## 5 Conclusions  
Based on the need to address the relative scarcity of high-quality reservoir logging data in practical production, we propose a workflow that combines enhanced sampling algorithms with machine learning models to achieve efficient and accurate lithology discrimination from well logging data. Applying a data synthesis balancing method that combines the ADASYN over-sampling algorithm with the ENN under-sampling algorithm to the problem of intelligent lithology discrimination can assist machine learning models in accurately predicting and identifying reservoirs with insufficient logging curve samples, thereby providing reliable references for production practices such as reservoir interpretation.  
The experimental results demonstrate the effectiveness of the proposed method. By comparing the changes in the dataset before and after data balancing, it was found that the comprehensive balancing method better maintains the original data's feature distribution compared to other data over-sampling or under-sampling algorithms, thereby increasing the number of learnable samples for high-quality reservoir lithology classes. Through testing and comparing the lithology discrimination performance of the SVM, RF, and GBDT models on the comprehensive balanced dataset and the original imbalanced dataset, evaluation metrics such as the Jaccard index and F1 score indicate that the comprehensive balancing method effectively improves the lithology discrimination performance of various machine learning models. Among them, the RF model performs the best and can accurately identify lithology in oil-bearing reservoirs.  
The enhanced sampling algorithms used in this study demonstrate strong versatility, as they not only enhance the performance of the well logging lithology discrimination model but also provide important methodological references for identifying shale reservoirs and predicting sweet spots and other related tasks. Future research should conduct a more comprehensive long-term evaluation of this workflow with more data and time resources.  
Data curation, J.L., F.T., A.Z. and W.C.; Funding acquisition, F.T.; Methodology, J.L.; Project administration, F.T.; Resources, F.T.; Software, W.Z.; Supervision, F.T.; Visualization, J.L., A.Z., W.Z. and W.C.; Writing--original draft, J.L.; Writing--review and editing, F.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Chinese National key research and development program, grant no. 2019 YFA0708301 and no. 2023 YFB3905005; the Youth Innovation Promotion Association Foundation of the Chinese Academy of Sciences (2021063); the Strategic Priority Research Program of the Chinese Academy of Sciences, grant no. XDA14050101; and the China National Petroleum Corporation (CNPC) Scientific research and technology development project, grant no. 2021 DJ05.And the APC was funded by Institute of Geology and Geophysics, Chinese Academy of Sciences.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
No conflicts of interest exit in the submission of this manuscript, and the manuscript is approved by all authors for publication.  
The abbreviations used in this article are summarized in below (term abbreviations used in this work).