We present an algorithm addressing well-control reservoir optimization problems in realistic situations. Primarily, we consider cases where the reservoir simulator is treated as a black box and the derivative information is unavailable. Then, in addition to the input constraints that can be imposed in the simulator, we also consider constraints that can only be evaluated after the simulation has been run, e.g., total water pro...

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 (R...