Seismic Facies Recognition of Ultra-Deep Carbonate Rocks Based on Convolutional Neural Network

Zhang Xuan; Yan Zhenhua; Xu Xiang; Li Xiang; Chen Zhigang; Li Jianhua; Ji Xuewu

doi:doi:10.11648/j.earth.20231202.11

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Seismic Facies Recognition of Ultra-Deep Carbonate Rocks Based on Convolutional Neural Network

Zhang Xuan, Yan Zhenhua, Xu Xiang, Li Xiang, Chen Zhigang, Li Jianhua, Ji Xuewu

Published in Earth Sciences (Volume 12, Issue 2)

Received: 13 April 2023 Accepted: 2 May 2023 Published: 10 May 2023

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Abstract

Seismic facies is a seismic reflection unit defined by specific seismic reflection characteristics, that is, the seismic responses of sedimentary facies or geological bodies, whose accuracy will directly affect the reliability of oil and gas exploration results. Currently, seismic facies is generally recognized depending upon the differences between certain single trace seismic attributes (waveform, frequency spectrum, and amplitude, etc.) and adjacent units to conduct cluster analysis. Such methods, however, have ambiguity in identifying special reflective structures with continuous waveforms (e.g. massive carbonate deposits). In order to solve this problem, this paper incorporates artificial intelligence (AI) technology into automatic recognition of seismic facies with special reflection structures. Firstly, a 2D seismic facies classification sample label set is designed and formed. Then, a seismic facies prediction model is designed and constructed using a multi-layer convolutional neural network (CNN). Finally, the trained model is used to automatically track the seismic facies in the study area. This method was applied to seismic facies recognition for the Sinian Dengying Formation in an area of the Sichuan Basin, and the seismic facies recognized were compared with artificially interpreted ones. It is confirmed that the proposed method provides a better effect than artificial interpretation, with greatly improved accuracy and efficiency.

Published in	Earth Sciences (Volume 12, Issue 2)
DOI	10.11648/j.earth.20231202.11
Page(s)	41-46
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

Artificial Intelligence, Convolutional Layer, Seismic Facies, Label, Model

References

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[3]	Yan Xingyu, Gu Hanming, Luo Hongmei et al. Intelligent recognition of seismic facies based on improved deep learning method [J]. Oil Geophysical Prospecting, 2020, 55 (06).
[4]	Xu Hai, Du Xiaofang, Gao Jun, et al. Research on quantitative interpretation technology of sedimentary microfacies based on wave-form clustering [J]. Geophysical Prospecting for Petroleum, 2018, 57 (5): 744-755.
[5]	Zhu Jianbing, Zhao Peikun, Advances in seismic facies classification technology abroad [J]. Progress in Exploration Geophysics, 2009, 32 (3): 167-171.
[6]	Li Jian, Wang Xiaoming, Zhang Yinghai et al. Research on the seismic phase picking method based on the deep convolutional neural network [J]. Chinese journal of geophysics, 2020, 63 (4): 1591-1606.
[7]	Liu Shiyou, Song Wei, Ying Mingxiong, et al. Density-based clustering seismic facies analysis of noisy corner gather waveforms [J]. Geophysical Prospecting for Petroleum, 2019, 58 (5): 773-782.
[8]	Liu Wanjun, Liang Xuejian, Qu Haicheng. Learning performance of convolutional neural networks with different pooling models [J]. Journal of Image and Graphics, 2016, 21 (9): 1178-1190.
[9]	Ma Xinhua, Yang Yu, Wen Long, et al. Distribution and exploration direction of medium- and large-sized marine carbonate gas fields in Sichuan Basin, SW China [J]. Petroleum Exploration and Development, 2019, 46 (1): 1-13.
[10]	Zhang Jiming, Huang Jianping. Carbonate oil and gas field in Sichuan Basin (Special) [J]. Natural Gas Exploration and Development, 1984 (4): 128-162.
[11]	Zhai Guangming. Petroleum geology of China (Vol. 10): Sichuan oiland gas area [M]. Beijing: Petroleum Industry Press, 1989.
[12]	Zou Caineng, Du Jinhu, Xu Chunchun, et al. Formation, distribution, resource potential and discovery of the Sinian-Cambrian giant gas field, Sichuan Basin, SW China [J]. Petroleum Exploration and Development, 2014, 41 (3): 278-293.
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[15]	Song Wenhai. Some new knowledge of Caledonian Paleo-uplift in Sichuan Basin [J]. Natural Gas Industry, 1987, 7 (3): 6-11.
[16]	Wei Guoqi, Yang Wei, Du Jinhu, et al. Geological characteristic of the Sinian-Early Cambrian intracratonic rift, Sichuan Basin [J]. Natural Gas Industry, 2015, 35 (1): 24-35.

