Performance comparison of bubble point pressure from oil PVT data: Several neurocomputing techniques compared

Author:

Ghorbani Hamzeh,Wood David A.,Choubineh Abouzar,Mohamadian Nima,Tatar Afshin,Farhangian Hamed,Nikooey Ali

Publisher

Springer Science and Business Media LLC

Reference78 articles.

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2. Ahmadi, M. A., Zendehboudi, S., Lohi, A., Elkamel, A., Chatzis., I. 2013. Application of hybrid genetic algorithm with particle swarm optimization and neural network for reservoir permeability prediction. J Geophys Prospect, 61:–598.

3. AlAjmi, M. D., Alarifi, S. A., Mahsoon, A. H. 2015. Improving multiphase choke performance prediction and well production test validation using artificial intelligence: A new milestone. In: Proceedings of the SPE Digital Energy Conference and Exhibition: SPE-173394-MS.

4. Al-Marhoun, M. A. 1988. PVT correlations for middle east crude oils. J Petrol Technol, 40:–666.

5. Al-Marhoun, M. A., Osman, E. A. 2002. Using artificial neural networks to develop new PVT correlations for saudi crude oils. In: Proceedings of the Abu Dhabi International Petroleum Exhibition and Conference: SPE-78592-MS.

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