Oil and Gas Pipeline Leakage Detection using IoT and Deep Learning Algorithm

Authors

  • Anietie P. Ekong Akwa Ibom State University, Nigeria
  • Gabriel Gregory James Topfaith University, Nigeria
  • Ifeoma Ohaeri Topfaith University, Nigeria
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DOI:

https://doi.org/10.51519/journalisi.v6i1.652

Keywords:

Artificial Intelligence, Convolutional Neural Networks, Computer Vision, Pipeline Leak Detection, Machine Learning

Abstract

Pipeline leaks are a frequent occurrence in oil and gas infrastructure worldwide. Though leak detection systems are expected to be installed on all pipelines in the near future, relying on human efforts to physically monitor these pipelines is and will continue to be challenging. Though today's leak detection techniques are not able to completely stop leaks from occurring or to detect most leaks, they are essential in lessening their effects. Despite recent developments toward solving this problem, the solution still falls short of expectations. This research presents an approach to pipeline leak detection by leveraging on the exceptional abilities of Convolutional Neural Network (CNN) and Internet of Things (IoT).  A comprehensive dataset on oil and pipeline leakage is collected, and the CNN model is developed and trained with the collected dataset. Thereafter, the trained model is integrated into the monitoring system to provide notifications of leaks. The model is adaptable and scalable and its performance, as evaluated, shows an improvement over existing systems with an accuracy of 97% hence well suited for deployment in various pipeline networks for the overall improvement of safety environment in the oil and gas sector.

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References

C. Lirong, W. Dingxiong, W. Hui, W. Xiaodong, G. Landing, M. Fen, X. Fei, F. Muqun, Y. Zhaofeng, L. Chao, J. Tiancheng, “Research on an oil pipeline anomaly identification method for distinguishing true and false anomalies,” Mobile Information Systems, vol. 2022.

O. Muhammad, H. Ali, “Deep Learning approach for oil pipeline leakage detection using image-based edge detection techniques,” Journal Européen des Systèmes Automatisés, vol. 56, no. 4, pp. 663-673, 2023.

W. Dongmei, S. Shaoxiong, L. Jingyi, H. Zhongrui & C. Jing, “Research on gas pipeline leakage model identification driven by digital twin,” Systems Science & Control Engineering, vol. 11, no. 1, 2023.

J. Renato, O. Thiago; P. Martin, “Leakage prevention and real-time internal detection in pipelines using a built-in wireless information and communication network.” SPE journal, vol. 25, no. 5, pp. 2496–2507, 2020.

A. Ekong, B. Ekong, A. Edet, “Supervised machine learning model for effective classification of patients with covid-19 symptoms based on bayesian belief network,” Journal of Information Systems and Informatics,” vol. 2, no. 1, 27-33, 2022.

A. Ekong, A. Etuk., S. Inyang, M. Ekere-obong, “Securing against zero-day attacks: a machine learning approach for classification and organizations’ perception of its impact,” Journal of Information Systems and Informatics, vol. 5, no. 3, pp. 1123-1140, 2023.

J. Choi, S. Im, “Application of CNN models to detect and classify leakages in water pipelines using magnitude spectra of vibration sound,” Applied Sciences, vol. 13. no. 5, 2023.

N. Ullah, Z. Ahmed, J. Kim “Pipeline leakage detection using acoustic emission and machine learning algorithms,” Sensors (basel), vol. 23, no. 6 pp. 3226, 2023.

S. Christos, T. Panayiotis, G. Fotis, G. Nektarios, P. Aret, “Evaluation of deep learning approaches for oil & gas pipeline leak detection using wireless sensor networks,” Engineering Applications of Artificial Intelligence, vol. 113, 2022.

A. July and C. Gomez, “Application of machine learning techniques for leak detection in horizontal pipelines” Webology, vol. 19, no. 4, 2022.

G. Jianfeng, Z. Yu, N. Kai, Z. Huaizhi, H. Bin, Y. Jialin, “Research on oil-gas Pipeline Leakage Detection Method Based on Particle Swarm Optimization Algorithm Optimized Support Vector Machine,” Journal of Physics: Conference Series, vol. 2076, 2021.

S. Moreira, V. Shah, M. Varela, A. Monteiro, G. Putnik “Supervised machine learning applied to gas leak detection in air conditioner cooling system,” The 14th international conference on axiomatic design iop conf. series,” Materials Science and Engineering, no. pp. 1174 012008, 2021.

A. Oluwatoyin, O. Adebayo, “Leak detection in natural gas pipelines using intelligent models,” SPE Nigeria annual international conference and exhibition, no 198738, 2019.

[ C. Owuru, J. Osakwe, S. Akinsola, “Utilisation of Information and Communication Technology for Environmental Sustainability: A Global Perspective” Journal of Information Systems and Informatics, vol. 3, no 3, pp. 403 -423, 2021.

A. Mutiu, F. Wai-Keung, K. Aditya, “Recent advances in pipeline monitoring and oil leakage detection technologies: principles and approaches,” Sensors, vol. 19, no. 11, pp. 1-36, 2019.

R. Balda, K. Civan, W. Companies, “Application of mass balance and transient flow modeling for leak detection in liquid pipelines,” SPE Oklahoma: Society of Petroleum Engineers, no. 164520, 2013.

A. Ekong A., “Evaluation of machine learning techniques towards early detection of cardiovascular diseases,” American Journal of Artificial Intelligence, vol. 7, no. 1, pp. 6-16, 2023.

A. Ekong, G. Ansa, N. Odikwa, E. Adabra, “A hybrid machine learning model for classifying phishing uniform resource locators,” International Journal of Engineering Research in Computer Science and Engineering, vol. 10, no. 5, pp. 63-67, 2023.

B. Ekong, O. Ekong, A. Silas, A. Edet, B. William, “Machine Learning Approach for Classification of Sickle Cell Anemia in Teenagers Based on Bayesian Network,” Journal of Information Systems and Informatics, vol. 5, no. 4, pp.1793-1808, 2023.

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Published

2024-03-27

Issue

Vol. 6 No. 1 (2024): March

Section

Articles

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How to Cite

[1]
A. P. Ekong, G. G. James, and I. Ohaeri, “Oil and Gas Pipeline Leakage Detection using IoT and Deep Learning Algorithm”, journalisi, vol. 6, no. 1, pp. 421–434, Mar. 2024, doi: 10.51519/journalisi.v6i1.652.

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