A method for modeling deep migration phase-controlled velocity inversion under complex deposition environment

Abstract

The application discloses a deep migration phase-controlled velocity inversion modeling method under a complex deposition environment, and comprises the following steps: constructing an initial depth domain layer velocity model; obtaining CIP gathers and stacked data thereof; obtaining a residual velocity field, and updating the velocity model; iteratively updating the velocity model, and obtaining an intermediate velocity model; obtaining intermediate prestack depth migration imaging result data; forming a deposition phase boundary constraint file; on the basis of the intermediate velocity model, implementing deposition phase control on the inversion process and result through the deposition phase boundary constraint file; and after multiple iterations, obtaining a final prestack depth migration layer velocity model and the like. The application is based on tomographic velocity inversion technology, and fuses a deposition phase model and a velocity model, so that the phase-controlled velocity model inversion modeling of the complex deposition environment is realized; the consistency of the constructed velocity model and the deposition phase model is better, the prestack depth migration imaging quality corresponding to the velocity model constructed by the application is better, and the homing is more accurate.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of oil and gas exploration and development, and particularly relates to a deep migration phase-controlled velocity inversion modeling method under complex sedimentary environment. BACKGROUND

[0002] The prestack depth migration is one of main seismic imaging methods at present, and the depth domain layer velocity model required by migration is a key factor influencing imaging effect. The existing depth domain velocity model is constructed by using the tomographic velocity inversion method, data driving, and the best depth domain layer velocity model is obtained after multiple iterations. The tomographic velocity inversion method has high efficiency in constructing the depth domain layer velocity model, but still has the following problems in practical application: 1) the existing technical scheme is only applicable to seismic data with high signal-to-noise ratio, and when the signal-to-noise ratio of seismic data is low, the existing technology will analyze the noise signal as effective signal to a certain extent, and the velocity model constructed is more conducive to the imaging of noise signal. 2) when the sedimentary and diagenetic environment of the research area is complex and changeable, some geological bodies have strong reflection interfaces, and the inside often has chaotic or blank reflection, the existing technical scheme cannot pick up the effective reflection signal inside the geological body, which affects the accuracy of velocity modeling and further affects the quality of seismic imaging. SUMMARY

[0003] The application is proposed to solve the uncontrollable problem of data-driven velocity inversion for low signal-to-noise ratio data and the inaccurate problem of velocity modeling under complex sedimentary environment, and the purpose is to provide a deep migration phase-controlled velocity inversion modeling method under complex sedimentary environment.

[0004] The application is implemented by the following technical scheme:

[0005] A deep migration phase-controlled velocity inversion modeling method under complex sedimentary environment, comprising the following steps:

[0006] S1, acquiring a time domain processed seismic data body and a prestack time migration velocity data body, and constructing an initial depth domain layer velocity model;

[0007] S2, carrying out prestack depth grid point migration to obtain CIP gathers and stacked data thereof; wherein, the grid S1, acquiring a time domain processed seismic data body and a prestack time migration velocity data body, and constructing an initial depth domain layer velocity model;

[0008] S2, carrying out prestack depth grid point migration to obtain CIP gathers and stacked data thereof;

[0009] S3, using the CIP gathers and stacked data thereof obtained in step S2, carrying out high-precision grid tomographic velocity inversion by using a conventional tomographic velocity inversion technology to obtain a residual velocity field and update the depth domain layer velocity model;

[0010] S4, repeatedly updating the depth domain layer velocity model in steps S2 and S3 until the CIP gathers are substantially flattened, and obtaining a stable velocity model as an intermediate depth domain layer velocity model;

[0011] S5, using the intermediate depth domain layer velocity model obtained in S4 to carry out prestack depth migration, and after trace stacking, time-depth conversion, post-stack denoising, frequency lifting and energy compensation processing, obtaining intermediate prestack depth migration imaging results;

[0012] S6, based on the intermediate results obtained in step S5, carrying out geological analysis, identifying the basin seismic facies according to the characteristics of the seismic profile, systematically determining the possible sedimentary facies types, implementing the distribution range, tracking the interpretation boundary, and forming a sedimentary facies boundary constraint file;

[0013] S7, based on the intermediate depth domain layer velocity model obtained in step S4, repeating steps S2 and S3, and adding the sedimentary facies boundary constraint file obtained in step S6 to the high-precision grid tomographic velocity inversion process, controlling the inversion process and results with the sedimentary facies, and after multiple iterations, obtaining a final prestack depth migration layer velocity model.

[0014] In the above technical solution, the depth domain layer velocity model constructed by the prestack time migration velocity is further interpolated, smoothed and time-depth converted.

[0015] In the above technical solution, the input data used in the conventional tomographic velocity inversion in step S3 is the CIP gathers and its stacking data obtained in step S2, the CIP gather data after optimization is used for residual velocity spectrum calculation, and the stacking data of the CIP gathers is used for constructing the dip angle parameter analysis.

[0016] In the above technical solution, the sedimentary types include fan delta facies, braided river delta facies, alluvial fan facies and gravity flow sedimentary facies.

