Artificial Intelligence Minor Fault Identification Technology and Its Application in BD1 Area

Chen Kang; Zhang Sheng; Zhang Xuan; Sun Desheng; Xu Xiang; Chen Zhigang; Cai Yintao; Wang Jie

doi:doi:10.11648/j.ijepe.20231204.11

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Artificial Intelligence Minor Fault Identification Technology and Its Application in BD1 Area

Chen Kang, Zhang Sheng, Zhang Xuan, Sun Desheng, Xu Xiang, Chen Zhigang, Cai Yintao, Wang Jie

Published in International Journal of Energy and Power Engineering (Volume 12, Issue 4)

Received: 19 July 2023 Accepted: 14 August 2023 Published: 28 August 2023

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Abstract

Traditional fault interpretation mainly relies on human-machine interaction, which has low efficiency and high human uncertainty. Coherence attribute is sensitive to the discontinuity characteristics of seismic data and can effectively identify high grade faults. The coherence algorithm has undergone three innovations: cross-correlation (C1), similarity (C2), and eigenstructure (C3). In addition to coherence, attributes such as curvature, dip angle, and ant tracking have been proposed, and the likelihood attribute has developed rapidly in recent years, which can accurately reflect larger fault structures and has certain discrimination ability for small faults. However, due to the small moment and short extension length of low grade faults, they do not necessarily exhibit discontinuous characteristics at the fault location (especially for strike-slip faults), and the traditional attributes have not achieved good results in identifying small-scale faults. With the development of artificial intelligence algorithms in the field of target detection, advanced neural networks have proven to surpass traditional attributes in identifying faults from seismic data. This article takes the BD1 area of the Sichuan Basin as an example and combines fault enhancement interpretive processing such as dip scanning, structure-guided filtering, edge-preserving filtering, and frequeency filtering with artificial intelligence algorithms and transfer learning techniques for low grade fault identification research, forming a precise and reasonable artificial intelligence low grade fault identification technology process. The results show that the artificial intelligence algorithm using a large sample library can identify low grade faults that cannot be detected by traditional methods, and the fault detection results of artificial intelligence are superior to traditional attributes in terms of noise resistance, accuracy, and computational efficiency.

Published in	International Journal of Energy and Power Engineering (Volume 12, Issue 4)
DOI	10.11648/j.ijepe.20231204.11
Page(s)	47-53
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Low Grade Fault, Discontinuity Attribuet, Fault Enhancement, Artificial Intelligence, Large Sample Library, Transfer Learning

References

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[7]	Hu bing. Application of Curvature-Based Fine Interpretation Technique in Complex Fault Area [J]. Offshore Oil, 2018, 38 (01): 22-27.
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[10]	Hale D. Methods to compute fault images, extract fault surfaces, and estimate fault throws from 3D seismic images [J]. Geophysics, 78 (2): O33-O43.
[11]	Pei Xiuxiu. Application of seismic maximum likelihood attribute on fault identification in Zhangchang area of Biyang sag [J]. PETROLEUM GEOLOGY AND ENGINEERING, 2020, 34 (05): 8-11.
[12]	Zhao xiaohui, Yu baoli, Cao xiaolu. Application of attribute fusion technology in the identification of micro-fractures [J]. Oil Geophysical Prospecting, 2017, 52 (S2): 164-169.
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[15]	CAICT, Artificial intelligence Alliance. The Era of Artificial Intelligence under Deep Learning [J]. Big Data Time, 2021 (6): 56-76.
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[17]	Zhuang Fuzhen, Luo ping, He qing, Shi zhongzhi. Survey on Transfer Learning Research [J]. Journal of Software, 2015, 26 (01): 26-39.

