Decision-Driven Geosteering Under Uncertainty: A Unified Framework for Sequential Decision Optimization
arXiv:2606.17331v1 Announce Type: new Abstract: Geosteering requires navigating a well trajectory through an unknown geological configuration, while sequentially updating decisions based on indirect measurements acquired during drilling. This work presents an uncertainty-aware geosteering framework that tightly integrates particle filtering for probabilistic subsurface interpretation with value-based reinforcement learning for sequential decision-making. Geological uncertainty ahead of the drill bit is represented explicitly through a particle filter (PF), enabling belief-informed control rath
The increasing maturity of AI techniques, specifically reinforcement learning and particle filtering, is enabling more sophisticated applications in complex, data-poor environments.
This development represents a significant step towards autonomous decision-making in critical industrial processes, potentially leading to increased efficiency and safety, while reducing human error and operational costs.
Geosteering operations can become more adaptive and less reliant on real-time human interpretation, leading to optimized well trajectories and improved resource extraction results.
- · Oil & Gas Industry
- · AI/ML Software Developers
- · Geological Survey Companies
- · Energy Sector
- · Traditional Geosteering Consultants
- · Companies without AI adoption strategies
Enhanced efficiency and resource recovery in drilling operations due to more precise subsurface navigation.
Reduced environmental impact of drilling as optimal paths minimize waste and maximize extraction from fewer wells.
Extension of similar AI-driven sequential decision optimization frameworks to other complex subsurface operations like mineral exploration or carbon sequestration.
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Read at arXiv cs.LG