“Investigating the performance of Robust Daily Production Optimization against a combined well-reservoir model”
Authors: Nima Janatian, Stein Krogstad and Roshan Sharma,Affiliation: University of South-Eastern Norway and SINTEF
Reference: 2024, Vol 45, No 2, pp. 65-80.
Keywords: ESP Lifted Oil Well, Scenario-based Robust Optimization, Constrained Optimization under Uncertainty, The MATLAB Reservoir Simulation Toolbox (MRST), Combinded Well--Reservoir Model
Abstract: This paper presents a scenario-based optimization framework applied to Daily Production Optimization (DPO) for an Electric Submersible Pump lifted oil field under parametric uncertainty. The study also develops a simplified combined well-reservoir model, which is used solely to assess the performance of the methods in a more realistic setting. The combined model consists of the steady-state model of wells combined with the reservoir model through bottom hole pressure and well flow. Moreover, it successfully represents the change in uncertain parameters based on reservoir dynamics rather than random variations. The superiority of scenario-based DPO and the importance of considering uncertainty are demonstrated through extensive comparisons between deterministic and robust methods. The comparisons show that the deterministic DPO fails to satisfy output constraints, leading to violations, particularly in wellhead pressure. Conversely, the scenario-based DPO exhibits significant potential for real oil field application, effectively respecting all input and output constraints. Nevertheless, this safety comes at the cost of sacrificing net profit to some extent. The research emphasizes the importance of considering uncertainty in DPO for oil field operations, providing valuable insights for achieving robustness and operational safety.
PDF (13558 Kb)
DOI: 10.4173/mic.2024.2.3References:
[1] Binder, B. J.T., Johansen, T.A., and Imsland, L. (2019). Improved predictions from measured disturbances in linear model predictive control, Journal of Process Control. 75:86--106. doi:10.1016/j.jprocont.2019.01.007
[2] Binder, B. J.T., Kufoalor, D. K.M., Pavlov, A., and Johansen, T.A. (2014). Embedded model predictive control for an electric submersible pump on a programmable logic controller, In 2014 IEEE Conference on Control Applications (CCA). IEEE, pages 579--585. doi:10.1109/CCA.2014.6981402
[3] Delou, P. d.A., deAzevedo, J.P., Krishnamoorthy, D., deSouzaJr, M.B., and Secchi, A.R. (2019). Model predictive control with adaptive strategy applied to an electric submersible pump in a subsea environment, IFAC-PapersOnLine. 52(1):784--789. doi:10.1016/j.ifacol.2019.06.157
[4] Epelle, E.I. and Gerogiorgis, D.I. (2019). Mixed-integer nonlinear programming (minlp) for production optimisation of naturally flowing and artificial lift wells with routing constraints, Chemical Engineering Research and Design. 152:134--148. doi:10.1016/j.cherd.2019.09.042
[5] Foss, B., Grimstad, B., and Gunnerud, V. (2015). Production optimization--facilitated by divide and conquer strategies, IFAC-PapersOnLine. 48(6):1--8. doi:10.1016/j.ifacol.2015.08.001
[6] Foss, B., Knudsen, B.R., and Grimstad, B. (2018). Petroleum production optimization--a static or dynamic problem? Computers & Chemical Engineering, 114:245--253. doi:10.1016/j.compchemeng.2017.10.009
[7] Hoffmann, A. and Stanko, M. (2017). Short-term model-based production optimization of a surface production network with electric submersible pumps using piecewise-linear functions, Journal of Petroleum Science and Engineering. 158:570--584. doi:10.1016/j.petrol.2017.08.063
[8] Janatian, N., Jayamanne, K., and Sharma, R. (2022). Model based control and analysis of gas lifted oil field for optimal operation, Scandinavian Simulation Society. pages 241--246. doi:10.3384/ecp21185241
[9] Janatian, N. and Sharma, R. (2022). Multi-stage scenario-based mpc for short term oil production optimization under the presence of uncertainty, Journal of Process Control. 118:95--105. doi:10.1016/j.jprocont.2022.08.012
[10] Janatian, N. and Sharma, R. (2023). A reactive approach for real-time optimization of oil production under uncertainty, In 2023 American Control Conference (ACC). IEEE, pages 2658--2663, 2023. doi:10.23919/ACC55779.2023.10156274
[11] Janatian, N. and Sharma, R. (2023). A robust model predictive control with constraint modification for gas lift allocation optimization, Journal of Process Control, 2023. 128:102996. doi:10.1016/j.jprocont.2023.102996
[12] Janatian, N. and Sharma, R. (2023). Short-term production optimization for electric submersible pump lifted oil field with parametric uncertainty, IEEE Access, 2023. doi:10.1109/ACCESS.2023.3312169
[13] Jansen, J.D., Fonseca, R.M., Kahrobaei, S., Siraj, M., VanEssen, G., and Vanden Hof, P. (2014). The egg model--a geological ensemble for reservoir simulation, Geoscience Data Journal. 1(2):192--195. doi:10.1002/gdj3.21
[14] Jordanou, J.P., Osnes, I., Hernes, S.B., Camponogara, E., Antonelo, E.A., and Imsland, L. (2022). Nonlinear model predictive control of electrical submersible pumps based on echo state networks, Advanced Engineering Informatics. 52:101553. doi:10.1016/j.aei.2022.101553
[15] Krishnamoorthy, D., Bergheim, E.M., Pavlov, A., Fredriksen, M., and Fjalestad, K. (2016). Modelling and robustness analysis of model predictive control for electrical submersible pump lifted heavy oil wells, IFAC-PapersOnLine, 2016. 49(7):544--549. doi:10.1016/j.ifacol.2016.07.399
[16] Krishnamoorthy, D., Fjalestad, K., and Skogestad, S. (2019). Optimal operation of oil and gas production using simple feedback control structures, Control Engineering Practice. 91:104107. doi:10.1016/j.conengprac.2019.104107
[17] Krishnamoorthy, D., Foss, B., and Skogestad, S. (2016). Real-time optimization under uncertainty applied to a gas lifted well network, Processes, 2016. 4(4):52. doi:10.3390/pr4040052
[18] Lie, K.A. and Moyner, O. (2021). Advanced modelling with the MATLAB reservoir simulation toolbox, Cambridge University Press.