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APA Style

Chen Kang, Zhang Sheng, Zhang Xuan, Sun Desheng, Xu Xiang, et al. (2023). Artificial Intelligence Minor Fault Identification Technology and Its Application in BD1 Area. International Journal of Energy and Power Engineering, 12(4), 47-53. https://doi.org/10.11648/j.ijepe.20231204.11

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ACS Style

Chen Kang; Zhang Sheng; Zhang Xuan; Sun Desheng; Xu Xiang, et al. Artificial Intelligence Minor Fault Identification Technology and Its Application in BD1 Area. Int. J. Energy Power Eng. 2023, 12(4), 47-53. doi: 10.11648/j.ijepe.20231204.11

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AMA Style

Chen Kang, Zhang Sheng, Zhang Xuan, Sun Desheng, Xu Xiang, et al. Artificial Intelligence Minor Fault Identification Technology and Its Application in BD1 Area. Int J Energy Power Eng. 2023;12(4):47-53. doi: 10.11648/j.ijepe.20231204.11

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@article{10.11648/j.ijepe.20231204.11,
  author = {Chen Kang and Zhang Sheng and Zhang Xuan and Sun Desheng and Xu Xiang and Chen Zhigang and Cai Yintao and Wang Jie},
  title = {Artificial Intelligence Minor Fault Identification Technology and Its Application in BD1 Area},
  journal = {International Journal of Energy and Power Engineering},
  volume = {12},
  number = {4},
  pages = {47-53},
  doi = {10.11648/j.ijepe.20231204.11},
  url = {https://doi.org/10.11648/j.ijepe.20231204.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijepe.20231204.11},
  abstract = {Traditional fault interpretation mainly relies on human-machine interaction, which has low efficiency and high human uncertainty. Coherence attribute is sensitive to the discontinuity characteristics of seismic data and can effectively identify high grade faults. The coherence algorithm has undergone three innovations: cross-correlation (C1), similarity (C2), and eigenstructure (C3). In addition to coherence, attributes such as curvature, dip angle, and ant tracking have been proposed, and the likelihood attribute has developed rapidly in recent years, which can accurately reflect larger fault structures and has certain discrimination ability for small faults. However, due to the small moment and short extension length of low grade faults, they do not necessarily exhibit discontinuous characteristics at the fault location (especially for strike-slip faults), and the traditional attributes have not achieved good results in identifying small-scale faults. With the development of artificial intelligence algorithms in the field of target detection, advanced neural networks have proven to surpass traditional attributes in identifying faults from seismic data. This article takes the BD1 area of the Sichuan Basin as an example and combines fault enhancement interpretive processing such as dip scanning, structure-guided filtering, edge-preserving filtering, and frequeency filtering with artificial intelligence algorithms and transfer learning techniques for low grade fault identification research, forming a precise and reasonable artificial intelligence low grade fault identification technology process. The results show that the artificial intelligence algorithm using a large sample library can identify low grade faults that cannot be detected by traditional methods, and the fault detection results of artificial intelligence are superior to traditional attributes in terms of noise resistance, accuracy, and computational efficiency.},
 year = {2023}
}

