Authors :
Mohaned Alhaj A. Mahdi; M. Amish; G. Oluyemi; Mohammed Abdulmoniem
Volume/Issue :
Volume 8 - 2023, Issue 11 - November
Google Scholar :
https://tinyurl.com/4wwsxzrn
Scribd :
https://tinyurl.com/3jw5ae3y
DOI :
https://doi.org/10.5281/zenodo.10251115
Abstract :
Artificial lift (AL) systems are crucial for
enhancing oil and gas production from reservoirs.
However, the failure of these systems can lead to
significant losses in production and revenue. This paper
explores the different types of AL failures and the causes
behind them. The article discusses the traditional
methods of identifying and mitigating these failures and
highlights the need for new designs and technologies to
improve the run life of AL systems. Advances in AL
system design and materials, as well as new methods for
monitoring and predicting failures using data analytics
and machine learning techniques, have been discussed.
The findings of this work provide valuable insights for
researchers and practitioners in the development of
more reliable and efficient AL systems.
Keywords :
Artificial Lift, Failure, Run Life, Machine Learning, Pump.
Artificial lift (AL) systems are crucial for
enhancing oil and gas production from reservoirs.
However, the failure of these systems can lead to
significant losses in production and revenue. This paper
explores the different types of AL failures and the causes
behind them. The article discusses the traditional
methods of identifying and mitigating these failures and
highlights the need for new designs and technologies to
improve the run life of AL systems. Advances in AL
system design and materials, as well as new methods for
monitoring and predicting failures using data analytics
and machine learning techniques, have been discussed.
The findings of this work provide valuable insights for
researchers and practitioners in the development of
more reliable and efficient AL systems.
Keywords :
Artificial Lift, Failure, Run Life, Machine Learning, Pump.
