Paper Info
Title
Accelerating Physics-Based Simulations Using End-to-End Neural Network Proxies: An Application in Oil Reservoir Modeling.
Abstract
We develop a proxy model based on deep learning methods to accelerate the simulations of oil reservoirs--by three orders of magnitude--compared to industry-strength physics-based PDE solvers. This paper describes a new architectural approach to this task, accompanied by a thorough experimental evaluation on a publicly available reservoir model. We demonstrate that in a practical setting a speedup of more than 2000X can be achieved with an average sequence error of about 10% relative to the oil-field simulator. The proxy model is contrasted with a high-quality physics-based acceleration baseline and is shown to outperform it by several orders of magnitude. We believe the outcomes presented here are extremely promising and offer a valuable benchmark for continuing research in oil field development optimization. Due to its domain-agnostic architecture, the presented approach can be extended to many applications beyond the field of oil and gas exploration.
Year
DOI
Venue
2019
10.3389/fdata.2019.00033
Frontiers Big Data
Keywords
DocType
Volume
deep neural network,long short-term memory cell,physics-based simulation,reservoir model,reservoir simulation,sequence-to-sequence model,surrogate model
Journal
2
ISSN
Citations 
PageRank 
2624-909X
2
0.47
References 
Authors
0
6
Name
Order
Citations
PageRank
Jiri Navratil131431.36
Alan King220.47
Jesus Rios320.47
Georgios Kollias420.47
Ruben Rodriguez Torrado522.16
Andrés Codas620.47