## 6 Conclusions  
This study addresses the technical challenges of real-time identification and precision drilling in strongly heterogeneous formations by proposing a collaborative-driven method for multi-source and multi-parameter data dimensionality reduction and unsupervised clustering based on UMAP. By integrating LWD logging parameters (such as natural gamma and electrical resistivity) and drilling engineering dynamic parameters (such as drilling rate and torque), a high-dimensional feature space is created that captures both the physical properties of formations and rock mechanics characteristics. The UMAP algorithm is employed for nonlinear dimensionality reduction, significantly enhancing the resolution of thin reservoir recognition while retaining lithology sensitivity and engineering response characteristics. A CatBoost lithology classification model is developed based on K-means clustering results, effectively addressing the challenge of sparse lithology labels in traditional supervised learning. This approach overcomes the difficulties associated with complex lithology and thin reservoir identification in heterogeneous formations and provides a geology-engineering integration framework for the real-time regulation of horizontal well trajectories. This is particularly significant for precision drilling in unconventional oil and gas reservoirs. Based on the measured data from eight wells in the Hugin Group of the Volve oilfield, the results indicate that the UMAP algorithm outperforms traditional PCA and t-SNE methods in dimensionality reduction. The Davies-Bouldin Index of UMAP is 61% lower than that of t-SNE, with particularly notable improvements in fine segmentation, especially for the silt-fine sandstone transition zone and the limestone independent cluster. After integrating the dimensionality reduction features, the CatBoost model achieves a balanced accuracy of 92.7% and an F1-score of 89.3%, representing an 18.6% improvement over the single logging parameter model. The recognition accuracy of coarse sand (F1 = 93.1%) and clay shale (F1 = 88.5%) shows the most significant improvement. These results demonstrate that the multi-source and multi-parameter collaborative analysis captures the coupling response between drilling rate variations and gamma-resistivity anomalies, with higher recognition accuracy for thin reservoirs (thickness < 0.3 m) compared with traditional methods. This validates the adaptability of the proposed method to strongly heterogeneous strata. Future research can focus on three key areas for further breakthroughs. First, improving the real-time performance of the optimization algorithm is crucial. By leveraging lightweight model architectures and edge computing technology, the data processing delay can be reduced to mere seconds, meeting the timing requirements for ultra-deep well drilling decision making. Second, expanding the dimensionality of multi-modal data fusion will be important. Integrating data sources such as geosteering, downhole imaging, and other relevant measurements will help build a multi-dimensional geo-engineeringfeature space, enhancing the robustness of recognition in complex structural areas like fault fracture zones. Third, exploring the fusion of self-supervised learning with physical constraints offers significant potential. Incorporating prior knowledge of stratigraphic deposition and rock mechanics could guide the unsupervised clustering process, reducing the algorithm's reliance on the number of samples. Additionally, strengthening the cross-block generalization ability and developing adaptive models suitable for different basin types will be essential to expand the application of intelligent identification technology in various geological settings. Conceptualization, A.Z. and F.T. ; Data curation, A.Z., Y.Y., B.W., Y.L. and J.L. ; Funding acquisition, Y.Y. and F.T. ; Investigation, B.W. ; Methodology, A.Z., W.Z. and F.T. ; Project administration, Y.Y. and F.T. ; Resources, Y.Y. ; Software, X.F. ; Supervision, Y.Y. ; Visualization, A.Z., B.W., Y.L., X.F.