Pre-stack depth migration seismic data processing method and device

Abstract

The invention discloses a seismic data processing method and device for prestack depth migration, and the method comprises the steps: obtaining the elevation data and micro-logging data of a to-be-processed work area, and respectively building a near-surface velocity model based on a real surface and a near-surface velocity model based on a small surface smooth surface; according to the static correction value of the real earth surface and the static correction value based on the earth surface small smooth surface, determining a residual correction value between the earth surface small smooth surface and the real earth surface; according to the determined residual error correction value, correcting the pre-stack data of the to-be-processed work area based on the real earth surface to first pre-stack data matched with the near-earth surface velocity model based on the earth surface small smooth surface; establishing a full-depth pre-stack depth migration velocity model according to the near-surface velocity model based on the surface small smooth surface, the VSP logging data of the to-be-processed work area and the geologic structure model; and performing depth migration imaging processing on the first pre-stack data by adopting a pre-stack depth migration velocity model to obtain seismic data after pre-stack depth migration.

Description

Technical Field

[0001] The present invention relates to the technical field of seismic data processing, and in particular to a method and device for seismic data processing of pre-stack depth migration. Background Art

[0002] The piedmont belt has developed anticlines and a set of thrust blocks, with good reservoir-caprock combinations and oil-generating traps, and has the conditions to form large oil and gas reservoirs, with huge oil and gas resources and good exploration prospects. However, due to the influence of complex surface, complex underground structure and low-quality seismic data, the imaging of the piedmont belt is often inconsistent with the actual situation in many cases. Therefore, velocity modeling and high-precision imaging of the piedmont belt have become the focus and difficulty of exploration and development research.

[0003] After decades of research, large enterprises and scientific research institutions have made some important progress in seismic exploration in the piedmont zone, but overall the exploration level is not high. The main reason is that conventional seismic data processing technology faces many difficulties, it is difficult to obtain reliable seismic imaging data, and the application of various seismic interpretation technologies is limited. Summary of the invention

[0004] The inventors of this application discovered that:

[0005] Traditional seismic data processing methods are mainly oriented to the time domain. Their idea is based on the relatively flat surface and underground strata. When faced with "double complex" geological conditions, the traditional "static correction + fixed surface offset" processing idea is unreasonable. For example, most of the current static correction technologies are theoretically based on the surface consistency assumption. The surface consistency assumption itself will cause distortion of seismic wave field characteristics. In the frontal belt area with large surface undulations and high-speed rock outcroppings, the distortion is aggravated because the surface consistency assumption is not met. There are also problems with traditional imaging technologies. In theory, they are all based on the assumption that the underground strata are laterally uniform. The diffraction energy is converged to or superimposed on the vertex of the diffraction time-distance curve according to the hyperbolic time-distance relationship, but the diffraction energy is not returned to its corresponding diffraction point. In the case of complex underground structures and large lateral velocity changes, there is a large deviation between the diffraction vertex and the diffraction point, which causes imaging distortion. These existing imaging problems will seriously affect geological understanding, and incorrect geological understanding will greatly increase exploration risks.

[0006] Therefore, in order to solve the above-mentioned problems, the inventors of the present application provide a seismic data processing method for pre-stack depth migration. Through this method, the near-surface velocity modeling accuracy and the degree of coupling with the real formation velocity in the "double complex" area will be improved, thereby improving the seismic imaging accuracy in the "double complex" area, reducing geophysical multi-solutions, improving the accuracy of geological understanding, and reducing exploration risks.

[0007] In a first aspect, the present application provides a seismic data processing method for prestack depth migration, the method comprising:

[0008] Obtain elevation data and micro-logging data of the work area to be processed, and establish a near-surface velocity model based on the real surface and a near-surface velocity model based on a small smooth surface on the surface;

[0009] According to the static correction amount of the real surface and the static correction amount based on the small smooth surface of the surface, the residual correction amount between the small smooth surface of the surface and the real surface is determined;

[0010] According to the determined residual correction amount, the pre-stack data based on the real surface of the work area to be processed is corrected to the first pre-stack data matching the near-surface velocity model based on the small smooth surface of the surface;

[0011] A full-depth prestack depth migration velocity model is established based on the near-surface velocity model based on the small smooth surface, the VSP logging data of the work area to be processed, and the geological structure model;

[0012] The prestack depth migration velocity model is used to perform depth migration imaging processing on the first prestack data to obtain seismic data after prestack depth migration.

[0013] In an exemplary embodiment, the process of establishing the near-surface velocity model based on the real surface is as follows:

[0014] Determine the first arrival picking time of the near offset of the pre-stack data based on the real surface, and establish a first near-surface velocity model by using a tomographic inversion method with the micro-logging data as a constraint;

[0015] The first arrival picking time of the far offset in the pre-stack data based on the real surface is determined, and a near-surface velocity model based on the real surface is established by using a tomographic inversion method with the first near-surface velocity model as a constraint.

[0016] In an exemplary embodiment, the process of establishing the near-surface velocity model based on the small smooth surface of the surface is as follows:

[0017] Interpolate the velocity data in the micro-logging data of the processing area to establish an initial near-surface velocity model;

[0018] Acquire pre-stack data based on a small smooth surface on the surface, determine the first arrival picking time of the near offset of the pre-stack data based on the small smooth surface on the surface, and obtain a second near-surface velocity model by using a tomographic inversion method with the initial near-surface velocity model as a constraint;

[0019] The first arrival picking time of the far offset in the pre-stack data based on the small surface smooth surface is determined, and the near surface velocity model based on the small surface smooth surface is obtained by using the tomographic inversion method with the second near surface velocity model as a constraint.

[0020] In an exemplary embodiment, the pre-stack data based on the small smooth surface of the ground surface is obtained by the following processing steps:

[0021] The elevation data is interpolated using the Kriging interpolation method to obtain three-dimensional small plane elevation data;

[0022] Smoothing the three-dimensional small plane elevation data by using a summation weighted smoothing method;

[0023] Calculate the elevation static correction values ​​corresponding to the elevation based on the real surface and the elevation based on the small smooth surface of the surface respectively, and obtain the residual data of the two.

