Parallel Automatic History Matching Algorithm Using Reinforcement Learning
Name
energies-16-00860.pdf
Size
3.06 MB
Format
Adobe PDF
Checksum (MD5)
7c1436a648f9f65594a003a324c1d5cd
Author(s)
Alolayan, Omar S.
Alomar, Abdullah O.
Williams, John R.
Date Issued
January 12, 2023
Publisher
Multidisciplinary Digital Publishing Institute
Citation
Energies 16 (2): 860 (2023)
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.3390/en16020860
