Shallow surface velocity field modeling method, computing device and storage medium
By acquiring and processing geological data in seismic exploration, generating three-dimensional structural fields and performing iterative inversion, the problem of modeling the shallow surface velocity field in complex geological exploration areas is solved, and the accuracy and success rate of seismic exploration are significantly improved.
Patent Information
- Application Number
- CN202311434611.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-10-31
- Publication Date
- 2025-05-02
AI Technical Summary
In seismic exploration, it is difficult for the existing technology to obtain the precise shallow surface velocity field in complex geological exploration areas such as mountains, resulting in the influencing factors that affect the success or failure of seismic exploration.
By obtaining the initial travel time and two-dimensional measuring line structure data collected in seismic exploration, a three-dimensional shallow surface structure field is generated, and velocity fills are performed to construct an initial velocity model. The shallow surface velocity field model of the target work area is obtained using fast ray tracing and iterative inversion methods.
The shallow surface velocity field modeling of complex geological exploration areas is achieved, which improves the accuracy and success rate of seismic exploration, especially in mountainous exploration, which significantly improves the accuracy of the near-surface velocity field.
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Figure CN119916449A_ABST
Abstract
Description
Technical Field
[0001] The invention relates to the technical field of oil and gas geophysical exploration engineering, and in particular to a shallow surface velocity field modeling method, a computing device and a storage medium. Background Art
[0002] As seismic exploration processing methods move from the time domain to the depth domain, and seismic exploration areas move from plains to complex geological exploration areas such as deserts, hills, and mountains, it is becoming more difficult to obtain more accurate underground structural imaging profiles. How to obtain the velocity description structure of the underground exploration area is still the most important factor that determines the success or failure of seismic exploration.
[0003] At present, the commonly used seismic exploration velocity modeling methods include velocity analysis in the time domain and velocity analysis in the depth domain. Velocity analysis in the time domain is to obtain TV (time-velocity) pairs by picking energy groups on the velocity spectrum, and to obtain T-Gamma (time-gamma) pairs by picking the residual velocity spectrum in the time domain to obtain high-precision pre-stack time migration velocity fields. For the depth domain, the main means of velocity modeling include two parts: first-arrival wave travel time tomography of the shallow layer and grid tomography of the medium and deep layers. For mountain exploration, industry experts generally believe that the accuracy of near-surface velocity modeling is particularly important. Due to the complexity of shallow surface structures in mountain data such as the foreland belt, conventional first-arrival wave ray tomography cannot obtain more accurate and effective near-surface velocity fields due to the sensitivity of the ray path to the exposure of high-speed layers.
[0004] Therefore, a method to construct the shallow surface velocity field near the surface is needed. Summary of the invention
[0005] The main purpose of the present invention is to provide a shallow layer velocity field modeling method, a computing device and a storage medium to improve the accuracy of shallow layer depth domain velocity field modeling.
[0006] The present invention provides a shallow surface velocity field modeling method, comprising: obtaining the first arrival travel time of a target work area collected in seismic exploration; obtaining two-dimensional survey line structure data of the target work area, generating a three-dimensional shallow surface structure field of the target work area according to the two-dimensional survey line structure data, performing velocity filling on the three-dimensional shallow surface structure field, and obtaining a reflectivity model; constructing an initial velocity model according to an undulating ground surface of the target work area model and a preset maximum model depth, performing fast ray tracing on the initial velocity model to obtain ray paths between shot points and receiver points in the target work area, and constructing a first arrival travel time tomography matrix based on the ray paths; based on the first arrival travel time, the first arrival travel time tomography matrix and the reflectivity model, performing iterative inversion on the initial velocity model according to a preset tomography matrix equation, and obtaining a shallow surface velocity field model of the target work area.
[0007] In one embodiment, the method further includes: selecting first arrival travel times within a preset offset range, and sorting the selected first arrival travel times according to their sizes.
[0008] In one embodiment, a three-dimensional shallow surface structure field of a target work area is generated based on two-dimensional survey line structure data, including: generating a three-dimensional shallow surface structure field of the target work area based on multiple structural profiles of geological outcrops at different angles of the target work area.
[0009] In one embodiment, micro-logging data of the target work area and data obtained by a small refraction method are used to perform velocity filling on a three-dimensional shallow surface structure field to obtain a reflectivity model.
[0010] In one embodiment, micro-logging data of the target work area and data obtained by a small refraction method are used to perform velocity filling on a three-dimensional shallow layer structure field to obtain a reflectivity model, including: filling the micro-logging data and the velocity obtained by a small refraction method into the three-dimensional shallow layer structure field to obtain a three-dimensional shallow layer structure constrained velocity model; performing edge detection on the three-dimensional shallow layer structure constrained velocity model to obtain a reflectivity model.
