Estimation of SARA composition of crudes purely from density and viscosity using machine learning based models

Accurate characterization of crude oils by determining the composition of saturates, aromatics, resins and asphaltenes (SARA) has always been a challenging task in the petroleum industry. However, conventional experimental methods for determination of SARA composition are labour intensive, timeconsuming and expensive. In the present study, artificial neural network (ANN) models were developed to predict the SARA composition from easily measurable parameters like density and viscosity. A dataset of 216 crude oil samples covering wide range of geographical locations was compiled from various literature sources. The ANN models with one hidden layer and six neurons are trained, tested and validated using MATLAB neural network toolbox. Results obtained on analysis revealed reasonably good accuracy of prediction of SARA components except for aromatics. The performance of developed ANN models was compared with various correlations reported in literature and found to be better in terms of mean squared error and coefficient of determination. The developed models hence provide a costeffective and time-efficient alternative to the conventional SARA characterization techniques. (c) 2024 Southwest Petroleum University. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/).