CN115994338B - A method, device, electronic device and medium for fusion of well-seismic information - Google Patents
A method, device, electronic device and medium for fusion of well-seismic informationInfo
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- CN115994338B CN115994338B CN202111210323.XA CN202111210323A CN115994338B CN 115994338 B CN115994338 B CN 115994338B CN 202111210323 A CN202111210323 A CN 202111210323A CN 115994338 B CN115994338 B CN 115994338B
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- structure tensor
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Abstract
The application discloses a well earthquake information fusion method, a well earthquake information fusion device, electronic equipment and a medium. The method comprises the steps of obtaining acoustic time difference logging data and an offset profile, carrying out median filtering and Gaussian filtering on the acoustic time difference logging data, extracting a structure tensor and a coherent body from the offset profile, calculating a structure tensor field by using the structure tensor and the coherent body, carrying out interpolation processing on the logging data by combining the structure tensor field by using a mixed neighborhood method to obtain an interpolation speed model and a diffusion time field, calculating a weighting coefficient by using the diffusion time field, and fusing the chromatography speed model and the interpolation speed model to obtain a fusion speed model. The application improves the precision and resolution of the chromatographic velocity model by using a small amount of logging data so as to efficiently obtain more accurate structural imaging results and guide the oil-gas exploration and development.
Description
Technical Field
The invention relates to the technical field of oil-gas seismic exploration, in particular to a well seismic information fusion method, a well seismic information fusion device, electronic equipment and a medium.
Background
In the field of oil-gas seismic exploration, a velocity modeling technology is the basis of subsequent seismic wave migration imaging and reservoir inversion, ray tomography inversion is used as the current mainstream velocity modeling technology, a relatively accurate low-frequency velocity model can be obtained, but a high wave number component of the velocity model is difficult to obtain, a wave tomography inversion method is generated, a more accurate velocity model can be obtained, in practical application, the problems of inversion efficiency, dependence on an initial model, data quality and the like still exist, the method is difficult to be widely applied, a set of methods between a conventional ray theory and a wave equation theory are developed in the industry, gaussian beam tomography realizes beam tomography of linear approximation of a wave equation by filtering, compared with the traditional ray tomography, the Gaussian beam tomography increases the coverage of rays, reduces the condition number of a nuclear matrix, reduces the disease state of a chromatographic equation set, and can obtain a more accurate velocity model, meanwhile, in a region to be researched, a small amount of well measurement data often exist, the acoustic time difference can accurately reflect the velocity information, and often serve as constraint conditions to be added into the chromatographic inversion system, the method can be limited by the constraint method, the method can be fast in the condition of the constraint method, but the well drilling accuracy is limited by the constraint on the condition of the constraint method can be greatly reduced, the accuracy of the well drilling information can be greatly reduced, and the method can not be limited by the constraint on the real-time information.
Aiming at the problems, the well earthquake information fusion method is expected to be provided, the accuracy and the resolution of a chromatographic velocity model are improved by using a small amount of logging data, so that more accurate structural imaging results are obtained efficiently, and the oil and gas exploration and development are guided.
The information disclosed in the background section of the invention is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.
Disclosure of Invention
The invention provides a well earthquake information fusion method, a well earthquake information fusion device, electronic equipment and a well earthquake information fusion medium, which at least solve the technical problems that the accuracy and the resolution of a logging data chromatographic velocity model are low, and an accurate structural imaging result cannot be obtained efficiently.
In a first aspect, an embodiment of the present disclosure provides a method for fusion of well seismic information, including:
acquiring acoustic time difference logging data and an offset profile;
Performing median filtering and Gaussian filtering on the acoustic time difference logging data;
extracting a structure tensor and a coherent body from the offset profile, and calculating a structure tensor field by using the structure tensor and the coherent body;
Performing interpolation processing on logging data by using a mixed neighborhood method and combining a structure tensor field to obtain an interpolation speed model and a diffusion time field;
and calculating a weighting coefficient by using the diffusion time field, and fusing the chromatographic velocity model and the interpolation velocity model to obtain a fusion velocity model.
