Accelerating Physics-Based Simulations Using End-to-End Neural Network Proxies: An Application in Oil Reservoir Modeling.

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 modeling a simulator as an end-to-end black box, 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 simulator.