Intelligent Identification and Prediction of Lithology of Volcanic Reservoirs Based on Machine Learning

doi:10.3969/j.issn.1006-6535.2022.01.006

Abstract

Abstract: To address the problem that the lithology of the volcanic reservoirs of Jiamuhe Formation in Well Block Jinlong 2, Junggar Basin is variable and difficult to be accurately identified by conventional methods, four algorithms in machine learning, including decision tree, random forest, gradient boosting tree and Bayes, were adopted to intelligently identify the lithology of the study area, and then determine eight characteristics parameters such as M and N, which are extremely sensitive to the lithology of volcanic rocks based on the analysis of the geological characteristics of volcanic reservoirs in the study area and the logging response characteristics of different lithologies. The results of the study proved that the random forest method was preferred with the best model, an accuracy rate of more than 90% and the highest model generalization. It was an effective method to identify volcanic rock lithology based on conventional logging curves. According to this study, the volcanic rock lithology can be identified and predicted with a high precision, laying a foundation for subsequent exploration and development of volcanic rock reservoirs.

Key words: volcanic rock, lithological characteristics, machine learning, intelligent recognition, decision tree, random forest

CLC Number:

TE312

Liu Kai, Zou Zhengyin, Wang Zhizhang, Jiang Qingping, Chang Tianquan, Wang Weifang, Yang Xiao. Intelligent Identification and Prediction of Lithology of Volcanic Reservoirs Based on Machine Learning[J]. Special Oil & Gas Reservoirs, 2022, 29(1): 38-45.

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