Integrative Approaches to Gas Hydrate Mitigation: From Molecular Inhibitors to Machine Learning Based Flow Assurance

Abstract

Gas hydrate formation in oil and gas pipelines, particularly under deepwater and high-pressure conditions, presents severe operational and safety risks. These ice-like crystalline compounds, composed of water and gas molecules, can block flow channels and disrupt production, posing significant threats to infrastructure reliability, flow assurance, and economic efficiency. This review offers a comprehensive analysis of hydrate formation mechanisms, including phase behavior, thermodynamic properties, and structural classifications across varied pipeline systems. It highlights conventional mitigation strategies such as thermodynamic inhibitors, kinetic hydrate inhibitors, and anti-agglomerants, while emphasizing recent advances in eco-friendly and dual-function inhibitors, including ionic liquids, amino acids, and nanoparticles, which promise improved efficiency with lower environmental impact.
In addition, the review critically evaluates predictive modelling techniques ranging from classical statistical and thermodynamic models to modern machine learning approaches such as Artificial Neural Networks and decision trees, which enable faster and more accurate hydrate risk assessments. By integrating core scientific principles with cutting-edge chemical and computational innovations, this study identifies sustainable, cost-effective, and scalable strategies for gas hydrate control. The paper further underscores the need for interdisciplinary collaboration between academia and industry to develop next-generation inhibitors and robust predictive frameworks tailored to evolving operational demands in flow assurance.