As a specific implementation manner of the embodiments of the present disclosure, the calculation formula of the structural tensor field is as follows:
Where S is the structure tensor extracted from the offset profile, c is the coherence volume extracted from the offset profile, and D is the structure tensor field.
As a specific implementation manner of the embodiment of the disclosure, the interpolation formula is as follows:
τ(x)=0,X∈χ
wherein τ is a diffusion time field, D is a structure tensor field, χ is an acoustic time difference logging position area, p is an acoustic time difference logging value with the shortest diffusion time, and q is an interpolation result.
As a specific implementation manner of the embodiment of the present disclosure, the fused calculation formula is as follows:
vmix＝l·vinterp+(1-l)·vtomo
Wherein T max is a time threshold, τ is a diffusion time field, l is a weighting coefficient, v interp is an interpolation velocity model, and v tomo is a chromatography velocity model.
In a second aspect, an embodiment of the present disclosure further provides a device for fusion of borehole seismic information, including:
the acquisition module is used for acquiring acoustic time difference logging data and an offset profile;
the processing module is used for carrying out median filtering and Gaussian filtering on the acoustic time difference logging data;
the calculation module is used for extracting a structure tensor and a coherent body from the offset profile and calculating a structure tensor field by using the structure tensor and the coherent body;
The interpolation processing module is used for carrying out interpolation processing on the logging data by utilizing a mixed neighborhood method and combining the structure tensor field to obtain an interpolation speed model and a diffusion time field;
and the fusion module is used for calculating a weighting coefficient by using the diffusion time field, fusing the chromatographic velocity model and the interpolation velocity model, and obtaining a fusion velocity model.
As a specific implementation manner of the embodiments of the present disclosure, the calculation formula of the structural tensor field is as follows:
Where S is the structure tensor extracted from the offset profile, c is the coherence volume extracted from the offset profile, and D is the structure tensor field.
As a specific implementation manner of the embodiment of the disclosure, the interpolation formula is as follows:
τ(x)=0,x∈χ
wherein τ is a diffusion time field, D is a structure tensor field, x is an acoustic time difference logging position area, p is an acoustic time difference logging value with the shortest diffusion time, and q is an interpolation result.
As a specific implementation manner of the embodiment of the present disclosure, the fused calculation formula is as follows:
vmix＝l·vinterp+(1-l)·vtomo
Wherein T max is a time threshold, τ is a diffusion time field, l is a weighting coefficient, v interp is an interpolation velocity model, and v tomo is a chromatography velocity model.
In a third aspect, embodiments of the present disclosure further provide an electronic device, including:
At least one processor, and
A memory communicatively coupled to the at least one processor, wherein,
The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of well intervention information fusion as described above.
In a fourth aspect, the presently disclosed embodiments also provide a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform a well-shock information fusion method as described above.
The invention has the beneficial effects that:
The method utilizes a mixed neighborhood method to interpolate a small amount of acoustic time difference logging data, adopts interpolation precision parameters to weight the multi-source velocity model, recovers high-frequency information of the velocity model to a certain extent, improves the precision and resolution of the chromatographic velocity model, realizes accurate imaging, and is simple and direct, and has higher efficiency.
According to the invention, the mixed neighborhood method is used for carrying out interpolation processing on logging data in combination with the structure tensor field, the diffusion time field is used for calculating the weighting coefficient, the chromatographic velocity model and the interpolation velocity model are fused, the fusion velocity model is obtained, the high-frequency information of the chromatographic velocity model is recovered to a certain extent, the resolution ratio of the velocity model is improved, a foundation is laid for subsequent high-precision imaging, and the method is simple and direct, and can realize rapid modeling imaging.
