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Support Vector Machine Algorithm for Automatically Identifying Depositional Microfacies Using Well Logs

Author

Listed:
  • Dahai Wang

    (College of Earth Science and Technology, Southwest Petroleum University, Chengdu 610500, China)

  • Jun Peng

    (College of Earth Science and Technology, Southwest Petroleum University, Chengdu 610500, China)

  • Qian Yu

    (Chengdu Center of China Geological Survey, Chengdu 610081, China)

  • Yuanyuan Chen

    (Southwest Geophysical Exploration Branch of Oriental Geophysical Company, Chengdu 610213, China)

  • Hanghang Yu

    (Southwest Oil & Gas Field Company Exploration Division, Petro China, Chengdu 610041, China)

Abstract

Depositional microfacies identification plays a key role in the exploration and development of oil and gas reservoirs. Conventionally, depositional microfacies are manually identified by geologists based on the observation of core samples. This conventional method for identifying depositional microfacies is time-consuming, and only the depositional microfacies in a few wells can be identified due to the limited core samples in these wells. In this study, the support vector machine (SVM) algorithm is proposed to identify depositional microfacies automatically using well logs. The application of SVM includes the following steps: First, the depositional microfacies are determined manually in several wells with core samples. Then, the training sets used in the SVM algorithm are extracted from the well logs. Finally, a quantitative discrimination model based on the SVM algorithm is established to realize the classification of depositional microfacies. Field application shows that this innovative and constructive solution can be effectively used in uncored wells to identify depositional microfacies with a rate of accuracy approaching 84%. It overcomes the limitation of the conventional manual method which greatly contributes to the cost-saving of core analysis and improves the sustainable profitability of oil and gas exploration.

Suggested Citation

  • Dahai Wang & Jun Peng & Qian Yu & Yuanyuan Chen & Hanghang Yu, 2019. "Support Vector Machine Algorithm for Automatically Identifying Depositional Microfacies Using Well Logs," Sustainability, MDPI, vol. 11(7), pages 1-15, March.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:7:p:1919-:d:218637
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    Citations

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    Cited by:

    1. Vivien Lai & Ali Najah Ahmed & M.A. Malek & Haitham Abdulmohsin Afan & Rusul Khaleel Ibrahim & Ahmed El-Shafie & Amr El-Shafie, 2019. "Modeling the Nonlinearity of Sea Level Oscillations in the Malaysian Coastal Areas Using Machine Learning Algorithms," Sustainability, MDPI, vol. 11(17), pages 1-26, August.
    2. Gibson Kimutai & Alexander Ngenzi & Rutabayiro Ngoga Said & Ambrose Kiprop & Anna Förster, 2020. "An Optimum Tea Fermentation Detection Model Based on Deep Convolutional Neural Networks," Data, MDPI, vol. 5(2), pages 1-26, April.
    3. Guangjuan Fan & Ting Dong & Yuejun Zhao & Yalou Zhou & Wentong Zhao & Jie Wang & Yilong Wang, 2023. "Establishment and Application of a Pattern for Identifying Sedimentary Microfacies of a Single Horizontal Well: An Example from the Eastern Transition Block in the Daqing Oilfield, Songliao Basin, Chi," Energies, MDPI, vol. 16(20), pages 1-19, October.
    4. Seyedalireza Khatibi & Azadeh Aghajanpour, 2020. "Machine Learning: A Useful Tool in Geomechanical Studies, a Case Study from an Offshore Gas Field," Energies, MDPI, vol. 13(14), pages 1-16, July.
    5. Chenglong Chen & Yikun Liu & Decai Lin & Guohui Qu & Jiqiang Zhi & Shuang Liang & Fengjiao Wang & Dukui Zheng & Anqi Shen & Lifeng Bo & Shiwei Zhu, 2021. "Research Progress of Oilfield Development Index Prediction Based on Artificial Neural Networks," Energies, MDPI, vol. 14(18), pages 1-25, September.

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