000			02160nab a2200205 4500
005			20260522181112.0
008			260224s2010 xxu
100	1		_aAshoori, S. _946168
100	1		_aAbedini, A. _946169
100	1		_aAbedini, R. _946170
245	0	0	_aComparison of scaling equation with neural network model for prediction of asphaltene precipitation
260			_cmayo 2010
270			_a03/08/2010 ; 03/08/2010
300			_a9 p. ; 186-194
520			_aTranscripción del resumen del autor. The precipitation and deposition of crude oil polar fractions such as asphaltenes in petroleum reservoirs reduce considerably the rock permeability and the oil recovery. Therefore, it is of great importance to determine "how much" the asphaltenes precipitate as a function of pressure, temperature and liquid phase composition. Extensive new experimental data for the amount of asphaltene precipitated in an Iranian crude oil has been determined with various solvents at different temperatures and dilution ratios. All experiments were carried out at atmospheric pressure. The experimental data obtained in this study were used to examine the scaling equations proposed by Rassamdana et al. and Hu et al. We introduced a modified version of their proposed scaling equation. Our observation showed that the results obtained from the present scaling equation are more satisfactory. Furthermore, an Artificial Neural Network (ANN) model was also designed and applied to predict the amount of asphaltene precipitation at a given operating condition. The predicted results of asphaltene precipitation from ANN model was also compared with the results of Rassamdana et al., Hu et al. and our proposed scaling equations. It was observed that there is more acceptable quantitative and qualitative agreement between experimental data and predicted amount of asphaltene precipitation through using ANN model and this model can be a more accurate method than scaling equations to predict the asphaltene precipitation.
581			_a1-2
773	0		_tJournal of Petroleum Science & Engineering _g72
942			_cARTICULO _2ddc
999			_c175681 _d175681