Model and Economic Uncertainties in Water-Flooding Optimization

1. SPE-173285-MSModel and Economic Uncertainties in Balancing Short-Term and Long-Term Objectives in Water-Flooding Optimization M. Mohsin Siraj1, Paul M.J. Van den Hof1 and Jan Dirk Jansen2 1 Dept. Electrical Engineering, TU/e 2 Dept. of Geoscience and Engineering, TUD

2. Slide 2 Oil Production Strategy that optimizes economic performance (life-cycle) Closed-loop reservoir management SPE-173285-MS • Model and Economic Uncertainties in Balancing Short-Term and Long-Term Objectives in Water-Flooding Optimization • M. Mohsin Siraj

3. Slide 3 Challenges • Uncertainty: • Parametric uncertainty • Economic uncertainty • Varying oil prices Decision making (model-based economic optimization) under geological parametric and economic uncertainty SPE-173285-MS • Model and Economic Uncertainties in Balancing Short-Term and Long-Term Objectives in Water-Flooding Optimization • M. Mohsin Siraj

4. Good optimization gives you the best choice based on the data. Great optimization is “robust” and resilient in the face of change and data errors. “The Optimization Edge” (2011) by Steve Shashihara

5. Slide 5 Contents • Introduction • Model-based optimization and Reactive strategy • Handling risk of uncertainty • Handling geological uncertainty • Handling economic uncertainty • Conclusions and future directions SPE-173285-MS • Model and Economic Uncertainties in Balancing Short-Term and Long-Term Objectives in Water-Flooding Optimization • M. Mohsin Siraj

6. Model-based optimization and Reactive strategy (1/2)

7. Model-based optimization and Reactive strategy (2/2) • Long-term gains • Short-term gains • Q1: Steps to gain confidence in a model-based optimization. • Q2: Indicators to reflect the confidence.

8. Handling risk of uncertainties (1/4) B A • Risk increases with time horizon. • Time-localized effect of uncertainty.

9. Handling risk of uncertainties (2/4) Optimization over an ensemble of possible realizations (geological scenarios) Van Essen, G., Zandvliet, M., Van den Hof, P. M. J., Bosgra, O., Jansen, J. D., 2009. Robust waterflooding optimization of multiple geological scenarios. SPE Journal 14 (01), 202–210, DOI: 10.2118/102913–PA. Improving economic performance and reducing effect of uncertainties in a hierarchical multi-objective optimization Van Essen, G., Van den Hof, P. M. J., Jansen, J. D., 2011. Hierarchical long-term and short-term production optimization. SPE Journal 16 (01), 191–199, DOI: 10.2118/124332–PA.

10. Handling risk of uncertainties (3/4) • Changing criterion an indirect way of steering the solution • The criterion should not change but the solution should. • Main Question: • ‘Whether, by explicit handling of uncertainty, the balance between short-term and long-term gains can be naturally obtained’. • Moving to robust optimization

11. Handling geological uncertaintyRobust optimization Time\data • Reducing uncertainty with measurements • Include uncertainty description in optimization and reduce sensitivity of optimal solution to uncertainty

12. Handling geological uncertaintyRobust optimization

13. Handling geological uncertaintyRobust optimization (100) Nominal optimization, Robust optimization, reactive strategy

14. Handling geological uncertaintyRobust optimization (100) Nominal optimization, Robust optimization, reactive strategy Short-term gains not improved!

15. “Flaw of averages” (2009,2012), Sam Savage.

16. Handling geological uncertaintyMean-variance optimization Capolei et al. (2013)

17. Handling geological uncertaintyMean-variance optimization (3) Mean-variance optimization, Robust optimization, reactive strategy

18. Handling geological uncertaintyMean-variance optimization (3) Mean-variance optimization, Robust optimization, reactive strategy

19. Handling economic uncertainty • An ensemble of varying oil prices Extreme-case Set-approach average-case Extreme-case

20. Handling economic uncertaintyRobust optimization

21. Handling economic uncertaintyMean-variance optimization

22. Handling economic uncertaintyMean-variance optimization • Comparison of the mean values of NPV from MVO, RO and reactive

23. Conclusions and future directions

24. References • Capolei, A., E. Suwartadi, B. Foss, and J. B. Jørgensen (2013). A mean-variance objective for robust production optimization in uncertain geological scenarios. J. Petrol. Sci. Eng.. • Fonseca, R. M., A. S. Stordal, O. Leeuwenburgh, P. M. J. Van Den Hof, and J. D. Jansen (2014). Robust ensemble-based multi-objective optimization. In ECMOR XIV-14th European conference on the mathematics of oil recovery. • Siraj, M. M., P. M. J. Van den Hof, and J. D. Jansen (2015). Handling risk of uncertainty in model-based production optimization: a robust hierarchical approach. Submitted to 2nd IFAC Workshop on Automatic Control in Offshore Oil and Gas Production in Florianpolis, Brazil. • Jansen, J. D., R. M. Fonseca, S. Kahrobaei, M. M. Siraj, G. M. Van Essen, and P. M. J. Van den Hof (2014). The egg model-a geological ensemble for reservoir simulation. Accepted for publication in Geoscience Data Journal.

25. Slide 25 Acknowledgements / Thank You / Questions The authors acknowledge financial support from the Recovery Factory program sponsored by Shell Global Solutions Internationals.