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APA Style

Zhang Xuan, Yan Zhenhua, Xu Xiang, Li Xiang, Chen Zhigang, et al. (2023). Seismic Facies Recognition of Ultra-Deep Carbonate Rocks Based on Convolutional Neural Network. Earth Sciences, 12(2), 41-46. https://doi.org/10.11648/j.earth.20231202.11

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ACS Style

Zhang Xuan; Yan Zhenhua; Xu Xiang; Li Xiang; Chen Zhigang, et al. Seismic Facies Recognition of Ultra-Deep Carbonate Rocks Based on Convolutional Neural Network. Earth Sci. 2023, 12(2), 41-46. doi: 10.11648/j.earth.20231202.11

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AMA Style

Zhang Xuan, Yan Zhenhua, Xu Xiang, Li Xiang, Chen Zhigang, et al. Seismic Facies Recognition of Ultra-Deep Carbonate Rocks Based on Convolutional Neural Network. Earth Sci. 2023;12(2):41-46. doi: 10.11648/j.earth.20231202.11

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@article{10.11648/j.earth.20231202.11,
  author = {Zhang Xuan and Yan Zhenhua and Xu Xiang and Li Xiang and Chen Zhigang and Li Jianhua and Ji Xuewu},
  title = {Seismic Facies Recognition of Ultra-Deep Carbonate Rocks Based on Convolutional Neural Network},
  journal = {Earth Sciences},
  volume = {12},
  number = {2},
  pages = {41-46},
  doi = {10.11648/j.earth.20231202.11},
  url = {https://doi.org/10.11648/j.earth.20231202.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.earth.20231202.11},
  abstract = {Seismic facies is a seismic reflection unit defined by specific seismic reflection characteristics, that is, the seismic responses of sedimentary facies or geological bodies, whose accuracy will directly affect the reliability of oil and gas exploration results. Currently, seismic facies is generally recognized depending upon the differences between certain single trace seismic attributes (waveform, frequency spectrum, and amplitude, etc.) and adjacent units to conduct cluster analysis. Such methods, however, have ambiguity in identifying special reflective structures with continuous waveforms (e.g. massive carbonate deposits). In order to solve this problem, this paper incorporates artificial intelligence (AI) technology into automatic recognition of seismic facies with special reflection structures. Firstly, a 2D seismic facies classification sample label set is designed and formed. Then, a seismic facies prediction model is designed and constructed using a multi-layer convolutional neural network (CNN). Finally, the trained model is used to automatically track the seismic facies in the study area. This method was applied to seismic facies recognition for the Sinian Dengying Formation in an area of the Sichuan Basin, and the seismic facies recognized were compared with artificially interpreted ones. It is confirmed that the proposed method provides a better effect than artificial interpretation, with greatly improved accuracy and efficiency.},
 year = {2023}
}

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TY  - JOUR
T1  - Seismic Facies Recognition of Ultra-Deep Carbonate Rocks Based on Convolutional Neural Network
AU  - Zhang Xuan
AU  - Yan Zhenhua
AU  - Xu Xiang
AU  - Li Xiang
AU  - Chen Zhigang
AU  - Li Jianhua
AU  - Ji Xuewu
Y1  - 2023/05/10
PY  - 2023
N1  - https://doi.org/10.11648/j.earth.20231202.11
DO  - 10.11648/j.earth.20231202.11
T2  - Earth Sciences
JF  - Earth Sciences
JO  - Earth Sciences
SP  - 41
EP  - 46
PB  - Science Publishing Group
SN  - 2328-5982
UR  - https://doi.org/10.11648/j.earth.20231202.11
AB  - Seismic facies is a seismic reflection unit defined by specific seismic reflection characteristics, that is, the seismic responses of sedimentary facies or geological bodies, whose accuracy will directly affect the reliability of oil and gas exploration results. Currently, seismic facies is generally recognized depending upon the differences between certain single trace seismic attributes (waveform, frequency spectrum, and amplitude, etc.) and adjacent units to conduct cluster analysis. Such methods, however, have ambiguity in identifying special reflective structures with continuous waveforms (e.g. massive carbonate deposits). In order to solve this problem, this paper incorporates artificial intelligence (AI) technology into automatic recognition of seismic facies with special reflection structures. Firstly, a 2D seismic facies classification sample label set is designed and formed. Then, a seismic facies prediction model is designed and constructed using a multi-layer convolutional neural network (CNN). Finally, the trained model is used to automatically track the seismic facies in the study area. This method was applied to seismic facies recognition for the Sinian Dengying Formation in an area of the Sichuan Basin, and the seismic facies recognized were compared with artificially interpreted ones. It is confirmed that the proposed method provides a better effect than artificial interpretation, with greatly improved accuracy and efficiency.
VL  - 12
IS  - 2
ER  -

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Author Information

Zhang Xuan

Exploration and Development Research Institute of PetroChina Southwest Oil & Gasfield Company, Chengdu, China
Yan Zhenhua

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

Exploration and Development Research Institute of PetroChina Southwest Oil & Gasfield Company, Chengdu, China
Li Xiang

