Research on the Application of Bayesian Machine Learning in Reservoir Prediction

Zhiguo Fu; Kang Chen; Juan Liao; Long Long; Da Peng

doi:doi:10.11648/j.pse.20230702.12

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Research on the Application of Bayesian Machine Learning in Reservoir Prediction

Zhiguo Fu^*, Kang Chen, Juan Liao, Long Long, Da Peng

Published in Petroleum Science and Engineering (Volume 7, Issue 2)

Received: 28 October 2023 Accepted: 21 November 2023 Published: 29 November 2023

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Abstract

As exploration and development of various types of oil and natural gas reservoirs underground continue to evolve, the more complex geological environment for seismic exploration in oil and gas reservoirs become focus increasingly. When the target reservoir is vertically interbedded and nested with various lithological strata, and exhibits poor lateral continuity, it becomes increasingly difficult for humans to distinguish oil and gas reservoirs from such complex backgrounds. It's also challenging to quantitatively assess and determine the accuracy of discrimination and achieve optimal reservoir identification results. In response to this issue, the Bayesian machine learning algorithm is introduced for automated target discrimination, enabling efficient differentiation between dolomite reservoirs, mudstones, and other lithological intercalations. The core of applying the Bayesian classifier is to establish a distribution model for target parameters, which is usually assumed to be a known distribution type such as Gaussian or Cauchy distribution. However, in petroleum seismic exploration, the distribution of oil and gas reservoir parameters is highly irregular and significantly different from these established distribution types, limiting the application of the Bayesian classification method. Therefore, we propose using a radial basis function neural network to estimate the prior distribution probability density of oil and gas reservoir parameters. This approach does not assume the prior distribution to be a certain predetermined model but instead builds the prior distribution model based on the numerical distribution characteristics of the target parameters themselves, enhancing the practicality of the Bayesian classification method. This method replaces manual reservoir identification processes, achieving high-precision, quantitative, and automated discrimination of reservoirs. Applied to actual seismic exploration data in oil fields for gas layer prediction, the discrimination results match with industrial gas wells, demonstrating the feasibility and effectiveness of the method.

Published in	Petroleum Science and Engineering (Volume 7, Issue 2)
DOI	10.11648/j.pse.20230702.12
Page(s)	35-42
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

Machine Learning, Bayes, Classification, Radial Basis Network, Reservoir Prediction

References

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APA Style

Fu, Z., Chen, K., Liao, J., Long, L., Peng, D. (2023). Research on the Application of Bayesian Machine Learning in Reservoir Prediction. Petroleum Science and Engineering, 7(2), 35-42. https://doi.org/10.11648/j.pse.20230702.12

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ACS Style

Fu, Z.; Chen, K.; Liao, J.; Long, L.; Peng, D. Research on the Application of Bayesian Machine Learning in Reservoir Prediction. Pet. Sci. Eng. 2023, 7(2), 35-42. doi: 10.11648/j.pse.20230702.12

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AMA Style

Fu Z, Chen K, Liao J, Long L, Peng D. Research on the Application of Bayesian Machine Learning in Reservoir Prediction. Pet Sci Eng. 2023;7(2):35-42. doi: 10.11648/j.pse.20230702.12

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@article{10.11648/j.pse.20230702.12,
  author = {Zhiguo Fu and Kang Chen and Juan Liao and Long Long and Da Peng},
  title = {Research on the Application of Bayesian Machine Learning in Reservoir Prediction},
  journal = {Petroleum Science and Engineering},
  volume = {7},
  number = {2},
  pages = {35-42},
  doi = {10.11648/j.pse.20230702.12},
  url = {https://doi.org/10.11648/j.pse.20230702.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.pse.20230702.12},
  abstract = {As exploration and development of various types of oil and natural gas reservoirs underground continue to evolve, the more complex geological environment for seismic exploration in oil and gas reservoirs become focus increasingly. When the target reservoir is vertically interbedded and nested with various lithological strata, and exhibits poor lateral continuity, it becomes increasingly difficult for humans to distinguish oil and gas reservoirs from such complex backgrounds. It's also challenging to quantitatively assess and determine the accuracy of discrimination and achieve optimal reservoir identification results. In response to this issue, the Bayesian machine learning algorithm is introduced for automated target discrimination, enabling efficient differentiation between dolomite reservoirs, mudstones, and other lithological intercalations. The core of applying the Bayesian classifier is to establish a distribution model for target parameters, which is usually assumed to be a known distribution type such as Gaussian or Cauchy distribution. However, in petroleum seismic exploration, the distribution of oil and gas reservoir parameters is highly irregular and significantly different from these established distribution types, limiting the application of the Bayesian classification method. Therefore, we propose using a radial basis function neural network to estimate the prior distribution probability density of oil and gas reservoir parameters. This approach does not assume the prior distribution to be a certain predetermined model but instead builds the prior distribution model based on the numerical distribution characteristics of the target parameters themselves, enhancing the practicality of the Bayesian classification method. This method replaces manual reservoir identification processes, achieving high-precision, quantitative, and automated discrimination of reservoirs. Applied to actual seismic exploration data in oil fields for gas layer prediction, the discrimination results match with industrial gas wells, demonstrating the feasibility and effectiveness of the method.
},
 year = {2023}
}

