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Parallel Automatic History Matching Algorithm Using Reinforcement Learning

2022-11-14 15:09:39
Omar S. Alolayan, Abdullah O. Alomar, John R. Williams
arXiv_AI
         
Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2211.07434

PDF

https://arxiv.org/pdf/2211.07434.pdf

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