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

2025-09-16

AI Summary

Well log interpretation faces challenges including heterogeneous tool responses, high noise levels, and scarce labeled data, hindering cross-well generalization and stratigraphic awareness. To address these, we propose a geology-aware multitask foundation model that innovatively integrates geologically informed masked modeling with sequence-level contrastive learning, enabling unsupervised stratigraphic structure modeling and reusable geological vocabulary construction. Our method comprises three stages: (1) tokenization of well log segments, (2) self-supervised pretraining, and (3) multitask fine-tuning—jointly optimizing masked-token prediction and hierarchical contrastive objectives. Evaluated on porosity estimation (MSE = 0.0038) and lithology classification (accuracy = 78.10%), the model significantly outperforms established baselines, demonstrating superior cross-well generalization and emergent incorporation of geological priors without explicit supervision.

Technology Category

• Machine Learning: Multimodal Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Interpreting heterogeneous well-log responses with noisy signals and limited labels
• Developing geological foundation model for multi-task subsurface characterization
• Creating transferable backbone for cross-well geological interpretation tasks

Innovation

• Pretrained foundation model using multi-curve well logs
• Self-supervised pretraining with masked-token modeling and contrastive learning
• Multi-task adaptation with few-shot fine-tuning for geological interpretation