Stabilization of artificial gas-lift process using nonlinear predictive generalized minimum variance control

Jing Shi, Ahmed Al-Durra, Rachid Errouissi, Igor Boiko

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


Artificial gas-lift (AGL) is one of the most widely used methods in oil production to maintain acceptable oil flow to the processing equipment and sales when the reservoir pressure is not high enough. In spite of its popularity, the AGL process is prone to casing-heading instability, which is revealed as significant flow oscillation. This is undesirable as it results in production losses and unstable behavior that has negative impact on the downstream equipment. Controller design for such a process is very challenging as it exhibits highly nonlinear dynamics. In this work, the predictive generalized minimum variance control (NPGMV) is employed to derive a robust controller based on the state estimation to stabilize AGL process when casing-heading phenomenon occurs. A closed-form optimal control law is obtained based on the Taylor series approximation. Further, a nonlinear state observer is produced and combined with the controller to ensure closed-loop control through variables that are most beneficial to the system performance, which are unmeasurable and can be obtained only via estimation. Through simulation studies, the effectiveness of the proposed controller is demonstrated.

Original languageEnglish
Pages (from-to)2031-2059
Number of pages29
JournalJournal of the Franklin Institute
Issue number4
Publication statusPublished - Mar 2019
Externally publishedYes

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications
  • Applied Mathematics


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