Seismic velocity model updating method and system based on well-controlled structural constraints

Abstract

The present invention discloses a method and system for updating a seismic velocity model based on well-controlled structural constraints, including: improving the accuracy and continuity of the velocity model. The main steps include converting the time domain velocity model to the depth domain, creating a preliminary structural model, and using logging data to constrain correction. Then, the logging velocity data is processed by Bucks averaging and the velocity change trend is fitted according to the corrected structural model to update and interpolate the depth domain velocity model. For new logging data, the velocity model is further optimized through local correction and velocity reconstruction, and finally applied to the seismic offset imaging process. The advantages of the present invention are: reducing the depth error of geological structure picking, and effectively reducing the computational burden of reconstructing the full-area velocity model caused by the addition of new logging data. By optimizing data utilization and computing resource management, the efficiency of seismic data processing is improved, and the reliability and interpretation ability of seismic imaging results are enhanced.

Description

Technical Field

[0001] The present invention relates to the field of seismic imaging technology, and in particular to a full-life cycle seismic velocity model updating method and system based on well-controlled structural constraints. Background Art

[0002] The quality of seismic migration imaging often depends on the accuracy of the velocity model. Traditional velocity modeling methods are mainly divided into two categories: methods based on seismic data can provide continuous velocity model information in the horizontal direction, while methods based on well logging data can provide more accurate velocity information in the vertical direction. The current popular method is to update and optimize the velocity model through a joint well-seismic approach, that is, using the structural information extracted from the migration imaging data and using well logging data to constrain the iterative update process of the velocity model.

[0003] However, there are some challenges in the existing joint well-seismic modeling methods. First, when the velocity model is inaccurate, there may be a large error between the structural information picked up from the offset data and the actual structural location, which limits the improvement effect of the updated velocity model. Second, when faced with new logging data, it is usually necessary to reuse all the data to reconstruct the velocity model of the entire work area. This comprehensive reconstruction method consumes a lot of computing resources, especially when processing large-scale data. Summary of the invention

[0004] In view of the defects of the prior art, the present invention provides a method and system for updating a seismic velocity model based on well-controlled structural constraints.

[0005] In order to achieve the above invention object, the technical solution adopted by the present invention is as follows:

[0006] A seismic velocity model updating method based on well-controlled structural constraints comprises the following steps:

[0007] S1: Use velocity analysis to obtain the initial velocity model in the time domain, use Dick's formula to convert the time domain velocity model to the depth domain velocity model, use the depth domain velocity model to perform prestack depth migration and layer picking, create a structural model, use the current logging data constraints to modify the structural model, and obtain the modified structural model.

[0008] S2: Perform Bax averaging on the acoustic logging velocity in the logging data to smooth the logging velocity data. Use the modified structural model in S1 to fit the velocity change trend of each point in the well section and calculate the linear change relationship between velocity and depth. Based on the modified structural model in S1, perform linear interpolation on the velocity between two adjacent wells to obtain the velocity change trend between the two adjacent wells and update the depth domain velocity model.

[0009] S3: When new logging data is available, the depth domain velocity model updated by S2 is used to re-perform migration imaging and pick up layers to build a structural model. The structural model is then locally corrected and the local velocity is reconstructed using the new logging data to obtain a new velocity model.

[0010] S4: Use the velocity model obtained in S3 to perform seismic migration imaging.

[0011] Preferably, the S1 comprises the following sub-steps:

[0012] S11: The initial velocity model in the time domain is obtained through velocity analysis, and then converted into a velocity model in the depth domain using Dick’s formula. The depth domain velocity model is used to perform prestack depth migration and layer picking to construct an initial structural model.

[0013] S12: Perform seismic migration imaging at the well location to obtain the structural depth, and subtract it from the layer depth interpreted by the well logging to obtain the difference between the picked layer and the layer interpreted by the well logging. The formula is as follows:

[0014]

[0015] In the formula, h i represents the layer number, w represents the well number, Indicates h i The seismic migration imaging pick-up structure depth of the layer at well w, Indicates h i The logging interpretation depth of the layer at well w is, Indicates h i The layer level difference of the layer at well w.

