Support vector regression between PVT data and bubble point pressure

Abstract

Accurate determination of oil bubble point pressure (Pb) from laboratory experiments is time, cost and labor intensive. Therefore, the quest for an accurate, fast, and cheap method of determining Pb is inevitable. Since support vector based regression satisfies all components of such a quest through a supervised learning algorithm plant based on statistical learning theory, it was employed to formulate available PVT data into Pb. Open-sources literature data were used for SVR model construction and Iranian Oils data were employed for model evaluation. A comparison among SVR, neural network and three well-known empirical correlations demonstrated superiority of SVR model.