Improving pore type identification from thin section images using an integrated fuzzy fusion of multiple classifiers

Author:

Mollajan Amir,Ghiasi-Freez Javad,Memarian Hossein

Funder

Iranian Central Oil Fields Company (ICOFC)

Publisher

Elsevier BV

Subject

Energy Engineering and Power Technology,Geotechnical Engineering and Engineering Geology,Fuel Technology

Reference28 articles.

1. An automated petrographic image analysis system: capillary pressure curves using confocal microscopy;Al Ibrahim,2012

2. Porosity, permeability and MHR calculations using SEM and thin-section images for charactrizing complex Mauddud – Burgan carbonate reservoir;Al-Bazzaz,2007

3. Combining image analysis and modular neural networks for classification of mineral inclusions and pores in archaeological potsherds;Aprile;J. Archaeol. Sci.,2014

4. Classification of carbonate reservoir rocks and petrophysical considerations;Archie;AAPG Bull.,1952

5. Mineral identification using color spaces and artificial neural networks;Baykan;J. Comput. Geosci.,2010

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