how metric space is used as internet search engine and image classification daily life
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the distribution of the models in Euclidean space provides a visualization of the model similarity - models close to each other are similar in terms of the distance measure. Those models far from each other are dissimilar in terms of the distance measure.
Another important step for sensitivity analysis and screening of reservoir models is clustering of the reservoir models. Clustering of the models allows for a classification of models into discrete groups. This is useful for sensitivity analysis (comparing model properties within each cluster and between clusters), and screening (selection of models based upon cluster).
An illustration is shown in the figure below. The models in the MDS plot are colored by cluster. The clustering has grouped together models with similar cumulative oil production, as seen in the figures above the MDS plot. Given this information, one can examine the model properties in each cluster and understand which properties have a significant impact on the cumulative oil.
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