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TY  - JOUR
T1  - Research on the Application of Bayesian Machine Learning in Reservoir Prediction
AU  - Zhiguo Fu
AU  - Kang Chen
AU  - Juan Liao
AU  - Long Long
AU  - Da Peng
Y1  - 2023/11/29
PY  - 2023
N1  - https://doi.org/10.11648/j.pse.20230702.12
DO  - 10.11648/j.pse.20230702.12
T2  - Petroleum Science and Engineering
JF  - Petroleum Science and Engineering
JO  - Petroleum Science and Engineering
SP  - 35
EP  - 42
PB  - Science Publishing Group
SN  - 2640-4516
UR  - https://doi.org/10.11648/j.pse.20230702.12
AB  - As exploration and development of various types of oil and natural gas reservoirs underground continue to evolve, the more complex geological environment for seismic exploration in oil and gas reservoirs become focus increasingly. When the target reservoir is vertically interbedded and nested with various lithological strata, and exhibits poor lateral continuity, it becomes increasingly difficult for humans to distinguish oil and gas reservoirs from such complex backgrounds. It's also challenging to quantitatively assess and determine the accuracy of discrimination and achieve optimal reservoir identification results. In response to this issue, the Bayesian machine learning algorithm is introduced for automated target discrimination, enabling efficient differentiation between dolomite reservoirs, mudstones, and other lithological intercalations. The core of applying the Bayesian classifier is to establish a distribution model for target parameters, which is usually assumed to be a known distribution type such as Gaussian or Cauchy distribution. However, in petroleum seismic exploration, the distribution of oil and gas reservoir parameters is highly irregular and significantly different from these established distribution types, limiting the application of the Bayesian classification method. Therefore, we propose using a radial basis function neural network to estimate the prior distribution probability density of oil and gas reservoir parameters. This approach does not assume the prior distribution to be a certain predetermined model but instead builds the prior distribution model based on the numerical distribution characteristics of the target parameters themselves, enhancing the practicality of the Bayesian classification method. This method replaces manual reservoir identification processes, achieving high-precision, quantitative, and automated discrimination of reservoirs. Applied to actual seismic exploration data in oil fields for gas layer prediction, the discrimination results match with industrial gas wells, demonstrating the feasibility and effectiveness of the method.

VL  - 7
IS  - 2
ER  -

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Author Information

Zhiguo Fu

School of Geoscience and Technology, Southwest Petroleum University, Chengdu, China

Contact Email
Kang Chen

Exploration and Development Research Institute, PetroChina Southwest Oil and Gas Field Company, Chengdu, China
Juan Liao

Southwest Institute of Exploration of Geophysics, BGP Inc., CNPC, Chengdu, China
Long Long

Exploration and Development Research Institute, PetroChina Southwest Oil and Gas Field Company, Chengdu, China
Da Peng

Exploration and Development Research Institute, PetroChina Southwest Oil and Gas Field Company, Chengdu, China

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APA Style

Fu, Z., Chen, K., Liao, J., Long, L., Peng, D. (2023). Research on the Application of Bayesian Machine Learning in Reservoir Prediction. Petroleum Science and Engineering, 7(2), 35-42. https://doi.org/10.11648/j.pse.20230702.12

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ACS Style

Fu, Z.; Chen, K.; Liao, J.; Long, L.; Peng, D. Research on the Application of Bayesian Machine Learning in Reservoir Prediction. Pet. Sci. Eng. 2023, 7(2), 35-42. doi: 10.11648/j.pse.20230702.12

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AMA Style

Fu Z, Chen K, Liao J, Long L, Peng D. Research on the Application of Bayesian Machine Learning in Reservoir Prediction. Pet Sci Eng. 2023;7(2):35-42. doi: 10.11648/j.pse.20230702.12

