Decision-driven geosteering under uncertainty: A unified framework for sequential decision optimization

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 rather than deterministic trajectory correction. The framework couples PF belief updates with belief-informed decision policies and evaluates three decision-making options that operate under identical uncertainty representations: an interpretable Approximate Dynamic Programming (ADP) scheme, a Deep Q-learning baseline, and a Dual Deep Reinforcement Learning (Dual DRL) architecture trained with a target Q-network scheme for stability, using a dueling (value/advantage) decomposition for Q-value parameterization. Beyond final placement performance, we assess policy behavior using a stability-oriented metric that quantifies steering smoothness over time, providing additional operational insight into how decision policies respond as uncertainty evolves. Using identical evaluation wells, operational limits, and reward definitions across methods, the experiments provide a controlled evaluation of how alternative decision policies behave throughout the drilling process, rather than judging them solely by the resulting well trajectories. Moreover, the framework is integrated with an API of an industrial geosteering simulator, and a representative case is validated end-to-end under realistic measurement noise and drilling constraints.