A method, device, electronic device and medium for fusion of well-seismic information

Abstract

This application discloses a method, device, electronic device, and medium for fusing well-seismic information. The method includes: acquiring acoustic time-of-day logging data and an offset profile; performing median filtering and Gaussian filtering on the acoustic time-of-day logging data; extracting a structural tensor and a coherence volume from the offset profile, and calculating a structural tensor field using the structural tensor and the coherence volume; interpolating the logging data using a hybrid neighborhood method in combination with the structural tensor field to obtain an interpolated velocity model and a diffusion time field; and calculating a weighting coefficient using the diffusion time field, fusing a tomographic velocity model and an interpolated velocity model, and obtaining a fused velocity model. The present invention utilizes a small amount of logging data to improve the accuracy and resolution of the tomographic velocity model, thereby efficiently obtaining more accurate structural imaging results and guiding oil and gas exploration and development.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas seismic exploration, and more specifically, to a well-seismic information fusion method, device, electronic equipment and medium. Background Art

[0002] In the field of oil and gas seismic exploration, velocity modeling technology is the basis for subsequent seismic wave migration imaging and reservoir inversion. Ray-type tomography inversion, as the current mainstream velocity modeling technology, can obtain relatively accurate low-frequency velocity models, but it is difficult to obtain high-wavenumber components of the velocity model. Wave-type tomography inversion methods have emerged. This type of method can obtain a more accurate velocity model. In practical applications, it still has problems such as inversion efficiency, dependence on initial models and data quality, so it is difficult to be widely used. The industry has developed a set of methods between conventional ray theory and wave equation theory. Gaussian beam tomography realizes the beam layer filtering of the linearized approximation of the wave equation. Compared with traditional ray tomography, Gaussian beam tomography increases the coverage of rays, reduces the condition number of the kernel matrix, reduces the ill-conditioning of the tomography equation group, and can obtain a more accurate velocity model. At the same time, there is often a small amount of logging data in the area to be studied. Acoustic time difference logging data can accurately and reliably reflect the formation velocity information and is often used as a constraint condition to add to the tomography inversion equation group. Although this method can reduce the ill-conditioning of the equation to a certain extent and improve the inversion accuracy, the high-frequency information of the logging data is lost in the constraint process. In addition, the well-constrained tomography method is limited by efficiency and cannot quickly build and image in real time to guide drilling.

[0003] To address the above issues, we hope to propose a well-seismic information fusion method that uses a small amount of well logging data to improve the accuracy and resolution of the tomographic velocity model, so as to efficiently obtain more accurate structural imaging results and guide oil and gas exploration and development.

[0004] The information disclosed in the background technology section of the present invention is only intended to deepen the understanding of the general background technology of the present invention, and should not be regarded as an admission or any form of suggestion that the information constitutes the prior art already known to those skilled in the art. Summary of the Invention

[0005] The present invention proposes a well-seismic information fusion method, device, electronic equipment and medium, which at least solves the technical problem that the accuracy and resolution of the tomographic velocity model of well logging data are low and accurate structural imaging results cannot be obtained efficiently.

[0006] In a first aspect, an embodiment of the present disclosure provides a method for fusing well and seismic information, comprising:

[0007] Acquire sonic time-of-day logging data and migration profiles;

[0008] performing median filtering and Gaussian filtering on the acoustic time difference logging data;

[0009] extracting a structure tensor and a coherence volume from the offset section, and calculating a structure tensor field using the structure tensor and the coherence volume;

[0010] The hybrid neighborhood method is used to interpolate the well logging data in combination with the structure tensor field to obtain the interpolation velocity model and diffusion time field.

[0011] The diffusion time field is used to calculate a weighting coefficient, and the tomographic velocity model and the interpolation velocity model are fused to obtain a fused velocity model.

[0012] As a specific implementation of the embodiment of the present disclosure, the calculation formula of the structure tensor field is as follows:

[0013]

[0014] Where S is the structure tensor extracted from the migration section, c is the coherence volume extracted from the migration section, and D is the structure tensor field.

[0015] As a specific implementation of the embodiment of the present disclosure, the interpolation formula is as follows:

[0016]

[0017] τ(x)=0,X∈χ

[0018]

[0019] Where τ is the diffusion time field, D is the structure tensor field, χ represents the sonic transit time logging location area, p is the sonic transit time logging value with the shortest diffusion time, and q is the interpolation result.

