Determination of dew point pressure in gas condensate reservoirs based on a hybrid neural genetic algorithm

Abstract

Knowing dew point pressure considers as one of the preliminary requirements in retrograde gas condensate reservoir simulations. When the pressure declines below the dew point pressure, the condensate dropouts form, which could lead to a substantial decrease in gas relative permeability and well deliverability. Different methods such as equation of states, empirical correlations and experimental procedures have been proposed to determine the dew point pressure. However, due to their convergence problem, being expensive and time consuming, great efforts have been taken to develop an alternative method. In this study, a new method based on artificial neural network has been developed and optimized by genetic algorithm as an evolutionary technique. A data set consists of 308 sample collected from different sources and literature including one of Iranian gas-condensate field is used. Reservoir temperature, mole percentage of gas components and heavy fractions properties were considered as input parameters to this model. The performance of the proposed model was compared with some of the common correlations and Peng–Robinson equation of state. The results confirmed the accuracy and capability of this model in determination of dew point pressure based on 2.46%, 3.66%, 95.91%, 0.02% and 24.39% as average absolute deviation, root mean square error, correlation of determination, minimum and maximum percentage error; respectively. The sensitivity analysis is also performed on variables to determine the impact and importance of each parameter on prediction of dew point pressure. The results show that plus fraction properties and C3–C4 fraction have the greatest positive and negative impacts on estimation of dew point pressure; respectively.