Parallel Automatic History Matching Algorithm Using Reinforcement Learning
This work addresses the computational bottleneck in reservoir engineering by providing a parallelizable method for history matching, though it appears incremental as it applies existing RL techniques to a specific domain problem.
The authors tackled the history matching problem in reservoir simulation by reformulating it as a Markov Decision Process, enabling the use of reinforcement learning with a deep neural network agent to find multiple solutions in parallel, achieving significant speed-up.
Reformulating the history matching problem from a least-square mathematical optimization problem into a Markov Decision Process introduces a method in which reinforcement learning can be utilized to solve the problem. This method provides a mechanism where an artificial deep neural network agent can interact with the reservoir simulator and find multiple different solutions to the problem. Such formulation allows for solving the problem in parallel by launching multiple concurrent environments enabling the agent to learn simultaneously from all the environments at once, achieving significant speed up.