[0020] As a specific implementation of the embodiment of the present disclosure, the calculation formula of the fusion is as follows:

[0021]

[0022] v mix =l·v interp +(1-l)·v tomo

[0023] Among them, T max is the time threshold, τ is the diffusion time field, l is the weighting coefficient, v interp is the interpolation velocity model, v tomo is the tomographic velocity model.

[0024] In a second aspect, the embodiments of the present disclosure further provide a well-seismic information fusion device, comprising:

[0025] Acquisition module, which acquires acoustic time difference logging data and migration profiles;

[0026] A processing module, performing median filtering and Gaussian filtering on the acoustic time difference logging data;

[0027] a calculation module, extracting a structure tensor and a coherence volume from the offset section, and calculating a structure tensor field using the structure tensor and the coherence volume;

[0028] The interpolation processing module uses the hybrid neighborhood method and the structural tensor field to interpolate the logging data to obtain the interpolation velocity model and diffusion time field;

[0029] The fusion module calculates a weighting coefficient using the diffusion time field, fuses the tomographic velocity model and the interpolation velocity model, and obtains a fused velocity model.

[0030] As a specific implementation of the embodiment of the present disclosure, the calculation formula of the structure tensor field is as follows:

[0031]

[0032] Where S is the structure tensor extracted from the migration section, c is the coherence volume extracted from the migration section, and D is the structure tensor field.

[0033] As a specific implementation of the embodiment of the present disclosure, the interpolation formula is as follows:

[0034]

[0035] τ(x)=0,x∈χ

[0036]

[0037] Where τ is the diffusion time field, D is the structure tensor field, x represents the sonic transit time logging location area, p is the sonic transit time logging value with the shortest diffusion time, and q is the interpolation result.

[0038] As a specific implementation of the embodiment of the present disclosure, the calculation formula of the fusion is as follows:

[0039]

[0040] v mix =l·v interp +(1-l)·v tomo

[0041] Among them, T max is the time threshold, τ is the diffusion time field, l is the weighting coefficient, v interp is the interpolation velocity model, v tomo is the tomographic velocity model.

[0042] In a third aspect, an embodiment of the present disclosure further provides an electronic device, the electronic device comprising:

[0043] at least one processor; and,

[0044] a memory communicatively connected to the at least one processor; wherein,

[0045] The memory stores instructions that can be executed by the at least one processor. The instructions are executed by the at least one processor to enable the at least one processor to perform the well-seismic information fusion method as described above.

[0046] In a fourth aspect, an embodiment of the present disclosure further provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the well-seismic information fusion method as described above.

[0047] The beneficial effects of the present invention are:

[0048] The present invention uses a hybrid neighborhood method to interpolate a small amount of acoustic time-difference logging data and adopts an interpolation accuracy parameter to weight the multi-source velocity model. This restores the high-frequency information of the velocity model to a certain extent, improves the accuracy and resolution of the tomographic velocity model, and achieves precise imaging. The method is simple, direct, and highly efficient.

[0049] The present invention uses a hybrid neighborhood method combined with a structural tensor field to interpolate logging data, and uses the diffusion time field to calculate weighting coefficients. The tomographic velocity model and the interpolation velocity model are then fused to obtain a fused velocity model. This method restores the high-frequency information of the tomographic velocity model to a certain extent, improves the resolution of the velocity model, and lays the foundation for subsequent high-precision imaging. The method is simple and direct and can achieve rapid modeling and imaging.

[0050] The methods and apparatus of the present invention have other features and advantages that will be apparent from or will be described in detail in the accompanying drawings and subsequent detailed descriptions incorporated herein, which together serve to explain the specific principles of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] 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, wherein like reference numerals generally represent like components throughout the exemplary embodiments of the present invention.

[0052] Figure 1 This is a flow chart of the well-seismic information fusion method of Example 1;

[0053] Figure 2 This is the marmousi standard model diagram used in Example 1;

[0054] Figure 3 is the Gaussian beam tomography velocity field diagram in Example 1;

[0055] Figure 4 This is the velocity model diagram of the mixed domain interpolation in Example 1;

[0056] Figure 5 This is the velocity model diagram after fusion in Example 1;

[0057] Figure 6 A comparison chart of the true velocity value, the tomographic velocity model, and the fusion velocity model in Example 1;

[0058] Figure 7 This is a structural block diagram of the well-seismic information fusion device of Example 2 of the present invention. DETAILED DESCRIPTION

[0059] 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.