[0024] The residual data is applied to the pre-stack data based on the real surface to obtain the pre-stack data based on the small smooth surface of the surface.

[0025] In an exemplary embodiment, the step of determining the residual correction amount between the small smooth surface and the real surface based on the static correction amount of the real surface and the static correction amount based on the small smooth surface of the surface includes:

[0026] Generate a high-speed top interface according to the near-surface velocity model based on the small smooth surface of the surface;

[0027] According to the high-speed top interface, a tomographic static correction corresponding to the real surface is calculated on a near-surface velocity model based on the real surface, and a corresponding residual static correction is determined;

[0028] According to the high-speed top interface, a tomographic static correction corresponding to the small surface smooth surface is calculated on a near-surface velocity model based on the small surface smooth surface, and a corresponding residual static correction is determined;

[0029] The residual formula is used to calculate the residual correction between the small smooth surface and the real surface.

[0030] In an exemplary embodiment, the residual formula is:

[0031] C(delta)=(C(t)+M(t))-(C(s)+M(s));

[0032] In the above formula, C(delta) represents the residual correction between the small smooth surface on the surface and the real surface, C(t) represents the tomographic static correction of the real surface, M(t) represents the real residual static correction, C(s) represents the tomographic static correction of the small smooth surface on the surface, and M(s) represents the residual static correction of the small smooth surface on the surface.

[0033] In an exemplary embodiment, the full-depth prestack depth migration velocity model is established according to the near-surface velocity model based on the small smooth surface, the VSP logging data of the work area to be processed, and the geological structure model, including:

[0034] Establishing an initial full velocity model based on a small smooth surface according to the near-surface velocity model based on the small smooth surface on the surface;

[0035] The initial full velocity model is optimized according to the VSP logging data and the geological structure model of the work area to be processed to obtain a prestack depth migration velocity model.

[0036] In an exemplary embodiment, establishing an initial full velocity model based on a small smooth surface according to the near-surface velocity model based on the small smooth surface on the surface includes:

[0037] Using the VSP logging data and the smoothed logging acoustic wave velocity values, a mid-deep layer velocity model is established;

[0038] Determining a splicing interface between the near-surface velocity model based on the small surface smooth surface and the mid-deep velocity model;

[0039] On the splicing interface, the near-surface velocity model based on the small smooth surface and the mid-deep velocity model are spliced ​​to obtain an initial full velocity model based on the small smooth surface.

[0040] In an exemplary embodiment, the use of the VSP logging data and the smoothed logging acoustic wave velocity values ​​to establish a mid-deep layer velocity model includes:

[0041] Using the VSP logging data and the smoothed logging acoustic wave velocity values, an initial mid-deep layer velocity model is established;

[0042] The initial mid-deep velocity model is optimized using a grid tomography optimization method to obtain a mid-deep velocity model.

[0043] In an exemplary embodiment, determining the splicing interface of the near-surface velocity model based on the small smooth surface on the surface and the mid-deep velocity model includes:

[0044] Determine a ray density body corresponding to the near-surface velocity model based on the small smooth surface of the surface, and pick up a velocity bottom interface according to the bottom boundary of the ray density body;

[0045] The velocity bottom interface is used as a splicing interface between the near-surface velocity model based on the small surface smooth surface and the mid-deep velocity model.

[0046] In an exemplary embodiment, the near-surface velocity model based on the small smooth surface on the surface and the mid-deep velocity model are spliced ​​on the splicing interface to obtain an initial full velocity model based on the small smooth surface, including:

[0047] On the splicing interface, comparing the velocity difference between the near-surface velocity model based on the small surface smooth surface and the mid-deep velocity model;

[0048] If the velocity difference between the two is less than a preset threshold, the near-surface velocity model based on the small smooth surface of the surface is spliced ​​with the mid-deep velocity model;

[0049] If the velocity difference between the two is greater than or equal to a preset threshold, the velocity difference between the two is adjusted to meet the threshold condition by setting a proportional factor a, and the near-surface velocity model based on the small smooth surface of the surface is spliced ​​with the mid-deep velocity model;

[0050] The velocity within a predetermined range of the splicing interface is smoothed by using a method of constructing constrained smoothing to obtain an initial full velocity model based on a small smooth surface.

[0051] In an exemplary embodiment, the initial mid-deep velocity model is optimized using a grid tomography optimization method to obtain the mid-deep velocity model, including:

[0052] Determining anisotropy parameters according to the VSP logging velocity value and the velocity value of the initial mid-deep velocity model;

[0053] Perform interpolation processing on all anisotropic parameter values ​​in the processing area to obtain an anisotropic initial parameter body;

[0054] According to the initial full velocity model based on the small smooth surface and the anisotropic initial parameter body, the grid points of the prestack data of the small smooth surface are migrated using the Gaussian beam migration method.

[0055] The structural interpretation horizon is used for control, and the grid tomography optimization method is used for grid tomography speed optimization iteration;

[0056] If the iteration meets the termination condition, the obtained velocity model is taken as the final full velocity model.

[0057] In a second aspect, an embodiment of the present invention provides a seismic data processing device for pre-stack depth migration, the device comprising: a memory and a processor; the memory is used to store a program for seismic data processing for pre-stack depth migration, and the processor is used to read and execute the program for seismic data processing for pre-stack depth migration, and execute any one of the methods described in the above embodiments.

[0058] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a data processing program stored thereon, and the data processing program is executed by a processor in the seismic data processing method for pre-stack depth migration described in any one of the above embodiments.

