Analysis of Human – Machine Interface for Drilling Rig Personnel to enable Remote Drilling Operations Support

Stephan Weichselbaum

Research output: ThesisMaster's Thesis

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A new visualization of real time drilling data to simplify detection of early drilling problems can result in a future developed driller rig interface. Through the development of new drilling technologies, equipment and monitoring systems, the requirements on drilling personnel increased rapidly. The ability to detect early drilling problems from the driller’s cabin, by observing trends of real time drilling data, is limited considering that the driller doesn’t have the time to follow single parameters over a longer period of time during rig operations. Various companies already provide early drilling problem detection software, but without taking a human-machine interface located in the driller’s cabin into account. A display located in the driller’s cabin, showing trend changes of main drilling parameters over a longer period of time is missing, but exactly this trend analysis of key drilling parameters are cause to detect drilling problems at the start of occurrence to enable earlier counter measures. A visualization is introduced called the Driller’s Display to present actual versus simulated key drilling parameters in addition to fingerprinting charts to observe three main rig operations. The simulation models and fingerprinting charts are newly developed. Various testing and evaluation phases have shown promising results. Through the reduction of displaying only the core parameters with trend analysis, the driller is able to detect drilling problems in an early stage with the advantage of counteracting as early as possible by adjusting drilling equipment directly controlled by the driller.
Translated title of the contributionAnalyse der Schnittstelle zwischen Mensch und Maschine in Bezug auf das Bohranlagen Personal um eine Fernbetätigung der Bohranlage zu ermöglichen
Original languageEnglish
  • Kucs, Richard, Supervisor (external), External person
  • Thonhauser, Gerhard, Supervisor (internal)
Award date27 Mar 2015
Publication statusPublished - 2015

Bibliographical note

embargoed until 27-02-2020


  • early drilling problem detection
  • driller rig interface
  • human machine interface
  • key drilling parameters
  • Driller's Display
  • fingerprinting charts
  • Artifical neuronal network simulation
  • Trend analysis

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