Nonlinear model predictive control of a Hammerstein Weiner model based experimental managed pressure drilling setup

Al Amin, Syed Imtiaz, Azizur Rahaman, Faisal Khan

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Design and implementation of a nonlinear model predictive controller (NMPC) on a pilot scale managed pressure drilling (MPD) setup is demonstrated. The goal of the controller is to maintain constant bottomhole pressure (BHP) and mitigate kick during reservoir influx scenario. Under normal condition, the controller tracks bottomhole pressure to a predefined setpoint. A Hammerstein Weiner nonlinear model has been used for pressure prediction, and genetic optimization algorithm for calculating optimal control input. During reservoir influx the controller switches to flow control mode to balance the pump flow and choke flow. After kick mitigation the controller switches back to pressure regulation mode by revising the setpoint pressure to estimated reservoir pressure. The NMPC controller delivered good performance over PI controller during normal operation, pump failure, and gas kick cases where flow demand changes frequently.

Original languageEnglish
JournalISA Transactions
DOIs
Publication statusAccepted/In press - 1 Jan 2018

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ASJC Scopus subject areas

  • Instrumentation
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics

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