VSP constraint-based near-surface velocity model establishment method and system

Abstract

The invention discloses a near-surface velocity model establishment method and system based on VSP constraint, and belongs to the technical field of near-surface velocity modeling methods. Comprising the following steps: calibrating a first-motion wave travel time tomography inversion speed model by using a VSP speed to obtain a calibrated tomography inversion speed model, and calculating speeds of different layer sections to obtain speed calibration values; carrying out longitudinal subdivision on the calibration chromatography inversion velocity model to generate a subdivision grid model, and calculating the velocity of different layer sections to obtain a velocity correction coefficient; according to a multi-point geostatistics algorithm, spatial interpolation extrapolation is carried out on each speed correction coefficient, and a final speed model correction coefficient body is obtained; and calculating to obtain a near-surface velocity model based on the first arrival wave travel time tomography inversion velocity model and the final velocity model correction coefficient body. According to the method, the characteristics of high spatial lateral resolution of the first arrival tomography inversion velocity model and high longitudinal resolution of the VSP velocity model are effectively combined, and near-surface fine modeling is realized.

Description

Technical Field

[0001] The invention belongs to the technical field of near-surface velocity modeling methods, and relates to a near-surface velocity model establishment method and system based on VSP constraints. Background Art

[0002] Complex surface conditions and significant changes in near-surface velocity pose great challenges to the inversion of near-surface velocity models. First-arrival travel-time tomography can simultaneously consider different types of first-arrival wave information such as direct waves, transmitted waves, reflected waves, and refracted waves, and can obtain velocity models of longitudinal and lateral changes in near-surface media. It is currently a key technology for near-surface velocity inversion. Due to factors such as near-surface anisotropy and absorption attenuation, the near-surface velocity obtained by the first-arrival tomography inversion algorithm is higher than the actual medium velocity, which is not conducive to velocity modeling and depth migration. Existing constrained inversion based on shallow small refraction data or micro-logging data is affected by the sparse spatial distribution of such data, and direct interpolation often results in "bull's eye" velocity anomalies. In the process of onshore complex piedmont seismic exploration, the accuracy of near-surface velocity modeling directly restricts the quality of seismic imaging. How to establish a high-precision near-surface velocity model is the key to restricting seismic exploration in complex near-surface areas. There is currently no suitable theoretical and technical method. Summary of the invention

[0003] The purpose of the present invention is to solve the technical problem of low accuracy of existing near-surface velocity models, and to provide a method and system for establishing a near-surface velocity model based on VSP constraints, which effectively combines the high spatial lateral resolution of the first-arrival wave traveltime tomographic inversion velocity model and the high vertical resolution of the VSP velocity model, thereby realizing fine near-surface modeling and improving the accuracy and reliability of the near-surface velocity model.

[0004] In order to achieve the above object, the present invention adopts the following technical solutions:

[0005] In a first aspect, the present invention provides a method for establishing a near-surface velocity model based on VSP constraints, comprising the following steps:

[0006] The VSP velocity is used to calibrate the first arrival wave travel time tomographic inversion velocity model, and a calibrated tomographic inversion velocity model is obtained. The velocity of different layers is calculated to obtain the velocity calibration value.

[0007] The calibrated tomographic inversion velocity model is vertically subdivided to generate a subdivided grid model, and the velocity of different layers is calculated to obtain the velocity correction coefficient;

[0008] According to the multi-point geostatistical algorithm, each velocity correction coefficient is spatially interpolated and extrapolated to obtain the final velocity model correction coefficient volume;

[0009] The near-surface velocity model is obtained based on the tomographic inversion velocity model of the first-arrival wave travel time and the final velocity model correction coefficient volume calculation.

[0010] A further improvement of the present invention is:

[0011] The first arrival wave travel time tomographic inversion velocity model is obtained by the following steps:

[0012] The tomographic velocity of the first arrival time of the full offset segment in the seismic information is picked up, and the tomographic velocity inversion is performed to obtain a shallow tomographic velocity model; the maximum offset distance of the tomographic velocity inversion is selected to be greater than or equal to 5000 meters to obtain a deep tomographic velocity model; the shallow tomographic velocity model and the deep tomographic velocity model constitute a tomographic inversion velocity model of the first arrival wave travel time.

