Estimating Asphaltene precipitation in the presence of co2 injection in oil reservoirs

In this research, use of multi layer perceptron (MLP) and radial basis function (RBF) structures of artificial neural network (ANN) for prediction of asphaltene precipitation were described and the models were contrasted with the modified Hirschberg et al., model. The essential data were gathered an...

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Main Author:	Akbari, Saeed
Format:	Thesis
Language:	English
Published:	2011
Subjects:	TP Chemical technology
Online Access:	http://eprints.utm.my/id/eprint/32776/1/SaeedAkbariMFKK2011.pdf
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spelling	my-utm-ep.327762018-05-27T07:51:31Z Estimating Asphaltene precipitation in the presence of co2 injection in oil reservoirs 2011-03 Akbari, Saeed TP Chemical technology In this research, use of multi layer perceptron (MLP) and radial basis function (RBF) structures of artificial neural network (ANN) for prediction of asphaltene precipitation were described and the models were contrasted with the modified Hirschberg et al., model. The essential data were gathered and after pre-treating was employed for training of ANN models. The performance of the best obtained model was checked by its generalization ability in predicting 30% of the unseen data. Excellent prediction with Mean Squared Error (MSE) of 0.0018 and Average Absolute Deviation (AAD %) of 1.4108 was observed. However the accuracies of RBF and MLP models may be evaluated relatively similar, it was obtained that the constructed MLP according to Levenberg-Marquardt (LM) optimization exhibited a high performance than RBF structure, and the modified Hirschberg to predict asphaltene precipitation. 2011-03 Thesis http://eprints.utm.my/id/eprint/32776/ http://eprints.utm.my/id/eprint/32776/1/SaeedAkbariMFKK2011.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Chemical Engineering Faculty of Chemical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TP Chemical technology
spellingShingle	TP Chemical technology Akbari, Saeed Estimating Asphaltene precipitation in the presence of co2 injection in oil reservoirs
description	In this research, use of multi layer perceptron (MLP) and radial basis function (RBF) structures of artificial neural network (ANN) for prediction of asphaltene precipitation were described and the models were contrasted with the modified Hirschberg et al., model. The essential data were gathered and after pre-treating was employed for training of ANN models. The performance of the best obtained model was checked by its generalization ability in predicting 30% of the unseen data. Excellent prediction with Mean Squared Error (MSE) of 0.0018 and Average Absolute Deviation (AAD %) of 1.4108 was observed. However the accuracies of RBF and MLP models may be evaluated relatively similar, it was obtained that the constructed MLP according to Levenberg-Marquardt (LM) optimization exhibited a high performance than RBF structure, and the modified Hirschberg to predict asphaltene precipitation.
format	Thesis
qualification_level	Master's degree
author	Akbari, Saeed
author_facet	Akbari, Saeed
author_sort	Akbari, Saeed
title	Estimating Asphaltene precipitation in the presence of co2 injection in oil reservoirs
title_short	Estimating Asphaltene precipitation in the presence of co2 injection in oil reservoirs
title_full	Estimating Asphaltene precipitation in the presence of co2 injection in oil reservoirs
title_fullStr	Estimating Asphaltene precipitation in the presence of co2 injection in oil reservoirs
title_full_unstemmed	Estimating Asphaltene precipitation in the presence of co2 injection in oil reservoirs
title_sort	estimating asphaltene precipitation in the presence of co2 injection in oil reservoirs
granting_institution	Universiti Teknologi Malaysia, Faculty of Chemical Engineering
granting_department	Faculty of Chemical Engineering
publishDate	2011
url	http://eprints.utm.my/id/eprint/32776/1/SaeedAkbariMFKK2011.pdf
_version_	1747816057700089856

Estimating Asphaltene precipitation in the presence of co2 injection in oil reservoirs

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