  DMDc and DMDspc successfully expedite the reconstruction and forecasting of
CO2 fluid flow with acceptable accuracy margins, aiding in the rapid
optimization of geological CO2 storage forecast and its optimization. DMDc and
DMDspc models were trained with weekly, monthly, and yearly ECLIPSE 300
simulation pressure and CO2 saturation fields. The domain of interest is a
large scale highly heterogeneous offshore reservoir model with over 100000
cells. Snapshot reconstruction significantly reduced simulation times from
several hours to mere minutes. DMDspc reduced the number of DMD modes for
pressure without losing accuracy while sometimes even improving accuracy. Two
operation cases were considered: 1. CO2 injection, 2. CO2 injection and water
production for pressure maintenance. For pressure, DMDspc achieved a slightly
higher than DMDc average error by removing several modes. On the other hand,
DMDspc showed limited success in reducing modes for CO2 saturation. The
forecast performance of DMD models was evaluated using percent change error,
mean absolute error, and Pearson R correlation coefficient metrics. Almost all
DMD pressure models managed to successfully forecast pressure fields while a
smaller number of DMD models managed to forecast CO2 saturation. While forecast
errors have a considerable range, especially for saturation, only DMD models
with errors below 5% PCE for pressure or 0.01 MAE for saturation were
considered acceptable for geological CO2 storage optimization. Optimized CO2
injection and water production amounts were consistent across selected DMD
models and all time scales. The DMDspc monitored cells approach, which only
reconstructs the monitored during optimization cells, reduced even further
optimization time while providing consistent results with the optimization that
used full snapshot reconstruction.