[0059] Compared with the related art, the present application provides a seismic data processing method and device for pre-stack depth migration, the method comprising: acquiring elevation data and micro-logging data of a work area to be processed, and establishing a near-surface velocity model based on the real surface and a near-surface velocity model based on a small smooth surface on the surface, respectively; determining a residual correction amount between the small smooth surface and the real surface based on the static correction amount of the real surface and the static correction amount based on the small smooth surface; correcting the pre-stack data based on the real surface of the work area to be processed to first pre-stack data that matches the near-surface velocity model based on the small smooth surface on the surface according to the determined residual correction amount; establishing a pre-stack depth migration velocity model according to the near-surface velocity model based on the small smooth surface on the surface, logging data, and geological strata; using the pre-stack depth migration velocity model, performing depth migration imaging processing on the first pre-stack data to obtain seismic data after pre-stack depth migration. This application establishes a near-surface velocity model based on a small smooth surface, and splices and couples it with a medium-deep velocity model to obtain a set of accurate depth domain velocity models. At the same time, a high-precision algorithm is used to improve the accuracy of seismic imaging in "double complex" areas, so that the structure and closure of the mountain front belt can be accurately determined, thereby greatly reducing the risk of oil exploration.

[0060] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or be understood by implementing the present application. Other advantages of the present application can be realized and obtained by the schemes described in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] The accompanying drawings are used to provide an understanding of the technical solution of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the technical solution of the present application and do not constitute a limitation on the technical solution of the present application.

[0062] Figure 1 This is a flow chart of a seismic data processing method for prestack depth migration in an embodiment of the present application;

[0063] Figure 2 is a schematic diagram of seismic migration datum smoothing in an exemplary embodiment;

[0064] Figure 3 is a schematic diagram of a near-surface velocity model before and after step-by-step constraint in an exemplary embodiment;

[0065] Figure 4 A schematic diagram showing the comparison of inversion accuracy at a VSP well before and after step-by-step constraint in an exemplary embodiment;

[0066] Figure 5 is a schematic diagram of surface smoothing with different parameters in an exemplary embodiment;

[0067] Figure 6 A schematic diagram of applying surface smoothing corrections with different parameters to a linear moving average shot gather in an exemplary embodiment;

[0068] Figure 7 is a schematic diagram of pre-stack shot gathering comparison before and after applying surface smoothing static correction in an exemplary embodiment;

[0069] Figure 8 is a schematic diagram of the fusion process of the near-surface velocity model and the mid-depth velocity model in an exemplary embodiment;

[0070] Fig. 9 A schematic diagram showing a comparison between a velocity update gather of the present invention and a conventionally processed gather in an exemplary embodiment;

[0071] Fig.10 A schematic diagram showing a comparison of the effects of the imaging method provided by the present invention and a conventional imaging method in an exemplary embodiment;

[0072] Fig.11 A schematic diagram showing the comparison of the seismic imaging obtained by the method of the present patent in an exemplary embodiment and the imaging obtained by the conventional processing method in the previous stage;

[0073] Fig.12 Schematic diagram of a seismic data processing device for prestack depth migration in an embodiment of the present application. DETAILED DESCRIPTION

[0074] The present application describes multiple embodiments, but the description is exemplary rather than restrictive, and it is obvious to those skilled in the art that there may be more embodiments and implementations within the scope of the embodiments described in the present application. Although many possible feature combinations are shown in the drawings and discussed in the specific embodiments, many other combinations of the disclosed features are also possible. Unless specifically limited, any feature or element of any embodiment may be used in combination with any other feature or element in any other embodiment, or may replace any other feature or element in any other embodiment.

[0075] The present application includes and contemplates combinations of features and elements known to those of ordinary skill in the art. The embodiments, features and elements disclosed in the present application may also be combined with any conventional features or elements to form a unique invention scheme defined by the claims. Any features or elements of any embodiment may also be combined with features or elements from other invention schemes to form another unique invention scheme defined by the claims. Therefore, it should be understood that any feature shown and / or discussed in the present application may be implemented individually or in any appropriate combination. Therefore, except for the limitations made according to the attached claims and their equivalents, the embodiments are not subject to other restrictions. In addition, various modifications and changes may be made within the scope of protection of the attached claims.

[0076] In addition, when describing representative embodiments, the specification may have presented the method and / or process as a specific sequence of steps. However, to the extent that the method or process does not rely on the specific order of the steps described herein, the method or process should not be limited to the steps of the specific order described. As will be understood by those of ordinary skill in the art, other sequences of steps are also possible. Therefore, the specific sequence of the steps set forth in the specification should not be interpreted as a limitation to the claims. In addition, the claims for the method and / or process should not be limited to the steps of performing them in the order written, and those skilled in the art can easily understand that these sequences can be changed and still remain within the spirit and scope of the embodiments of the present application.

[0077] The embodiment of the present invention provides a seismic data processing method for pre-stack depth migration, such as Figure 1 As shown, the device includes steps S100 to S140;

[0078] S100: Acquire elevation data and micro-logging data of the work area to be processed, and respectively establish a near-surface velocity model based on the real surface and a near-surface velocity model based on a small smooth surface of the surface;

[0079] S110: Determine a residual correction amount based on the small smooth surface and the real surface according to the static correction amount of the real surface and the static correction amount based on the small smooth surface of the surface;

[0080] S120: According to the determined residual correction amount, the pre-stack data based on the real surface of the work area to be processed is corrected to the first pre-stack data matching the near-surface velocity model based on the small smooth surface of the surface;

[0081] S130: Establishing a full-depth prestack depth migration velocity model according to the near-surface velocity model based on the small surface smooth surface, the VSP logging data of the work area to be processed, and the geological structure model;

[0082] S140: Using the pre-stack depth migration velocity model, perform depth migration imaging processing on the first pre-stack data to obtain seismic data after pre-stack depth migration.

[0083] In an exemplary embodiment, before performing the seismic data processing method for prestack depth migration, data preparation work needs to be performed, including:

[0084] (1) Data collection and preparation: full-offset seismic shot data, full-offset first-arrival travel time picking, receiver point elevation data and shot point elevation data for the entire work area, micro-logging data, surface outcrop profiles, logging velocity and layering data, VSP logging velocity and corridor stacking, anisotropic parameter survey data, and structural interpretation horizons.

[0085] (2) Data quality evaluation: Check and evaluate the collected first arrival picking time, micro-logging data, logging data, and anisotropic parameter data, and correct or optimize the problematic data.

