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Lithology Classification Based on Set-Valued Identification Method

文献类型：期刊论文

作者Li Jing4; Wu Lifang2,3; Lu Wenjun4; Wang Ting1; Kang Yu4; Feng Deyong6; Zhou Hansheng5
刊名JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY
出版日期2022-06-15
页码16
ISSN号1009-6124
关键词DT lithology classification LR RF set-valued model SVM
DOI10.1007/s11424-022-1059-y
英文摘要Lithology classification using well logs plays a key role in reservoir exploration. This paper studies the problem of lithology identification based on the set-valued method (SV), which uses the SV model to establish the relation between logging data and lithologic types at a certain depth point. In particular, the system model is built on the assumption that the noise between logging data and lithologic types is normally distributed, and then the system parameters are estimated by SV method based on the existing identification criteria. The logging data of Shengli Oilfield in Jiyang Depression are used to verify the effectiveness of SV method. The results indicate that the SV model classifies lithology more accurately than the Logistic Regression model (LR) and more stably than uninterpretable models on imbalanced dataset. Specifically, the Macro-F1 of the SV models (i.e., SV(3), SV(5), and SV(7)) are higher than 85%, where the sandstone samples account for only 22%. In addition, the SV(7) lithology identification system achieves the best stability, which is of great practical significance to reservoir exploration.
资助项目National Key Research and Development Project of China[2018AAA0100800] ; National Key Research and Development Project of China[2018YFE0106800] ; SINOPEC Programmes for Science and Technology Development[PE19008-8] ; National Natural Science Foundation of China[61725304] ; National Natural Science Foundation of China[61803370] ; National Natural Science Foundation of China[61903353] ; Major Science and Technology Project of Anhui Province[201903a07020012] ; University Synergy Innovation Program of Anhui Province[GXXT2021-010] ; Fundamental Research Funds for the Central Universities[WK2100000013]
WOS研究方向Mathematics
语种英语
出版者SPRINGER HEIDELBERG
WOS记录号WOS:000812046900001
源URL[http://ir.amss.ac.cn/handle/2S8OKBNM/61547]  
专题中国科学院数学与系统科学研究院
通讯作者Lu Wenjun
作者单位1.Univ Sci & Technol Beijing, Inst Artificial Intelligence, Beijing 100083, Peoples R China
2.Univ Chinese Acad Sci, Sch Math Sci, Beijing 100190, Peoples R China
3.Chinese Acad Sci, Acad Math & Syst Sci, Inst Syst Sci, Key Lab Syst & Control, Beijing 100190, Peoples R China
4.Univ Sci & Technol China, Dept Automat, Hefei 230027, Peoples R China
5.Hefei Comprehens Natl Sci Ctr, Inst Artificial Intelligence, Hefei 230088, Peoples R China
6.SINOPEC Grp, Shengli Geophys Res Inst, Dongying 257022, Peoples R China
推荐引用方式
GB/T 7714		 Li Jing,Wu Lifang,Lu Wenjun,et al. Lithology Classification Based on Set-Valued Identification Method[J]. JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY,2022:16.
APA Li Jing.,Wu Lifang.,Lu Wenjun.,Wang Ting.,Kang Yu.,...&Zhou Hansheng.(2022).Lithology Classification Based on Set-Valued Identification Method.JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY,16.
MLA Li Jing,et al."Lithology Classification Based on Set-Valued Identification Method".JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY (2022):16.

入库方式： OAI收割

来源：数学与系统科学研究院

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