The Interplay between Experimental Data and Uncertainty Analysis in Quantifying CO₂ Trapping during Geological Carbon Storage

Numerical simulation is a widely used tool for studying CO₂ storage in porous media. It enables the representation of trapping mechanisms and CO₂ retention capacity. The complexity of the involved physicochemical phenomena necessitates multiphase flow, accurate fluid and rock property representation, and their interactions. These include CO₂ solubility, diffusion, relative permeabilities, capillary pressure hysteresis, and mineralization, all crucial in CO₂ trapping during carbon storage simulations. Experimental data is essential to ensure accurate quantification. However, due to the extensive data required, modeling under uncertainty is often needed to assess parameter impacts on CO₂ trapping and its interaction with geological properties like porosity and permeability. This work proposes a framework combining laboratory data and stochastic parameter distribution to map uncertainty in CO₂ retention over time. Published data representing solubility, residual trapping, and mineral trapping are used to calibrate prediction models. Geological property variations, like porosity and permeability, are coupled to quantify uncertainty. Results from a saline sandstone aquifer model demonstrate significant variation in CO₂ trapping, ranging from 17% (P10 estimate) to 56% (P90), emphasizing the importance of considering uncertainty in CO₂ storage projects. Quadratic response surfaces and Monte Carlo simulations accurately capture this uncertainty, resulting in calibrated models with an R-squared coefficient above 80%. In summary, this work provides a practical and comprehensive framework for studying CO₂ retention in porous media, addressing uncertainty through stochastic parameter distributions, and highlighting its importance in CO₂ storage projects.