Handling Geological and Economic Uncertainties in Balancing Short-Term and Long-Term Objectives in Waterflooding Optimization
- M. Mohsin Siraj (Eindhoven University of Technology) | Paul M. J. Van den Hof (Eindhoven University of Technology) | Jan-Dirk Jansen (Delft University of Technology)
- Document ID
- Society of Petroleum Engineers
- SPE Journal
- Publication Date
- August 2017
- Document Type
- Journal Paper
- 1,313 - 1,325
- 2017.Society of Petroleum Engineers
- balancing short-term and long-term gains, robust optimization, water flooding optimization, improving robustness, uncertainty handling and quantification
- 2 in the last 30 days
- 211 since 2007
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Model-based economic optimization of oil production has a significant scope to increase financial life-cycle performance. The net-present-value (NPV) objective in this optimization, because of its nature, focuses on long-term gains, whereas short-term production is not explicitly addressed. At the same time, the achievable NPV is highly uncertain because of strongly varying economic conditions and limited knowledge of the reservoir-model parameters. The prime focus of this work is to develop optimization strategies that balance both long-term and short-term economic objectives and also offer robustness to the long-term NPV. An earlier robust hierarchical optimization method honoring geological uncertainty with robust long-term and short-term NPV objectives serves as a starting base of this work. We address the issue of extending this approach to include economic uncertainty and aim to analyze how the optimal solution reduces the uncertainty in the achieved average NPV. An ensemble of varying oil prices is used to model economic uncertainty with average NPVs as robust objectives in the hierarchical approach. A weighted-sum approach is used with the same objectives to quantify the effect of uncertainty. To reduce uncertainty, a mean-variance-optimization (MVO) objective is then considered to maximize the mean and also minimize the variance. A reduced effect of uncertainty on the long-term NPV is obtained compared with the uncertainty in the mean-optimization (MO) objectives. Last, it is investigated whether, because of the better handling of uncertainty in MVO, a balance between short-term and long-term gains can be naturally obtained by solving a single-objective MVO. Simulation examples show that a faster NPV buildup is naturally achieved by choosing appropriate weighting of the variance term in the MVO objective.
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Aziz, K. and Settari, A. 1979. Petroleum Reservoir Simulation. London. Applied Science Publishers.
Bhattacharyya, S. C. and Timilsina, G. R. 2010. A Review of Energy System Models. Int. J. Energ. Sec. Mgmt. 4 (4): 494–518. https://doi.org/10.1108/17506221011092742.
Birol, F. 2010. World Energy Outlook 2010. Paris: International Energy Agency.
Brouwer, D. R. and Jansen, J. D. 2004. Dynamic Optimization of Waterflooding With Smart Wells Using Optimal Control Theory. SPE J. 9 (4): 391–402. SPE-78278-PA. https://doi.org/10.2118/78278-PA.
Capolei, A., Foss, B., and Jorgensen, J. B. 2015a. Profit and Risk Measures in Oil Production Optimization. Proc., 2nd IFAC Workshop on Automatic Control in Offshore Oil and Gas Production, Floriano´polis, Brazil, 27–29 May, Vol. 48, Issue 6, 214–220. https://doi.org/10.1016/j.ifacol.2015.08.034.
Capolei, A., Suwartadi, E., Foss, B. et al. 2015b. A Mean–Variance Objective for Robust Production Optimization in Uncertain Geological Scenarios. J. Pet. Sci. Eng. 125 (January): 23–37. https://doi.org/10.1016/j.petrol.2014.11.015.
Chen, C., Li, G., Reynolds, A. et al. 2012. Robust Constrained Optimization of Short- and Long-Term Net Present Value for Closed-Loop Reservoir Management. SPE J. 17 (3): 849–864. SPE-141314-PA. https://doi.org/10.2118/141314-PA.
Chen, Y. and Oliver, D. S. 2010. Ensemble-Based Closed-Loop Optimization Applied to Brugge Field. SPE J. 13 (1): 56–71. SPE-118926-PA. https://doi.org/10.2118/118926-PA.
Chen, C., Wang, Y., Li, G. et al. 2010. Closed-Loop Reservoir Management on the Brugge Test Case. Computational Geosciences 14 (4): 691–703. https://doi.org/10.1007/s10596-010-9181-7.
Criqui, P. 2001. POLES: Prospective Outlook on Long-Term Energy Systems. Information document, LEPII-EPE, Grenoble, France, http://web.upmfgrenoble.fr/lepiiepe/textes/POLES8p_01.pdf (accessed 18 January 2017).
Fonseca, R. M., Stordal, A. S., Leeuwenburgh, O. et al. 2014. Robust Ensemble-Based Multi-Objective Optimization. Proc., ECMOR XIV: 14th European Conference on Mathematics in Oil Recovery, Catania, Italy, 8–11 September. https://doi.org/10.3997/2214-4609.20141895.
Foss, B. 2012. Process Control in Conventional Oil and Gas Fields–Challenges and Opportunities. Control. Eng. Pract. 20 (10): 1058–1064. https://doi.org/10.1016/j.conengprac.2011.11.009.
Haimes, Y. Y. and Li, D. 1988. Hierarchical Multiobjective Analysis for Large-Scale Systems: Review and Current Status. Automatica 24 (1): 53–69. https://doi.org/10.1016/0005-1098(88)90007-6.
Jansen, J. D. 2011. Adjoint-Based Optimization of Multi-Phase Flow Through Porous Media–A Review. Comput. Fluid. 46 (1): 40–51. https://doi.org/10.1016/j.compfluid.2010.09.039.
