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Authors = Razan M. Alzannan

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15 pages, 3534 KiB  
Article
An Anomaly Detection Model for Oil and Gas Pipelines Using Machine Learning
by Sumayh S. Aljameel, Dorieh M. Alomari, Shatha Alismail, Fatimah Khawaher, Aljawharah A. Alkhudhair, Fatimah Aljubran and Razan M. Alzannan
Computation 2022, 10(8), 138; https://doi.org/10.3390/computation10080138 - 10 Aug 2022
Cited by 56 | Viewed by 10929
Abstract
Detection of minor leaks in oil or gas pipelines is a critical and persistent problem in the oil and gas industry. Many organisations have long relied on fixed hardware or manual assessments to monitor leaks. With rapid industrialisation and technological advancements, innovative engineering [...] Read more.
Detection of minor leaks in oil or gas pipelines is a critical and persistent problem in the oil and gas industry. Many organisations have long relied on fixed hardware or manual assessments to monitor leaks. With rapid industrialisation and technological advancements, innovative engineering technologies that are cost-effective, faster, and easier to implement are essential. Herein, machine learning-based anomaly detection models are proposed to solve the problem of oil and gas pipeline leakage. Five machine learning algorithms, namely, random forest, support vector machine, k-nearest neighbour, gradient boosting, and decision tree, were used to develop detection models for pipeline leaks. The support vector machine algorithm, with an accuracy of 97.4%, overperformed the other algorithms in detecting pipeline leakage and thus proved its efficiency as an accurate model for detecting leakage in oil and gas pipelines. Full article
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