Modeling dew point pressure of gas condensate reservoirs: Comparison of hybrid soft computing approaches, correlations, and thermodynamic models

Optimal future management of gas condensate reservoirs requires reliable estimation of the dew point pressure (Pd). Due to the limitations of the available Pd determination methods, such as cost and the prohibitive time for experimental approaches, and inaccuracies and lack of generalization for predictive approaches, it is still necessary to establish more accurate and user friendly Pd paradigms. In this study, various methodologies based on soft computing (SC) techniques, optimization algorithms, and generalized reduced gradient (GRG) method were implemented to develop Pd models based on a widespread databank. Two types of artificial neural networks, namely radial basis function (RBF) neural networks and Multilayer perceptron (MLP) are the employed SC methods. To improve the prediction capability of the latter, Levenberg-Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugate Gradient (SCG) algorithms were used in the training phase of MLP, while three nature-inspired algorithms, namely Genetic Algorithm (GA), Bat Algorithm (BA), and Salp Swarm Algorithm (SSA) were first considered in the RBF learning phase. Then, the four best-found models were assembled beneath a unified paradigm utilizing a committee machine intelligent system (CMIS). Also, a correlation was developed using GRG. The developed CMIS and GRG correlation were compared with four empirical correlations as well as seven equations of state (EOSs). Based on the results obtained, CMIS model exhibits very satisfactory Pd predictions with an overall average absolute percent relative error (AAPRE) of 5.28%, and outperforms largely the other existing predictive approaches. Furthermore, the developed correlation provided more accurate results compared to existing correlations and EOSs.