Study Of The Effectiveness Of Subsea Pipeline Leak Detection Methods

Authors

  • Hamzah Hamzah

DOI:

https://doi.org/10.62012/collaborate.v1i2.37

Keywords:

Detection, Monitoring, Pipe Leak, Subsea Pipeline

Abstract

The oil and gas exploration and production industry significantly contributes to the global economy, and undersea pipelines are the safest and most economical route to transport natural gas and crude oil from offshore to land. However, undersea pipeline networks are prone to leaks that can cause financial losses and extreme environmental pollution. Therefore, it is essential to monitor pipelines to detect leaks promptly or even predict leaks to reduce the impact of oil spills on society. Various leak detection methods have been developed, and further research is needed to identify research gaps for future work. There are various methods for detecting pipe leaks, each with advantages and disadvantages. External-based methods tend to be more accurate in finding leaks, while internal/computational methods can determine the level of leaks. The acoustic method is considered the most efficient for inspecting subsea pipelines and detecting leaks. However, each method has strengths and limitations that must be considered before choosing the proper method. Advances in computing technology have also enabled the use of dynamic modeling approaches more popular in the oil and gas industry. Factors such as cost, sensitivity, accuracy, ease of use, and the type and location of the pipeline also need to be considered in selecting a suitable pipeline leak detection method.

 

Downloads

Download data is not yet available.

References

Du, F., Li, C., & Wang, W. (2023). Development of Subsea Pipeline Buckling, Corrosion and Leakage Monitoring. Journal of Marine Science and Engineering, 11(1), 188.

Diao, X., Chi, Z., Jiang, J., Mebarki, A., Ni, L., Wang, Z., & Hao, Y. (2020). Leak detection and location of flanged pipes: An integrated approach of principal component analysis and guided wave mode. Safety Science, 129, 104809.

Adegboye, M. A., Fung, W. K., & Karnik, A. (2019). Recent advances in pipeline monitoring and oil leakage detection technologies: Principles and approaches. Sensors, 19(11), 2548.

Geiger, G., Vogt, D., & Tetzner, R. (2006). State-of-the-art in leak detection and localization. Oil Gas European Magazine, 32(4), 193.

Xu, J., Nie, Z., Shan, F., Li, J., Luo, Y., Yuan, Q., & Chen, H. (2013). Leak detection methods overview and summary. In ICPTT 2012: Better Pipeline Infrastructure for a Better Life (pp. 1034-1050).

Zhang, J., Hoffman, A., Murphy, K., Lewis, J., & Twomey, M. (2013, April). Review of pipeline leak detection technologies. In PSIG Annual Meeting. OnePetro.

Korlapati, N. V. S., Khan, F., Noor, Q., Mirza, S., & Vaddiraju, S. (2022). Review and analysis of pipeline leak detection methods. Journal of Pipeline Science and Engineering, 100074.

Kousiopoulos, G.-.P., Kampelopoulos, D., Karagiorgos, N., Papastavrou, G.-.N., Konstantakos, V., Nikolaidis, S., 2022. Acoustic leak localization method for pipelines in high noise environment using time-frequency signal segmentation. IEEE Trans. Instrument. Meas. 71. doi:10.1109/TIM.2022.3150864.

Geiger, G.; Vogt, D.; Tetzner, R. State-of-the-art in leak detection and localization. Oil Gas Eur. Mag. 2006, 32, 1–26.

Scott, S.L.; Barrufet, M.A. Worldwide Assessment of Industry Leak Detection Capabilities for Single&MultiphasPipelines.OffshoreTechnologyResearchCenterCollegeStation.2003.Availableonline:http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.118.6455&rep=rep1&type=pdf (accessed on 17 December 2018).

Martins, J.C.; Seleghim, P. Assessment of the performance of acoustic and mass balance methods for leak detection in pipelines for transporting liquids. J. Fluids Eng. 2010, 132, 011401–011413.

Shama, A. M., Bady, A., El-Shaib, M. N., & Kotb, M. A. (2017, October). Review of leakage detection methods for the subsea pipeline. In Proceedings of the 17th International Congress of the International Maritime Association of the Mediterranean, Lisbon, Portugal (pp. 9-11).