[0011] In one embodiment, micro-logging data and the velocity obtained by the small refraction method are filled into the three-dimensional shallow surface structure field, including: filling the average velocity of the layer in the corresponding layer of the three-dimensional shallow surface structure field according to the micro-logging data and the velocity obtained by the small refraction method.
[0012] In one embodiment, based on the first arrival travel time, the first arrival travel time tomography matrix and the reflectivity model, the initial velocity model is iteratively inverted according to a preset tomography matrix equation, including: using the first arrival travel time to iterate the following steps: using the first arrival travel time tomography matrix and the reflectivity model to jointly solve the preset tomography matrix equation to obtain a velocity update, and using the velocity update to update the initial velocity model of the target work area.
[0013] In one embodiment, the preset tomographic matrix equation is represented by the following formula:
[0014] L(m)=||kΔm-Δt|| 2 +θ||Γ(m′)Δm+∈|| 2
[0015] Where L(m) represents the target functional of the first arrival travel time, Δm represents the velocity update to be calculated, K represents the first arrival travel time tomography matrix, θ represents the structural constraint regularization coefficient, Γ(m′) represents the reflectivity model, ∈ represents the stability factor, and Δt represents the difference between the first arrival travel time and the calculated travel time.
[0016] The present invention provides a computing device, comprising a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the above-mentioned shallow layer velocity field modeling method is implemented.
[0017] The present invention provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the above-mentioned shallow surface velocity field modeling method is implemented.
[0018] The present invention proposes a structurally constrained seismic exploration shallow surface depth domain velocity modeling method for shallow surface velocity field modeling in exploration areas with complex surface conditions. The method of the present invention can accurately obtain the shallow surface depth domain velocity field model of the underground medium, thereby improving the success rate of seismic exploration. BRIEF DESCRIPTION OF THE DRAWINGS
[0019] The drawings constituting a part of the present application are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:
[0020] Figure 1 is a flow chart of a shallow layer velocity field modeling method according to an exemplary embodiment of the present application;
[0021] Figure 2 is a projection of first arrival data on shot gather data according to an embodiment of the present application;
[0022] Figure 3 A schematic diagram of a two-dimensional survey line structure in a geological profile according to an embodiment of the present application;
[0023] Figure 4 is a schematic diagram of a three-dimensional shallow surface structure field according to an embodiment of the present application;
[0024] Figure 5 is a schematic diagram of a reflectivity model according to an embodiment of the present application;
[0025] Figure 6 Schematic diagram of a shallow surface velocity field model according to an embodiment of the present application. DETAILED DESCRIPTION
[0026] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.
[0027] refer to Figure 1 This embodiment provides a shallow layer velocity field modeling method, which may include:
[0028] S100: Obtain the first arrival travel time of the target work area collected in the seismic exploration.
[0029] S200: Acquire two-dimensional survey line structure data of the target work area, generate a three-dimensional shallow surface structure field of the target work area according to the two-dimensional survey line structure data, perform velocity filling on the three-dimensional shallow surface structure field, and obtain a reflectivity model.
[0030] S300: construct an initial velocity model according to the undulating surface of the target work area model and the preset maximum model depth, perform fast ray tracing on the initial velocity model to obtain the ray paths between the target work area shot points and receiver points, and construct the first arrival travel time tomography matrix based on the ray paths.
[0031] S400: Based on the first arrival travel time, the first arrival travel time tomography matrix and the reflectivity model, the initial velocity model is iteratively inverted according to the preset tomography matrix equation to obtain the shallow surface velocity field model of the target work area.
[0032] In one embodiment, the method may further include: selecting first arrival travel times within a preset offset range, and sorting the selected first arrival travel times according to their sizes.
[0033] In this embodiment, the first arrival travel time can be screened according to the offset distance. For example, the largest and smallest first arrival travel time can be removed, or only the first arrival travel time within the preset offset distance can be retained, etc. This application does not make specific limitations on this. Sorting the screened first arrival travel time by size is conducive to the subsequent iteration process to iterate according to the size of the first arrival travel time. Figure 2 , which is a projection of the first arrival data of an embodiment on the shot gather data.
[0034] In one embodiment, generating a three-dimensional shallow surface structure field of a target work area based on two-dimensional survey line structural data may include: generating a three-dimensional shallow surface structure field of the target work area based on multiple structural profiles of geological outcrops at different angles of the target work area.
