🤖 AI Summary
This work addresses the limitations of conventional sliding-window approaches in capturing the global geological context of well-log data, which often leads to misaligned stratigraphic boundaries and violations of geological sequence. To overcome this, we propose LithoFormer—the first application of a full-sequence Seq2Seq Transformer architecture to stratigraphic inference. Our model integrates a channel-agnostic PatchTST backbone, rotary position embeddings (RoPE), and a decoupled multi-task head to jointly predict lithological zones and boundary probabilities. Furthermore, we incorporate geological prior constraints into the loss function to enforce stratigraphic consistency. Evaluated on three real-world datasets, LithoFormer reduces the median boundary error by 90%, entirely eliminates sequence violations, and cuts expert annotation effort by 80%, thereby substantially enhancing the reliability and scalability of large-scale subsurface modeling.
📝 Abstract
Accurate geological characterization of subsurface reservoirs from well log data is essential to support projects such as carbon capture and storage (CCS), geothermal development, and extraction of natural resources. Existing automated techniques for geological characterization primarily use sliding-window classification, which limits their ability to understand broader geological contexts, often leading to misaligned formation layers. To overcome these limitations, we introduce LithoFormer, a robust framework for stratigraphic inference using a Seq2Seq transformer model that ingests entire multivariate well logs in a single pass. The framework utilizes a channel-independent PatchTST backbone enhanced with rotary positional embeddings (RoPE) to capture long-range geological dependencies across entire multivariate well logs. A decoupled multi-task head is employed to jointly predict geological zonation and precise boundary probabilities, while a geology-informed loss function enforces physical constraints such as the Law of Superposition. Validated and deployed on three real-world datasets, LithoFormer demonstrates a 90% reduction in median boundary error and eliminates stratigraphic order violations compared to traditional sliding-window baselines. It also achieves a 80% reduction in manual expert labor and eliminates stratigraphic inconsistencies, providing a scalable and reliable solution for large-scale subsurface modeling.