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

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), 2023. 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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[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.
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Cite This Article
  • 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
  • Exploration and Development Research Institute of PetroChina Southwest Oil and Gasfield Company, Chengdu, China

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

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

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

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

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

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

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

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