用深度学习预测多孔介质中矿物反应溶解，速度快4000倍。
A Deep-Learning Iterative Stacked Approach for Prediction of Reactive Dissolution in Porous Media
- 基于时序堆叠网络，同时捕捉空间与时间演化信息
- 相比传统模拟提速约10⁴倍，精度优于现有方法
- 适合碳封存、油气开采等需要快速模拟的场景
模拟多孔介质中固体矿物的反应性溶解在碳捕集与封存（CCS）、地热系统和油气开采等领域有广泛应用。传统直接数值模拟计算成本高，亟需更快更高效的替代方案。近年来，基于卷积神经网络（CNN）的深度学习方法被提出用于解决此问题，但大多仅能近似预测域内单一场（如速度场）。本文提出一种新型深度学习方法，结合时空信息，从输入状态序列中预测固定时间步长下的未来溶解状态。在数值模拟数据集上验证，该方法在速度和预测精度方面均优于现有技术，相比传统数值模拟提速约10⁴倍。
原文摘要 · Abstract (English)
Simulating reactive dissolution of solid minerals in porous media has many subsurface applications, including carbon capture and storage (CCS), geothermal systems and oil & gas recovery. As traditional direct numerical simulators are computationally expensive, it is of paramount importance to develop faster and more efficient alternatives. Deep-learning-based solutions, most of them built upon convolutional neural networks (CNNs), have been recently designed to tackle this problem. However, these solutions were limited to approximating one field over the domain (e.g. velocity field). In this manuscript, we present a novel deep learning approach that incorporates both temporal and spatial information to predict the future states of the dissolution process at a fixed time-step horizon, given a sequence of input states. The overall performance, in terms of speed and prediction accuracy, is demonstrated on a numerical simulation dataset, comparing its prediction results against state-of-the-art approaches, also achieving a speedup around $10^4$ over traditional numerical simulators.
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