[0013] The use of VSP velocity to calibrate the first arrival wave travel time tomographic inversion velocity model is achieved by the following steps:

[0014] Load all VSP curves in the work area, compare the VSP curves with the chromatography velocity, and obtain the optimal VSP curve;

[0015] Adjust the reference plane of the VSP curve to be consistent with the reference plane of the tomographic velocity, compare the P-wave velocity curve in the preferred VSP curve with the tomographic velocity, and determine the reliable depth of the tomographic inversion velocity model during the travel time of the first arrival wave;

[0016] Based on the first-arrival traveltime tomographic inversion velocity model, the first-arrival traveltime tomographic inversion ray density distribution and the reliable depth of the first-arrival traveltime tomographic inversion velocity model, the reliable model bottom interface of the first-arrival traveltime tomographic inversion velocity model of the whole area is established, and the calibration of the first-arrival traveltime tomographic inversion velocity model is completed.

[0017] The vertical subdivision of the calibration tomographic inversion velocity model is achieved by the following steps:

[0018] Based on the first-arrival wave traveltime tomographic inversion velocity model, the bottom interface of the reliable model and the surface elevation information, the first-arrival wave traveltime tomographic inversion near-surface velocity model is vertically subdivided to obtain a subdivided grid model.

[0019] The vertical subdivision adopts geometric subdivision method; the accuracy of the vertical subdivision is the same as the vertical accuracy of the VSP well velocity layering data.

[0020] The calculation of the velocity of different layers to obtain the velocity calibration value and the calculation of the velocity of different layers to obtain the velocity correction coefficient are achieved by the following steps:

[0021] Based on the spatial position relationship between the VSP well trajectory data and the subdivided grid model, a discrete well trajectory grid model is obtained; the discrete well trajectory grid model is abstracted into a spatial polyline, called the well trajectory polyline L, and the velocity correction coefficient L is directly calculated from the scattered points on the polyline. i The intersection point of the well trajectory line L and all the subdivision levels of the subdivision grid model is recorded as j, and multiple subdivision levels adjacent to point j are selected to calculate the distance weight coefficient W i At the same time, the VSP discrete points of the adjacent points are used to calculate the scattered point velocity correction coefficient Li, and the inverse distance weighted algorithm is used to obtain the velocity correction coefficient D at point j j :

[0022] L i =L ivsp / L ifb

[0023]

[0024]

[0025] Where i is a point on the well trajectory line L; L i is the direct calculation speed correction coefficient of point i; L ivsp is the VSP speed value at point i; L ifb is the tomographic inversion velocity value at point i; W i is the weight coefficient of the distance point i; d i is the distance from point i to point j; n is the maximum value of the calculated distance; D j It is the speed correction coefficient after inverse distance weighted calculation.

[0026] The method of spatially interpolating and extrapolating each velocity correction coefficient according to the multi-point geostatistical algorithm to obtain the final velocity model correction coefficient body is implemented by the following steps:

[0027] The subdivided grid model is divided into different velocity change zones in the horizontal direction. The variograms of different layers in each segment of the velocity change zone are calculated respectively, and the corresponding velocity correction coefficients are interpolated using the variograms:

[0028]

[0029] Where r(h) is the variogram; E represents the expectation; Z(i) and Z(i+h) are the observations at two locations with a distance h in the spatial region.

[0030] In a second aspect, the present invention provides a near-surface velocity model establishment system based on VSP constraints, characterized in that it includes:

[0031] The calibration unit uses the VSP velocity to calibrate the tomographic inversion velocity model of the first arrival wave travel time, obtains the calibrated tomographic inversion velocity model, and calculates the velocity of different layers to obtain the velocity calibration value;

[0032] Subdividing the grid unit, vertically subdividing the calibrated tomographic inversion velocity model, generating a subdivided grid model, calculating the velocity of different layers to obtain the velocity correction coefficient;

[0033] The correction unit performs spatial interpolation and extrapolation of each velocity correction coefficient according to a multi-point geostatistical algorithm to obtain a final velocity model correction coefficient body;

[0034] The calculation unit calculates the near-surface velocity model based on the tomographic inversion velocity model of the first arrival wave travel time and the final velocity model correction coefficient volume.

