1. Fundamental Science on Radioactive Geology and Exploration Technology Laboratory,East China University of Technology,Nanchang 330013,China 2. State Key Laboratory of Nuclear Resources and Environment,East China University of Technology,Nanchang 330013,China 3. Liaoxing Oil and Gas Development Company,Petro China Liaohe Oilfield,Panjing 124000,China
Specific computational tools assist geologists in identifying and classifying the lithology of rocks in oil well exploration,reducing costs,and enhancing operational efficiency. Machine learning methods integrate a vast amount of information,enabling efficient pattern recognition and accurate decision-making. This article categorizes the lithology of five oil wells in the Norwegian Sea,randomly dividing the data into a training set (70%) and a test set (30%). Using multivariate well log parameter data for training and validation,the application effectiveness of models such as Multilayer Perceptron (MLP),Decision Tree,Random Forest,and XGBoost is compared. The research results indicate that the XGBoost model outperforms others in terms of data generalization,achieving an accuracy of 95%. The Random Forest model follows with an accuracy of 94%. Meanwhile,Multilayer Perceptron (MLP) and Decision Tree models exhibit good robustness,with accuracies of 92% and 90%,respectively.
江丽, 张智谟, 王琦玮, 封志兵, 张博程, 任腾飞. 基于不同机器学习模型的石油测井数据岩性分类对比研究[J]. 物探与化探, 2024, 48(2): 489-497.
JIANG Li, ZHANG Zhi-Mo, WANG Qi-Wei, FENG Zhi-Bing, ZHANG Bo-Cheng, REN Teng-Fei. Comparative study on lithology classification of oil logging data based on different machine learning models. Geophysical and Geochemical Exploration, 2024, 48(2): 489-497.
徐德龙, 李涛, 黄宝华, 等. 利用交会图法识别国外 M 油田岩性与流体类型的研究[J]. 地球物理学进展, 2012, 27(3):1123-1132.
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