Benchmarking Machine Learning Models for Penetration Rate Prediction in Rotary Drilling: A Case Study from the Sungun Copper Mine
DOI:
https://doi.org/10.17794/Keywords:
Rate of penetration, Mining, Machine learning models, Drilling, Sungun copper mineAbstract
Mining operations require an accurate prediction of rotary drilling penetration rate (ROP) to optimize efficiency and reduce costs. ROP estimation is challenging due to complex, nonlinear relationships among rock properties, equipment specifications, and operational parameters. This study applies six machine learning models, Random Forest, CatBoost, LightGBM, XGBoost, Support Vector Machine, and Extra Trees to forecast ROP using data from 85 drillholes at the Sungun copper mine, Iran. Input features include mining rock mass rating, Schmidt hammer rebound number, rotation pressure, and weight on bit. The Extra Trees model achieved the best performance, with R² values of 1 (training) and 0.93 (testing) and RMSE of 0 and 0.937, respectively, demonstrating high accuracy and generalization. The results confirm that machine learning, particularly Extra Trees, can effectively model complex ROP dependencies, enabling improved equipment selection, operational planning, and cost estimation in rotary drilling.
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Copyright (c) 2026 Ali Nemati Vardin, Masoud Monjezi, Mojtaba Rezakhah, Manoj Khandelwal, Hasel Amini Khoshalan
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