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Identifying lithofacies from subsurface well data is a foundational task in geoscience, but traditional workflows commonly rely on manual interpretation, which limits scalability and consistency across large datasets. Machine learning approaches can provide fast, reproducible alternatives to manual interpretation.
We train a transformer-based neural network to classify lithology classes in wireline well logs using nine commonly available measurement curves. Despite only training on end-member intervals, our model shows a high level of agreement with traditional petrophysical interpretation results when generalised to entire wells. Because our training data is derived from end-member picks, additional annotations can be produced by interpreters relatively quickly for the purposes of fine-tuning for particular wells or basins.