Data-driven Detection and Identification of Undesirable Events in Subsea Oil Wells

Abstract

Condition-Based Monitoring (CBM) systems have grown in popularity in recent years owing to innovations in areas, such as sensor-technology, communication systems, and computing. That has fostered the development of more efficient systems to monitor, analyze, and identify failures in industrial plants, production lines, and machinery. Gas and oil industries lose billions of dollars yearly related to abnormal events and systems failures. Thus, Abnormal Event Management (AEM), which aims at early detection and identification of these events, has become their number one priority so that preventive actions can be taken timely. This work addresses the issue of detection and classification of faults in offshore oil wells. The aim is to create a CBM system based on the random forest classifier to support decision-making. The events used in this work are part of the 3W database developed by Petrobras, Brazil, one of the world's largest oil producer. Seven events categorized as faulty events are considered, as well as several instances labeled as normal operation. We conducted two experiments related to two different classification scenarios. The proposed systems achieved an overall accuracy of 90\%, indicating that the system is not only able to detect faulty events but can also anticipate incoming failures successfully.