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Abstract

H034 Experimental Design and Genetic Algorithms Approach to Quantify Model Uncertainty – A Case Study H. Gross* (Chevron) A. Castellini (Chevron) I. Gullapalli (Chevron) & V. Hoang (Chevron) SUMMARY Recent developments combine genetic algorithms and experimental design to provide fast and efficient model building tools. Beyond single-number forecasts probabilistic predictions enhance the accuracy of simulation and allow quantifying risks and opportunities in field developments. Often history-matching focuses solely on production forecast uncertainty. Model uncertainty upstream from simulators has more information content: mapping regions with high variability on its physical parameters but good match with past production data makes it possible

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/content/papers/10.3997/2214-4609.201401691
2007-06-11
2020-07-06
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http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201401691
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