Method to Identify Complicated Lithology of Clastic Rocks in Tahe Oilfield
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摘要: 为了准确识别塔河油田石炭系复杂碎屑岩储集层的岩性,研究了基于常规测井资料的岩性识别方法.根据取心资料将储集层的碎屑岩分为砂岩、含泥砂岩、含砾砂岩和含钙砂岩4种类型;从常规测井资料中提取对岩性反映相对敏感的多种信息,并进行归一化处理;采用常规支持向量机方法和最小二乘支持向量机方法对少量已知岩性的取心岩样进行学习,识别碎屑岩岩性,建立了适用于塔河油田石炭系复杂碎屑岩储集层的岩性识别方法.塔河油田X区块的石炭系碎屑岩岩性识别表明,基于最小二乘支持向量机方法的岩性识别符合率比常规支持向量机方法提高10百分点.研究结果表明,基于最小二乘支持向量机的岩性识别方法能有效识别塔河油田石炭系复杂碎屑岩储集层的岩性.Abstract: In order to correctly identify the complicated lithology of clasolite in Carboniferous reservoir in the Tahe Oilfield,the identification method was studied on the basis of conventional logging data.According to coring data,the lithology of the reservoir was divided into four types:sandstone,shaly sand,pebbly sandstone and calcareous sandstone.Extensive pertinent information regarding the lithology was taken from conventional logging data before processing in a normalization of data phase.The information of some known lithologies of coring samples was studied with conventional SVM and LSSVM,an identification method of lithology that suitable for characterizing carboniferous clasolite of the Tahe Oilfield has been obtained.In X block,the accuracy of the identification of the lithology with LSSVM was 10 Percentage point higher than that of conventional SVM.The result of the lithology identification showed that LSSVM was effective for identifying the complicated Carboniferous clasolite in the Tahe Oilfield reservoir.
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[1] 于代国,孙建孟,张振城,等.支持向量机方法识别储集层流体性质[J].新疆石油地质,2005,26(6):675-677. Yu Daiguo,Sun Jianmeng,Zhang Zhencheng,et al.Reservoir fluid property identification with support vector machine method[J].Xinjiang Petroleum Geology,2005,26(6):675-677. [2] 魏茂安,马海,孙正义,等.基于支持向量机的虚拟测井声波速度重构技术研究[J].石油钻探技术,2006,34(6):83-87. Wei Maoan,Ma Hai,Sun Zhengyi,et al.The reconstruction of the virtual well-log acoustic velocity technology based on support vector machine[J].Petroleum Drilling Techniques,2006,34(6):83-87. [3] 肖京男,汪志明,魏建光,等.改进LSSVM在水平井产能预测中的应用[J].石油钻探技术,2010,38(6):95-98. Xiao Jingnan,Wang Zhiming,Wei Jianguang,et al.Application of LSSVM improved horizontal well productivity prediction[J].Petroleum Drilling Techniques,2010,38(6):95-98. [4] 陈民锋,白晓虎,郑伟,等.基于支持向量机的早期聚合物驱动态预测研究[J].断块油气田,2012,19(2):199-202. Chen Mingfeng,Bai Xiaohu,Zheng Wei,et al.Development index prediction of early polymer flooding based on support vector machine [J].Fault-Block Oil Gas Field,2012,19(2):199-202. [5] 朱怡翔,石广仁.火山岩岩性的支持向量机识别[J].石油学报,2013,34(2):312-322. Zhu Yixiang,Shi Guangren.Identification of lithologic characteristics of volcanic rocks by support vector machine[J].Acta Petrolei Sinica,2013,34(2):312-322. [6] 钟仪华,李榕.基于主成分分析的最小二乘支持向量机岩性识别方法[J].测井技术,2009,33(5):425-429. Zhong Yihua,Li Rong.Application of principal component analysis and least square support vector machine to lithology identification[J].Well Logging Technology,2009,33(5):425-429. [7] 韩学辉,卢时林,支乐菲,等.应用最小二乘支持向量机识别J13井区杜家台油层岩性[J].特种油气藏,2011,18(6):18-21. Han Xuehui,Lu Shilin,Zhi Lefei,et al.Identification of the lithology of Dujiatai Formation in J13 Well Block with least squares support vector machine[J].Special Oil Gas Reservoirs,2011,18(6):18-21. [8] 韩学辉,支乐菲,刘荣,等.应用最小二乘支持向量机识别广利油田沙四段储层岩性[J].地球物理学进展,2013,28(4):1886-1892. Han Xuehui,Zhi Lefei,Liu Rong,et al.A lithologic identification method in Es4 reservoir of Guangli Oilfield with least square support vector machine[J].Progress in Geophysics,2013,28(4):1886-1892. [9] Vapnik V.The nature of statistical learning theory[M].2nd ed.NY:Springer-Verlag,1999:21-36.
[10] 白鹏,张喜斌,张斌,等.支持向量机理论及工程应用实例[M].西安:西安电子科技大学出版社,2008:32-35. Bai Peng,Zhang Xibin,Zhang Bin,et al.Support vector machine and its application in mixed gas infrared spectrum analysis[M].Xi’an:Xidian University Press,2008:32-35. [11] 邓乃扬,田英杰.数据挖掘中的新方法:支持向量机[M].北京:科学出版社,2004:236-243. Deng Naiyang,Tian Yingjie.Data mining in the new method:the study of support vector machine[M].Beijing:Science Press,2004:236-243. [12] Suykens J A K,Vandewalle J.Least squares support vector machine classifiers[J].Neural Processing Letters,1999,9(3):293-300.
[13] Suykens J A K,Gestel T V,Brabanter J D,et al.Least squares support vector machines[M].Singapore:World Scientific Publishing,2002:79-93.
[14] 宋延杰,张剑风,闫伟林,等.基于支持向量机的复杂岩性测井识别方法[J].大庆石油学院学报,2007,31(5):18-20. Song Yanjie,Zhang Jianfeng,Yan Weilin,et al.A new identification method for complex lithology with support vector machine[J].Journal of Daqing Petroleum Institute,2007,31(5):18-20. [15] 周继宏,袁瑞.基于支持向量机的复杂碎屑岩储层岩性识别[J].石油天然气学报,2012,34(7):72-75. Zhou Jihong,Yuan Rui.Identifying complex lithology of clastic rocks reservoir by support vector machines[J].Journal of Oil and Gas Technology,2012,34(7):72-75. [16] 肖冬生,乔东生.低阻油层识别新方法及其应用[J].断块油气田,2010,17(4):509-512. Xiao Dongsheng,Qiao Dongsheng.New method for identifying low resistivity reservoirs and its application[J].Fault-Block Oil Gas Field,2010,17(4):509-512. [17] 张莹,潘保芝.支持向量机与微电阻率成像测井识别火山岩岩性[J].物探与化探,2011,35(5):634-638. Zhang Ying,Pan Baozhi.The application of SVM and FMI to the lithologic identification of volcanic rocks[J].Geophysical Geochemical Exploration,2011,35(5):634-638. -
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