The traditional identification method of watered-out layer is mainly based on logging interpretation, the evaluate model is established according to the change of logging curve of watered-out layer. but the error is usually very large. Based on ensemble learning method, this paper selects random forests algorithm to establish classifiers of different reservoir watered-out levels, and it verifies with actual oilfield data. The results show that the accuracy of this method reaches 96.2%, and it can be effectively used to identify watered-out layers.
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