The release of GEMS 6.5

Geostatistics with SGeMS A Users Guide

Publisher: Cambridge University Press, ISBN:

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    ABSTRACT: Subsurface flow and solute transport simulations are performed using different scenarios of permeability fields generated from the sequential Gaussian simulation method (SGS), the multiple-point FILTERSIM algorithm and a new multiple-point wavelet-based simulation method (SWS). The SWS method is a multiple-point pattern-based simulation method which uses discrete wavelet transformation for the representation of geologic heterogeneity. For pattern-based simulation, patterns are generated by scanning a training image with a spatial template. The pattern classifications were performed after reducing the dimension of patterns by wavelet decomposition at the suitable scale and by taking only scaling components of wavelet decomposed patterns. The simulation is performed in a sequential manner by finding the best-matched class corresponding to the conditioning data and by randomly sampling a pattern from the best-matched class. The developed method is compared with two other multi-point simulation algorithms, FLTERSIM and SIMPAT. The comparative results revealed that the proposed method is computationally faster than the other two methods while the simulation maps are comparable. Numerical simulations of two flow problems are performed using SGS, SWS and FILTERSIM realizations. The numerical results show a superiority of the SWS method over SGS and FILTERSIM in terms of reproduction of the reference images main features, and agreement with flow and transport results obtained on reference images.

    Advances in Water Resources 04/2013; 54:22–37. · 2.41 Impact Factor

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    ABSTRACT: The Direct Sampling (DS) algorithm is a recently developed multiple-point statistical simulation technique. It directly scans the training image (TI) for a given data event instead of storing the training probability values in a catalogue prior to simulation. By using distances between the given data events and the TI patterns, DS allows to simulate categorical, continuous and multivariate problems. Benefiting from the wide spectrum of potential applications of DS, requires understanding of the user-defined input parameters. Therefore, we list the most important parameters and assess their impact on the generated simulations. Real case TIs are used, including an image of ice-wedge polygons, a marble slice and snow crystals, all three as continuous and categorical images. We also use a 3D categorical TI representing a block of concrete to demonstrate the capacity of DS to generate 3D simulations. First, a quantitative sensitivity analysis is conducted on the three parameters balancing simulation quality and CPU time: the acceptance threshold t, the fraction of TI to scan f and the number of neighbors n. Next to a visual inspection of the generated simulations, the performance is analyzed in terms of speed of calculation and quality of pattern reproduction. Whereas decreasing the CPU time by influencing t and n is at the expense of simulation quality, reducing the scanned fraction of the TI allows substantial computational gains without degrading the quality as long as the TI contains enough reproducible patterns. We also illustrate the quality improvement resulting from post-processing and the potential of DS to simulate bivariate problems and to honor conditioning data. We report a comprehensive...
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Cambridge University Press By Nicolas Remy - Applied Geostatistics with SGeMS: A User's Guide
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