Abstract
Various studies have witnessed the wide application of assisted history matching for the calibration of dynamic reservoir models. Although the proposed algorithms have the potential to improve the history matching process in some synthetic cases, most of them have failed or have partially succeeded when applied to real, complex reservoirs. Thus far, identifying the most efficient optimization strategy for history matching has remained a challenging topic for research. In this paper, a sequential approach is adopted whereby a reservoir model is replaced by a proxy model, and multiobjective optimization algorithms are applied on misfit functions that were defined by the combination of the proxy models and historical data. The proposed approach was tested on a case study involving a benchmark synthetic reservoir model with 14 years of production data. The data were freely provided by Imperial College London. The effectiveness of using individual optimization algorithms was quantified by using normalized root-mean-square error. The proposed approach is found to be efficient, robust, and flexible.
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References
Imperial college London, D.o.E.S. https://www.imperial.ac.uk/engineering/departments/earth-science/research/research-groups/perm/standard-models/eclipse-dataset/. 2015/09/10].
Wu, X.-H., L. Bi, and S. Kalla, Effective parametrization for reliable reservoir performance predictions. International Journal for Uncertainty Quantification, 2012. 2(3).
Oliver, D.S. and Y. Chen, Recent progress on reservoir history matching: a review. Computational Geosciences, 2011. 15(1): p. 185–221.
Rwechungura, R.W., M. Dadashpour, and J. Kleppe. Advanced History Matching Techniques Reviewed. in SPE Middle East Oil and Gas Show and Conference. 2011: Society of Petroleum Engineers.
Tavassoli, Z., J.N. Carter, and P.R. King, Errors in History Matching.
Verga, F., M. Cancelliere, and D. Viberti, Improved application of assisted history matching techniques. Journal of Petroleum Science and Engineering, 2013. 109: p. 327–347.
Arief, I.H., Computer assisted history matching: A comprehensive study of methodology. 2013.
Gu, Y. and D.S. Oliver, History matching of the PUNQ-S3 reservoir model using the ensemble Kalman filter. SPE journal, 2005. 10(02): p. 217–224.
Yang, X. and T. DelSole, The diffuse ensemble filter. Nonlinear Processes in Geophysics, 2009. 16(4): p. 475–486.
Zubarev, D.I. Pros and cons of applying proxy-models as a substitute for full reservoir simulations. in SPE Annual Technical Conference and Exhibition. 2009: Society of Petroleum Engineers.
Goodwin, N., Bridging the Gap Between Deterministic and Probabilistic Uncertainty Quantification Using Advanced Proxy Based Methods, Society of Petroleum Engineers.
Dehghan Monfared, A., et al., A Global Optimization Technique Using Gradient Information for History Matching. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2014. 36(13): p. 1414–1428.
Sisman, Y. and S. Bektas, Linear regression methods according to objective functions. ACTA MONTANISTICA SLOVACA, 2012. 17(3): p. 209–217.
Acknowledgements
The authors would like to thank Elf Exploration Company and Imperial College of Earth Sciences and Engineering for making PUNQ-S3 model dataset available online. The authors also thank Universiti Teknologi PETRONAS for providing access to the commercial software required to complete the work.
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Negash, B.M., Ayoub, M.A., Jufar, S.R., Robert, A.J. (2017). History Matching Using Proxy Modeling and Multiobjective Optimizations. In: Awang, M., Negash, B., Md Akhir, N., Lubis, L., Md. Rafek, A. (eds) ICIPEG 2016. Springer, Singapore. https://doi.org/10.1007/978-981-10-3650-7_1
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DOI: https://doi.org/10.1007/978-981-10-3650-7_1
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