[19] Lucia, S., Finkler, T., and Engell, S. (2013). Multi-stage nonlinear model predictive control applied to a semi-batch polymerization reactor under uncertainty, Journal of process control. 23(9):1306--1319. doi:10.1016/j.jprocont.2013.08.008
[20] Mejia, J. A.P., Silva, L.A., and Florez, J. A.P. (2018). Control strategy for oil production wells with electrical submersible pumping based on the nonlinear model-based predictive control technique, In 2018 IEEE ANDESCON. IEEE, pages 1--6. doi:10.1109/ANDESCON.2018.8564581
[21] Mohammadzaheri, M., Tafreshi, R., Khan, Z., Franchek, M., and Grigoriadis, K. (2016). An intelligent approach to optimize multiphase subsea oil fields lifted by electrical submersible pumps, Journal of Computational Science. 15:50--59. doi:10.1016/j.jocs.2015.10.009
[22] Muller, E.R., Camponogara, E., Seman, L.O., Hulse, E.O., Vieira, B.F., Miyatake, L.K., and Teixeira, A.F. (2022). Short-term steady-state production optimization of offshore oil platforms: Wells with dual completion (gas-lift and esp) and flow assurance, Top. 30(1):152--180. doi:10.1007/s11750-021-00604-2
[23] Pavlov, A., Krishnamoorthy, D., Fjalestad, K., Aske, E., and Fredriksen, M. (2014). Modelling and model predictive control of oil wells with electric submersible pumps, In 2014 IEEE Conference on Control Applications (CCA). IEEE, pages 586--592. doi:10.1109/CCA.2014.6981403
[24] Santana, B.A., Fontes, R.M., Schnitman, L., and Martins, M.A. (2021). An adaptive infinite horizon model predictive control strategy applied to an esp-lifted oil well system, IFAC-PapersOnLine. 54(3):176--181. doi:10.1016/j.ifacol.2021.08.238
[25] Serghides, T. (1984). Estimate friction factor accurately, Chemical engineering (New York, NY). 91(5):63--64.
[26] Sharma, R. and Glemmestad, B. (2013). Optimal control strategies with nonlinear optimization for an electric submersible pump lifted oil field, Modeling, Identification and Control. 34(2):55--67. doi:10.4173/mic.2013.2.2
[27] Sharma, R. and Glemmestad, B. (2014). Mixed integer nonlinear optimization for esp lifted oil field and improved operation through production valve choking, International Journal of Modeling and Optimization, 2014. 4(6):465. doi:10.7763/IJMO.2014.V4.419
[28] Sharma, R. and Glemmestad, B. (2014). Modeling and simulation of an electric submersible pump lifted oil field, International Journal of Petroleum Science and Technology, 2014. 8(1):39--68.
[29] Sharma, R. and Glemmestad, B. (2014). Nonlinear model predictive control for optimal operation of electric submersible pump lifted oil field, In Proceedings of the IASTED International Conference on Modelling, Identification and Control. pages 229--236, 2014. doi:10.2316/P.2014.809-042
[30] Stenhouse, B., Woodman, M., and Griffiths, P. (2010). Model based operational support-adding assurance to operational decision making, In SPE Intelligent Energy Conference and Exhibition. OnePetro, pages--. doi:10.2118/128694-MS
[31] Takacs, G. (2017). Electrical submersible pumps manual: design, operations, and maintenance, Gulf professional publishing.
[32] Teixeira, A.F., deCampos, M., Barreto, F.P., Stender, A.S., Arraes, F.F., and Rosa, V.R. (2013). Model based production optimization applied to offshore fields, In OTC Brasil. OnePetro, pages--. doi:10.4043/24301-MS
BibTeX:
@article{MIC-2024-2-3,
title={{Investigating the performance of Robust Daily Production Optimization against a combined well-reservoir model}},
author={Janatian, Nima and Krogstad, Stein and Sharma, Roshan},
journal={Modeling, Identification and Control},
volume={45},
number={2},
pages={65--80},
year={2024},
doi={10.4173/mic.2024.2.3},
publisher={Norwegian Society of Automatic Control}
};