Asphaltene precipitation and consequent deposition may result in several operational problems ranging from the wellbore to transmission lines. Despite several studies, stability conditions of the asphaltene in crude oil are still a challenging issue and a potential area of investigation. Refractive Index (RI) is a parameter indicative of the region at which asphaltene becomes stable. In this study, a Committee Machine Intelligent System (CMIS) is incorporated to predict the RI of different crude oils through the existing SARA fractions experimental data. The CMIS itself utilizes different artificial neural networks: Multilayer Perceptron (MLP), Radial Basis Function (RBF), and Least Squares Support Vector Machine (LSSVM). By comparing the results of each artificial neural network with the final output, it was demonstrated that the CMIS increases the generalization capability of the utilized artificial network. The results were compared with two well‐known classical correlations. It was proven that the proposed intelligent system outperforms the classical correlations. At the end, outlier detection was performed to identify data which deviate from the bulk of the data points and obtain the applicability domain of the CMIS model.

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Most industrial areas, especially oilfield operations and geothermal reservoirs, deal with varying viscosities in multicomponent electrolyte solutions. An accurate estimate of this property as a function of pressure, temperature, and varying salt concentrations is highly desirable. Although a number of empirical correlations have already been developed, they are still limited to single electrolyte solutions and can only operate over specified temperature and pressure ranges. In this study, a highly accurate model based on an adaptive network-based fuzzy inference system was developed, mainly devoted to dynamic viscosity prediction in aqueous multicomponent chloride solutions. Crisp input data were transformed into fuzzy sets employing the subtractive clustering algorithm with an effective radius optimized by a hybrid of genetic algorithm and particle swarm optimization technique. Comparing the model with thousands of experimental data concluded in squared correlation coefficient (R 2) of 0.9986 and an average absolute error of 1.59%. The developed model was also found to outperform a number of empirical correlations that are employed for the viscosity determination of single electrolyte solutions.

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