Advancing Geological Carbon Storage Monitoring With 3d Digital Shadow Technology

TL;DR

This paper presents a 3D Digital Shadow framework that enhances geological carbon storage monitoring by integrating advanced seismic imaging, machine learning, and reservoir modeling to improve accuracy and risk assessment.

Contribution

It extends existing 2D Digital Shadow methods to 3D, incorporating seismic data and uncertainty modeling for more precise CO2 plume tracking.

Findings

3D monitoring improves spatial accuracy of CO2 migration detection

Machine learning techniques effectively update digital models with field data

Enhanced uncertainty quantification aids in risk mitigation

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.