Multi-Phase Flow Metering for Offshore Pipeline Leak Detection: Anomaly Detection Using AI Algorithms

Abstract

Offshore oil and gas operations have long relied on Multi-Phase FlowMetering (MPFM) technology to accurately monitor the flow of oil, gas, and water within pipelines. As offshore pipeline networks expand and age, the risks of undetected leaks grow significantly, leading to economic losses and environmental disasters. Traditional MPFM systems focus on fluid composition and flow rate monitoring but fall short in providing real-time leak detection capabilities. The integration of Artificial Intelligence (AI), specifically anomaly detection algorithms, into MPFM systems presents a promising solution for addressing this critical gap in pipeline monitoring. Anomaly detection using AI involves training predictive models on historical pipeline data to identify irregularities in flow patterns that could signify a leak. By utilizing machine learning techniques such as random forests, k-means clustering, and autoencoders, AI models can detect subtle deviations in pipeline behavior that are often overlooked by conventional monitoring methods. This paper explores the application of AI-driven anomaly detection to offshore pipeline leak detection, offering a comprehensive review of the existing literature on MPFM technology, AI in industrial settings, and the evolving role of predictive analytics in oil and gas operations. The methodology outlines the steps taken to develop and validate a predictive model using historical MPFM data from a real-world offshore pipeline in Nigeria. Through a case study focusing on an offshore pipeline in the Niger Delta, this research demonstrates the effectiveness of AI in identifying leaks before they result in major economic or environmental damage. Results from the case study highlight significant improvements in leak detection accuracy, early identification of leaks, and cost savings through the implementation of AI-enhanced MPFM systems. By integrating real-time data, such as pressure changes, flow rates, and fluid composition, the proposed AI-driven model offers a dynamic approach to pipeline monitoring. This approach not only automates the detection of potential leaks but also reduces the time it takes to identify and mitigate leaks, ultimately leading to enhanced operational efficiency and environmental safety. Additionally, the study explores the economic and environmental impact of early leak detection, emphasizing the importance of incorporating AI into MPFM systems for long-term sustainability in offshore oil and gas operations. The findings of this research suggest that AI has the potential to revolutionize pipeline monitoring by enhancing the capabilities of MPFM systems, making them more responsive to leaks and better equipped to handle the complexities of multi-phase flow in offshore environments. As offshore pipelines become more susceptible to leaks due to age and harsh operational conditions, the integration of AI-driven anomaly detection will be key to ensuring the continued viability and safety of offshore oil and gas infrastructure. This paper concludes by outlining future research directions and recommending best practices for deploying AI-enhanced MPFM systems to optimize leak detection and pipeline integrity management in offshore settings.