A new correlation and intelligent modeling of asphaltene precipitation in live and tank crude oil systems

The most important parameters in asphaltene precipitation modeling and prediction are the asphaltene and oil solvent solubility parameters which are very sensitive to reservoir and operational conditions. The driving force of asphaltene flocculation is the difference between asphaltene and oil solvent solubility parameter. Since the nature of asphaltene solubility is yet unknown, the existing prediction models may fail in prediction the asphaltene precipitation in crude oil systems and it is the case that we have the opportunity to use artificial intelligence techniques. This paper introduces a new implementation of the artificial intelligent computing technology in petroleum engineering. We have proposed a new approach to prediction of the asphaltene precipitation in crude oil systems using fuzzy logic, neural networks and genetic algorithms. Results of this research indicate that the proposed correlation model with recognizing the possible patterns between input and output variables can successfully predict and model asphaltene precipitation in tank and live crude oils with a good accuracy.

Keywords:

Asphaltene precipitation modeling , Neural network , Fuzzy logic , Genetic algorithm , Intelligent modeling