Full-waveform inversion gradient preprocessing method and device based on block similarity

Abstract

The invention relates to the field of seismic velocity modeling and imaging in oil-gas exploration and development, and particularly discloses a full-waveform inversion gradient preprocessing method and device based on block similarity, and the method comprises the steps: calculating Euclidean metrics of an imaging point and internal points of a nearby search region block; calculating a similarity weight factor of the imaging point and an internal point based on the Euclidean metric; calculating stratigraphic dip constraint operators of the nearby search region blocks; and preprocessing the gradient to obtain a noiseless gradient of the imaging point. According to the method, gradient preprocessing is carried out through block similarity, noise interference and abnormal information in the full-waveform inversion gradient can be well eliminated, stratigraphic dip constraint is added, the range and direction of gradient processing search blocks are controlled, the search efficiency can be remarkably improved, and when the method is applied to full-waveform inversion, the search efficiency is improved. The method can effectively improve the precision of the full-waveform inversion gradient, enables the gradient to better conform to the geological background, and improves the full-waveform inversion precision and convergence efficiency.

Description

Technical Field

[0001] The invention relates to the field of seismic velocity modeling and imaging in oil and gas exploration and development, and in particular to a full waveform inversion gradient preprocessing method and device based on block similarity. Background Art

[0002] Full waveform inversion technology is one of the most accurate inversion methods currently. Its key problem is reliable gradient calculation. However, since seismic data often contain more noise and lower quality, the full waveform inversion gradient calculation contains more noise or abnormal information. Traditional full waveform inversion methods often solve this problem by smoothing the gradient. However, this method has limited processing effect and is difficult to obtain satisfactory results. It also reduces the convergence efficiency of full waveform inversion or directly obtains wrong inversion results. Therefore, a full waveform inversion gradient preprocessing method is expected to better solve this problem, obtain quality gradients, and improve the accuracy of full waveform inversion.

[0003] Based on this technical background, the present invention studies a full waveform inversion gradient preprocessing method and device based on block similarity. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a full waveform inversion gradient preprocessing method and device based on block similarity. The method performs gradient preprocessing through block similarity, which can better eliminate noise interference and abnormal information in the full waveform inversion gradient, and adds formation dip constraints to control the range and direction of the gradient processing search block, which can significantly improve the search efficiency. Applying it to full waveform inversion can effectively improve the accuracy of the full waveform inversion gradient, make the gradient more consistent with the geological background, thereby improving the accuracy and convergence efficiency of the full waveform inversion.

[0005] In order to achieve the above object, a first aspect of the present invention provides a full waveform inversion gradient preprocessing method based on block similarity, comprising:

[0006] The inversion results are obtained by performing prestack AVAZ inversion based on the optimized omnidirectional OVT gathers or angle domain ES360 gathers.

[0007] The inversion results are subjected to ellipse fitting to obtain the fracture development density and fracture orientation.

[0008] A second aspect of the present invention provides a full waveform inversion gradient preprocessing device based on block similarity, comprising:

[0009] Prestack AVAZ inversion module, used to perform prestack AVAZ inversion based on optimized omnidirectional OVT gathers or angle domain ES360 gathers to obtain inversion results;

[0010] The ellipse fitting module is used to perform ellipse fitting on the inversion result to obtain the fracture development density and fracture orientation.

[0011] A third aspect of the present invention provides an electronic device, the electronic device comprising:

[0012] A memory storing executable instructions;

[0013] A processor runs the executable instructions in the memory to implement the full waveform inversion gradient preprocessing method based on block similarity described in the first aspect.

[0014] The fourth aspect of the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the full waveform inversion gradient preprocessing method based on block similarity described in the first aspect.

[0015] The beneficial effects of the present invention include:

[0016] (1) The full waveform inversion gradient preprocessing method based on block similarity proposed in the present invention can effectively eliminate noise interference and abnormal information in the full waveform inversion gradient by performing gradient preprocessing based on block similarity, and add formation dip constraints to control the range and direction of the gradient processing search block, which can significantly improve the search efficiency. Applying it to full waveform inversion can effectively improve the accuracy of the full waveform inversion gradient and make the gradient more consistent with the geological background, thereby improving the accuracy and convergence efficiency of the full waveform inversion.

[0017] (2) The full waveform inversion gradient preprocessing method based on block similarity proposed in the present invention solves the problems of noise interference and abnormal information in the conventional full waveform inversion gradient by performing gradient preprocessing based on block similarity and adding formation dip constraints, thereby improving the accuracy of the full waveform inversion gradient and having strong practicality.

