# EUR Prediction for Shale Gas Wells Based on the ROA-CatBoost-AM Model
## 3 Analysis of Primary Production Control Factors and EUR Prediction Model  
### Experimental Data and Preprocessing  
The CN block features relatively gentle stratigraphic undulations, with underdeveloped large faults and simple structural characteristics. The area includes syncline structures and multiple anticline structures of varying scales. Among these, the Jianwu Syncline is a  
Figure 3: Attention mechanism flow chart. broad and gentle east-west syncline, while the CN Anticline is the largest structure in the area. The CN Anticline trends northwest-southeast, with a gentle southwest flank and a steeper northeast flank [35]. The data used in this study were sourced from the Ning201 and Ning209 well blocks in the CN shale gas field, comprising production data from a total of 200 wells. These two well blocks are representative core areas within the CN block, covering the major geological characteristics and production conditions, including variations in reservoir depth, permeability ranges, and hydraulic fracturing designs. Given the high typicality of Ning201 and Ning209 in terms of reservoir properties and extraction techniques, the data can effectively reflect the overall characteristics of the CN block. Therefore, this study, based on data from these two well blocks, provides a robust foundation for modeling and extending the research findings to other areas within the CN block. A total of 16 factors influencing EUR were identified and screened. These data were derived from integrated well logging interpretation, drilling operation reports, fracturing operation summaries, and production dynamics. In this study, multiple measures were implemented during the data preprocessing phase to ensure data quality and consistency. First, min-max Scaling was applied to normalize all input features to a range of [0, 1], eliminating dimensional differences between features and enhancing the stability of gradient optimization in the model. Second, non-numerical data and missing values were handled through forced conversion and row removal (dropna), ensuring the completeness and consistency of the training data. Additionally, the Isolation Forest algorithm was used to detect and label outliers, reducing the impact of extreme values on model training. Random sampling was also employed to mitigate distribution bias, further improving the representativeness of the sample and the credibility of the data. These preprocessing steps provided a robust foundation for the performance and reliability of the model. Normalization, a critical step in data preprocessing, was applied to scale feature values to a fixed range, thereby reducing scale differences between features. This process effectively improves model training speed and stability, particularly in distance-based algorithms such as KNN, SVM, and neural networks. Normalization maps feature values to a fixed interval (commonly [0, 1] or [\(-\)1, 1]) while maintaining the relative relationships between features. The normalization formula is:  
\[X_{\text{normalized}}=\frac{X-X_{\text{min}}}{X_{\text{max}}-X_{\text{min}}} \tag{4}\]  
where \(X\) represents the original feature value, \(X_{\text{min}}\) is the minimum value of feature \(X\), and \(X_{\text{max}}\) is the maximum value of feature \(X\). This study adopts min-max normalization instead of z-score standardization for the following reasons. First, min-max normalization is suitable for non-normally distributed data, allowing for all features to be mapped to the same scale without altering their original distribution, thereby improving model stability. Second, although CatBoost can inherently handle features with different scales, min-max normalization helps reduce the impact of numerical range differences on feature importance evaluation, further optimizing the attention mechanism's feature weighting process.