Tomographic Inversion Method and Device for Small-Scale Anomalies Based on Ray Density Equilibrium

Abstract

The present invention relates to the technical field of geophysical exploration, and provides a tomography inversion method, device and electronic equipment for small-scale abnormal bodies based on ray density equalization. Among them, the tomography inversion method for small-scale abnormal bodies based on ray density equalization includes: performing tomography inversion on seismic data to obtain an initial ray density data volume; based on the initial ray density data volume, obtaining a ray density automatic equalization regularization operator through iterative calculation until the equalized density standard deviation is less than a preset threshold; calculating a tomography inversion objective function according to the ray density automatic equalization regularization operator, and performing tomography inversion of small-scale abnormal bodies based on the tomography inversion objective function. The present invention improves the ray density uniformity in the small-size abnormal body area and the modeling accuracy of the tomography inversion speed of small-scale abnormal bodies, and finally improves the imaging quality.

Description

Technical Field

[0001] The present invention relates to the field of geophysical exploration technology, and more specifically, to a small-scale abnormal body tomography inversion method, device and electronic equipment based on ray density balance, which can be applied to the depth domain velocity modeling stage in petroleum geophysical exploration. Background Art

[0002] With the continuous deepening of oil and gas exploration and development, the exploration target has changed from simple large-scale structures to small-scale anomaly characterization, which requires higher-precision seismic data processing technology, especially higher precision requirements for tomographic inversion velocity modeling. High-precision velocity model is the key to seismic data imaging. At present, grid tomography is still commonly used in the industry. The velocity model obtained by inversion of this technology can often only characterize the low-wave number component of velocity, which is good for large-scale structure characterization, but poor for small-scale anomaly characterization.

[0003] Based on this, many methods for characterizing small-scale anomalies have emerged, such as Gaussian beam tomography inversion technology, which can invert the medium-wave number component of the velocity model, improve the accuracy of the velocity model to a certain extent, and solve the inversion problem of small-scale anomalies. However, the accuracy needs to be further improved, especially for velocity anomalies with drastic scale changes. The inversion accuracy of Gaussian beam is sometimes still unsatisfactory. In comparison, the more advanced full waveform inversion technology can invert a higher beam velocity model with higher accuracy, but this technology has extremely high requirements for data quality and huge calculation amount. At present, there are many problems in practical applications and good results have not been achieved. The main reason is that the ray density is uneven in the modeling of small-scale anomalies, which leads to insufficient inversion accuracy.

[0004] Therefore, it is expected to propose a high-precision tomographic inversion method for small-scale anomalies. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a small-scale anomaly tomographic inversion method, device and electronic equipment based on ray density equalization. The small-scale anomaly tomographic inversion method based on ray density equalization automatically calculates the ray density calculated by conventional tomographic inversion, and then introduces it into the target functional of Gaussian beam tomographic velocity modeling, thereby achieving a fine characterization of the small-scale anomaly velocity model.

[0006] In order to achieve the above object, the first aspect of the present invention provides a small-scale anomaly tomography inversion method based on ray density equalization, comprising:

[0007] Perform tomographic inversion on seismic data to obtain initial ray density data volume;

[0008] Based on the initial ray density data volume, an automatic ray density equalization regularization operator is obtained through iterative calculation until the standard deviation of the equalized density is less than a preset threshold;

[0009] According to the automatic ray density equalization regularization operator calculate the tomographic inversion objective function, and perform tomographic inversion of small-scale anomalies based on the tomographic inversion objective function.

[0010] Optionally, tomographic inversion of the seismic data is performed by Gaussian beam tomographic inversion.

[0011] Optionally, the automatic ray density equalization regularization operator is obtained through iterative calculation by the following formula (1) until the standard deviation of the equalized density is less than a preset threshold:

[0012]

[0013] where G(z i ) represents the initial ray density, represents the integral of the ray density above the Z depth, () -1 represents the inverse operation, Max() represents taking the maximum value, i represents the longitudinal sampling point, taking integer values, ε is the constraint weight factor, and its value range is 0-1, represents the automatic ray density equalization regularization operator obtained in the previous round, The initial value of is obtained by performing tomographic inversion on the seismic data to obtain the initial ray density data volume.

