Post-injection trapping of mobile CO2 in deep aquifers: Assessing the importance of model and parameter uncertainties

• Published in: Computational geosciences Vol. 20; no. 6; pp. 1251 - 1267
• Main Authors: Manceau, JC, Rohmer, J.
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 2016
• Subjects: Earth and Environmental Science; Earth Sciences; Geotechnical Engineering & Applied Earth Sciences; Hydrogeology; Mathematical Modeling and Industrial Mathematics; Original Paper; Soil Science & Conservation; ACOSSO metamodelling; 49Q12 Calculus of variations and optimal control; optimization/sensitivity analysis; Global sensitivity analysis; Post-injection; storage; CO; trapping; Mobile CO; 68U20 Computer science/simulation; 62K20 Statistics/response surface designs
• ISSN: 1420-0597; 1573-1499
• DOI: 10.1007/s10596-016-9588-x

Predicting the fate of the injected CO 2 is crucial for the safety of carbon storage operations in deep saline aquifers: especially the evolution of the position, the spreading and the quantity of the mobile CO 2 plume during and after the injection has to be understood to prevent any loss of containment. Fluid flow modelling is challenging not only given the uncertainties on subsurface formation intrinsic properties (parameter uncertainty) but also on the modelling choices/assumptions for representing and numerically implementing the processes occurring when CO 2 displaces the native brine (model uncertainty). Sensitivity analysis is needed to identify the group of factors which contributes the most to the uncertainties in the predictions. In this paper, we present an approach for assessing the importance of model and parameter uncertainties regarding post-injection trapping of mobile CO 2 . This approach includes the representation of input parameters, the choice of relevant simulation outputs, the assessment of the mobile plume evolution with a flow simulator and the importance ranking for input parameters. A variance-based sensitivity analysis is proposed, associated with the ACOSSO-like meta-modelling technique to tackle the issues linked with the computational burden posed by the use of long-running simulations and with the different types of uncertainties to be accounted for (model and parameter). The approach is tested on a potential site for CO 2 storage in the Paris basin (France) representative of a project in preliminary stage of development. The approach provides physically sound outcomes despite the challenging context of the case study. In addition, these outcomes appear very helpful for prioritizing the future characterisation efforts and monitoring requirements, and for simplifying the modelling exercise.