PVT Publications

Evaluation of Below Bubble Point Viscosity Correlations and Construction of a New Neural Network Model

M.A. Ayoub, SPE, D.M. Raja, SPE, PETROTEL Inc., (formerly attached to UTP, Malaysia), and M.A. Al-Marhoun, SPE, KFUPM (Saudi Arabia)

Copyright 2007. Society of Petroleum Engineers

This paper was prepared for presentation at the Asia Pacific Oil and Gas Conference and Exhibition held in Jakarta, Indonesia., 30 October-1 November 2007.

Abstract:

This paper, precisely, evaluates two famous below bubble point viscosity correlations and tries to create a new Neural Network model for estimating this property. The new created model outperforms the two investigated correlations namely Khan Model (1987) and Labedi Model (1992). The new technique (Artificial neural network) found to be successful in developing a model for predicting viscosity below bubble point with an outstanding correlation coefficient of 99.3%. A limited number of data points have been collected from Pakistani fields in order to construct, test, and validate the model. Viscosity from 99 sets of differential liberation data covering a wide range of pressure, temperature, and oil density were used to validate the correlations and to develop the new model. A series of statistical and graphical analysis were conducted also to show the superiority of the model that has been formulated through an Artificial Neural Network technique. A thorough literature review is also made to check the applicability of the existing correlations and their drawbacks.

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