[0016] S13: Based on the location of the well and the difference between the layers Constructing data pairs where x w Represents the horizontal coordinate value of well w. For two adjacent wells j and m, construct a data pair and The trend of the horizon difference between the two wells is calculated by linear interpolation, and the horizon surface is corrected to be consistent with the horizon interpreted by well logging.

[0017] S14: After correction, the least squares method is used to fit the strike trend function y of the fault plane. f (x), determine the reconstructed position of the fault and obtain the revised structural model.

[0018] Preferably, S2 comprises the following sub-steps:

[0019] S21: Perform Bucks averaging processing on the acoustic logging velocity in the logging data to smooth the data and calculate the Bucks average value of each point.

[0020] S22: Fit the velocity variation trend of the well section between each layer plane. For well w, the part w between each layer plane i , where i is the well section number, calculate the distance from each point to the upper interface and the corresponding speed value The least square method is used for linear fitting to obtain the velocity change trend of the well section, as shown in the following formula:

[0021]

[0022] In the formula, Indicates well section w i In Depth The speed change trend at the .

[0023] and is the coefficient obtained by least squares fitting, which is used to describe the well section i at different depths. The speed change relationship below.

[0024] It is the distance between the well section i and the horizon, that is, the depth.

[0025] S23: Using the velocity transformation trend β of two adjacent wells j and m j1 , β j2 , β m1 , β m2 , interpolate the velocity of the well sections between the same layer planes. By linear interpolation, calculate the velocity of any position x w Speed ​​change trend of the layer plane:

[0026]

[0027] In the formula, represents the depth D of well section i in well w dk The speed change trend at the .

[0028] β1 and β2 are coefficients obtained by least squares fitting, which are used to describe the well section i at different depths D dk The speed change relationship below.

[0029] D dk It is the distance between well section i on the horizon, usually used to express depth or distance.

[0030] According to the above calculation results, the depth domain velocity model is updated.

[0031] Preferably, S3 includes the following sub-steps:

[0032] S31: When there is a new well, the latest migration imaging result needs to be processed for local layer update, and the distribution of the layer difference change satisfies the first-order Gaussian function:

[0033]

[0034] In the formula, a is the amplitude of the layer difference, b is the center position of the layer difference change, and c is 2 is the variance of the Gaussian function, which determines the width of the variation

[0035] For the new well n and the old well m, the depth difference between the two wells With well position x n 、x m , construct data pairs Substitute the data pair into the first-order Gaussian function f(x) and make At the highest point:

[0036]

[0037] Right now:

[0038]

[0039] S32: After completing the update of the local layer, it is necessary to perform logging velocity interpolation on the updated layer. This step is similar to S23, and the velocity change trend of the adjacent well section is used to perform velocity interpolation on the local layer to further improve the updated velocity model.

[0040] The present invention also discloses a seismic velocity model updating system based on well control structural constraints, which can be used to implement the above-mentioned seismic velocity model updating method, specifically comprising:

[0041] Initial velocity model building module: Use velocity analysis to obtain the initial velocity model in the time domain, use Dick's formula to convert the time domain velocity model into a depth domain velocity model, perform prestack depth migration and layer picking, and create an initial structural model.

[0042] Structural model correction module: Use the current logging data to constrain the initial structural model and obtain the corrected structural model.

[0043] Well logging data processing module: Perform Bax averaging on the acoustic logging velocity and smooth the velocity data to reduce noise and improve data quality.

[0044] Velocity model update module: Based on the revised structural model, fit the velocity change trend of each point in the well section, calculate the linear change relationship between velocity and depth, and update the depth domain velocity model.

[0045] New data processing and velocity reconstruction module: When there is new logging data, the updated depth domain velocity model is used to re-perform migration imaging. The new layer structure model is picked up, and the structure model is locally corrected and the local velocity is reconstructed. The updated velocity model is obtained to ensure the timeliness and accuracy of the model.

[0046] Seismic Migration Imaging Module: Seismic migration imaging uses the latest velocity models to generate high-resolution images of underground structures.

