A method for improving velocity model accuracy using azimuth information

Abstract

The application belongs to the technical field of oil exploration, and discloses a method for improving the precision of a velocity model by using azimuth information, which comprises the following steps: improving the signal-to-noise ratio of shot gather data; establishing an initial velocity model in the depth domain; improving the signal-to-noise ratio of pre-stack depth migration profiles; outputting reflection angle gathers; picking up azimuth residual depth differences; obtaining azimuth residual depth differences after ray tracing; obtaining new velocities and velocity updating amounts; obtaining final velocities; and performing depth migration processing, and iteratively updating the new velocities until the reflection angle gathers and common offset gathers are flattened. The application improves the velocity inversion precision of middle and deep layers and effectively improves the velocity inversion precision of shallow layers. The application is suitable for establishing a velocity model in the depth domain.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of oil exploration, in particular to a method for improving the precision of velocity model by using azimuth information. BACKGROUND

[0002] In oil exploration, the heterogeneity of underground medium is universal. Due to the heterogeneity of lithology, physical property and oil content of reservoir, the difference in sand body distribution and connectivity in vertical and horizontal directions, etc., the velocity distortion of seismic wave in the propagation process is caused, thus the precision of conventional velocity modeling is seriously reduced.

[0003] The precision of velocity modeling directly affects the quality of seismic imaging. Velocity analysis and modeling are the key technologies for ensuring the acquisition of seismic profile with high signal-to-noise ratio, high resolution and high fidelity through seismic imaging. The current more advanced velocity modeling technology is the grid tomography velocity inversion technology based on pre-stack depth migration gathers. Since the seismic wave propagates in different directions is obviously different due to the influence of geological conditions, and the conventional velocity modeling assumes that the velocity of each imaging point is unique, and the change of ray path caused by the different geological bodies in the propagation process of seismic wave in different directions is ignored. This assumption weakens the influence of seismic wave propagation in different directions, which is beneficial to the effective establishment of depth domain velocity model, but the depth-velocity model established by this method reduces the precision of pre-stack depth migration imaging, which is not conducive to accurate depth migration imaging.

[0004] At present, the multi-azimuth tomography velocity modeling technology based on the idea of azimuth processing restricts the precision of velocity modeling because each azimuth data contains angle information in a large angle range, which is approximately processed as the same azimuth angle. In addition, on the basis of traditional azimuth grid tomography, people have proposed OVT full-azimuth grid tomography technology, which has made certain progress in the utilization of angle information, but also has certain limitations. OVT is also an azimuth approximation technology. This approximation, especially in the analysis of azimuth anisotropy of fractured reservoir, equates the shot-receiver azimuth angle to the incident azimuth angle or the scattering azimuth angle of underground wave field, and the azimuth attribute analysis result is affected by the overlying strata, which cannot accurately describe the geological properties of the target layer. SUMMARY

[0005] The purpose of the present application is to provide a method for improving the precision of velocity model by using azimuth information, so as to eliminate the velocity difference caused by geological heterogeneity and improve the precision of modeling.

[0006] In order to achieve the above purpose, the technical method adopted by the present application is as follows:

[0007] A method for improving the precision of velocity model by using azimuth information, comprising the following steps:

[0008] S1, obtaining a ground elevation surface text file and shot gather data after a general pre-stack series processing, performing a signal-to-noise ratio improvement processing on the shot gather data to obtain a gather;

[0009] S2, establishing a depth domain initial velocity model;

[0010] S3, performing a pre-stack depth migration on the depth domain initial velocity model in step S2 to obtain a pre-stack depth migration profile, performing a signal-to-noise ratio improvement processing on the pre-stack depth migration profile, and then performing a profile scanning on the pre-stack depth migration profile after the signal-to-noise ratio improvement to obtain an inclination angle, an azimuth angle and a quality factor;

[0011] S4, performing a beam migration processing on a target point of the gather obtained in step S1 by using the depth domain initial velocity model in step S2 to output a reflection angle gather;

[0012] S5, performing a signal-to-noise ratio improvement and energy adjustment processing on the reflection angle gather of the target point, and then picking up an azimuth residual depth difference on the reflection angle gather after the signal-to-noise ratio improvement and the energy adjustment;

[0013] S6, adding the inclination angle and the azimuth angle information obtained in step S3 to constrain the picked-up azimuth residual depth difference, and then performing a ray tracing to obtain an azimuth residual depth difference after the ray tracing;

[0014] S7, performing a velocity inversion processing on the azimuth residual depth difference after the ray tracing to obtain a new velocity and a velocity update amount;

[0015] S8, performing a smoothing processing on the new velocity obtained in step S7 to obtain an updated final velocity;

[0016] S9, performing a depth migration processing on the shot gather data after the signal-to-noise ratio improvement in step S1 by using the final velocity in step S8 to obtain a reflection angle gather and a common offset gather, judging whether the reflection angle gather and the common offset gather are flattened, if not, replacing the velocity after the iteration along the layer and the vertical velocity in step S2 with the updated final velocity obtained in step S8 to perform an iteration update, repeating steps S3-S9 until the reflection angle gather and the common offset gather are flattened to obtain a precision-improved velocity model.

