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Influence of geological parameters on CO2 storage prediction in deep saline aquifer at industrial scale

Abstract : Consequences of uncertainties on geological parameters are examined using 2D models at large extension. Reduction of the uncertainties on predictions is also investigated, either because parameter's influence is negligible for the project design, or by showing for which parameters additional data will significantly increase the quality of prediction. TOUGH2/ECO2N is used to simulate the injection of millions tonnes of CO2 for the specific case of the Dogger aquifer (carbonates aquifer in the Paris Basin) with high lateral and vertical heterogeneities and for which few data are available. Studied parameters are spatial variability and correlation length of permeability, value of absolute permeability, pore compressibility, caprock permeability and relative permeability curves. Several numerical models of permeability are constructed: two uniform cases (two values of permeability) and 200 geostatistical initial realizations, which are modified according to the studied parameters. Results are compared in terms of propagation of pressure perturbations, injectivity (pressure in the vicinity of the well) and in terms of gas migration and dissolution. The results indicate, for the specific scale and values, that: (1) Pore compressibility, absolute value and spatial variability of permeability have the main influence on pressure propagation and injectivity. Relative permeability curves and correlation lengths have a weaker influence for the peak of pressure but tend to increase the variations for maximum/minimum cases. (2) Relative permeability curves and heterogeneities have a significant impact on prediction of gas dissolution and migration. At last, we also investigate the possibility to reduce the number of runs.
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Submitted on : Friday, January 18, 2013 - 12:20:46 PM
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  • HAL Id : hal-00777894, version 1


Sarah Bouquet, Dominique Bruel, Chantal de Fouquet. Influence of geological parameters on CO2 storage prediction in deep saline aquifer at industrial scale. TOUGH Symposium 2012, Sep 2012, Berkeley, United States. pp.NA. ⟨hal-00777894⟩



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