الفهرس 
The use of connectivity clusters and percolation concepts in stochastic modeling of highly heterogeneous porous media
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Abstract
The main objective of this research is to present a sampling framework to reduce the number of the required Monte Carlo realizations utilizing the connectivity properties of the hydraulic conductivity distributions in three dimensional domains.This objective is achieved through studying the influence of the connectivity and preferential flow paths on transport modeling in highly heterogonous media and testing different sampling techniques to select a compact yet representative sample. Applying different sampling techniques together with several indicators suggested that a compact sample representing only 10% of the total number of realizations can be used to produce results close to the results of the full set of realizations. In addition to that, Artificial Neural Network (ANN) is utilized to predict the transport outputs using the clusters connectivity indicators.The results are in good match with the traditional transport modelling using MT3DMS.The developed sampling frame work is applied to the famous MADE-2 site experiment.The selected compact sample succeeded to match the observed plume concentrations along the simulated domain