Hybrid particle swarm neural network and regression models for dew-point pressure prediction

Accurate prediction of dew-point pressure (DPP) is essential for the development and production management of gas condensate reservoirs; however, existing empirical correlations and standalone machine learning models often suffer from limited generalization and sensitivity to data variability. This study addresses this gap by developing and comparing a nonlinear multiple regression (NLMR) correlation and a hybrid particle swarm optimized neural network (PSONN) model using a large and diverse dataset of 880 experimental samples collected from published literature and Middle East reservoirs. The PSONN model was selected due to its capability to overcome neural network limitations such as slow convergence by optimizing network weights through global swarm intelligence. The hybrid PSONN model achieved superior predictive accuracy with APRE of 2.45% and CC of 0.997, outperforming both the proposed NLMR correlation and previously published models. The results demonstrate that the developed hybrid framework can significantly improve dew-point pressure estimation, thereby supporting reliable reservoir characterization, surface facility design, and production planning in gas condensate fields.