# EUR Prediction for Shale Gas Wells Based on the ROA-CatBoost-AM Model
###### Abstract  
Shale gas is a critical energy resource, and estimating its ultimate recoverable reserves (EUR) is a key indicator for evaluating the development potential and effectiveness of gas wells. To address the challenges in accurately predicting shale gas EUR, this study analyzed production data from 200 wells in the CN block. Sixteen potential factors influencing EUR were considered, and key geological, engineering, and production factors were identified using Spearman correlation analysis and mutual information methods to exclude highly linearly correlated variables. An attention mechanism was introduced to weight input features prior to model training, enhancing the interpretability of feature contributions. The hyperparameters of the model were optimized using the Rabbit Optimization Algorithm (ROA), and 10-fold cross-validation was employed to improve the stability and reliability of model evaluation, mitigating overfitting and bias. The performance of four machine learning models was compared, and the optimal model was selected. The results indicated that the ROA-CatBoost-AM model exhibited superior performance in both fitting accuracy and prediction effectiveness. This model was subsequently applied for EUR prediction and for identifying the primary factors controlling productivity, providing effective guidance for development practices. The dominant factors and production forecasts determined by the model offer valuable references for optimizing block development strategies. m achemine learning; shale gas; production prediction; primary control factors 2025  
of China's natural gas supply. China boasts abundant shale gas resources, with marine shale gas reserves being the most substantial [2]. In 2023, China's shale gas production reached 25 billion cubic meters, accounting for over 10% of the nation's total natural gas output for that year, and 57% of unconventional natural gas production (Figure 1). Shale gas has become one of the primary drivers of incremental growth in China's natural gas production [3]. Under the \"dual carbon\" strategy, promoting the efficient development of shale gas has become an urgent task for China to ensure a balanced supply and demand for natural gas [4]. Estimating the ultimate recovery (EUR) for individual shale gas wells has long been a technical challenge in the dynamic analysis of shale gas reservoirs. As a critical indicator of development potential and efficiency, EUR estimation directly impacts the formulation of production control strategies, the deployment of well patterns, and the rational and scientific planning of capacity replacement programs [5]. Traditional prediction methods include empirical methods, numerical analysis, and material balance approaches [5; 6]. While material balance and decline curve analysis methods are suitable for long-term production forecasting, they struggle to address the dynamic changes in complex flow regimes. The simplifications inherent in traditional methods fail to adequately account for the interactions among multiple factors and the multi-scale flow characteristics of shale gas wells, resulting in significant limitations in accuracy, timeliness, and applicability [7; 8; 9; 10; 11; 12]. These constraints have driven the development of new approaches based on machine learning and advanced statistical methods to better meet the needs of shale gas development. With the advancement of big data and artificial intelligence technologies, machine learning has demonstrated great potential in unconventional oil and gas development. Time-series models such as LSTM and CNN effectively handle dynamic data changes, significantly improving prediction accuracy [13; 14; 15]. Machine learning models like Random Forest and XGBoost [16; 17] have been successfully applied to predict enhanced recovery rates through CO\({}_{2}\) injection in shale gas reservoirs [18], severe slugging lengths in well trajectories [19], AGC [20], and production rates [21; 22; 23; 24; 25].