[0035] In a third aspect, the present invention provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method for establishing a near-surface velocity model based on VSP constraints when executing the computer program.

[0036] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the method for establishing a near-surface velocity model based on VSP constraints.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] The present invention discloses a method for establishing a near-surface velocity model based on VSP constraints. The VSP velocity is used to calibrate the first arrival wave travel time tomographic inversion velocity model. After obtaining the calibrated tomographic inversion velocity model, the velocities of different layers are calculated to obtain velocity calibration values. The method effectively combines the characteristics of high spatial lateral resolution of the first arrival tomographic inversion velocity model and high vertical resolution of the VSP velocity model. The tomographic velocity calibration using VSP constraints is more stable and reliable than calibrating the tomographic velocity by modifying the first arrival wave, making the calibrated tomographic inversion velocity model more reliable than the first arrival wave travel time tomographic inversion velocity model. Fine; on the basis of the calibrated tomographic inversion velocity model, it is further subdivided vertically to achieve near-surface fine modeling; according to the multi-point geostatistical algorithm, each velocity correction coefficient is spatially interpolated and extrapolated to obtain the final velocity model correction coefficient body; the near-surface velocity model is calculated based on the tomographic inversion velocity model and the final velocity model correction coefficient body of the first arrival wave travel time, and a high-precision near-surface velocity model is obtained with high calculation efficiency. The near-surface velocity model based on VSP constraints is conducive to depth migration velocity modeling, which can improve the imaging accuracy of pre-stack depth migration starting from the real surface. The accuracy and reliability of the shallow tomographic velocity model are improved, and an effective technical solution is provided for solving the time domain static correction and depth domain imaging velocity modeling of complex surface exploration areas in seismic data processing, which can improve the velocity inversion accuracy of pre-stack depth migration and reservoir imaging, and the near-surface velocity model based on VSP constraints is conducive to depth migration velocity modeling, which can improve the imaging accuracy of pre-stack depth migration starting from the real surface, and can be widely used in the near-surface seismic exploration process of onshore piedmont belt. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] 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.

[0040] Figure 1 A flow chart of a method for establishing a near-surface velocity model based on VSP constraints in the present invention;

[0041] Figure 2 A diagram showing the specific steps of the method for establishing a near-surface velocity model based on VSP constraints in the present invention;

[0042] Figure 3 The first arrival wave travel time tomographic inversion velocity model obtained by the first arrival wave travel time tomographic inversion of the present invention;

[0043] Figure 4It is a reliable model bottom interface and surface elevation surface of the velocity model of the tomographic inversion of the first arrival wave travel time after VSP calibration in the present invention;

[0044] Figure 5 This is a longitudinal subdivision effect diagram of the first arrival wave travel time tomographic inversion velocity model of the present invention;

[0045] Figure 6 Comparison between the first arrival wave travel time tomographic inversion velocity model of the present invention and the VSP velocity. Figure 6 (a) is the comparison of speed curves before constraint. Figure 6 (b) is the comparison of velocity curves after constraint, where the scattered points are VSP velocities and the continuous broken lines are first-arrival tomographic inversion velocities;

[0046] Figure 7 Establishing a system diagram for the near-surface velocity model based on VSP constraints in the present invention;

[0047] Figure 8 This is a module diagram of an electronic device in the present invention. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0049] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0051] The present invention is further described in detail below in conjunction with the accompanying drawings:

[0052] See also Figure 1 The embodiment of the present invention proposes a near-surface modeling method based on VSP velocity constraints, comprising the following steps:

