  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 general-purpose
deep-learning modeling suite that can act as an alternative to conventional
numerical simulators for carbon capture and storage (CCS) problems where CO$_2$
is injected into saline aquifers in 2d-radial systems. 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.