[0018] Other features and advantages of the present invention will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] 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 with reference to the accompanying drawings.

[0020] Figure 1 The figure is a flow chart of the full waveform inversion gradient preprocessing method based on block similarity proposed by the present invention.

[0021] Figure 2 A schematic diagram of a conventional full waveform inversion gradient in a specific implementation of the full waveform inversion gradient preprocessing method based on block similarity proposed by the present invention.

[0022] Figure 3 A schematic diagram of a gradient after being processed by the method of the present invention in a specific implementation of the full waveform inversion gradient preprocessing method based on block similarity proposed by the present invention. DETAILED DESCRIPTION

[0023] The 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 to the embodiments set forth herein.

[0024] The present invention provides a full waveform inversion gradient preprocessing method based on block similarity, such as Figure 1 As shown, including:

[0025] Calculate the Euclidean metric between the image point and the internal points of the nearby search area block;

[0026] Calculate the similarity weight factor between the imaging point and the internal point based on the Euclidean metric;

[0027] Calculate the formation dip constraint operator of nearby search area blocks;

[0028] The gradient is preprocessed to obtain the noise-free gradient of the imaging point.

[0029] In the present invention, gradient preprocessing is performed through block similarity, which can better eliminate noise interference and abnormal information in the full waveform inversion gradient, and adding formation dip constraints to control the range and direction of the gradient processing search block can significantly improve the search efficiency. Applying it to full waveform inversion can effectively improve the accuracy of the full waveform inversion gradient, make the gradient more consistent with the geological background, thereby improving the accuracy and convergence efficiency of the full waveform inversion.

[0030] According to the present invention, the formula used to calculate the Euclidean metric is:

[0031]

[0032] Among them, x and z are the horizontal and vertical positions of the imaging point respectively, x r 、z r are the horizontal and vertical positions of the inner points of the nearby search area block with a distance r from the imaging point, g(x,z) is the gradient value of the imaging point, and g(x r ,z r ) is the gradient value of the internal point of the nearby search area block with a distance r from the imaging point, and O(x,z) is the Euclidean metric between the imaging point and the internal point of the nearby search area block.

[0033] According to the present invention, the mathematical expression of the imaging point gradient is:

[0034] g(x,z)=g%(x,z)+n(x,z);

[0035] Among them, g%(x,z) is the noise-free gradient of the imaging point, and n(x,z) is the noise in the gradient of the imaging point.

[0036] According to the present invention, the formula used to calculate the similarity weight factor is:

[0037]

[0038] Among them, f is the filtering parameter, c(x,z; x r ,z r ) is the similarity weight factor between the imaging point and the internal point.

[0039] According to the present invention, the value range of the similarity weight factor is 0≤c≤1.

[0040] Preferably, the formula used to calculate the formation dip constraint operator is:

[0041]

[0042] Among them, M and N are the vertical and horizontal points of the nearby search area block, and α is the formation dip.

[0043] Preferably, the formula used for gradient pretreatment is:

[0044]

[0045] Where R is the search area block with a distance r from the imaging point (x, z).

[0046] In the present invention, gradient preprocessing is performed through block similarity, and formation dip constraints are added to solve the problems of noise interference and abnormal information in conventional full waveform inversion gradients, improve the accuracy of full waveform inversion gradients, and have strong practicality.

[0047] The present invention will be described in more detail below by way of examples.

[0048] Embodiment 1:

[0049] This embodiment provides a full waveform inversion gradient preprocessing method based on block similarity, and the specific steps are as follows:

[0050] Step 1: Full waveform inversion gradient parameterization: The full waveform inversion gradient can be expressed as

[0051] g(x,z)=g%(x,z)+n(x,z) (1)

[0052] Where x, z are the horizontal and vertical grid point positions, g%(x,z) is the noise-free gradient, and n(x,z) is the noise in the gradient;

[0053] Step 2: Calculate the image point (x, z) and the internal point (x r ,z r ) is:

[0054]

[0055] Among them, G is the Gaussian filter kernel function;

[0056] Step 3: Calculate the similarity weight factor between the imaging point and the neighborhood:

[0057]

[0058] Among them, f is the filtering parameter, and the weight factor c(x r ,z r ) depends on the imaging point (x,z) and (x r ,z r ), the value range is 0≤c≤1;

[0059] Step 4: Calculate the formation dip constraint operator:

[0060]

[0061] Among them, M and N represent the number of vertical and horizontal points in the search area block, respectively, and α represents the formation dip angle. The formation dip angle constraint can control the range and direction of the gradient processing search block to improve the search efficiency;

[0062] Step 5: Gradient preprocessing:

[0063]

[0064] in, This is the final gradient result after processing.

