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Advanced Workflow for Taking High-Quality Increment Cores - New Techniques and Devices
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A sticking predictor construction and evaluation method for drill tools sticking prediction.

Honglin Wu1, Zhongbin Wang1, Lei Si1

  • 1School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, Jiangsu, China.

The Review of Scientific Instruments
|January 31, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for predicting drill tool sticking, crucial for preventing accidents. The developed sticking predictor effectively identifies and tracks sticking trends, improving engineering safety.

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Area of Science:

  • Drilling Engineering
  • Predictive Maintenance
  • Geotechnical Engineering

Background:

  • Drill tool sticking is a significant issue in drilling operations, leading to costly downtime and potential accidents.
  • Accurate prediction of sticking events is essential for operational efficiency and safety in the oil and gas industry.
  • Existing methods for predicting drill tool sticking often lack the precision to track evolving trends.

Purpose of the Study:

  • To develop and evaluate a novel sticking predictor for rod-deflection sticking accidents.
  • To identify and select robust feature parameters that accurately reflect the progression of sticking events.
  • To establish a reliable evaluation model for assessing the performance of the proposed sticking predictor.

Main Methods:

  • Extraction of relevant features from drill tool sticking signals using various signal processing techniques.
  • Application of the Mann-Kendall trend test to assess the significance and evolutionary tracking capability of extracted features.
  • Construction of the sticking predictor by calculating weighted values of the selected, trend-reflecting features.
  • Development of a dedicated evaluation model to rigorously test the predictor's effectiveness.

Main Results:

  • The proposed sticking predictor demonstrated superior performance compared to models using alternative input features.
  • Selected features effectively captured and tracked the evolutionary trends indicative of sticking.
  • The evaluation model confirmed the practical utility and accuracy of the developed predictor.
  • The predictor provides a valuable reference for real-world engineering applications in preventing sticking accidents.

Conclusions:

  • The developed sticking predictor offers a significant advancement in the field of drill tool sticking prediction.
  • The methodology provides a robust framework for feature selection and predictor construction.
  • This work offers practical insights and a reliable tool for enhancing safety and efficiency in drilling operations.