Early Stuck Pipe Detection based on Real-Time Data Analysis

Ahmed Msahli

Research output: ThesisMaster's Thesis

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This research looks at the work previously done by other scholars regarding pipe sticking prediction, especially the ones using real-time data, then goes on to prove it possible to predict impending sticking events using real-time and simulated data. An algorithm is created based on case based reasoning and improved methods from previous work. This algorithm is then tested on historical real-time data to come to the conclusion that it can predict pipe sticking. This work sheds the light on the potential developments in drilling towards full automation and better economical practices.
Translated title of the contributionFrühe Stuck-Pipe Vorbeugungen basierend auf Echtzeit Datenanalysen
Original languageEnglish
  • Doppringer, Philipp, Supervisor (external)
  • Thonhauser, Gerhard, Supervisor (internal)
  • Wolf-Zöllner, Philipp, Co-Supervisor (external), External person
Award date20 Oct 2017
Publication statusPublished - 2017

Bibliographical note

embargoed until 06-09-2020


  • pipe sticking
  • drilling
  • real time
  • case based reasoning
  • CBR
  • drilling problems
  • prediction

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