Jansen, J. D., Bosgra, O. H., and Van den Hof, P. M. J. 2008. Model-Based Control of Multiphase Flow in Subsurface Oil Reservoirs. J. Process Contr. 18 (9): 846–855. https://doi.org/10.1016/j.jprocont.2008.06.011.
Jansen, J. D., Fonseca, R. M., Kahrobaei, S. et al. 2014. The Egg Model–A Geological Ensemble for Reservoir Simulation. Geosci. Dat. J. 1 (2): 192–195. https://doi.org/10.1002/gdj3.21.
Lapillonne, B., Chateau, B., Criqui, P. et al. 2007. World Energy Technology Outlook: 2050–WETO-H2. Brussels, Belgium: European Commission.
Lie, K.-A., Krogstad, S., Ligaarden, I. S. et al. 2012. Open-Source MATLAB Implementation of Consistent Discretisations on Complex Grids. Computat. Geosci. 16 (2): 297–322. https://doi.org/10.1007/s10596-011-9244-4.
Liu, X. and Reynolds, A. C. 2015a. Multiobjective Optimization for Maximizing Expectation and Minimizing Uncertainty or Risk with Application to Optimal Well Control. Presented at the SPE Reservoir Simulation Symposium, Houston, 23–25 February. SPE-173216-MS. https://doi.org/10.2118/173216-MS.
Liu, X. and Reynolds, A. C. 2015b. Pareto Optimal Solutions for Minimizing Risk and Maximizing Expected Value of Life-Cycle NPV of Production under Nonlinear Constraints. Presented at the SPE Reservoir Simulation Symposium, Houston, 23–25 February. SPE-173274-MS. https://doi.org/10.2118/173274-MS.
Ljung, L. 1999. System Identification–Theory for the User. Upper Saddle River, New Jersey: Prentice Hall.
Markowitz, H. 1952. Portfolio Selection. J. Financ. 7 (1): 77–91. https://doi.org/10.1111/j.1540-6261.1952.tb01525.x.
Nocedal, J. and Wright, S. 2006. Numerical Optimization. New York City: Springer Science & Business Media.
Rockafellar, R. T. 2007. Coherent Approaches to Risk in Optimization Under Uncertainty. In INFORMS Tutorials in Operations Research: OR Tools and Applications—Glimpses of Future Technologies, ed. P. Gray, Chap. 3, 38–61. Catonsville, Maryland: INFORMS. https://doi.org/10.1287/educ.1073.0032.
Sarma, P., Aziz, K., and Durlofsky, L. J. 2005. Implementation of Adjoint Solution for Optimal Control of Smart Wells. Presented at the SPE Reservoir Simulation Symposium, The Woodlands, Texas, 31 January–2 February. SPE-92864-MS. https://doi.org/10.2118/92864-MS.
Siraj, M. M., Van den Hof, P. M. J., and Jansen, J. D. 2015a. Model and Economic Uncertainties in Balancing Short-Term and Long-Term Objectives in Water-Flooding Optimization. Presented at the SPE Reservoir Simulation Symposium, Houston, 23–25 February. SPE-173285-MS. https://doi.org/10.2118/173285-MS.
Siraj, M. M., Van den Hof, P. M. J., and Jansen, J. D. 2015b. Handling Risk of Uncertainty in Model-Based Production Optimization: A Robust Hierarchical Approach. Proc., 2nd IFAC Workshop on Automatic Control in Offshore Oil and Gas Production, Floriano´polis, Brazil, 27–29 May, Vol. 48, Issue 6, 248–253. https://doi.org/10.1016/j.ifacol.2015.08.039.
Siraj, M. M., Van den Hof, P. M. J., and Jansen, J. D. 2015c. Risk Management in Oil ReservoirWaterflooding Under Economic Uncertainty. Proc., 54th IEEE Conference on Decision and Control, Osaka, Japan, 15–18 December, 7542–7547. https://doi.org/10.1109/CDC.2015.7403410.
Strang, G. 2011. Introduction to Linear Algebra. Wellesley, Massachusetts: Wellesley-Cambridge Press.
Van den Hof, P. M. J., Jansen, J. D., and Heemink, A. 2012. Recent Developments in Model-Based Optimization and Control of Subsurface Flow in Oil Reservoirs. Proc., 1st IFAC Workshop on Automatic Control in Offshore Oil and Gas Production, Trondheim, Norway, Vol. 45, Issue 8, 189–200. https://doi.org/10.3182/20120531-2-NO-4020.00047.
van Essen, G., Van den Hof, P. M. J., and Jansen, J. D. 2011. Hierarchical Long-Term and Short-Term Production Optimization. SPE J. 16 (1): 191–199. SPE-124332-PA. https://doi.org/10.2118/124332-PA.
van Essen, G., Zandvliet, M., Van den Hof, P. M. J. et al. 2009. Robust Waterflooding Optimization of Multiple Geological Scenarios. SPE J. 14 (1): 202–210. SPE-102913-PA. https://doi.org/10.2118/102913-PA.
Yasari, E., Pishvaie, M. R., Khorasheh, F. et al. 2013. Application of Multi-Criterion Robust Optimization in Waterflooding of Oil Reservoir. J. Pet. Sci. Eng. 109 (September): 1–11. https://doi.org/10.1016/j.petrol.2013.07.008.
Yeten, B., Durlofsky, L. J., and Aziz, K. 2003. Optimization of Nonconventional Well Type, Location, and Trajectory. SPE J. 8 (3): 200–210. SPE-86880-PA. https://doi.org/10.2118/86880-PA.