# EUR Prediction for Shale Gas Wells Based on the ROA-CatBoost-AM Model
## 4 Discussion  
In this study, the EUR data were obtained from the system, with the initial estimates computed using the 2016 version Harmony software and subsequently refined through manual adjustments. Harmony software employs decline curve analysis (DCA), Rate Transient Analysis (RTA), and analytical modeling techniques to estimate EUR based on historical production data and well performance parameters. While these methods provide a systematic approach to EUR estimation, they may not fully capture the complexities of the reservoir and production anomalies. Therefore, manual adjustments were incorporated to account for site-specific conditions, operational constraints, and expert knowledge, compensating for potential discrepancies arising from automated modeling. However, traditional EUR estimation methods, such as DCA, material balance equations, and numerical reservoir simulation, heavily rely on empirical assumptions and predefined decline models, which limits their applicability, particularly in unconventional reservoirs characterized by highly variable production behavior. To overcome these limitations, machine learning (ML) techniques have emerged as a powerful alternative for EUR prediction. Unlike traditional methods that rely on predefined production trends, ML algorithms can learn complex, nonlinear relationships among reservoir properties, completion parameters, and production history, thereby improving predictive accuracy. Moreover, ML models can adapt to heterogeneous reservoir conditions, account for multiphase flow effects, and efficiently process large datasets, making them particularly suitable for unconventional resource development. Recent studies have demonstrated that ML-based EUR estimation approaches, such as XGBoost, deep neural networks (DNNs), and recurrent neural networks (RNNs), can reduce human bias while enhancing forecast reliability. Additionally, hybrid models integrating physics-driven decline curve  
Figure 9: Feature importance analysis histogram. models with ML-based pattern recognition techniques have shown promise in improving prediction robustness. This study significantly improved shale gas EUR prediction by integrating the Rabbit Optimization Algorithm (ROA), attention mechanism (AM), and 10-fold cross-validation into the machine learning model. Among the four models compared, the ROA-CatBoost-AM model achieved the best balance between accuracy, robustness, and generalization ability. As shown in Figure 10, CatBoost achieved the lowest RMSE among the compared models, outperforming XGBoost, Random Forest, SVR, and DNN, demonstrating its superior predictive performance in this study. XGBoost and Random Forest performed similarly but were slightly inferior to CatBoost, while SVR had the highest RMSE, indicating significant limitations in handling this dataset. In contrast, DNN exhibited a relatively lower RMSE but still fell short of CatBoost. Overall, the CatBoost model effectively captured complex feature interactions and significantly reduced prediction errors, providing reliable support for the findings of this study. By comparing the model performance between all sixteen features and the selected eight features (as shown in Figure 11), it is evident that removing low-importance features significantly improved predictive accuracy (MSE reduced from 0.0275 to 0.0071, and R\({}^{2}\) increased from 0.6664 to 0.9134). This indicates that low-importance features may have introduced noise or redundancy, while high-importance features (e.g., true vertical depth and thickness of high-quality reservoir) were the primary drivers of model performance. The results in Figure 12 demonstrate that ROA-CatBoost-AM outperforms PSO-CatBoost-AM and GA-CatBoost-AM across all performance metrics. Specifically, ROA-CatBoost-AM achieves the lowest MAE, MSE, and RMSE, indicating minimal prediction errors and reduced variability. Additionally, its R\({}^{2}\) value is the highest, approaching 1, reflecting superior fitting performance.