Natural gas hydrates identification and characterization are the key tasks throughout the exploration and development phase of marine energy resources.However,due to the complex nonlinear relationship between logging data and reservoirs,as well as the imbalance of logging data,traditional reservoir identification methods often show low accuracy,which severely limited the progress of energy exploration in the study area.To address the above is-sues,a composite method for reservoir identification is proposed.The improved SMOTE algorithm is used to increase the number of minority class reservoir samples and denoise the data,which effectively solves the issues of data im-balance.The XGBoost algorithm is then used to identify reservoirs.The results show that compared with traditional machine learning method,the RLSMOTE-XGB method has higher effectiveness and accuracy in reservoir identifica-tion.This method addresses the limitations of traditional machine learning methods in the case of imbalanced sample classes,increasing the reservoir identification accuracy from 66.7％to 86.4％and significantly improving the algo-rithm's performance.This study can effectively improve the identification effect of natural gas hydrates and natural gas reservoirs,which is of great significance for achieving intelligent reservoir identification.
维普
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