Real-time relative permeability prediction using deep learning-Reference-Cited by-同舟云学术

Real-time relative permeability prediction using deep learning

Published:2018-11-24 Issue:2 Volume:9 Page:1271-1284
ISSN:2190-0558
Container-title:Journal of Petroleum Exploration and Production Technology
language:en
Short-container-title:J Petrol Explor Prod Technol

Author:

Arigbe O. D.,Oyeneyin M. B.,Arana I.,Ghazi M. D.

Publisher

Springer Science and Business Media LLC

Subject

General Energy,Geotechnical Engineering and Engineering Geology

Link

http://link.springer.com/content/pdf/10.1007/s13202-018-0578-5.pdf

Reference24 articles.

1. Al-Fattah SMA (2013) Artificial neural network models for determining relative permeability of hydrocarbon reservoirs. U.S. Patent No. 8,510,242. U.S. Patent and Trademark Office, Washington, DC

2. Al-Fattah SM, Al-Naim HA (2009) Artificial-intelligence technology predicts relative permeability of giant carbonate reservoirs. SPE Reserv Eval Eng 12(01):96–103

3. Baker L (1988) Three-phase relative permeability correlations. In: SPE enhanced oil recovery symposium. Society of petroleum engineers

4. Buckley SE, Leverett M (1942) Mechanism of fluid displacement in sands. Trans AIME 146(01):107–116

5. Corey A et al (1956) Three-phase relative permeability. J Petrol Technol 8(11):63–65

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