[0086] The content of data quality evaluation includes:

[0087] a) Pickup inspection during the first wave travel: Check whether the initial wave is picked up properly and whether the picking rate is up to standard; ensure that the accurate picking rate of the first wave reaches more than 90%;

[0088] b) Micro-well logging basic data inspection: interpolation forms velocity profiles, combined with surface outcrop data, and superimposed to determine whether they conform to geological and geophysical laws, and correct or eliminate micro-well logging points that do not conform to geological laws;

[0089] c) Logging data check: First, conduct a well-connected stratification survey to ensure the accuracy of the logging stratification; second, check the overall curve for parameters such as logging speed and correct the problematic data before use.

[0090] According to different data inspection results, high-quality basic data that meet the conditions are selected for preliminary optimization processing to better meet the seismic data processing requirements for pre-stack depth migration.

[0091] In an exemplary embodiment, the travel time of pre-stack depth migration calculation requires the known positions of its shot points and detection points. Therefore, it is particularly important to maintain the authenticity of the shot and detection point positions during the processing process for the accuracy of travel time calculation. In different offset distances at the same CMP position, the shot and detection points are not at the same altitude, and the greater the height difference, the greater the error, which is easy to cause imaging illusions. In order to eliminate the influence of this factor, the elevation needs to be smoothed to achieve an effect close to the real surface and better maintain the wave field dynamic characteristics. The data is processed based on a small smooth surface in the time domain to maintain the authenticity of the shot and detection point positions. After the data is corrected to the small smooth surface, at the same position, the CMP point, shot point, and detection point are all at different elevation positions. Therefore, when calculating the travel time, the travel time of the downgoing wave and the travel time of the upgoing wave need to be considered separately, which is the double square root dynamic correction formula. In addition, the smoothing parameters of the surface need to be able to smooth out the high-frequency jitter of the surface while retaining the characteristics of the surface terrain changes. This can not only ensure the accuracy of the wave field as much as possible, but also avoid the distortion problem of travel time calculation caused by high-frequency jitter of the surface, thereby causing erroneous seismic imaging.

[0092] The pre-stack data based on the small smooth surface of the ground surface is obtained through the following processing steps:

[0093] The first step is to use the Kriging interpolation method to interpolate the elevation data to obtain three-dimensional small plane elevation data;

[0094] The second step is to smooth the three-dimensional small plane elevation data by using a summation weighted smoothing method;

[0095] The third step is to calculate the elevation static correction values ​​(S1 and S2) corresponding to the real surface elevation and the smoothed surface elevation, and obtain the residual S (DELTA) between the two.

[0096] The fourth step is to apply the residual data S (DELTA) to the pre-stack data based on the real surface to obtain the pre-stack data based on the small smooth surface of the surface.

[0097] In an exemplary embodiment, the more advanced the prestack depth migration algorithm is, the higher the model accuracy requirement is. When the velocity model accuracy is not high, the imaging effect of the advanced algorithm is not as good as the traditional method. Therefore, improving the accuracy of the velocity model is crucial in prestack depth migration. Since the signal-to-noise ratio of the shallow layer is relatively low, there is very little reflection wave information available. It is difficult to obtain accurate shallow layer velocity by traditional methods such as tomographic inversion based on reflection waves. As we all know, the accuracy of the shallow layer velocity seriously affects the accuracy of the mid-deep layer velocity model of the depth migration, so that accurate structural imaging cannot be obtained. Therefore, obtaining the velocity model of the shallow layer is crucial to the establishment of an accurate velocity-depth model. Based on this, the industry establishes the near-surface velocity model by using the statically corrected velocity model as the shallow velocity model of the velocity-depth model. This velocity model has a suitable trend, but the accuracy is not enough. Therefore, for the acquisition of the near-surface velocity model, this paper proposes a near-surface velocity modeling method with step-by-step constraints. Through the sequence of micro-logging, near-offset distance, medium- and far-offset distance, it is iterated step by step. In this way, the velocity model's extremely shallow and relatively deep formation velocities are in good agreement with the VSP velocity.

[0098] The near-surface velocity modeling method with step-by-step constraints is as follows:

[0099] 1. The process of establishing the near-surface velocity model based on the real surface is as follows:

[0100] The first step is to determine the first arrival picking time of the near-offset distance of the pre-stack data based on the real surface, and use the micro-logging data as a constraint to establish a first near-surface velocity model V1 using a tomographic inversion method;

[0101] The second step is to determine the first arrival picking time of the far offset in the pre-stack data based on the real surface, and to establish the near-surface velocity model V based on the real surface by using the tomographic inversion method with the first near-surface velocity model V1 as a constraint.

[0102] 2. The process of establishing the near-surface velocity model based on the small smooth surface is as follows:

[0103] The first step is to interpolate the velocity data in the micro-logging data of the processing area to establish an initial near-surface velocity model;

[0104] The second step is to obtain pre-stack data based on a small smooth surface on the surface, determine the first arrival picking time of the near offset of the pre-stack data based on the small smooth surface on the surface, and obtain the second near-surface velocity model V2 by using the tomographic inversion method with the initial near-surface velocity model as a constraint;

[0105] The third step is to determine the first arrival picking time of the far offset in the pre-stack data based on the small surface smooth surface, and use the second near-surface velocity model V2 as a constraint to obtain the near-surface velocity model V(S) based on the small surface smooth surface using the tomographic inversion method.

[0106] In an exemplary embodiment, data matching an offset reference surface is acquired.

[0107] When performing depth migration, the migration datum of the velocity model must be precisely matched with the data migration datum. In the above steps, the near-surface velocity model V(S) based on the small smooth surface is preferred. Based on this, it is necessary to eliminate the residual correction amount S(DELTA) between the small smooth surface and the real surface, and then replace it with the residual of the datum correction amount of the near-surface velocity model V based on the real surface and the near-surface velocity model V(S) based on the small smooth surface obtained by step-by-step constraints, and finally obtain a set of pre-stack migration data that fully matches the small smooth surface. Among them, the pre-stack data based on the small smooth surface of the surface is obtained by the following processing steps:

[0108] The first step is to use the Kriging interpolation method to interpolate the elevation data to obtain three-dimensional small plane elevation data;

[0109] The second step is to smooth the three-dimensional small plane elevation data by using a summation weighted smoothing method;

[0110] The third step is to calculate the elevation static correction values ​​(S1 and S2) corresponding to the elevation based on the real surface and the elevation based on the small smooth surface of the surface, and obtain the residual data S (DELTA) of the two.

