VSP interval velocity inversion method and device, electronic equipment and storage medium

Abstract

The invention discloses a VSP interval velocity inversion method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out the calculation based on an initial velocity model through employing an eikonal equation finite difference solution formula and employing a forward modeling method, and obtaining first-motion wave travel time; establishing a VSP interval velocity inversion objective function, wherein the objective function represents an error between the first arrival travel time calculated by a forward modeling method and the first arrival travel time of actual observation VSP data; and solving the objective function, iteratively inverting the velocity model through a global optimization method, and obtaining a group of interval velocities enabling the objective function value to be minimum as a VSP interval velocity model. According to the method, the VSP interval velocity modeling precision can be effectively improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of velocity modeling and seismic imaging in oil and gas exploration and development, and more specifically, relates to a VSP layer velocity inversion method, device, electronic equipment and storage medium. Background Art

[0002] VSP data contains more accurate data information, and VSP layer velocity inversion is of great help to formation velocity modeling. Traditional VSP layer velocity inversion methods are generally based on the vertical propagation path assumption or inclined straight ray incidence. These methods often require correction of VSP offset distances, but the assumptions cannot accurately describe the actual propagation path of seismic waves, and the modeling accuracy is limited. Summary of the invention

[0003] The purpose of the present invention is to provide a VSP layer velocity inversion method, device, electronic equipment and storage medium to improve the accuracy of VSP layer velocity modeling.

[0004] To achieve the above objectives, in a first aspect, the present invention proposes a VSP layer velocity inversion method, comprising:

[0005] Based on the initial velocity model, the travel time of the first arrival wave is calculated using the forward modeling method using the finite difference solution formula of the Eikonal equation.

[0006] Establishing a VSP layer velocity inversion objective function, the objective function represents the error between the first arrival travel time calculated by the forward modeling method and the first arrival travel time of the actual observed VSP data;

[0007] The objective function is solved, and the velocity model is iteratively inverted by a global optimization method to obtain a set of layer velocities that minimize the objective function value as the VSP layer velocity model.

[0008] Optionally, the global optimization method comprises:

[0009] The objective function is calculated based on the first arrival wave travel time calculated by the forward modeling method and the first arrival travel time of the actual observed VSP data to obtain the travel time error;

[0010] Calculating model acceptance based on the travel time error;

[0011] determining whether to accept the model according to the acceptance degree, if accepted, updating the inversion velocity model as a candidate velocity model, otherwise recalculating the candidate velocity model;

[0012] It is determined whether the maximum number of iterations meets the convergence condition. If not, the inversion velocity model is updated iteratively. If it is satisfied, the iteration is stopped and the final candidate velocity model is output as the VSP layer velocity model.

[0013] Optionally, model acceptance is calculated via the Metropolis criterion.

[0014] Optionally, the finite difference solution formula of the eikonal equation is:

[0015]

[0016] Among them, i and j represent the positions of the grid points in the x and y directions respectively, T represents the propagation time of the seismic wave, S represents the slowness of the seismic wave propagation, p represents the x and y directions of the spatial point, M[] represents the maximum value, and Δp represents the grid point spacing.

[0017] Optionally, the inversion objective function is:

[0018] J(T)=T sim -T obs (2)

[0019] Where J(T) is the travel time error, T sim The first-arrival wave travel time is calculated using the forward modeling method, T obs is the first arrival travel time of the actual observed VSP data.

[0020] In a second aspect, the present invention provides an electronic device, the electronic device comprising:

[0021] at least one processor; and,

[0022] a memory communicatively connected to the at least one processor; wherein,

[0023] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the VSP layer velocity inversion method described in any one of the first aspects.

[0024] In a third aspect, the present invention provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute any VSP layer velocity inversion method described in the first aspect.

[0025] In a fourth aspect, the present invention provides a VSP layer velocity inversion device, comprising:

[0026] The first arrival wave travel time calculation module is used to calculate the first arrival wave travel time based on the initial velocity model using the finite difference solution formula of the eikonal equation using the forward method;

[0027] An objective function establishment module is used to establish an objective function for VSP layer velocity inversion, wherein the objective function represents the error between the first arrival travel time calculated by the forward modeling method and the first arrival travel time of the actual observed VSP data;

[0028] The velocity model calculation module solves the objective function, iteratively inverts the velocity model through a global optimization method, and obtains a set of layer velocities that minimize the objective function value as the VSP layer velocity model.

