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An artificial lift selection approach using machine learning: a case study in Sudan.

Mahdi, Mohaned Alhaj A.; Amish, Mohamed; Oluyemi, Gbenga



This article presents a machine learning (ML) application to examine artificial lift (AL) selection, using only field production datasets from a Sudanese oil field. Five ML algorithms were used to develop a selection model, and the results demonstrated the ML capabilities in the optimum selection, with accuracy reaching 93%. Moreover, the predicted AL has a better production performance than the actual ones in the field. The research shows the significant production parameters to consider in AL type and size selection. The top six critical factors affecting AL selection are gas, cumulatively produced fluid, wellhead pressure, GOR, produced water, and the implemented EOR. This article contributes significantly to the literature and proposes a new and efficient approach to selecting the optimum AL to maximize oil production and profitability, reducing the analysis time and production losses associated with inconsistency in selection and frequent AL replacement. This study offers a universal model that can be applied to any oil field with different parameters and lifting methods.


MAHDI, M.A.A., AMISH, M. and OLUYEMI, G. 2023. An artificial lift selection approach using machine learning: a case study in Sudan. Energies [online], 16(6), article number 2853. Available from:

Journal Article Type Article
Acceptance Date Mar 16, 2023
Online Publication Date Mar 19, 2023
Publication Date Mar 31, 2023
Deposit Date Mar 21, 2023
Publicly Available Date Mar 21, 2023
Journal Energies
Electronic ISSN 1996-1073
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 16
Issue 6
Article Number 2853
Keywords Artificial lift; Machine learning; Supervised learning; Oil and gas engineering; Sudan; Algorithms; Production data
Public URL


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