[0053] S1, use the VSP velocity to calibrate the first arrival wave travel time tomographic inversion velocity model, obtain the calibrated tomographic inversion velocity model, calculate the velocity of different layers to obtain the velocity calibration value;

[0054] S2, vertically subdividing the calibration tomographic inversion velocity model to generate a subdivided grid model, calculating the velocity of different layers to obtain the velocity correction coefficient;

[0055] S3, according to the multi-point geostatistical algorithm, the velocity correction coefficients are spatially interpolated and extrapolated to obtain the final velocity model correction coefficient body;

[0056] S4, the near-surface velocity model is obtained based on the tomographic inversion velocity model of the first arrival wave travel time and the final velocity model correction coefficient volume calculation.

[0057] This method effectively combines the high spatial lateral resolution of the tomographic inversion velocity model of the first arrival wave travel time and the high vertical resolution of the VSP velocity model, and realizes fine modeling of the near surface; using VSP for tomographic velocity calibration is more stable and reliable than calibrating the tomographic velocity by modifying the first arrival wave; if there are multiple VSPs in the work area, vertical and horizontal calibration can also be considered; the tomographic inversion velocity model of the first arrival wave travel time constrained by VSP is conducive to deep migration velocity modeling, which can improve the imaging accuracy of prestack depth migration starting from the real surface. This method can obtain a high-precision near-surface velocity model based on the first arrival wave information and VSP velocity information of seismic data, and the calculation method is efficient, so this method can be widely used in the near-surface seismic exploration process of onshore piedmont belt.

[0058] See also Figure 2 The embodiment of the present invention proposes a near-surface modeling method based on VSP velocity constraint, which uses the tomographic inversion of the first arrival wave travel time to obtain the near-surface shallow layer background velocity model, obtains the velocity calibration value of the well point position inversion model and the VSP velocity based on the VSP data, vertically subdivides the calibrated tomographic inversion velocity model, generates a subdivided grid model, calculates the velocity of different layers to obtain the velocity correction coefficient; then uses the multi-point geostatistical method to perform spatial interpolation and extrapolation on the velocity correction coefficient, thereby realizing the calibration of the velocity model of the entire work area. The method includes the following steps: using the tomographic inversion of the first arrival wave travel time to obtain the tomographic inversion velocity model of the first arrival wave travel time; selecting The reliable VSP P-wave velocity curve is compared with the first-arrival tomographic inversion velocity. According to the VSP velocity stratification characteristics, the VSP curve is used to calibrate and fit the data of different layers to obtain the velocity calibration values ​​of different depths of each VSP well. The calibrated tomographic inversion velocity model is vertically subdivided to generate a subdivided grid model, and the velocities of different layers are calculated to obtain the velocity correction coefficient. According to the multi-point geostatistical algorithm, the obtained velocity correction coefficient is spatially interpolated and extrapolated. The velocity correction coefficient body obtained after interpolation is used to perform velocity model correction on the first-arrival tomographic inversion velocity body of each small layer to obtain the near-surface velocity model data body used for depth migration.

[0059] The content of the present invention is described in detail below in conjunction with specific embodiments:

[0060] Step 1: First arrival travel time tomographic inversion

[0061] Pick up the tomographic velocity of the first arrival travel time of the seismic data, try to pick up the first arrival time of the full offset segment, use the first arrival to perform travel time tomographic inversion, and obtain a shallow tomographic velocity model. The maximum offset distance in the inversion is selected to be above 5000 meters as much as possible to obtain a deep tomographic velocity model; the shallow tomographic velocity model and the deep tomographic velocity model constitute the first arrival wave travel time tomographic inversion velocity model, such as Figure 3 shown.