[0029] Optionally, the global optimization method comprises:

[0030] The objective function is calculated based on the first arrival wave travel time calculated by the forward modeling method and the first arrival travel time of the actual observed VSP data to obtain the travel time error;

[0031] Calculating model acceptance based on the travel time error;

[0032] determining whether to accept the model according to the acceptance degree, if accepted, updating the inversion velocity model as a candidate velocity model, otherwise recalculating the candidate velocity model;

[0033] It is determined whether the maximum number of iterations meets the convergence condition. If not, the inversion velocity model is updated iteratively. If it is satisfied, the iteration is stopped and the final candidate velocity model is output as the VSP layer velocity model.

[0034] Optionally, model acceptance is calculated via the Metropolis criterion.

[0035] The beneficial effects of the present invention are:

[0036] The present invention improves the simulation accuracy of traveltime by using high-order difference approximation based on solving the traveltime by the eikonal equation, can avoid the traveltime error caused by offset correction, obtains a high-precision layer velocity model by iterative inversion using a global optimal method, and improves the accuracy of VSP layer velocity modeling.

[0037] The system of the present invention has other characteristics and advantages, which will be apparent from the drawings incorporated herein and the following detailed description, or will be described in detail in the drawings incorporated herein and the following detailed description, which together serve to explain the specific principles of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The above and other objects, features and advantages of the present invention will become more apparent through a more detailed description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, in which like reference numerals generally represent like components.

[0039] Figure 1 A step diagram of a VSP layer velocity inversion method according to Embodiment 1 of the present invention is shown.

[0040] Figure 2A flow chart of a VSP layer velocity inversion method according to Embodiment 2 of the present invention is shown.

[0041] Figure 3 A schematic diagram of the actual speed model in Example 2 of the present invention is shown.

[0042] Figure 4a A schematic diagram of the VSP layer velocity inversion result obtained by using the VSP layer velocity inversion method of the present invention is shown.

[0043] Figure 4b A schematic diagram of the VSP layer velocity inversion results obtained using the shortest path layer velocity inversion method is shown. DETAILED DESCRIPTION

[0044] The present invention will be described in more detail below with reference to the accompanying drawings. Although preferred embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to make the present invention more thorough and complete, and to fully convey the scope of the present invention to those skilled in the art.

[0045] Example 1

[0046] like Figure 1 As shown, the present invention proposes a VSP layer velocity inversion method, comprising:

[0047] S1: Based on the initial velocity model, the travel time of the first arrival wave is calculated using the forward method using the finite difference solution formula of the Eikonal equation;

[0048] In this step, the finite difference solution formula of the eikonal equation is:

[0049]

[0050] Among them, i and j represent the positions of the grid points in the x and y directions respectively, T represents the propagation time of the seismic wave, S represents the slowness of the seismic wave propagation, p represents the x and y directions of the spatial point, M[] represents the maximum value, and Δp represents the grid point spacing.

[0051] S2: Establish the VSP layer velocity inversion objective function, which represents the error between the first arrival travel time calculated by the forward modeling method and the first arrival travel time of the actual observed VSP data;

[0052] In this step, the inversion objective function is:

[0053] J(T)=T sim -T obs (2)

[0054] Where J(T) is the travel time error, T simThe first-arrival wave travel time is calculated using the forward modeling method, T obs is the first arrival travel time of the actual observed VSP data.

[0055] S3: Solve the objective function, iteratively invert the velocity model through the global optimization method, and obtain a set of layer velocities that minimize the objective function value as the VSP layer velocity model.

[0056] In this step, the global optimization method includes:

[0057] The travel time error is obtained by calculating the first arrival wave travel time and the first arrival travel time calculation objective function of the actual observed VSP data according to the forward modeling method;

[0058] Calculate model acceptance based on travel time error;

[0059] Determine whether to accept the model according to the acceptance degree. If accepted, update the inversion velocity model as the candidate velocity model. Otherwise, recalculate the candidate velocity model.

[0060] It is determined whether the maximum number of iterations meets the convergence condition. If not, the inversion velocity model is updated iteratively. If it is satisfied, the iteration is stopped and the final candidate velocity model is output as the VSP layer velocity model.

