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

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

Abstract

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.

Citation

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: https://doi.org/10.3390/en16062853

• 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
• DOI: https://doi.org/10.3390/en16062853
• Keywords: Artificial lift; Machine learning; Supervised learning; Oil and gas engineering; Sudan; Algorithms; Production data
• Public URL: https://rgu-repository.worktribe.com/output/1919553