Speed modeling method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a speed modeling method and device, electronic equipment and a storage medium. The method comprises the steps of determining actual horizon data explained by an ith iteration period through horizon explanation according to prestack depth migration data of the ith iteration period; determining expected horizon data of seismic interpretation of the (i + 1) th iteration period according to the prior geological information of the target complex structure area, the geological recognition of the adjacent work area and the interpretation result of the earlier stage processing link; determining an (i + 1) th iteration speed model according to the expected horizon data, the actual horizon data and the isotropic depth domain speed model based on a ray tracing tomography inversion mode; and determining whether to output the (i + 1) th iteration speed model as a speed modeling result or not according to the (i + 1) th iteration period pre-stack depth migration data corresponding to the (i + 1) th iteration speed model and an iteration ending condition. According to the method, the velocity model conforming to geological knowledge is obtained, the problem of prestack depth migration of a mountain complex structure area is effectively solved, and the imaging effect of seismic data is improved.

Description

Technical Field

[0001] The invention relates to the technical field of oil and gas seismic exploration in complex structural areas, and in particular to a velocity modeling method, device, electronic equipment and storage medium. Background Art

[0002] Similar to obtaining the internal structure of the human body through CT, seismic data processing requires imaging to obtain underground structure information. Imaging depends on CMP gathers and velocity models. The rationality of the velocity model determines the accuracy of the stratigraphic structure information. The most widely used depth domain velocity modeling methods include model tomography inversion method and grid tomography inversion method. Model tomography inversion method: tomography inversion based on interpretation of stratigraphic control, mainly through the ray path and residual delay in multiple velocity interfaces of simple layered models for velocity inversion. Its advantages are: it can simplify complex geological models into seismic stratigraphic models under the constraints of geological structures, and the model has geological significance; the disadvantages are: it is necessary to interpret the geological stratigraphic layers, and the interpretation of stratigraphic layers in low signal-to-noise ratio areas is difficult, resulting in low accuracy of the divided velocity interface model, and this simplified model can only invert the trend velocity field, lacking detailed information on interlayer velocity. Grid tomography inversion method: Velocity inversion is performed by obtaining ray paths and residual delays in the global grid space. It does not require interpretation of the layer and is purely data-driven. It overcomes the defect of the layered model that the interlayer velocity details are insufficient. The inversion accuracy is relatively high, and a relatively accurate (relative) high-frequency component of the velocity field can be obtained. However, the inversion model is not constrained by the geological layer, and it is not easy to converge to the actual velocity model, and may even produce inversion results that do not conform to geological laws.

[0003] Therefore, simple underground structures can be accurately imaged through conventional velocity modeling methods; but for complex structural areas, direct reliance on automated calculations may increase the uncertainty of inversion and introduce false structural information.

[0004] Therefore, the current technology has the technical problem of unreasonable velocity modeling methods for complex structural areas. Summary of the invention

[0005] In order to alleviate the technical problem of unreasonable velocity modeling methods for complex structural areas existing in the current technology, the embodiments of the present invention provide a velocity modeling method, device, electronic device and storage medium.

[0006] In a first aspect, an embodiment of the present invention provides a velocity modeling method for seismic data in a complex structural region, comprising:

[0007] According to the pre-stack depth migration data of the i-th iteration period, the actual layer data interpreted in the i-th iteration period are determined through layer interpretation;

[0008] Determine the expected horizon data of the seismic interpretation of the i+1th iteration cycle according to the prior geological information of the target complex structural area, the geological knowledge of the adjacent work area and the interpretation results of the previous processing link; the prior geological information includes logging and drilling information, and the previous processing link includes the previous pre-stack time migration;

[0009] Based on the ray tracing tomographic inversion mode, the i+1th iteration velocity model is determined according to the expected layer data of the seismic interpretation of the i+1th iteration cycle, the actual layer data interpreted in the i-th iteration cycle, and the isotropic depth domain velocity model;

[0010] According to the pre-stack depth migration data of the i+1th iteration period corresponding to the i+1th iteration velocity model and the iteration end condition, it is determined whether to output the i+1th iteration velocity model as the velocity modeling result of the seismic data corresponding to the target complex structural area.

[0011] In some implementations, determining whether to output the (i+1)th iterative velocity model as a velocity modeling result according to the (i+1)th iterative cycle prestack depth migration data corresponding to the (i+1)th iterative velocity model and an iteration end condition includes:

[0012] Determining whether the pre-stack depth migration data of the (i+1)th iteration cycle matches the priori geological information;

[0013] Determining whether the common imaging point gathers of the prestack depth migration data of the (i+1)th iteration period are flattened;

[0014] Determining whether the wave group characteristics of the prestack depth migration data of the (i+1)th iteration period are reasonable;

[0015] If the pre-stack depth migration data of the i+1th iteration cycle matches the priori geological information, the common imaging point gathers are flattened, and the wave group characteristics are reasonable, the i+1th iteration velocity model is output as the velocity modeling result.