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@article{10.11648/j.pse.20230702.12,
  author = {Zhiguo Fu and Kang Chen and Juan Liao and Long Long and Da Peng},
  title = {Research on the Application of Bayesian Machine Learning in Reservoir Prediction},
  journal = {Petroleum Science and Engineering},
  volume = {7},
  number = {2},
  pages = {35-42},
  doi = {10.11648/j.pse.20230702.12},
  url = {https://doi.org/10.11648/j.pse.20230702.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.pse.20230702.12},
  abstract = {As exploration and development of various types of oil and natural gas reservoirs underground continue to evolve, the more complex geological environment for seismic exploration in oil and gas reservoirs become focus increasingly. When the target reservoir is vertically interbedded and nested with various lithological strata, and exhibits poor lateral continuity, it becomes increasingly difficult for humans to distinguish oil and gas reservoirs from such complex backgrounds. It's also challenging to quantitatively assess and determine the accuracy of discrimination and achieve optimal reservoir identification results. In response to this issue, the Bayesian machine learning algorithm is introduced for automated target discrimination, enabling efficient differentiation between dolomite reservoirs, mudstones, and other lithological intercalations. The core of applying the Bayesian classifier is to establish a distribution model for target parameters, which is usually assumed to be a known distribution type such as Gaussian or Cauchy distribution. However, in petroleum seismic exploration, the distribution of oil and gas reservoir parameters is highly irregular and significantly different from these established distribution types, limiting the application of the Bayesian classification method. Therefore, we propose using a radial basis function neural network to estimate the prior distribution probability density of oil and gas reservoir parameters. This approach does not assume the prior distribution to be a certain predetermined model but instead builds the prior distribution model based on the numerical distribution characteristics of the target parameters themselves, enhancing the practicality of the Bayesian classification method. This method replaces manual reservoir identification processes, achieving high-precision, quantitative, and automated discrimination of reservoirs. Applied to actual seismic exploration data in oil fields for gas layer prediction, the discrimination results match with industrial gas wells, demonstrating the feasibility and effectiveness of the method.
},
 year = {2023}
}

Copy | Download

TY  - JOUR
T1  - Research on the Application of Bayesian Machine Learning in Reservoir Prediction
AU  - Zhiguo Fu
AU  - Kang Chen
AU  - Juan Liao
AU  - Long Long
AU  - Da Peng
Y1  - 2023/11/29
PY  - 2023
N1  - https://doi.org/10.11648/j.pse.20230702.12
DO  - 10.11648/j.pse.20230702.12
T2  - Petroleum Science and Engineering
JF  - Petroleum Science and Engineering
JO  - Petroleum Science and Engineering
SP  - 35
EP  - 42
PB  - Science Publishing Group
SN  - 2640-4516
UR  - https://doi.org/10.11648/j.pse.20230702.12
AB  - As exploration and development of various types of oil and natural gas reservoirs underground continue to evolve, the more complex geological environment for seismic exploration in oil and gas reservoirs become focus increasingly. When the target reservoir is vertically interbedded and nested with various lithological strata, and exhibits poor lateral continuity, it becomes increasingly difficult for humans to distinguish oil and gas reservoirs from such complex backgrounds. It's also challenging to quantitatively assess and determine the accuracy of discrimination and achieve optimal reservoir identification results. In response to this issue, the Bayesian machine learning algorithm is introduced for automated target discrimination, enabling efficient differentiation between dolomite reservoirs, mudstones, and other lithological intercalations. The core of applying the Bayesian classifier is to establish a distribution model for target parameters, which is usually assumed to be a known distribution type such as Gaussian or Cauchy distribution. However, in petroleum seismic exploration, the distribution of oil and gas reservoir parameters is highly irregular and significantly different from these established distribution types, limiting the application of the Bayesian classification method. Therefore, we propose using a radial basis function neural network to estimate the prior distribution probability density of oil and gas reservoir parameters. This approach does not assume the prior distribution to be a certain predetermined model but instead builds the prior distribution model based on the numerical distribution characteristics of the target parameters themselves, enhancing the practicality of the Bayesian classification method. This method replaces manual reservoir identification processes, achieving high-precision, quantitative, and automated discrimination of reservoirs. Applied to actual seismic exploration data in oil fields for gas layer prediction, the discrimination results match with industrial gas wells, demonstrating the feasibility and effectiveness of the method.

VL  - 7
IS  - 2
ER  -

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