New Method of Pore Network Extraction from 3D CT scan Images of Rock

Document Type : Original Article

Authors

1 Petroleum Engineering, Chemical & Petroleum Engineering, Sharif, Tehran, Iran

2 Chemical & Petroleum Engineering Department /Sharif University of Technology/Tehran/Iran

Abstract

Reservoir rock geometry is one of the important factors in the analysis of static and dynamic properties. CT scan images are used to obtain the reservoir rock geometry. Because these images are not easy computable, the pore-throat network models are used to geometrical conversation. The process of extracting these models from CT images is done using statistical probabilistic image processing based methods, such as the modified maximal ball algorithm. In this study, new methods for extracting the pore network model and pore connection detection are presented. Throat spanning probability is a criterion for determining the presence of connections. In this method, the geometric static parameters of the rock, including the porosity, the porosity-based homogeneity index of rock, the distribution, and the average pore size& coordination number are calculated. Finally, using the image of a synthetic silica sample, the results of the proposed method are compared with the results of the modified maximal ball (MMB) method. Good agreement was found between the results of the models. Also, the pore network extracted from sandstone, carbonate and synthetic rocks and corresponding porosity-based homogeneity index are presented and discussed.

Keywords

References

Al-Kharusi, A. S., & Blunt, M. J. (2007). Network extraction from sandstone and carbonate pore space images. Journal of Petroleum Science and Engineering, 56(4), 219-231 .
Baldwin, C. A., Sederman, A. J., Mantle, M. D., Alexander, P., & Gladden, L. F. (19 .)96Determination and characterization of the structure of a pore space from 3D volume images. Journal of Colloid and Interface Science, 181(1), 79-92 .
Blunt, M., King, M. J., & Scher, H. (1992). Simulation and theory of two-phase flow in porous media .Physical Review A, 46(12), 7680 .
Bryant, S., & Blunt, M. (1992). Prediction of relative permeability in simple porous media. Physical Review A,46(4), 2004 .
Bryant, S., & Raikes, S. (1995). Prediction of elastic-wave velocities in sandstones using structural models. Geophysics, 60(2), 437-446 .
Bryant, S. L., King, P. R., & Mellor, D. W. (1993). Network model evaluation of permeability and spatial correlation in a real random sphere packing. Transport in porous media, 11(1), 53-70 .
Bryant, S. L., Mellor, D. W., & Cade, C. A. (1993). Physically representative network models of transport in porous media. AIChE Journal, 39(3), 387-396 .
Dong, H., & Blunt, M. J. (2009). Pore-network extraction from micro-computerized-tomography images. Physical review E, 8 .036307 ,(3)0
Gatos, B., Pratikakis, I., & Perantonis, S. J. (2006). Adaptive degraded document image binarization. Pattern recognition, 39(3), 317-327 .
Gostick, J. T. (2017). Versatile and efficient pore network extraction method using marker-based watershed segmentation. Physical review E, 96(2), 023307 .
Johannsen, G., & Bille, J. (1982). A threshold selection method using information measures. Paper presented at the ICPR.
Kapur, J. N., Sahoo, P. K., & Wong, A. K. (1985). A new method for gray-level picture thresholding using the entropy of the histogram. Computer vision, graphics, and image processing, 29(3), 273-285 .
Kittler, J., & Illingworth, J. (1986). Minimum error thresholding. Pattern recognition, 19(1), 41-47 .
Lindquist, W. B., Lee, S. M ,.Coker, D. A., Jones, K. W., & Spanne, P. (1996). Medial axis analysis of void structure in three‐dimensional tomographic images of porous media. Journal of Geophysical Research: Solid Earth, 101(B4), 8297-8310 .
Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics, 9(1), 62-66 .
Rabbani, A., Jamshidi, S., & Salehi, S. (2014). An automated simple algorithm for realistic pore network extraction from micro-tomography images. Journal of Petroleum Science and Engineering, 123, 164-171 .
Raeini, A. Q., Bijeljic, B., & Blunt, M. J. (2017). Generalized network modeling: Network extraction as a coarsescale discretization of the void space of porous media. Physical review E, 96(1), 013312
Silin, D., & Patzek, T. (2006). Pore space morphology analysis using maximal inscribed spheres. Physica A: Statistical Mechanics and its Applications, 371(2), 336-360 .
Thepade, S., Das, R., & Ghosh, S. (2014). A Novel Feature Extraction Technique Using Binarization of Bit Planes for Content Based Image Classification. Journal of Engineering. doi:10.1155/2014/439218.
Xiong, Q., Baychev, T. G., & Jivkov, A. P. (2016). Review of pore network modelling of porous media: experimental characterisations, network constructions and applications to reactive transport. Journal of contaminant hydrology, 192, 101-117 .
Journal of Petroleum Geomechanics
Volume 3, Issue 3
December 2019
Pages 1-13

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