• DocumentCode
    693781
  • Title

    Managed Pressure Drilling System Using Adaptive Control

  • Author

    Magzoub, Muawia A. ; Saad, Nordin B. ; Ibrahim, Rosdiazli B.

  • Author_Institution
    Dept. of Electr. & Electron. Eng., UTP, Tronoh, Malaysia
  • fYear
    2013
  • fDate
    3-5 Dec. 2013
  • Firstpage
    165
  • Lastpage
    170
  • Abstract
    The research study that has been presented in this paper depicts automatic control of down whole pressure by topside choking in managed-pressure-drilling (MPD) that is an example of automated drilling and also elaborates automatic control needs for drilling operations. For a set of normal operations, MPD operations require a specified accuracy, including rate changes and set point ramping during connections, swab and surge, as well as certain failure operations namely, gas kicks, blocked choke and power loss. In the future, adaptive control solutions for drilling are anticipated to be extensively used. Furthermore, in this research study, two methods have been implemented for adaptive control for Automatic Managed Pressure Drilling System that are the Massachusetts Institute of Technology (MIT) rule and Lyapunov, and later-on comparison was also conducted between the two. In the end, few viewpoints for the future of intelligent-drilling operations with growing adaptation were also included.
  • Keywords
    Lyapunov methods; adaptive control; drilling; Lyapunov rule; MIT rule; MPD operation; Massachusetts Institute of Technology rule; adaptive control solution; automated drilling; automatic control; automatic managed pressure drilling system; down whole pressure; intelligent-drilling operation; managed-pressure-drilling; set point ramping; topside choking; Accuracy; Adaptive control; Control systems; Inductors; Mathematical model; Noise; Transfer functions; Adaptive control; Lyapunov theorem; Managed pressure drilling (MPD); The Massachusetts Institute of Technology (MIT) rule;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Artificial Intelligence, Modelling and Simulation (AIMS), 2013 1st International Conference on
  • Conference_Location
    Kota Kinabalu
  • Print_ISBN
    978-1-4799-3250-4
  • Type

    conf

  • DOI
    10.1109/AIMS.2013.33
  • Filename
    6959911