimization of the velocity model. The high signal-to-noise ratio data obtains an accurate velocity model through layered iterative inversion. The low signal-to-noise ratio data uses the layer velocity of similar strata in the surrounding blocks as a reference, and adopts layer-by-layer velocity scanning and migration. After determining the layer velocity range between different marker layers, the low signal-to-noise ratio velocity model is established by gradient filling layer velocity from shallow to deep in combination with the marker layer information. Finally, the velocity models of the high signal-to-noise ratio data and the low signal-to-noise ratio data are fused, and the local position is manually optimized in combination with the migration results. At the same time, the velocity model is laterally smoothed along each marker layer to ensure that the velocity model is consistent with the structural characteristics. In order to eliminate high-frequency variations in velocity, a small-scale global inversion is performed to further optimize the depth-domain velocity model, ultimately forming a complete depth-domain modeling method for deep complex structures.

[0057] like Figure 1 As shown, the present invention is a deep domain modeling method for deep complex structures, and the processing flow includes the following steps:

[0058] Step 1: Import the shot and receiver elevation data to create a surface elevation surface library file. Apply a small smoothing radius to smooth the elevation surface library file and create a starting surface for depth domain migration of the approximate true surface. The depth migration and travel time calculation start from the approximate true surface undulating surface, such as Figure 2 shown.

[0059] Step 2: Establish a shallow velocity model through the first arrival information of single shots in the entire area, update the shallow velocity model by combining the shallow velocity model and ray density iterative inversion, and select the bottom boundary envelope of the ray density to determine the depth of the shallow model.

[0060] Step 3: Based on the shallow velocity model, the RMS velocity model of the middle and deep layers is established by combining the well velocity, geological information, and manually picked velocity spectrum in the work area. The RMS velocity is converted into layer velocity using the Dix formula to establish the shallow, middle and deep layer velocity model. The initial depth domain velocity model is established, such as Figure 3 shown.

[0061] Step 4: After the initial depth domain velocity model is established, large-scale grid migration is performed first. Then, the residual curvature of the depth domain gather after pre-stack depth migration is picked up. Finally, the depth domain velocity model is updated after 2-3 rounds of iterations based on the data-driven tomographic inversion model.

[0062] Step 5: Use the velocity model updated by the overall inversion to perform pre-stack depth migration of the monitoring line, convert the migrated data into time domain data through deep time, combine geological information to interpret the main marker layers on the time domain migration data, and use the map migration technology to convert the time domain layers into depth domain layers in the subsequent layered inversion, such as Figure 4 shown.

[0063] Step 6: Optimize depth migration parameters, including anti-aliasing and migration frequency, and perform trimming, denoising, and filtering on the migrated data gathers to improve the signal-to-noise ratio. This facilitates residual curvature delay picking and enhances inversion accuracy. Simultaneously, combine the migration results to calculate the formation dip field file and refine the post-inversion velocity model.

[0064] Step 7: Combine the optimized gathers, geological dip field information, and depth domain marker layer information, and use data-driven layer-by-layer stripping inversion to calibrate the layer velocity model layer by layer. High signal-to-noise ratio data is improved by migration imaging after layer-by-layer tomography inversion, and accurate depth domain velocity models are obtained from high signal-to-noise ratio data, such as Figure 5 shown.

[0065] Step 8: In view of the defects of the tomographic inversion strategy for low signal-to-noise ratio data, the velocity range of similar layers in the surrounding blocks is used as a reference. The data in the local low signal-to-noise ratio area are scanned and migrated layer by layer from shallow to deep layers. The velocity model in the depth domain of the corresponding layer is determined by the rationality of the migration imaging.

[0066] Step 9: Based on the velocity scan migration of the low signal-to-noise ratio data layer, the velocity model of the low signal-to-noise ratio area is established by gradient filling from shallow to deep between layers in combination with the marker layer information, such as Figure 6 shown.

[0067] Step 10: Fusion of velocity models of high and low SNR areas is performed. Based on the rationality of the structural imaging results after migration, the local velocity model is optimized by manually modifying the depth domain layer velocity to meet the requirements of the consistency between the velocity model and the geological structure and the rationality of imaging of deep complex structures. The overall velocity model is optimized.

[0068] Step 11: After optimizing the velocity model by manually optimizing the depth domain layer velocity, the velocity model is further optimized by using the large-scale lateral smoothing technology along each marker layer to make the velocity model more consistent with the structural characteristics, such as Figure 7 shown.

[0069] Step 12: Perform small-scale global inversion on the established depth-domain velocity model to further optimize the high-frequency variation of inter-layer velocity. Finally, a complete depth-domain velocity model of deep complex structures is obtained. The migration profile after pre-stack depth migration is consistent with the structural trend of the depth-domain velocity model. Figure 8 shown.

[0070] Beneficial effects:

[0071] The first aspect is to use the elevations of all the shots and receiver points in the area to establish an approximate true surface depth domain migration starting surface, and to establish a shallow high-precision velocity model through the first arrival information to ensure the accuracy of the shallow velocity model.

[0072] Secondly, we use data-driven iterative inversion to optimize the velocity model based on the high-precision shallow-layer velocity model. High-SNR data are used through layer-by-layer iterative inversion to obtain an accurate velocity model. Low-SNR data are subjected to layer-by-layer velocity scanning migration. After determining the inter-layer velocity range between different marker layers, the low-SNR velocity model is established by gradient filling layer-by-layer from shallow to deep, combining the marker layer information.

