Lithology Classification Based on Set-Valued Identification Method  

作　　者：LI Jing WU Lifang LU Wenjun WANG Ting KANG Yu FENG Deyong ZHOU Hansheng 

机构地区：[1]Department of Automation,University of Science and Technology of China,Hefei 230027,China [2]Key Laboratory of Systems and Control,Institute of Systems Science,Academy of Mathematics and Systems Science,Chinese Academy of Sciences,Beijing 100190,China [3]School of Mathematical Sciences,University of Chinese Academy of Sciences,Beijing 100190,China [4]Institute of Artificial Intelligence,University of Science and Technology Beijing,Beijing 100083,China [5]Shengli Geophysical Research Institute,SINOPEC Group,Dongying 257022,China [6]Institute of Artificial Intelligence,Hefei Comprehensive National Science Center,Hefei 230088,China

出　　处：《Journal of Systems Science & Complexity》2022年第5期1637-1652,共16页系统科学与复杂性学报（英文版）

基　　金：supported in part by the National Key Research and Development Project of China under Grant Nos.2018AAA0100800 and 2018YFE0106800;in part by the SINOPEC Programmes for Science and Technology Development(PE19008-8);in part by the National Natural Science Foundation of China under Grant Nos.61725304,61803370,and 61903353;in part by the Major Science and Technology Project of Anhui Province(201903a07020012);in part by the University Synergy Innovation Program of Anhui Province(GXXT-2021-010);in part by the Fundamental Research Funds for the Central Universities(WK2100000013)。

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.

关 键 词：DT lithology classification LR RF set-valued model SVM 

分 类 号：P631.81[天文地球—地质矿产勘探] TP181[天文地球—地质学]