RamySaleem/Machine-Predict-Lithologies-Using-Wireline-logs
To identify lithologies, geoscientists use subsurface data such as wireline logs and petrophysical data. However, this process is often tedious, repetitive, and time-consuming. This project aims to use machine learning techniques to predict lithology from petrophysical logs, which are direct indicators of lithology.
Implements supervised classification algorithms (scikit-learn, XGBoost) on multi-feature wireline log datasets from offshore Norwegian wells, processing 18+ petrophysical parameters (GR, RHOB, NPHI, DTC, etc.) to classify five lithology types. Trains on LAS-formatted well log data spanning diverse geological formations from Permian evaporites to Brent delta facies, leveraging open-source Python stack (NumPy, Pandas, Matplotlib) for feature engineering and model evaluation.
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MIT
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Feb 22, 2025
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