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

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

doi:10.1109/ACC.2016.7525577

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

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

Artificial gas-lift (AGL) process is one of the techniques used in the oil industry to maintain the oil flow from the well to the production line when the reservoir pressure drops. Controller design for such a system is very challenging as it exhibits highly nonlinear dynamics. In this work, the predictive generalized minimum variance control (PGMVC) is employed to derive a robust controller for artificial gas-lift process (AGL). A closed-form optimal control law is obtained based on Taylor series approximation. Moreover, a nonlinear disturbance observer is combined with the controller to ensure zero-steady state error under model uncertainty and external disturbance. The composite controller is applied to stabilize casing-heading instability occurring in wells. Through simulation studies, the effectiveness of the proposed controller is demonstrated.

Original language	English
Title of host publication	2016 American Control Conference, ACC 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4169-4174
Number of pages	6
ISBN (Electronic)	9781467386821
DOIs	https://doi.org/10.1109/ACC.2016.7525577
Publication status	Published - Jul 28 2016
Externally published	Yes
Event	2016 American Control Conference, ACC 2016 - Boston, United States Duration: Jul 6 2016 → Jul 8 2016

Publication series

Name	Proceedings of the American Control Conference
Volume	2016-July
ISSN (Print)	0743-1619

Conference

Conference	2016 American Control Conference, ACC 2016
Country/Territory	United States
City	Boston
Period	7/6/16 → 7/8/16

ASJC Scopus subject areas

Electrical and Electronic Engineering

Access to Document

10.1109/ACC.2016.7525577

Cite this

Shi, J., Errouissi, R., Al-Durra, A., & Boiko, I. (2016). Stabilization of artificial gas-lift process using nonlinear predictive generalized minimum variance control. In 2016 American Control Conference, ACC 2016 (pp. 4169-4174). [7525577] (Proceedings of the American Control Conference; Vol. 2016-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACC.2016.7525577

Stabilization of artificial gas-lift process using nonlinear predictive generalized minimum variance control. / Shi, Jing; Errouissi, Rachid; Al-Durra, Ahmed et al.
2016 American Control Conference, ACC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 4169-4174 7525577 (Proceedings of the American Control Conference; Vol. 2016-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Shi, J, Errouissi, R, Al-Durra, A & Boiko, I 2016, Stabilization of artificial gas-lift process using nonlinear predictive generalized minimum variance control. in 2016 American Control Conference, ACC 2016., 7525577, Proceedings of the American Control Conference, vol. 2016-July, Institute of Electrical and Electronics Engineers Inc., pp. 4169-4174, 2016 American Control Conference, ACC 2016, Boston, United States, 7/6/16. https://doi.org/10.1109/ACC.2016.7525577

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AB - Artificial gas-lift (AGL) process is one of the techniques used in the oil industry to maintain the oil flow from the well to the production line when the reservoir pressure drops. Controller design for such a system is very challenging as it exhibits highly nonlinear dynamics. In this work, the predictive generalized minimum variance control (PGMVC) is employed to derive a robust controller for artificial gas-lift process (AGL). A closed-form optimal control law is obtained based on Taylor series approximation. Moreover, a nonlinear disturbance observer is combined with the controller to ensure zero-steady state error under model uncertainty and external disturbance. The composite controller is applied to stabilize casing-heading instability occurring in wells. Through simulation studies, the effectiveness of the proposed controller is demonstrated.

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

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