[0016] In some embodiments, before the step of determining the actual horizon data interpreted in the i-th iteration period through horizon interpretation based on the prestack depth migration data in the i-th iteration period, the method further includes:

[0017] Acquire a seismic imaging data volume corresponding to the mountain seismic data;

[0018] Determining a preset number of events marking strata according to the horizon interpretation data on the seismic imaging data volume;

[0019] The event axis of the marker stratum is traced to obtain actual stratum data interpreted in the i-th iteration cycle.

[0020] In some embodiments, the desired horizon data of the seismic interpretation of the i+1th iteration cycle is determined based on the prior geological information and seismic data of the target complex structural region, including:

[0021] Determining prestack time migration data based on seismic data of the target complex structural area;

[0022] converting the prestack time migration data into depth domain data;

[0023] Based on the prior geological information, a horizon interpretation is performed on the depth domain data converted by the prestack time migration to determine the expected horizon data.

[0024] In some embodiments, before the step of determining the expected horizon data of the (i+1)th iteration period according to the prestack depth migration data of the i-th iteration period, the method further includes:

[0025] Determining a preset tomography inversion mode, wherein the preset tomography inversion mode includes a model tomography inversion mode or a grid tomography inversion mode;

[0026] The isotropic depth-domain velocity model is determined according to the preset tomographic inversion mode.

[0027] In some embodiments, the i+1th iteration velocity model is determined by a ray tracing-based tomographic inversion mode according to the expected horizon data of the seismic interpretation of the i+1th iteration cycle, the actual horizon data interpreted in the i-th iteration cycle, and the isotropic depth domain velocity model, including:

[0028] Determine the residual of the i-th iteration cycle according to the actual layer data and the expected layer data of the seismic interpretation of the i+1-th iteration cycle;

[0029] Based on the ray tracing tomographic inversion mode, the i+1th iteration velocity model is determined according to the residual of the i-th iteration cycle, the isotropic depth domain velocity model and the layer interpretation data.

[0030] In some embodiments, the i+1th iteration velocity model is determined according to the i-th iteration period residual, the isotropic depth domain velocity model and the horizon interpretation data through a ray tracing-based tomographic inversion mode, including:

[0031] Determining updated variables of anisotropic parameters of each target point according to the isotropic depth-domain velocity model; the target point includes a target point between the three-dimensional grid and the ray pair of the i-th iteration velocity model;

[0032] Determining an update variable of the P-wave velocity and an update variable of the depth value of each target point according to the residual error of the i-th iteration period and the layer interpretation data;

[0033] Determining the travel time variables of each ray pair model parameter according to the update variables of the anisotropy parameter, the update variables of the longitudinal wave velocity and the update variables of the depth value;

[0034] The (i+1)th iterative velocity model is obtained according to the travel time variable and the target matrix vector.

[0035] In a second aspect, an embodiment of the present invention provides a velocity modeling device for seismic data in a complex structural region, the device comprising:

[0036] The first module is used to determine the actual horizon data interpreted in the i-th iteration period through horizon interpretation according to the pre-stack depth migration data of the i-th iteration period;

[0037] The second module is used to determine the expected layer data of the seismic interpretation of the i+1th iteration cycle according to the prior geological information of the target complex structural area, the geological knowledge of the adjacent work area and the interpretation results of the previous processing link; the prior geological information includes logging and drilling information, and the previous processing link includes the previous pre-stack time migration;

[0038] The third module is used for determining the i+1th iteration velocity model in a tomographic inversion mode based on ray tracing according to the expected layer data of the seismic interpretation of the i+1th iteration cycle, the actual layer data interpreted in the i-th iteration cycle, and the isotropic depth domain velocity model;

[0039] The fourth module is used to determine whether to output the i+1th iterative velocity model as the velocity modeling result of the seismic data corresponding to the target complex structural area according to the pre-stack depth migration data of the i+1th iterative cycle corresponding to the i+1th iterative velocity model and the iteration end condition.

[0040] In a third aspect, an embodiment of the present invention provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the method described in the first aspect is implemented.

[0041] In a fourth aspect, an embodiment of the present invention provides a storage medium having a computer program stored thereon. When the computer program is executed by one or more processors, the method described in the first aspect is implemented.

