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Reconstruction of porous media using multiple-point statistics with data conditioning

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Abstract

The reconstruction of porous media is of great importance in predicting fluid transport properties, which are widely used in various fields such as catalysis, oil recovery, medicine and aging of building materials. The real three-dimensional structural data of porous media are helpful to describe the irregular topologic structures of porous media. By using multiple-point statistics (MPS) to extract the characteristics of real porous media acquired from micro computed tomography (micro-CT) scanning, the probabilities of structural characteristics of pore spaces are obtained first, and then reproduced in the reconstructed regions. One solution to overcome the anisotropy of training images is to use real 3D volume data as a training image (TI). The CPU cost and memory burden brought up by 3D simulations can be reduced greatly by selecting the optimal multiple-grid template size that is determined by the entropy of a TI. Moreover, both soft data and hard data are integrated in MPS simulation to improve the accuracy of reconstructed images. The variograms and permeabilities, computed by lattice Boltzmann method, of the reconstructed images and the target image obtained from real volume data are compared, showing that the structural characteristics of reconstructed porous media using our method are similar to those of real volume data.

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Acknowledgments

This work is supported by National Program on Key Basic Research Project of China (973 Program, No. 2011CB707305), the National Science and Technology Major Project (No. 2011ZX05009-006), CAS Strategic Priority Research Program (XDB10030402), Shanghai Municipal Natural Science Foundation (No. 11ZR1413700, 12ZR1412000), Talented People Introduction Foundation of Shanghai University of Electric Power (No. K2012-004, K-2013-019, K2014-020), the Excellent University Young Teachers Training Program of Shanghai Municipal Education Commission (No. ZZsdl12002, ZZsd113015), and Software Service Engineering Key Discipline Construction (fourth stage) of Shanghai Second Polytechnic University (No. XXKZD1301).

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Authors and Affiliations

  1. College of Computer Science and Technology, Shanghai University of Electric Power, 2588 Changyang Road, Shanghai, 200090, People’s Republic of China

    Ting Zhang

  2. School of Computer and Information, Shanghai Second Polytechnic University, 2360 Jinhai Road, Shanghai, 201209, People’s Republic of China

    Yi Du

  3. Department of Modern Mechanics, University of Science and Technology of China, 96 Jinzhai Road, Hefei, 230027, People’s Republic of China

    Tao Huang & Xue Li

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  1. Ting Zhang
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  2. Yi Du
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  3. Tao Huang
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  4. Xue Li
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Correspondence to Yi Du.

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Zhang, T., Du, Y., Huang, T. et al. Reconstruction of porous media using multiple-point statistics with data conditioning. Stoch Environ Res Risk Assess 29, 727–738 (2015). https://doi.org/10.1007/s00477-014-0947-7

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  • DOI: https://doi.org/10.1007/s00477-014-0947-7

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