Identification of reservoir types in deep carbonates based on mixedkernel machine learning using geophysical logging data  

作　　者：Jin-Xiong Shi Xiang-Yuan Zhao Lian-Bo Zeng Yun-Zhao Zhang Zheng-Ping Zhu Shao-Qun Dong 

机构地区：[1]Key Laboratory of Exploration Technologies for Oil and Gas Resources of the Ministry of Education,Yangtze University,Wuhan,4310,Hubei,China [2]School of Geosciences,Yangtze University,Wuhan,430100,Hubei,China [3]College of Geosciences,China University of Petroleum(Beijing),Beijing,102249,China [4]Petroleum Exploration and Production Research Institute of SINOPEC,Beijing,100083,China [5]School of Earth Sciences and Engineering,Xi'an Shiyou University,Xi'an 710065,Shaanxi,China

出　　处：《Petroleum Science》2024年第3期1632-1648,共17页石油科学（英文版）

基　　金：supported by the National Natural Science Foundation of China(No.U21B2062);the Natural Science Foundation of Hubei Province(No.2023AFB307)。

摘　　要：Identification of reservoir types in deep carbonates has always been a great challenge due to complex logging responses caused by the heterogeneous scale and distribution of storage spaces.Traditional cross-plot analysis and empirical formula methods for identifying reservoir types using geophysical logging data have high uncertainty and low efficiency,which cannot accurately reflect the nonlinear relationship between reservoir types and logging data.Recently,the kernel Fisher discriminant analysis(KFD),a kernel-based machine learning technique,attracts attention in many fields because of its strong nonlinear processing ability.However,the overall performance of KFD model may be limited as a single kernel function cannot simultaneously extrapolate and interpolate well,especially for highly complex data cases.To address this issue,in this study,a mixed kernel Fisher discriminant analysis(MKFD)model was established and applied to identify reservoir types of the deep Sinian carbonates in central Sichuan Basin,China.The MKFD model was trained and tested with 453 datasets from 7 coring wells,utilizing GR,CAL,DEN,AC,CNL and RT logs as input variables.The particle swarm optimization(PSO)was adopted for hyper-parameter optimization of MKFD model.To evaluate the model performance,prediction results of MKFD were compared with those of basic-kernel based KFD,RF and SVM models.Subsequently,the built MKFD model was applied in a blind well test,and a variable importance analysis was conducted.The comparison and blind test results demonstrated that MKFD outperformed traditional KFD,RF and SVM in the identification of reservoir types,which provided higher accuracy and stronger generalization.The MKFD can therefore be a reliable method for identifying reservoir types of deep carbonates.

关 键 词：Reservoir type identification Geophysical logging data Kernel Fisher discriminantanalysis Mixedkernel function Deep carbonates 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] P618.13[自动化与计算机技术—控制科学与工程] P631.81[天文地球—矿床学]