Parallel Automatic History Matching Algorithm Using Reinforcement Learning

2022/11/14 by Omar S. Alolayan, Alolayan, Omar S., Abdullah O. Alomar +3
Engineering · Mathematics · #Algorithm #Artificial Intelligence (cs.AI) #Artificial intelligence #Artificial neural network #Computer science #FOS: Computer and information sciences #Machine Learning (cs.LG) #Markov decision process #Markov process #Matching (statistics) #Mathematical optimization #Mathematics #Optimization problem #Process (computing) #Q-learning #Reinforcement learning #Reservoir Engineering and Simulation Methods #Water resources management and optimization

paper · pdf · doi:10.48550/arxiv.2211.07434

published in arXiv (Cornell University) (Cornell University)

openalex publication_date 2022/11/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06

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.

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