Reliable modeling of constant volume depletion (CVD) behaviors in gas condensate reservoirs

Abstract

It is important to access a clear understanding about the phase behavior of gas condensate reservoirs in order to forecast the future performance of such reservoirs. In this communication, different models based on multilayer perceptron network (MLP NN), least square support vector machine (LSSVM), adaptive neuro inference system (ANFIS) and radial basis function networks optimized by genetic algorithm (GA-RBF NN) were developed for estimation of amount of produced gas using constant volume depletion (CVD) tests of retrograde gas condensate reservoirs. Results show that the developed models are capable of accurately estimating the cumulative produced gas (Gp) as an output parameter by utilizing various input parameters including temperature, pressure, composition of gas, and properties of plus fraction. The analysis of results reveals that the GA-RBF NN presents more accurate results in comparison with MLP NN, ANFIS and LSSVM models. Moreover, comparison between GA-RBF NN model as the most accurate model developed in the present work and two literature models shows the superiority of GA-RBF NN. Results of this study can be used in PVT softwares to enhance the accuracy and precision of CVD modeling of gas condensate reservoirs.