“Model-free optimal anti-slug control of a well-pipeline-riser in the K-Spice/LedaFlow simulator”

Authors: Christer Dalen, David Di Ruscio and Roar Nilsen,
Affiliation: Telemark University College and Kongsberg Oil and Gas Technologies
Reference: 2015, Vol 36, No 3, pp. 179-188.

Keywords: optimal controller, integral action, PI controller, Kalman filter, system identification, anti-slug, well-pipeline-riser

Abstract: Simplified models are developed for a 3-phase well-pipeline-riser and tested together with a high fidelity dynamic model built in K-Spice and LedaFlow. These models are developed from a subspace algorithm, i.e. Deterministic and Stochastic system identification and Realization (DSR), and implemented in a Linear Quadratic optimal Regulator (LQR) for stabilizing the slugging regime. We are comparing LQR with PI controller using different performance measures.

PDF PDF (1591 Kb)        DOI: 10.4173/mic.2015.3.5

DOI forward links to this article:
[1] Christer Dalen and David Di Ruscio (2016), doi:10.4173/mic.2016.1.4
[2] Christer Dalen and David Di Ruscio (2019), doi:10.4173/mic.2019.4.2
References:
[1] Alvarez, C. and Al-Malki, S. (2003). Using gas injection for reducing pressure losses in multiphase pipelines, International Journal of Multiphase Flow. doi:10.2118/84503-MS
[2] DiRuscio, D. (1996). Combined Deterministic and Stochastic System Identification and Realization: DSR - A Subspace Approach Based on Observations, Modeling, Identification and Control. 17(3):193--230. doi:10.4173/mic.1996.3.3
[3] DiRuscio, D. (2010). On Tuning PI Controllers for Integrating Plus Time Delay Systems, Modeling, Identification and Control. 31(4):145--164. doi:10.4173/mic.2010.4.3
[4] DiRuscio, D. (2012). Discrete LQ optimal control with integral action: A simple controller on incremental form for MIMO systems, Modeling, Identification and Control. 33(2):35--44. doi:10.4173/mic.2012.2.1
[5] Godhavn, J.-M., Fard, M.P., and Fuchs, P.H. (2005). New slug control strategies, tuning rules and experimental results, Journal of Process Control. 15(5):547 -- 557. doi:10.1016/j.jprocont.2004.10.003
[6] Jahanshahi, E. and Skogestad, S. (2015). Anti-slug control solutions based on identified model, Journal of Process Control. 30(0):58 -- 68. doi:10.1016/j.jprocont.2014.12.007
[7] K-Spice. (0). K-Spice, 2014. Kongsberg.com/k-spice.
[8] LedaFlow. (0). LedaFlow, 2014. Kongsberg.com/ledaflow.
[9] OgaziAI, Y. H. . L.L., CaoY. (2010). Slug control with large valve openings to maximize oil production, SPE Journal. 15(3):812--821. doi:10.2118/124883-PA
[10] Skogestad, S. (2009). Feedback: Still the Simplest and Best Solution, Modeling, Identification and Control. 30(3):149--155. doi:10.4173/mic.2009.3.5
[11] Soederstroem, T. and Stoica, P. (1989). System Identification, Prentice Hall.
[12] Sotomayor, O.A., Park, S.W., and Garcia, C. (2003). Multivariable identification of an activated sludge process with subspace-based algorithms, Control Engineering Practice. 11(8):961 -- 969. doi:10.1016/S0967-0661(02)00210-1
[13] Storkaas, E. and Skogestad, S. (2007). Controllability analysis of two-phase pipeline-riser systems at riser slugging conditions, Control Engineering Practice. 15(5):567 -- 581. doi:10.1016/j.conengprac.2006.10.007
[14] Storkaas, E., Skogestad, S., and Alstad, V. (2001). Stabilisation of desired flow regimes in pipelines, AIChE Annual meeting, Chicago 10–14 Nov., 1996, Paper 45f, 200.


BibTeX:
@article{MIC-2015-3-5,
  title={{Model-free optimal anti-slug control of a well-pipeline-riser in the K-Spice/LedaFlow simulator}},
  author={Dalen, Christer and Di Ruscio, David and Nilsen, Roar},
  journal={Modeling, Identification and Control},
  volume={36},
  number={3},
  pages={179--188},
  year={2015},
  doi={10.4173/mic.2015.3.5},
  publisher={Norwegian Society of Automatic Control}
};