Development of an expert-informed rig state classifier using naive Bayes algorithm for invisible loss time measurement.

Youcefi, Mohamed Riad; Boukredera, Farouk Said; Ghalem, Khaled; Hadjadj, Ahmed; Ezenkwu, Chinedu Pascal

Abstract

The rig state plays a crucial role in recognizing the operations carried out by the drilling crew and quantifying Invisible Lost Time (ILT). This lost time, often challenging to assess and report manually in daily reports, results in delays to the scheduled timeline. In this paper, the Naive Bayes algorithm was used to establish a novel rig state. Training data, consisting of a large set of rules, was generated based on drilling experts' recommendations. This dataset was then employed to build a Naive Bayes classifier capable of emulating the cognitive processes of skilled drilling engineers and accurately recognizing the actual drilling operation from surface data. The developed model was used to process high-frequency drilling data collected from three wells, aiming to derive the Key Performance Indicators (KPIs) related to each drilling crew's efficiency and quantify the ILT during the drilling connections. The obtained results revealed that the established rig state excelled in automatically recognizing drilling operations, achieving a high success rate of 99.747%. The findings of this study offer valuable insights for drillers and rig supervisors, enabling real-time visual assessment of efficiency and prompt intervention to reduce ILT.

Citation

YOUCEFI, M.R., BOUKREDERA, F.S., GHALEM, K., HADJADJ, A. and EZENKWU, C.P. 2024. Development of an expert-informed rig state classifier using naive Bayes algorithm for invisible loss time measurement. Applied intelligence [online], 54(17-18), pages 7659-7673. Available from: https://doi.org/10.1007/s10489-024-05560-5

• Journal Article Type: Article
• Acceptance Date: May 24, 2024
• Online Publication Date: Jun 11, 2024
• Publication Date: Sep 30, 2024
• Deposit Date: Jun 13, 2024
• Publicly Available Date: Jun 12, 2025
• Journal: Applied intelligence
• Print ISSN: 0924-669X
• Electronic ISSN: 1573-7497
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Volume: 54
• Issue: 17-18
• Pages: 7659-7673
• DOI: https://doi.org/10.1007/s10489-024-05560-5
• Keywords: Rig state; Invisible lost time (ILT); Naive Bayes algorithm; Drilling efficiency; Drilling performance; Oil and gas industry
• Public URL: https://rgu-repository.worktribe.com/output/2372695