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TY  - JOUR
T1  - Artificial Intelligence Minor Fault Identification Technology and Its Application in BD1 Area
AU  - Chen Kang
AU  - Zhang Sheng
AU  - Zhang Xuan
AU  - Sun Desheng
AU  - Xu Xiang
AU  - Chen Zhigang
AU  - Cai Yintao
AU  - Wang Jie
Y1  - 2023/08/28
PY  - 2023
N1  - https://doi.org/10.11648/j.ijepe.20231204.11
DO  - 10.11648/j.ijepe.20231204.11
T2  - International Journal of Energy and Power Engineering
JF  - International Journal of Energy and Power Engineering
JO  - International Journal of Energy and Power Engineering
SP  - 47
EP  - 53
PB  - Science Publishing Group
SN  - 2326-960X
UR  - https://doi.org/10.11648/j.ijepe.20231204.11
AB  - Traditional fault interpretation mainly relies on human-machine interaction, which has low efficiency and high human uncertainty. Coherence attribute is sensitive to the discontinuity characteristics of seismic data and can effectively identify high grade faults. The coherence algorithm has undergone three innovations: cross-correlation (C1), similarity (C2), and eigenstructure (C3). In addition to coherence, attributes such as curvature, dip angle, and ant tracking have been proposed, and the likelihood attribute has developed rapidly in recent years, which can accurately reflect larger fault structures and has certain discrimination ability for small faults. However, due to the small moment and short extension length of low grade faults, they do not necessarily exhibit discontinuous characteristics at the fault location (especially for strike-slip faults), and the traditional attributes have not achieved good results in identifying small-scale faults. With the development of artificial intelligence algorithms in the field of target detection, advanced neural networks have proven to surpass traditional attributes in identifying faults from seismic data. This article takes the BD1 area of the Sichuan Basin as an example and combines fault enhancement interpretive processing such as dip scanning, structure-guided filtering, edge-preserving filtering, and frequeency filtering with artificial intelligence algorithms and transfer learning techniques for low grade fault identification research, forming a precise and reasonable artificial intelligence low grade fault identification technology process. The results show that the artificial intelligence algorithm using a large sample library can identify low grade faults that cannot be detected by traditional methods, and the fault detection results of artificial intelligence are superior to traditional attributes in terms of noise resistance, accuracy, and computational efficiency.
VL  - 12
IS  - 4
ER  -

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Author Information

Chen Kang

Exploration and Development Research Institute of PetroChina Southwest Oil and Gasfield Company, Chengdu, China
Zhang Sheng

Geological Research Center of Research Institute of BGP, Zhuozhou, China
Zhang Xuan

Exploration and Development Research Institute of PetroChina Southwest Oil and Gasfield Company, Chengdu, China
Sun Desheng

Geological Research Center of Research Institute of BGP, Zhuozhou, China
Xu Xiang

Exploration and Development Research Institute of PetroChina Southwest Oil and Gasfield Company, Chengdu, China
Chen Zhigang

Geological Research Center of Research Institute of BGP, Zhuozhou, China
Cai Yintao

Geological Research Center of Research Institute of BGP, Zhuozhou, China
Wang Jie

Geological Research Center of Research Institute of BGP, Zhuozhou, China

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Plain Text BibTeX RIS

APA Style

Chen Kang, Zhang Sheng, Zhang Xuan, Sun Desheng, Xu Xiang, et al. (2023). Artificial Intelligence Minor Fault Identification Technology and Its Application in BD1 Area. International Journal of Energy and Power Engineering, 12(4), 47-53. https://doi.org/10.11648/j.ijepe.20231204.11

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ACS Style

Chen Kang; Zhang Sheng; Zhang Xuan; Sun Desheng; Xu Xiang, et al. Artificial Intelligence Minor Fault Identification Technology and Its Application in BD1 Area. Int. J. Energy Power Eng. 2023, 12(4), 47-53. doi: 10.11648/j.ijepe.20231204.11

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AMA Style

Chen Kang, Zhang Sheng, Zhang Xuan, Sun Desheng, Xu Xiang, et al. Artificial Intelligence Minor Fault Identification Technology and Its Application in BD1 Area. Int J Energy Power Eng. 2023;12(4):47-53. doi: 10.11648/j.ijepe.20231204.11

