On Optimal Selection of Objective Grouping for Multiobjective History Matching
- Junko Hutahaean (Heriot-Watt University) | Vasily Demyanov (Heriot-Watt University) | Michael A. Christie (Heriot-Watt University)
- Document ID
- Society of Petroleum Engineers
- SPE Journal
- Publication Date
- August 2017
- Document Type
- Journal Paper
- 1,296 - 1,312
- 2017.Society of Petroleum Engineers
- Multi-Objective Optimisation, History Matching, Objective Grouping
- 17 in the last 30 days
- 127 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 10.00|
|SPE Non-Member Price:||USD 30.00|
Multiobjective history matching has gained popularity in the last decade. It provides an ensemble of diverse set and good matched models that should lead to improved forecasting. Moreover, in some cases, multiobjective history matching provides faster and more-robust convergence than the single-objective approach. In multiobjective, objective components (usually groups of them) guide the algorithm to different areas of objective space that lead to a diverse set of optimal solutions. These algorithms are widely established and well-developed for problems with two or three objectives. Under an increasing number of objective components, such as in a reservoir model with multiple wells and production data, multiobjective-history-matching performance (convergence speed and match quality) can deteriorate. One effective approach is grouping objective components to reduce the number of objectives. However, the existing literature does not present sufficient information on appropriate grouping techniques and ways of combining objective components.
We present a novel technique to group the objective components depending on analysis of the nonparametric-conflict information obtained from a set with a limited number of initial solutions. By grouping the objectives depending on the conflict between them, we aim to achieve better performance in history matching. We apply this framework to history matching of an industry-standard reservoir model and a real-field case study. We also perform history-matching runs of groupings with different degree of conflict, and then analyze the performance among them with the statistical-significance test.
Our extensive simulation results show that the proposed conflict-based strategy can be used as a guideline to help select a grouping of the objective components in multiobjective history matching optimally. By calculating the conflict between objectives a priori, we can identify which grouping scheme will result in a better performance. This technique can significantly improve the fitness quality of the matched model given the same number of flow simulations, and can also obtain a diverse set of models.
|File Size||2 MB||Number of Pages||17|
Abdollahzadeh, A., Reynolds, A., Christie, M. et al. 2012. Bayesian Optimization Algorithm Applied to Uncertainty Quantification. SPE J. 17 (3): 865–873. SPE-143290-PA. https://doi.org/10.2118/143290-PA.
Abdollahzadeh, A., Reynolds, A., Christie, M., et al. 2013. Estimation of Distribution Algorithms Applied to History Matching. SPE J. 18 (3): 508–517. SPE-141161-PA. https://doi.org/10.2118/141161-PA.
Bauer, D. F. 1972. Constructing Confidence Sets Using Rank Statistics. J. Am. Stat. Assoc. 67 (339): 687–690. https://doi.org/10.1080/01621459.1972.10481279.
Bos, C. F. M. 2000. Production Forecasting with Uncertainty Quantification. Final Report, NITG 99–255, Netherlands Institute of Applied Geoscience, The Hague, The Netherlands.
Christie, M., Eydinov, D., Demyanov, V. et al. 2013. Use of Multi-Objective Algorithms in History Matching of a Real Field. Presented at the SPE Reservoir Simulation Symposium, The Woodlands, Texas, 18–20 February. SPE-163580-MS. https://doi.org/10.2118/163580-MS.
Deb, K. 2001. Multiobjective Optimization using Evolutionary Algorithms. Chichester, UK: John Wiley & Sons Ltd.
Derrac, J., García, S., Molina, D. et al. 2011. A Practical Tutorial on the Use of Nonparametric Statistical Tests as a Methodology for Comparing Evolutionary and Swarm Intelligence Algorithms. Swarm Evol. Comput. 1 (1): 3–18. https://doi.org/10.1016/j.swevo.2011.02.002.
ECLIPSE Dataset. 2017. Imperial College London, http://www.imperial.ac.uk/earth-science/research/research-groups/perm/standard-models/eclipse-dataset/.
Efron, B. and Tibshirani, R. J. 1994. An Introduction to the Bootstrap. Boca Raton, Florida: CRC Press.
Ferraro, P. and Verga, F. 2009. Use of Evolutionary Algorithms in Single and Multi-Objective Optimization Techniques for Assisted History Matching. Presented at the Offshore Mediterranean Conference and Exhibition, Ravenna, Italy, 25–27 March. OMC-2009-079.
Floris, F. J. T., Bush, M., Cuypers, M. et al. 2001. Methods for Quantifying the Uncertainty of Production Forecasts: A Comparative Study. Petrol. Geosci. 7 (S): S87–S96. https://doi.org/10.1144/petgeo.7.S.S87.
de Freitas, A. R. R., Fleming, P. J., Guimara˜es, F. G. 2013. A Non-Parametric Harmony-Based Objective Reduction Method for Many-Objective Optimization (pp. 651–656). Proc., IEEE International Conference on Systems, Man, and Cybernetics (SMC), Manchester, UK, 13–16 October, 651–656. https://doi.org/10.1109/SMC.2013.116.
de Freitas, A. R. R., Fleming, P. J., Guimara˜es, F. G. 2015. Aggregation Trees for Visualization and Dimension Reduction in Many-Objective Optimization. Inform. Sciences 298 (20 March): 288–314. https://doi.org/10.1016/j.ins.2014.11.044.
