Digital petrography: Mineralogy and porosity identification using machine learning algorithms in petrographic thin section images-Reference-Cited by-同舟云学术

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Digital petrography: Mineralogy and porosity identification using machine learning algorithms in petrographic thin section images

Published:2019-12 Issue: Volume:183 Page:106382
ISSN:0920-4105
Container-title:Journal of Petroleum Science and Engineering
language:en
Short-container-title:Journal of Petroleum Science and Engineering

Author:

Rubo Rafael Andrello,de Carvalho Carneiro Cleyton,Michelon Mateus Fontana,Gioria Rafael dos Santos

Funder

CENPES

QEMSCAN

Publisher

Elsevier BV

Subject

Geotechnical Engineering and Engineering Geology,Fuel Technology

Reference64 articles.

1. Geology of Carbonate Reservoirs: the Identification, Description, and Characterization of Hydrocarbon Reservoirs in Carbonate Rocks;Ahr,2008

2. Artificial neural networks workflow and its application in the petroleum industry;Al-Bulushi;Neural Comput. Appl.,2012

3. Shear wave travel time estimation from petrophysical logs using ANFIS-PSO algorithm: a case study from Ab-Teymour Oilfield;Anemangely;J. Nat. Gas Sci. Eng.,2017

4. Machine learning technique for prediction of shear wave velocity using petrophysical logs;Anemangely;J. Pet. Sci. Eng.,2019

5. Geomechanical parameter estimation from mechanical specific energy using artificial intelligence;Anemangely;J. Pet. Sci. Eng.,2019

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