The data used includes a wide range of temperature and pressure, and the models presented in this research are the most accurate models to date for predicting the condensate viscosity. The input parameters for the development of the models presented in this research were temperature, pressure and condensate composition. According to the mentioned cases, in this research, unlike the research done in the literature, Rs parameter was not used to develop the models. Also, measuring this parameter in the laboratory requires spending time and money. Measuring Rs in wellhead requires special equipment and is somewhat difficult. In models presented in the literature, one of the input parameters for the development of the models is solution gas oil ratio (Rs). Several intelligent techniques, including Ensemble methods, support vector regression (SVR), K-nearest neighbors (KNN), Radial basis function (RBF), and Multilayer Perceptron (MLP) optimized by Bayesian Regularization and Levenberg–Marquardt were applied for modeling. In this study, the most comprehensive database related to the viscosity of gas condensate, including 1370 laboratory data was used. This goal is possible if the amount of viscosity of the liquids released below the dew point is available. Estimation of production rate in these reservoirs is important. In gas-condensate reservoirs, liquid dropout occurs by reducing the pressure below the dew point pressure in the area near the wellbore.
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