[0017] As a limitation: the signal-to-noise ratio improvement processing on the shot gather data in step S1 is a four-dimensional denoising and a five-dimensional interpolation processing in sequence; the shot gather data contains a trace information line number, a point number, a shot point coordinate and a receiver point coordinate; the ground elevation surface text file contains a line number, a point number, a coordinate X, a coordinate Y and a ground elevation, which are used as a calculation surface for the target point migration.

[0018] As a further limitation: the method for establishing the initial velocity model in step S2 is to sort the shot gather data obtained in step S1 into common mid-point gathers, to obtain common reflection point gathers after pre-stack time migration processing, to iteratively obtain the final velocity of pre-stack time migration using the common reflection point gathers, to establish the velocity model in the depth domain through velocity constraint inversion of the final velocity of pre-stack time migration, to perform velocity nesting of the near-surface forward velocity model, and to iteratively obtain the initial velocity model in the depth domain along the layer and the vertical velocity.

[0019] As a further limitation: the method for establishing the initial velocity model in step S2 is to sort the shot gather data obtained in step S1 into common mid-point gathers, to obtain common reflection point gathers after pre-stack time migration processing, to iteratively obtain the final velocity of pre-stack time migration using the common reflection point gathers, to establish the velocity model in the depth domain through velocity constraint inversion of the final velocity of pre-stack time migration, to perform velocity nesting of the near-surface forward velocity model, and to iteratively obtain the initial velocity model in the depth domain along the layer and the vertical velocity.

[0020] As a further limitation: the method for establishing the initial velocity model in step S2 is to sort the shot gather data obtained in step S1 into common mid-point gathers, to obtain common reflection point gathers after pre-stack time migration processing, to iteratively obtain the final velocity of pre-stack time migration using the common reflection point gathers, to establish the velocity model in the depth domain through velocity constraint inversion of the final velocity of pre-stack time migration, to perform velocity nesting of the near-surface forward velocity model, and to iteratively obtain the initial velocity model in the depth domain along the layer and the vertical velocity.

[0021] As a further limitation: the method for establishing the initial velocity model in step S2 is to sort the shot gather data obtained in step S1 into common mid-point gathers, to obtain common reflection point gathers after pre-stack time migration processing, to iteratively obtain the final velocity of pre-stack time migration using the common reflection point gathers, to establish the velocity model in the depth domain through velocity constraint inversion of the final velocity of pre-stack time migration, to perform velocity nesting of the near-surface forward velocity model, and to iteratively obtain the initial velocity model in the depth domain along the layer and the vertical velocity.

[0022] The application has the beneficial effects compared with the prior art:

[0023] The application provides a method for improving the precision of a velocity model using azimuth information, and a pre-stack depth migration velocity iteration processing method, which eliminates the velocity variation caused by azimuth anisotropy; grid point migration processing is performed using beam migration, and the characteristics of the shallow layer information and the azimuth information of the reflection angle gather are used to improve the velocity inversion precision of the middle and deep layers and effectively improve the velocity inversion precision of the shallow layer.

[0024] The application is suitable for establishing a velocity model in the depth domain. BRIEF DESCRIPTION OF DRAWINGS

[0025] The application will be described in further detail below with reference to the drawings and specific embodiments.

[0026] Figure 1 The common offset gather after pre-stack depth migration velocity iteration of the conventional method;

[0027] Figure 2 The reflection angle gather after pre-stack depth migration velocity iteration of the conventional method;

[0028] Figure 3 (a) The depth velocity profile after velocity iteration of the conventional method;

[0029] Figure 3 (b) is the depth velocity profile obtained by the embodiment of the present application;

[0030] Figure 4 (a) is the post-migration reflection angle gather after velocity iteration by the conventional method;

[0031] Figure 4 (b) is the post-migration reflection angle gather obtained by the embodiment of the present application;

[0032] Figure 5 (a) is the post-migration profile after velocity iteration by the conventional method;

[0033] Figure 5 (b) is the post-migration profile obtained by the embodiment of the present application. DETAILED DESCRIPTION

[0034] The present application will be further described in conjunction with the embodiments. However, those skilled in the art should understand that the present application is not limited to the following embodiments, and any improvement and equivalent change made on the basis of the specific embodiments of the present application are within the scope of protection of the present application.

