The machine learning method is the main technical means of carbonate lithofacies log identification. Selecting the appropriate machine learning method according to the different geological conditions and data is one of the key factors for high-precision identification of lithofacies. However

there are few researches on the applicability of machine learning identification methods. In this paper

four most commonly used machine learning methods for identifying lithofacies are studied

including Self Organizing Maps（SOM）

Multi-Resolution Graph-based Clustering（MRGC）

K Nearest Neighbor（KNN）

and Artificial Neural Network（ANN）. By comparing the principle and practical application effects of these methods

the advantages

disadvantages and applicability of the four machine learning methods have been summarized. When there are few core samples

MRGC is preferred

while when there are more core data

KNN is preferred as well as MRGC. Their application of lithofacies identification in the Longwangmiao Formation in the MX area in Sichuan Basin shows that MRGC and KNN are the best

SOM is the second

and ANN is the worst. This study of the application effects of machine learning methods provides a guidance for the identification of carbonate rock facies in other layers and regions

and has strong practical value.