Artificial lift selection methods in conventional and unconventional wells: a summary and review from old techniques to machine learning applications.

Mahdi, Mohaned Alhaj A.; Amish, M.; Oluyemi, G.

Abstract

Artificial lift (AL) selection is an important process in enhancing oil and gas production from reservoirs. This article explores the old and current states of AL selection in conventional and unconventional wells, identifying the challenges faced in the process. The role of various factors such as production and reservoir data and economic and environmental considerations is highlighted. The article also examines the use of machine learning (ML) techniques in the AL selection process, emphasising their potential to increase the accuracy of selection and reduce data analysis time. The findings of this article provide valuable insights for researchers and practitioners in the oil and gas industry, as well as for those interested in the development of AL selection methods.

Citation

MAHDI, M.A.A., AMISH, M. and OLUYEMI, G. 2024. Artificial lift selection methods in conventional and unconventional wells: a summary and review from old techniques to machine learning applications. International journal of innovative science and research technology [online], 9(3), pages 2342-2356. Available from: https://doi.org/10.38124/ijisrt/IJISRT24MAR2108

• Journal Article Type: Article
• Acceptance Date: Apr 9, 2024
• Online Publication Date: Apr 9, 2024
• Publication Date: Mar 31, 2024
• Deposit Date: Apr 15, 2024
• Publicly Available Date: Apr 16, 2024
• Journal: International journal of innovative science and research technology (IJISRT)
• Electronic ISSN: 2456-2165
• Publisher: IJISRT
• Peer Reviewed: Peer Reviewed
• Volume: 9
• Issue: 3
• Pages: 2342-2356
• DOI: https://doi.org/10.38124/ijisrt/ijisrt24mar2108
• Keywords: Artificial lift; Selection; Conventionals; Unconventionals; Machine learning
• Public URL: https://rgu-repository.worktribe.com/output/2303104