Numerical simulation is an essential tool for many applications involving subsurface flow and transport, yet often suffers from computational challenges due to the multi-physics nature, highly non-linear governing equations, inherent parameter uncertainties, and the need for high spatial resolutions to capture multi-scale heterogeneity. We developed CCSNet, a deep-learning modeling suite that can act as an alternative to conventional numerical simulators for carbon capture and storage (CCS) problems well-represented by a 2D radial grid, for example, injection into an infinite acting saline formation with no or very small dip. CCSNet consists of a sequence of deep learning models producing all the outputs that a numerical simulator typically provides, including saturation distributions, pressure buildup, dry-out, fluid densities, mass balance, solubility trapping, and sweep efficiency. The results are 10${}^3$ to 10${}^4$ times faster than conventional numerical simulators. As an application of CCSNet illustrating the value of its high computational efficiency, we developed rigorous estimation techniques for the sweep efficiency and solubility trapping.

数值模拟是涉及地下流动和运输的许多应用的重要工具，但由于多物理性质、高度非线性控制方程、固有参数不确定性以及需要高空间分辨率来捕获多-规模异质性。我们开发了 CCSNet，这是一种深度学习建模套件，可以作为传统数值模拟器的替代方案，用于解决由 2D 径向网格很好地表示的碳捕获和储存 (CCS) 问题，例如，注入无限作用的盐水地层，没有或非常小的倾角。CCSNet 由一系列深度学习模型组成，这些模型产生数值模拟器通常提供的所有输出，包括饱和度分布、压力增加、干燥、流体密度、质量平衡、溶解性捕集和扫描效率。结果是 10${}^3$ 到 10${}^4$比传统的数值模拟器快几倍。作为说明其高计算效率价值的 CCSNet 的应用，我们开发了用于扫描效率和溶解度捕获的严格估计技术。