  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 based on
online information. We integrate an RL-based geosteering with PF to address
realistic geosteering scenarios. Our framework deploys PF to process real-time
well-log data to estimate the location of the well relative to the
stratigraphic layers, which then informs the RL-based decision-making process.
We compare our method's performance with that of using solely either RL or PF.
Our findings indicate a synergy between RL and PF in yielding optimized
geosteering decisions.
