Anomaly detection in oil-producing wells: a comparative study of one-class classifiers in a multivariate time series dataset

Anomaly detection in oil-producing wells: a comparative study of one-class classifiers in a multivariate time series dataset

Abstract Anomalies in oil-producing wells can have detrimental financial implications, leading to production disruptions and increased maintenance costs. Machine learning techniques offer a promising solution for detecting and preventing such anomalies, minimizing these disruptions and expenses. In...

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Bibliographic Details
Main Authors: Wander Fernandes, Karin Satie Komati, Kelly Assis de Souza Gazolli
Format: Article
Language:English
Published: SpringerOpen 2023-11-01
Series:Journal of Petroleum Exploration and Production Technology
Subjects:
Oil well monitoring
3W dataset
LSTM (Long short-term memory)
One-class Support Vector Machine (OCSVM)
Local Outlier Factor (LOF)
Elliptical envelope
Online Access:https://doi.org/10.1007/s13202-023-01710-6

Internet

https://doi.org/10.1007/s13202-023-01710-6

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