Control-oriented modelling for managed pressure drilling automation using model order reduction

Sajad Naderilordejani (Corresponding author), B. Besselink, Mohammad H. Abbasi, G.O. Kaasa, Wil H.A. Schilders, Nathan van de Wouw

Research output: Contribution to journalArticleAcademicpeer-review

3 Citations (Scopus)
132 Downloads (Pure)


Automation of Managed Pressure Drilling (MPD) enables fast and accurate pressure control in drilling operations. The performance that can be achieved by automated MPD is determined by, firstly, the controller design and, secondly, the hydraulics model that is used as a basis for controller design. On the one hand, such hydraulics model should be able to accurately capture essential flow dynamics, e.g., wave propagation effects, for which typically complex models are needed.
On the other hand, a suitable model should be simple enough to allow for extensive simulation studies supporting well scenario analysis and high-performance controller design.
In this paper, we develop a model order reduction approach for the derivation of such a control-oriented model for {single-phase flow} MPD {operations}. In particular, a nonlinear model order reduction procedure is presented that preserves key system properties such as stability and provides guaranteed (accuracy) bounds on the reduction error. To demonstrate the quality of the derived control-oriented model, {comparisons with field data and} both open-loop and closed-loop simulation-based case studies are presented.
Original languageEnglish
Article number9099213
Pages (from-to)1161-1174
Number of pages14
JournalIEEE Transactions on Control Systems Technology
Issue number3
Publication statusPublished - May 2021


FundersFunder number
Horizon 2020 Framework Programme
Horizon 2020675731


    • Automatic control
    • managed pressure drilling
    • model order reduction
    • modeling
    • wave propagation


    Dive into the research topics of 'Control-oriented modelling for managed pressure drilling automation using model order reduction'. Together they form a unique fingerprint.

    Cite this