Image Processing and Machine Learning Approaches for Petrographic Thin Section Analysis

Published on Oct 16, 2017
· DOI :10.2118/187885-MS
Semen Budennyy4
Estimated H-index: 4
(MIPT: Moscow Institute of Physics and Technology),
Alexey Pachezhertsev1
Estimated H-index: 1
(MIPT: Moscow Institute of Physics and Technology)
+ 3 AuthorsBoris Belozerov6
Estimated H-index: 6
(Gazprom Neft)
Source
Abstract
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1 Author (E. Pavlovskiy)
1982
References17
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Porosity estimation from thin section image using digital image processing is critical for petrography study since it gives a brief description on the 2D porosity of the sample. The standard routine uses the binarization process that converts the colour (RGB) image into a binary image using pixel value treshold. The idea is that the treshold value must accomodate all the blue regions correlate to pore and turns it into white in the resulting binary image. Errors come from mis-conversion when the...
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Identification of different types of porosity within a reservoir rock is a functional parameter for reservoir characterization since various pore types play different roles in fluid transport and also, the pore spaces determine the fluid storage capacity of the reservoir. The present paper introduces a model for semi-automatic identification of porosity types within thin section images. To get this goal, a pattern recognition algorithm is followed. Firstly, six geometrical shape parameters of si...
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