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Abstract

Summary

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.

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/content/papers/10.3997/2214-4609.202576017
2025-11-10
2026-02-11
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References

  1. Hall, B. [2016] Facies classification using machine learning. The Leading Edge, 35(10), 906–909.
    [Google Scholar]
  2. Lamont, M.G., Thompson, T.A. and Bevilacqua, C. [2008] Drilling success as a result of probabilistic lithology and fluid prediction: A case study in the Carnarvon Basin, WA. APPEA Journal, 48(1), 273–288.
    [Google Scholar]
  3. Lin, T.-Y.et al. [2020] Focal loss for dense object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(2), 318–327.
    [Google Scholar]
  4. Prajapati, R. et al. [2024] Machine learning assisted lithology prediction using geophysical logs: A case study from Cambay basin. J Earth Syst Sci, 133, 108.
    [Google Scholar]
  5. Vaswani, A. et al. [2017] Attention is all you need. Advances in Neural Information Processing Systems, 30, 5998–6008.
    [Google Scholar]
  6. Xie, D. et al. [2024]. A Transformer and LSTM- Based Approach for Blind Well Lithology Prediction. Symmetry, 16(5), 616.
    [Google Scholar]
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