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

## TL;DR

This paper introduces a deep learning proxy model that accelerates oil reservoir simulations by over 2000 times, maintaining reasonable accuracy, and surpasses traditional physics-based methods, with potential for broader applications.

## Contribution

A novel neural network architecture for physics-based simulation acceleration, achieving unprecedented speedups and accuracy in oil reservoir modeling.

## Key findings

- Over 2000x speedup compared to traditional PDE solvers
- Average sequence error of about 10% relative to industry simulators
- Outperforms physics-based acceleration baselines by several orders of magnitude

## 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.