[1] Teng QizhiYang Dan, Xu ZhiLi ZhengjiHe Xiaohai,. Training image analysis for three-dimensional reconstructionof porous media [J]. Journal of Southeast University (English Edition), 2012, 28 (4): 415-421. [doi:10.3969/j.issn.1003-7985.2012.04.008]

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Training image analysis for three-dimensional reconstructionof porous media()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

28

Issue:

2012 4

Page:

415-421

Research Field:

Energy and Power Engineering

Publishing date:

2012-12-30

Info

Title:

Training image analysis for three-dimensional reconstructionof porous media

Author(s):

Teng QizhiYang Dan;  Xu ZhiLi ZhengjiHe Xiaohai

School of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China

Keywords:

three-dimensional reconstruction;  training image;  stationarity;  porous media;  multiple-point statistics

PACS:

TE319

DOI:

10.3969/j.issn.1003-7985.2012.04.008

Abstract:

In order to obtain a better sandstone three-dimensional(3D)reconstruction result which is more similar to the original sample, an algorithm based on stationarity for a two-dimensional(2D)training image is proposed. The second-order statistics based on texture features are analyzed to evaluate the scale stationarity of the training image. The multiple-point statistics of the training image are applied to obtain the multiple-point statistics stationarity estimation by the multi-point density function. The results show that the reconstructed 3D structures are closer to reality when the training image has better scale stationarity and multiple-point statistics stationarity by the indications of local percolation probability and two-point probability. Moreover, training images with higher multiple-point statistics stationarity and lower scale stationarity are likely to obtain closer results to the real 3D structure, and vice versa. Thus, stationarity analysis of the training image has far-reaching significance in choosing a better 2D thin section image for the 3D reconstruction of porous media. Especially, high-order statistics perform better than low-order statistics.

References:

[1] Tang T, Teng Q Z, He X H, et al. A pixel selection rule based on the number of different-phase neighbours for the simulated annealing reconstruction of sandstone microstructure [J]. Journal of Microscopy, 2009, 234(3): 262-268.
[2] Liang Z R, Fernandes C P, Magnani F S, et al. A reconstruction technique for three-dimensional porous media using image analysis and Fourier transforms [J]. Journal of Petroleum Science and Engineering, 1998, 21(3/4): 273-283.
[3] Tahmasebi P, Hezarkhani A, Muhammad S. Multiple-point geostatistical modeling based on the cross-correlation functions [J].Computational Geosciences, 2012, 16(3):779-797.
[4] ˇ/Capek P, Hejtmánek V, Brabec L, et al. Stochastic reconstruction of particulate media using simulated annealing: improving pore connectivity [J]. Transport in Porous Media, 2009, 76(2): 179-198.
[5] Liu X F, Sun J M, Wang H T. Reconstruction of 3-D digital cores using a hybrid method [J]. Applied Geophysics, 2009, 6(2): 105-112.
[6] Jiao Y, Stillinger F H, Torquato S. Modeling heterogeneous materials via two-point correlation functions.Ⅱ.algorithmic details and applications [J]. Physical Review E, 2008, 77(3): 031135.
[7] Strebelle S. Conditional simulation of complex geological structures using multiple-point statistics [J]. Mathematical Geology, 2002, 34(1): 1-21.
[8] Hajizadeh A, Safekordi A, Farhadpour F A. A multiple-point statistics algorithm for 3D pore space reconstruction from 2D images [J]. Advances in Water Resources, 2011, 34(10): 1256-1267.
[9] Caers J, Zhang T. Multiple-point geostatistics: A quantitative vehicle for integrating geologic analogs into multiple reservoir models [C]//Integration of Outcrop and Modern Analogs in Reservoir Modeling. Tulsa: American Association of Petroleum Geologists, 2004: 383-394.
[10] Illian J, Penttinen A, Stoyan H, et al. Statistical analysis and modeling of spatial point patterns [M]. Chichester: John Wiley & Sons, 2008:173-291.
[11] Okabe H, Blunt M J. Pore space reconstruction using multiple-point statistics [J]. Journal of Petroleum Science and Engineering, 2005, 46(1/2): 121-137.
[12] Mirowski P W, Tetzlaff D M, Davies R C, et al. Stationarity scores on training images for multipoint geostatistics [J]. Mathematical Geosciences, 2009, 41(4): 447-474.
[13] Prats-Montalbán J M, de Juan A, Ferrer A. Multivariate image analysis: A review with applications [J]. Chemometrics and Intelligent Laboratory Systems, 2011, 107(1):1-23.
[14] Boisvert J B, Pyrcz M J, Deutsch C V. Multiple-point statistics for training image selection [J]. Natural Resources Research, 2008, 16(4): 313-321.
[15] Haralick R M, Shanmugam K, Dinstein I. Textural features for image classification [J]. IEEE Trans on Systems, Man, and Cybernetics, 1973, SMC-3(6): 610-621.
[16] Manivannan K, Aggarwal P, Devabhaktuni V, et al. Particulate matter characterization by gray level co-occurrence matrix based support vector machines [J]. Journal of Hazardous Materials, 2012, 223/224:94-103.
[17] Abdi H, Williams L J. Principal component analysis [J]. Wiley Interdisciplinary Reviews: Computational Statistics, 2010, 2(4): 433-459.
[18] Latief F D E, Biswal B, Fauzi U, et al. Continuum reconstruction of the pore scale microstructure for Fontainebleau sandstone [J]. Physica A: Statistical Mechanics and Its Applications, 2010, 389(8):1607-1618.
[19] Hu J, Stroeven P. Local porosity analysis of pore structure in cement paste [J]. Cement and Concrete Research, 2005, 35(2): 233-242.

Memo

Memo:

Biography: Teng Qizhi(1961—), female, doctor, professor, qzteng@scu.edu.cn.
Foundation item: The National Natural Science Foundation of China(No.60972130).
Citation: Teng Qizhi, Yang Dan, Xu Zhi, et al.Training image analysis for three-dimensional reconstruction of porous media[J].Journal of Southeast University(English Edition), 2012, 28(4):415-421.[doi:10.3969/j.issn.1003-7985.2012.04.008]

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