Uncertainty Quantification and Optimization for the Scale-Up of Geological Carbon Storage

1. Reducing geological uncertainty through coupled flow-geomechanics based surrogate models and rejection sampling of CO2 plume prediction. Saleh, W. B., & Zhang, B. (2025). Geoenergy Science and Engineering, 246, 213652.
2. Uncertainty Quantification Through the Assimilation of CO2 Plume Size from 4D Seismic Survey. Saleh, W. Ben, and B. Zhang (2024). SPE Canadian Energy Technology Conference. SPE, 2024.

This project focuses on geological carbon storage (GCS) in deep sedimentary formations where limited geological information is available. Leveraging the available data for measurement, monitoring, and verification (MMV) program in ongoing GCS projects, an integrated data assimilation and optimization workflow will be developed to manage the geological uncertainty throughout the project life.

The proposed methodology will provide optimization for injection scheme and monitoring strategy in onshore deep saline formation with comparable geology. The injectivity and storage capacity are the two major metrics while also consider the potential containment risks, including caprock failure, re-activation of faults, and contamination of groundwater. The proposed workflow for uncertainty quantification and optimization is anticipated to provide guidance on the new or expended GCS projects with geological uncertainty.