Advancing Geological Carbon Storage Monitoring With 3d Digital Shadow Technology

TL;DR

This work addresses the challenge of monitoring subsurface CO2 migration in Geological Carbon Storage (GCS) by developing a 3D Digital Shadow assimilating 4D seismic data. It extends prior 2D uncertainty-aware approaches by formulating 3D plume dynamics with $x_k = M_k(x_{k-1}, κ_k)$, $κ_k ∼ p(κ)$, and observations $y_k = H_k(x_k) + ε_k$, and by learning the posterior with conditional Normalizing Flows to estimate $p(x_k|y_{1:k})$. A synthetic Compass North Sea case demonstrates that 3D CNF-based posteriors improve 3D plume shape/size fidelity and provide well-calibrated uncertainty when conditioned on 4D seismic data. This framework supports robust decision-making and risk mitigation in GCS and paves the way toward a 3D Digital Twin for optimized CO2 storage operations.

Abstract

Geological Carbon Storage (GCS) is a key technology for achieving global climate goals by capturing and storing CO2 in deep geological formations. Its effectiveness and safety rely on accurate monitoring of subsurface CO2 migration using advanced time-lapse seismic imaging. A Digital Shadow framework integrates field data, including seismic and borehole measurements, to track CO2 saturation over time. Machine learning-assisted data assimilation techniques, such as generative AI and nonlinear ensemble Bayesian filtering, update a digital model of the CO2 plume while incorporating uncertainties in reservoir properties. Compared to 2D approaches, 3D monitoring enhances the spatial accuracy of GCS assessments, capturing the full extent of CO2 migration. This study extends the uncertainty-aware 2D Digital Shadow framework by incorporating 3D seismic imaging and reservoir modeling, improving decision-making and risk mitigation in CO2 storage projects.

Figures (6)

• Figure 1: 4D seismic observation
• Figure 2: Unknown ground truth plume
• Figure 3: Training plume
• Figure 4: Assimilated plume
• Figure 5: Estimated plume error