Abstract—Dead oil viscosity is a critical design factor for oilfields and refineries. From available literature, crude oil viscosity is found to be a strong function of pressure, temperature, bubble-point pressure, gas-oil ratio, gas gravity, and oil gravity. Oil viscosity is generally determined from laboratory experiments and empirically derived correlations. Reliable measurements of dead oil viscosity are difficult to obtain due to lack of lab equipment or liquid samples. Based on API oil gravity & temperature, various correlations have been used to predict dead oil viscosity. In addition to correlations, recently data mining techniques like Artificial Neural Networks (ANN) and Support Vector Machines (SVM) have been used to predict petroleum viscosity. The aim of this paper is to introduce the ensemble model of bagging as an important data mining technique to predict dead oil viscosity. The ensemble model predicted the viscosity accurately with a correlation coefficient of 0.99, an accuracy that is comparable to that of ANN as found in literature. It was also observed that bagging lowered the relative error of the base classifier (ANN) from 10% to about 8%, thereby stabilizing the ANN while retaining its accuracy.
Index Terms—API gravity, bagging, data mining, dead oil viscosity.
Bharat B. Gulyani and B. G. Prakash Kumar are with Department of Chemical Engineering, BITS Pilani, Dubai Campus, Academic City, Dubai 345055, UAE (e-mail: gulyanibb@gmail.com, prakash@dubai.bits-pilani.ac.in). Arshia Fathima is with Nanolabs, Alfaisal University (e-mail: arshiafathima92@gmail.com).
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Cite: Bharat B. Gulyani, B. G. Prakash Kumar, and Arshia Fathima, "Bagging Ensemble Model for Prediction of Dead Oil Viscosity," International Journal of Chemical Engineering and Applications vol. 8, no. 2, pp. 102-105, 2017.
