TITLE:
Effective Electrical Submersible Pump Management Using Machine Learning
AUTHORS:
Son Tung Pham, Phi Son Vo, Dac Nhat Nguyen
KEYWORDS:
Machine Learning, Electrical Submersible Pump, Decision Tree, Random For-est, Gradient Boosting Machine
JOURNAL NAME:
Open Journal of Civil Engineering,
Vol.11 No.1,
March
10,
2021
ABSTRACT: Artificial lift plays an important role in petroleum industry to sustain
production flowrate and to extend the lifespan of oil wells. One of the most
popular artificial lift methods is Electric Submersible Pumps (ESP) because it
can produce high flowrate even for wells with great depth. Although ESPs are
designed to work under extreme conditions such as corrosion, high temperatures
and high pressure, their lifespan is much shorter than expected. ESP failures
lead to production loss and increase the cost of replacement, because the cost
of intervention work for ESP is much higher than for other artificial lift
methods, especially for offshore wells. Therefore, the prediction of ESP
failures is highly valuable in oil production and contributes a lot to the design, construction and operation of
oil wells. The contribution of this study is to use 3 machine learning
algorithms, which are Decision Tree, Random Forest and Gradient Boosting
Machine, to build predictive models for ESP lifespan while using both dynamic
and static ESP parameters. The results of these models were compared to find out the most suitable model for the prediction of ESP life cycle.
In addition, this study also evaluated the influence factor of various
operating parameters to forecast the most
impact parameters on the duration of ESP. The results of this study can provide
a better understanding of ESP behavior so that early actions can be realized to
prevent potential ESP failures.