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April 15, 2026ProcessesOpen Access

Intelligent Identification of Drilling Operation Statuses Under Ultra-Deep High-Temperature and High-Pressure Conditions

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Authors

YZYafei ZhaoTSTing SunCYChen Yuan

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Overview

Data-driven framework identifies drilling operation statuses in HPHT environments, suggesting increased operational safety and efficiency.

Key Points

  • The aim is to create an automated system for recognizing drilling operation statuses in extreme HPHT conditions using machine learning.
  • Developed a support vector machine-based classification workflow
  • Focused on nine representative drilling operation statuses
  • Incorporated a sliding window-based time-series optimization strategy
  • Optimized model for better accuracy in identifying HPHT-related operations
  • Achieved a classification accuracy of 91.33%
  • Improved overall accuracy to 95.22% through optimization
  • Increased recognition accuracy for HPHT-related operations from 77.67% to 89.33%
  • Demonstrated strong adaptability and stability under extreme conditions

Cite This Study

Zhao et al. (2026) studied this question.

synapsesocial.com/papers/69df2ae6e4eeef8a2a6afd86https://doi.org/10.3390/pr14081237
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