The method and apparatus of the present invention have other features and advantages which will be apparent from or are set forth in detail in the accompanying drawings and the following detailed description, which are incorporated herein, and which together serve to explain certain principles of the present invention.
Drawings
The foregoing and other objects, features and advantages of the invention will be apparent from the following more particular descriptions of exemplary embodiments of the invention as illustrated in the accompanying drawings wherein like reference numbers generally represent like parts throughout the exemplary embodiments of the invention.
FIG. 1 is a flow chart of a method of fusion of well seismic information according to embodiment 1;
FIG. 2 is a diagram of marmousi standard models employed in example 1;
FIG. 3 is a Gaussian beam tomography velocity field diagram of example 1;
FIG. 4 is a velocity model diagram of the mixed domain interpolation in example 1;
FIG. 5 is a graph of the velocity model after fusion in example 1;
FIG. 6 is a graph showing the comparison of the true velocity values, the chromatographic velocity model and the fusion velocity model in example 1;
fig. 7 is a block diagram of a well seismic information fusion apparatus according to embodiment 2 of the present invention.
Detailed Description
Preferred embodiments of the present invention will be described in more detail below. While the preferred embodiments of the present invention are described below, it should be understood that the present invention may be embodied in various forms and should not be limited to the embodiments set forth herein.
The invention provides a well earthquake information fusion method, which comprises the following steps:
acquiring acoustic time difference logging data and an offset profile;
Performing median filtering and Gaussian filtering on the acoustic time difference logging data;
extracting a structure tensor and a coherent body from the offset profile, and calculating a structure tensor field by using the structure tensor and the coherent body;
Performing interpolation processing on logging data by using a mixed neighborhood method and combining a structure tensor field to obtain an interpolation speed model and a diffusion time field;
and calculating a weighting coefficient by using the diffusion time field, and fusing the chromatographic velocity model and the interpolation velocity model to obtain a fusion velocity model.
In one example, the structural tensor field is calculated as follows:
Where S is the structure tensor extracted from the offset profile, c is the coherence volume extracted from the offset profile, and D is the structure tensor field.
In one example, the interpolation formula is as follows:
τ(x)=0,x∈χ
wherein τ is a diffusion time field, D is a structure tensor field, χ is an acoustic time difference logging position area, p is an acoustic time difference logging value with the shortest diffusion time, and q is an interpolation result.
In one example, the fused calculation formula is as follows:
vmix＝l·vinterp+(1-l)·vtomo
Wherein T max is a time threshold, τ is a diffusion time field, l is a weighting coefficient, v interp is an interpolation velocity model, and v tomo is a chromatography velocity model.
The invention also provides a well earthquake information fusion device, which comprises:
the acquisition module is used for acquiring acoustic time difference logging data and an offset profile;
the processing module is used for carrying out median filtering and Gaussian filtering on the acoustic time difference logging data;
the calculation module is used for extracting a structure tensor and a coherent body from the offset profile and calculating a structure tensor field by using the structure tensor and the coherent body;
The interpolation processing module is used for carrying out interpolation processing on the logging data by utilizing a mixed neighborhood method and combining the structure tensor field to obtain an interpolation speed model and a diffusion time field;
and the fusion module is used for calculating a weighting coefficient by using the diffusion time field, fusing the chromatographic velocity model and the interpolation velocity model, and obtaining a fusion velocity model.
In one example, the structural tensor field is calculated as follows:
Where S is the structure tensor extracted from the offset profile, c is the coherence volume extracted from the offset profile, and D is the structure tensor field.
In one example, the interpolation formula is as follows:
τ(x)=0,x∈χ
wherein τ is a diffusion time field, D is a structure tensor field, χ is an acoustic time difference logging position area, p is an acoustic time difference logging value with the shortest diffusion time, and q is an interpolation result.
