The Application and Effect of AI Fault Interpretation Technology in the Laoyemiao Area

Zeng Cheng; Sun Lizhi; Bi Yongbin; Xu Bo; Duan Jian; Xu Yingxin; Qian Liping; Zhang Wanfu; Zhang Hao; Ying Zijuan

doi:doi:10.11648/j.earth.20241302.14

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The Application and Effect of AI Fault Interpretation Technology in the Laoyemiao Area

Zeng Cheng, Sun Lizhi^*, Bi Yongbin, Xu Bo, Duan Jian, Xu Yingxin, Qian Liping, Zhang Wanfu, Zhang Hao, Ying Zijuan

Published in Earth Sciences (Volume 13, Issue 2)

Received: 7 December 2023 Accepted: 8 April 2024 Published: 29 April 2024

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Abstract

The complex faults, especially mid-deep faults, in the Laoyemiao area of the Nanpu Sag, the Bohai Bay Basin, are unclearly understood for their characteristics, constraining the structural and geological delineation of the area. The hydrocarbon enrichment in the Laoyemiao area is closely related to the faults, and thus the precise identification of mid-deep faults is of great significance for understanding the structural system and reservoir distribution in the area. In the past twenty years, artificial intelligence (AI) scholars developed new technologies and methods to solve engineering problems. Typically, the AI seismic data interpretation technology plays a critical role in improving the accuracy and efficiency of fault interpretation. In order to define the structural characteristics of the Laoyemiao area, the "2W1H" seismic data were processed by fault-constrained structure-oriented filtering, and then interpreted using the EasyTrack module of GeoEast independently developed by BGP. It is found that the imaging quality and accuracy of mid-deep faults are improved effectively. On this basis, the SN-trending strike-slip fault systems were discovered, and the structural pattern and evolution law of mid-deep faults in the Laoyemiao area were re-understood. The results are of great significance for the structural identification, reservoir evaluation and selection of exploration targets in this area.

Published in	Earth Sciences (Volume 13, Issue 2)
DOI	10.11648/j.earth.20241302.14
Page(s)	76-85
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

Laoyemiao, Nanpu Sag, AI, Strike-Slip Fault, Structure-Oriented Filtering, Likelihood Attribute, Xinanzhuang Fault

References

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[4]	Liu Haiqing, Jin Pengbo, Liu Jingdong et al. The fault characteristics of the Laoyemiao structural belt in the Nanpu Depression and its control on hydrocarbon accumulation [J/OL]. Geology in China, 2023, 3: 1-13. https://doi.org/10.12029/gc20220910001
[5]	Sun Simin, Ji Hancheng, Liu Xiao et al. Architecture of sequence stratigraphy responding to segmentation of boundary fault: taking an example of Dongying Formation on hanging wall of Xinanzhuang Fault in Nanpu Sag [J]. Journal of Jilin University (Earth Science Edition), 2017, 47(02): 382-392. https://doi.org/10.13278/j.cnki.jjuese.201702105
[6]	Liu Xingke, Gan Huajun, Chen Si. Evolution of sedimentary system of continental faulted lacustrine basin under high-precision sequence framework: A case from the third member of Dongying Formation in Laoyemiao area, Nanpu Sag [J]. Geological Science and Technology Information, 2019, 38(03): 88-102. https://doi.org/10.3799/dqkx.2020.064
[7]	Wu Jizhong, He Shumei, Yang Qianqian et al. Research on low-order fault interpretation method based on fully convolutional neural network (FCN) [C]. SPG/SEG, Nanjing, 2020: 1010-1012. https://doi.org/10.3390/jmse9030259
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[11]	Yang Yong. Application progress of big data & AI technologies in exploration and development of Shengli Oilfield [J]. Petroleum Geology and Recovery Efficiency, 2022, 29(01): 1-10. https://doi.org/10.13673/j.cnki.cn37-1359/te.2022.01.001
[12]	Hale Dave. Methods to compute fault images, extract fault planes, and estimate fault throws from 3D seismic images [J]. Geophysics, 2013, 78(2): 33-43. https://doi.org/10.1190/geo2012-0331.1
[13]	Chen Weichang, Yan Jingjing, Sun Guanyu et al. Fault tectonics and petroleum entrapment in the Laoyemiao Region of the Nanpu Depression [J]. Journal of Southwest Petroleum University (Science & Technology Edition), 2018, 40(02): 46-56. https://doi.org/10.11885/j.issn.1674-5086.2016.11.04.02
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APA Style

Cheng, Z., Lizhi, S., Yongbin, B., Bo, X., Jian, D., et al. (2024). The Application and Effect of AI Fault Interpretation Technology in the Laoyemiao Area. Earth Sciences, 13(2), 76-85. https://doi.org/10.11648/j.earth.20241302.14

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ACS Style

Cheng, Z.; Lizhi, S.; Yongbin, B.; Bo, X.; Jian, D., et al. The Application and Effect of AI Fault Interpretation Technology in the Laoyemiao Area. Earth Sci. 2024, 13(2), 76-85. doi: 10.11648/j.earth.20241302.14