[0061] Preferably, the model acceptance is calculated by the Metropolis criterion.

[0062] Example 2

[0063] This embodiment provides a VSP layer velocity inversion method, the method flow is as follows Figure 2 As shown, the specific steps include:

[0064] Step 1: Use high-order difference approximation to establish the finite difference solution formula for the eikonal equation:

[0065]

[0066] Among them, i and j represent the grid point positions in the x and y directions respectively, T represents the seismic wave propagation time, and S represents the seismic wave propagation slowness.

[0067] Step 2: Establish the VSP layer velocity inversion objective function.

[0068] J(T)=T sim -T obs (2)

[0069] Among them, T sim To calculate the first arrival wave travel time using the first step forward modeling method, T obs is the travel time of the first arrival wave of the actual observed VSP data.

[0070] Step 3: Solve the objective function and obtain a set of layer velocities to minimize J(T) through global optimization method.

[0071] Among them, the global optimization method mainly includes calculating the likelihood function by simulating travel time and observation data, calculating the model acceptance according to the Metropolis criterion, and updating the inversion model as a candidate model.

[0072] Step 4: Determine whether the maximum number of iterations meets the convergence condition. If not, return to the first step. If so, stop the iteration and the output velocity model is the VSP layer velocity model.

[0073] In one example, using Figure 3 The actual velocity model shown is used for inversion test. The model depth is 2000m, the source is located 50m above the ground, and a well receiver is set at 25m.

[0074] The VSP layer velocity inversion method of the present invention and the traditional shortest path layer velocity inversion method are used to model the VSP layer velocity. The obtained inversion results are as follows: Figure 4a and Figure 4b As shown in the inversion results, it can be seen that the inversion layer velocity ( Figure 4a ) is more consistent with the actual velocity and has a smaller error, while the inversion result based on the shortest path method has a large error, which shows that the inversion accuracy of the VSP layer velocity is higher in the method of the present invention.

[0075] Example 3

[0076] This embodiment provides a VSP layer velocity inversion device, including:

[0077] The first arrival wave travel time calculation module is used to calculate the first arrival wave travel time based on the initial velocity model using the finite difference solution formula of the eikonal equation using the forward method;

[0078] An objective function establishment module is used to establish an objective function for VSP layer velocity inversion, wherein the objective function represents the error between the first arrival travel time calculated by the forward modeling method and the first arrival travel time of the actual observed VSP data;

[0079] The velocity model calculation module solves the objective function, iteratively inverts the velocity model through a global optimization method, and obtains a set of layer velocities that minimize the objective function value as the VSP layer velocity model.

[0080] Wherein, the global optimization method comprises:

[0081] The objective function is calculated based on the first arrival wave travel time calculated by the forward modeling method and the first arrival travel time of the actual observed VSP data to obtain the travel time error;

[0082] Calculating model acceptance based on the travel time error;

[0083] determining whether to accept the model according to the acceptance degree, if accepted, updating the inversion velocity model as a candidate velocity model, otherwise recalculating the candidate velocity model;

[0084] It is determined whether the maximum number of iterations meets the convergence condition. If not, the inversion velocity model is updated iteratively. If it is satisfied, the iteration is stopped and the final candidate velocity model is output as the VSP layer velocity model.

[0085] Preferably, the model acceptance is calculated by the Metropolis criterion.

[0086] Example 4

[0087] This embodiment provides an electronic device, the electronic device comprising:

[0088] at least one processor; and,

[0089] a memory communicatively connected to the at least one processor; wherein,

[0090] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the VSP layer velocity inversion method described in the above embodiment 1 or 2.

[0091] The electronic device according to an embodiment of the present disclosure includes a memory and a processor, and the memory is used to store non-temporary computer-readable instructions. Specifically, the memory may include one or more computer program products, and the computer program product may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include a random access memory (RAM) and / or a cache memory (cache), etc. The non-volatile memory may, for example, include a read-only memory (ROM), a hard disk, a flash memory, etc.

[0092] The processor may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment of the present disclosure, the processor is used to run the computer-readable instructions stored in the memory.

[0093] Those skilled in the art should be able to understand that in order to solve the technical problem of how to obtain a good user experience, the present embodiment may also include well-known structures such as a communication bus and an interface, and these well-known structures should also be included in the protection scope of the present disclosure.

