A novel hybrid method of lithology identification based on k-means++ algorithm and fuzzy decision tree

Abstract

Lithology identification methods based on conventional logging data are essential in reservoir geological evaluation. Due to the highly non-linear relationship between lithology and various logging parameters, conventional methods cannot meet the requirements. In recent years, machine learning methods such as Neural Networks and decision tree have been applied to the field of lithology identification and achieved good effects. However, there is no obvious difference in logging parameters for various types of lithology, and at the same time, there is a large amount of information redundancy between each logging curve. Therefore, its uncertainty and fuzziness are high, which interferes with the result of lithology identification. Combining fuzzy theory, decision tree and K-means++ algorithm, this paper proposes a novel hybrid technique of lithology identification which can better overcome the ambiguity and uncertainty of logging data. In the actual data test, we select six logging parameters: density (RHOB), neutron porosity (NPHI), natural gamma (GR), longitudinal wave velocity (VP), shallow formation resistivity (LLS), and deep formation resistivity (LLD). Then K-means++ clustering algorithm was used for clustering analysis on logging data. Finally, the triangular membership function is selected to fuzz the logging data according to the obtained clustering center points, and a fuzzy decision tree lithology identification model is constructed. The prediction accuracy of the model reached 93.92%. The fuzzy decision tree algorithm was also compared with five machine learning algorithms, including decision tree, extremely randomized trees (ET), Adaboost, random forest (RF) and gradient boosting decision tree (GBDT). The results show that the modeling results of fuzzy decision tree algorithm outperform other algorithms. In summary, the fuzzy decision tree model developed in the study is a practical and effective model for complex lithology identification, providing a new idea for lithology identification. • A fuzzy decision tree model using logging data for lithology identification is proposed. • The k-means++ algorithm optimizes the selection of initial clustering centers. • Logging data is fuzzily processed by the triangular membership function. • Pre-pruning the fuzzy decision tree by controlling the values of significance level α and truth degree threshold β. • The accuracy of the model can reach 93.92%, which is better than other algorithms.