A systematic review of real-time monitoring systems for oil and gas pipeline leakage identification based on deep learning approaches

Gabriel James; Ini Umoeka; Edidiong Nkang; Emmanuel Etim; David Egete; John Odey; Bassey Ele; Ekemini Okpongkpong; Enefiok Etuk; Tope Jimoh; Idara Essien

doi:10.61298/rans.2026.4.1.250

Authors

Gabriel James
[email protected]

Department of Computer Science, Federal University of Technology, Ikot Abasi, Nigeria
Ini Umoeka
Department of Computer Science, University of Uyo, Uyo, Nigeria
Edidiong Nkang
Department of Computer Science, Federal University of Technology, Ikot Abasi, Nigeria
Emmanuel Etim
Department of Cybersecurity, Federal University of Technology, Ikot Abasi, Nigeria
David Egete
Department of Computer Science, University of Calabar, Calabar, Nigeria
John Odey
Department of Computer Science, University of Calabar, Calabar, Nigeria
Bassey Ele
Department of Computer Science, University of Calabar, Calabar, Nigeria
Ekemini Okpongkpong
Department of Software Engineering, Federal University of Technology, Ikot Abasi, Nigeria
Enefiok Etuk
Department of Computer Science, Michael Okpara University of Agriculture, Umudike, Nigeria
Tope Jimoh
Department of Computer Science, Federal University of Technology, Ikot Abasi, Nigeria
Idara Essien
Department of Computer Science, Federal University of Technology, Ikot Abasi, Nigeria

Keywords:

Oil and Gas Pipeline Monitoring, Real-Time Leak Detection, Internet of Things (IoT), Deep Learning Models, Anomaly Detection in Industrial Systems

Abstract

Leakage in oil and gas pipelines threatens environmental safety, operational integrity, and economic stability. Recent advances have integrated Internet of Things (IoT) technologies and deep learning to improve real-time leak detection and response systems. This study presents a systematic review of state-of-the-art IoT-based and deep learning approaches for pipeline leakage detection, guided by the PRISMA methodology. From 450 articles published between 2015 and 2025, 144 high-quality studies focusing on real-time monitoring were selected for in-depth analysis. Key performance metrics, including accuracy, precision, recall, F1-score, sensitivity, and specificity, were evaluated. Hybrid deep learning models, such as Deep Autoencoder with XGBoost (DAE+XGB) and Bidirectional LSTM (BiLSTM), achieved the strongest performance, with accuracies reaching 99.78%, particularly for fine leaks as small as 0.5 mm. IoT frameworks leveraging cloud computing and reinforcement learning showed strong scalability and adaptability for remote operations. Despite these advances, challenges remain, including limited real-world validation, a lack of standardized datasets, energy inefficiency, cybersecurity risks, and limited model interpretability. Future research should emphasize field deployments, energy-aware IoT designs, improved security protocols, and explainable artificial intelligence to enhance transparency and trust. This review summarizes current progress, challenges, and future opportunities in intelligent, real-time pipeline leak detection for safer and more sustainable oil and gas infrastructure.

Dimensions

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