[0042] Compared with the prior art, one or more embodiments of the present invention can at least bring the following beneficial effects:

[0043] The embodiment of the present invention provides a velocity modeling method, device, electronic device and storage medium; the method comprises determining the actual stratigraphic data interpreted in the i-th iteration period through stratigraphic interpretation according to the pre-stack depth migration data of the i-th iteration period; determining the expected stratigraphic data of the seismic interpretation in the i+1-th iteration period according to the prior geological information of the target complex structural area, the geological knowledge of the adjacent work area and the interpretation results of the previous processing link; the prior geological information comprises logging and drilling information, and the previous processing link comprises the previous pre-stack time migration; based on the tomographic inversion mode of ray tracing, determining the i+1-th iteration velocity model according to the expected stratigraphic data of the seismic interpretation in the i+1-th iteration period, the actual stratigraphic data interpreted in the i-th iteration period and the isotropic depth domain velocity model; determining whether to output the i+1-th iteration velocity model as the velocity modeling result of the seismic data corresponding to the target complex structural area according to the pre-stack depth migration data of the i+1-th iteration period corresponding to the i+1-th iteration velocity model and the iteration end condition. In the method provided in this scheme, velocity modeling is carried out under the constraint of prior geological information to obtain a velocity model that conforms to geological understanding. Prestack depth migration is carried out through this velocity model. The wave group characteristics of the processing results are clear, the stratigraphic contact relationship is clear, the stratigraphic distribution characteristics and structure are consistent with the geological understanding of this area and neighboring areas, and the common imaging point gathers have a good flattening effect, which effectively solves the prestack depth migration in complex structural areas and improves the imaging effect of seismic data. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0045] Figure 1 A first flow chart of a velocity modeling method provided in an embodiment of the present application;

[0046] Figure 2 A second flow chart of the velocity modeling method provided in an embodiment of the present application;

[0047] Figure 3 A cross-sectional diagram of pre-stack depth migration imaging data of adjacent work areas provided in an embodiment of the present application;

[0048] Figure 4 The imaging data profiles corresponding to the velocity modeling method of the present invention before and after the present invention is used are provided in the embodiments of the present application;

[0049] Figure 5 A schematic diagram of the structure of a velocity modeling device provided in an embodiment of the present application.

[0050] In the drawings, the same reference numerals are used for the same components, and the drawings are not drawn to scale. DETAILED DESCRIPTION

[0051] The following will describe the implementation methods of the present application in detail with reference to the accompanying drawings and embodiments, so that the implementation process of how the present application applies technical means to solve technical problems and achieve corresponding technical effects can be fully understood and implemented accordingly. The embodiments of the present application and the various features in the embodiments can be combined with each other without conflict, and the technical solutions formed are all within the scope of protection of the present application.

[0052] Meanwhile, in the following description, many specific details are set forth for the purpose of explanation to provide a thorough understanding of the embodiments of the present invention. However, it is obvious to those skilled in the art that the present invention can be implemented without the specific details or the specific manner described herein.

[0053] Embodiment 1

[0054] Figure 1 For a first flow chart of the velocity modeling method provided in the embodiment of the present application, please refer to Figure 1 , the velocity modeling method provided in this embodiment includes:

[0055] Step S110: According to the pre-stack depth migration data of the i-th iteration period, actual layer data interpreted in the i-th iteration period are determined through layer interpretation.

[0056] In some embodiments, before the step of determining the actual stratigraphic data interpreted in the i-th iteration period through stratigraphic interpretation based on the pre-stack depth migration data of the i-th iteration period, the step also includes: acquiring a seismic imaging data body corresponding to the seismic data of the complex structural area; determining a preset number of stratigraphic axes of marker strata based on the stratigraphic interpretation data on the seismic imaging data body; and performing stratigraphic tracking on the stratigraphic axes of the marker strata to obtain the actual stratigraphic data interpreted in the i-th iteration period.

[0057] Specifically, through the interpretation of the horizon on the seismic imaging data volume, the horizon tracking is performed on several sets of event axes of the marker strata to obtain the actual horizon data.

[0058] In some embodiments, before the step of determining the expected layer data of the (i+1)th iteration period based on the pre-stack depth migration data of the i-th iteration period, it also includes: determining a preset tomography inversion mode, the preset tomography inversion mode including a model tomography inversion mode or a grid tomography inversion mode; and determining the isotropic depth domain velocity model according to the preset tomography inversion mode.

[0059] Specifically, the isotropic depth domain velocity model is obtained through the model tomography inversion method or the network tomography inversion method; on this basis, subsequent work is carried out.

[0060] Step S120: Determine the expected horizon data of the seismic interpretation of the i+1th iteration cycle based on the prior geological information of the target complex structural area, the geological knowledge of the adjacent work area, and the interpretation results of the previous processing link.

[0061] In this application, the prior geological information includes logging and drilling information, i.e., logging curves, drilling depth values, etc.; the geological understanding includes horizon interpretation; and the preliminary processing includes preliminary pre-stack time migration. In this embodiment, the richness of geological information directly determines the reliability of subsequent velocity model inversion. The logging and drilling information can finely control the longitudinal velocity value and depth value near the well point in the vertical direction, and the horizon interpretation data can be used to constrain the control in the horizontal direction.

