Upscaling of solute transport in disordered porous media by wavelet transformations

作者：Moslehi Mahsa; de Barros Felipe P J; Ebrahimi Fatemeh; Sahimi Muhammad

来源：Advances in Water Resources, 2016, 96: 180-189.

DOI：10.1016/j.advwatres.2016.07.013

• Flow and transport
• Heterogeneous porous media
• Upscaling wavelet transform
• Computational efficiency

Modeling flow and solute transport in large-scale (e.g. on the order of 10(3) m) heterogeneous porous media involves substantial computational burden. A common approach to alleviate the problem is to utilize an upscaling method that generates models that require less intensive computations. The method must also preserve the important properties of the spatial distribution of the hydraulic conductivity (K) field. We use an upscaling method based on the wavelet transformations (WTs) that coarsens the computational grid based on the spatial distribution of K. The technique is applied to a porous formation with broadly distributed and correlated K values, and the governing equation for solute transport in the formation is solved numerically. The WT upscaling preserves the resolution of the initial highly-resolved computational grid in the high K zones, as well as that of the zones with sharp contrasts between the neighboring K, whereas the low-K zones are averaged out. To demonstrate the accuracy of the method, we simulate fluid flow and nonreactive solute transport in both the high-resolution and upscaled grids, and compare the concentration profiles and the breakthrough times. The results indicate that the WT upscaling of a K field generates non-uniform upscaled grids with a number of grid blocks that on average is about two percent of the number of the blocks in the original high-resolution computational grids, while the concentration profiles, the breakthrough times and the second moment of the concentration distribution, computed for both models, are virtually identical. A systematic parametric study is also carried out in order to investigate the sensitivity of the method to the broadness of the K field, the nature of the correlations in the field (positive versus negative), and the size of the computational grid. As the broadness of the K field and the size of the computational domain increase, better agreement between the results for the high -resolution and upscaled models is obtained.

• 出版日期2016-10