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.

本论文基于深度学习方法开发了一个代理模型，以加速油藏模拟，相较于现有业界物理学基础偏微分方程求解器提高了三个数量级，并进行了深入的实验评估。我们证明，在实际环境中，可以实现超过2000倍的加速，平均序列误差约为10％相对于油田模拟器。该代理模型与高质量的基于物理学的加速基线进行对比，并显示其性能优于其几个数量级。本论文的结果非常有前途，并为持续开展油田开发优化方面的研究提供了有价值的基准。

利用神经网络代理加速基于物理的仿真：油藏建模应用