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Lithology Classification Based on Set-Valued Identification Method

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Li Jing 1   Wu Lifang 2,3   Lu Wenjun 1 *   Wang Ting 4   Kang Yu 1   Feng Deyong 5   Zhou Hansheng 6  
文摘 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.
来源 Journal of Systems Science and Complexity ,2022,35(5):1637-1652 【核心库】
DOI 10.1007/s11424-022-1059-y
关键词 DT ; lithology classification ; LR ; RF ; set-valued model ; SVM
地址

1. Department of Automation, University of Science and Technology of China, Hefei, 230027  

2. Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Key Laboratory of Systems and Control, Chinese Academy of Sciences, Beijing, 100190  

3. School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, 100190  

4. Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, 100083  

5. Shengli Geophysical Research Institute, SINOPEC Group, Dongying, 257022  

6. Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088

语种 英文
文献类型 研究性论文
ISSN 1009-6124
学科 地质学;自动化技术、计算机技术
基金 supported in part by the National Key Research and Development Project of China ;  the SINOPEC Programmes for Science and Technology Development ;  国家自然科学基金 ;  the Major Science and Technology Project of Anhui Province ;  the University Synergy Innovation Program of Anhui Province ;  the Fundamental Research Funds for the Central Universities
文献收藏号 CSCD:7394048

参考文献 共 35 共2页

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