Probabilistic Joint Recovery Method for CO$_2$ Plume Monitoring
This work addresses uncertainty quantification for CCS monitoring, which is incremental as it builds on existing seismic imaging methods.
The paper tackles the challenge of predicting fluid flow patterns in Carbon Capture and Storage (CCS) due to uncertainties in CO2 plume dynamics and reservoir properties, proposing the Probabilistic Joint Recovery Method (pJRM) to provide uncertainty information for improved risk assessment.
Reducing CO$_2$ emissions is crucial to mitigating climate change. Carbon Capture and Storage (CCS) is one of the few technologies capable of achieving net-negative CO$_2$ emissions. However, predicting fluid flow patterns in CCS remains challenging due to uncertainties in CO$_2$ plume dynamics and reservoir properties. Building on existing seismic imaging methods like the Joint Recovery Method (JRM), which lacks uncertainty quantification, we propose the Probabilistic Joint Recovery Method (pJRM). By estimating posterior distributions across surveys using a shared generative model, pJRM provides uncertainty information to improve risk assessment in CCS projects.