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Applying Machine Learning to Predict Pipeline Failures
Subject area: Science,Engineering and Technology · Area of research: Oil and Gas
Abstract
Pipelines are vital for transporting oil and gas, but leaks can have serious consequences such as fires, injuries, pollution, and property damage. Therefore, preserving pipeline integrity is crucial for a safe and sustainable energy supply. The rapid progress of machine learning (ML) technologies provides an advantageous opportunity to develop predictive models that can effectively tackle these challenges. This article explores the significant challenges in managing pipeline infrastructure, including the safety and reliability of its pipeline network. The aging infrastructure, coupled with varying environmental conditions and operational stresses, increases the risk of leaks, which can lead to safety hazards, environmental damage, and financial losses. Traditional leak detection methods are reactive, identifying issues only after significant damage has occurred. The advent of machine learning (ML) presents a significant opportunity to enhance gas pipeline operations through improved efficiency, predicting potential leaks, and prioritizing maintenance. This paper delves into the implementation of machine learning in gas pipelines, focusing on three major areas: Enhanced Safety, Operational Efficiency, and Regulatory Compliance. This paper presents a comprehensive approach to build a machine learning model to predict potential pipeline leaks. This model will integrate operational, environmental, and geospatial data to predict potential pipeline leaks. Early detection of leaks not only prevents environmental damage but also safeguards public safety. This paper addresses challenges primarily in data, model, and computational aspects. By implementing ML in pipeline operations, companies can benefit in cost savings, improved safety records, and reduced environmental impact.
Keywords
Machine Learning (ML), Utilities, Geographic Information Systems (GIS), Enhanced Safety, Operational Efficiency, and Regulatory Compliance.
References
[1] Singh, R., & Misra, D. (2020). Machine learning applications in oil and gas pipeline leak detection: An ensemble approach using decision trees and SVMs. Journal of Pipeline Engineering and Safety.
[2] Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735- 1780.
[3] LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. (For CNN-based methods applied to spatial data analysis.)
[4] Adadi, A., & Berrada, M. (2018). Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI). IEEE Access, 6, 52138-52160.
[5] Ahammed, M. (1998). Probabilistic estimation of remaining life of a pipeline in the presence of active corrosion defects. International Journal of Pressure Vessels and Piping, 75(4), 321- 329.
[6] Xu, X., & Denka, D. (2019). Hybrid machine learning for improved pipeline failure prediction. Journal of Safety and Reliability, 14(3), 123-133.
[7] Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer. (A comprehensive source on feature selection and data preprocessing.)
[8] Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and Practice. OTexts. (Includes ARIMA and time-series feature engineering methods.)
[9] Longley, P., Goodchild, M. F., Maguire, D. J., & Rhind, D. W. (2015). Geographic Information Science and Systems. John Wiley & Sons.
[10] Korlapati, Naga Venkata Saidileep, et al. "Review and analysis of pipeline leak detection methods.” Journal of pipeline science and engineering 2.4 (2022): 100074.
[11] Su, Yue, et al. "Fast and accurate prediction of failure pressure of oil and gas defective pipelines using the deep learning model." Reliability Engineering & System Safety 216 (2021): 108016.
[12] Batista, G. E., & Monard, M. C. (2003). An analysis of four missing data treatment methods for supervised learning. Applied Artificial Intelligence, 17(5-6), 519-533.
[13] Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
[14] Jain, A. K. (2010). Data clustering: 50 years beyond K-means. Pattern Recognition Letters, 31(8), 651-666.
[15] Kiefner, J. F., & Associates. (2013). Integrity Management of Pipelines. ASME Press. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
How to cite this paper
@article{1708456,
author = {David Dayo Osantola},
title = {Applying Machine Learning to Predict Pipeline Failures},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {8},
number = {11},
pages = {2382-2388},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1708456.pdf},
abstract = {Pipelines are vital for transporting oil and gas, but leaks can have serious consequences such as fires, injuries, pollution, and property damage. Therefore, preserving pipeline integrity is crucial for a safe and sustainable energy supply. The rapid progress of machine learning (ML) technologies provides an advantageous opportunity to develop predictive models that can effectively tackle these challenges. This article explores the significant challenges in managing pipeline infrastructure, including the safety and reliability of its pipeline network. The aging infrastructure, coupled with varying environmental conditions and operational stresses, increases the risk of leaks, which can lead to safety hazards, environmental damage, and financial losses. Traditional leak detection methods are reactive, identifying issues only after significant
damage has occurred. The advent of machine learning (ML) presents a significant opportunity to enhance gas pipeline operations through improved efficiency, predicting potential leaks, and prioritizing maintenance. This paper delves into the implementation of machine learning in gas pipelines, focusing on three major areas: Enhanced Safety, Operational Efficiency, and Regulatory Compliance. This paper presents a comprehensive approach to build a machine learning model to predict potential pipeline leaks. This model will integrate operational, environmental, and geospatial data to predict potential pipeline leaks. Early detection of leaks not only prevents environmental damage but also safeguards public safety. This paper addresses challenges primarily in data, model, and computational aspects. By implementing ML in pipeline operations, companies can benefit in cost savings, improved safety records, and reduced environmental impact.
},
keywords = {Machine Learning (ML), Utilities, Geographic Information Systems (GIS), Enhanced Safety, Operational Efficiency, and Regulatory Compliance.},
month = {May},
}