In one example, the fused calculation formula is as follows:
vmix＝l·vinterp+(1-l)·vtomo
Wherein T max is a time threshold, τ is a diffusion time field, l is a weighting coefficient, v interp is an interpolation velocity model, and v tomo is a chromatography velocity model.
The present invention also provides an electronic device including:
and a memory communicatively coupled to the at least one processor, wherein,
The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of well intervention information fusion as described above.
The present invention also provides a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform a well shock information fusion method as described above.
In order to facilitate understanding of the solution and the effects of the embodiments of the present invention, four specific application examples are given below. It will be understood by those of ordinary skill in the art that the examples are for ease of understanding only and that any particular details thereof are not intended to limit the present invention in any way.
Example 1
FIG. 1 shows a flow chart of the steps of a method of fusion of well seismic information, as shown in FIG. 1, according to one embodiment of the invention, comprising:
s01, acquiring acoustic time difference logging data and an offset profile;
S02, carrying out median filtering and Gaussian filtering on the acoustic time difference logging data;
S03, extracting a structure tensor and a coherent body from the offset profile, and calculating a structure tensor field by using the structure tensor and the coherent body;
S04, carrying out interpolation processing on logging data by utilizing a mixed neighborhood method and combining a structure tensor field to obtain an interpolation speed model and a diffusion time field;
And S05, calculating a weighting coefficient by using the diffusion time field, and fusing the chromatographic velocity model and the interpolation velocity model to obtain a fusion velocity model.
Performing interpolation processing on logging data by combining a structure tensor field by using a mixed neighborhood method, calculating a weighting coefficient by using a diffusion time field, fusing a chromatographic velocity model and an interpolation velocity model to obtain a fusion velocity model, the method has the advantages that the high-frequency information of the chromatographic velocity model is recovered to a certain extent, the resolution of the velocity model is improved, a foundation is laid for subsequent high-precision imaging, and the method is simple and direct and can realize rapid modeling imaging.
Fig. 2 is a marmousi standard model diagram adopted in the embodiment of the present invention, a gaussian beam chromatography velocity model in the embodiment of the present invention of fig. 3 is selected as an example for verification, a geophysical standard model marmousi model is selected for verification, a velocity model is obtained by using reflected wave gaussian beam chromatography, real velocity values at positions x=200m and x=1100 m are taken as logging data, a structure tensor and a coherent body are extracted from an offset section, the logging data is interpolated by using a hybrid neighborhood method, and the interpolated velocity model is obtained, as shown in fig. 4. The interpolated velocity model has a higher frequency than the tomographic velocity model, with velocity values near the well location closer to the true velocity model, and velocity values further from the well location offset from the true value. Setting the time threshold as 2000ms, calculating a weighting coefficient by using a diffusion time field, fusing the two velocity models to obtain a fused velocity model, taking X=500m and X=1500m data, comparing the real velocity value, the chromatographic velocity model and the fused velocity model, and displaying the result that the fused velocity model is closer to the real velocity value, as shown in fig. 6.
Example 2
FIG. 7 illustrates a borehole seismic information fusion apparatus in accordance with one embodiment of the invention.
As shown in fig. 7, the well-shock information fusion device includes:
the acquisition module is used for acquiring acoustic time difference logging data and an offset profile;
the processing module is used for carrying out median filtering and Gaussian filtering on the acoustic time difference logging data;
the calculation module is used for extracting a structure tensor and a coherent body from the offset profile and calculating a structure tensor field by using the structure tensor and the coherent body;
The interpolation processing module is used for carrying out interpolation processing on the logging data by utilizing a mixed neighborhood method and combining the structure tensor field to obtain an interpolation speed model and a diffusion time field;
and the fusion module is used for calculating a weighting coefficient by using the diffusion time field, fusing the chromatographic velocity model and the interpolation velocity model, and obtaining a fusion velocity model.
As a specific implementation manner of the embodiments of the present disclosure, the calculation formula of the structural tensor field is as follows:
Where S is the structure tensor extracted from the offset profile, c is the coherence volume extracted from the offset profile, and D is the structure tensor field.