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AMA Style

Cheng Z, Lizhi S, Yongbin B, Bo X, Jian D, et al. The Application and Effect of AI Fault Interpretation Technology in the Laoyemiao Area. Earth Sci. 2024;13(2):76-85. doi: 10.11648/j.earth.20241302.14

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@article{10.11648/j.earth.20241302.14,
  author = {Zeng Cheng and Sun Lizhi and Bi Yongbin and Xu Bo and Duan Jian and Xu Yingxin and Qian Liping and Zhang Wanfu and Zhang Hao and Ying Zijuan},
  title = {The Application and Effect of AI Fault Interpretation Technology in the Laoyemiao Area
},
  journal = {Earth Sciences},
  volume = {13},
  number = {2},
  pages = {76-85},
  doi = {10.11648/j.earth.20241302.14},
  url = {https://doi.org/10.11648/j.earth.20241302.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.earth.20241302.14},
  abstract = {The complex faults, especially mid-deep faults, in the Laoyemiao area of the Nanpu Sag, the Bohai Bay Basin, are unclearly understood for their characteristics, constraining the structural and geological delineation of the area. The hydrocarbon enrichment in the Laoyemiao area is closely related to the faults, and thus the precise identification of mid-deep faults is of great significance for understanding the structural system and reservoir distribution in the area. In the past twenty years, artificial intelligence (AI) scholars developed new technologies and methods to solve engineering problems. Typically, the AI seismic data interpretation technology plays a critical role in improving the accuracy and efficiency of fault interpretation. In order to define the structural characteristics of the Laoyemiao area, the "2W1H" seismic data were processed by fault-constrained structure-oriented filtering, and then interpreted using the EasyTrack module of GeoEast independently developed by BGP. It is found that the imaging quality and accuracy of mid-deep faults are improved effectively. On this basis, the SN-trending strike-slip fault systems were discovered, and the structural pattern and evolution law of mid-deep faults in the Laoyemiao area were re-understood. The results are of great significance for the structural identification, reservoir evaluation and selection of exploration targets in this area.
},
 year = {2024}
}

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TY  - JOUR
T1  - The Application and Effect of AI Fault Interpretation Technology in the Laoyemiao Area

AU  - Zeng Cheng
AU  - Sun Lizhi
AU  - Bi Yongbin
AU  - Xu Bo
AU  - Duan Jian
AU  - Xu Yingxin
AU  - Qian Liping
AU  - Zhang Wanfu
AU  - Zhang Hao
AU  - Ying Zijuan
Y1  - 2024/04/29
PY  - 2024
N1  - https://doi.org/10.11648/j.earth.20241302.14
DO  - 10.11648/j.earth.20241302.14
T2  - Earth Sciences
JF  - Earth Sciences
JO  - Earth Sciences
SP  - 76
EP  - 85
PB  - Science Publishing Group
SN  - 2328-5982
UR  - https://doi.org/10.11648/j.earth.20241302.14
AB  - The complex faults, especially mid-deep faults, in the Laoyemiao area of the Nanpu Sag, the Bohai Bay Basin, are unclearly understood for their characteristics, constraining the structural and geological delineation of the area. The hydrocarbon enrichment in the Laoyemiao area is closely related to the faults, and thus the precise identification of mid-deep faults is of great significance for understanding the structural system and reservoir distribution in the area. In the past twenty years, artificial intelligence (AI) scholars developed new technologies and methods to solve engineering problems. Typically, the AI seismic data interpretation technology plays a critical role in improving the accuracy and efficiency of fault interpretation. In order to define the structural characteristics of the Laoyemiao area, the "2W1H" seismic data were processed by fault-constrained structure-oriented filtering, and then interpreted using the EasyTrack module of GeoEast independently developed by BGP. It is found that the imaging quality and accuracy of mid-deep faults are improved effectively. On this basis, the SN-trending strike-slip fault systems were discovered, and the structural pattern and evolution law of mid-deep faults in the Laoyemiao area were re-understood. The results are of great significance for the structural identification, reservoir evaluation and selection of exploration targets in this area.

VL  - 13
IS  - 2
ER  -

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Author Information

Zeng Cheng

Nanpu Oilfield Operation Area, China National Petroleum Corporation Jidong Oilfield Branch, Tangshan, China
Sun Lizhi

Research Institute, Bureau of Geophysical Prospecting INC., China National Petroleum Corporation (BGP), Baoding, China

Contact Email
Bi Yongbin

Nanpu Oilfield Operation Area, China National Petroleum Corporation Jidong Oilfield Branch, Tangshan, China
Xu Bo

Nanpu Oilfield Operation Area, China National Petroleum Corporation Jidong Oilfield Branch, Tangshan, China
Duan Jian

Nanpu Oilfield Operation Area, China National Petroleum Corporation Jidong Oilfield Branch, Tangshan, China
Xu Yingxin

Research Institute, Bureau of Geophysical Prospecting INC., China National Petroleum Corporation (BGP), Baoding, China
Qian Liping

Research Institute, Bureau of Geophysical Prospecting INC., China National Petroleum Corporation (BGP), Baoding, China
Zhang Wanfu

