Machine Learning for Well Log Analysis in Uranium Mining

Mostafa, Tanzim [Idaho National Laboratory] (ORCID:0009000122917982); Hamed, Ahmed [Idaho National Laboratory]

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DOE OSTI · 3363246

Machine Learning for Well Log Analysis in Uranium Mining

Mostafa, Tanzim [Idaho National Laboratory] (ORCID:0009000122917982)·Hamed, Ahmed [Idaho National Laboratory]

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Abstract

This project explores the use of Artificial Intelligence (AI) and Machine Learning (ML) techniques to automate well log analysis for uranium mining. Geophysical log data—spontaneous potential, resistivity, and gamma ray—were used to classify lithology, correlate well logs and identify roll front zonation patterns, which are critical for locating uranium ore bodies. Supervised ML algorithms such as eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Random Forest were trained to classify lithology with high accuracy. Gradient Boosting Machines (GBM), XGBoost, Random Forest, and Neural Networks were also used for role front zone identification. Moreover, a Fast Dynamic Time Warping (FastDTW) algorithm was employed for well log correlation. Additionally, sample lag was addressed using dynamic programming. Results demonstrate the potential of AI and ML to streamline well log analysis and enhance uranium exploration workflows.

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11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS 58 - GEOSCIENCES 97 - MATHEMATICS AND COMPUTING Lithology Classification Machine Learning Sample Lag Identification Uranium Mining Well Log Correlation

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Mostafa, Tanzim [Idaho National Laboratory] (ORCID:0009000122917982), Hamed, Ahmed [Idaho National Laboratory]. 2025-07-31. Machine Learning for Well Log Analysis in Uranium Mining. https://www.osti.gov/biblio/3363246

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