fdata-02-00033-g0005_Accelerating Physics-Based Simulations Using End-to-End Neural Network Proxies: An Application in Oil Reservoir Modeling.tif

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. The task involves varying well locations and varying geological realizations. The end-to-end proxy model is contrasted with several baselines, including upscaling, and is shown to outperform these by two 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.

本研究开发了一种基于深度学习方法的代理模型，旨在加速油田储层模拟的计算过程，其速度比业界主流的基于物理偏微分方程求解器快三个数量级。本文详细阐述了针对此任务的一种新型架构方法，即将模拟器建模为端到端的黑盒，并伴随着对公开可用的储层模型进行的全面实验评估。我们证实，在实际应用场景中，可以实现超过2000倍的加速，同时与模拟器的平均序列误差约为10%。该任务涉及井位变化和地质实体的变化。端到端的代理模型与包括尺度扩展在内的多个基线进行了对比，并显示出优于这两个数量级的性能。我们认为，所呈现的成果极具前景，并为油田开发优化的持续研究提供了宝贵的基准。由于其领域无关的架构，所提出的方法可以扩展到油气勘探领域之外的其他许多应用中。