AI-Driven Smart Proxy Model for Optimizing Offshore Pipeline Flow Behavior and Early Leakage Detection

Abstract Accurate pipeline leak detection is critical for ensuring safety, operational efficiency, and environmental sustainability in offshore oil and gas operations. Traditional methods, such as pressure-based monitoring and manual inspections, often fail to detect small or gradual leaks under dynamic multiphase flow conditions, leading to delayed responses and significant environmental and economic risks. This study introduces an AI-driven leak detection framework that leverages two independently trained Artificial Neural Network (ANN) models to address these challenges. Model 1 utilizes real-time Multiphase Flow Meter (MPFM) data to establish a baseline for normal operating conditions, enabling the detection of deviations indicative of leaks. Model 2 relies on oil flow rate predictions generated by the OLGA transient flow simulator, providing a robust alternative for scenarios where real-time sensor data is unavailable. The framework was tested using synthetic leak scenarios of varying sizes and locations introduced into the OLGA model, simulating real-world pipeline conditions. Results demonstrate that the ANN, trained exclusively on non-leak scenarios, accurately distinguishes between normal operational fluctuations and leak-induced anomalies. Model 1 detects deviations with a stringent 1% error threshold, serving as an early warning system, while Model 2 validates potential anomalies, ensuring reliable leak detection without dependence on physical sensors. This dual-model approach provides a scalable, cost-effective, and adaptive solution for offshore pipeline monitoring, capable of addressing operational complexities in remote environments. The study highlights the transformative potential of AI-driven methodologies to enhance pipeline integrity management, reduce environmental risks, and optimize decision-making in offshore operations.