Review on Resistivity Inversion of Underground Abnormal Bodies

Yanchang Liu; Weifang Kong; Ke Du; Tongming Liu; Yuli Wang

doi:doi:10.11648/j.jenr.20221102.12

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Review on Resistivity Inversion of Underground Abnormal Bodies

Yanchang Liu, Weifang Kong, Ke Du, Tongming Liu, Yuli Wang

Published in Journal of Energy and Natural Resources (Volume 11, Issue 2)

Received: 31 May 2022 Published: 1 June 2022

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Abstract

With the development of petroleum in China, most oilfields have entered the stage of high water cut development. Well ground resistivity method as a new type of electric prospecting method, which have influence on the formation of small, measuring low cost advantages, gradually become one of the key technology of remaining oil distribution in the detection of resistivity inversion is through observation of the underground space apparent resistivity data reconstruction of the underground resistivity distribution, can realize the resistivity imaging of the underground space. In order to achieve the morphological characterization and spatial location of the abnormal area of underground resistivity, and then to carry out geological interpretation, the definition and properties of resistivity inversion are summarized, and the bottleneck problems encountered in practical engineering application are re-recognized and analyzed. On this basis, the theoretical method, numerical method and inversion method based on machine learning are introduced to solve the inverse resistivity problem of underground abnormal body. The inversion method based on deep learning is emphatically introduced, and its advantages and disadvantages and applicability are evaluated. It is pointed out that inversion is an ideal tool for data analysis. Then, it is pointed out that the development direction of resistivity inversion of underground abnormal body is to propose an optimized inversion network architecture based on deep learning.

Published in	Journal of Energy and Natural Resources (Volume 11, Issue 2)
DOI	10.11648/j.jenr.20221102.12
Page(s)	37-43
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), 2022. Published by Science Publishing Group

Keywords

Underground Resistivity, Abnormal Body, Inversion Method, Deep Learning

References

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APA Style

Yanchang Liu, Weifang Kong, Ke Du, Tongming Liu, Yuli Wang. (2022). Review on Resistivity Inversion of Underground Abnormal Bodies. Journal of Energy and Natural Resources, 11(2), 37-43. https://doi.org/10.11648/j.jenr.20221102.12

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ACS Style

Yanchang Liu; Weifang Kong; Ke Du; Tongming Liu; Yuli Wang. Review on Resistivity Inversion of Underground Abnormal Bodies. J. Energy Nat. Resour. 2022, 11(2), 37-43. doi: 10.11648/j.jenr.20221102.12

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AMA Style

Yanchang Liu, Weifang Kong, Ke Du, Tongming Liu, Yuli Wang. Review on Resistivity Inversion of Underground Abnormal Bodies. J Energy Nat Resour. 2022;11(2):37-43. doi: 10.11648/j.jenr.20221102.12

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@article{10.11648/j.jenr.20221102.12,
  author = {Yanchang Liu and Weifang Kong and Ke Du and Tongming Liu and Yuli Wang},
  title = {Review on Resistivity Inversion of Underground Abnormal Bodies},
  journal = {Journal of Energy and Natural Resources},
  volume = {11},
  number = {2},
  pages = {37-43},
  doi = {10.11648/j.jenr.20221102.12},
  url = {https://doi.org/10.11648/j.jenr.20221102.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jenr.20221102.12},
  abstract = {With the development of petroleum in China, most oilfields have entered the stage of high water cut development. Well ground resistivity method as a new type of electric prospecting method, which have influence on the formation of small, measuring low cost advantages, gradually become one of the key technology of remaining oil distribution in the detection of resistivity inversion is through observation of the underground space apparent resistivity data reconstruction of the underground resistivity distribution, can realize the resistivity imaging of the underground space. In order to achieve the morphological characterization and spatial location of the abnormal area of underground resistivity, and then to carry out geological interpretation, the definition and properties of resistivity inversion are summarized, and the bottleneck problems encountered in practical engineering application are re-recognized and analyzed. On this basis, the theoretical method, numerical method and inversion method based on machine learning are introduced to solve the inverse resistivity problem of underground abnormal body. The inversion method based on deep learning is emphatically introduced, and its advantages and disadvantages and applicability are evaluated. It is pointed out that inversion is an ideal tool for data analysis. Then, it is pointed out that the development direction of resistivity inversion of underground abnormal body is to propose an optimized inversion network architecture based on deep learning.},
 year = {2022}
}

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TY  - JOUR
T1  - Review on Resistivity Inversion of Underground Abnormal Bodies
AU  - Yanchang Liu
AU  - Weifang Kong
AU  - Ke Du
AU  - Tongming Liu
AU  - Yuli Wang
Y1  - 2022/06/01
PY  - 2022
N1  - https://doi.org/10.11648/j.jenr.20221102.12
DO  - 10.11648/j.jenr.20221102.12
T2  - Journal of Energy and Natural Resources
JF  - Journal of Energy and Natural Resources
JO  - Journal of Energy and Natural Resources
SP  - 37
EP  - 43
PB  - Science Publishing Group
SN  - 2330-7404
UR  - https://doi.org/10.11648/j.jenr.20221102.12
AB  - With the development of petroleum in China, most oilfields have entered the stage of high water cut development. Well ground resistivity method as a new type of electric prospecting method, which have influence on the formation of small, measuring low cost advantages, gradually become one of the key technology of remaining oil distribution in the detection of resistivity inversion is through observation of the underground space apparent resistivity data reconstruction of the underground resistivity distribution, can realize the resistivity imaging of the underground space. In order to achieve the morphological characterization and spatial location of the abnormal area of underground resistivity, and then to carry out geological interpretation, the definition and properties of resistivity inversion are summarized, and the bottleneck problems encountered in practical engineering application are re-recognized and analyzed. On this basis, the theoretical method, numerical method and inversion method based on machine learning are introduced to solve the inverse resistivity problem of underground abnormal body. The inversion method based on deep learning is emphatically introduced, and its advantages and disadvantages and applicability are evaluated. It is pointed out that inversion is an ideal tool for data analysis. Then, it is pointed out that the development direction of resistivity inversion of underground abnormal body is to propose an optimized inversion network architecture based on deep learning.
VL  - 11
IS  - 2
ER  -