[0062] In some embodiments, based on prior geological information and seismic data of a target complex structural area, expected stratigraphic data of seismic interpretation in the i+1th iteration cycle is determined, including: determining pre-stack time migration data based on the seismic data of the target complex structural area; converting the pre-stack time migration data into depth domain data; and determining the expected stratigraphic data based on the prior geological information and the stratigraphic interpretation of the seismic imaging data volume and the stratigraphic interpretation of the depth domain data.

[0063] Specifically, through the geological understanding and pre-stack time migration results of this work area and adjacent work areas, we can provide global guidance for the stratigraphic interpretation on the seismic imaging data volume in the depth domain. If there are local areas on the seismic imaging data volume that do not conform to the geological understanding, we can perform stratigraphic interpretation based on the geological understanding, or we can experiment with stratigraphic interpretation on the data converted from pre-stack time migration to the depth domain to obtain the expected stratigraphic data for the i+1th iteration cycle.

[0064] Step S130: Based on the ray tracing tomographic inversion mode, the i+1th iteration velocity model is determined according to the expected layer data of the seismic interpretation of the i+1th iteration cycle, the actual layer data interpreted in the i-th iteration cycle, and the isotropic depth domain velocity model.

[0065] In some embodiments, the i+1th iteration velocity model is determined according to the expected layer data of the seismic interpretation of the i+1th iteration cycle, the actual layer data interpreted in the i-th iteration cycle, and the isotropic depth domain velocity model through a tomographic inversion mode based on ray tracing, including: determining the residual of the i-th iteration cycle according to the expected layer data of the seismic interpretation of the i+1th iteration cycle; determining the i+1th iteration velocity model according to the residual of the i-th iteration cycle, the isotropic depth domain velocity model and the layer interpretation data through a tomographic inversion mode based on ray tracing.

[0066] In this embodiment, the actual layer data and the expected layer data are combined to obtain the residual between the two.

[0067] In some embodiments, the i+1th iterative velocity model is determined according to the i-th iterative cycle residual, the isotropic depth-domain velocity model and the layer interpretation data through a tomographic inversion mode based on ray tracing, including: determining the updated variables of the anisotropic parameters of each target point according to the isotropic depth-domain velocity model; the target point includes the target point between the three-dimensional grid and the ray pair of the i-th iterative velocity model; determining the updated variables of the longitudinal wave velocity and the updated variables of the depth value of each target point according to the i-th iterative cycle residual and the layer interpretation data; determining the travel time variables of the model parameters of each ray pair according to the updated variables of the anisotropic parameters, the updated variables of the longitudinal wave velocity and the updated variables of the depth value; and obtaining the i+1th iterative velocity model according to the travel time variables and the target matrix vector.

[0068] Specifically, the velocity model is updated using a tomographic inversion technique based on ray tracing, combining residuals, horizon interpretation data, and an isotropic depth-domain velocity model: During tomographic inversion, the target points formed by the model's three-dimensional grid and ray pairs are regular in the horizontal direction and irregular in the vertical direction, and these grid points are consistent with the structural interface of the interpreted horizon. The updated model parameters are displayed as a sparse grid, and the rays are emitted from a finer horizontal grid. The travel time variable of the model parameters for each ray pair is given by the following formula:

[0069]

[0070] Where i can be regarded as the index number of the target point launched to the structural interface on the velocity model. ΔV, Δε, Δδ, Δz represent the updated variables of the P-wave velocity, two anisotropic parameters and depth value respectively. Represents the following matrix part:

[0071]

[0072] where t is the total ray pair travel time, and m = V, ε, δ, z; each ray pair generates an equation, which together define an overdetermined system of linear equations (the number of rays is much greater than the number of model nodes). This can be written as a matrix-vector expression:

[0073] A V ΔV+A ε Δε+A δ Δδ+A z Δz=0 (3)

[0074] By solving the matrix, an updated velocity model can be obtained, and the prestack depth migration result of this velocity model is close to the prior geological information.

[0075] Step S140: Determine whether to output the i+1th iterative velocity model as the velocity modeling result of the seismic data corresponding to the target complex structural area according to the pre-stack depth migration data of the i+1th iterative cycle corresponding to the i+1th iterative velocity model and the iteration end condition.

[0076] In some embodiments, the determining whether to output the i+1th iterative velocity model as a velocity modeling result according to the pre-stack depth migration data of the i+1th iterative velocity model corresponding to the i+1th iterative velocity model and the iteration end condition includes: judging whether the pre-stack depth migration data of the i+1th iterative velocity model matches the priori geological information; judging whether the common imaging point gathers of the pre-stack depth migration data of the i+1th iterative velocity model are flattened; judging whether the wave group characteristics of the pre-stack depth migration data of the i+1th iterative velocity model are reasonable; if the pre-stack depth migration data of the i+1th iterative velocity model matches the priori geological information, the common imaging point gathers are flattened, and the wave group characteristics are reasonable, then outputting the i+1th iterative velocity model as a velocity modeling result; if the pre-stack depth migration data of the i+1th iterative velocity model does not match the priori geological information, the common imaging point gathers are not flattened, and the wave group characteristics are unreasonable, then it is necessary to re-determine the actual layer data of the next iterative cycle according to the pre-stack depth migration data of the i+1th iterative velocity model, and then execute step S130 and step S140.