[0047] The present invention also discloses a computer device, including a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned seismic velocity model updating method when executing the program.

[0048] The present invention also discloses a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the above-mentioned seismic velocity model updating method is implemented.

[0049] Compared with the prior art, the advantages of the present invention are:

[0050] 1. Reduced depth error of seismic migration imaging: By using well control data to constrain the velocity model, the depth error between the picked structure and the real structure during seismic migration imaging can be significantly reduced, thereby improving the accuracy of the underground structure.

[0051] 2. Reduced demand for computing resources: The strategy of updating the local velocity model avoids the need to re-use all logging data to build the velocity model of the entire work area every time new logging data is available, thereby effectively reducing the consumption of computing resources and processing time costs.

[0052] 3. Enhanced resolution and accuracy of seismic imaging: Through more accurate velocity model updates, the resolution and quality of seismic imaging can be improved, making the details of underground structures clearer and more reliable.

[0053] 4. Improved reliability of seismic interpretation: The updated velocity model combines well location data and structural constraints to make seismic interpretation results more reliable and accurate, and helps to accurately identify stratigraphic interfaces and geological bodies.

[0054] 5. Optimized data processing efficiency: The optimized speed model update method reduces the time and resource overhead of repeated calculations and data processing, and improves the efficiency of data processing and the effectiveness of the overall workflow. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 This is a flow chart of a method for updating a seismic velocity model according to an embodiment of the present invention;

[0056] Figure 2The figure is a migration imaging result diagram of an embodiment of the present invention. (a) is the initial velocity model in the depth domain obtained by velocity analysis and time-depth conversion, (b) is the migration imaging result of the initial velocity model, and (c) is the migration imaging result of the theoretical model.

[0057] Figure 3 : is the first iteration result diagram of the embodiment of the present invention, wherein (a) is the layer picking result of the initial velocity model migration result, (b) is the velocity model after the first iteration reconstruction, and (c) is the first iteration reconstruction velocity model migration imaging result.

[0058] Figure 4 : This is a result diagram of the second iteration of an embodiment of the present invention, wherein (a) is the layer picking result of the first iteration reconstruction velocity model migration result, (b) is the velocity model after the second iteration reconstruction, and (c) is the second iteration reconstruction velocity model migration imaging result;

[0059] Figure 5 : This is a result diagram of the third iteration of the embodiment of the present invention, wherein (a) is the layer picking result of the second iteration reconstruction velocity model migration result, (b) is the velocity model after the third iteration reconstruction, and (c) is the third iteration reconstruction velocity model migration imaging result;

[0060] Figure 6 Comparison diagram of CIG gathers after model migration, where (a) is the CIG gather after initial velocity model migration, (b) is the CIG gather after the third iterative velocity model migration, and (c) is the CIG gather after theoretical model migration. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and examples.

[0062] like Figure 1 As shown, a seismic velocity model updating method based on well-controlled structural constraints includes the following steps:

[0063] S1: An initial depth domain velocity model is established based on velocity analysis and time-to-depth conversion. On this basis, prestack depth migration is performed and the layers are picked to construct a structural model. The structural model is constrained and modified using the current logging data to obtain a revised structural model.

[0064] S2: Reconstruct the velocity model by combining the constraint-corrected structural model with the logging data to obtain a new velocity model.

[0065] S3: When new logging data is available, the latest velocity model is used to re-perform migration imaging and pick up layers to build a structural model. The new logging data is used to perform local corrections and local velocity reconstruction on the structural model to obtain a new velocity model.

[0066] The S1 comprises the following sub-steps:

[0067] S11: The initial velocity model in the time domain is obtained by velocity analysis, and converted to the depth domain using Dick’s formula. The depth domain velocity model is used to perform prestack depth migration and layer picking to create a structural model.

[0068] S12: Subtract the structural depth picked up by seismic migration imaging at the well location from the depth of the layer interpreted by logging to obtain the layer surface difference between the picked layer and the layer interpreted by logging:

[0069]

[0070] Among them, h i represents the layer number, w represents the well number, Indicates h i The seismic migration imaging pick-up structure depth of the layer at well w, Indicates h i The logging interpretation layer depth of the layer at well w, Indicates h i The layer level difference of the layer at well w.

