A Real-time Lithological Identification Method based on SMOTE-Tomek and ICSA Optimization  

作　　者：DENG Song PAN Haoyu LI Chaowei YAN Xiaopeng WANG Jiangshuai SHI Lin PEI Chunyu CAI Meng 

机构地区：[1]College of Petroleum Engineering,Changzhou University,Changzhou,Jiangsu 213000,China

出　　处：《Acta Geologica Sinica(English Edition)》2024年第2期518-530,共13页地质学报（英文版）

基　　金：supported by CNPC-CZU Innovation Alliance;supported by the Program of Polar Drilling Environmental Protection and Waste Treatment Technology (2022YFC2806403)。

摘　　要：In petroleum engineering,real-time lithology identification is very important for reservoir evaluation,drilling decisions and petroleum geological exploration.A lithology identification method while drilling based on machine learning and mud logging data is studied in this paper.This method can effectively utilize downhole parameters collected in real-time during drilling,to identify lithology in real-time and provide a reference for optimization of drilling parameters.Given the imbalance of lithology samples,the synthetic minority over-sampling technique(SMOTE)and Tomek link were used to balance the sample number of five lithologies.Meanwhile,this paper introduces Tent map,random opposition-based learning and dynamic perceived probability to the original crow search algorithm(CSA),and establishes an improved crow search algorithm(ICSA).In this paper,ICSA is used to optimize the hyperparameter combination of random forest(RF),extremely random trees(ET),extreme gradient boosting(XGB),and light gradient boosting machine(LGBM)models.In addition,this study combines the recognition advantages of the four models.The accuracy of lithology identification by the weighted average probability model reaches 0.877.The study of this paper realizes high-precision real-time lithology identification method,which can provide lithology reference for the drilling process.

关 键 词：mud logging data real-time lithological identification improved crow search algorithm petroleum geological exploration SMOTE-Tomek 

分 类 号：P618.13[天文地球—矿床学]