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Research Article
Aug 14, 2018

The Application of Downhole Vibration Factor in Drilling Tool Reliability Big Data Analytics—A Review

Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering
Volume 5, Issue 1

Abstract

In the challenging downhole environment, drilling tools are normally subject to high temperature, severe vibration, and other harsh operation conditions. The drilling activities generate massive field data, namely field reliability big data (FRBD), which includes downhole operation, environment, failure, degradation, and dynamic data. Field reliability big data has large size, high variety, and extreme complexity. FRBD presents abundant opportunities and great challenges for drilling tool reliability analytics. Consequently, as one of the key factors to affect drilling tool reliability, the downhole vibration factor plays an essential role in the reliability analytics based on FRBD. This paper reviews the important parameters of downhole drilling operations, examines the mode, physical and reliability impact of downhole vibration, and presents the features of reliability big data analytics. Specifically, this paper explores the application of vibration factor in reliability big data analytics covering tool lifetime/failure prediction, prognostics/diagnostics, condition monitoring (CM), and maintenance planning and optimization. Furthermore, the authors highlight the future research about how to better apply the downhole vibration factor in reliability big data analytics to further improve tool reliability and optimize maintenance planning. This article is available in the ASME Digital Collection at https://doi.org/10.1115/1.4040407.

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Information

Published In

Go to ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering
Volume 5Issue 1March 2019

History

Received: Sep 1, 2017
Revision received: May 23, 2018
Published online: Aug 14, 2018
Published in print: Mar 1, 2019

Authors

Affiliations

Department of Computer Science, Georgia Institute of Technology, North Avenue, Atlanta, GA 30332 e-mail: [email protected]
Department of Electrical and Computer Engineering, University of Houston, Engineering Bldg 1, 4726 Calhou Road, Houston, TX 77204 e-mail: [email protected]
Jinwei Jiang [email protected]
Department of Mechanical Engineering, University of Houston, Engineering Bldg 1, 4726 Calhoun Road, Houston, TX 77204 e-mail: [email protected]
Junxiao Zhu [email protected]
Department of Mechanical Engineering, University of Houston, Engineering Bldg 1, 4726 Calhoun Road, Houston, TX 77204 e-mail: [email protected]
Gangbing Song [email protected]
Department of Mechanical Engineering, University of Houston, Engineering Building 2, 4726 Calhoun Road, Houston, TX 77204 e-mail: [email protected]
Xuemin Chen [email protected]
Department of Engineering, Texas Southern University, Houston, Leonard H. O. Spearman Technology Building, 3100 Cleburne Avenue, Houston, TX 77004 e-mail: [email protected]

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