[0111] The fourth step is to apply the residual data S (DELTA) to the pre-stack data based on the real surface to obtain the pre-stack data based on the small smooth surface of the surface.

[0112] In an exemplary embodiment, determining a residual correction amount based on a small smooth surface on the ground and a real ground surface according to a static correction amount of the real ground surface and a static correction amount based on a small smooth surface on the ground surface includes:

[0113] The first step is to generate a high-speed top interface H(v) according to the near-surface velocity model based on the small smooth surface of the surface;

[0114] Step 2: According to the high-speed top interface H(v), the tomographic static correction C(t) corresponding to the real surface is calculated on the near-surface velocity model V based on the real surface, and the corresponding residual static correction M(t) is determined;

[0115] The third step is to calculate the tomographic static correction C(s) corresponding to the small surface smooth surface on the near surface velocity model V(s) based on the small surface smooth surface according to the high-speed top interface H(v), and determine the corresponding residual static correction M(s);

[0116] Step 4: Calculate the residual correction amount between the small smooth surface and the real surface using the residual formula;

[0117] The residual formula is:

[0118] C(delta)=(C(t)+M(t))-(C(s)+M(s));

[0119] In the above formula, C(delta) represents the residual correction between the small smooth surface on the surface and the real surface, C(t) represents the tomographic static correction of the real surface, M(t) represents the real residual static correction, C(s) represents the tomographic static correction of the small smooth surface on the surface, and M(s) represents the residual static correction of the small smooth surface on the surface.

[0120] In an exemplary embodiment, during the oil seismic exploration process, due to reasons such as the design of the acquisition observation system, the signal-to-noise ratio of the shallow layer is relatively low, and very little reflected wave information can be used. It is difficult to obtain accurate shallow layer velocity using traditional methods such as tomographic inversion based on reflected waves. Therefore, in this embodiment, a near-surface velocity model based on a small smooth surface is first obtained. For the medium-deep velocity model, an initial medium-deep velocity model is obtained after mainly using information constraints such as seismic data and logging, combined with high-precision grid tomography for optimization. After establishing a near-surface velocity model with a relatively high accuracy and a medium-deep velocity model with a reasonable velocity trend, the near-surface velocity of the small smooth surface and the medium-deep velocity are spliced ​​according to the ray bottom boundary of the near-surface inversion and the characteristics of the shallow geological formation, thereby obtaining an initial velocity model of full velocity.

[0121] Specifically, a full-depth prestack depth migration velocity model is established according to the near-surface velocity model based on the small smooth surface, the VSP logging data of the work area to be processed, and the geological structure model, including:

[0122] Establishing an initial full velocity model based on a small smooth surface according to the near-surface velocity model V(S) based on the small smooth surface of the surface;

[0123] The initial full velocity model is optimized according to the VSP logging data and the geological structure model of the work area to be processed to obtain a prestack depth migration velocity model.

[0124] In an exemplary embodiment, the process of establishing a full-depth prestack depth migration velocity model is as follows:

[0125] The first step is to establish a medium-deep layer velocity model V(h) using the VSP logging velocity value and the smoothed logging acoustic wave velocity value, including:

[0126] S11. Using the VSP logging data and the smoothed logging acoustic velocity values, an initial mid-deep velocity model is established;

[0127] S12. The initial mid-deep velocity model is optimized using a grid tomography optimization method to obtain a mid-deep velocity model V(h), including:

[0128] S121. Determine an anisotropy parameter δ according to the VSP logging velocity value and the velocity value of the initial mid-deep velocity model;

[0129] S122. interpolating all anisotropic parameter values ​​δ in the processing area to obtain an anisotropic initial parameter body;

[0130] S123. Using the Gaussian beam migration method to migrate the grid points of the pre-stack data of the small smooth surface according to the initial full velocity model based on the small smooth surface and the anisotropic initial parameter volume;

[0131] S124. Use structural interpretation horizons for control and grid tomography optimization method for grid tomography velocity optimization iteration;

[0132] S125. If the iteration meets the termination condition, the obtained velocity model is used as the final full velocity model.

[0133] The second step is to determine the splicing interface between the near-surface velocity model V(S) based on the small smooth surface and the mid-deep velocity model V(h), including:

[0134] S21. Determine a ray density body corresponding to the near-surface velocity model V(S) based on the small smooth surface of the surface, and pick up a velocity bottom interface according to the bottom boundary of the ray density body;

[0135] S22. Using the velocity bottom interface as the splicing interface H(ray) between the near-surface velocity model V(S) based on the small surface smooth surface and the mid-deep velocity model V(h).

[0136] The third step is to splice the near-surface velocity model V(S) based on the small smooth surface and the mid-deep velocity model V(h) on the splicing interface H(ray) to obtain an initial full velocity model based on the small smooth surface, including:

[0137] S31. Compare the velocity difference between the near-surface velocity model V(S) based on the small smooth surface on the surface and the mid-deep velocity model V(h) on the splicing interface H(ray);

[0138] S32. If the velocity difference between the two is less than a preset threshold, the near-surface velocity model V(S) based on the small smooth surface of the surface is spliced ​​with the mid-deep velocity model V(h);

[0139] S33. If the velocity difference between the two is greater than or equal to a preset threshold, the velocity difference between the two is adjusted to meet the threshold condition by setting a proportional factor a, and the near-surface velocity model V(S) based on the small smooth surface of the surface is spliced ​​with the mid-deep velocity model V(h);

[0140] S34. Smoothing the velocity within a predetermined range of the H(ray) interface using a method of constructing constrained smoothing to obtain an initial full velocity model based on a small smooth surface.

