# EUR Prediction for Shale Gas Wells Based on the ROA-CatBoost-AM Model
## 4 Discussion
These findings highlight that ROA, compared to traditional optimization algorithms (PSO and GA), exhibits greater efficiency and accuracy  
Figure 10: Comparison with baseline models. in optimizing CatBoost hyperparameters, effectively enhancing predictive performance and reducing errors, thereby providing better modeling support for complex datasets. The introduction of the attention mechanism enabled the model to focus on key influencing factors, enhancing its ability to handle complex high-dimensional data and improving interpretability. The Rabbit Optimization Algorithm efficiently fine-tuned the hyperparameters, reducing the likelihood of overfitting and improving the model's  
Figure 11: Comparison before and after removing redundant features. Figure 12: Comparison of optimization algorithms. adaptability to different data partitions. As shown in Figure 13, the inclusion of the attention mechanism and the optimization by the Rabbit Algorithm significantly enhanced the model's generalization ability and reduced errors. Furthermore, the application of 10-fold cross-validation ensured the reliability of the results, mitigating the randomness caused by specific data splits. Figure 14 demonstrates that true vertical depth and drilled length of high-quality reservoir consistently hold high attention weights across different wells and depth groups, indicating their critical and consistent role in the model's predictions. Meanwhile, the distribution of secondary feature weights exhibits dynamic adaptability; for instance, shallow wells place greater emphasis on permeability and thickness of high-quality reservoir, whereas deeper wells show significantly increased reliance on true vertical depth. Additionally, individual wells exhibit personalized distributions for features like average pumping rate and stage length, highlighting the model's capability to dynamically adjust feature importance based on geological conditions and well characteristics, thereby enhancing its predictive power and interpretability in complex reservoir environments. Figure 15 indicates that the model exhibits minimal prediction errors for most samples, with residuals concentrated within the range of [\(-\)0.05, 0.05] and a symmetric distribution, suggesting no significant systematic overestimation or underestimation. However, a small number of samples with larger residuals (e.g., \(<\)\(-\)0.1 or \(>\)0.1) may be associated with complex geological conditions or feature anomalies, such as deep reservoirs or wells with low flowback rates. Additionally, optimizing feature engineering and model structure (e.g., adaptive loss functions or enhanced attention mechanisms) could improve the model's adaptability to anomalous wells, further enhancing prediction accuracy and robustness. Moreover, the analysis of influencing factors provided practical guidance for the productivity development of the CN block. For instance, this study identified that depth, high-quality reservoir encountered length, and FBR330 (330-day flowback ratio) were the most critical factors affecting EUR. These findings offer valuable insights for well placement, the target zone selection of high-quality reservoirs, and hydraulic fracturing optimization, thereby improving the overall development efficiency of the block. Figure 13: Comparison of model improvement effects. True vertical depth (TVD) is a key geological parameter of shale gas reservoirs that significantly influences the Estimated Ultimate Recovery (EUR). From a geological perspective, TVD determines the formation pressure gradient, where greater depths are typically associated with higher formation pressures, providing a stronger driving force for shale gas desorption and flow. Additionally, TVD is directly related to formation temperature, with deeper reservoirs exhibiting higher temperatures, which facilitates phase transitions and desorption behaviors, thereby enhancing gas production potential. Although permeability  
Figure 14: Distribution of attention weights by depth. Figure 15: Residual distribution.