Peng, Z.; Wang, J.; Han, X. A study of harmful pressure wave method based on HAAR wavelet transform in ship piping leakage detection system. In Proceedings of the 2011 IEEE 2nd International Conference on Computing, Control and Industrial Engineering (CCIE), Wuhan, China, 20–21 August 2011; IEEE: Piscataway, NJ, USA, 2011; pp. 111–113.

Yonghong, S.; Zhenhua, W. Detection of small leakage from pipeline based on improved harmonic wavelet. Proceedings of the 2012 7th International Conference on Computer Science & Education (ICCSE), Melbourne, VIC, Australia, 14–17 July 2012; IEEE: Piscataway, NJ, USA, 2012; pp. 45–49.

Chen, Z.; Lian, X.; Yu, Z. Leakage detection for oil pipelines based on Independent Component Analysis. Proceedings of the 29th Chinese Control Conference (CCC), Beijing, China, 29–31 July 2010; IEEE: Piscataway, NJ, USA, 2010; pp. 4009–4013.

Li, Q.; Li, M.; Zhang, X.; Ba, W. Research on mixed signal separation method for pipeline leakage based on RobustICA. In Proceedings of the 2016 International Conference on Robotics and Automation Engineering (ICRAE), Jeju, South Korea, 27–29 August 2016; IEEE: Piscataway, NJ, USA, 2016; pp. 70–74.

Li, H.; Xiao, D.; Zhao, X. Morphological filtering assisted field-pipeline small leakage detection. In Proceedings of the 2009 IEEE International Conference on System, Man and Cybernetics, SMC 2009, San Antonio, TX, USA, 11–14 October 2009; pp. 3769–3774.

Zhang, G.; Zhu, J.; Song, Y.; Peng, C.; Song, G. A Time Reversal Based Pipeline Leakage Localization Method with the Adjustable Resolution. IEEE Access 2018, 6, 26993–27000.

Fu, H., Ling, K., Pu, H., 2022. Identifying two-point leakages in parallel pipelines based on flow parameter analysis. J. Pipeline Sci. Eng. 100052.

Meng, Q., Lang, X., Lin, M., Cai, Z., Zheng, H., Song, H., et al., 2022. Leak localization of gas pipeline based on the combination of EEMD and cross-spectrum analysis. IEEE Trans. Instrum. Meas. 71.

Zheng, J., Liang, Y., Xu, N., Wang, B., Zheng, T., Li, Z., et al., 2021. Deep Pipe: a customized generative model for estimations of liquid pipeline leakage parameters. Comput. Chem. Eng. 149. doi:10.1016/j.compchemeng.2021.107

Hassan, S., Wang, J., Kontovas, C., Bashir, M., 2022. An assessment of causes and failure likelihood of cross-country pipelines under uncertainty using Bayesian networks. Reliab. Eng. Syst. Saf. 218. doi:10.1016/j.ress.2021.108171.

Spandonidis, C., Theodoropoulos, P., Giannopoulos, F., Galiatsatos, N., Petsa, A., 2022. Evaluating deep learning approaches for oil and gas pipeline leak detection using wireless sensor networks. Eng. Appl. Artif. Intel. 113, 104890. doi:10.1016/j.engappai.2022.104890

Behari, N., Sheriff, M. Z., Rahman, M. A., Nounou, M., Hassan, I., & Nounou, H. (2020). Chronic leak detection for single and multiphase flow: A critical review on onshore and offshore subsea and arctic conditions. Journal of Natural Gas Science and Engineering, 81, 103460.

Yang, L., & Zhao, Q. (2021). A BiLSTM-based pipeline leak detection and disturbance-assisted localization method. IEEE Sensors Journal, 22(1), 611-620.

Sekhavati, J., Hashemabadi, S.H., Soroush, M., 2022. Computational methods for pipeline leakage detection and localization: a review and comparative study. J. Loss Prev. Process Ind. 77, 104771. doi:10.1016/j.jlp.2022.104771.

H. Palippui, “Analysis Of The Installation Of Subsea Pipelines To Support The Need For Clean Water In Supporting Tourism Development On Kayangan Island”, Journal of Maritime Technology and Society, vol. 1, no. 1, pp. 1-9, Feb. 2022.

Downloads

Published

2023-12-28

How to Cite

Hamzah, H. (2023). Study Of The Effectiveness Of Subsea Pipeline Leak Detection Methods. Collaborate Engineering Daily Book Series, 1(2), 45–56. https://doi.org/10.62012/collaborate.v1i2.37