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Applying Machine Learning to Predict the Rate of Penetration for Geothermal Drilling Located in the Utah FORGE Site

Author

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  • Mohamed Arbi Ben Aoun

    (Department of Civil, Geological and Mining Engineering, Polytechnique Montréal, 2500 Chemin de Polytechnique, Montréal, QC H3T 1J4, Canada
    Institute of Environmental Management, University of Miskolc, 3515 Miskolc-Egyetemváros, Hungary)

  • Tamás Madarász

    (Institute of Environmental Management, University of Miskolc, 3515 Miskolc-Egyetemváros, Hungary)

Abstract

Well planning for every drilling project includes cost estimation. Maximizing the rate of penetration (ROP) reduces the time required for drilling, resulting in reducing the expenses required for the drilling budget. The empirical formulas developed to predict ROP have limited field applications. Since real-time drilling data acquisition and computing technologies have improved over the years, we implemented the data-driven approach for this purpose. We investigated the potential of machine learning and deep learning algorithms to predict the nonlinear behavior of the ROP. The well was drilled to confirm the geothermal reservoir characteristics for the FORGE site. After cleaning and preprocessing the data, we selected two models and optimized their hyperparameters. According to our findings, the random forest regressor and the artificial neural network predicted the behavior of our field ROP with a maximum absolute mean error of 3.98, corresponding to 19% of the ROP’s standard deviation. A tool was created to assist engineers in selecting the best drilling parameters that increase the ROP for future drilling tasks. The tool can be validated with an existing well from the same field to demonstrate its capability as an ROP predictive model.

Suggested Citation

  • Mohamed Arbi Ben Aoun & Tamás Madarász, 2022. "Applying Machine Learning to Predict the Rate of Penetration for Geothermal Drilling Located in the Utah FORGE Site," Energies, MDPI, vol. 15(12), pages 1-21, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:12:p:4288-:d:836548
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    References listed on IDEAS

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    1. BARTEN, Anton P., 1987. "The coefficient of determination for regression without a constant term," LIDAM Reprints CORE 766, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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    Cited by:

    1. Zhai, Haizhen & Jin, Guangrong & Liu, Lihua & Su, Zheng & Zeng, Yuchao & Liu, Jie & Li, Guangyu & Feng, Chuangji & Wu, Nengyou, 2023. "Parametric study of the geothermal exploitation performance from a HDR reservoir through multilateral horizontal wells: The Qiabuqia geothermal area, Gonghe Basin," Energy, Elsevier, vol. 275(C).
    2. Jianxin Ding & Rui Zhang & Xin Wen & Xuesong Li & Xianzhi Song & Baodong Ma & Dayu Li & Liang Han, 2023. "Interpretable Feature Construction and Incremental Update Fine-Tuning Strategy for Prediction of Rate of Penetration," Energies, MDPI, vol. 16(15), pages 1-16, July.

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