[0062] First-arrival wave tomographic inversion process: First, carry out the screening of small refraction and micro-logging scattered data in the work area, and use the Delaunay triangulation interpolation algorithm to establish the micro-logging velocity model of the extremely shallow layer. Then, perform micro-logging constrained near-path inversion (within 500 meters of shot offset), and combine the characteristics of micro-logging accuracy on the "point" and the stable trend of the first-arrival inversion results on the "surface". Take the extremely shallow layer information as a constraint condition, and together with the travel time residual, form the objective function, solve the Lagrangian constrained objective function least squares solution, and obtain high-precision extremely shallow layer velocity information (20-50 meters). Subsequently, the shot offset restriction is gradually lifted, and the shallower velocity inverted from the near-offset is used as a constraint condition, and the inversion of medium and long shot offsets is gradually carried out.

[0063] In tomographic inversion, the tomographic equations established by M rays and N unknowns can be expressed as:

[0064] AΔS=ΔT (1)

[0065] Where A represents the Jacob matrix; ΔS represents the slowness correction; ΔT represents the travel time residual.

[0066] In actual production, step-by-step constrained first-arrival wave tomographic inversion generally requires three rounds of inversion. The first step is to use micro-logging to constrain the near-offset inversion to obtain accurate extremely shallow layer velocities; the second step is to perform medium-offset (within 3000 meters) constrained inversion to obtain a stable high-speed top interface and calculate the static correction; finally, full-offset constrained inversion is performed to obtain the maximum detection depth velocity model. Step-by-step constrained inversion can ensure the maximum depth range of the final inversion result, while not losing the low-velocity information of the extremely shallow layer and maintaining velocity accuracy.

[0067] Step 2: Comparative analysis of VSP P-wave velocity curve and first-arrival tomography inversion velocity

[0068] (1) Import the VSP P-wave velocity curves of all well points in the work area using the ipresis software platform, exclude wells with abnormal VSP curves and insufficient measurement depth, and select the VSP curve; (2) The VSP measurement datum is the true surface elevation of the wellhead, and the tomographic inversion velocity datum is the unified elevation depth of the work area (-3000m for complex mountainous work areas). Adjust the datum of the imported VSP velocity curve to be consistent with the tomographic velocity datum; (3) Compare the reliable VSP P-wave velocity curve with the first-arrival tomographic inversion velocity, and determine the reliable depth and lateral distribution of the inversion model based on the similarity of the two in the vertical range and the distribution of lateral velocity differences in the entire work area; (4) Use the first-arrival tomographic inversion velocity model and the first-arrival inversion ray density distribution in step 1, combined with the reliable depth of the model obtained by VSP analysis, to establish the reliable model bottom interface of the first-arrival wave travel time tomographic inversion velocity model of the entire area, and complete the calibration of the first-arrival wave travel time tomographic inversion velocity model, such as Figure 4 shown.

[0069] Step 3: Calculate the velocity calibration values ​​for different VSP well points in the work area

[0070] Select typical VSP points in different velocity change areas such as conglomerate area and Gobi area, take non-conglomerate area as the main, perform different fittings on conglomerate area and non-conglomerate area respectively, and divide the velocity layer information, calculate the velocity calibration value of each layer layer by layer; (This velocity calibration value is the velocity calibration reference value of different lithology formations, which is used to verify the calculation accuracy of subsequent velocity correction coefficient)

[0071] Step 4: Subdividing the lattice model

[0072] Based on the reliable model bottom interface and surface elevation information of the first-arrival wave travel time tomographic inversion velocity model obtained in step 1 and the first-arrival wave travel time tomographic inversion velocity model of the entire area generated in step 2, the first-arrival wave travel time tomographic inversion velocity model is vertically subdivided to generate a subdivided grid model. The subdivision method is the geometric subdivision method, and the vertical accuracy of the subdivided grid model refers to the vertical accuracy of the VSP well velocity layering data, such as Figure 5 shown.

