A general regression neural network model offers reliable prediction of CO2 minimum miscibility pressure
Author:
Publisher
Springer Science and Business Media LLC
Subject
General Energy,Geotechnical Engineering and Engineering Geology
Link
http://link.springer.com/article/10.1007/s13202-015-0196-4/fulltext.html
Reference40 articles.
1. Ahmed, T (1997) A generalized methodology for minimum miscibility pressure. Paper No. SPE 39034. In: Fifth Latin American and Caribbean Petroleum Engineering Conference and Exhibition, Rio de Janeiro, Aug 30–Sep 3
2. Al-Dousari MM, Garrouch AA (2013) An artificial neural network model for predicting the recovery performance of surfactant polymer floods. J Pet Sci Eng 109:51–62
3. Alomair O, Malallah A, Elsharkawy A, Iqbal M, Predicting CO2 minimum miscibility pressure (MMP) using alternating conditional expectation (ACE) algorithm. Oil Gas Sci Technol Accepted for publication on December 2012, Published online in June 2013
4. Alston RB, Kokolis GP, James CF (1985) CO2 minimum miscibility pressure: a correlation for impure CO 2 streams and live oil systems. SPEJ April issue:268–75
5. Alvarado V, Manrique E (2010) Enhanced oil recovery: an update review. Energies 3:1529–1575
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