Computational Geosciences

, Volume 20, Issue 6, pp 1251–1267 | Cite as

Post-injection trapping of mobile CO 2 in deep aquifers: Assessing the importance of model and parameter uncertainties

  • JC. ManceauEmail author
  • J. Rohmer
Original Paper


Predicting the fate of the injected CO2 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 CO2 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 CO2 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 CO2. 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 CO2 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.


CO2 storage Post-injection Mobile CO2 trapping Global sensitivity analysis ACOSSO metamodelling 

Mathematics Subject Classification (2010)

49Q12 Calculus of variations and optimal control optimization/sensitivity analysis 68U20 Computer science/simulation 62K20 Statistics/response surface designs 


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.BRGMOrléansFrance

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