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April 10, 2026Journal of Petroleum Geology

Machine Learning–Based Real‐Time Lithology Identification From Drilling Data in the Ca Tam Offshore Field

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Authors

HNHung T. NguyenHanoi University of Mining and GeologyDVDuong Hong VuHanoi University of Mining and GeologyHNHoa Minh NguyenHanoi University of Mining and Geology

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Overview

Data-driven approaches classify lithological layers in real-time, enhancing drilling efficiency and safety.

Key Points

  • The aim is to develop a method for real-time lithology classification during drilling operations.
  • Time-series datasets from wells used for training
  • Four drilling parameters identified: weight on bit, torque, standpipe pressure, rate of penetration
  • Noise and outlier removal via modified Z-score method
  • Four machine learning algorithms tested: Random Forest, Extreme Gradient Boosting, SVM, and ANN
  • Random Forest model achieved the highest accuracy of 0.873
  • XGBoost closely followed with an accuracy of 0.87
  • RF maintained high reliability with 84.23% accuracy on an independent well
  • Demonstrated strong generalization ability with minimal overfitting

Cite This Study

Nguyen et al. (2026) studied this question.

synapsesocial.com/papers/69d893406c1944d70ce04420https://doi.org/10.1111/jpg.70062
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