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

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

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

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

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APA Style

Zhang Xuan, Yan Zhenhua, Xu Xiang, Li Xiang, Chen Zhigang, et al. (2023). Seismic Facies Recognition of Ultra-Deep Carbonate Rocks Based on Convolutional Neural Network. Earth Sciences, 12(2), 41-46. https://doi.org/10.11648/j.earth.20231202.11

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ACS Style

Zhang Xuan; Yan Zhenhua; Xu Xiang; Li Xiang; Chen Zhigang, et al. Seismic Facies Recognition of Ultra-Deep Carbonate Rocks Based on Convolutional Neural Network. Earth Sci. 2023, 12(2), 41-46. doi: 10.11648/j.earth.20231202.11

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AMA Style

Zhang Xuan, Yan Zhenhua, Xu Xiang, Li Xiang, Chen Zhigang, et al. Seismic Facies Recognition of Ultra-Deep Carbonate Rocks Based on Convolutional Neural Network. Earth Sci. 2023;12(2):41-46. doi: 10.11648/j.earth.20231202.11

Copy | Download

@article{10.11648/j.earth.20231202.11,
  author = {Zhang Xuan and Yan Zhenhua and Xu Xiang and Li Xiang and Chen Zhigang and Li Jianhua and Ji Xuewu},
  title = {Seismic Facies Recognition of Ultra-Deep Carbonate Rocks Based on Convolutional Neural Network},
  journal = {Earth Sciences},
  volume = {12},
  number = {2},
  pages = {41-46},
  doi = {10.11648/j.earth.20231202.11},
  url = {https://doi.org/10.11648/j.earth.20231202.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.earth.20231202.11},
  abstract = {Seismic facies is a seismic reflection unit defined by specific seismic reflection characteristics, that is, the seismic responses of sedimentary facies or geological bodies, whose accuracy will directly affect the reliability of oil and gas exploration results. Currently, seismic facies is generally recognized depending upon the differences between certain single trace seismic attributes (waveform, frequency spectrum, and amplitude, etc.) and adjacent units to conduct cluster analysis. Such methods, however, have ambiguity in identifying special reflective structures with continuous waveforms (e.g. massive carbonate deposits). In order to solve this problem, this paper incorporates artificial intelligence (AI) technology into automatic recognition of seismic facies with special reflection structures. Firstly, a 2D seismic facies classification sample label set is designed and formed. Then, a seismic facies prediction model is designed and constructed using a multi-layer convolutional neural network (CNN). Finally, the trained model is used to automatically track the seismic facies in the study area. This method was applied to seismic facies recognition for the Sinian Dengying Formation in an area of the Sichuan Basin, and the seismic facies recognized were compared with artificially interpreted ones. It is confirmed that the proposed method provides a better effect than artificial interpretation, with greatly improved accuracy and efficiency.},
 year = {2023}
}

Copy | Download

TY  - JOUR
T1  - Seismic Facies Recognition of Ultra-Deep Carbonate Rocks Based on Convolutional Neural Network
AU  - Zhang Xuan
AU  - Yan Zhenhua
AU  - Xu Xiang
AU  - Li Xiang
AU  - Chen Zhigang
AU  - Li Jianhua
AU  - Ji Xuewu
Y1  - 2023/05/10
PY  - 2023
N1  - https://doi.org/10.11648/j.earth.20231202.11
DO  - 10.11648/j.earth.20231202.11
T2  - Earth Sciences
JF  - Earth Sciences
JO  - Earth Sciences
SP  - 41
EP  - 46
PB  - Science Publishing Group
SN  - 2328-5982
UR  - https://doi.org/10.11648/j.earth.20231202.11
AB  - Seismic facies is a seismic reflection unit defined by specific seismic reflection characteristics, that is, the seismic responses of sedimentary facies or geological bodies, whose accuracy will directly affect the reliability of oil and gas exploration results. Currently, seismic facies is generally recognized depending upon the differences between certain single trace seismic attributes (waveform, frequency spectrum, and amplitude, etc.) and adjacent units to conduct cluster analysis. Such methods, however, have ambiguity in identifying special reflective structures with continuous waveforms (e.g. massive carbonate deposits). In order to solve this problem, this paper incorporates artificial intelligence (AI) technology into automatic recognition of seismic facies with special reflection structures. Firstly, a 2D seismic facies classification sample label set is designed and formed. Then, a seismic facies prediction model is designed and constructed using a multi-layer convolutional neural network (CNN). Finally, the trained model is used to automatically track the seismic facies in the study area. This method was applied to seismic facies recognition for the Sinian Dengying Formation in an area of the Sichuan Basin, and the seismic facies recognized were compared with artificially interpreted ones. It is confirmed that the proposed method provides a better effect than artificial interpretation, with greatly improved accuracy and efficiency.
VL  - 12
IS  - 2
ER  -

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