High-Precision Geosteering via Reinforcement Learning and Particle Filters

Geosteering, a key component of drilling operations, traditionally involves manual interpretation of various data sources such as well-log data. This introduces subjective biases and inconsistent procedures. Academic attempts to solve geosteering decision optimization with greedy optimization and Approximate Dynamic Programming (ADP) showed promise but lacked adaptivity to realistic diverse scenarios. Reinforcement learning (RL) offers a solution to these challenges, facilitating optimal decision-making through reward-based iterative learning. State estimation methods, e.g., particle filter (PF), provide a complementary strategy for geosteering decision-making …

academic biases cs.ai cs.lg data data sources decision diverse dynamic filters interpretation key log data operations optimization physics.geo-ph precision programming reinforcement reinforcement learning solve via