Incipient fault detection for geological drilling processes using multivariate generalized Gaussian distributions and Kullback–Leibler divergence. (December 2021)

• Record Type: Journal Article
• Authors: Li, Yupeng; Cao, Weihua; Hu, Wenkai; Xiong, Ying; Wu, Min
• Is Part Of: Control engineering practice. Volume 117(2021)
• Publication Date: 2021-12
• Subjects: Geological drilling process -- Fault detection -- Multivariate generalized Gaussian distribution -- Kullback-Leibler divergence; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2021.104937
• Languages: English
• ISSNs: 0967-0661

Abstract

Abstract: Early detection of downhole faults in geological drilling processes can effectively reduce downtime and prevent catastrophic drilling accidents. Considering that the changes of drilling signals in early faults are difficult to observe while the data distribution may have significant deviations, a systematic incipient fault detection method is proposed for drilling processes based on the Multivariate Generalized Gaussian Distributions (MGGDs) and Kullback-Leibler Divergence (KLD). The contributions are twofold: (1) A new dissimilarity index is proposed for incipient fault detection by estimating the KLD between the MGGD of the historical data under the normal condition and that of the real-time data; (2) an adaptive alarm limit design approach is proposed for updating the alarm limits that adapt to the increasing of the drilling depth. Industrial case studies from a real drilling project are presented to demonstrate the effectiveness and practicability of the proposed method. Highlights: A systematic framework is proposed for incipient downhole fault detection. A new dissimilarity index is developed based on MGGD and KLD. An adaptive alarm limit design approach is proposed for updating the alarm limits.