Upgrading fuzzy logic by GA-PS to determine asphaltene stability in crude oil

Abstract

Precipitation and deposition of asphaltene are undesirable phenomena that arise during petroleum production which give rise to a pronounced rate of increase in operational cost and adversely affect production rates as well. Hence, it is imperative to develop a mathematical model for the assessment of asphaltene stability in crude oil. In the present study, delta RI which constitutes the difference between refractive index of crude oil (RI) and refractive index of crude oil at the onset of asphaltene precipitation (PRI) is employed as the principal factor for determining the asphaltene stability of the region. Fuzzy logic is a potent tool capable of extracting the underlying dependency between SARA fractions (saturate, aromatic, resin, and asphaltene) data and delta RI for the inexpensive and rapid diagnosis of asphaltene stability. In this study a novel strategy known as hybrid genetic algorithm-pattern search (GA-PS) is suggested for the development of an optimal fuzzy logic model as a reliable alternative for the widely-applied subtractive clustering (SC) method. While SC solely optimizes mean of input Gaussian membership functions (GMFs), GA-PS tool optimizes both mean and variance of input GMFs. Comparison between GA-PS and SC methods confirmed the capability of GA-PS for developing an optimal fuzzy logic model.