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.
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)。