[0073] Step 5: Calculate the velocity correction coefficients for each layer of the subdivided grid model

[0074] Based on computational geometry algorithms, the spatial position relationship between the imported well trajectory data and the grid model grid is automatically calculated, and a discrete well trajectory grid model is automatically generated. The well trajectory is abstracted as a spatial polyline, called the well trajectory polyline L, and the scattered points on the polyline directly calculate the velocity correction coefficient as L i The intersection point of the well trajectory line L and all the subdivision levels of the subdivision grid model is recorded as j, and multiple subdivision levels adjacent to point j are selected to calculate the distance weight coefficient W iAt the same time, the VSP discrete points of the adjacent points are used to calculate the scattered point velocity correction coefficient Li, and the inverse distance weighted algorithm is used to obtain the velocity correction coefficient D at point j j At the same time, check the calculated scattered velocity correction coefficient Li to ensure that the velocity in the small layer is within the velocity calibration value range obtained in step three to ensure the accuracy of the calculation.

[0075] L i =L ivsp / L ifb (2)

[0076]

[0077]

[0078] Where i is a point on the well trajectory line L; L i is the direct calculation speed correction coefficient of point i; L ivsp is the VSP speed value at point i; L ifb is the tomographic inversion velocity value at point i; W i is the weight coefficient of the distance point i; d i is the distance from point i to point j; n is the maximum value of the calculated distance; D j It is the speed correction coefficient after inverse distance weighted calculation.

[0079] Step 6: Spatial interpolation of coefficients based on geostatistics

[0080] The velocity model of the first arrival wave travel time tomography inversion is divided into subdivided layers according to the topography and the top of the high-speed layer, such as Figure 5 As shown; then it is divided into different velocity change zones in the horizontal direction, and the variogram is calculated for each section of different layers, and the VSP velocity correction coefficient in the section is interpolated by Kriging using this variogram. During the interpolation process, single-well or multi-well extrapolation method can be selected to ensure the stability of the interpolation and obtain the velocity correction coefficient body.

[0081]

[0082] Where r(h) is the variogram; E represents the expectation; Z(i) and Z(i+h) are the observations at two locations with a distance h in the spatial region.

[0083] Step 7: Tomographic inversion velocity model correction

[0084] Using the first arrival wave travel time tomographic inversion velocity model obtained in step 1 and the velocity correction coefficient volume calculated in step 6, the near-surface velocity model after VSP constraint correction is calculated:

[0085] Vol vsp =Vol fb *D (6)

[0086] Among them, Vol. fb is the velocity model of the first arrival wave travel time tomographic inversion; D is the velocity correction coefficient calculated in step 6; Vol vsp Represents the output constraint-corrected near-surface velocity model.

[0087] See also Figure 7 The embodiment of the present invention proposes a near-surface modeling system based on VSP velocity constraints, including:

[0088] The calibration unit uses the VSP velocity to calibrate the tomographic inversion velocity model of the first arrival wave travel time, obtains the calibrated tomographic inversion velocity model, and calculates the velocity of different layers to obtain the velocity calibration value;

[0089] Subdividing the grid unit, vertically subdividing the calibrated tomographic inversion velocity model, generating a subdivided grid model, calculating the velocity of different layers to obtain the velocity correction coefficient;

[0090] The correction unit performs spatial interpolation and extrapolation of each velocity correction coefficient according to a multi-point geostatistical algorithm to obtain a final velocity model correction coefficient body;

[0091] The calculation unit calculates the near-surface velocity model based on the tomographic inversion velocity model of the first arrival wave travel time and the final velocity model correction coefficient volume.

[0092] The system uses the tomographic inversion of the seismic first-arrival wave travel time to obtain the tomographic inversion velocity model of the first-arrival wave travel time, uses the VSP velocity to calibrate the tomographic inversion velocity model of the first-arrival wave travel time, calculates the calibration values ​​of different VSP well points in the work area, vertically subdivides the calibrated tomographic inversion velocity model, generates a subdivision grid model, calculates the velocity of different layers to obtain the velocity correction coefficient; then uses the tomographic inversion velocity model of the first-arrival wave travel time to obtain the spatial variation function, and performs spatial interpolation and extrapolation of the velocity correction coefficient based on the Kriging method to complete the calibration of the velocity model of the entire area. This system improves the accuracy and reliability of shallow velocity, solves the technical problems of how to perform time domain static correction and depth domain imaging velocity modeling in complex surface exploration areas in seismic data processing, and can improve the velocity inversion accuracy of pre-stack depth migration and reservoir imaging.

