Predicting Dew Point Pressure: Using a Hybrid Intelligent Network

Abstract

This article presents a new model in predicting dew point pressure, which is reduced the complexities of the prior fuzzy model by implementing genetic algorithm as a feature recognition tool. Feature recognition is a tool that makes the problem less complex and create the opportunity to have a better model. In this study, 15 parameters decreased to six by using the feature recognition tool. This tool discovered if fuzzy model inputs include reservoir temperature and mole fractions of H2S, N2, C3, iC5, and C6, the proposed model captures the physical trend better than previous models. The recommended model has an average relative deviation of 1.16% and average absolute deviation of 3.08% for testing data points.

Keywords:

• dew point pressure
• gas condensate
• adaptive network fuzzy inference system
• genetic algorithm
• intelligent network