  This paper contributes to the development and evaluation of a deep learning
workflow that accurately and efficiently predicts the temporal-spatial
evolution of pressure and CO2 plumes during injection and post-injection
periods of geologic CO2 sequestration (GCS) operations. Based on a Fourier
Neuron Operator, the deep learning workflow takes input variables or features
including rock properties, well operational controls and time steps, and
predicts the state variables of pressure and CO2 saturation. To further improve
the predictive fidelity, separate deep learning models are trained for CO2
injection and post-injection periods due the difference in primary driving
force of fluid flow and transport during these two phases. We also explore
different combinations of features to predict the state variables. We use a
realistic example of CO2 injection and storage in a 3D heterogeneous saline
aquifer, and apply the deep learning workflow that is trained from
physics-based simulation data and emulate the physics process. Through this
numerical experiment, we demonstrate that using two separate deep learning
models to distinguish post-injection from injection period generates the most
accurate prediction of pressure, and a single deep learning model of the whole
GCS process including the cumulative injection volume of CO2 as a deep learning
feature, leads to the most accurate prediction of CO2 saturation. For the
post-injection period, it is key to use cumulative CO2 injection volume to
inform the deep learning models about the total carbon storage when predicting
either pressure or saturation. The deep learning workflow not only provides
high predictive fidelity across temporal and spatial scales, but also offers a
speedup of 250 times compared to full physics reservoir simulation, and thus
will be a significant predictive tool for engineers to manage the long term
process of GCS.
