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Identification of Karst Cavities from 2D Seismic Wave Impedance Images Based on Gradient-Boosting Decision Trees Algorithms (GBDT): Case of Ordovician Fracture-Vuggy Carbonate Reservoir, Tahe Oilfield, Tarim Basin, China

Author

Listed:
  • Allou Koffi Franck Kouassi

    (Key Laboratory of Theory and Technology of Petroleum Exploration and Development in Hubei Province, China University of Geosciences, Wuhan 430074, China)

  • Lin Pan

    (Key Laboratory of Theory and Technology of Petroleum Exploration and Development in Hubei Province, China University of Geosciences, Wuhan 430074, China)

  • Xiao Wang

    (Key Laboratory of Theory and Technology of Petroleum Exploration and Development in Hubei Province, China University of Geosciences, Wuhan 430074, China)

  • Zhangheng Wang

    (Key Laboratory of Theory and Technology of Petroleum Exploration and Development in Hubei Province, China University of Geosciences, Wuhan 430074, China)

  • Alvin K. Mulashani

    (Key Laboratory of Theory and Technology of Petroleum Exploration and Development in Hubei Province, China University of Geosciences, Wuhan 430074, China
    Department of Geoscience and Mining Technology, College of Engineering and Technology, Mbeya University of Science and Technology, Mbeya P.O. Box 131, Tanzania)

  • Faulo James

    (Key Laboratory of Theory and Technology of Petroleum Exploration and Development in Hubei Province, China University of Geosciences, Wuhan 430074, China)

  • Mbarouk Shaame

    (Key Laboratory of Theory and Technology of Petroleum Exploration and Development in Hubei Province, China University of Geosciences, Wuhan 430074, China
    Department of Petroleum and Energy Engineering, College of Earth Sciences and Engineering, The University of Dodoma, Dodoma P.O. Box 259, Tanzania)

  • Altaf Hussain

    (Key Laboratory of Theory and Technology of Petroleum Exploration and Development in Hubei Province, China University of Geosciences, Wuhan 430074, China)

  • Hadi Hussain

    (Key Laboratory of Theory and Technology of Petroleum Exploration and Development in Hubei Province, China University of Geosciences, Wuhan 430074, China)

  • Edwin E. Nyakilla

    (Key Laboratory of Theory and Technology of Petroleum Exploration and Development in Hubei Province, China University of Geosciences, Wuhan 430074, China)

Abstract

The precise characterization of geological bodies in fracture-vuggy carbonates is challenging due to their high complexity and heterogeneous distribution. This study aims to present the hybrid of Visual Geometry Group 16 (VGG-16) pre-trained by Gradient-Boosting Decision Tree (GBDT) models as a novel approach for predicting and generating karst cavities with high accuracy on various scales based on uncertainty assessment from a small dataset. Seismic wave impedance images were used as input data. Their manual interpretation was used to build GBDT classifiers for Light Gradient-Boosting Machine (LightGBM) and Unbiased Boosting with Categorical Features (CatBoost) for predicting the karst cavities and unconformities. The results show that the LightGBM was the best GBDT classifier, which performed excellently in karst cavity interpretation, giving an F1-score between 0.87 and 0.94 and a micro-G-Mean ranging from 0.92 to 0.96. Furthermore, the LightGBM performed better in cave prediction than Linear Regression (LR) and Multilayer Perceptron (MLP). The prediction of karst cavities according to the LightGBM model was performed well according to the uncertainty quantification. Therefore, the hybrid VGG16 and GBDT algorithms can be implemented as an improved approach for efficiently identifying geological features within similar reservoirs worldwide.

Suggested Citation

  • Allou Koffi Franck Kouassi & Lin Pan & Xiao Wang & Zhangheng Wang & Alvin K. Mulashani & Faulo James & Mbarouk Shaame & Altaf Hussain & Hadi Hussain & Edwin E. Nyakilla, 2023. "Identification of Karst Cavities from 2D Seismic Wave Impedance Images Based on Gradient-Boosting Decision Trees Algorithms (GBDT): Case of Ordovician Fracture-Vuggy Carbonate Reservoir, Tahe Oilfield," Energies, MDPI, vol. 16(2), pages 1-26, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:2:p:643-:d:1026071
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    References listed on IDEAS

    as
    1. Haghighat, Fatemeh, 2021. "Predicting the trend of indicators related to Covid-19 using the combined MLP-MC model," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    2. Saifur Rahman & Muhammad Irfan & Mohsin Raza & Khawaja Moyeezullah Ghori & Shumayla Yaqoob & Muhammad Awais, 2020. "Performance Analysis of Boosting Classifiers in Recognizing Activities of Daily Living," IJERPH, MDPI, vol. 17(3), pages 1-15, February.
    3. Juan He & Aowei Li & Shanshan Wu & Ruixue Tang & Dongliang Lv & Yongren Li & Xiaobo Li, 2020. "Experimental Investigation on Injection and Production Pattern in Fractured-Vuggy Carbonate Reservoirs," Energies, MDPI, vol. 13(3), pages 1-24, January.
    4. Shuozhen Wang & Shuoliang Wang & Chunlei Yu & Haifeng Liu, 2021. "Single Well Productivity Prediction Model for Fracture-Vuggy Reservoir Based on Selected Seismic Attributes," Energies, MDPI, vol. 14(14), pages 1-10, July.
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