Parallel Automatic History Matching Algorithm Using Reinforcement Learning

Reformulating the history matching problem from a least-square mathematical optimization into Markov Decision Process introduces method in which reinforcement learning can be utilized to solve problem. This provides mechanism where an artificial deep neural network agent interact with reservoir simulator and find multiple different solutions Such formulation allows for solving parallel by launching concurrent environments enabling learn simultaneously all at once, achieving significant speed up.