AUTHOR=Navrátil Jiří , King Alan , Rios Jesus , Kollias Georgios , Torrado Ruben , Codas Andrés 
  
TITLE=Accelerating Physics-Based Simulations Using End-to-End Neural Network Proxies: An Application in Oil Reservoir Modeling
  
JOURNAL=Frontiers in Big Data
  
VOLUME=Volume 2 - 2019
  
YEAR=2019
  
URL=https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2019.00033
  
DOI=10.3389/fdata.2019.00033
  
ISSN=2624-909X
  
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.