Asphaltene precipitation of titration data modeling through committee machine with stochastically optimized fuzzy logic and optimized neural network

• In this study, precipitated asphaltene amount is predicted from titration data.
• Neural network (NN), fuzzy logic (FL) were used as modeling tools.
• NN and FL were then optimized by GA-PS to enhance prediction accuracy.
• A CM was then constructed to integrate aforementioned models and enhances precision.
• Implementation of the proposed strategy considerably saves time and reduces costs.

Deposition of asphaltene during crude oil production is a challenging issue in oil industry which causes considerable loss of production efficiency as well as imposes negative impacts on production rates. Upon variation in pressure, temperature and crude oil composition, asphaltene begins to precipitate and deposits in reservoir rock and consequently causes formation damage owing to mechanisms of wettability alteration and pore throat blockage. In the present study a sophisticated method, called committee machine with optimized intelligent systems was utilized to predict the amount of asphaltene precipitation from experimental titration data. The committee machine is composed of optimized neural network and optimized fuzzy logic. Stochastic optimization of neural network and fuzzy logic by virtue of hybrid genetic algorithm-pattern search technique significantly enhances their efficiencies. The committee machine provides a further improvement in accuracy of final prediction through integrating optimized intelligent systems and consequent reaping of their benefits. The committee machine model was applied to experimental data reported in the open-source literature. It was observed that there was an acceptable agreement between experimental data and committee machine predicted values. Finally, performance of committee machine model was compared with other intelligent systems used for prediction of asphaltene precipitation. Results showed superiority of committee machine in asphaltene precipitation modeling to optimized neural network and optimized fuzzy logic.