Energy method of geophysical logging lithology based on K-means dynamic clustering analysis

Abstract Lithology identification is an important part of reservoir evaluation and reservoir description when processing and interpreting geophysical record data. Clustering analysis refers to the analysis process of grouping a collection of physical or abstract objects into several classes composed of similar objects. K-means clustering algorithm is an iterative clustering analysis algorithm. In this paper, seven mechanical property parameters of 49 rock samples are selected as experimental data in an engineering survey, and the geophysical logging method of K-means dynamic clustering analysis is adopted. The rock samples are divided into three categories, and the classification results are matched by mechanical property parameter method. By changing the order of data grouping, the misjudgment rates were 0.021, 0.021 and 0.102, respectively. Therefore, it is feasible and effective to use k-means dynamic clustering analysis to classify lithology. The number of samples decreased to 15, and the misjudgment rate increased to 0.267 The results of K-means dynamic clustering analysis may be different from the actual situation of rock sample data selection.