[0077] Specifically, if the prestack depth migration results of the newly determined velocity modeling results are consistent with the prior geological knowledge, the CIP gathers (i.e., common imaging point gathers) after migration have a good flattening effect, and the wave group characteristics on the prestack depth migration data body are normal, and there is no abnormal phase axis stringing phenomenon, then the velocity modeling is completed.

[0078] Figure 2 For a second flow chart of the velocity modeling method provided in the embodiment of the present application, please refer to Figure 2 , the velocity modeling method provided in this embodiment includes:

[0079] S210: Interpret the horizon to obtain the expected horizon.

[0080] In this application, the prior geological information includes logging curves, drilling depth values ​​(i.e. logging and drilling data) and geologists' geological understanding of adjacent similar work areas (i.e., the geological understanding of the structural distribution of adjacent work areas); among which, geological understanding includes stratigraphic interpretation.

[0081] In this embodiment, the richness of geological information directly determines the reliability of subsequent velocity model inversion. Well logging and drilling information can precisely control the longitudinal velocity and depth values ​​near the well point vertically, and stratigraphic interpretation data can constrain control horizontally.

[0082] In this embodiment, the actual stratigraphic data is obtained by performing stratigraphic tracking on the phase axes of several sets of marker strata through interpretation on the seismic imaging data body. The geological knowledge and prestack time migration results of the work area and adjacent work areas can be used to guide the stratigraphic interpretation on the depth domain seismic imaging data body from a global perspective. If there are local areas on the seismic imaging data body that do not conform to the geological knowledge, stratigraphic interpretation can be performed according to the geological knowledge, or the stratigraphic interpretation can be performed on the data converted from prestack time migration to the depth domain to obtain the expected stratigraphic position.

[0083] S220: Determine actual layer data.

[0084] In this embodiment, determining the actual horizon data includes:

[0085] S220a1: In the i-th iteration period, obtain the velocity model of the i-th iteration period.

[0086] S220a2: Prestack depth migration.

[0087] S220a3: Actual stratigraphic data interpreted on pre-stack depth migration data.

[0088] S230: Calculate the difference between the two (calculate the residual).

[0089] In this embodiment, the actual layer data and the expected layer data are combined to obtain the residual between the two.

[0090] S240: Tomographic inversion.

[0091] In this embodiment, the velocity model is updated by combining residuals, horizon interpretation data and isotropic depth domain velocity model using tomographic inversion technology based on ray tracing: During tomographic inversion, the target points formed by the three-dimensional grid and ray pairs of the model are regular in the horizontal direction and irregular in the vertical direction. These grid points are consistent with the structural interface of the interpretation horizon. The updated model parameters are displayed as a sparse grid, and the rays are emitted from a finer horizontal grid. The travel time variable of the model parameters for each ray pair is given by the following formula:

[0092]

[0093] Where i can be regarded as the index number of the target point launched to the structural interface on the velocity model. ΔV, Δε, Δδ, Δz represent the updated variables of the P-wave velocity, two anisotropic parameters and depth value respectively. Represents the following matrix part:

[0094]

[0095] where t is the total ray pair travel time, and m = V, ε, δ, z; each ray pair generates an equation, which together define an overdetermined system of linear equations (the number of rays is much greater than the number of model nodes). This can be written as a matrix-vector expression:

[0096] A V ΔV+A ε Δε+A δ Δδ+A z Δz=0 (3)

[0097] S250: The new Speed ​​model.

[0098] In this embodiment, by solving the matrix-vector expression (3) in the previous step, an updated velocity model can be obtained, and the prestack depth migration result of the velocity model is close to the prior geological information.

[0099] S260: Prestack depth migration.

[0100] In this embodiment, prestack depth migration is performed using the new velocity model obtained in the previous step.

[0101] S270: Determine whether the underground structure is consistent with geological knowledge (whether the offset CIP gather is flattened; whether the wave group characteristics of the offset profile are reasonable).

[0102] In this embodiment, if the pre-stack depth migration result of the new velocity model is consistent with the prior geological knowledge, the CIP gather flattening effect after migration is good, and the wave group characteristics on the pre-stack depth migration data body are normal, and there is no abnormal phase axis stringing phenomenon, then the velocity modeling is completed; otherwise, it is necessary to re-interpret the stratigraphic position on the newly migrated depth domain data body to obtain a new actual stratigraphic position, and then iterate steps S230 and S240.

