New correlation and intelligent modeling of experimental data of slim tube permeability reduction by asphaltene precipitation in Iranian crude oil reservoirs

When during oil production the thermodynamic conditions within the near-wellbore formation lie inside the asphaltene deposition envelope of the reservoir fluid, the flocculated asphaltene cause formation damage. Mathematically, formation damage is a reduction in the hydrocarbon effective mobility. This paper introduces a new implementation of the artificial intelligent computing technology in permeability reduction prediction by asphaltene precipitation in slim tube and use of our experimental data provided by Ashoori. Results of this research indicate that the proposed correlation and intelligent prediction model with recognizing the possible patterns between experimental data can successfully predict and model permeability reduction in slim tube by asphaltene precipitation with a good accuracy.

Keywords:

Asphaltene precipitation permeability reduction modeling , Neural network , Genetic algorithm , Intelligent modeling