[0141] The seismic data processing method for prestack depth migration in the embodiment of the present application has the following technical effects:

[0142] 1. Based on the difference in information between micro-logging and offset, the velocity model obtained by iterating step by step in the order of micro-logging, near offset, medium and far offset is in good agreement with the VSP velocity and can be used as a shallow surface velocity model for pre-stack depth migration.

[0143] 2. The seismic data processing method of pre-stack depth migration of the present application is to combine the method of structural constraint smoothing to realize the organic splicing of the near-surface velocity model of the small smooth surface and the mid-deep velocity model;

[0144] 3. In the face of "double complex" seismic data, a high-precision velocity optimization method with high signal-to-noise ratio gathers based on structural constraints was implemented;

[0145] 4. Solve the problems of large changes in surface elevation, low signal-to-noise ratio of seismic data and drastic changes in time difference between traces, improve the accuracy of seismic imaging in "double complex" areas, accurately determine the structure and closure of the foreland belt, and thus greatly reduce the risk of oil exploration.

[0146] In a second aspect, an embodiment of the present invention provides a seismic data processing device for pre-stack depth migration, such as Fig.12 As shown, the device includes: a memory 1200 and a processor 1210; the memory is used to store a program for seismic data processing for pre-stack depth migration, and the processor is used to read and execute the program for seismic data processing for pre-stack depth migration, and execute any one of the methods in the above embodiments.

[0147] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a data processing program stored thereon, and the data processing program is executed by a processor in the seismic data processing method for pre-stack depth migration described in any one of the above embodiments.

[0148] Example 1

[0149] To make the technical scheme of the present invention clearer, the technical scheme in the embodiment of the present invention will be described clearly and completely in combination with the accompanying drawings in the embodiment of the present invention. The seismic data in the example comes from a certain oil field block F in western China. Block F is located in the frontal zone of Kunlun Mountain. The surface elevation is high in the north and low in the south. The altitude is between 1750-2330m, and the local drop is large, which can reach 260 meters. Due to the multi-stage strong tectonic compression of Kunlun Mountain, a large number of Paleozoic strata and even basement are thrust to the surface, forming a foreland fold-thrust belt. Various lithologies change rapidly, and lithology mutations occur at a parallel distance of about 500m on the surface. The stratum dip angle is 70-80 degrees; the north is restricted by the Bachu paleo-uplift, and an inherited foreland basin is formed between the thrust belt and the paleo-uplift. The profile of the foreland basin is dustpan-shaped, dipping from northeast to southwest, which leads to a chaotic seismic wave field, an extremely low signal-to-noise ratio, and a very fast lateral velocity change. It is very difficult to accurately establish a seismic velocity field. According to conventional processing technology and velocity modeling methods, the imaging results obtained by processing have a low signal-to-noise ratio, the main shallow structures can hardly be accurately imaged, and the imaging of deep structures is not clear and complete. Through a new round of technical research and development, this patented method is used to accurately image shallow structures, make deep structures clearer and more complete, basically match the occurrence of the well, the location of the mid-deep breakpoints is clearer, basically consistent with the actual drilling, and the relationship between multiple rows of structures is clear.

[0150] The following steps are implemented in the embodiment of the present application:

[0151] 1. Collected pre-stack shot data from the F work area in the piedmont zone, more than 100 micro-well logging data, 3 well logging layering and velocity data, geological interpretation layer data, etc.;

[0152] 2. Evaluate and correct the quality of the collected data, optimize the collection of micro-logging and well logging data, and obtain reliable basic data as input for the next step;

[0153] 3. Extract the real elevation information of the receiver points and shot points from the pre-stack shot data, and use the corresponding smoothing parameters to obtain a small smooth surface relative to the surface. Figure 2 As shown, a schematic diagram of a small smooth surface relative to the ground surface, namely the smooth surface of the seismic offset datum.

[0154] 4. Perform a small smoothing process on the real surface to obtain pre-stack data based on the real surface and the small smoothing surface of the surface;

[0155] 5. Accurately pick up the full offset first arrival and take-off time based on the true surface and the small smooth surface of the surface;

[0156] 6. Based on the optimized micro-logging data, the first arrival pickup time of the offset distance of 0-1000 meters and the first arrival pickup time of the offset distance of 0-5000 meters, the near-surface velocity model based on the real surface and the near-surface velocity model based on the small smooth surface are obtained by adopting the near-surface model establishment technology of step-by-step constraint. Figure 3 As shown in the figure, the near-surface velocity profiles at different offsets after the step-by-step velocity constraint are compared. It can be seen that the step-by-step velocity constraint model not only retains the shallow velocity accuracy of the near-offset constraint, but also improves the velocity accuracy of the deep layer. Figure 4 The schematic diagram of the comparison of inversion accuracy at the VSP well before and after the step-by-step constraint is shown. The obtained near-surface velocity model based on the small smooth surface is compared with the VSP velocity. It is found that the near-surface velocity based on the small smooth surface after the step-by-step constraint inversion is highly accurate compared with the VSP velocity.

[0157] 7. Calculate the reference surface static corrections of step 5 and step 6 respectively. Based on the static corrections calculated respectively, iterate the residual static corrections to obtain the total static corrections based on the real surface and the small smooth surface.

[0158] 8. Calculate the residual correction of the total static correction of the real surface and the smooth surface, and correct the pre-stack data based on the real surface of the work area to be processed to the first pre-stack data matching the near-surface velocity model based on the small smooth surface of the surface; realize the smooth terrain correction processing; Figure 5 and Figure 6 As shown in the figure, after applying appropriate smoothing parameters, the application of small smoothing surface correction can effectively remove high-frequency jitter and retain the terrain undulations.

[0159] 9. Apply to pre-stack shot collection, compare single shot after small smooth terrain correction and real surface for quality control; Figure 7 Schematic diagram of comparison of pre-stack shot gathering before and after applying surface smoothing static correction in an example of the present invention. Figure 7 It can be expressed that after applying a small amount of smooth surface correction, high-frequency jitter is well removed, terrain undulations are preserved, and the wave field morphology is maintained.

