PREDICTION OF CRUDE OIL VISCOSITY USING FEED-FORWARD BACK-PROPAGATION NEURAL NETWORK (FFBPNN)

Makinde, F. A. and Ako , C. T. and Orodu, O. D. and Asuquo, I. U. (2012) PREDICTION OF CRUDE OIL VISCOSITY USING FEED-FORWARD BACK-PROPAGATION NEURAL NETWORK (FFBPNN). Petroleum & Coal, 54 (2). pp. 120-131. ISSN 1337-7027

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Abstract

Crude oil viscosity is an important governing parameter of fluid flow both in the porous media and in pipelines. So, estimating the oil viscosity at various operating conditions with accuracy is of utmost importance to petroleum engineers. Usually, oil viscosity is determined by laboratory measurements at reservoir temperature. However, laboratory experiments are rather expensive and in most cases, the data from such experiments are not reliable. So, petroleum engineers prefer to use published correlations but these correlations are either too simple or too complex and so many of them are region-based not generic. To tackle the above enumerated drawbacks, in this paper, a Feed-Forward Back-Propagation Neural Network (FFBPNN) model has been developed to estimate the crude oil viscosity (μo) of Undersaturated reservoirs in the Niger Delta region of Nigeria. The newly developed FFBPNN model shows good results compared to the existing empirical correlations. The μo FFBPNN model achieved an average absolute relative error of 0.01998 and the correlation coefficient (R2) of 0.999 compared to the existing empirical correlations. From the performance plots for the FFBPNN model and empirical correlations against their experimental values, the FFBPNN model's performance was excellent.

Item Type:	Article
Subjects:	T Technology > TP Chemical technology
Divisions:	Faculty of Engineering, Science and Mathematics > School of Engineering Sciences
Depositing User:	Mr Adewole Adewumi
Date Deposited:	14 Dec 2012 14:13
Last Modified:	14 Dec 2012 14:13
URI:	http://eprints.covenantuniversity.edu.ng/id/eprint/952

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