[0060] The present invention provides a well-seismic information fusion method, comprising:

[0061] Acquire sonic transit time logging data and migration profiles;

[0062] performing median filtering and Gaussian filtering on the acoustic time difference logging data;

[0063] extracting a structure tensor and a coherence volume from the offset section, and calculating a structure tensor field using the structure tensor and the coherence volume;

[0064] The hybrid neighborhood method is used to interpolate the well logging data in combination with the structure tensor field to obtain the interpolation velocity model and diffusion time field.

[0065] The diffusion time field is used to calculate a weighting coefficient, and the tomographic velocity model and the interpolation velocity model are fused to obtain a fused velocity model.

[0066] In one example, the calculation formula of the structure tensor field is as follows:

[0067]

[0068] Where S is the structure tensor extracted from the migration section, c is the coherence volume extracted from the migration section, and D is the structure tensor field.

[0069] In one example, the interpolation formula is as follows:

[0070]

[0071] τ(x)=0,x∈χ

[0072]

[0073] Where τ is the diffusion time field, D is the structure tensor field, χ represents the sonic transit time logging location area, p is the sonic transit time logging value with the shortest diffusion time, and q is the interpolation result.

[0074] In one example, the fusion calculation formula is as follows:

[0075]

[0076] v mix =l·v interp +(1-l)·v tomo

[0077] Among them, T max is the time threshold, τ is the diffusion time field, l is the weighting coefficient, v interp is the interpolation velocity model, v tomo is the tomographic velocity model.

[0078] The present invention also provides a well-seismic information fusion device, comprising:

[0079] Acquisition module, which acquires acoustic time difference logging data and migration profiles;

[0080] A processing module, performing median filtering and Gaussian filtering on the acoustic time difference logging data;

[0081] a calculation module, extracting a structure tensor and a coherence volume from the offset section, and calculating a structure tensor field using the structure tensor and the coherence volume;

[0082] The interpolation processing module uses the hybrid neighborhood method and the structural tensor field to interpolate the logging data to obtain the interpolation velocity model and diffusion time field;

[0083] The fusion module calculates a weighting coefficient using the diffusion time field, fuses the tomographic velocity model and the interpolation velocity model, and obtains a fused velocity model.

[0084] In one example, the calculation formula of the structure tensor field is as follows:

[0085]

[0086] Where S is the structure tensor extracted from the migration section, c is the coherence volume extracted from the migration section, and D is the structure tensor field.

[0087] In one example, the interpolation formula is as follows:

[0088]

[0089] τ(x)=0,x∈χ

[0090]

[0091] Where τ is the diffusion time field, D is the structure tensor field, χ represents the sonic transit time logging location area, p is the sonic transit time logging value with the shortest diffusion time, and q is the interpolation result.

[0092] In one example, the fusion calculation formula is as follows:

[0093]

[0094] v mix =l·v interp +(1-l)·v tomo

[0095] Among them, T max is the time threshold, τ is the diffusion time field, l is the weighting coefficient, v interp is the interpolation velocity model, v tomo is the tomographic velocity model.

[0096] The present invention further provides an electronic device, comprising:

[0097] at least one processor; and a memory communicatively connected to the at least one processor; wherein,

[0098] The memory stores instructions that can be executed by the at least one processor. The instructions are executed by the at least one processor to enable the at least one processor to perform the well-seismic information fusion method as described above.

[0099] The present invention also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the well-seismic information fusion method as described above.

[0100] To facilitate understanding of the solutions and effects of the embodiments of the present invention, four specific application examples are given below. Those skilled in the art should understand that these examples are only for facilitating understanding of the present invention, and any specific details thereof are not intended to limit the present invention in any way.

[0101] Example 1

[0102] Figure 1 FIG. 1 is a flow chart showing the steps of a well-seismic information fusion method according to an embodiment of the present invention. Figure 1 As shown in FIG, the well-seismic information fusion method includes:

[0103] S01: Acquire acoustic time-of-day logging data and migration profiles;

[0104] S02: performing median filtering and Gaussian filtering on the acoustic time difference logging data;

[0105] S03: extracting a structure tensor and a coherence volume from the offset section, and calculating a structure tensor field using the structure tensor and the coherence volume;

[0106] S04: Use the hybrid neighborhood method and the structure tensor field to interpolate the logging data to obtain the interpolation velocity model and diffusion time field;

[0107] S05: Calculating a weighting coefficient using the diffusion time field, fusing the tomographic velocity model and the interpolation velocity model to obtain a fused velocity model.

