| Summary: | In order to master the development effect of polymer flooding well group, it is necessary to accurately analyze the influence of different factors in the whole polymer flooding process on the development index. Combined with the principle of big data analysis, based on the neighborhood rough set theory and Kmeans clustering algorithm, an intelligent analysis algorithm is proposed to determine the achievement of development indicators of polymer flooding well group. Firstly, the neighborhood rough set was used to reduce the attributes of the influencing factors of the Wells with and without the standards. Secondly, Kmeans algorithm was used to cluster the reduced influencing factors to delete the data inconsistent with the actual compliance. Finally, the clustering model is used to judge the standard status of other well groups, and the practical application effect is very good.
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