Attempt of early stuck detection using unsupervised deep learning with probability mixture model

Tomoya Inoue, Yujin Nakagawa, Ryota Wada, Keisuke Miyoshi, Shungo Abe, Kouhei Kuroda, Hitoshi Tamamura, Hakan Bilen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

The early detection of a stuck pipe during drilling operations is challenging and crucial. Some of the studies on stuck detection have adopted supervised machine learning approaches with ordinal support vector machines or neural networks using datasets for “stuck” and “normal”. However, for early detection before stuck occurs, the application of ordinal supervised machine learning has several concerns, such as limited stuck data, lack of an exact “stuck sign” before it occurs, and the various mechanisms involved in pipe sticking. This study acquires surface drilling data from various wells belonging to several agencies, examines the effectiveness of multiple learning models, and discusses the possibility of the early detection of pipe sticking before it occurs. Unsupervised machine learning using data on the normal activities is a possible advanced method for early stuck detection, which is adopted in this study. In addition, as a countermeasure to another concern that even normal activities involve various operations, we apply unsupervised learning with multiple learning models.

Original languageEnglish
Title of host publicationProceedings of the ASME 2021 40th International Conference on Ocean, Offshore and Arctic Engineering
Subtitle of host publicationVolume 10: Petroleum Technology
PublisherAmerican Society of Mechanical Engineers(ASME)
Number of pages6
Volume10
ISBN (Electronic)9780791885208
DOIs
Publication statusPublished - 11 Oct 2021
Event2021 40th International Conference on Ocean, Offshore and Arctic Engineering, - Virtual, Online
Duration: 21 Jun 202130 Jun 2021
https://event.asme.org/OMAE-2

Conference

Conference2021 40th International Conference on Ocean, Offshore and Arctic Engineering,
Abbreviated titleOMAE 2021
CityVirtual, Online
Period21/06/2130/06/21
Internet address

Keywords / Materials (for Non-textual outputs)

  • Pipe sticking
  • Probability mixture model
  • Unsupervised machine learning

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