Open AccessImplementing the autonomous adaptive algorithm to manage ESP operation in harsh reservoir conditions
Author(s) -
Miroslav Antonic,
Mišo Soleša,
G Thonhauser,
A.B. Zolotukhin,
Maja Aleksić
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1201/1/012083
Subject(s) - artificial lift , lift (data mining) , computer science , scaling , point (geometry) , petroleum engineering , machine learning , engineering , geometry , mathematics
The well geometries with a shallow kick-off point in conjunction with surface infrastructure limitations have led to Electrical Submersible Pump (ESP) technologies' application as one of the most suitable artificial lift methods for the harsh reservoir conditions. However, the harsh reservoir conditions in terms of the low reservoir pressure, high reservoir temperature, scaling problems in various forms, and high gas content at the pump intake have reduced the ESP system run life. Therefore, this research represents the Autonomous Adaptive Algorithm (A 3 ) as a holistic approach to integrate analytical and machine learning models to assist production engineers in the early detection of operating problems. The A 3 relies on different data sources and uses unique, well diagnostics logic to generate valuable features and prepare data for training. Finally, the paper evaluates different classifiers and explores the possibilities of application A 3 as a flexible edge solution. The research benefits will be demonstrated for several problematic ESP wells.