[0103] S280: Output the final result.

[0104] In this embodiment, following the previous step, after the velocity modeling is completed, the final velocity modeling result is output.

[0105] In summary, this embodiment performs velocity modeling under the constraints of prior geological information to obtain a velocity model that conforms to geological knowledge. Prestack depth migration is performed through the velocity model. The wave group characteristics of the processing results are clear, the stratigraphic contact relationship is clear, the stratigraphic distribution characteristics and structure are consistent with the geological knowledge of this area and neighboring areas, and the common imaging point gather flattening effect is good, which effectively solves the prestack depth migration in complex mountainous structural areas and improves the imaging effect of seismic data.

[0106] Embodiment 2

[0107] Figure 5 For a schematic diagram of the structure of the velocity modeling device provided in the embodiment of the present application, please refer to Figure 5 , the velocity modeling device provided in this embodiment includes:

[0108] The first module 510 is used to determine the actual horizon data interpreted in the i-th iteration period through horizon interpretation according to the pre-stack depth migration data of the i-th iteration period;

[0109] The second module 520 is used to determine the expected horizon data of the seismic interpretation of the i+1th iteration cycle according to the prior geological information of the target complex structural area, the geological knowledge of the adjacent work area and the interpretation results of the previous processing link; the prior geological information includes logging and drilling information, and the previous processing link includes the previous pre-stack time migration;

[0110] The third module 530 is used to determine the i+1th iteration velocity model based on the tomographic inversion mode of ray tracing according to the expected layer data of the seismic interpretation of the i+1th iteration cycle, the actual layer data interpreted in the i-th iteration cycle, and the isotropic depth domain velocity model;

[0111] The fourth module 540 is used to determine whether to output the i+1th iterative velocity model as the velocity modeling result of the seismic data corresponding to the target complex structural area based on the pre-stack depth migration data of the i+1th iterative cycle corresponding to the i+1th iterative velocity model and the iteration end condition.

[0112] The specific implementation examples based on the above-mentioned modules for executing the method have been described in detail in the first embodiment and will not be repeated here.

[0113] Based on the above records, this application is now described in detail in combination with specific scenarios.

[0114] The velocity modeling method of the present application is used to obtain a velocity model that conforms to geological knowledge, effectively solves the pre-stack depth migration of complex structures, and improves the imaging effect of seismic data. The specific effect is described in the following research example.

[0115] The work area is located in the high and steep fold belt of the X Basin. The terrain has both hilly and mountainous characteristics, with an altitude of 300m-1300m. The mountains are high and the slopes are steep, gullies are crisscrossed, there are many cliffs and steep ridges, and the terrain is undulating. Surface conditions such as surface collapse, steep cliffs, steep ridges, steep slopes, half-mountains, piedmont broken zones, landslides, exposed rocks and broken areas are distributed throughout the area, resulting in relatively complex surface seismic geological conditions in the work area.

[0116] The high-steep structures and limestone areas in the study area have complex geological structures, developed faults, and large changes in stratum dip, which have a great impact on the propagation of seismic waves. The wave field is relatively complex, especially in the high-steep structures. The reflection wave field is complex, the signal-to-noise ratio is low, the continuity is poor, and imaging is difficult. It is necessary to confirm the structural details and fault distribution characteristics through pre-stack depth migration.

[0117] like Figure 3 This is a profile of prestack depth migration imaging data from an adjacent work area. The eastern wing of the fault in the O3w formation is an anticline structure, which is consistent with the geological understanding of drilling and imaging logging. Under similar surface and underground conditions in this work area, the imaging results of prestack time migration show that the eastern wing of the fault in this formation is an anticline structure, but the prestack depth migration data volume obtained by conventional modeling methods shows that the eastern wing of the fault in this formation is a monocline formation, and the depth domain velocity model may be unreasonable.

[0118] The pre-stack time migration data volume is scaled to the depth domain, and the O3w layer interpreted in the depth domain is taken as the expected layer. The O3w layer interpreted on the current pre-stack depth migration data volume is taken as the actual layer. The difference between the two is calculated. The difference, the current depth domain velocity model and the actual layer are input, and the updated velocity model is obtained through the above-mentioned ray tracing inversion algorithm. The pre-stack depth migration data volume corresponding to the velocity models before and after the update is shown in FIG. Figure 4 As shown, it can be seen that the velocity model obtained by the velocity modeling method of the present application makes the imaged structure more consistent with geological understanding (this is a qualitative evaluation. In addition, it is still necessary to combine the offset CIP gather flattening, whether it conforms to the logging and drilling data and other criteria to judge the reliability of the imaging results).

[0119] Embodiment 3

[0120] This embodiment provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the speed modeling method described in Embodiment 1 is implemented. It is understood that the electronic device may also include an input / output (I / O) interface and a communication component.

