The Method of Volume Averaging

Author(s):  
Stephen Whitaker
Author(s):  
Michel Quintard ◽  
Stephen Whitaker

Most porous media of practical importance are hierarchical in nature; that is, they are characterized by more than one length-scale. When these length-scales are disparate, the hierarchical structure can be analyzed by the method of volume averaging (Anderson and Jackson, 1967; Marie, 1967; Slattery, 1967; Whitaker, 1967). In this approach, macroscopic quantities at a given length-scale are defined in terms of a boundary value problem that describes the phenomena at a smaller length-scale, and information is filtered from one scale to another by a series of volume and area integrals. Other methods, such as ensemble averaging (Matheron, 1965; Dagan, 1989) or homogenization theory (Bensoussan et al, 1978; Sanchez-Palencia, 1980), have been used to study hierarchical systems, and developments specific to the problems under consideration in this chapter can be found in Bourgeat (1984), Auriault (1987), Amaziane and Bourgeat (1988), and Sáez et al. (1989). The transformation from the Darcy scale to the large scale is a recurrent problem in reservoir and aquifer engineering. A detailed description of reservoir properties is available through geostatistical analysis (Journel, 1996) on a fine grid with a length-scale much smaller than the scale of the blocks in the reservoir simulator. “Effective” or “pseudo” properties are assigned to the coarse grid blocks, while the forms of the large-scale equations are required to be the same as those used at the Darcy scale (Coats et al., 1967; Hearn, 1971; Jacks et al., 1972; Kyte and Berry, 1975; Dake, 1978; Killough and Foster, 1979; Yokoyama and Lake, 1981; Kortekaas, 1983; Thomas, 1983; Kossack et al., 1990). A detailed discussion of the comparison between the several approaches is beyond the scope of this chapter; however, one can read Bourgeat et al. (1988) for an introductory comparison between the method of volume averaging and the homogenization theory, and Ahmadi et al. (1993) for a discussion of the various classes of pseudofunction theories.


1986 ◽  
Vol 41 (2) ◽  
pp. 227-235 ◽  
Author(s):  
G.H. Crapiste ◽  
E. Rotstein ◽  
S. Whitaker

1996 ◽  
Vol 16 (4) ◽  
pp. 650-658 ◽  
Author(s):  
Carolyn Cidis Meltzer ◽  
Jon Kar Zubieta ◽  
Jonathan M. Links ◽  
Paul Brakeman ◽  
Martin J. Stumpf ◽  
...  

Partial volume and mixed tissue sampling errors can cause significant inaccuracy in quantitative positron emission tomographic (PET) measurements. We previously described a method of correcting PET data for the effects of partial volume averaging on gray matter (GM) quantitation; however, this method may incompletely correct GM structures when local tissue concentrations are highly heterogeneous. We have extended this three-compartment algorithm to include a fourth compartment: a GM volume of interest (VOI) that can be delineated on magnetic resonance (MR) imaging. Computer simulations of PET images created from human MR data demonstrated errors of up to 120% in assigned activity values in small brain structures in uncorrected data. Four-compartment correction achieved full recovery of a wide range of coded activity in GM VOIs such as the amygdala, caudate, and thalamus. Further validation was performed in an agarose brain phantom in actual PET acquisitions. Implementation of this partial volume correction approach in [18F]fluorodeoxyglucose and [11C]-carfentanil PET data acquired in a healthy elderly human subject was also performed. This newly developed MR-based partial volume correction algorithm permits the accurate determination of the true radioactivity concentration in specific structures that can be defined by MR by accounting for the influence of heterogeneity of GM radioactivity.


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