基于改进型随机森林算法的页岩岩性识别——以准噶尔盆地芦草沟组为例  

Shale Lithology Identification Based on Improved Random Forest Algorithm:A Case of Lucaogou Formation in Junggar Basin

作　　者：秦志军 操应长[1] 冯程[3] QIN Zhijun;CAO Yingchang;FENG Cheng(School of Geosciences,China University of Petroleum(East China),Qingdao,Shandong 266580,China;Research Institute of Exploration and Development,Xinjiang Oilfield Company,PetroChina,Karamay,Xinjiang 834000,China;College of Petroleum,Karamay Campus,China University of Petroleum(Beijing),Xinjiang 834000,China)

机构地区：[1]中国石油大学(华东)地球科学与技术学院,山东青岛266580 [2]中国石油新疆油田分公司勘探开发研究院,新疆克拉玛依834000 [3]中国石油大学(北京)克拉玛依校区石油学院,新疆克拉玛依834000

出　　处：《新疆石油地质》2024年第5期595-603,共9页Xinjiang Petroleum Geology

基　　金：国家自然科学基金(42364007,42004089);新疆维吾尔自治区自然科学基金(2021D01E22)。

摘　　要：在储集层岩性识别的应用中,特别是对页岩等非均质性较强的非常规储集层的岩性识别,机器学习算法的高效性、准确性和有效信息整合能力已经得到了充分验证。考虑到岩性识别的特征参数优选问题,优选自然伽马、T2几何平均值、结构指数、骨架密度指数、密度和深侧向电阻率,采用结合递归特征消除的随机森林算法,对准噶尔盆地中二叠统芦草沟组页岩储集层的主要岩性进行识别;利用传统的随机森林算法和支持向量机法,对同一套资料进行岩性预测,并与岩石薄片鉴定结果对比。结合递归特征消除的随机森林算法只需选择一半的测井参数,便能够达到更好的效果,而且通过优选特征参数,缩短了算法的运行时间。因此,结合递归特征消除的随机森林算法能够实现测井特征参数的优选,提高页岩岩性识别的准确率,缩短运行时间,为复杂岩性识别和多参数选择提供了新的思路。In the application of reservoir lithology identification,the efficiency,accuracy and effective information integration ability of machine learning algorithm have been fully verified,especially in unconventional reservoirs with strong heterogeneity such as shale.Based on the optimal selection of parameters such as natural gamma,T2 geometric mean,structural index,skeleton density index,density,and deep lateral resistivity,and using a random forest algorithm combined with recursive feature elimination(RF⁃RFE),major lithologies of the shale reservoirs in the Middle Permian Lucaogou formation in the Junggar basin were identified.Lithology prediction was conducted on the same dataset using conventional RF and support vector machine(SVM)algorithms,and the results were compared with those obtained from thinsection identifications.It is found that RF⁃RFE yields better results with only half of the logging parameters,and the parameters defined by optimal selection help reduce the algorithm’s running time.Thus,the use of RF⁃RFE algorithm can realize optimal selection of characteristic logging parameters,more accurate identification of shale lithology,and reduction of running time.The algorithm provides a new approach for complex lithology identification and multi⁃parameter selection.

关 键 词：随机森林算法 递归特征消除 特征选择 中二叠统 芦草沟组 页岩储集层 岩性识别 

分 类 号：TE122.221[石油与天然气工程—油气勘探]