[0071] S13: Construct a data pair based on the well location and the difference between the well and the layers above the well. where x w Represents the horizontal coordinate value of well w. Corresponding to two adjacent wells j and m, a data pair can be constructed Use two points to do linear interpolation to get the trend of the difference between the layers of each point between wells j and m, so as to correct the layer surface to the same depth as the layer interpreted by logging. According to the linear transformation formula:

[0072] y=kx+b

[0073] The difference transformation trend between the x-matching layer plane and the picked layer plane at each point between the wells can be obtained:

[0074]

[0075] in:

[0076]

[0077] S14: After the layer correction, the fault needs to be reconstructed. The horizontal and vertical positions of the corrected positions of the intersection points of the layer before correction and the fault are regarded as coordinates (x1, y1), (x2, y2)…(x n ,y n ), the least squares method is used to fit the fault plane trend function y fIn this way, the reconstructed fault is obtained, and the residual can be defined as:

[0078] e w =y w -(β0+β1x w )

[0079] Summing the squared residuals yields:

[0080]

[0081] At the same time, taking the derivative of E and making it equal to 0 gives:

[0082]

[0083]

[0084] The trend transformation function of the fault plane is obtained as follows:

[0085] y f (x) = β1x + β0

[0086] The S2 comprises the following sub-steps:

[0087] S21: Perform Bucks averaging on the acoustic logging velocity in the logging data:

[0088]

[0089] in: represents the Bucks mean of the i-th point, M is the half-width of the window (the window size is 2M+1), d i+j is the logging velocity of the i+jth point.

[0090] S22: Fit the velocity variation trend of the well section between each layer plane, and calculate the distance from each point in the part of well w between the layer planes to the upper interface as The velocity value of this point is This constructs the speed pair The velocity change trend of the well section can be obtained by linear fitting using the least squares method.

[0091]

[0092] S23: Given the velocity change trend of all the layers between the wells, the velocity interpolation can be performed for the interlayer of the same layer between two adjacent wells. wFor any well, it has a similar velocity change trend in the vertical direction with the interlayer part of the adjacent well in the same layer plane. In the adjacent two wells j and m, the velocity change trend between the same layer plane is affected by β j1 , β j2 , β m1 , β m2 With Depth Therefore, the linear interpolation method can be used to obtain the value x at any position between wells j and m. w The velocity change trend of the same layer plane is used to update the velocity model. j ,β j1 )、(x m ,β m1 ) and (x j ,β j2 )、(x m ,β m2 ) are linearly interpolated to obtain the transformation trend of parameters β1 and β2 between wells j and m. and

[0093]

[0094] The speed change trend F of the corresponding segment at any position x can be obtained vw (D dk ):

[0095] F vw (D dk )=β1·D dk +β2

[0096] The S3 comprises the following sub-steps:

[0097] S31: When there is a new well, the latest migration imaging result needs to be processed for local layer update, and the distribution of the layer difference change satisfies the first-order Gaussian function:

[0098]

[0099] For a new well n and an old well m, the depth difference between the two wells can be With well position x n 、x m , construct data pairs Substitute the data pair into the first-order Gaussian function f(x) and make At the highest point:

[0100]

[0101] Right now:

[0102]

[0103] S32: After the local layer is updated, the logging velocity interpolation needs to be performed on the updated layer, and the method is the same as S23.

[0104] Taking the test model as an example, the full life cycle seismic velocity model update method based on well control structural constraints is divided into four steps:

[0105] In the first step, the initial depth domain velocity model is constructed by velocity analysis and time-depth conversion. This model is used as the initial velocity model, and the structure model is created by performing migration imaging and picking layers. Figure 2 and Figure 3 shown.

[0106] The second step is to implement the steps from S12 to S14 using the current logging layer data to obtain an optimized structural model.

[0107] The third step is to implement the steps from S21 to S23 using the current logging velocity data to obtain an updated velocity model, such as Figure 3 (b) as shown.

