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September 29, 2025JES. Journal of Engineering Sciences/JES. Journal of engineering sciencesOpen Access

A Note on Predicting Rate of Penetration Using Machine Learning Models

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Authors

AHA.A. HusseinySuez UniversityAAAttia Mahmoud AttiaBritish University in EgyptAHAhmed HagagArab Open University

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Overview

This work demonstrates improved ROP prediction in directional wells using simple machine learning models, highlighting accuracy and efficiency.

Key Points

  • XGBoost achieved a remarkable increase in R² scores for ROP predictions across three directional offshore wells, showcasing its effectiveness.
  • Integrating Mechanical Specific Energy and D-exponent into the dataset significantly enhanced the performance of machine learning algorithms.
  • This approach eliminates complex programming requirements, making ROP optimization more accessible for directional drilling projects.
  • Feature importance analyses confirmed the critical role of domain-specific parameters in improving machine learning model accuracy.

Cite This Study

Husseiny et al. (2025) studied this question.

synapsesocial.com/papers/68da58dcc1728099cfd113e5https://doi.org/10.21608/jesaun.2025.397652.1574
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Ensemble Machine Learning for Data-Driven Predictive Analytics of Drilling Rate of Penetration (ROP) Modeling: A Case Study in a Southern Iraqi Oil Field2023 · 8 citations
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  3. 3Hybrid Strategies for Interpretability of Rate of Penetration Prediction: Automated Machine Learning and SHAP Interpretation2024 · 4 citations
  4. 4Enhancing Geothermal Drilling Performance: Predicting Rate of Penetration with Machine Learning Utilizing Geomechanical and Petrophysical Data2025 · 2 citations
  5. 5A Tuned-Filtration and Supervised Machine-Learning Framework for Robust Rate of Penetration Prediction and Drilling Optimization2026