[0035] A method for improving the precision of a velocity model using azimuth information

[0036] A method for improving the precision of a velocity model using azimuth information, comprising the following steps:

[0037] S1, obtaining a surface elevation plane text file and shot gather data after a conventional pre-stack series processing, sequentially performing four-dimensional denoising and five-dimensional interpolation processing on the shot gather data to improve the signal-to-noise ratio, and obtaining a gather; the shot gather data contains trace header information, line number, point number, shot point coordinates and receiver point coordinates; the surface elevation plane text file contains line number, point number, coordinate X, coordinate Y and surface elevation, serving as a starting surface for target point migration.

[0038] S2, sorting the gather obtained in step S1 to a common midline point gather, performing pre-stack time migration processing to obtain a common reflection point gather, using the common reflection point gather to iteratively calculate a final velocity of the pre-stack time migration, and using the final velocity of the pre-stack time migration to establish a velocity model in the depth domain through velocity constraint inversion, performing near-surface forward velocity model velocity nesting, and then performing along-layer and vertical velocity iteration to obtain a depth domain initial velocity model; the depth domain initial velocity model contains trace header information, line number, point number, coordinate X and coordinate Y.

[0039] S3, pre-stack depth migration is performed on the depth domain initial velocity model in step S2 to obtain a pre-stack depth migration profile, the pre-stack depth migration profile is sequentially subjected to removal of migration "arc drawing" and four-dimensional noise removal processing to improve signal-to-noise ratio, and then the pre-stack depth migration profile after the signal-to-noise ratio is improved is subjected to profile scanning to obtain dip angle, azimuth angle and quality factor; the pre-stack depth migration profile contains trace header information line number, point number, coordinate X and coordinate Y.

[0040] S4, beam migration processing is performed on the target points of the gather obtained in step S1 by using the depth domain initial velocity model in step S2, and a reflection angle gather is output; the reflection angle gather contains trace header information line number, point number, coordinate X, coordinate Y, azimuth angle and dip angle.

[0041] S5, the reflection angle gather of the target points is sequentially subjected to removal of linear interference and multiple wave processing to improve signal-to-noise ratio, and then is sequentially subjected to filtering and dynamic balance processing to adjust energy, and then the azimuth residual depth difference is picked up on the reflection angle gather after the signal-to-noise ratio is improved and the energy is adjusted;

[0042] S6, the picked-up azimuth residual depth difference is constrained by adding the dip angle and azimuth angle information obtained in step S3, and then ray tracing is performed to obtain the azimuth residual depth difference after the ray tracing;

[0043] S7, velocity inversion processing is performed on the azimuth residual depth difference after the ray tracing to obtain new velocity and velocity update amount;

[0044] S8, smoothing processing is performed on the new velocity obtained in step S7 to obtain the updated final velocity;

[0045] S9, depth migration processing is performed on the shot gather data after the signal-to-noise ratio is improved in step S1 by using the final velocity in step S8 to obtain a reflection angle gather and a common offset gather, whether the reflection angle gather and the common offset gather are flattened is judged, if not, the updated final velocity obtained in step S8 is used to replace the velocity after the iteration along the layer and the vertical velocity in step S2, iteration update is performed, steps S3-S9 are repeated until the reflection angle gather and the common offset gather are flattened, and a velocity model with improved precision is obtained.

[0046] The working area of the embodiment is the southern margin area of the Junggar Basin, the southern margin area of the Junggar Basin is a complex area with strong reservoir heterogeneity, the common offset gather after the pre-stack depth migration processing of the depth domain initial velocity model obtained in step S2 has been flattened, as shown in FIG. 2; Figure 1 and the reflection angle gather after the pre-stack depth migration processing still has an obvious situation that the velocity changes with the azimuth, as shown in FIG. 3; Figure 2 Figure 3 (a) is a depth velocity profile after the velocity iteration of the conventional method, Figure 3 ​(b) is the depth-velocity profile obtained by the embodiment of the present application, and is integrated Figure 3 (a) and Figure 3 (b) can be known, the embodiment obtains the updated final velocity after step S8, the small-scale velocity variation is effectively characterized, and the range of the conglomerate area is also well characterized; Figure 4 (a) is the migrated reflection angle gather after the velocity iteration of the conventional method, Figure 4 (b) is the migrated reflection angle gather obtained by the embodiment of the present application, and is integrated Figure 4 (a) and Figure 4 (b) can be known, the velocity inversion of the embodiment and the comparison of the migrated reflection angle gather of the iteration velocity of the conventional method can be known, it can be seen that the phenomenon of the reflection angle gather changing with the azimuth is eliminated after the application of the inversion velocity of the embodiment, the comparison of the migration profiles before and after is shown as Figure 5 , the phenomenon of the unclear imaging of the shallow layer caused by the inaccurate velocity of the low-velocity conglomerate in the shallow layer is effectively improved, the phenomenon of the false uplift of the deep layer caused by the inaccurate inversion of the high-velocity conglomerate is eliminated, the problem of the low resolution caused by the inaccurate velocity is solved, and the signal-to-noise ratio of the profile is obviously improved. It can be known that Figures 3-5 the method of the embodiment obviously improves the precision of the velocity model.

