WLFM: A Well-Logs Foundation Model for Multi-Task and Cross-Well Geological Interpretation

• URL: http://arxiv.org/abs/2509.18152v1
• Date: Tue, 16 Sep 2025 14:59:45 GMT
• Title: WLFM: A Well-Logs Foundation Model for Multi-Task and Cross-Well Geological Interpretation
• Authors: Zhenyu Qi, Qing Yu, Jichen Wang, Yun-Bo Zhao, Zerui Li, Wenjun Lv,
• Abstract summary: We propose WLFM, a foundation model pretrained on multi-curve logs from 1200 wells.<n> WLFM consistently outperforms state-of-the-art baselines, achieving 0.0041 MSE in porosity estimation and 74.13% accuracy in lithology classification.<n>These results establish WLFM as a scalable, interpretable, and transferable backbone for geological AI, with implications for multi-modal integration of logs, seismic, and textual data.
• Score: 12.858491655938026
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Well-log interpretation is fundamental for subsurface characterization but remains challenged by heterogeneous tool responses, noisy signals, and limited labels. We propose WLFM, a foundation model pretrained on multi-curve logs from 1200 wells, comprising three stages: tokenization of log patches into geological tokens, self-supervised pretraining with masked-token modeling and stratigraphy-aware contrastive learning, and multi-task adaptation with few-shot fine-tuning. WLFM consistently outperforms state-of-the-art baselines, achieving 0.0041 MSE in porosity estimation and 74.13\% accuracy in lithology classification, while WLFM-Finetune further improves to 0.0038 MSE and 78.10\% accuracy. Beyond predictive accuracy, WLFM exhibits emergent layer-awareness, learns a reusable geological vocabulary, and reconstructs masked curves with reasonable fidelity, though systematic offsets are observed in shallow and ultra-deep intervals. Although boundary detection is not explicitly evaluated here, clustering analyses suggest strong potential for future extension. These results establish WLFM as a scalable, interpretable, and transferable backbone for geological AI, with implications for multi-modal integration of logs, seismic, and textual data.

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