基于梯度提升算法的岩性识别方法

doi:10.13278/j.cnki.jjuese.20200081

摘要/Abstract

摘要： 传统的岩性识别方法如岩屑录井、钻井取心及测井资料解释等技术，对录井质量的依赖程度较高，识别精度与效率低，泛化能力差。随着计算机技术的迅速发展，将测井资料与计算机技术相结合开展岩性研究已成为岩性识别的有效手段。本文提出了一种基于梯度提升算法XGBoost和LightGBM的岩性识别方法。以苏里格气田苏东41-33区块下碳酸盐岩储层为例进行测试验证，采用该方法结合测井资料中的声波时差、自然伽马、光电吸收截面指数、密度、深侧向电阻率和补偿中子等6种参数进行岩性识别，并与KNN （K近邻分类器）、朴素贝叶斯和支持向量机等传统算法进行对比，结果表明，3种传统算法的岩性识别准确率分别为78.45%、74.43%和78.72%，基于梯度提升算法XGBoost和LightGBM的识别准确率分别达到了98.90%和98.72%，远高于传统算法。

关键词: 岩性识别, 梯度提升算法, 碳酸盐岩, 决策树

Abstract: Traditional lithology identification methods, such as cuttings logging, drilling coring, and logging data interpretation techniques, are highly dependent on logging quality, have low identification accuracy and efficiency, and have poor generalization capabilities. With the rapid development of computer technology, combining logging data with computer technology to carry out lithology research has become an effective means of lithology identification. This paper proposes a lithology recognition method based on gradient boosting algorithms XGBoost and LightGBM. Taking the lower carbonate reservoir in Block 41-33 of Sudong gas field in Sulige gas field as an example, test and verify it, using the acoustic time difference, natural gamma, photoelectric absorption cross-section index, density, and deep lateral resistivity in the logging data. Lithology identification is carried out with six parameterssuch as compensation neutron, and compared with traditional algorithms such as KNN, naive Bayes and support vector machine. The results show that the accuracy of lithology identification of the three traditional algorithms is 78.45%,74.43% and 78.72%, the recognition accuracy rates of XGBoost and LightGBM based on gradient boosting algorithms reached 98.90% and 98.72% respectively, which are much higher than traditional algorithms.

Key words: lithology identification, gradient boosting algorithm, carbonate rock, decision tree

中图分类号:

TP181

王恒, 姜亚楠, 张欣, 仲鸿儒, 陈庆轩, 高世臣. 基于梯度提升算法的岩性识别方法[J]. 吉林大学学报(地球科学版), 2021, 51(3): 940-950.

Wang Heng, Jiang Yanan, Zhang Xin, Zhong Hongru, Chen Qingxuan, Gao Shichen. Lithology Identification Method Based on Gradient Boosting Algorithm[J]. Journal of Jilin University(Earth Science Edition), 2021, 51(3): 940-950.

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