Name
Title | ||
|---|---|---|
Accelerating Physics-Based Simulations Using 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 | Venue | DocType |
|---|---|---|
2019 | CoRR | Journal |
Volume | Citations | PageRank |
abs/1906.01510 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jirí Navrátil | 1 | 0 | 0.34 |
| Alan King | 2 | 0 | 0.34 |
| Jesus Rios | 3 | 0 | 0.34 |
| Georgios Kollias | 4 | 4 | 2.12 |
| Ruben Rodriguez Torrado | 5 | 2 | 2.16 |
| Andres Codas | 6 | 0 | 0.34 |