Comparative evaluation of back-propagation neural network learning algorithms and empirical correlations for prediction of oil PVT properties in Iran oilfields-Reference-Cited by-同舟云学术

Comparative evaluation of back-propagation neural network learning algorithms and empirical correlations for prediction of oil PVT properties in Iran oilfields

Published:2011-08 Issue:2 Volume:78 Page:464-475
ISSN:0920-4105
Container-title:Journal of Petroleum Science and Engineering
language:en
Short-container-title:Journal of Petroleum Science and Engineering

Author:

Asadisaghandi Jalil,Tahmasebi Pejman

Publisher

Elsevier BV

Subject

Geotechnical Engineering and Engineering Geology,Fuel Technology

Reference30 articles.

1. Predicting natural-gas production using artificial neural network;Al-Fattah,2001

2. PVT correlations for Middle East crude oils;Al-Marhoun;JPT,1988

3. Improved PVT correlations for UAE crude oils;Al-mehaideb,1997

4. Bubble point pressure and oil formation volume factor correlations;Al-Shammasi,1997

5. Approximation by superpositions of a sigmoidal function;Cybenko;Math. Control Signals Syst.,1989

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