[0017] In the above technical solution, the sedimentary facies control includes a tomographic inversion method with a facies control file constraint and a sedimentary facies constraint correction method for the inversion results.

[0018] In the above technical solution, the number of iterations in step S7 is three or four.

[0019] In the above technical solution, in the iteration process in step S7, when the imaging of the external geometric shape and internal reflection structure of the target geological body changes significantly, steps S5 and S6 are repeated to re-analyze the sedimentary facies and improve the boundary constraint file.

[0020] The beneficial effects of the present application are:

[0021] This invention provides a method for phase-controlled velocity inversion modeling of depth migration in complex sedimentary environments. This method implements phase control during the velocity inversion process, integrating sedimentary facies analysis with tomographic velocity inversion. This method is suitable for deep-domain layer velocity inversion modeling of seismic data processed under complex geological conditions (particularly turbidite fan bodies such as slump turbidite fans, nearshore underwater fans, and fan deltas). This method effectively addresses the issue of inaccurate velocity modeling in complex sedimentary environments, resulting in a more consistent velocity model with sedimentary facies characteristics and improved prestack depth migration imaging quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a schematic flow diagram of the method of the present invention;

[0023] Figure 2 It is the initial depth domain layer velocity model diagram of the present invention;

[0024] Figure 3 It is a velocity model diagram of the intermediate depth domain constructed by the conventional tomographic inversion technology in the present invention;

[0025] Figure 4 It is the top and bottom envelope morphology diagram of the fan body tracked by geological analysis in the present invention;

[0026] Figure 5 It is a quality control constraint file diagram formed by the fan envelope in the present invention;

[0027] Figure 6 This is a depth domain layer velocity model diagram of phase-controlled tomography velocity inversion obtained based on the technical solution of the present invention;

[0028] Figure 7 This is a comparison of the conventional tomographic inversion depth domain layer velocity model migration imaging (left) and the phase-controlled tomographic velocity inversion depth domain layer velocity model migration imaging (right).

[0029] For ordinary technicians in this field, other relevant drawings can be obtained based on the above drawings without any creative work. DETAILED DESCRIPTION

[0030] In order to enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention will be further described below with reference to the accompanying drawings and through specific implementation methods.

[0031] like Figure 1 As shown in FIG, a method for depth migration phase-controlled velocity inversion modeling in a complex sedimentary environment includes the following steps:

[0032] S1. Obtain the time domain processed seismic data volume and the pre-stack time migration velocity data volume, convert the pre-stack time migration velocity into layer velocity using the DIX formula, and generate the initial depth domain layer velocity model through interpolation, smoothing and time-depth conversion. The obtained initial depth domain layer velocity model is as follows: Figure 2 As shown;

[0033] S2. Use the time-domain processed seismic data volume and the initial depth-domain layer velocity model to perform pre-stack depth grid point migration to obtain CIP (Common Imaging Point) gathers and their stacking data. The grid point migration bin size is consistent with the initial depth-domain layer velocity model. The obtained CIP gathers are optimized by denoising and amplitude compensation.

[0034] S3. Use conventional tomographic velocity inversion technology to carry out high-precision grid tomographic velocity inversion to obtain the residual velocity field and update the depth domain layer velocity model;

[0035] The input data used for conventional tomographic velocity inversion is the CIP gathers obtained in step S2 and their stacked data. The optimized CIP gather data are used for residual velocity spectrum calculation, and the stacked CIP gather data are used for structural dip parameter analysis.

[0036] S4, repeating steps S2 and S3 multiple times to iteratively update the velocity model until the events of the obtained CIP gathers are basically flattened, and a set of stable velocity models is obtained as the velocity model of the intermediate depth domain layer;

[0037] The stability of the velocity model is mainly determined by the flattening degree of the CIP gather event axis after migration. In general, the conventional tomographic inversion method driven by data is used. After three or four rounds of iteration, the gather event axis is basically flattened and the overall spatial structure of the velocity model is basically stable. The velocity model of the intermediate depth domain layer is obtained as follows: Figure 3 ;

[0038] S5. Using the intermediate depth domain layer velocity model obtained in S4, prestack depth migration is performed to generate common offset data. Common imaging point (CIP) data is formed through extraction and stacking. Post-stack denoising, frequency boosting, and energy compensation are then performed to improve the signal-to-noise ratio of the depth migration imaging data, resulting in intermediate prestack depth migration imaging data.

[0039] S6. Conduct geological analysis based on the intermediate data obtained in step S5;

[0040] Based on the amplitude, phase, and frequency characteristics of the seismic profile, we identify the seismic facies in the basin, systematically determine the possible sedimentary facies types, determine the distribution range, trace the interpretation boundaries, and generate sedimentary facies boundary constraint files.

[0041] The method is mainly aimed at the geological body with chaotic internal structure or blank of seismic reflection, and the main sedimentary types correspond to the fan delta facies, the braided river delta facies, the alluvial fan facies and the gravity flow sedimentary facies.

[0042] The top and bottom envelope shape diagram of the fan body tracked by the geological analysis of the embodiment is shown in Figure 4 , three turbidite fans are spatially superimposed and successively developed, and the sedimentary facies boundary constraint file formed is shown in Figure 5 .