[0093] like Figure 8 As shown, the third object of the present invention is to provide an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method for establishing a near-surface velocity model based on VSP constraints when executing the computer program.

[0094] The method for establishing a near-surface velocity model based on VSP constraints comprises the following steps:

[0095] The VSP velocity is used to calibrate the first arrival wave travel time tomographic inversion velocity model, and a calibrated tomographic inversion velocity model is obtained. The velocity of different layers is calculated to obtain the velocity calibration value.

[0096] The calibrated tomographic inversion velocity model is vertically subdivided to generate a subdivided grid model, and the velocity of different layers is calculated to obtain the velocity correction coefficient;

[0097] According to the multi-point geostatistical algorithm, each velocity correction coefficient is spatially interpolated and extrapolated to obtain the final velocity model correction coefficient volume;

[0098] The near-surface velocity model is obtained based on the tomographic inversion velocity model of the first-arrival wave travel time and the final velocity model correction coefficient volume calculation.

[0099] A fourth object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the method for establishing a near-surface velocity model based on VSP constraints.

[0100] The method for establishing a near-surface velocity model based on VSP constraints comprises the following steps:

[0101] The VSP velocity is used to calibrate the first arrival wave travel time tomographic inversion velocity model, and a calibrated tomographic inversion velocity model is obtained. The velocity of different layers is calculated to obtain the velocity calibration value.

[0102] The calibrated tomographic inversion velocity model is vertically subdivided to generate a subdivided grid model, and the velocity of different layers is calculated to obtain the velocity correction coefficient;

[0103] According to the multi-point geostatistical algorithm, each velocity correction coefficient is spatially interpolated and extrapolated to obtain the final velocity model correction coefficient volume;

[0104] The near-surface velocity model is obtained based on the tomographic inversion velocity model of the first-arrival wave travel time and the final velocity model correction coefficient volume calculation.

[0105] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0106] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0107] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0108] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps of the functions specified in one or more boxes, such as Figure 8 shown.

[0109] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for establishing a near-surface velocity model based on VSP constraints, characterized in that: The following steps are involved: The VSP velocity is used to calibrate the first arrival wave travel time tomographic inversion velocity model, and a calibrated tomographic inversion velocity model is obtained. The velocity of different layers is calculated to obtain the velocity calibration value. The calibrated tomographic inversion velocity model is vertically subdivided to generate a subdivided grid model, and the velocity of different layers is calculated to obtain the velocity correction coefficient; According to the multi-point geostatistical algorithm, each velocity correction coefficient is spatially interpolated and extrapolated to obtain the final velocity model correction coefficient volume; The near-surface velocity model is obtained based on the tomographic inversion velocity model of the first-arrival wave travel time and the final velocity model correction coefficient volume calculation.

2. The method for establishing a near-surface velocity model based on VSP constraints according to claim 1, characterized in that: The first arrival wave travel time tomographic inversion velocity model is obtained by the following steps: The tomographic velocity of the first arrival time of the full offset segment in the seismic information is picked up, and the tomographic velocity inversion is performed to obtain a shallow tomographic velocity model; the maximum offset distance of the tomographic velocity inversion is selected to be greater than or equal to 5000 meters to obtain a deep tomographic velocity model; the shallow tomographic velocity model and the deep tomographic velocity model constitute a tomographic inversion velocity model of the first arrival wave travel time.

3. The method for establishing a near-surface velocity model based on VSP constraints according to claim 1, characterized in that: The use of VSP velocity to calibrate the first arrival wave travel time tomographic inversion velocity model is achieved by the following steps: Load all VSP curves in the work area, compare the VSP curves with the chromatography velocity, and obtain the optimal VSP curve; Adjust the reference plane of the VSP curve to be consistent with the reference plane of the tomographic velocity, compare the P-wave velocity curve in the preferred VSP curve with the tomographic velocity, and determine the reliable depth of the tomographic inversion velocity model during the travel time of the first arrival wave; Based on the first-arrival traveltime tomographic inversion velocity model, the first-arrival traveltime tomographic inversion ray density distribution and the reliable depth of the first-arrival traveltime tomographic inversion velocity model, the reliable model bottom interface of the first-arrival traveltime tomographic inversion velocity model of the whole area is established, and the calibration of the first-arrival traveltime tomographic inversion velocity model is completed.

