fdata-02-00033-g0004_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，旨在加速油气藏的模拟过程，其速度相较于业界强大的基于物理偏微分方程求解器提高了三个数量级。本文阐述了针对此任务的一种新的架构方法，即将模拟器建模为一个端到端的全黑盒，并伴随对公开可用油气藏模型进行的详尽实验评估。我们展示了在实际应用中，通过该模型可以达到超过2000倍的速度提升，同时相对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现变化。端到端的代理模型与包括上采样在内的多个基线进行了对比，并显示出其性能优于后者两个数量级。我们认为本文所呈现的结果极具前景，并为油气田开发优化方面的持续研究提供了宝贵的基准。鉴于其领域无关的架构，所提出的方法可扩展应用于油气勘探领域之外的众多应用。