Using artificial neural networks to estimate the z-factor for natural hydrocarbon gases
- Author
- KAMYAB, Mohammadreza1 ; SAMPAIO, Jorge H. B2 ; QANBARI, Farhad1 ; EUSTES, Alfred W3
[1] Department of Petroleum Engineering, Petroleum University of Technology, Tehran, Iran, Islamic Republic of
[2] Department of Petroleum Engineering, Curtin University of Technology, Bentley WA, Australia
[3] Department of Petroleum Engineering, Colorado School of Mines, Golden CO, United States - Source
Journal of petroleum science & engineering. 2010, Vol 73, Num 3-4, pp 248-257, 10 p ; ref : 1/4 p
- Publisher
- Elsevier, Oxford
- Publication country
- United Kingdom
- Document type
- Article
- Language
- English
- Author keyword
- DAK artificial Neural Network gas compressibility factor gas flow simulator numerical reservoir simulator z-factor
- Keyword (fr)
- Compressibilité Corrélation Ecoulement gaz Hydrocarbure Norme Performance Pression Pétrole Réseau neuronal
- Keyword (en)
- Compressibility Correlation Gas flow Hydrocarbon Standards Performance Pressure Petroleum Neural network
- Keyword (es)
- Compresibilidad Correlación Flujo gas Hidrocarburo Norma Rendimiento Presión Petróleo Red neuronal
- Classification
- Pascal
- 001 Exact sciences and technology / 001D Applied sciences / 001D06 Energy / 001D06B Fuels / 001D06B02 Crude oil, natural gas and petroleum products
- Discipline
- Energy
- Origin
- Inist-CNRS
- Database
- PASCAL
- INIST identifier
- 23264169
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
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DOI
10.1016/j.petrol.2010.07.006 
https://dx.doi.org/10.1016/j.petrol.2010.07.006
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