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Classifying Facies in 3D Digital Rock Images Using Supervised and Unsupervised Approaches

Author

Listed:
  • Cenk Temizel

    (Saudi Aramco, Dhahran 31311, Saudi Arabia)

  • Uchenna Odi

    (Aramco Americas, Houston, TX 77002, USA)

  • Karthik Balaji

    (Tau Drones, Grand Forks, ND 58201, USA)

  • Hakki Aydin

    (Department of Petroleum and Natural Gas Engineering, Middle East Technical University, Ankara 06800, Turkey)

  • Javier E. Santos

    (Los Alamos National Laboratory, Center for NonLinear Studies, Los Alamos, NM 87545, USA)

Abstract

Lithology is one of the critical parameters influencing drilling operations and reservoir production behavior. Well completion is another important area where facies type has a crucial influence on fracture propagation. Geological formations are highly heterogeneous systems that require extensive evaluation with sophisticated approaches. Classification of facies is a critical approach to characterizing different depositional systems. Image classification is implemented as a quick and easy method to detect different facies groups. Artificial intelligence (AI) algorithms are efficiently used to categorize geological formations in a large dataset. This study involves the classification of different facies with various supervised and unsupervised learning algorithms. The dataset for training and testing was retrieved from a digital rock database published in the data brief. The study showed that supervised algorithms provided more accurate results than unsupervised algorithms. In this study, the extreme gradient boosted tree regressor was found to be the best algorithm for facies classification for the synthetic digital rocks.

Suggested Citation

  • Cenk Temizel & Uchenna Odi & Karthik Balaji & Hakki Aydin & Javier E. Santos, 2022. "Classifying Facies in 3D Digital Rock Images Using Supervised and Unsupervised Approaches," Energies, MDPI, vol. 15(20), pages 1-15, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:20:p:7660-:d:944914
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    References listed on IDEAS

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    1. Zhixue Sun & Baosheng Jiang & Xiangling Li & Jikang Li & Kang Xiao, 2020. "A Data-Driven Approach for Lithology Identification Based on Parameter-Optimized Ensemble Learning," Energies, MDPI, vol. 13(15), pages 1-15, July.
    2. Chuanbo Shen & Solomon Asante-Okyere & Yao Yevenyo Ziggah & Liang Wang & Xiangfeng Zhu, 2019. "Group Method of Data Handling (GMDH) Lithology Identification Based on Wavelet Analysis and Dimensionality Reduction as Well Log Data Pre-Processing Techniques," Energies, MDPI, vol. 12(8), pages 1-16, April.
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