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October 8, 2025

Enhancing Geothermal Drilling Performance: Predicting Rate of Penetration with Machine Learning Utilizing Geomechanical and Petrophysical Data

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Authors

ASAhmed SaadSuez UniversityATAmira TamanMansoura UniversityDRD. C. RedaSuez University

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Overview

This study demonstrates how advanced machine learning incorporates geomechanical and petrophysical data to improve rate of penetration predictions in geothermal energy operations.

Key Points

  • Incorporating geomechanical and petrophysical data improved rate of penetration predictions significantly.
  • Random Forest achieved the highest predictive accuracy with an R2 of 0.87, while K-Nearest Neighbors had an R2 of 0.81.
  • Advanced machine learning methods were applied to augment drilling data, enhancing both accuracy and operational efficiency.
  • The developed Python web application allows for real-time rate of penetration predictions, aiding decision-making in geothermal drilling.

Cite This Study

Saad et al. (2025) studied this question.

synapsesocial.com/papers/68e5c1be6950a706b22b5925https://doi.org/10.2118/226760-ms
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