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Ensemble SVM for characterisation of crude oil viscosity

Research output: Contribution to journalArticlepeer-review

This paper develops ensemble machine learning model for the prediction of dead oil, saturated and undersaturated viscosities. Easily acquired field data have been used as the input parameters for the machine learning process. Different functional forms for each property have been considered in the simulation. Prediction performance of the ensemble model is better than the compared commonly used correlations based on the error statistical analysis. This work also gives insight into the reliability and performance of different functional forms that have been used in the literature to formulate these viscosities. As the improved predictions of viscosity are always craved for, the developed ensemble support vector regression models could potentially replace the empirical correlation for viscosity prediction.
Original languageEnglish
Number of pages16
JournalJournal of Petroleum Exploration and Production Technology
Early online date1 Jun 2017
Publication statusEarly online - 1 Jun 2017


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