Parallel Automatic History Matching Algorithm Using Reinforcement Learning

Omar S. Alolayan (), Abdullah O. Alomar and John R. Williams
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Omar S. Alolayan: Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Abdullah O. Alomar: Electrical Engineering & Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
John R. Williams: Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA

Energies, 2023, vol. 16, issue 2, 1-27

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 a 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.

Keywords: artificial intelligence; reinforcement learning; parallel actor–critic; history matching; reservoir simulation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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