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Fault Detection in Managed Pressure Drilling using Slow Feature Analysis

Gao, Xiaoyong, Li, Haishou, Wang, Yuhong, Chen, Tao, Zuo, Xin and Zhong, Lei (2018) Fault Detection in Managed Pressure Drilling using Slow Feature Analysis IEEE Access.

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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 (MPD) 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 (PCA) and independent component analysis (ICA), 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. 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.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Chemical and Process Engineering
Authors :
Gao, Xiaoyong
Li, Haishou
Wang, Yuhong
Zuo, Xin
Zhong, Lei
Date : 2018
Copyright Disclaimer : © 2017 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See for more information.
Uncontrolled Keywords : Fault detection; Managed pressure drilling; Slow feature analysis; Drillbench
Related URLs :
Depositing User : Clive Harris
Date Deposited : 08 Jun 2018 11:36
Last Modified : 16 Jan 2019 19:11

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