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Author Information

Yanchang Liu

Department of Electrical Information Engineering, Northeast Petroleum University, Qinhuangdao, China
Weifang Kong

School of Physical and Electrical Engineering, Northeast Petroleum University, Daqing, China
Ke Du

School of Physical and Electrical Engineering, Northeast Petroleum University, Daqing, China
Tongming Liu

School of Physical and Electrical Engineering, Northeast Petroleum University, Daqing, China
Yuli Wang

School of Stomatology, China Medical University, Shenyang, China

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Plain Text BibTeX RIS

APA Style

Yanchang Liu, Weifang Kong, Ke Du, Tongming Liu, Yuli Wang. (2022). Review on Resistivity Inversion of Underground Abnormal Bodies. Journal of Energy and Natural Resources, 11(2), 37-43. https://doi.org/10.11648/j.jenr.20221102.12

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ACS Style

Yanchang Liu; Weifang Kong; Ke Du; Tongming Liu; Yuli Wang. Review on Resistivity Inversion of Underground Abnormal Bodies. J. Energy Nat. Resour. 2022, 11(2), 37-43. doi: 10.11648/j.jenr.20221102.12

Copy | Download

AMA Style

Yanchang Liu, Weifang Kong, Ke Du, Tongming Liu, Yuli Wang. Review on Resistivity Inversion of Underground Abnormal Bodies. J Energy Nat Resour. 2022;11(2):37-43. doi: 10.11648/j.jenr.20221102.12

Copy | Download

@article{10.11648/j.jenr.20221102.12,
  author = {Yanchang Liu and Weifang Kong and Ke Du and Tongming Liu and Yuli Wang},
  title = {Review on Resistivity Inversion of Underground Abnormal Bodies},
  journal = {Journal of Energy and Natural Resources},
  volume = {11},
  number = {2},
  pages = {37-43},
  doi = {10.11648/j.jenr.20221102.12},
  url = {https://doi.org/10.11648/j.jenr.20221102.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jenr.20221102.12},
  abstract = {With the development of petroleum in China, most oilfields have entered the stage of high water cut development. Well ground resistivity method as a new type of electric prospecting method, which have influence on the formation of small, measuring low cost advantages, gradually become one of the key technology of remaining oil distribution in the detection of resistivity inversion is through observation of the underground space apparent resistivity data reconstruction of the underground resistivity distribution, can realize the resistivity imaging of the underground space. In order to achieve the morphological characterization and spatial location of the abnormal area of underground resistivity, and then to carry out geological interpretation, the definition and properties of resistivity inversion are summarized, and the bottleneck problems encountered in practical engineering application are re-recognized and analyzed. On this basis, the theoretical method, numerical method and inversion method based on machine learning are introduced to solve the inverse resistivity problem of underground abnormal body. The inversion method based on deep learning is emphatically introduced, and its advantages and disadvantages and applicability are evaluated. It is pointed out that inversion is an ideal tool for data analysis. Then, it is pointed out that the development direction of resistivity inversion of underground abnormal body is to propose an optimized inversion network architecture based on deep learning.},
 year = {2022}
}

Copy | Download

TY  - JOUR
T1  - Review on Resistivity Inversion of Underground Abnormal Bodies
AU  - Yanchang Liu
AU  - Weifang Kong
AU  - Ke Du
AU  - Tongming Liu
AU  - Yuli Wang
Y1  - 2022/06/01
PY  - 2022
N1  - https://doi.org/10.11648/j.jenr.20221102.12
DO  - 10.11648/j.jenr.20221102.12
T2  - Journal of Energy and Natural Resources
JF  - Journal of Energy and Natural Resources
JO  - Journal of Energy and Natural Resources
SP  - 37
EP  - 43
PB  - Science Publishing Group
SN  - 2330-7404
UR  - https://doi.org/10.11648/j.jenr.20221102.12
AB  - With the development of petroleum in China, most oilfields have entered the stage of high water cut development. Well ground resistivity method as a new type of electric prospecting method, which have influence on the formation of small, measuring low cost advantages, gradually become one of the key technology of remaining oil distribution in the detection of resistivity inversion is through observation of the underground space apparent resistivity data reconstruction of the underground resistivity distribution, can realize the resistivity imaging of the underground space. In order to achieve the morphological characterization and spatial location of the abnormal area of underground resistivity, and then to carry out geological interpretation, the definition and properties of resistivity inversion are summarized, and the bottleneck problems encountered in practical engineering application are re-recognized and analyzed. On this basis, the theoretical method, numerical method and inversion method based on machine learning are introduced to solve the inverse resistivity problem of underground abnormal body. The inversion method based on deep learning is emphatically introduced, and its advantages and disadvantages and applicability are evaluated. It is pointed out that inversion is an ideal tool for data analysis. Then, it is pointed out that the development direction of resistivity inversion of underground abnormal body is to propose an optimized inversion network architecture based on deep learning.
VL  - 11
IS  - 2
ER  -

Copy | Download