[0035] In this implementation, the structural profile can be converted into a binary seismic data format, and then the binary seismic data can be interpolated and extended to generate a three-dimensional shallow surface structural field of the target work area. Figure 3 , which is a schematic diagram of a two-dimensional survey line structure in an address profile of an embodiment; Figure 4 , which is a schematic diagram of a three-dimensional shallow surface structure field according to an embodiment.
[0036] In one embodiment, micro-logging data of the target work area and data obtained by a small refraction method are used to perform velocity filling on a three-dimensional shallow surface structure field to obtain a reflectivity model.
[0037] In this embodiment, in addition to the micro-logging data and the data obtained by the small refraction method, the velocity data obtained by other methods can also be used to fill the velocity of the three-dimensional shallow surface structure field, and this application does not make specific limitations on this.
[0038] In one embodiment, using micro-logging data of the target work area and data obtained by the small refraction method to fill the three-dimensional shallow layer structure field with velocity to obtain a reflectivity model may include: filling the micro-logging data and the velocity obtained by the small refraction method into the three-dimensional shallow layer structure field to obtain a three-dimensional shallow layer structure constrained velocity model; performing edge detection on the three-dimensional shallow layer structure constrained velocity model to obtain a reflectivity model. Figure 5 , which is a schematic diagram of a reflectivity model of an embodiment.
[0039] In one embodiment, filling micro-logging data and the velocity obtained by the small refraction method into the three-dimensional shallow structure field may include: filling the average velocity of the layer in the corresponding layer of the three-dimensional shallow structure field according to the micro-logging data and the velocity obtained by the small refraction method.
[0040] In other implementations, other velocities of the corresponding layer may also be filled in the corresponding layer, such as the median velocity, any velocity, etc., which is not specifically limited in the present application.
[0041] In one embodiment, based on the first arrival travel time, the first arrival travel time tomographic matrix and the reflectivity model, the initial velocity model is iteratively inverted according to the preset tomographic matrix equation, which may include: using the first arrival travel time to iterate the following steps: using the first arrival travel time tomographic matrix and the reflectivity model to jointly solve the preset tomographic matrix equation to obtain the velocity update amount, and using the velocity update amount to update the initial velocity model of the target work area. Figure 6 , is a schematic diagram of a shallow surface velocity field model according to an embodiment.
[0042] In one embodiment, the preset tomographic matrix equation is represented by the following formula:
[0043] L(m)=||KΔm-Δt|| 2 +θ||Γ(m′)Δm+∈|| 2
[0044] Where L(m) represents the target functional of the first arrival travel time, Δm represents the velocity update to be calculated, K represents the first arrival travel time tomography matrix, θ represents the structural constraint regularization coefficient, Γ(m′) represents the reflectivity model, ∈ represents the stability factor, and Δt represents the difference between the first arrival travel time and the calculated travel time.
[0045] The present invention proposes a structurally constrained seismic exploration shallow surface depth domain velocity modeling method for shallow surface velocity field modeling in exploration areas with complex surface conditions. The method of the present invention can accurately obtain the shallow surface depth domain velocity field model of the underground medium, thereby improving the success rate of seismic exploration.
[0046] This embodiment provides a computing device, including a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the above-mentioned shallow layer velocity field modeling method is implemented.
[0047] In one embodiment, the computing device may include one or more processors (CPU), input / output interfaces, network interfaces, and memory.
[0048] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash memory (FLASH RAM). The memory is an example of a computer-readable medium.
[0049] This embodiment provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the above-mentioned shallow layer velocity field modeling method is implemented.
[0050] The computer program may be implemented in any combination of one or more storage media, which may be readable signal media or readable storage media.
[0051] The readable storage medium may include, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) may include: an electrical connection with one or more conductors, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.
[0052] The readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, wherein a readable computer program is carried. Such propagated data signals may take a variety of forms, for example, may include electromagnetic signals, optical signals, or any suitable combination of the above. The readable signal medium may also be any storage medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.
[0053] The computer program contained on the storage medium may be transmitted using any appropriate medium, including, for example, wireless, wired, optical cable, RF, etc., or any suitable combination of the above.
[0054] The computer program for performing the operation of the present invention may be written in any combination of one or more programming languages. The programming language may include an object-oriented programming language, such as Java, C++, etc., and may also include a conventional procedural programming language, such as "C" language or similar programming languages. The computer program may be executed entirely on the user computing device, partially on the user device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network (e.g., it may include a local area network or a wide area network), or it may be connected to an external computing device (e.g., using an Internet service provider to connect through the Internet).
[0055] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. When the terms "include" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or their combinations.
