基于测井曲线深程度耦合的页岩岩相智能识别方法  

Intelligent identification methods for shale lithology based on the coupling deeply of logging curves

作　　者：刘粤蛟 赖富强 徐浩 王濡岳 张晓树 罗彤彤 杨彬跃 LIU Yuejiao;LAI Fuqiang;XU Hao;WANG Ruyue;ZHANG Xiaoshu;LUO Tongtong;YANG Binyue(Chongqing Key Laboratory of Complex Oilfield Exploration and Development,Chongqing University of Science and Technology,Chongqing 401331,China;SINOPEC Petroleum Exploration and Production Research Institute,Beijing 102206,China;Geological Exploration and Development Research Institute,CNPC Chuanqing Drilling Engineering Co.,Ltd.,Chengdu 610051,China;School of Geosciences,China University of Petroleum(East China),Qingdao Shandong 266000,China)

机构地区：[1]重庆科技大学复杂油田勘探开发重庆市重点实验室,重庆401331 [2]中国石油化工股份有限公司石油勘探开发研究院,北京102206 [3]中国石油集团川庆钻探工程有限公司地质勘探开发研究院,成都610051 [4]中国石油大学(华东)地球科学与技术学院,山东青岛266000

出　　处：《地质科技通报》2025年第1期308-320,共13页Bulletin of Geological Science and Technology

基　　金：国家自然科学基金项目(41402118);重庆市基础研究与前沿探索项目(cstc2018jcyjAX0503);页岩气勘探开发国家地方联合工程研究中心开放课题(YYQKTKFGJDFLHGCYJZX-201901);重庆市研究生科研创新项目(CYS22722);重庆科技学院研究生创新计划项目(YKJCX2220109)。

摘　　要：四川盆地渝西区块五峰组-龙马溪组是国内典型的页岩气储层,其层间强非均质性,导致采集的测井曲线信息存在大量冗余且曲线间耦合关系复杂,岩相测井识别难度高、精度低,亟需技术方法创新。本文在岩相划分与分析的基础上,联合主成分分析法与随机森林算法构建了一种岩相智能识别方法。研究结果表明:①利用主成分分析法对测井曲线进行优化,可以使测井曲线深度耦合,削减测井信息冗余及曲线间复杂耦合关系等因素对岩相识别的影响,可得到更加科学有效的数据信息;②向原始数据添加不改变其岩相的微量变化,可以达到数据增强的效果,在一定程度上解决随机森林算法由于数据集比较小或者不平衡时,模型的泛化能力和稳定性差的问题;③联合主成分分析法与随机森林算法构建的岩相智能识别方法运用识别准确率达83%以上,适用性强,准确率高。该方法不仅在一定程度上克服了研究区岩相识别困难的问题,也极大地提高了岩相识别效率,对促进研究区页岩气经济高效开发具有重要意义。[Objective]The Wufeng-Longmaxi formations in the Yuxi Block of the Sichuan Basin,China are typical shale gas reservoirs.The strong heterogeneities of these formations leads to both information redundancy and complex coupling relationships of logging curves,which is challenging and inaccurate for traditional lithofacies identification.[Methods]This study developed an intelligent lithofacies identification method that integrated with both principal component analysis(PCA)and the random forest algorithm based on lithofacies classification and analysis.[Results]Research findings were given as follows:First,PCA optimization can strengthen the coupling of logging curves,reducing the impact of lithofacies identification such as logging curve information redundancy and complex relationships.Second,data augmentation was achieved by including minor changes to the original data without impacting lithofacies,improving model generalization and stability during handling small or imbalanced datasets.Finally,lithofacies identification accuracy based on PCA with the random forest algorithm achievedabove 83%,with a high precision and a strong applicability.[Conclusion]This method not only overcomes the difficulty of lithofacies identification in the study area,but also greatly improves the efficiency of lithofacies identification,which is of great significance for promoting the economic and efficient development of shale gas in the study area.

关 键 词：渝西区块 岩相识别 测井曲线深程度耦合 主成分分析法 随机森林算法 页岩 

分 类 号：P618.12[天文地球—矿床学]