  A deep-learning-based surrogate model capable of predicting flow and
geomechanical responses in CO2 storage operations is presented and applied. The
3D recurrent R-U-Net model combines deep convolutional and recurrent neural
networks to capture the spatial distribution and temporal evolution of
saturation, pressure and surface displacement fields. The method is trained
using high-fidelity simulation results for 2000 storage-aquifer realizations
characterized by multi-Gaussian porosity and log-permeability fields. These
numerical solutions are expensive because the domain that must be considered
for the coupled problem includes not only the storage aquifer but also a
surrounding region, overburden and bedrock. The surrogate model is trained to
predict the 3D CO2 saturation and pressure fields in the storage aquifer, and
2D displacement maps at the Earth's surface. Detailed comparisons between
surrogate model and full-order simulation results for new (test-case)
storage-aquifer realizations are presented. The saturation, pressure and
surface displacement fields provided by the surrogate model display a high
degree of accuracy, both for individual test-case realizations and for ensemble
statistics. Finally, the the recurrent R-U-Net surrogate model is applied with
a rejection sampling procedure for data assimilation. Although the observations
consist of only a small number of surface displacement measurements,
significant uncertainty reduction in pressure buildup at the top of the storage
aquifer (caprock) is achieved.
