Advancing Geological Carbon Storage Monitoring With 3d Digital Shadow Technology
This research addresses the problem of accurate monitoring of CO2 storage for stakeholders involved in GCS projects, which is crucial for achieving global climate goals.
The study tackled the problem of monitoring subsurface CO2 migration in Geological Carbon Storage (GCS) by developing a 3D Digital Shadow framework, resulting in enhanced spatial accuracy of GCS assessments. The 3D approach improves upon 2D methods by capturing the full extent of CO2 migration.
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.