Deep reinforcement learning for optimal well control in subsurface systems with uncertain geology. (arXiv:2203.13375v1 [physics.comp-ph] CROSS LISTED)

A general control policy framework based on deep reinforcement learning (DRL)
is introduced for closed-loop decision making in subsurface flow settings.
Traditional closed-loop modeling workflows in this context involve the repeated
application of data assimilation/history matching and robust optimization
steps. Data assimilation can be particularly challenging in cases where both
the geological style (scenario) and individual model realizations are
uncertain. The closed-loop reservoir management (CLRM) problem is formulated
here as a partially observable Markov decision process, with the associated
optimization …

arxiv geology learning physics reinforcement reinforcement learning systems uncertain