[0014] Optionally, the automatic ray density equalization regularization operator at different depths Z can be calculated by formula (1) Through the automatic ray density equalization regularization operator Iterative calculation is performed to enhance the consistency of the lateral ray density at different depths, and hierarchical processing of the ray density is realized.

[0015] Optionally, the standard deviation of the equalized density is as shown in formula (2):

[0016]

[0017] where σ is the standard deviation of the equalized density, average() represents taking the average value, and n represents the number of plane samples of the initial ray density data volume.

[0018] Optionally, the tomographic inversion objective function S(m) is as shown in formula (3):

[0019]

[0020] where z represents the depth sampling point, zpick is the picked reflection point position, z true is the true reflection point position, and ε1 is the constraint weight factor.

[0021] Optionally, the value range of the constraint weight factor ε1 is 0-1.

[0022] The second aspect of the present invention provides a small-scale anomaly tomographic inversion device based on ray density equalization, including:

[0023] An initial ray density data volume acquisition module, which is used to perform tomographic inversion on seismic data to obtain an initial ray density data volume;

[0024] A regularization operator calculation module, which is used to obtain a ray density automatic equalization regularization operator through iterative calculation based on the initial ray density data volume until the standard deviation of the equalized density is less than a preset threshold;

[0025] A tomographic inversion objective function calculation module, which is used to calculate a tomographic inversion objective function according to the ray density automatic equalization regularization operator and perform small-scale anomaly tomographic inversion based on the tomographic inversion objective function.

[0026] Optionally, the ray density automatic equalization regularization operator is obtained through iterative calculation by the following formula (1) until the standard deviation of the equalized density is less than a preset threshold:

[0027]

[0028] where G(z i ) represents the initial ray density, represents the integral of the ray density above the Z depth, () -1 represents the inverse operation, Max() represents taking the maximum value, z i represents the depth of the i-th longitudinal sampling point, i takes positive integer values, ε is the constraint weight factor, and its value range is 0-1, represents the ray density automatic equalization regularization operator obtained in the previous round.

[0029] The third aspect of the present invention provides an electronic device, and the electronic device includes:

[0030] A memory, storing executable instructions;

[0031] A processor, and the processor runs the executable instructions in the memory to implement the small-scale anomaly tomographic inversion method according to any item in the first aspect.

[0032] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method for tomographic inversion of small-scale anomalies based on ray density equalization according to any one of the first aspect.

[0033] The beneficial effects of the present invention are as follows: By performing ray density automatic equalization calculation on the ray density data volume of conventional tomographic inversion calculation, an automatically equalized ray density regularization operator after equalization is obtained, and then introduced into the objective function of tomographic velocity modeling, which improves the ray density uniformity in the anomaly region and the tomographic inversion velocity modeling accuracy of small-scale anomalies, and finally improves the imaging quality.

[0034] Other features and advantages of the present invention will be described in detail in the following specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] By describing the exemplary embodiments of the present invention in more detail in conjunction with the drawings, the above and other objects, features, and advantages of the present invention will become more obvious. Among them, in the exemplary embodiments of the present invention, the same reference numerals generally represent the same components.

[0036] Figure 1 It is a flowchart of the method for tomographic inversion of small-scale anomalies based on ray density equalization in the embodiment of the present invention;

[0037] Figure 2 It is a schematic diagram of ray tracing for conventional tomographic inversion of small-scale anomalies in the embodiment of the present invention.

[0038] Figure 3 It is a ray density slice obtained without using the ray density automatic equalization method in the embodiment of the present invention.

[0039] Figure 4 It is a ray density slice obtained by using the ray density automatic equalization method in the embodiment of the present invention.

[0040] Figure 5 It is an imaging gather obtained without using the ray density automatic equalization method in the embodiment of the present invention.

[0041] Figure 6 It is an imaging gather obtained by using the ray density automatic equalization method in the embodiment of the present invention.

[0042] Figure 7 It is a migrated section obtained without using the ray density automatic equalization method in the embodiment of the present invention.

[0043] Figure 8 It is a migrated section obtained by using the ray density automatic equalization method in the embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0044] Preferred embodiments of the present invention will be described in more detail below. Although the preferred embodiments of the present invention are described below, 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.