[0043] S7, on the basis of the intermediate depth domain interval velocity model obtained in step S4, steps S2 and S3 are repeated, the sedimentary facies boundary constraint file is added in the high-precision grid tomographic velocity inversion process, the inversion process and result are implemented to be controlled by the sedimentary facies, and after multiple iterations, a final pre-stack depth migration interval velocity model is obtained. Figure 6 .

[0044] The sedimentary facies control includes a tomographic inversion method with a phase control file constraint and a sedimentary facies constraint correction method for the inversion result.

[0045] Three or four rounds of iterations are needed, and the interpolation velocity value is adjusted round by round to improve the precision of the velocity model. When the external geometry and internal reflection structure imaging of the target geological body change significantly, steps S5 and S6 need to be repeated, the sedimentary facies is reanalyzed, and the boundary constraint file is improved.

[0046] The result profile of the conventional tomographic inversion depth domain interval velocity model migration imaging is shown on the left side of Figure 7 , and the result profile of the phase-controlled tomographic inversion depth domain interval velocity model migration imaging of the embodiment is shown on the right side of Figure 7 . Compared with the conventional method, the fan body boundary and the internal imaging obtained by the method of the embodiment are clearer, and the boundary fracture imaging effect is obviously improved.

[0047] The applicant declares that the above description is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto. It should be understood by those skilled in the art that any changes or replacements within the technical scope disclosed by the present application can be easily thought of by those skilled in the art, and all fall within the protection scope and disclosure scope of the present application.

Claims

1. A method for depth migration phase-controlled velocity inversion modeling in complex sedimentary environments, characterized by: The following steps are involved: S1. Acquire time-domain processed seismic data and pre-stack time migration velocity data to construct an initial depth-domain layer velocity model; S2. Carry out pre-stack depth grid point migration to obtain CIP gathers and their stacking data; S3, using the CIP gathers obtained in step S2 and the stacked data thereof, a conventional tomographic velocity inversion technique is used to perform high-precision grid tomographic velocity inversion, obtain the residual velocity field, and update the depth domain layer velocity model; S4, repeating steps S2 and S3 multiple times to iteratively update the depth domain layer velocity model until the obtained CIP gather events are basically flattened, and a set of stable velocity models is obtained as the intermediate depth domain layer velocity model; S5: Using the intermediate depth domain velocity model obtained in S4, pre-stack depth migration is performed. After channel stacking, time-depth conversion, post-stack denoising, frequency enhancement, and energy compensation, intermediate pre-stack depth migration imaging data is obtained. S6. Conduct geological analysis based on the intermediate data obtained in step S5. Identify the seismic facies of the basin according to the seismic profile characteristics, systematically determine the possible sedimentary facies types, determine the distribution range, trace the interpretation boundaries, and form a sedimentary facies boundary constraint file. S7. Based on the intermediate depth domain layer velocity model obtained in step S4, repeat steps S2 and S3, add the sedimentary facies boundary constraint file obtained in step S6 to the high-precision grid tomography velocity inversion process, implement sedimentary facies control on the inversion process and results, and obtain the final pre-stack depth migration layer velocity model after multiple iterations.

2. The method for depth migration phase-controlled velocity inversion modeling in a complex sedimentary environment according to claim 1, characterized in that: The depth domain layer velocity model constructed by the pre-stack time migration velocity is further interpolated, smoothed and time-to-depth converted.

3. The method for depth migration phase-controlled velocity inversion modeling in a complex sedimentary environment according to claim 1, characterized in that: The input data used for conventional tomographic velocity inversion in step S3 is the CIP gathers and their superposition data obtained in step S2. The optimized CIP gather data are used for residual velocity spectrum calculation, and the superposition data of the CIP gathers are used for structural dip parameter analysis.

4. The method for depth migration phase-controlled velocity inversion modeling in a complex sedimentary environment according to claim 1, characterized in that: The sedimentary facies types include fan delta facies, braided river delta facies, alluvial fan facies and gravity flow sedimentary facies.

5. The method for depth migration phase-controlled velocity inversion modeling in a complex sedimentary environment according to claim 1, characterized in that: The sedimentary facies control includes a tomographic inversion method that adds facies control file constraints and a sedimentary facies constraint correction method for the inversion results.

6. The method for depth migration phase-controlled velocity inversion modeling in a complex sedimentary environment according to claim 1, characterized in that: The number of iterations in step S7 is three or four.

7. The method for depth migration phase-controlled velocity inversion modeling in a complex sedimentary environment according to claim 1, characterized in that: During the iterative process of step S7, when the external geometry and internal reflection structure imaging of the target geological body change significantly, steps S5 and S6 are repeated to reanalyze the sedimentary facies and improve the boundary constraint file.

8. The method for depth migration phase-controlled velocity inversion modeling in a complex sedimentary environment according to claim 1, characterized in that: The grid point offset bin size in step S2 is consistent with the depth domain layer velocity model, and the obtained CIP gather is subjected to denoising and amplitude compensation optimization processing.

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