[0073] Third, the velocity models of high-SNR and low-SNR data were integrated, and the migration results were combined to manually optimize the model at local locations. The velocity model was also smoothed laterally within each marker layer. To eliminate velocity differences between the high-SNR and low-SNR data, a global inversion was performed to further optimize the high-frequency velocity model, ultimately forming a complete deep-domain modeling method for deep, complex structures.

[0074] The above specific implementation methods further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above are only specific implementation methods of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A deep domain modeling method based on deep complex structures, characterized in that: The modeling method includes: Import the elevation data of the shot and detection points to establish the surface elevation ground surface database file; Establish a shallow velocity model based on the first arrival information of single shots in the entire area, and determine the shallow model depth; On the basis of the shallow velocity model, an initial depth domain velocity model is established; Iteratively updating the initial depth-domain velocity model to obtain an updated depth-domain velocity model; Converting time domain horizon data into depth domain horizon data using a map migration technique according to the updated depth domain velocity model; Optimizing depth migration parameters, finely constraining the updated depth domain velocity model, and obtaining optimized information; Based on the optimized information, a data-driven layer-by-layer stripping inversion is used to calibrate the layer velocity model layer by layer. The high signal-to-noise ratio data is improved by migration imaging after layer-by-layer tomography inversion, and an accurate depth-domain velocity model is obtained with the high signal-to-noise ratio data. Based on the velocity scan migration of the low signal-to-noise ratio data layer and the information of the marker layer, a velocity model of the low signal-to-noise ratio area is established by gradient filling from shallow to deep between layers. The velocity models of high and low signal-to-noise ratio areas are integrated for migration to obtain a complete depth-domain velocity model of deep and complex structures.

2. The method for deep domain modeling based on deep complex structures according to claim 1, characterized in that: The process of importing shot and detection point elevation data to establish a surface elevation ground surface database file specifically includes: Import the elevation data of the shot and detection points to establish the surface elevation ground surface database file; Applying a small smoothing radius to smooth the elevation surface library file; The starting surface of the approximate true surface depth domain migration is established, and the depth migration and travel time calculation start from the approximate true surface undulating surface.

3. The method for deep domain modeling based on deep complex structures according to claim 1, characterized in that: The establishment of a shallow velocity model based on the first arrival information of a single shot in the entire area and determination of the shallow model depth specifically include: A shallow velocity model was established based on the first arrival information of single shots in the entire area; The shallow velocity model is updated by iterative inversion based on the shallow velocity model and the ray density, and the ray density bottom boundary envelope is selected to determine the shallow model depth.

4. The method for deep domain modeling based on deep complex structures according to claim 1, characterized in that: The step of establishing an initial depth-domain velocity model based on the shallow velocity model specifically includes: On the basis of shallow velocity model, the RMS velocity model of middle and deep layers is established according to the geological information of the work area; The Dicks formula is used to convert the root mean square velocity into layer velocity to establish the shallow, medium and deep layer velocity model and the initial depth domain velocity model.

5. The method for deep domain modeling based on deep complex structure according to claim 4, characterized in that: The geological information of the work area specifically includes: well velocity in the work area, geological information, and manually picked velocity spectrum.

6. The method for deep domain modeling based on deep complex structures according to claim 1, characterized in that: The iterative updating of the initial depth-domain velocity model to obtain an updated depth-domain velocity model specifically includes: Performing large-scale grid migration on the initial depth-domain velocity model; Pick the residual curvature of the depth domain gather after pre-stack depth migration; The depth domain velocity model is updated after 2-3 rounds of iterations based on the data-driven tomographic inversion model to obtain an updated depth domain velocity model.

7. The method for deep domain modeling based on deep complex structures according to claim 1, characterized in that: The converting of the time domain horizon into the depth domain horizon data by using the map migration technology according to the updated depth domain velocity model specifically includes: Use the velocity model updated by the overall inversion to perform prestack depth migration of the monitoring line; Convert the migrated data to time domain data through deep time conversion; Combined with geological information, the horizons of the main marker layers are interpreted on the time domain migration data. In the subsequent layered inversion, the map migration technology is used to convert the time domain horizons into depth domain horizon data.

8. The method for deep domain modeling based on deep complex structure according to claim 1, characterized in that: Optimizing the depth migration parameters and finely constraining the updating of the depth domain velocity model specifically includes: Optimize depth migration parameters including anti-aliasing, migration frequency, and perform trimming, denoising, and filtering on the depth migration gathers to improve the signal-to-noise ratio of the migrated gathers. Combined with the migration results, the formation dip field file is calculated to finely constrain the velocity model after inversion.

9. The method for deep domain modeling based on deep complex structures according to claim 1, characterized in that: The optimized information specifically includes: optimized gathers, geological dip field information, and depth domain marker layer information.

10. The method for deep domain modeling based on deep complex structure according to claim 1, characterized in that: The method of fusing the velocity models of the high signal-to-noise ratio area and the low signal-to-noise ratio area to perform migration and obtain a complete depth-domain velocity model of the deep complex structure specifically includes: Migrate by fusing the velocity models of high and low signal-to-noise ratio areas. Optimize the local velocity model and the overall velocity model by manually modifying the depth domain layer velocity. The velocity model is further optimized by using large-scale lateral smoothing technology along each marker horizon, so that the velocity model is more consistent with the structural characteristics. The established depth-domain velocity model is subjected to small-scale global inversion to further optimize the high-frequency variation of inter-layer velocity and obtain a complete depth-domain velocity model of deep complex structures.

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