  We present an uncertainty-aware Digital Twin (DT) for geologic carbon storage
(GCS), capable of handling multimodal time-lapse data and controlling CO2
injectivity to mitigate reservoir fracturing risks. In GCS, DT represents
virtual replicas of subsurface systems that incorporate real-time data and
advanced generative Artificial Intelligence (genAI) techniques, including
neural posterior density estimation via simulation-based inference and
sequential Bayesian inference. These methods enable the effective monitoring
and control of CO2 storage projects, addressing challenges such as subsurface
complexity, operational optimization, and risk mitigation. By integrating
diverse monitoring data, e.g., geophysical well observations and imaged
seismic, DT can bridge the gaps between seemingly distinct fields like
geophysics and reservoir engineering. In addition, the recent advancements in
genAI also facilitate DT with principled uncertainty quantification. Through
recursive training and inference, DT utilizes simulated current state samples,
e.g., CO2 saturation, paired with corresponding geophysical field observations
to train its neural networks and enable posterior sampling upon receiving new
field data. However, it lacks decision-making and control capabilities, which
is necessary for full DT functionality. This study aims to demonstrate how DT
can inform decision-making processes to prevent risks such as cap rock
fracturing during CO2 storage operations.
