Real-Time Multi-Event Anomaly Detection using Elliptic Envelope and A Deep Neural Network for Enhanced MPD Robustness

Abstract

In managed pressure drilling (MPD), robust and fast event detection is critical for timely event identification and diagnosis, as well as executing well control actions as quickly as possible. In current event detection systems (EDSs), signal noise and uncertainties often cause missed and false alarms, and automated diagnosis of the event type is usually restricted to certain event types. A new EDS method is proposed in this paper to overcome these shortcomings. The new approach uses a multivariate online change point detection (OCPD) method based on elliptic envelope for event detection. The method is robust against signal noise and uncertainties, and is able to detect abnormal features within a minute or less, using only a few data points. A deep neural network (DNN) is utilized for estimating the occurrence probability of various drilling events, currently encompassing (but not limited to) six event types: liquid kick, gas kick, lost circulation, plugged choke, plugged bit, and drillstring washout. The OCPD and the DNN are integrated together and demonstrate better performance with respect to robustness and accuracy. The training and testing of the OCPD and the DNN were conducted on a large dataset representing various drilling events, which was generated using a field-validated two-phase hydraulics software. Compared to current EDS methods, the new system shows the following advantages: (1) lower missed alarm rate; (2) lower false alarm rate; (3) earlier alarming; and (4) significantly improved classification capability that also allows for further extension to even more drilling events.