Probabilistic Joint Recovery Method for CO$_2$ Plume Monitoring

TL;DR

This paper introduces a probabilistic method for joint seismic imaging in CO$_2$ storage, providing uncertainty quantification to enhance risk assessment in CCS projects.

Contribution

The paper presents the Probabilistic Joint Recovery Method (pJRM), which incorporates uncertainty estimation into seismic imaging for CO$_2$ plume monitoring, advancing beyond existing deterministic approaches.

Findings

pJRM effectively estimates posterior distributions across surveys.

Uncertainty quantification improves risk assessment in CCS.

Method enhances the reliability of CO$_2$ plume monitoring.

Abstract

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.