机器学习法是碳酸盐岩岩相测井识别的主要技术手段,针对不同地质情况和资料,选择适用的机器学习方法是高精度识别岩相的关键因素之一,然而针对机器学习识别岩相方法的适用性研究较少,为此列举了4种最常用的机器学习识别岩相方法：自组织神经网络聚类分析法（SOM）、基于图像多分辨率聚类分析法（MRGC）、K最近邻分类算法（KNN）和神经网络法（ANN）。通过对比方法的原理及方法的实际应用效果,总结出这4种机器学习法的优缺点和适用性,少量岩心样本情况下,优选MRGC法;在较多数量岩心资料情况下,优选KNN或MRGC法。其在四川盆地MX地区龙王庙组地层岩相识别应用中表明：MRGC和KNN法效果最好,其次为SOM法,ANN法效果最差。不同机器学习方法实际应用及对比分析成果,对于碳酸盐岩岩相测井识别方法在其他层组或其他工区的应用起到借鉴作用,并具有较强的实用价值。

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.