[0065] In this embodiment, simulated data is used to perform effect testing. Figure 2 The gradient obtained by the conventional full waveform inversion method is preprocessed using the method of the present invention, and the obtained result is as follows Figure 3 As shown, it can be seen that the method of the present invention effectively suppresses the abnormal noise in the gradient, the effective information is significantly enhanced, and the gradient is more consistent with the geological background, which proves the effectiveness of the method of the present invention.

[0066] Embodiment 2:

[0067] This embodiment provides a full waveform inversion gradient preprocessing method based on block similarity, such as Figure 1 As shown, including:

[0068] Calculate the Euclidean metric between the image point and the internal points of the nearby search area block;

[0069] Calculate the similarity weight factor between the imaging point and the internal point based on the Euclidean metric;

[0070] Calculate the formation dip constraint operator of nearby search area blocks;

[0071] Preprocess the gradient to obtain the noise-free gradient of the imaging point;

[0072] The formula used to calculate the Euclidean metric is:

[0073]

[0074] Among them, x and z are the horizontal and vertical positions of the imaging point respectively, x r 、z r are the horizontal and vertical positions of the inner points of the nearby search area block with a distance r from the imaging point, g(x,z) is the gradient value of the imaging point, and g(x r ,z r ) is the gradient value of the internal point of the nearby search area block with a distance r from the imaging point, O(x,z) is the Euclidean metric between the imaging point and the internal point of the nearby search area block;

[0075] The mathematical expression of the imaging point gradient is:

[0076] g(x,z)=g%(x,z)+n(x,z);

[0077] in, is the noise-free gradient of the imaging point, and n(x,z) is the noise in the gradient of the imaging point.

[0078] According to the present invention, the formula used to calculate the similarity weight factor is:

[0079]

[0080] Among them, f is the filtering parameter, c(x,z; x r ,z r ) is the similarity weight factor between the imaging point and the internal point.

[0081] According to the present invention, the value range of the similarity weight factor is 0≤c≤1;

[0082] The formula used to calculate the formation dip constraint operator is:

[0083]

[0084] Among them, M and N are the number of vertical and horizontal points in the nearby search area block, and α is the formation dip;

[0085] The formula used for gradient preconditioning is:

[0086]

[0087] Where R is the search area block with a distance r from the imaging point (x, z).

[0088] Embodiment three:

[0089] This embodiment provides a full waveform inversion gradient preprocessing device based on block similarity, comprising:

[0090] The Euclidean metric calculation module is used to calculate the Euclidean metric between the imaging point and the internal points of the nearby search area block;

[0091] A weight factor calculation module, used for calculating similarity weight factors of imaging points and internal points based on Euclidean metric;

[0092] A constraint operator calculation module is used to calculate the formation dip constraint operator of the nearby search area block;

[0093] A gradient preprocessing module is used to preprocess the gradient to obtain a noise-free gradient of the imaging point;

[0094] The formula used to calculate the Euclidean metric is:

[0095]

[0096] Among them, x and z are the horizontal and vertical positions of the imaging point respectively, x r 、z r are the horizontal and vertical positions of the inner points of the nearby search area block with a distance r from the imaging point, g(x,z) is the gradient value of the imaging point, and g(x r ,z r ) is the gradient value of the internal point of the nearby search area block with a distance r from the imaging point, O(x,z) is the Euclidean metric between the imaging point and the internal point of the nearby search area block;

[0097] The mathematical expression of the imaging point gradient is:

[0098] g(x,z)=g%(x,z)+n(x,z);

[0099] Among them, g%(x,z) is the noise-free gradient of the imaging point, and n(x,z) is the noise in the gradient of the imaging point.

[0100] According to the present invention, the formula used to calculate the similarity weight factor is:

[0101]

[0102] Among them, f is the filtering parameter, c(x,z; x r,z r ) is the similarity weight factor between the imaging point and the internal point.

[0103] According to the present invention, the value range of the similarity weight factor is 0≤c≤1;

[0104] The formula used to calculate the formation dip constraint operator is:

[0105]

[0106] Among them, M and N are the number of vertical and horizontal points in the nearby search area block, and α is the formation dip;

[0107] The formula used for gradient preconditioning is:

[0108]

[0109] Where R is the search area block with a distance r from the imaging point (x, z).