Hajizadeh, Y., Christie, M. A., and Demyanov, V. 2010. Comparative Study of Novel Population-Based Optimization Algorithms for History Matching and Uncertainty Quantification: PUNQ-S3 Revisited. Presented at the Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, 1–4 November. SPE-136861-MS. https://doi.org/10.2118/136861-MS.
Hajizadeh, Y., Demyanov, V., Mohamed, L. et al. 2011a. Comparison of Evolutionary and Swarm Intelligence Methods for History Matching and Uncertainty Quantification in Petroleum Reservoir Models. In Intelligent Computational Optimization in Engineering, Vol. 366, ed. M. Koppen, G. Schaefer, and A. Abraham, 209–240. Berlin: Springer.
Hajizadeh, Y., Christie, M. A., and Demyanov, V. 2011b. Towards Multiobjective History Matching: Faster Convergence and Uncertainty Quantification. Presented at SPE Reservoir Simulation Symposium, The Woodlands, Texas, 21–23 February. SPE-141111-MS. https://doi.org/10.2118/141111-MS.
Hutahaean, J. J., Demyanow, V., and Christie, M. A. 2015. Impact of Model Parameterisation and Objective Choices on Assisted History Matching and Reservoir Forecasting. Presented at the SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition, Bali, Indonesia, 20–22 October. SPE-176389-MS. https://doi.org/10.2118/176389-MS.
Ishibuchi, H., Tsukamoto, N., and Nojima, Y. 2008. Evolutionary Many-Objective Optimization: A Short Review. Proc., IEEE World Congress on Computational Intelligence, Hong Kong, 1–6 June, 2419–2426. https://doi.org/10.1109/CEC.2008.4631121.
Kennedy, J. and Eberhart, R. 1995. Particle Swarm Optimization. Proc., IEEE International Conference on Neural Networks, Perth, Australia, 1942–1948.
Mohamed, L., Christie, M. A., and Demyanov, V. 2010a. Comparison of Stochastic Sampling Algorithms for Uncertainty Quantification. SPE J. 15 (1): 31–38. SPE-119139-PA. https://doi.org/10.2118/119139-PA.
Mohamed, L., Christie, M. A., Demyanov, V. et al. 2010b. Application of Particle Swarms for History Matching in the Brugge Reservoir. Presented at the SPE Annual Technical Conference and Exhibition, Florence, Italy, 19–22 September. SPE-135264-MS. https://doi.org/10.2118/135264-MS.
Mohamed, L., Christie, M. A., Demyanov, V. 2011. History Matching and Uncertainty Quantification: Multiobjective Particle Swarm Optimisation Approach. Presented at the SPE EUROPEC/EAGE Annual Conference and Exhibition, Vienna, Austria, 23–26 May. SPE-143067-MS. https://doi.org/10.2118/143067-MS.
Park, H.-Y., Datta-Gupta, A., and King, M. J. 2013. Handling Conflicting Multiple Objectives Using Pareto-Based Evolutionary Algorithm for History Matching of Reservoir Performance. Presented at the SPE Reservoir Simulation Symposium, The Woodlands, Texas, 18–20 February. SPE-163623-MS. https://doi.org/10.2118/163623-MS.
Purshouse, R. C. and Fleming, P. J. 2003. Conflict, Harmony, and Independence: Relationships in Evolutionary Multi-Criterion Optimisation. In Evolutionary Multi-Criterion Optimization, Vol. 2632, ed. C. Fonseca, P. Flemming, E. Zitzler, et al., 16–30. Berlin: Springer.
Romero, C. E., Carter, J. N., Zimmerman, R. W. et al. 2000. Improved Reservoir Characterization through Evolutionary Computation. Presented at the SPE Annual Technical Conference and Exhibition, Dallas, 1–4 October. SPE-62942-MS. https://doi.org/10.2118/62942-MS.
Sani, F. and Todman, J. 2008. Experimental Design and Statistics for Psychology: A First Course. Oxford, UK. Blackwell Publishing.
Schervish, M. J. 1996. P Values: What They Are and What They Are Not. Am. Stat. 50 (3): 203–206. https://doi.org/10.1080/00031305.1996.10474380.
Schulze-Riegert, R. W., Krosche, M., Fahimuddin, A. et al. 2007. Multi-Objective Optimization with Application to Model Validation and Uncertainty Quantification. Presented at the SPE Middle East Oil and Gas Show and Conference, Manama, Bahrain, 11–14 March. SPE-105313-MS. http://doi.org/10.2118/105313-MS.
Trelea, I. C. 2003. The Particle Swarm Optimization Algorithm: Convergence Analysis and Parameter Selection. Inform. Process. Lett. 85 (6): 317–325. https://doi.org/10.1016/S0020-0190(02)00447-7.