# Identification of Complicated Lithology with Machine Learning
## 4 Conclusions  
This study focuses on the second member of the Lucaogou Formation in the Santanghu Basin, using three machine learning methods to classify and identify lithology in the study area based on conventional logging curves and core data. The specific conclusions of this study are as follows:  
1. By analyzing the correlations between logging curves, the best feature curves for training were selected. The GR, AC, CN, DEN, RS, and RD curves were chosen as the input features. Three methods--XGBoost, random forest, and support vector regression--were used to predict the felsic content. The predictive performance of the models was evaluated using four evaluation metrics. The random forest method yielded the best performance with R\({}^{2}\) = 0.43, MAE = 0.11, MSE = 0.020, and RMSE = 0.14 when using 80% of the training data, followed by XGBoost (R\({}^{2}\) = 0.42, MAE = 0.12, MSE = 0.022, RMSE = 0.15) and then Support Vector Regression (R\({}^{2}\) = 0.41, MAE = 0.11, MSE = 0.021, RMSE = 0.14). While these R\({}^{2}\) values indicate moderate predictive capability, they represent a significant improvement over traditional methods in this complex geological setting. By comparing the predicted felsic content with the actual core felsic content at specific depths, it was demonstrated that lithology identification based on predicted felsic content achieves sufficient accuracy for practical reservoir characterization applications. 2. In this study, three machine learning methods were adopted to identify the lithology of complex carbonate rocks in the Luijagou Formation. Compared with previous studies, our random forest model has achieved better results in the prediction of feldspar content by optimizing feature selection. Although the R\({}^{2}\) value of the model is at a medium level, it is comparable to the previous achievements under similar complex geological conditions and achieves a good balance between computational efficiency and accuracy. Verified by actual core data, the lithology identification method based on predicted feldspar content has practical value, providing a reliable technical solution for complex lithology identification and having reference significance for reservoir evaluation in similar areas. 3. Although the prediction model constructed in this study performed well in the lithology identification of the Lucaogou Formation, there are still some limitations and uncertainties: On the one hand, the logging data itself contains various noise sources and uncertainties. Although certain data preprocessing has been carried out, some noises may still affect the prediction accuracy. On the other hand, the number of XRD samples available for model training in this study is limited, which may affect the model's ability to fully capture the geological complexity of the region. Secondly, there is a potential sampling deviation. Some core samples may not perfectly represent all the lithological changes existing in the formation. To alleviate these uncertainties, future work should focus on expanding the training dataset, adding additional logging curves when available, and developing more complex model validation techniques. 4. Although this research has achieved certain results, there is still room for improvement. Future research can be carried out from the following perspectives: Firstly, on the basis of sufficient data, deep learning methods such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) can be used to predict complex rock types. Deep learning methods have good effects in capturing the tiny changes between different rock types. Secondly, other data types such as nuclear magnetic resonance (NMR) can be combined to enhance the lithology discrimination ability of the model in the case of overlapping conventional logging responses. **Author Contributions:** L.C. : writing--review and editing, supervision, and validation, L.H. : writing--original draft and writing--review and editing. J.X. : data curation. Q.H. : methodology and supervision. J.F.