[0108] In the fourth step, whenever a new well is added, the local velocity model is updated by implementing steps S31 to S32 using the new logging data, such as Figure 4 , 5 Shown is the iterative process of adding two new wells one after the other.

[0109] like Figure 6 As shown, it can be seen that the CIG gathers after the third iteration are similar to the real model CIG gathers in depth and morphology. Compared with the initial model CIG gathers, the iterative gathers correct the imaging points to the real imaging positions, and the event axes are also more stable.

[0110] In yet another embodiment of the present invention, a seismic velocity model updating system based on well-controlled structural constraints is provided. The system can be used to implement the above-mentioned seismic velocity model updating method based on well-controlled structural constraints, and specifically includes:

[0111] Initial velocity model building module: Use velocity analysis to obtain the initial velocity model in the time domain, use Dick's formula to convert the time domain velocity model into a depth domain velocity model, perform prestack depth migration and layer picking, and create an initial structural model.

[0112] Structural model correction module: Use the current logging data to constrain the initial structural model and obtain the corrected structural model.

[0113] Well logging data processing module: Perform Bax averaging on the acoustic logging velocity and smooth the velocity data to reduce noise and improve data quality.

[0114] Velocity model update module: Based on the revised structural model, fit the velocity change trend of each point in the well section, calculate the linear change relationship between velocity and depth, and update the depth domain velocity model.

[0115] New data processing and velocity reconstruction module: When there is new logging data, the updated depth domain velocity model is used to re-perform migration imaging. The new layer structure model is picked up, and the structure model is locally corrected and the local velocity is reconstructed. The updated velocity model is obtained to ensure the timeliness and accuracy of the model.

[0116] Seismic Migration Imaging Module: Seismic migration imaging uses the latest velocity models to generate high-resolution images of underground structures.

[0117] In another embodiment of the present invention, a terminal device is provided, the terminal device includes a processor and a memory, the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, which are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding functions; the processor described in the embodiment of the present invention can be used for the operation of a seismic velocity model updating method based on well control structural constraints.

[0118] In another embodiment of the present invention, the present invention further provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and the extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory, or a non-volatile memory (non-volatile memory), such as at least one disk storage.

[0119] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the seismic velocity model updating method based on well-controlled structural constraints in the above-mentioned embodiment; one or more instructions in the computer-readable storage medium are loaded and executed by the processor.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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 for the functions specified in one or more boxes.