[0094] For detailed description of this embodiment, reference may be made to the corresponding descriptions in the aforementioned embodiments, which will not be repeated here.

[0095] Example 5

[0096] This embodiment provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable a computer to execute the VSP layer velocity inversion method described in the above embodiment 1 or 2.

[0097] According to the computer-readable storage medium of the embodiment of the present disclosure, non-transitory computer-readable instructions are stored thereon. When the non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the above-mentioned methods of each embodiment of the present disclosure are executed.

[0098] The above-mentioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or mobile hard disk), media with built-in rewritable non-volatile memory (e.g., memory card) and media with built-in ROM (e.g., ROM box).

[0099] The embodiments of the present invention have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A VSP layer velocity inversion method, It is characterized in that include: Based on the initial velocity model, the travel time of the first arrival wave is calculated using the forward modeling method using the finite difference solution formula of the Eikonal equation. Establishing a VSP layer velocity inversion objective function, the objective function represents the error between the first arrival travel time calculated by the forward modeling method and the first arrival travel time of the actual observed VSP data; The objective function is solved, and the velocity model is iteratively inverted by a global optimization method to obtain a set of layer velocities that minimize the objective function value as the VSP layer velocity model.

2. The VSP layer velocity inversion method according to claim 1, It is characterized in that The global optimization method comprises: The objective function is calculated based on the first arrival wave travel time calculated by the forward modeling method and the first arrival travel time of the actual observed VSP data to obtain the travel time error; Calculating model acceptance based on the travel time error; determining whether to accept the model according to the acceptance degree, if accepted, updating the inversion velocity model as a candidate velocity model, otherwise recalculating the candidate velocity model; It is determined whether the maximum number of iterations meets the convergence condition. If not, the inversion velocity model is updated iteratively. If it is satisfied, the iteration is stopped and the final candidate velocity model is output as the VSP layer velocity model.

3. The VSP layer velocity inversion method according to claim 2, It is characterized in that Model acceptance was calculated using the Metropolis criterion.

4. The VSP layer velocity inversion method according to claim 1, It is characterized in that The finite difference solution formula of the eikonal equation is: Among them, i and j represent the positions of the grid points in the x and y directions respectively, T represents the propagation time of the seismic wave, S represents the slowness of the seismic wave propagation, p represents the x and y directions of the spatial point, M[] represents the maximum value, and Δp represents the grid point spacing.

5. The VSP layer velocity inversion method according to claim 1, It is characterized in that The inversion objective function is: J(T)=T sim -T obs (2) Where J(T) is the travel time error, T sim The first-arrival wave travel time is calculated using the forward modeling method, T obs is the first arrival travel time of the actual observed VSP data.

6. An electronic device, It is characterized in that The electronic device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the VSP layer velocity inversion method according to any one of claims 1 to 5.

7. A non-transitory computer-readable storage medium, It is characterized in that The non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the VSP layer velocity inversion method according to any one of claims 1 to 5.

8. A VSP layer velocity inversion device, It is characterized in that include: The first arrival wave travel time calculation module is used to calculate the first arrival wave travel time based on the initial velocity model using the finite difference solution formula of the eikonal equation using the forward method; An objective function establishment module is used to establish an objective function for VSP layer velocity inversion, wherein the objective function represents the error between the first arrival travel time calculated by the forward modeling method and the first arrival travel time of the actual observed VSP data; The velocity model calculation module solves the objective function, iteratively inverts the velocity model through a global optimization method, and obtains a set of layer velocities that minimize the objective function value as the VSP layer velocity model.

9. The VSP layer velocity inversion device according to claim 8, It is characterized in that The global optimization method comprises: The objective function is calculated based on the first arrival wave travel time calculated by the forward modeling method and the first arrival travel time of the actual observed VSP data to obtain the travel time error; Calculating model acceptance based on the travel time error; determining whether to accept the model according to the acceptance degree, if accepted, updating the inversion velocity model as a candidate velocity model, otherwise recalculating the candidate velocity model; It is determined whether the maximum number of iterations meets the convergence condition. If not, the inversion velocity model is updated iteratively. If it is satisfied, the iteration is stopped and the final candidate velocity model is output as the VSP layer velocity model.

10. The VSP layer velocity inversion device according to claim 9, It is characterized in that Model acceptance was calculated using the Metropolis criterion.

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