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Predicting rate of penetration (ROP) with machine learning models: A physics-validated comparative case study of Gradient Boosting Regressor (GBR), Random Forest (RF), and K-Nearest Neighbours (KNN)

Author

Listed:
  • Musaab Magzoub
  • Noora Al Mansoori
  • Mohammed Abdalla Ayoub Mohammed
  • Almostafa Alhadi
  • Kaushik Sivaramakrishnan
  • Munawar Shaik
  • Abu Bakar Adni

Abstract

A successful drilling operation plays a vital role in the profitability of exploration and production of hydrocarbon reserves. However, these operations can come with high drilling rig rental costs. Reliable predictions of Rate of Penetration (ROP) can avoid unnecessary drilling equipment and rig rental expenses. In this study, Gradient Boosting Regressor, Random Forest, and K-Nearest Neighbours were developed to predict rate of penetration based on drilling operational data. The machine learning models were benchmarked with established empirical models, such as the Bingham and Bourgoyne and Young models, which are typically used to predict the rate of penetration. All machine learning models achieved, coefficient of determination (R2) > 0.93 and Average Absolute Percentage Error (AAPE)

Suggested Citation

  • Musaab Magzoub & Noora Al Mansoori & Mohammed Abdalla Ayoub Mohammed & Almostafa Alhadi & Kaushik Sivaramakrishnan & Munawar Shaik & Abu Bakar Adni, 2026. "Predicting rate of penetration (ROP) with machine learning models: A physics-validated comparative case study of Gradient Boosting Regressor (GBR), Random Forest (RF), and K-Nearest Neighbours (KNN)," PLOS ONE, Public Library of Science, vol. 21(11), pages 1-43, November.
    • Handle: RePEc:plo:pone00:0360046
    DOI: 10.1371/journal.pone.0360046
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