Fault diagnosis based on feature clustering of time series data for loss and kick of drilling process. (June 2021)

• Record Type: Journal Article
• Authors: Zhang, Zheng; Lai, Xuzhi; Wu, Min; Chen, Luefeng; Lu, Chengda; Du, Sheng
• Is Part Of: Journal of process control. Volume 102(2021)
• Page Start: 24
• Page End: 33
• Publication Date: 2021-06
• Subjects: Drilling process -- Fault diagnosis -- Feature clustering -- Kick -- Loss -- Time series; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2021.03.004
• Languages: English
• ISSNs: 0959-1524

Abstract

Abstract: With the increase of drilling depth, complicated geological environments lead to a high risk of loss and kick. Fault diagnosis plays an essential role in minimizing the financial and environmental losses of the drilling process. On account of the temporal correlation of drilling parameters, a fault diagnosis method based on feature clustering of time series data for loss and kick of the drilling process is presented in this paper. Distance correlation is conducted for parameter combination to retain the whole information of drilling process. Global trend, local trends, and approximate entropy features are extracted to illustrate the characteristic of the time series. Density-based clustering method is performed for each combination to mine the local similarity among drilling parameters. Based on the clustering results of each combination as the inputs, the Bayesian classifier is further utilized to obtain the final fault diagnosis result. Experiments are executed with the actual data collected from a practical drilling process. The results indicate that the proposed method has both low false alarm rate and low miss alarm rate. Highlights: Distance correlation is applied to discovery the nonlinear relationships of drilling parameters. Local trend features are introduced to illustrate the internal characteristic of the time series. Density-based clustering is conducted to mine the local similarity among drilling parameters.