4. The method for establishing a near-surface velocity model based on VSP constraints according to claim 1, characterized in that: The vertical subdivision of the calibration tomographic inversion velocity model is achieved by the following steps: Based on the first-arrival wave traveltime tomographic inversion velocity model, the bottom interface of the reliable model and the surface elevation information, the first-arrival wave traveltime tomographic inversion near-surface velocity model is vertically subdivided to obtain a subdivided grid model.

5. The method for establishing a near-surface velocity model based on VSP constraints according to claim 4, characterized in that: The vertical subdivision adopts geometric subdivision method; the accuracy of the vertical subdivision is the same as the vertical accuracy of the VSP well velocity layering data.

6. The method for establishing a near-surface velocity model based on VSP constraints according to claim 4, characterized in that: The calculation of the velocity of different layers to obtain the velocity calibration value and the calculation of the velocity of different layers to obtain the velocity correction coefficient are achieved by the following steps: Based on the spatial position relationship between the VSP well trajectory data and the subdivided grid model, a discrete well trajectory grid model is obtained; the discrete well trajectory grid model is abstracted into a spatial polyline, called the well trajectory polyline L, and the velocity correction coefficient L is directly calculated from the scattered points on the polyline. i The intersection point of the well trajectory line L and all the subdivision levels of the subdivision grid model is recorded as j, and multiple subdivision levels adjacent to point j are selected to calculate the distance weight coefficient W i At the same time, the VSP discrete points of the adjacent points are used to calculate the scattered point velocity correction coefficient Li, and the inverse distance weighted algorithm is used to obtain the velocity correction coefficient D at point j j : L i =L ivsp / L ifb (1) Where i is a point on the well trajectory line L; L i is the direct calculation speed correction coefficient of point i; L ivsp is the VSP speed value at point i; L ifb is the tomographic inversion velocity value at point i; W i is the weight coefficient of the distance point i; d i is the distance from point i to point j; n is the maximum value of the calculated distance; D j It is the speed correction coefficient after inverse distance weighted calculation.

7. The method for establishing a near-surface velocity model based on VSP constraints according to claim 1, characterized in that: The method of spatially interpolating and extrapolating each velocity correction coefficient according to the multi-point geostatistical algorithm to obtain the final velocity model correction coefficient body is implemented by the following steps: The subdivided grid model is divided into different velocity change zones in the horizontal direction. The variograms of different layers in each segment of the velocity change zone are calculated respectively, and the corresponding velocity correction coefficients are interpolated using the variograms: Where r(h) is the variogram; E represents the expectation; Z(i) and Z(i+h) are the observations at two locations with a distance h in the spatial region.

8. A near-surface velocity model building system based on VSP constraints, characterized in that: include: The calibration unit uses the VSP velocity to calibrate the tomographic inversion velocity model of the first arrival wave travel time, obtains the calibrated tomographic inversion velocity model, and calculates the velocity of different layers to obtain the velocity calibration value; Subdividing the grid unit, vertically subdividing the calibrated tomographic inversion velocity model, generating a subdivided grid model, calculating the velocity of different layers to obtain the velocity correction coefficient; The correction unit performs spatial interpolation and extrapolation of each velocity correction coefficient according to a multi-point geostatistical algorithm to obtain a final velocity model correction coefficient body; The calculation unit calculates the near-surface velocity model based on the tomographic inversion velocity model of the first arrival wave travel time and the final velocity model correction coefficient volume.

9. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method for establishing a near-surface velocity model based on VSP constraints as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of the method for establishing a near-surface velocity model based on VSP constraints according to any one of claims 1 to 7.

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