[0121] The processor is used to execute all or part of the steps in the speed modeling method in Embodiment 1. The memory is used to store various types of data, which may include instructions of any application or method in the terminal device, and data related to the application.

[0122] The processor can be an application specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a controller, a microcontroller, a microprocessor or other electronic components, and is used to execute the speed modeling method in the above-mentioned embodiment 1.

[0123] The memory can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0124] Embodiment 4

[0125] This embodiment also provides a computer-readable storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory (for example, an SD or DX memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a disk, an optical disk, a server, an App application store, etc., wherein a computer program is stored on the storage medium, and when the computer program is executed by a processor, the following method steps can be implemented:

[0126] According to the pre-stack depth migration data of the i-th iteration period, the actual layer data interpreted in the i-th iteration period are determined through layer interpretation;

[0127] Determine the expected horizon data of the seismic interpretation of the i+1th iteration cycle according to the prior geological information of the target complex structural area, the geological knowledge of the adjacent work area and the interpretation results of the previous processing link; the prior geological information includes logging and drilling information, and the previous processing link includes the previous pre-stack time migration;

[0128] Based on the ray tracing tomographic inversion mode, the i+1th iteration velocity model is determined according to the expected layer data of the seismic interpretation of the i+1th iteration cycle, the actual layer data interpreted in the i-th iteration cycle, and the isotropic depth domain velocity model;

[0129] According to the pre-stack depth migration data of the i+1th iteration period corresponding to the i+1th iteration velocity model and the iteration end condition, it is determined whether to output the i+1th iteration velocity model as the velocity modeling result of the seismic data corresponding to the target complex structural area.

[0130] The specific implementation process of the above method steps can be found in Example 1, and this embodiment will not be repeated here.

[0131] In summary, the present application provides a velocity modeling method, device, electronic device and storage medium; the method includes determining the actual stratigraphic data interpreted in the i-th iteration period through stratigraphic interpretation based on the pre-stack depth migration data of the i-th iteration period; determining the expected stratigraphic data of the seismic interpretation in the i+1-th iteration period according to the prior geological information of the target complex structural area, the geological knowledge of the adjacent work area and the interpretation results of the previous processing link; the prior geological information includes logging and drilling information, and the previous processing link includes the previous pre-stack time migration; based on the tomographic inversion mode of ray tracing, the i+1-th iteration velocity model is determined according to the expected stratigraphic data of the seismic interpretation in the i+1-th iteration period, the actual stratigraphic data interpreted in the i-th iteration period, and the isotropic depth domain velocity model; according to the pre-stack depth migration data of the i+1-th iteration period corresponding to the i+1-th iteration velocity model, and the iteration end condition, it is determined whether to output the i+1-th iteration velocity model as the velocity modeling result of the seismic data corresponding to the target complex structural area. In the method provided in this scheme, velocity modeling is carried out under the constraint of prior geological information to obtain a velocity model that conforms to geological understanding. Prestack depth migration is carried out through this velocity model. The wave group characteristics of the processing results are clear, the stratigraphic contact relationship is clear, the stratigraphic distribution characteristics and structure are consistent with the geological understanding of this area and neighboring areas, and the common imaging point gathers have a good flattening effect, which effectively solves the prestack depth migration in complex mountainous structural areas and improves the imaging effect of seismic data.

[0132] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0133] Although the implementation methods disclosed in this application are as above, the contents described are only implementation methods adopted for facilitating the understanding of this application, and are not intended to limit this application. Any technician in the technical field to which this application belongs can make any modifications and changes in the form and details of implementation without departing from the spirit and scope disclosed in this application, but the scope of patent protection of this application shall still be based on the scope defined in the attached claims.

Claims

1. A velocity modeling method for seismic data in complex structural areas, characterized in that: include: According to the pre-stack depth migration data of the i-th iteration period, the actual layer data interpreted in the i-th iteration period are determined through layer interpretation; Determine the expected horizon data of the seismic interpretation of the i+1th iteration cycle according to the prior geological information of the target complex structural area, the geological knowledge of the adjacent work area and the interpretation results of the previous processing link; the prior geological information includes logging and drilling information, and the previous processing link includes the previous pre-stack time migration; Based on the ray tracing tomographic inversion mode, the i+1th iteration velocity model is determined according to the expected layer data of the seismic interpretation of the i+1th iteration cycle, the actual layer data interpreted in the i-th iteration cycle, and the isotropic depth domain velocity model; According to the pre-stack depth migration data of the i+1th iteration period corresponding to the i+1th iteration velocity model and the iteration end condition, it is determined whether to output the i+1th iteration velocity model as the velocity modeling result of the seismic data corresponding to the target complex structural area.