Copy | Download

@article{10.11648/j.ijepe.20231204.11,
  author = {Chen Kang and Zhang Sheng and Zhang Xuan and Sun Desheng and Xu Xiang and Chen Zhigang and Cai Yintao and Wang Jie},
  title = {Artificial Intelligence Minor Fault Identification Technology and Its Application in BD1 Area},
  journal = {International Journal of Energy and Power Engineering},
  volume = {12},
  number = {4},
  pages = {47-53},
  doi = {10.11648/j.ijepe.20231204.11},
  url = {https://doi.org/10.11648/j.ijepe.20231204.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijepe.20231204.11},
  abstract = {Traditional fault interpretation mainly relies on human-machine interaction, which has low efficiency and high human uncertainty. Coherence attribute is sensitive to the discontinuity characteristics of seismic data and can effectively identify high grade faults. The coherence algorithm has undergone three innovations: cross-correlation (C1), similarity (C2), and eigenstructure (C3). In addition to coherence, attributes such as curvature, dip angle, and ant tracking have been proposed, and the likelihood attribute has developed rapidly in recent years, which can accurately reflect larger fault structures and has certain discrimination ability for small faults. However, due to the small moment and short extension length of low grade faults, they do not necessarily exhibit discontinuous characteristics at the fault location (especially for strike-slip faults), and the traditional attributes have not achieved good results in identifying small-scale faults. With the development of artificial intelligence algorithms in the field of target detection, advanced neural networks have proven to surpass traditional attributes in identifying faults from seismic data. This article takes the BD1 area of the Sichuan Basin as an example and combines fault enhancement interpretive processing such as dip scanning, structure-guided filtering, edge-preserving filtering, and frequeency filtering with artificial intelligence algorithms and transfer learning techniques for low grade fault identification research, forming a precise and reasonable artificial intelligence low grade fault identification technology process. The results show that the artificial intelligence algorithm using a large sample library can identify low grade faults that cannot be detected by traditional methods, and the fault detection results of artificial intelligence are superior to traditional attributes in terms of noise resistance, accuracy, and computational efficiency.},
 year = {2023}
}

Copy | Download

TY  - JOUR
T1  - Artificial Intelligence Minor Fault Identification Technology and Its Application in BD1 Area
AU  - Chen Kang
AU  - Zhang Sheng
AU  - Zhang Xuan
AU  - Sun Desheng
AU  - Xu Xiang
AU  - Chen Zhigang
AU  - Cai Yintao
AU  - Wang Jie
Y1  - 2023/08/28
PY  - 2023
N1  - https://doi.org/10.11648/j.ijepe.20231204.11
DO  - 10.11648/j.ijepe.20231204.11
T2  - International Journal of Energy and Power Engineering
JF  - International Journal of Energy and Power Engineering
JO  - International Journal of Energy and Power Engineering
SP  - 47
EP  - 53
PB  - Science Publishing Group
SN  - 2326-960X
UR  - https://doi.org/10.11648/j.ijepe.20231204.11
AB  - Traditional fault interpretation mainly relies on human-machine interaction, which has low efficiency and high human uncertainty. Coherence attribute is sensitive to the discontinuity characteristics of seismic data and can effectively identify high grade faults. The coherence algorithm has undergone three innovations: cross-correlation (C1), similarity (C2), and eigenstructure (C3). In addition to coherence, attributes such as curvature, dip angle, and ant tracking have been proposed, and the likelihood attribute has developed rapidly in recent years, which can accurately reflect larger fault structures and has certain discrimination ability for small faults. However, due to the small moment and short extension length of low grade faults, they do not necessarily exhibit discontinuous characteristics at the fault location (especially for strike-slip faults), and the traditional attributes have not achieved good results in identifying small-scale faults. With the development of artificial intelligence algorithms in the field of target detection, advanced neural networks have proven to surpass traditional attributes in identifying faults from seismic data. This article takes the BD1 area of the Sichuan Basin as an example and combines fault enhancement interpretive processing such as dip scanning, structure-guided filtering, edge-preserving filtering, and frequeency filtering with artificial intelligence algorithms and transfer learning techniques for low grade fault identification research, forming a precise and reasonable artificial intelligence low grade fault identification technology process. The results show that the artificial intelligence algorithm using a large sample library can identify low grade faults that cannot be detected by traditional methods, and the fault detection results of artificial intelligence are superior to traditional attributes in terms of noise resistance, accuracy, and computational efficiency.
VL  - 12
IS  - 4
ER  -

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