[0045] A tomography inversion method for small-scale anomalies based on ray density equalization, comprising:

[0046] Performing tomography inversion on seismic data to obtain an initial ray density data volume;

[0047] Based on the initial ray density data volume, obtaining a ray density automatic equalization regularization operator through iterative calculation Until the standard deviation of the equalized density is less than a preset threshold;

[0048] According to the ray density automatic equalization regularization operator Calculating a tomography inversion objective function, and performing tomography inversion of small-scale anomalies based on the tomography inversion objective function.

[0049] Optionally, performing tomography inversion on the seismic data by Gaussian beam tomography inversion.

[0050] Optionally, obtaining the ray density automatic equalization regularization operator through iterative calculation by the following formula (1) Until the standard deviation of the equalized density is less than a preset threshold:

[0051]

[0052] Wherein, G(z i ) represents the initial ray density, represents the ray density integral above the Z depth, () -1 represents the inverse operation, Max() represents taking the maximum value, i represents the longitudinal sampling point, taking an integer value, ε is a constraint weight factor, and its value range is 0-1, represents the ray density automatic equalization regularization operator obtained in the previous round, The initial value of is obtained by performing tomography inversion on seismic data to obtain an initial ray density data volume.

[0053] The ray density automatic equalization regularization operator at different depths Z can be calculated through formula (1) Through the ray density automatic equalization regularization operator Iteratively calculating to enhance the consistency of the transverse ray density at different depths, and realizing the layered processing of the ray density.

[0054] Optionally, the standard deviation of the equalized density is as shown in formula (2):

[0055]

[0056] Among them, σ is the standard deviation of the equilibrium density, average() represents taking the average value, and n represents the number of plane sample points of the initial ray density data volume.

[0057] σ is used to control the number of iterations. For example, if it is set that the standard deviation of the equilibrium density shall not be greater than 0.1, then the above formula (3) needs to be iterated multiple times, and the iteration stops when the standard deviation σ ≤ 0.1.

[0058] Optionally, the tomographic inversion objective function S(m) is as shown in formula (3):

[0059]

[0060] Among them, z represents the depth sampling point, z pick is the picked reflection point position, z true is the true reflection point position, and ε1 is the constraint weight factor.

[0061] In traditional tomographic inversion, the ray density in the small-scale anomaly region is extremely uneven, resulting in insufficient inversion accuracy of small-scale anomalies, distortion of the underlying structure of the anomaly in the imaging result, and generation of structural artifacts. The objective function of conventional tomographic inversion is usually as follows:

[0062]

[0063] When the tomographic velocity is accurate, S(m) is the smallest. In practical applications, only using the above formula to calculate the velocity model has poor effects, especially for small-scale special anomalies, it is difficult to obtain a good modeling and imaging effect. This application adopts the ray density automatic equalization technology to obtain a more uniform ray density in the anomaly regions of different scales, and applies it to the objective function for regularization constraints to obtain the objective function shown in formula (3). It can improve the uniformity of the ray density in the anomaly region and achieve the resolution of small-scale anomalies.

[0064] Optionally, the value range of the constraint weight factor ε1 is 0-1.

[0065] Example 1:

[0066] A tomographic inversion method for small-scale anomalies based on ray density equalization, including: performing tomographic inversion on seismic data to obtain an initial ray density data volume; based on the initial ray density data volume, obtaining a ray density automatic equalization regularization operator through iterative calculation until the standard deviation of the equilibrium density is less than a preset threshold; according to the ray density automatic equalization regularization operator The tomographic inversion objective function is calculated, and the tomographic inversion of small-scale anomalies is performed based on the tomographic inversion objective function. The method improves the uniformity of the ray density in the small-size anomaly area and the accuracy of the tomographic inversion velocity modeling of the small-scale anomaly, and ultimately improves the imaging quality. The technical effects of the present invention are illustrated below with reference to the embodiments:

[0067] From the conventional tomographic inversion ray tracing diagram for small-scale anomalies (such as Figure 2 As shown in the figure, it can be seen that due to the influence of the small-scale Permian igneous rocks with drastic lateral changes, the ray density is extremely uneven, which affects the subsequent velocity modeling accuracy and migration imaging quality.

[0068] The ray density slices (such as Figure 3 As shown in FIG. 1 , the ray density is extremely uneven, with white representing ray-dense areas and gray representing ray-sparse areas. After the small-scale anomaly tomography inversion method based on ray density balance provided by the present invention is adopted, the ray density is very uniform (as shown in FIG. 1 ). Figure 4 The problem of uneven lateral ray density distribution no longer occurs.