[0056] It should be noted that the terms "first", "second", etc. in the specification, claims and drawings of this application are used to distinguish similar objects, rather than to describe a specific order or sequence. It should be understood that the terms used in this way can be interchangeable under appropriate circumstances.
[0057] It should be understood that the exemplary embodiments in this specification can be implemented in a variety of different forms and should not be interpreted as being limited to the embodiments described herein. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps. These embodiments are provided to make the disclosure of this application thorough and complete, and to fully convey the concepts of these exemplary embodiments to those of ordinary skill in the art, and should not be construed as limitations on the present invention.
[0058] Although the spirit and principle of the present invention have been described with reference to several specific embodiments, it should be understood that the present invention is not limited to the disclosed specific embodiments, and the division of various aspects does not mean that the features in these aspects cannot be combined to benefit, and such division is only for the convenience of expression. The present invention is intended to cover various modifications and equivalent arrangements included in the spirit and scope of the attached claims.
Claims
1. A shallow surface velocity field modeling method, characterized in that: include: Obtain the first arrival travel time of the target work area collected in seismic exploration; Acquire two-dimensional survey line structure data of the target work area, generate a three-dimensional shallow layer structure field of the target work area according to the two-dimensional survey line structure data, perform velocity filling on the three-dimensional shallow layer structure field, and obtain a reflectivity model; An initial velocity model is constructed according to the undulating surface of the target work area model and a preset maximum model depth, and a fast ray tracing is performed on the initial velocity model to obtain the ray paths between the shot points and receiver points of the target work area, and a first arrival travel time tomography matrix is constructed based on the ray paths; Based on the first arrival travel time, the first arrival travel time tomographic matrix and the reflectivity model, the initial velocity model is iteratively inverted according to a preset tomographic matrix equation to obtain a shallow surface velocity field model of the target work area.
2. The shallow surface velocity field modeling method according to claim 1 is characterized in that: Also includes: The first arrival travel times within the preset offset range are screened out, and the screened out first arrival travel times are sorted by size.
3. The shallow surface velocity field modeling method according to claim 1 is characterized in that: Generating a three-dimensional shallow surface structure field of a target work area according to the two-dimensional survey line structure data includes: Based on multiple structural profiles of the geological outcrops at different angles in the target work area, a three-dimensional shallow surface structural field of the target work area is generated.
4. The shallow surface velocity field modeling method according to claim 1 is characterized in that: The three-dimensional shallow surface structure field is velocity filled using micro-logging data of the target work area and data obtained by a small refraction method to obtain a reflectivity model.
5. The shallow surface velocity field modeling method according to claim 4 is characterized in that: The three-dimensional shallow surface structure field is velocity filled using micro-logging data of the target work area and data obtained by the small refraction method to obtain a reflectivity model, including: Filling the micro-logging data and the velocity obtained by the small refraction method into the three-dimensional shallow surface structure field to obtain a three-dimensional shallow surface structure constrained velocity model; Edge detection is performed on the three-dimensional shallow layer structure constraint velocity model to obtain a reflectivity model.
6. The shallow surface velocity field modeling method according to claim 5, characterized in that: Filling the micro-logging data and the velocity obtained by the small refraction method into the three-dimensional shallow surface structure field includes: According to the micro-logging data and the velocity obtained by the small refraction method, the average velocity of the layer is filled in the corresponding layer of the three-dimensional shallow layer structure field.
7. The shallow surface velocity field modeling method according to claim 1, characterized in that: Based on the first arrival travel time, the first arrival travel time tomographic matrix and the reflectivity model, the initial velocity model is iteratively inverted according to a preset tomographic matrix equation, including: The following steps are iterated using the first arrival travel time: The first arrival travel time tomography matrix and the reflectivity model are used to jointly solve a preset tomography matrix equation to obtain a velocity update amount, and the velocity update amount is used to update the initial velocity model of the target work area.
8. The shallow surface velocity field modeling method according to claim 1 is characterized in that: The preset tomography matrix equation is represented by the following formula: L(m)=||KΔm-Δt|| 2 +θ||Γ(m′)Δm+∈|| 2 Where L(m) represents the target functional of the first arrival travel time, Δm represents the velocity update to be calculated, K represents the first arrival travel time tomography matrix, θ represents the structural constraint regularization coefficient, Γ(m′) represents the reflectivity model, ∈ represents the stability factor, and Δt represents the difference between the first arrival travel time and the calculated travel time.
9. A computing device, characterized in that The method comprises a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the shallow layer velocity field modeling method according to any one of claims 1 to 8 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 a processor, the shallow layer velocity field modeling method according to any one of claims 1 to 8 is implemented.