Fault Detection in Managed Pressure Drilling Using Slow Feature Analysis

Correct detection of drilling abnormal incidents while minimizing false alarms is a crucial measure to decrease the non-productive time and, thus, decrease the total drilling cost. With the recent development of drilling technology and innovation of down-hole signal transmitting method, abundant drilling data are collected and stored in the electronic driller's database. The availability of such data provides new opportunities for rapid and accurate fault detection; however, data-driven fault detection has seen limited practical application in well drilling processes. One particular concern is how to distinguish “controllable”process changes, e.g., due to set-point changes, from truly abnormal events that should be considered as faults. This is highly relevant for the managed pressure drilling technology, where the operating pressure window is often narrow resulting in necessary set-point changes at different depths. However, the classical data-driven fault detection methods, such as principal component analysis and independent component analysis, are unable to distinguish normal set-point changes from abnormal faults. To address this challenge, a slow feature analysis (SFA)-based fault detection method is applied. The SFA-based method furnishes four monitoring charts containing more information that could be synthetically utilized to correctly differentiate set-point changes from faults. Furthermore, the evaluation about controller performance is provided for drilling operator. Simulation studies with a commercial high-fidelity simulator, Drillbench, demonstrate the effectiveness of the introduced approach.