Automatic clustering and batch marking method for indicator diagram of pumping well based on unsupervised learning
In order to make full use of a large number of unmarked samples and save manpower and time,an automatic clustering and batch marking method for indicator diagram of pumping well based on unsupervised learning was proposed.First,the displace-ment and load data generated by the reciprocating motion of the pumping unit horsehead were converted into the sample of the indica-tor diagram,where the abscissa of the indicator diagram was the displacement and the ordinate is the load.Secondly,the convolu-tion neural network model with a series of weight parameters and strong feature extraction ability that had been trained on ImageNet was loaded.Then,the full connection layer of the network model was removed,and the network model to extract the characteristics of indicator diagram image samples was used.Finally,k-means clustering algorithm was used to cluster the extracted features and cluster the indicator diagrams with similar features into the same folder.Batch of indicator diagram clustering results were quickly marked to form a sample set of indicator diagrams for fault diagnosis of pumping wells.Twenty thousand indicator diagram data from 100 pumping wells were randomly collected.The results show that the automatic clustering and batch marking method for indicator diagram of pumping well based on unsupervised learning is time-efficient and highly accurate.This method provides an efficient method for indicator sample set marking,which has exemplary significance for fully mining the application value of oilfield big data.
万方数据
维普
