Data-Driven Fault Detection for Transmitter in Logging-While-Drilling Tool
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Abstract
Logging tools widely used in the oil and gas industry are exposed to demanding environmental conditions that can lead to faster degradation and unexpected failures. These events can reduce productivity, delay deliverables, or even bring entire drilling operations to an end. However, such accidents can be avoided using a prognostics and health management approach. This paper presents a data-driven fault detection method for transmitter in logging-while-drilling tool adopting a support vector machine classifier. The health analyzer determines the component’s physical condition in just a few minutes, demonstrating an exceptional value for both field and maintenance engineers. This work is part of a long-term project aimed at constructing a digital fleet management system for downhole testing tools.
How to Cite
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Fault Detection, Data-Driven, Logging tools, Support vector machine, Classification
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