[0110] Embodiment 4:

[0111] An embodiment of the present invention provides an electronic device including a memory and a processor.

[0112] A memory storing executable instructions;

[0113] The processor runs the executable instructions in the memory to implement a full waveform inversion gradient preprocessing method based on block similarity.

[0114] The memory is used to store non-temporary computer-readable instructions. Specifically, the memory may include one or more computer program products, which 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 random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.

[0115] 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 one embodiment of the present invention, the processor is used to run the computer-readable instructions stored in the memory.

[0116] 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 invention.

[0117] For detailed description of this embodiment, reference may be made to the corresponding descriptions in the aforementioned embodiments, which will not be repeated here.

[0118] Embodiment five:

[0119] An embodiment of the present invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, a full waveform inversion gradient preprocessing method based on block similarity is implemented.

[0120] The computer-readable storage medium according to the embodiment of the present invention stores non-transitory computer-readable instructions, and when the non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the above-mentioned methods of the embodiments of the present invention are executed.

[0121] 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).

[0122] The full waveform inversion gradient preprocessing method based on block similarity proposed in an embodiment of the present invention uses all-round OVT domain data set or angle domain ES360 data set to carry out pre-stack AVAZ direct inversion, and performs gradient preprocessing through block similarity, which can better eliminate noise interference and abnormal information in the full waveform inversion gradient, and add formation dip constraints to control the gradient processing search block range and direction, which can significantly improve the search efficiency. Applying it to full waveform inversion can effectively improve the accuracy of the full waveform inversion gradient, make the gradient more consistent with the geological background, thereby improving the accuracy and convergence efficiency of the full waveform inversion.

[0123] 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 full waveform inversion gradient preprocessing method based on block similarity, It is characterized in that include: Calculate the Euclidean metric between the image point and the internal points of the nearby search area block; Calculating similarity weight factors of the imaging points and the internal points based on the Euclidean metric; Calculating a formation dip angle constraint operator of the nearby search area block; The gradient is preprocessed to obtain a noise-free gradient of the imaging point.

2. The method according to claim 1, It is characterized in that The formula used to calculate the Euclidean metric is: Among them, x and z are the horizontal and vertical positions of the imaging point respectively, x r 、z r are the horizontal and vertical positions of the inner points of the nearby search area block with a distance r from the imaging point, g(x,z) is the gradient value of the imaging point, and g(x r ,z r ) is the gradient value of the internal point of the nearby search area block with a distance r from the imaging point, and O(x,z) is the Euclidean metric between the imaging point and the internal point of the nearby search area block.

3. The method according to claim 2, It is characterized in that The mathematical expression of the imaging point gradient is: in, is the noise-free gradient of the imaging point, and n(x,z) is the noise in the gradient of the imaging point.

4. The method according to claim 3, It is characterized in that The formula used to calculate the similarity weight factor is: Among them, f is the filtering parameter, c(x,z; x r ,z r ) is the similarity weight factor between the imaging point and the internal point.

5. The method according to claim 4, It is characterized in that The value range of the similarity weight factor is 0≤c≤1.

6. The method according to claim 4, It is characterized in that The formula used to calculate the formation dip constraint operator is: Among them, M and N are the vertical and horizontal points of the nearby search area block, and α is the formation dip.

7. The method according to claim 6, It is characterized in that The formula used for the gradient pretreatment is: Where R is the search area block with a distance r from the imaging point (x, z).

8. A full waveform inversion gradient preprocessing device based on block similarity, It is characterized in that include: The Euclidean metric calculation module is used to calculate the Euclidean metric between the imaging point and the internal points of the nearby search area block; A weight factor calculation module, used for calculating the similarity weight factor between the imaging point and the internal point based on the Euclidean metric; A constraint operator calculation module, used to calculate the formation dip constraint operator of the nearby search area block; The gradient preprocessing module is used to preprocess the gradient to obtain the noise-free gradient of the imaging point.

9. An electronic device, It is characterized in that The electronic device comprises: A memory storing executable instructions; A processor, wherein the processor runs the executable instructions in the memory to implement the full waveform inversion gradient preprocessing method based on block similarity according to any one of claims 1-7.

10. A computer-readable storage medium, It is characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the full waveform inversion gradient preprocessing method based on block similarity according to any one of claims 1 to 7 is implemented.

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