[0160] 10. Using all VSP velocity functions and smoothed logging acoustic velocity functions in the work area, and using the method of geostatistical constraints, an initial velocity model of the mid-deep layer is established, and then data grid tomography optimization is carried out to obtain a relatively accurate initial mid-deep layer velocity model;

[0161] 11. According to the ray density curve of the near-surface velocity model of the small smooth surface, pick the ray density bottom boundary as the velocity splicing surface;

[0162] 12. On the basis of the interface, the near-surface velocity model of the small smooth surface is spliced ​​with the initial mid-deep velocity model, and the structural constraint smoothing is performed on the splicing surface to obtain the final initial full velocity model; Figure 8 As shown in Figure 1, it is a schematic diagram of the fusion process of the near-surface velocity model and the mid-deep velocity model.

[0163] 13. Based on the full velocity model, the initial anisotropic parameter volume is obtained by using the difference in velocity from the well logging.

[0164] 14. Carry out Gaussian beam migration based on small smoothing. On the basis of beam migration gathers, combined with structural geological layer constraints, the velocity model and anisotropic parameter model are optimized layer by layer; Fig. 9 It is a schematic diagram for comparing the offset gather of the present invention and the conventionally processed offset gather in the example of the present invention; it can be found from the figure that the offset gather of the new invention has less offset noise, higher signal-to-noise ratio, and is more conducive to speed optimization and iteration.

[0165] 15. Based on the principle of gather flattening and accurate profile imaging, the process is repeated until the velocity model and imaging results are recognized by the interpreters, so that they can identify structures and traps.

[0166] Fig.10 This is a schematic diagram comparing the effects of the imaging method provided by the present invention and the conventional imaging method in an example of the present invention; in the present method, Gaussian beam migration can better solve the problems of multi-value travel time, shadow, caustics, etc., and ultimately achieves accurate imaging through Gaussian beam migration. The imaging of the Gaussian beam at the red arrow is significantly better than the imaging effect of the conventional Kirchhoff depth migration.

[0167] Fig.11 It is a schematic diagram of comparative analysis of the seismic imaging results obtained by the method of the present application and the imaging results obtained by the previous conventional processing method in the example of the present invention. The seismic imaging results obtained by the method of the present application are significantly better than the imaging results obtained by the previous conventional processing method. The shallow imaging is significantly improved, the occurrence is basically consistent with the well, the position of the mid-deep breakpoints is clearer, basically consistent with the actual drilling, and the relationship between the multi-row structural structures is clearer.

[0168] The seismic data processing method for pre-stack depth migration implemented in the embodiment of the present application has been shown through the above-mentioned actual data application that due to the large changes in surface elevation, low signal-to-noise ratio of seismic data and drastic changes in inter-trace time differences, imaging in "double complex" areas becomes extremely difficult. Based on this, this time, through the processing method for depth migration, the imaging accuracy is improved, the exploration and development efficiency is improved, and the exploration risk is reduced, which has a very broad prospect and potential for promotion and application.

[0169] The present application includes and contemplates combinations of features and elements known to those of ordinary skill in the art. The embodiments, features and elements disclosed in the present application may also be combined with any conventional features or elements to form a unique invention scheme defined by the claims. Any features or elements of any embodiment may also be combined with features or elements from other invention schemes to form another unique invention scheme defined by the claims. Therefore, it should be understood that any feature shown and / or discussed in the present application may be implemented individually or in any appropriate combination. Therefore, except for the limitations made according to the attached claims and their equivalents, the embodiments are not subject to other restrictions. In addition, various modifications and changes may be made within the scope of protection of the attached claims.

[0170] In addition, when describing representative embodiments, the specification may have presented the method and / or process as a specific sequence of steps. However, to the extent that the method or process does not rely on the specific order of the steps described herein, the method or process should not be limited to the steps of the specific order described. As will be understood by those of ordinary skill in the art, other sequences of steps are also possible. Therefore, the specific sequence of the steps set forth in the specification should not be interpreted as a limitation to the claims. In addition, the claims for the method and / or process should not be limited to the steps of performing them in the order written, and those skilled in the art can easily understand that these sequences can be changed and still remain within the spirit and scope of the embodiments of the present application.

Claims

1. A seismic data processing method for prestack depth migration, characterized in that: The method comprises: Obtain elevation data and micro-logging data of the work area to be processed, and establish a near-surface velocity model based on the real surface and a near-surface velocity model based on a small smooth surface on the surface; According to the static correction amount of the real surface and the static correction amount based on the small smooth surface of the surface, the residual correction amount between the small smooth surface of the surface and the real surface is determined; According to the determined residual correction amount, the pre-stack data based on the real surface of the work area to be processed is corrected to the first pre-stack data matching the near-surface velocity model based on the small smooth surface of the surface; A full-depth prestack depth migration velocity model is established based on the near-surface velocity model based on the small smooth surface, the VSP logging data of the work area to be processed, and the geological structure model; The prestack depth migration velocity model is used to perform depth migration imaging processing on the first prestack data to obtain seismic data after prestack depth migration.

2. The seismic data processing method for prestack depth migration according to claim 1, characterized in that: The process of establishing the near-surface velocity model based on the real surface is as follows: Determine the first arrival picking time of the near offset of the pre-stack data based on the real surface, and establish a first near-surface velocity model by using a tomographic inversion method with the micro-logging data as a constraint; The first arrival picking time of the far offset in the pre-stack data based on the real surface is determined, and a near-surface velocity model based on the real surface is established by using a tomographic inversion method with the first near-surface velocity model as a constraint.

3. The seismic data processing method for prestack depth migration according to claim 2, characterized in that: The process of establishing the near-surface velocity model based on the small smooth surface is as follows: Interpolate the velocity data in the micro-logging data of the processing area to establish an initial near-surface velocity model; Acquire pre-stack data based on a small smooth surface on the surface, determine the first arrival picking time of the near offset of the pre-stack data based on the small smooth surface on the surface, and obtain a second near-surface velocity model by using a tomographic inversion method with the initial near-surface velocity model as a constraint; The first arrival picking time of the far offset in the pre-stack data based on the small surface smooth surface is determined, and the near surface velocity model based on the small surface smooth surface is obtained by using the tomographic inversion method with the second near surface velocity model as a constraint.

