Abstract: Lithology identification has always been an important project in oil and gas exploration of volcanic rocks, and based on logging data, it has become a need for volcanic rock research. Machine learning algorithms under the background of big data provide a new direction for logging lithology identification. In order to improve the lithology recognition accuracy of volcanic rocks in the study area, K-nearest neighbor (KNN), support vector machine (SVM), and adaptive boosting (Ada Boosting) various classic machine learning algorithms are used here to identify six types of volcanic rocks,which consist of basalt, non-compacted basalt, trachyte, non-compacted trachyte, gabbro and diabase. Five types of well logging parameters sensitive to the lithology of the study area are selected from the conventional logging series as input vectors. 1 440 logging data points are selected from five wells with core samples or segmented data, 960 of them are used as training samples, and the remaining 480 are used as test samples. Using recognition accuracy and time as evaluation indicators, the recognition results of the three algorithms are compared and analyzed. The experiments show that the classification accuracy of the Ada Boosting algorithm is the highest with an average recognition rate of 82.10% for six types of lithology; The SVM algorithm performs well with an average recognition rate of 81.04%; The recognition rate of KNN algorithm is 76.04%.

Key words: K nearest neighbor, support vector machine, Ada Boosting, volcanic rocks, lithology identification