Real-Time IoT-Integrated Multiphase Flow Modeling for Enhanced Flow Assurance and Leakage Detection in Offshore Pipeline Systems

Abstract

 Offshore pipeline systems are increasingly challenged by flow assurance issues such as undetected leaks, pressure surges, and inefficient maintenance practices, largely due to limited real-time monitoring and the difficulty of integrating traditional modeling approaches with modern sensing technologies. Many existing models, including mechanistic, empirical, and computational fluid dynamics (CFD) models, often lack the adaptability required to respond effectively to transient flow conditions in subsea environments. This study addresses this critical gap by exploring the integration of Internet of Things (IoT) technologies with multiphase flow modeling for improved flow assurance and predictive maintenance in offshore pipelines. A laboratory-based experimental system was constructed using a 3D rubber pipeline equipped with embedded IoT sensors. Water was used as the working fluid. Flow rates and leakage levels were recorded and analyzed via the ThingSpeak platform. Pressure pulses were monitored at different points to simulate fault localization, and data was visualized through heatmaps to track leakage severity over time. Results showed that stable flow rates between 185 and 205 indicated no leakage, while higher flow rates (208–239) led to significant increases in leakage levels—peaking at 100 during critical flow conditions. Pressure pulse analysis confirmed the accuracy of sensor placement in detecting anomalies based on distance and amplitude variation. The study concludes that combining IoT with flow modeling improves the accuracy of anomaly detection and enhances maintenance planning. It is recommended that offshore operators adopt IoT-integrated systems for real-time monitoring and ensure infrastructure is compatible with smart technologies for long-term operational efficiency.