4. The seismic data processing method for prestack depth migration according to claim 1, characterized in that: The pre-stack data based on the small smooth surface of the ground surface is obtained through the following processing steps: The elevation data is interpolated using the Kriging interpolation method to obtain three-dimensional small plane elevation data; Smoothing the three-dimensional small plane elevation data by using a summation weighted smoothing method; Calculate the elevation static correction values ​​corresponding to the elevation based on the real surface and the elevation based on the small smooth surface of the surface respectively, and obtain the residual data of the two. The residual data is applied to the pre-stack data based on the real surface to obtain the pre-stack data based on the small smooth surface of the surface.

5. The seismic data processing method for prestack depth migration according to claim 4, characterized in that: The method of determining the residual correction amount between the small smooth surface and the real surface according to the static correction amount of the real surface and the static correction amount based on the small smooth surface of the surface comprises: Generate a high-speed top interface according to the near-surface velocity model based on the small smooth surface of the surface; According to the high-speed top interface, a tomographic static correction corresponding to the real surface is calculated on a near-surface velocity model based on the real surface, and a corresponding residual static correction is determined; According to the high-speed top interface, a tomographic static correction corresponding to the small surface smooth surface is calculated on a near-surface velocity model based on the small surface smooth surface, and a corresponding residual static correction is determined; The residual formula is used to calculate the residual correction between the small smooth surface and the real surface.

6. The seismic data processing method for prestack depth migration according to claim 5, characterized in that: The residual formula is: C(delta)=(C(t)+M(t))-(C(s)+M(s)); In the above formula, C(delta) represents the residual correction between the small smooth surface on the surface and the real surface, C(t) represents the tomographic static correction of the real surface, M(t) represents the residual static correction of the real surface, C(s) represents the tomographic static correction of the small smooth surface on the surface, and M(s) represents the residual static correction of the small smooth surface on the surface.

7. The seismic data processing method for prestack depth migration according to claim 5, characterized in that: The method of establishing a full-depth prestack depth migration velocity model based on the near-surface velocity model based on the small smooth surface, the VSP logging data of the work area to be processed, and the geological structure model includes: Establishing an initial full velocity model based on a small smooth surface according to the near-surface velocity model based on the small smooth surface on the surface; The initial full velocity model is optimized according to the VSP logging data and the geological structure model of the work area to be processed to obtain a prestack depth migration velocity model.

8. The seismic data processing method for prestack depth migration according to claim 7, characterized in that: The step of establishing an initial full velocity model based on a small smooth surface according to the near-surface velocity model based on the small smooth surface of the surface comprises: Using the VSP logging data and the smoothed logging acoustic wave velocity values, a mid-deep layer velocity model is established; Determining a splicing interface between the near-surface velocity model based on the small surface smooth surface and the mid-deep velocity model; On the splicing interface, the near-surface velocity model based on the small smooth surface and the mid-deep velocity model are spliced ​​to obtain an initial full velocity model based on the small smooth surface.

9. The seismic data processing method for prestack depth migration according to claim 8, characterized in that: The method of using the VSP logging data and the smoothed logging acoustic wave velocity value to establish a mid-deep layer velocity model includes: Using the VSP logging data and the smoothed logging acoustic wave velocity values, an initial mid-deep layer velocity model is established; The initial mid-deep velocity model is optimized using a grid tomography optimization method to obtain a mid-deep velocity model.

10. The seismic data processing method for prestack depth migration according to claim 8, characterized in that: The step of determining a splicing interface between the near-surface velocity model based on a small smooth surface and the mid-deep velocity model comprises: Determine a ray density body corresponding to the near-surface velocity model based on the small smooth surface of the surface, and pick up a velocity bottom interface according to the bottom boundary of the ray density body; The velocity bottom interface is used as a splicing interface between the near-surface velocity model based on the small surface smooth surface and the mid-deep velocity model.

11. The seismic data processing method for prestack depth migration according to claim 8, characterized in that: The near-surface velocity model based on the small smooth surface and the mid-deep velocity model are spliced ​​on the splicing interface to obtain an initial full velocity model based on the small smooth surface, including: On the splicing interface, comparing the velocity difference between the near-surface velocity model based on the small surface smooth surface and the mid-deep velocity model; If the velocity difference between the two is less than a preset threshold, the near-surface velocity model based on the small smooth surface of the surface is spliced ​​with the mid-deep velocity model; If the velocity difference between the two is greater than or equal to a preset threshold, the velocity difference between the two is adjusted to meet the threshold condition by setting a proportional factor a, and the near-surface velocity model based on the small smooth surface of the surface is spliced ​​with the mid-deep velocity model; The velocity within a predetermined range of the interface is smoothed by using a method of constructing constrained smoothing to obtain an initial full velocity model based on a small smooth surface.

12. The seismic data processing method for prestack depth migration according to claim 11, characterized in that: The initial medium-deep velocity model is optimized by using a grid tomography optimization method to obtain a medium-deep velocity model, including: Determining anisotropy parameters according to the VSP logging velocity value and the velocity value of the initial mid-deep velocity model; Perform interpolation processing on all anisotropic parameter values ​​in the processing area to obtain an anisotropic initial parameter body; According to the initial full velocity model based on the small smooth surface and the anisotropic initial parameter body, the grid points of the prestack data of the small smooth surface are migrated using the Gaussian beam migration method. The structural interpretation horizon is used for control, and the grid tomography optimization method is used for grid tomography speed optimization iteration; If the iteration meets the termination condition, the obtained velocity model is taken as the final full velocity model.

13. A seismic data processing device for pre-stack depth migration, characterized in that: The device comprises: a memory and a processor; the memory is used to store a program for seismic data processing for pre-stack depth migration, and the processor is used to read and execute the program for seismic data processing for pre-stack depth migration, and execute the method described in any one of claims 1-12.

14. A computer-readable storage medium having a data processing program stored thereon, wherein the data processing program is executed by a processor to implement the seismic data processing method for prestack depth migration according to any one of claims 1 to 12.

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