The recent advances in computer assisted history matching have enabled the asset team to investigate multiple alternative reservoir descriptions (SPE89974, Williams et al). The systems are being asked to work in a high number of dimensions, and yet we know that the problem is tractable as we are able to find models that satisfy history match criterion. What we need is a measure of efficiency or elegance to the finding of the alternative solutions, to then allow optimization of the search and an objective discussion of the way in which different strategies interact with the task. This paper covers two case studies, at different stages in their lifecycle, and how different choices of genetic algorithm parameters modify the efficiency.


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