Literature DB >> 15457789

Modeling multiscale heterogeneity and aquifer interconnectivity.

Christopher J Proce1, Robert W Ritzi, David F Dominic, Zhenxue Dai.   

Abstract

A number of methods involving indicator geostatistics were combined in a methodology for characterizing and modeling multiscale heterogeneity. The methodology circumvents sources of bias common in data from borehole logs. We applied this methodology to the complex heterogeneity within a regional system of buried valley aquifers, which occurs in the western glaciated plains of North America and includes the Spiritwood Aquifer. The region is conceptualized as having a hierarchical organization with three facies assemblage types (large-scale heterogeneity) and two facies types within each assemblage (small-scale heterogeneity). We statistically characterized the sedimentary architecture at both scales, formulated indicator correlation models from those characterizations, and used the models to simulate the architecture in a multiscale realization. We focused on the interconnectivity of units creating higher-permeability pathways. Higher-permeability pathways span the realization even though the proportion of higher-permeability facies is less than the percolation threshold. Thus, geologic structures as represented in the indicator correlation models create interconnectivity above that which would occur if the higher-permeability facies were randomly placed. This amount of interconnection among higher-permeability facies within the multiscale realization is consistent with that suggested in prior hydraulic and geochemical studies of the regional system.

Mesh:

Year:  2004        PMID: 15457789     DOI: 10.1111/j.1745-6584.2004.tb02720.x

Source DB:  PubMed          Journal:  Ground Water        ISSN: 0017-467X            Impact factor:   2.671


  1 in total

1.  Improved estimation of hydraulic conductivity by combining stochastically simulated hydrofacies with geophysical data.

Authors:  Lin Zhu; Huili Gong; Yun Chen; Xiaojuan Li; Xiang Chang; Yijiao Cui
Journal:  Sci Rep       Date:  2016-03-01       Impact factor: 4.379

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.