[0069] The imaging gathers (such as Figure 5 As shown in the figure, it can be seen that due to the influence of uneven ray density, the accuracy of the velocity model corresponding to the small-scale anomaly is limited, the imaging gathers are not completely straightened, and the imaging quality needs to be improved; and the imaging gathers obtained by using the small-scale anomaly tomography inversion method based on ray density balance (as shown in the figure) Figure 6 Compared to Figure 5 The accuracy of the velocity model corresponding to the small-scale anomaly is significantly improved, the imaging gathers are basically completely straightened, and the imaging quality is improved.

[0070] The imaging sections (such as Figure 7 As shown in the figure, it can be seen that the velocity accuracy is insufficient due to the uneven ray density. The deep layer of the imaging section is affected by the shallow small-scale anomaly. The depth indicated by the arrow shows obvious twisting, and the imaging quality is poor. The imaging section obtained by using the small-scale anomaly tomography inversion method based on ray density balance (as shown in the figure) Figure 8 The twisting at the depth indicated by the arrow in the figure has been effectively eliminated, and the imaging quality has been significantly improved.

[0071] Embodiment 2:

[0072] A small-scale abnormal body tomographic inversion device based on ray density balance, comprising:

[0073] An initial ray density data volume acquisition module, configured to perform tomographic inversion on seismic data to obtain an initial ray density data volume;

[0074] A regularization operator calculation module, configured to iteratively calculate a ray density automatic equalization regularization operator based on the initial ray density data volume until the equalized density standard deviation is less than a preset threshold;

[0075] A tomographic inversion objective function calculation module, configured to calculate a tomographic inversion objective function according to the ray density automatic equalization regularization operator and perform tomographic inversion of small-scale anomalies based on the tomographic inversion objective function.

[0076] Optionally, perform tomographic inversion on the seismic data through Gaussian beam tomographic inversion.

[0077] Optionally, obtain the ray density automatic equalization regularization operator through iterative calculation using the following formula (1) until the equalized density standard deviation is less than a preset threshold:

[0078]

[0079] where G(z i ) represents the initial ray density, represents the integral of the ray density above the Z depth, () -1 represents the inverse operation, Max() represents taking the maximum value, i represents the longitudinal sampling point, taking integer values, ε is a constraint weight factor, and its value range is 0-1, represents the ray density automatic equalization regularization operator obtained in the previous round, and the initial value of is obtained by performing tomographic inversion on the seismic data to obtain the initial ray density data volume.

[0080] Optionally, the ray density automatic equalization regularization operator at different depths Z can be calculated through formula (1) Through the ray density automatic equalization regularization operator Iteratively calculate to enhance the consistency of the lateral ray density at different depths and achieve hierarchical processing of the ray density.

[0081] Optionally, the equalized density standard deviation is as shown in formula (2):

[0082]

[0083] where σ is the equalized density standard deviation, average() represents taking the average value, and n represents the number of plane samples of the initial ray density data volume.

[0084] Optionally, the tomographic inversion objective function S(m) is as shown in formula (3):

[0085]

[0086] where z represents the depth sampling point, z pick is the picked reflection point position, z true is the true reflection point position, and ε1 is the constraint weight factor.

[0087] Optionally, the value range of the constraint weight factor ε1 is 0 - 1.

[0088] Example 3:

[0089] An embodiment of the present invention provides an electronic device including a memory and a processor.

[0090] The memory stores executable instructions;

[0091] The processor runs the executable instructions in the memory to implement the tomographic inversion method for small-scale anomalies based on ray density equilibrium.

[0092] This memory is used to store non-temporary computer-readable instructions. Specifically, the memory may include one or more computer program products, and these computer program products may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. This volatile memory may include, for example, random access memory (RAM) and / or cache memory, etc. This non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0093] The processor may be a central processing unit (CPU) or other forms of processing units with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In an embodiment of the present invention, the processor is used to run the computer-readable instructions stored in the memory.

[0094] Those skilled in the art should understand that, in order to solve the technical problem of how to obtain a good user experience effect, this embodiment may also include well-known structures such as communication buses, interfaces, etc., and these well-known structures should also be included in the protection scope of the present invention.

[0095] For a detailed description of this embodiment, reference may be made to the corresponding descriptions in the foregoing embodiments, and details will not be repeated here.