[0124] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the implementation methods of the present invention, and should be understood that the protection scope of the present invention is not limited to such special statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A seismic velocity model updating method based on well-controlled structural constraints, characterized in that: The steps include: S1: Use velocity analysis to obtain the initial velocity model in the time domain, use Dicks formula to convert the time domain velocity model to the depth domain velocity model, use the depth domain velocity model to perform prestack depth migration and layer picking, create a structural model, use the current logging data constraints to modify the structural model, and obtain the modified structural model; S1 includes the following sub-steps: S11: Obtain the initial velocity model in the time domain through velocity analysis, and then convert it into a velocity model in the depth domain using Dick’s formula; use the depth domain velocity model to perform prestack depth migration and perform layer picking to construct an initial structural model; S12: Perform seismic migration imaging at the well location to obtain the structural depth, and subtract it from the layer depth interpreted by the well logging to obtain the difference between the picked layer and the layer interpreted by the well logging. The formula is as follows: In the formula, h i represents the layer number, w represents the well number, Indicates h i The seismic migration imaging pick-up structure depth of the layer at well w, Indicates h i The logging interpretation depth of the layer at well w is, Indicates h i The difference in the layer level at well w; S13: Based on the location of the well and the difference between the layers Constructing data pairs where x w Represents the lateral coordinate value of well w; for two adjacent wells j and m, construct a data pair and The trend of the difference in the horizon value at each point between the two wells is calculated by linear interpolation, and the horizon plane is corrected to be consistent with the horizon interpreted by the well logging; S14: After correction, the least squares method is used to fit the strike trend function y of the fault plane. f (x), determine the reconstructed position of the fault and obtain the revised structural model; S2: Perform Bax averaging on the acoustic logging velocity in the logging data to smooth the logging velocity data; use the modified structural model in S1 to fit the velocity variation trend of each point in the well section and calculate the linear variation relationship between velocity and depth; based on the modified structural model in S1, perform linear interpolation on the velocity between two adjacent wells to obtain the velocity variation trend between the two adjacent wells and update the depth domain velocity model; The S2 comprises the following sub-steps: S21: Performing Bax averaging processing on the acoustic logging velocity in the logging data to smooth the data and calculate the Bax average value of each point; S22: Fit the velocity variation trend of the well section between each layer plane; for well w, the part w between each layer plane i , where i is the well section number, calculate the distance from each point to the upper interface and the corresponding speed value The least square method is used for linear fitting to obtain the velocity change trend of the well section, as shown in the following formula: In the formula, Indicates well section w i In Depth The speed change trend at and is the coefficient obtained by least squares fitting, which is used to describe the well section i at different depths. The speed change relationship under is the distance between the well section i and the horizon, i.e. the depth; S23: Using the velocity transformation trend β of two adjacent wells j and m j1 , β j2 , β m1 , β m2 , interpolate the velocity of the well sections between the same layer planes; calculate the velocity of any position x by linear interpolation w Speed ​​change trend of the layer plane: In the formula, represents the depth D of well section i in well w dk The speed change trend at β1 and β2 are coefficients obtained by least squares fitting, which are used to describe the well section i at different depths D dk The speed change relationship under D dk is the distance between well section i and the horizon, usually used to express depth or distance; According to the above calculation results, the depth domain velocity model is updated; S3: When new logging data is available, the depth domain velocity model updated by S2 is used to re-perform migration imaging and pick up layers to build a structural model. The structural model is then locally corrected and the local velocity is reconstructed using the new logging data to obtain a new velocity model. S4: Use the velocity model obtained in S3 to perform seismic migration imaging.

2. The method for updating the seismic velocity model according to claim 1, characterized in that: The S3 comprises the following sub-steps: S31: When there is a new well, the latest migration imaging result needs to be processed for local layer update, and the distribution of the layer difference change satisfies the first-order Gaussian function: In the formula, a is the amplitude of the layer difference, b is the center position of the layer difference change, and c is 2 is the variance of the Gaussian function, which determines the width of the variation For the new well n and the old well m, the depth difference between the two wells With well position x n 、x m , construct data pairs Substitute the data pair into the first-order Gaussian function f(x) and make At the highest point: Right now: S32: After completing the update of the local layer, it is necessary to perform logging velocity interpolation on the updated layer; this step is similar to S23, and the velocity change trend of the adjacent well section is used to perform velocity interpolation on the local layer to further improve the updated velocity model.

3. A seismic velocity model updating system based on well control structural constraints, characterized in that: The system can be used to implement the seismic velocity model updating method described in one of claims 1 or 2, specifically comprising: Initial velocity model building module: Use velocity analysis to obtain the initial velocity model in the time domain, use Dick's formula to convert the time domain velocity model into a depth domain velocity model, perform prestack depth migration and layer picking, and create an initial structural model; Structural model correction module: use the current logging data to constrain and correct the initial structural model to obtain the corrected structural model; Well logging data processing module: Perform Bax averaging on the acoustic well logging velocity to smooth the velocity data; Velocity model update module: Based on the revised structural model, fit the velocity change trend of each point in the well section; calculate the linear change relationship between velocity and depth, and update the depth domain velocity model; New data processing and velocity reconstruction module: when there is new logging data, use the updated depth domain velocity model to re-perform migration imaging; pick up the new layer structure model, and perform local correction and local velocity reconstruction on the structure model; obtain the updated velocity model; Seismic Migration Imaging Module: Seismic migration imaging uses the latest velocity models to generate high-resolution images of underground structures.

4. A computer device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the seismic velocity model updating method according to one of claims 1 or 2 is implemented.

5. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the program is executed by a processor, the seismic velocity model updating method described in one of claims 1 or 2 is implemented.

Images