Developing grey-box model to diagnose asphaltene stability in crude oils: Application of refractive index

Asphaltene precipitation can cause serious problems in petroleum industry while diagnosing the asphaltene stability conditions in crude oil system is still a challenge and has been subject of many investigations. To monitor and diagnose asphaltene stability, high performance intelligent approaches based bio-inspired science like artificial neural network which have been optimized by various optimization techniques have been carried out. The main purpose of the implemented optimization algorithms is to decide high accurate interconnected weights of proposed neural network model. The proposed intelligent approaches are examined by using extensive experimental data reported in open literature. Moreover, to highlight robustness and precision of the addressed approaches, two different regression models have been developed and results obtained from the aforementioned intelligent models and regression approaches are compared with the corresponding refractive index data measured in laboratory. Based on the results, hybrid of genetic algorithm and particle swarm optimization have high performance and average relative absolute deviation between the model outputs and the relevant experimental data was found to be less than 0.2%. Routs from this work indicate that implication of HGAPSO-ANN in monitoring refractive index can lead to more reliable estimation of addressed issue which can lead to design of more reliable phase behavior simulation and further plans of oil production.