شماره ركورد كنفرانس :
4518
عنوان مقاله :
Artificial Neural Network Based Model for Crude Oil Viscosity Prediction
Author/Authors :
Siyamak Moradi Petroleum University of Technology- Abadan Faculty of Petroleum Engineering- Northern Bowarde, Abadan , Jamshid Moghadasi Petroleum University of Technology- Abadan Faculty of Petroleum Engineering- Northern Bowarde, Abadan , Koorosh Kazemi Petroleum University of Technology- Abadan Faculty of Petroleum Engineering- Northern Bowarde, Abadan , Saadat Mohammad Hosein Zadeh Petroleum University of Technology- Abadan Faculty of Petroleum Engineering- Northern Bowarde, Abadan
كليدواژه :
Iranian Crude Oils , Empirical Correlations , Artificial Neural Network , Viscosity
عنوان كنفرانس :
The 7th International Chemical Engineering Congress & Exhibition (IChEC 2011
چكيده لاتين :
Viscosity is a crucial physical property of crude oil which is used in the calculations of formation
evaluation, fluid flow through porous media and the design of production and surface facilities,
and pipeline. A feed-forward back-propagation neural network model with with Levenberg-
Marquardt training algorithm is presented based on 357 data sets of Iranian crudes for estimation
of saturated and undersaturated oil viscosity. The developed model is tested and compared to some
empirical corrolations by 90 data sets. The neural network model is generally more accurate than
correlations. It outperformed corrolations with highest corrolation coeficients and lowest average
absolute relative errors.
