An Artificial Lift Selection Approach Using Machine Learning: A Case Study in Sudan

Mohaned Alhaj A. Mahdi (), Mohamed Amish and Gbenga Oluyemi
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Mohaned Alhaj A. Mahdi: School of Engineering, Robert Gordon University, Garthdee Road, Aberdeen AB10 7GJ, UK
Mohamed Amish: School of Engineering, Robert Gordon University, Garthdee Road, Aberdeen AB10 7GJ, UK
Gbenga Oluyemi: School of Engineering, Robert Gordon University, Garthdee Road, Aberdeen AB10 7GJ, UK

Energies, 2023, vol. 16, issue 6, 1-15

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.

Keywords: artificial lift; machine learning; production data; supervised learning; algorithms (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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