Pattern recognition-based approach finds rotating steering system faults

摘要

A pattern recognition-based anomaly detection system for bottomhole assemblies can determine whether the tools in the BHA are operating in a nominal or degraded mode. The developed system has demonstrated that it can detect faults in a rotating steering system tool and can provide about 16 hr of warning before tool failure. As the costs of rig downtime continue to increase, it is becoming increasingly important to mitigate or prevent downhole failures. This article presents a new, empirical model-based approach for detecting faults before failure in components of BHA tools. The goal in fault or anomaly detection is to detect subtle changes in process parameters beyond those normally expected. The petroleum industry has restricted the field of anomaly detection to the problem of monitoring mud flow for well control. The system described in the next section demonstrates its application for detecting anomalies in a rotating steering system using real-world data.