A Unified Drift–Flux Framework for Predictive Analysis of Flow Patterns and Void Fractions in Vertical Gas Lift Systems

This study utilizes the drift–flux model to develop a new flow pattern map designed to facilitate an accurate estimation of gas void fraction (αg) in vertical upward flow. The map is parameterized by mixture velocity (um) and gas volumetric quality (βg), integrating transition criteria from the established literature. For applications characterized by significant pressure gradients, such as gas lift, these criteria were reformulated as functions of pressure, enabling direct estimation from operational data. A critical component of this methodology for the estimation of αg is the estimation of the distribution parameter (C0). An analysis of experimental data, spanning pipe diameters from 1.27 to 15 cm across the full void fraction ranges (0<αg<1), reveals a critical αg threshold beyond which C0 exhibits a distinct decreasing trend. To characterize this phenomenon, the parameter of the distribution-weighted void fraction (αc=αgC0) is introduced. This parameter, representing the dynamically effective void fraction, identifies the critical threshold at its inflection point. The proposed model subsequently defines C0 using a two-part function of αc. This generalized approach simplifies the complexity inherent in existing correlations and demonstrates superior predictive accuracy, reducing the average error in αg estimations to 5.4% and outperforming established methods. Furthermore, the model’s parametric architecture is explicitly designed to support the optimization and fine-tuning of coefficients, enabling future use of machine learning for various fluids and complex industrial cases.