New Framework Optimizes Geosteering Decisions Under Geological Uncertainty

Geosteering, the process of guiding a well trajectory through unknown geological formations, requires continuous decision updates based on real-time measurements. A new framework has been developed to address this challenge by explicitly incorporating geological uncertainty into the decision-making process. The framework combines particle filtering to create a probabilistic understanding of the subsurface ahead of the drill bit with value-based reinforcement learning for sequential decision optimization. This allows for "belief-informed control" rather than relying on deterministic trajectory corrections. It evaluates three decision-making approaches: Approximate Dynamic Programming (ADP), Deep Q-learning, and a Dual Deep Reinforcement Learning (DRL) architecture. The framework is validated using an industrial geosteering simulator, assessing not only final well placement but also policy behavior through stability-oriented metrics, providing deeper operational insights into how decisions evolve with uncertainty.