[0108] The hybrid neighborhood method combined with the structural tensor field is used to interpolate the logging data, and the diffusion time field is used to calculate the weighting coefficient. The tomographic velocity model and the interpolation velocity model are fused to obtain a fused velocity model. This method restores the high-frequency information of the tomographic velocity model to a certain extent, improves the resolution of the velocity model, and lays the foundation for subsequent high-precision imaging. This method is simple and direct and can achieve rapid modeling and imaging.

[0109] Figure 2 is a diagram of the marmousi standard model used in the embodiment of the present invention, Figure 3 The Gaussian beam tomography velocity model in the embodiment of the present invention is verified by taking the geophysical standard model Marmousi model as an example. The velocity model is obtained by using reflected wave Gaussian beam tomography. The true velocity values ​​at positions X = 200m and X = 1100m are taken as logging data. The structural tensor and coherence volume are extracted from the migration section. The logging data are interpolated using the mixed neighborhood method to obtain the interpolated velocity model, as shown in FIG. Figure 4 As shown in Figure 2, the interpolated velocity model has a higher frequency than the tomographic velocity model. The velocity values ​​near the well location are closer to the true velocity model, while the velocity values ​​far from the well location deviate from the true value. The time threshold is set to 2000ms, and the weighting coefficient is calculated using the diffusion time field. The two velocity models are fused to obtain the fused velocity model, as shown in Figure 2. Figure 5 As shown in the figure, two data traces at X = 500m and X = 1500m are taken to compare the true velocity value, the tomographic velocity model and the fusion velocity model, as shown in the figure. Figure 6 As shown in Figure 3, the results show that the fused velocity model is closer to the actual velocity value.

[0110] Example 2

[0111] Figure 7 A well-seismic information fusion device according to an embodiment of the present invention is shown.

[0112] like Figure 7 As shown, the well-seismic information fusion device includes:

[0113] Acquisition module, which acquires acoustic time difference logging data and migration profiles;

[0114] A processing module, performing median filtering and Gaussian filtering on the acoustic time difference logging data;

[0115] a calculation module, extracting a structure tensor and a coherence volume from the offset section, and calculating a structure tensor field using the structure tensor and the coherence volume;

[0116] The interpolation processing module uses the hybrid neighborhood method and the structural tensor field to interpolate the logging data to obtain the interpolation velocity model and diffusion time field;

[0117] The fusion module calculates a weighting coefficient using the diffusion time field, fuses the tomographic velocity model and the interpolation velocity model, and obtains a fused velocity model.

[0118] As a specific implementation of the embodiment of the present disclosure, the calculation formula of the structure tensor field is as follows:

[0119]

[0120] Where S is the structure tensor extracted from the migration section, c is the coherence volume extracted from the migration section, and D is the structure tensor field.

[0121] As a specific implementation of the embodiment of the present disclosure, the interpolation formula is as follows:

[0122]

[0123] τ(x)=0,x∈χ

[0124]

[0125] Where τ is the diffusion time field, D is the structure tensor field, χ represents the sonic transit time logging location area, p is the sonic transit time logging value with the shortest diffusion time, and q is the interpolation result.

[0126] As a specific implementation of the embodiment of the present disclosure, the calculation formula of the fusion is as follows:

[0127]

[0128] v mix =l·v interp +(1-l)·vtomo

[0129] Among them, T max is the time threshold, τ is the diffusion time field, l is the weighting coefficient, v interp is the interpolation velocity model, v tomo is the tomographic velocity model.

[0130] Example 3

[0131] The present disclosure provides an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein,

[0132] The memory stores instructions that can be executed by the at least one processor. The instructions are executed by the at least one processor to enable the at least one processor to perform the well-seismic information fusion method described above.

[0133] An electronic device according to an embodiment of the present disclosure includes a memory and a processor.

[0134] The memory is used to store non-transitory 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 include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), a hard disk, flash memory, etc.

