Enhancing CO₂ storage confidence through spatial history matching, AI-driven interpretation, and dynamic simulation

Accurately predicting CO₂ plume migration is critical for the success of carbon capture, utilization, and storage (CCUS) projects. However, there is an opportunity to further enhance existing workflows by incorporating time-lapse monitoring data alongside modelling assumptions.

In this study, a novel workflow was developed to integrate 4D seismic imaging with dynamic reservoir simulation, enabling spatial history matching and improved plume prediction using CMG GEM, Sharp Reflection, and Bluware AI tools.

Applied to the Sleipner CO₂ storage project, the approach demonstrated efficient plume identification using AI-driven seismic interpretation, direct integration of seismic plume geometry into simulation models, improved history matching using spatial comparison metrics, and quantification of plume growth for Area of Review (AoR) assessment.

Outcome: Matching plume geometry, and not just production data, fundamentally improves confidence in CO₂ storage predictions.