As a specific implementation manner of the embodiment of the disclosure, the interpolation formula is as follows:
τ(x)=0,x∈χ
wherein τ is a diffusion time field, D is a structure tensor field, χ is an acoustic time difference logging position area, p is an acoustic time difference logging value with the shortest diffusion time, and q is an interpolation result.
As a specific implementation manner of the embodiment of the present disclosure, the fused calculation formula is as follows:
vmix＝l·vinterp+(1-l)·vtomo
Wherein T max is a time threshold, τ is a diffusion time field, l is a weighting coefficient, v interp is an interpolation velocity model, and v tomo is a chromatography velocity model.
Example 3
The present disclosure provides an electronic device comprising at least one processor, and a memory communicatively coupled to the at least one processor, wherein,
The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of seismic information fusion described above.
An electronic device according to an embodiment of the present disclosure includes a memory and a processor.
The memory is for storing non-transitory computer readable instructions. In particular, 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 (cache), and the like. The non-volatile memory may include, for example, read Only Memory (ROM), hard disk, flash memory, and the like.
The processor may be a Central Processing Unit (CPU) or other form of processing unit having data processing and/or instruction execution capabilities, and may control other components in the electronic device to perform the desired functions. In one embodiment of the present disclosure, the processor is configured to execute the computer readable instructions stored in the memory.
It should be understood by those skilled in the art that, in order to solve the technical problem of how to obtain a good user experience effect, the present embodiment may also include well-known structures such as a communication bus, an interface, and the like, and these well-known structures are also included in the protection scope of the present disclosure.
The detailed description of the present embodiment may refer to the corresponding description in the foregoing embodiments, and will not be repeated herein.
Example 4
Embodiments of the present disclosure provide a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform a well-shock information fusion method as described above.
The non-transitory computer readable storage medium according to embodiments of the present disclosure, when executed by a processor, performs all or part of the steps of the methods of embodiments of the present disclosure described above.
Such 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., tape or removable hard disk), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).
It will be appreciated by persons skilled in the art that the above description of embodiments of the invention has been given for the purpose of illustrating the benefits of embodiments of the invention only and is not intended to limit embodiments of the invention to any examples given.
The foregoing description of embodiments of the invention has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the various embodiments described.
Claims (6)
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| CN112017289A (en) * | 2020-08-31 | 2020-12-01 | 电子科技大学 | Well-seismic combined initial lithology model construction method based on deep learning |
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| CN106703787B (en) * | 2017-02-27 | 2019-11-29 | 中国石油大学(北京) | A kind of the well track parameter calculation method and device of radially horizontal well |
| CN109884700B (en) * | 2019-03-20 | 2021-02-26 | 中国石油化工股份有限公司 | Multi-information fusion seismic velocity modeling method |
| CN110333551B (en) * | 2019-07-26 | 2020-09-25 | 长江大学 | Dolostone reservoir prediction method and system based on well-seismic combination and storage medium |
| CN111502647B (en) * | 2020-03-27 | 2021-02-02 | 中国石油化工股份有限公司石油工程技术研究院 | Method and device for determining drilling geological environment factors and storage medium |
| EP4133310B1 (en) * | 2020-04-07 | 2025-11-12 | Chevron U.S.A. Inc. | System and method for seismic inversion |
| CN112433247B (en) * | 2020-11-17 | 2022-09-02 | 中国石油化工股份有限公司 | While-drilling adjusting method and device for position of stratum to be drilled |
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| CN104200115A (en) * | 2014-09-12 | 2014-12-10 | 中国石油集团川庆钻探工程有限公司地球物理勘探公司 | Geostatistics simulation based full-formation velocity modeling method |
| CN112017289A (en) * | 2020-08-31 | 2020-12-01 | 电子科技大学 | Well-seismic combined initial lithology model construction method based on deep learning |
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