Large-scale implementation of geological CO2 sequestration requires quantification of risk and leakage potential. One potentially important leakage pathway for the injected CO2 involves existing oil and gas wells. Wells are particularly important in North America, where more than a century of drilling has created millions of oil and gas wells. Models of CO 2 injection and leakage will involve large uncertainties in parameters associated with wells, and therefore a probabilistic framework is required. These models must be able to capture both the large-scale CO 2 plume associated with the injection and the small-scale leakage problem associated with localized flow along wells. Within a typical simulation domain, many hundreds of wells may exist. One effective modeling strategy combines both numerical and analytical models with a specific set of simplifying assumptions to produce an efficient numerical-analytical hybrid model. The model solves a set of governing equations derived by vertical averaging with assumptions of a macroscopic sharp interface and vertical equilibrium. These equations are solved numerically on a relatively coarse grid, with an analytical model embedded to solve for wellbore flow occurring at the sub-gridblock scale. This vertical equilibrium with sub-scale analytical method (VESA) combines the flexibility of a numerical method, allowing for heterogeneous and geologically complex systems, with the efficiency and accuracy of an analytical method, thereby eliminating expensive grid refinement for sub-scale features. Through a series of benchmark problems, we show that VESA compares well with traditional numerical simulations and to a semi-analytical model which applies to appropriately simple systems. We believe that the VESA model provides the necessary accuracy and efficiency for applications of risk analysis in many CO2 sequestration problems. © 2009 Springer Science+Business Media B.V.
|Original language||English (US)|
|Number of pages||13|
|State||Published - Apr 23 2009|
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUS-F1-026-41
Acknowledgements: This work was funded in part by a grantto from BP through funding of the Carbon Mitigation Initiativeat Princeton University, and the King Abdullah University ofScience and Technology through a research fellowship for S.Gasda (Award no. KUS-F1-026-41)
This publication acknowledges KAUST support, but has no KAUST affiliated authors.