Natural Gas Engineering and Optimization of Gas Production Systems Using Advanced Control Techniques

Abstract

The optimization of natural gas production systems is essential for enhancing hydrocarbon recovery, maintaining operational safety, and minimizing costs in increasingly complex and dynamic energy environments. Advanced control techniques, including model predictive control (MPC), fuzzy logic controllers, and artificial intelligence (AI)-based optimization methods, have become indispensable tools for managing the nonlinear behavior, multivariable interactions, and operational uncertainties inherent in gas production systems. These techniques are applied across critical areas such as flow regulation, well performance optimization, compressor station management, and pipeline operations, enabling real-time decision-making, predictive maintenance, and improved process efficiency. This review provides a comprehensive analysis of current methodologies, illustrating how advanced control strategies improve system stability, energy efficiency, and reliability while addressing operational challenges such as equipment limitations, data integration, and dynamic reservoir behavior. Key case studies and recent research findings are discussed, emphasizing the potential of integrating AI and predictive control frameworks to further enhance production performance. Finally, future research directions are outlined, focusing on the development of adaptive, intelligent, and hybrid control strategies capable of meeting the demands of modern gas production systems while supporting sustainable and cost-effective operations.