2008
DOI: 10.1007/s10596-008-9084-z
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Special issue on multiscale methods for flow and transport in heterogeneous porous media

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Cited by 14 publications
(7 citation statements)
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“…The assessment of hydraulic properties of deep fractured rock masses has attracted attention in many fields, such as CO 2 sequestration [1,2], enhanced oil recovery [3,4], and groundwater use [5][6][7][8][9]. In tight rocks (i.e., granite and basalt), the permeability of fractures is much larger than that of the rock matrix, and as a result, the rock matrix is assumed to be impermeable [10][11][12][13].…”
Section: Introductionmentioning
confidence: 99%
“…The assessment of hydraulic properties of deep fractured rock masses has attracted attention in many fields, such as CO 2 sequestration [1,2], enhanced oil recovery [3,4], and groundwater use [5][6][7][8][9]. In tight rocks (i.e., granite and basalt), the permeability of fractures is much larger than that of the rock matrix, and as a result, the rock matrix is assumed to be impermeable [10][11][12][13].…”
Section: Introductionmentioning
confidence: 99%
“…Facies data and inference are then used to populate reservoir properties like porosity and permeability on a fine grid, known as a static model (or geomodel). The number of cells in the geomodel is typically too large to perform reservoir flow studies, so a dynamic model is built from either upscaling procedures [e.g., Durlofsky, 1991;Chen et al, 2003] or multiscale techniques [e.g., Arbogast, 2002;Jenny et al, 2003;Aarnes, 2004;Aarnes et al, 2005;Kippe et al, 2008;Juanes and Dub, 2008;Juanes and Tchelepi, 2008], which solve the global reservoir flow equations on a coarser grid. Rock-physics properties like porosity and permeability, and reservoir dynamics properties like During the past two decades, joint flow-geophysics inversion has received increased attention, especially in the context of hydrogeophysics [e.g., Hubbard and Rubin, 2000].…”
Section: Introductionmentioning
confidence: 99%
“…Within this framework, the focus has largely been on the development of better preconditioners for the linearized system, such as multiscale methods (Juanes and Tchelepi 2008;Nordbotten 2009), two-stage preconditioners (Cao et al 2005;Stüben et al 2007;Wallis et al 1985), sequential splitting strategies (Watts 1986), and domain decomposition (DD) methods (Quarteroni and Valli 1999;Smith et al 1996;Toselli and Widlund 2005). It should, however, be noted that some multiscale methods also take the full nonlinear problem into account, to a varying degree (Jenny et al 2006;Juanes and Tchelepi 2008). The aim of these and other linear preconditioners is to lower the number of linear iterations needed, thus increasing the computational efficiency of the code.…”
Section: Introductionmentioning
confidence: 99%