2. The velocity modeling method according to claim 1, characterized in that: The determining whether to output the (i+1)th iterative velocity model as a velocity modeling result according to the (i+1)th iterative cycle prestack depth migration data corresponding to the (i+1)th iterative velocity model and an iteration end condition comprises: Determining whether the pre-stack depth migration data of the (i+1)th iteration cycle matches the priori geological information; Determining whether the common imaging point gathers of the prestack depth migration data of the (i+1)th iteration period are flattened; Determining whether the wave group characteristics of the prestack depth migration data of the (i+1)th iteration period are reasonable; If the pre-stack depth migration data of the i+1th iteration cycle matches the priori geological information, the common imaging point gathers are flattened, and the wave group characteristics are reasonable, the i+1th iteration velocity model is output as the velocity modeling result.

3. The velocity modeling method according to claim 1, characterized in that: Before the step of determining the actual horizon data interpreted in the i-th iteration period through horizon interpretation according to the pre-stack depth migration data of the i-th iteration period, the method further includes: Acquire a seismic imaging data volume corresponding to the seismic data of the complex structural area; Determining a preset number of events marking strata according to the horizon interpretation data on the seismic imaging data volume; The event axis of the marker stratum is traced to obtain actual stratum data interpreted in the i-th iteration cycle.

4. The velocity modeling method according to claim 3, characterized in that: Based on the prior geological information and seismic data of the target complex structural area, the expected horizon data of the seismic interpretation of the i+1th iteration cycle is determined, including: Determining prestack time migration data based on seismic data of the target complex structural area; converting the prestack time migration data into depth domain data; Based on the prior geological information, the expected horizon data are determined according to the horizon interpretation of the seismic imaging data volume and the horizon interpretation of the depth domain data.

5. The velocity modeling method according to claim 1, characterized in that: Before the step of determining the expected horizon data of the (i+1)th iteration period according to the pre-stack depth migration data of the i-th iteration period, the method further includes: Determining a preset tomography inversion mode, wherein the preset tomography inversion mode includes a model tomography inversion mode or a grid tomography inversion mode; The isotropic depth-domain velocity model is determined according to the preset tomographic inversion mode.

6. The velocity modeling method according to any one of claims 1 to 5, characterized in that: Determining the i+1th iteration velocity model through a tomographic inversion mode based on ray tracing according to the expected horizon data of the seismic interpretation of the i+1th iteration cycle, the actual horizon data interpreted in the i-th iteration cycle, and the isotropic depth domain velocity model, including: Determine the residual of the i-th iteration cycle according to the actual layer data and the expected layer data of the seismic interpretation of the i+1-th iteration cycle; Based on the ray tracing tomographic inversion mode, the i+1th iteration velocity model is determined according to the residual of the i-th iteration cycle, the isotropic depth domain velocity model and the layer interpretation data.

7. The velocity modeling method according to claim 6, characterized in that: Determining the i+1th iteration velocity model according to the residual of the i-th iteration cycle, the isotropic depth domain velocity model and the horizon interpretation data through a tomographic inversion mode based on ray tracing, including: Determining updated variables of anisotropic parameters of each target point according to the isotropic depth-domain velocity model; the target point includes a target point between the three-dimensional grid and the ray pair of the i-th iteration velocity model; Determining an update variable of the P-wave velocity and an update variable of the depth value of each target point according to the residual error of the i-th iteration period and the layer interpretation data; Determining the travel time variables of each ray pair model parameter according to the update variables of the anisotropy parameter, the update variables of the longitudinal wave velocity and the update variables of the depth value; The (i+1)th iterative velocity model is obtained according to the travel time variable and the target matrix vector.

8. A velocity modeling device for seismic data in complex tectonic areas, characterized in that: include: The first module is used to determine the actual horizon data interpreted in the i-th iteration period through horizon interpretation according to the pre-stack depth migration data of the i-th iteration period; The second module is used to determine the expected layer data of the seismic interpretation of the i+1th iteration cycle according to the prior geological information of the target complex structural area, the geological knowledge of the adjacent work area and the interpretation results of the previous processing link; the prior geological information includes logging and drilling information, and the previous processing link includes the previous pre-stack time migration; The third module is used for determining the i+1th iteration velocity model in a tomographic inversion mode based on ray tracing according to the expected layer data of the seismic interpretation of the i+1th iteration cycle, the actual layer data interpreted in the i-th iteration cycle, and the isotropic depth domain velocity model; The fourth module is used to determine whether to output the i+1th iterative velocity model as the velocity modeling result of the seismic data corresponding to the target complex structural area according to the pre-stack depth migration data of the i+1th iterative cycle corresponding to the i+1th iterative velocity model and the iteration end condition.

9. An electronic device, characterized in that: The method comprises a memory and a processor; a computer program is stored in the memory, and when the computer program is executed by the processor, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by one or more processors, the method according to any one of claims 1 to 7 is implemented.

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