[0135] The processor may be a central processing unit (CPU) or other form of processing unit having data processing capability and / or instruction execution capability, 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 execute the computer-readable instructions stored in the memory.

[0136] Those skilled in the art should understand that in order to solve the technical problem of how to obtain a good user experience, this 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 scope of protection of this disclosure.

[0137] For detailed description of this embodiment, please refer to the corresponding description in the aforementioned embodiments, which will not be repeated here.

[0138] Example 4

[0139] An embodiment of the present disclosure provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the well-seismic information fusion method described above.

[0140] According to the non-transitory computer-readable storage medium of the embodiment of the present disclosure, when the non-transitory computer-readable instructions are executed by the processor, all or part of the steps of the aforementioned methods of the various embodiments of the present disclosure are executed.

[0141] The above-mentioned computer-readable storage media include, but are not limited to, optical storage media (e.g., CD-ROMs and DVDs), magneto-optical storage media (e.g., MOs), magnetic storage media (e.g., magnetic tapes or mobile hard disks), media with built-in rewritable non-volatile memory (e.g., memory cards), and media with built-in ROM (e.g., ROM cartridges).

[0142] Those skilled in the art should understand that the above description of the embodiments of the present invention is only for the purpose of illustrative purposes only to illustrate the beneficial effects of the embodiments of the present invention, and is not intended to limit the embodiments of the present invention to any given examples.

[0143] While various embodiments of the present invention have been described above, the above description is intended to be illustrative, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A method for fusing well and seismic information, characterized in that: include: Acquire sonic time-of-day logging data and migration profiles; performing median filtering and Gaussian filtering on the acoustic time difference logging data; extracting a structure tensor and a coherence volume from the offset section, and calculating a structure tensor field using the structure tensor and the coherence volume; The hybrid neighborhood method is used to interpolate the well logging data in combination with the structure tensor field to obtain the interpolation velocity model and diffusion time field. Calculating a weighting coefficient using the diffusion time field, fusing a tomographic velocity model and an interpolation velocity model to obtain a fused velocity model; The calculation formula of the structure tensor field is as follows: ， Where S is the structure tensor extracted from the migration section, c is the coherence volume extracted from the migration section, and D is the structure tensor field; The calculation formula of the fusion speed is as follows: ， Among them, T max is the time threshold, τ is the diffusion time field, l is the weighting coefficient, v interp is the interpolation velocity model, v tomo is the tomographic velocity model, v mix is the fusion speed.

2. The well-seismic information fusion method according to claim 1, characterized in that: The interpolation formula is as follows: ， Where τ is the diffusion time field, D is the structure tensor field, χ represents the sonic transit time logging location area, p is the sonic transit time logging value with the shortest diffusion time, and q is the interpolation result.

3. A well-seismic information fusion device, characterized in that: Acquisition module, which acquires acoustic time difference logging data and migration profiles; A processing module, performing median filtering and Gaussian filtering on the acoustic time difference logging data; a calculation module, extracting a structure tensor and a coherence volume from the offset section, and calculating a structure tensor field using the structure tensor and the coherence volume; The interpolation processing module uses the hybrid neighborhood method and the structural tensor field to interpolate the logging data to obtain the interpolation velocity model and diffusion time field; A fusion module calculates a weighting coefficient using the diffusion time field, fuses the tomographic velocity model and the interpolation velocity model, and obtains a fused velocity model; The calculation formula of the structure tensor field is as follows: ， Where S is the structure tensor extracted from the migration section, c is the coherence volume extracted from the migration section, and D is the structure tensor field; The calculation formula of the fusion speed is as follows: ， Among them, T max is the time threshold, τ is the diffusion time field, l is the weighting coefficient, v interp is the interpolation velocity model, v tomo is the tomographic velocity model, v mix is the fusion speed.

4. The well-seismic information fusion device according to claim 3, characterized in that: The interpolation formula is as follows: ， Where τ is the diffusion time field, D is the structure tensor field, χ represents the sonic transit time logging location area, p is the sonic transit time logging value with the shortest diffusion time, and q is the interpolation result.

5. An electronic device, 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 that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the well-seismic information fusion method according to claim 1 or 2.

6. A non-transitory computer-readable storage medium, characterized in that The non-transitory computer-readable storage medium stores computer instructions, which are used to enable a computer to execute the well-seismic information fusion method according to claim 1 or 2.

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