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Experimental Design and Genetic Algorithms Approach to Quantify Model Uncertainty – A Case Study
- Publisher: European Association of Geoscientists & Engineers
- Source: Conference Proceedings, 69th EAGE Conference and Exhibition incorporating SPE EUROPEC 2007, Jun 2007, cp-27-00273
- ISBN: 978-90-73781-54-2
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