Flow Instability Detection in Offshore Oil Wells with Multivariate Time Series Machine Learning Classifiers

In offshore petroleum exploration, subsea systems are susceptible to a variety of undesirable events or faults, in which oil wells operation is considered abnormal. Proper detection and classification of such events is crucial in order to reduce downtime, maintenance costs, and even damage to installations. Flow instability is a type of event inherently related to hydrocarbon multiphase flow and root cause of equipment stress and failure. This work investigates applying binary machine learning classifiers on real world captured data for the task of flow instability fault detection. Four different evaluation scenarios were considered. The mostly common scenarios used by the machine learning research community showed that even simple algorithms can reach high classification performance. The remaining scenarios, however, try to avoid the similarity bias problem and showed more realistic results.