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Geological storage of CO2: Application, feasibility and efficiency of global sensitivity analysis and risk assessment using the arbitrary polynomial chaos


Geological storage of CO2 is a proposed interim solution for mitigating the climate change. Modeling CO2 storage is accompanied by huge geological uncertainties and excessive computational demands. However, the considerable costs and potential hazards of the technique require feasibility studies to assess all possible risks. This makes computationally efficient methods for sensitivity analysis, uncertainty quantification and probabilistic risk assessment indispensable.

Our goal is to demonstrate the application and feasibility of the arbitrary polynomial chaos expansion (aPC) for these tasks under realistic conditions. We model a typical CO2 injection scenario in realistic geological realizations of a shallow marine deposit. Our scenario features uncertain parameters that control the structure of geological heterogeneities, including the density of barriers, the aggradation angle, fault transmissibility and regional groundwater effects. The aPC approximates the models by a polynomial-based response surface to speed up the involved statistical analysis of an otherwise expensive simulation tool.

We demonstrate how such an analysis can guide further exploration and the design process of finding suitable injection rates. Our case study demonstrates clearly that the aPC is an efficient, feasible and hence valuable approach in this context, and we strongly encourage its future use. A key advantage of the aPC over more conventional polynomial chaos methods is the flexibility to work with arbitrary probability distributions of uncertain parameters. From our featured parameters, we found the aggradation angle to be the most and the regional groundwater effect to be the least influential one. To the best of our knowledge, this is the first analysis of structural parameters for geological heterogeneities in the CO2 context and within a probabilistic setting.


Academic article




  • Seyed Meisam Ashraf
  • Sergey Oladyshkin
  • Wolfgang Nowak


  • University of Bergen
  • SINTEF Digital / Mathematics and Cybernetics
  • University of Stuttgart



Published in

International Journal of Greenhouse Gas Control








704 - 719

View this publication at Cristin