Research Institute, Bureau of Geophysical Prospecting INC., China National Petroleum Corporation (BGP), Baoding, China
Zhang Hao

Research Institute, Bureau of Geophysical Prospecting INC., China National Petroleum Corporation (BGP), Baoding, China
Ying Zijuan

Research Institute, Bureau of Geophysical Prospecting INC., China National Petroleum Corporation (BGP), Baoding, China

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Plain Text BibTeX RIS

APA Style

Cheng, Z., Lizhi, S., Yongbin, B., Bo, X., Jian, D., et al. (2024). The Application and Effect of AI Fault Interpretation Technology in the Laoyemiao Area. Earth Sciences, 13(2), 76-85. https://doi.org/10.11648/j.earth.20241302.14

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ACS Style

Cheng, Z.; Lizhi, S.; Yongbin, B.; Bo, X.; Jian, D., et al. The Application and Effect of AI Fault Interpretation Technology in the Laoyemiao Area. Earth Sci. 2024, 13(2), 76-85. doi: 10.11648/j.earth.20241302.14

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AMA Style

Cheng Z, Lizhi S, Yongbin B, Bo X, Jian D, et al. The Application and Effect of AI Fault Interpretation Technology in the Laoyemiao Area. Earth Sci. 2024;13(2):76-85. doi: 10.11648/j.earth.20241302.14

Copy | Download

@article{10.11648/j.earth.20241302.14,
  author = {Zeng Cheng and Sun Lizhi and Bi Yongbin and Xu Bo and Duan Jian and Xu Yingxin and Qian Liping and Zhang Wanfu and Zhang Hao and Ying Zijuan},
  title = {The Application and Effect of AI Fault Interpretation Technology in the Laoyemiao Area
},
  journal = {Earth Sciences},
  volume = {13},
  number = {2},
  pages = {76-85},
  doi = {10.11648/j.earth.20241302.14},
  url = {https://doi.org/10.11648/j.earth.20241302.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.earth.20241302.14},
  abstract = {The complex faults, especially mid-deep faults, in the Laoyemiao area of the Nanpu Sag, the Bohai Bay Basin, are unclearly understood for their characteristics, constraining the structural and geological delineation of the area. The hydrocarbon enrichment in the Laoyemiao area is closely related to the faults, and thus the precise identification of mid-deep faults is of great significance for understanding the structural system and reservoir distribution in the area. In the past twenty years, artificial intelligence (AI) scholars developed new technologies and methods to solve engineering problems. Typically, the AI seismic data interpretation technology plays a critical role in improving the accuracy and efficiency of fault interpretation. In order to define the structural characteristics of the Laoyemiao area, the "2W1H" seismic data were processed by fault-constrained structure-oriented filtering, and then interpreted using the EasyTrack module of GeoEast independently developed by BGP. It is found that the imaging quality and accuracy of mid-deep faults are improved effectively. On this basis, the SN-trending strike-slip fault systems were discovered, and the structural pattern and evolution law of mid-deep faults in the Laoyemiao area were re-understood. The results are of great significance for the structural identification, reservoir evaluation and selection of exploration targets in this area.
},
 year = {2024}
}

Copy | Download

TY  - JOUR
T1  - The Application and Effect of AI Fault Interpretation Technology in the Laoyemiao Area

AU  - Zeng Cheng
AU  - Sun Lizhi
AU  - Bi Yongbin
AU  - Xu Bo
AU  - Duan Jian
AU  - Xu Yingxin
AU  - Qian Liping
AU  - Zhang Wanfu
AU  - Zhang Hao
AU  - Ying Zijuan
Y1  - 2024/04/29
PY  - 2024
N1  - https://doi.org/10.11648/j.earth.20241302.14
DO  - 10.11648/j.earth.20241302.14
T2  - Earth Sciences
JF  - Earth Sciences
JO  - Earth Sciences
SP  - 76
EP  - 85
PB  - Science Publishing Group
SN  - 2328-5982
UR  - https://doi.org/10.11648/j.earth.20241302.14
AB  - The complex faults, especially mid-deep faults, in the Laoyemiao area of the Nanpu Sag, the Bohai Bay Basin, are unclearly understood for their characteristics, constraining the structural and geological delineation of the area. The hydrocarbon enrichment in the Laoyemiao area is closely related to the faults, and thus the precise identification of mid-deep faults is of great significance for understanding the structural system and reservoir distribution in the area. In the past twenty years, artificial intelligence (AI) scholars developed new technologies and methods to solve engineering problems. Typically, the AI seismic data interpretation technology plays a critical role in improving the accuracy and efficiency of fault interpretation. In order to define the structural characteristics of the Laoyemiao area, the "2W1H" seismic data were processed by fault-constrained structure-oriented filtering, and then interpreted using the EasyTrack module of GeoEast independently developed by BGP. It is found that the imaging quality and accuracy of mid-deep faults are improved effectively. On this basis, the SN-trending strike-slip fault systems were discovered, and the structural pattern and evolution law of mid-deep faults in the Laoyemiao area were re-understood. The results are of great significance for the structural identification, reservoir evaluation and selection of exploration targets in this area.

VL  - 13
IS  - 2
ER  -

Copy | Download