Inscrição na biblioteca: Guest
Portal Digital Begell Biblioteca digital da Begell eBooks Diários Referências e Anais Coleções de pesquisa
Journal of Porous Media
Fator do impacto: 1.752 FI de cinco anos: 1.487 SJR: 0.43 SNIP: 0.762 CiteScore™: 2.3

ISSN Imprimir: 1091-028X
ISSN On-line: 1934-0508

Volumes:
Volume 23, 2020 Volume 22, 2019 Volume 21, 2018 Volume 20, 2017 Volume 19, 2016 Volume 18, 2015 Volume 17, 2014 Volume 16, 2013 Volume 15, 2012 Volume 14, 2011 Volume 13, 2010 Volume 12, 2009 Volume 11, 2008 Volume 10, 2007 Volume 9, 2006 Volume 8, 2005 Volume 7, 2004 Volume 6, 2003 Volume 5, 2002 Volume 4, 2001 Volume 3, 2000 Volume 2, 1999 Volume 1, 1998

Journal of Porous Media

DOI: 10.1615/JPorMedia.v18.i5.40
pages 507-518

PORE, THROAT, AND GRAIN DETECTION FOR ROCK SEM IMAGES USING DIGITALWATERSHED IMAGE SEGMENTATION ALGORITHM

Amirhossein Tavanaei
The Center for Advance Computer Studies, University of Louisiana at Lafayette, LA 70504
Saeed Salehi
Petroleum Engineering Department, University of Louisiana at Lafayette, LA 70504

RESUMO

Estimation of rock porosity by automatic and fast methods is one of the critical challenges in recent studies due to its importance in formation properties and prediction of other parameters. This paper proposes a method to separate pores, grains, and throats in scanning electron microscope (SEM) images. The method utilizes a digital image segmentation algorithm to distinguish the pores and their connections from grains. The proposed method is called watershed image segmentation, which uses gradient image for pore detection. Furthermore, morphological concepts are applied in this algorithm to separate the grains, pores, and throats. In addition, as SEM images, like other data sources, are polluted by noise and lack of information in their pixel values, required digital image preprocessing is done to provide the highquality images and consequently desirable results. This investigation, in comparison with constant threshold based methods for pore detection, demonstrates the acceptable estimation in pore-grain recognition. Finally, porosity values of high perm and low perm sample rocks are calculated.


Articles with similar content:

NOISE REDUCTION METHOD IN FLOW VISUALIZATION USING DSPI
Journal of Flow Visualization and Image Processing, Vol.6, 1999, issue 4
Yaozu Song, Wei Zhang
NUMERICAL PREDICTION OF EFFECTIVE THERMAL CONDUCTIVITY OF CATALYST LAYERS IN PROTON EXCHANGE MEMBRANE FUEL CELLS
4th Thermal and Fluids Engineering Conference, Vol.8, 2019, issue
Ruiyuan Zhang, Chen Li, Wenzhen Fang, Wen-Quan Tao
GAS DIFFUSIVITY IN POROUS MEDIA: DETERMINATION BY MERCURY INTRUSION POROSIMETRY AND CORRELATION TO POROSITY AND PERMEABILITY
Journal of Porous Media, Vol.16, 2013, issue 7
Zhiye Gao, Qinhong Hu, Hecheng Liang
Methods for Blind Evaluation of Noise Variance in Multichannel Optical and Radar Images
Telecommunications and Radio Engineering, Vol.65, 2006, issue 6-10
Benoit Vozel, Kacem Chehdi, N. N. Ponomarenko, S. K. Abramov
COMPARING THREE IMAGE PROCESSING ALGORITHMS TO ESTIMATE THE GRAIN-SIZE DISTRIBUTION OF POROUS ROCKS FROM BINARY 2D IMAGES AND SENSITIVITY ANALYSIS OF THE GRAIN OVERLAPPING DEGREE
Special Topics & Reviews in Porous Media: An International Journal, Vol.6, 2015, issue 1
Arash Rabbani, Shahab Ayatollahi