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Leak Detection in Natural Gas Pipelines Using Intelligent Models

Authors
Oluwatoyin Akinsete (University of Ibadan) | Adebayo Oshingbesan (University of Ibadan)
DOI
https://doi.org/10.2118/198738-MS
Document ID
SPE-198738-MS
Publisher
Society of Petroleum Engineers
Source
SPE Nigeria Annual International Conference and Exhibition, 5-7 August, Lagos, Nigeria
Publication Date
2019
Document Type
Conference Paper
Language
English
ISBN
978-1-61399-691-1
Copyright
2019. Society of Petroleum Engineers
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6 in the last 30 days
91 since 2007
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SPE Member Price: USD 9.50
SPE Non-Member Price: USD 28.00

Detection of small leaks in gas pipelines is an important and persistent problem in the oil and gas industry. However, the industry is beginning to investigate how tools of Machine Learning, Artificial Intelligence, Big Data, etc. can be used to improve current industry processes.

This work aims to study the ability of intelligent models to detect small leaks in a natural gas pipeline using operational parameters such as pressure, temperature and flowrate through existing industry performance metrics (sensitivity, reliability, robustness and accuracy). Observer design technique was applied to detect leaks in a gas pipeline using a regresso-classification hierarchical model where an intelligent model acts as a regressor and a leak detection algorithm acts as a classifier. Five intelligent models (Gradient Boosting, Decision Trees, Random Forest, Support Vector Machine and Artificial Neural Network) were used in this present work.

Results showed that the Random Forest and Decision Tree models are the most sensitive as they can detect a leak of 0.1% of nominal flow in about 2 hours. All the intelligent models had high reliability with zero false alarm rate in testing phase. However, due to this level of reliability, the models had low accuracy with the Artificial Neural Network and Support Vector Machine performing best and better regressors than the others. All the intelligent models are robust. The average time to leak detection for different leak sizes for all the intelligent models were compared to a real time transient model in literature. The intelligent models had a time savings of 25% to 48%.

Results in this present work further suggest that intelligent models could be used alongside a real time transient model to improve leak detection. Also, that the tools of big data, data analytics, artificial intelligence can be harnessed to improving leak detection results.

File Size  836 KBNumber of Pages   10

Afebu, K. O., Abbas, A. J., Nasr, G. G. and Kadir, A. 2015. Integrated Leak Detection in Gas Pipelines Using OLGA Simulator and Artificial Neural Network. Paper presented at the Abu Dhabi International Petroleum Exhibition and Conference held in Abu Dhabi, UAE, 9 - 12 November 2015.

Barbagelata, L. 2011. Acoustic Leak Detector Underwater Acoustic for Leak Detection. Subsea and Arctic Leak Detection Symposium (SALDS), Houston, TX, 2011.

Belsito, S., Lommbardi, P., Andreussi, P. and Banerjee, S. 1998. Leak Detection in Liquefied Gas Pipeline by Artificial Neural Networks AIChE Journal Vol 44, Issue 12.

Billmann, L. and Isermann, R. 1987. Leak Detection Methods for Pipelines. Automatica 23(3): 381-385.

Chen, Z., Xu, X., Du, X., Zhang, J. and Yu, M. 2018. Leakage Detection in Pipelines using Decision Tree and Multi-Support Vector Machine, Advances in Engineering Research, Volume 140.

Cui-wei, L., Yu-Xing, L., Jun-Tao, F. and Guang-xiao, L. 2015. Experimental study on acoustic propagation- characteristics-based leak location method for natural gas pipelines, Process Safety and Environmental Protection, pp. 43-60, 2015.

Da Silva, H.V., Morooka, C. K., Guilherme, I. R., Da Fonseca, T. C. and Mended, J. R. P. 2005. Leak Detection in Petroleum Pipelines Using a Fuzzy System, Journal of Petroleum Science and Engineering vol. 49, pp 223-238.

Desmet, A. and Delore, M. 2017. Leak Detection in Compressed Air Systems using Unsupervised Anomaly Detection Techniques. Annual Conference of the Prognostics and Health Management Society 2017.

Hint Dossier. Gas Pipeline Explosion at Ghislenghien, Belgium, Ghislenghien, 2005.

Isehunwa, S. O., Ipinsokan, S. B. and Akinsete, O. O. 2014. Pressure Transient Analysis of Multiple Leakages in a Natural Gas Pipeline. Journal of Petroleum and Gas Engineering Vol. 5, No. 1: 1-8.

Kim, S. H. 2005. Extensive Development of Leak Detection Algorithm by Impulse Response Method. J. Hydraul. Eng. ASCE 131(3): 201-208.

Klein, W. R. 1993. Acoustic Leak Detection. Am. Soc. Mech. Eng. Petrol. Div. (Publication) PD 55:57-61.

Maicol, M. M. 2017. Recent Technological Advances in Large Natural Gas Pipeline Leak Detection and Prediction- A General Survey, ASME, 2017

McEntire, D., Long, L., Kendra, J. and Kelly, J. 2013. Spontaneous Planning after the San Bruno Gas Pipeline Explosion: A Case Study of Anticipation and Improvisation during Response and Recovery Operations. Journal of Homeland Security and Emergency Management, vol. 10, No. 1, pp. 161-185.

Morgan, H., Carpenter, P. and Nicholas, E. 2016. Pipeline Leak Detection Handbook. DOI: http://dx.doi.or/10.1016/B978-0-12-802240-5.00001-7 pp 1-115.

Scott, S. L., Liu, L. and Yi, J. 1999. Modelling the Effects of a Deepwater Leak on Behaviour of a Multiphase Production Flow Line. Paper presented at the 1999 SPE/EPA E&P Environmental Conference, Austin, Texas.

Shields, D. N., Ashton, S. A. and Daley, S. 2001. Design of Nonlinear Observers for Detecting Faults in Hydraulic Sub-sea Pipelines. Contr. Eng. Pract. 9(3): 297-311.

Tiang, X. 1997. Non-isothermal Long Pipeline Leak Detection and Location. Atca Scientiarum Naturalium Universitis Pekinensis 33(5):574-580.

Walker, I. 2011. Leak Detection using Distributed Acoustic Sensing. Subsea and Arctic Leak Detection Symposium (SALDS), Houston, TX, 2011.

Wang, H. and Duncan, I. J. 2014. Likelihood, Causes, and Consequences of Focused Leakage and Rupture of U.S. Natural Gas Transmission Pipelines, Journal of Loss Prevention in the Process Industries, pp. 177-187.

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