Gas lift remains one of the most reliable an‎d versatile artificial lift methods in petroleum production. However, optimizing its performance poses substantial technical an‎d operational challenges due to nonlinear flow behaviors, multiple constraints, an‎d the dynamic interactions between wells. This study provides a comprehensive academic review of gas lift optimization methods from classical numerical approaches to modern AI-enabled, real-time systems. It begins by outlining the physical principles of gas lift operation an‎d its importance in sustaining production from mature fields. The paper then categorizes optimization strategies into two major groups: numerical methods, such as the Equal Slope an‎d Newton Reduction techniques, an‎d meta-heuristic methods, including GA, PSO, an‎d ACO. Comparative analyses demonstrate that while traditional methods offer precision an‎d analytical clarity, meta-heuristics an‎d AI-based algorithms provide flexibility, robustness, an‎d adaptability to complex field environments. Further sections explore real-time optimization (RTO) frameworks an‎d integrated asset modeling (IAM), which combine simulation data with machine learning to achieve continuous optimization an‎d predictive control. The findings highlight that hybrid workflows integrating physics-based an‎d AI- driven techniques deliver superior performance in maximizing production an‎d reducing gas waste. Ultimately, this review contributes to the ongoing transition from static, rule-based operations to intelligent, self-optimizing gas lift systems, paving the way for smarter an‎d more sustainable field management.

11/10/2025 12:00:00 AM

89160

1404/09/28

A Comprehensive Review of Gas Lift Optimization Methods

Gas Lift, , Gas Lift Optimization, , Artificial Lift, , Numerical Methods, , Meta-Heuristic Algorithms, , GA, , PSO, , ACO, , Real-Time Optimization, , Smart Gas Lift, , Integrated Asset Modeling.