Claims

1. A method for improving the accuracy of a velocity model using azimuth information, characterized in that, Includes the following steps: S1. Obtain the surface elevation surface text file and the shot gather data after conventional pre-stack series processing, and perform signal-to-noise ratio improvement processing on the shot gather data to obtain the gathers; S2. Establish the initial velocity model in the depth domain; S3. Perform pre-stack depth migration on the initial velocity model in the depth domain in step S2 to obtain a pre-stack depth migration profile. Improve the signal-to-noise ratio of the pre-stack depth migration profile, and then perform profile scanning on the pre-stack depth migration profile with improved signal-to-noise ratio to obtain the dip angle, azimuth angle and quality factor. S4. Using the initial velocity model in the depth domain from step S2, perform beam offset processing on the target points of the gather obtained in step S1, and output the reflection angle gather. S5. Improve the signal-to-noise ratio and adjust the energy of the reflection angle gather of the target point, and then pick the azimuth residual depth difference on the reflection angle gather after improving the signal-to-noise ratio and adjusting the energy. S6. Add the tilt angle and azimuth angle information obtained in step S3 to the picked azimuth remaining depth difference for constraint, and then perform ray tracing to obtain the azimuth remaining depth difference after ray tracing. S7. Perform velocity inversion processing on the azimuth residual depth difference after ray tracing to obtain the new velocity and velocity update amount; S8. Smooth the new speed obtained in step S7 to obtain the updated final speed; S9. Using the final velocity from step S8, perform depth migration processing on the shot gather data after improving the signal-to-noise ratio in step S1 to obtain the reflection angle gather and the common offset gather. Determine whether the reflection angle gather and the common offset gather are flattened. If they are not flattened, replace the velocity after the iteration of the layer and vertical velocities in step S2 with the updated final velocity obtained in step S8, and perform iterative updates. Repeat steps S3-S9 until the reflection angle gather and the common offset gather are flattened to obtain the velocity model with improved accuracy.

2. The method for improving the accuracy of a velocity model using azimuth information according to claim 1, characterized in that, In step S1, the signal-to-noise ratio improvement processing of the shot gather data involves sequential four-dimensional denoising and five-dimensional interpolation. The shot gather data includes track head information, line number, point number, shot point coordinates, and receiver point coordinates. The surface elevation surface text file includes line number, point number, X coordinates, Y coordinates, and surface elevation, which serves as the starting surface for target point offset.

3. The method for improving the accuracy of a velocity model using azimuth information according to claim 2, characterized in that, The method for establishing the initial velocity model in step S2 is as follows: the shot gather data obtained in step S1 is sorted into common centerline point gathers, and common reflection point gathers are obtained after pre-stack time migration. The final velocity of the pre-stack time migration is obtained by iteratively using the common reflection point gathers. The final velocity of the pre-stack time migration is used to establish a depth domain velocity model through velocity constraint inversion. Velocity nesting of near-surface forward model velocity model is performed, and then the depth domain initial velocity model is obtained by iteratively performing velocity along the layer and vertically. The depth domain initial velocity model includes the trace head information line number, point number, coordinate X and coordinate Y.

4. The method for improving the accuracy of a velocity model using azimuth information according to claim 3, characterized in that, In step S3, the pre-stack depth migration profile signal-to-noise ratio improvement process specifically involves sequentially removing the offset "arc drawing" and performing four-dimensional denoising. The pre-stack depth migration profile contains trace head information, line number, point number, X coordinate, and Y coordinate.

5. The method for improving the accuracy of a velocity model using azimuth information according to claim 4, characterized in that, The reflection angle gather in step S4 includes track head information such as line number, point number, X coordinate, Y coordinate, azimuth angle, and inclination angle.

6. The method for improving the accuracy of a velocity model using azimuth information according to claim 5, characterized in that, In step S5, the signal-to-noise ratio (SNR) improvement processing and energy adjustment of the target point's reflection angle gather are specifically performed by sequentially removing linear interference and performing multiple wave processing to improve the SNR, followed by sequentially performing filtering and dynamic balancing processing to adjust the energy.

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