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Uncertainty Analysis in Predictive Reservoir Simulation Using Gradient Information
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AI
DOI：10.2118/57594-PA.png)
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We demonstrate how gradient-based techniques can be used to estimate uncertainty in predictive reservoir simulations made following history matching. We discuss how the gradient calculations can help in finding the parameters that make the largest contributions to the uncertainty, estimating the uncertainty on these parameters, and estimating the uncertainty in predicted quantities by use of a linear analysis. The methodology is illustrated on two field examples, and compared with other methods of uncertainty quantification.
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