Application of Artificial Intelligence to Monitor Leaks from Pumps

O. K. Dankwa, J. S. Mensah, E. M. Amarfio and E. P. Amenyah Kove
Additional contact information
O. K. Dankwa: Petroleum and Natural Gas Engineering Department, University of Mines and Technology (UMaT), Tarkwa
J. S. Mensah: Petroleum and Natural Gas Engineering Department, University of Mines and Technology (UMaT), Tarkwa
E. M. Amarfio: Petroleum and Natural Gas Engineering Department, University of Mines and Technology (UMaT), Tarkwa
E. P. Amenyah Kove: Petroleum and Natural Gas Engineering Department, University of Mines and Technology (UMaT), Tarkwa

International Journal of Research and Innovation in Applied Science, 2024, vol. 9, issue 3, 28-34

Abstract: During the production of hydrocarbons, offshore platforms frequently leak large volumes of oil and gas. Traditional methods for detecting these leaks are prone to errors, making them inefficient at detecting leaks precisely. These leaks, however, have damaging effects on the environment and humans, posing economic risks to companies as well. This study explores the application of artificial intelligence to monitor pump leaks. The data used in this work was obtained from Kaggle, which contained sensor readings from pumps. Supervised (random forest, support vector machine, and naïve bayes) and unsupervised (isolation forest) machine learning algorithms were employed for leak detection. The results showed that supervised machine learning algorithms were more accurate, with random forest having the greatest F1-score (0.993). Leveraging artificial intelligence for leak monitoring proved effective, offering a promising alternative to traditional methods.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.rsisinternational.org/journals/ijrias/ ... -9-issue-3/28-34.pdf (application/pdf)
https://rsisinternational.org/journals/ijrias/arti ... or-leaks-from-pumps/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bjf:journl:v:9:y:2024:i:3:p:28-34

Access Statistics for this article

International Journal of Research and Innovation in Applied Science is currently edited by Dr. Renu Malsaria

More articles in International Journal of Research and Innovation in Applied Science from International Journal of Research and Innovation in Applied Science (IJRIAS)
Bibliographic data for series maintained by Dr. Renu Malsaria ().