Neuro-fuzzy Inference System for Modeling Equilibrium Ratio for Heptanes-plus Fraction

Equilibrium constant has many applications in solving problems in reservoir engineering and petroleum processing. Various correlations are available for estimating K- values for heptanes plus fractions. These correlations can be classified into simple and complicated. However these correlations are not able to predict K values adequately for a wide range of conditions. They lose validity in specific range of pressure and temperature and exhibit some error. In this work neuro-fuzzy modeling techniques (ANFIS) is developed to predict K- values for heavy fractions. A large collection of K- values data points (more than 1340 data points) were extracted from experimental 570 PVT reports using the principal of material balance are used in developing the neuro- fuzzy model. 80% of the data points were used to train ANFIS model and 20% of data sets were used to validate, and test the model. Statistical analysis (average absolute percent error, correlation coefficient, standard deviation, maximum error, minimum error, etc.) is used for comparison the proposed model with empirical correlations. Graphical tools have also been utilized for the sake of comparison the performance of the new model and experimental data. Results showed that the new hybrid neural fuzzy model outperforms some available empirical correlations.