  As the global deployment of carbon capture and sequestration (CCS) technology
intensifies in the fight against climate change, it becomes increasingly
imperative to establish robust monitoring and detection mechanisms for
potential underground CO2 leakage, particularly through pre-existing or induced
faults in the storage reservoir's seals. While techniques such as history
matching and time-lapse seismic monitoring of CO2 storage have been used
successfully in tracking the evolution of CO2 plumes in the subsurface, these
methods lack principled approaches to characterize uncertainties related to the
CO2 plumes' behavior. Inclusion of systematic assessment of uncertainties is
essential for risk mitigation for the following reasons: (i) CO2 plume-induced
changes are small and seismic data is noisy; (ii) changes between regular and
irregular (e.g., caused by leakage) flow patterns are small; and (iii) the
reservoir properties that control the flow are strongly heterogeneous and
typically only available as distributions. To arrive at a formulation capable
of inferring flow patterns for regular and irregular flow from well and seismic
data, the performance of conditional normalizing flow will be analyzed on a
series of carefully designed numerical experiments. While the inferences
presented are preliminary in the context of an early CO2 leakage detection
system, the results do indicate that inferences with conditional normalizing
flows can produce high-fidelity estimates for CO2 plumes with or without
leakage. We are also confident that the inferred uncertainty is reasonable
because it correlates well with the observed errors. This uncertainty stems
from noise in the seismic data and from the lack of precise knowledge of the
reservoir's fluid flow properties.
