I’ll discuss some recent developments in robust optimization of water flooding, in particular the use of non-symmetric probability density functions to maximize the expected objection function value while minimizing the downside. I’ll also present some older results to determine (approximate) upper and lower bounds of production forecasts based on history matched models, using redundancy in the parameter space during the history matching process.
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