[0096] Example 4:

[0097] An embodiment of the present invention provides a computer-readable storage medium storing a computer program, which when executed by a processor implements a tomographic inversion method for small-scale anomalies based on ray density equalization.

[0098] The computer-readable storage medium according to the embodiment of the present invention stores non-temporary computer-readable instructions. When the non-temporary computer-readable instructions are run by a processor, all or part of the steps of the methods of the various embodiments of the present invention described above are executed.

[0099] The above computer-readable storage medium includes but is not limited to: optical storage media (such as CD-ROMs and DVDs), magneto-optical storage media (such as MOs), magnetic storage media (such as magnetic tapes or external hard drives), media with built-in rewritable non-volatile memories (such as memory cards), and media with built-in ROMs (such as ROM cartridges).

[0100] The various embodiments of the present invention have been described above. The above description is exemplary and not exhaustive, and is also not limited to the disclosed embodiments. Many modifications and variations are obvious to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A tomographic inversion method for small-scale abnormal bodies based on ray density equilibrium, characterized in that, Including: Performing tomographic inversion on seismic data to obtain an initial ray density data volume; Based on the initial ray density data volume, an automatic equalization regularization operator for ray density is obtained through iterative calculation until the standard deviation of the equalized density is less than a preset threshold value; According to the ray density automatic equalization regularization operator Calculate the tomographic inversion objective function, and perform tomographic inversion of small-scale anomalies based on the tomographic inversion objective function; The ray density automatic equalization regularization operator is obtained through iterative calculation using the following formula (1). until the standard deviation of the equalized density is less than a preset threshold value: Among them, G(z i ) represents the initial ray density, represents the integral of the ray density above the z depth, () -1 represents the inverse operation, Max() represents taking the maximum value, z i represents the depth of the i-th longitudinal sampling point, where i takes positive integer values, and ε is the constraint weight factor, whose value range is 0 - 1, represents the ray density automatic equalization regularization operator obtained in the previous round.

2. The tomographic inversion method for small-scale anomalies based on ray density equilibrium according to claim 1, characterized in that Performing tomographic inversion on the seismic data through Gaussian beam tomographic inversion.

3. The tomographic inversion method for small-scale anomalies based on ray density equilibrium according to claim 1, wherein The balanced density standard deviation is as shown in formula (2): Wherein, σ is the balanced density standard deviation, average() represents taking the average value, and n represents the number of plane samples of the initial ray density data volume.

4. The tomographic inversion method for small-scale anomalies based on ray density equilibrium according to claim 1, wherein The tomographic inversion objective function S(m) is as shown in formula (3): where z represents the depth sampling point, z pick is the picked reflection point position, and z true is the true reflection point position, and ε1 is the constraint weight factor.

5. The tomographic inversion method for small-scale anomalies based on ray density balance according to claim 4, characterized in that The value range of the constraint weight factor ε1 is 0-1.

6. A tomographic inversion device for small-scale abnormal bodies based on ray density equilibrium, characterized in that Including: An initial ray density data volume acquisition module, configured to perform tomographic inversion on seismic data to obtain an initial ray density data volume; A regularization operator calculation module, configured to iteratively calculate a ray density automatic equalization regularization operator based on the initial ray density data volume until the standard deviation of the equalized density is less than a preset threshold value; The tomographic inversion objective function calculation module is used to automatically balance the regularization operator according to the ray density Calculate the tomographic inversion objective function, and perform tomographic inversion of small-scale anomalies based on the tomographic inversion objective function: Obtaining the ray density automatic balancing regularization operator H(z) through iterative calculation using the following formula (1) until the balanced density standard deviation is less than a preset threshold: Among them, G(z i ) represents the initial ray density, represents the integral of the ray density above the z depth, () -1 represents the inverse operation, Max() represents taking the maximum value, z i represents the depth of the i-th longitudinal sampling point, i takes positive integer values, ε is the constraint weight factor, and its value range is 0 - 1, represents the ray density automatic equalization regularization operator obtained in the previous round.

7. An electronic device, characterized in that, The electronic device includes: A memory storing executable instructions; A processor, the processor running the executable instructions in the memory to implement the tomographic inversion method for small-scale abnormal bodies based on ray density balancing according to any one of claims 1-5.

8. 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, it implements the tomographic inversion method for small-scale abnormal bodies based on ray density balancing according to any one of claims 1-5.

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