## 3 Key Processes for Deep Learning-Based Seepage Field Prediction
### Comparative Framework for Deep Learning Architectures in Seepage Modeling
#### 3.4.3 Model Selection Decision Tree  
This decision tree aims to select the optimal Deep Learning model for the seepage field prediction task based on specific task requirements and data characteristics. The following is a detailed description of the logic of each node:  
1. Initial judgment: are there enough data? Sufficient data (yes): Enter real-time requirements analysis for scenarios with well-developed historical databases or rich experimental data (e.g., long-term monitoring data from mature oil fields). Insufficient data (No): Enter data structure analysis, applicable to the early stage of exploration or scenarios with high experimental costs (e.g., small amount of logging data in new areas of unconventional reservoirs). 2. Data-sufficient branching: is high real time required? Need high real-time performance (yes): Choose FFN (FeedForward Neural) network or CNN (Convolutional Neural Network) Applicable Scenario: Real-time monitoring of production parameters in a single well (FFN: [PERSON] et al. [5]) Dynamic visualization of injection response (CNN: [PERSON] et al. [15]) Technical Advantage: An FFN/CNN has a simple structure and high computational efficiency, can complete the prediction in seconds, and is adapted to the deployment of edge devices. No need for high real time (No): Access to field continuity judgment; suitable for medium-to-long-term development scenario optimization or research-level simulation tasks. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Model Type** & **Study Example** & \begin{tabular}{l} **MSE (Pressure** \\ **Field)** \\ \end{tabular} & \begin{tabular}{l} **Physics Violation** \\ **Rate** \\ \end{tabular} & \
\begin{tabular}{l} **Training Time (h)** \\ \end{tabular} &
\begin{tabular}{l} **Data** \\ **Requirement** \\ \end{tabular} \\ \hline FFN & [PERSON] et al. [10] & 0.32 & 28\% & 2.1 & High \\ CNN & [PERSON] et al. [3] & 0.18 & 15\% & 5.3 & Medium \\ GNN & [PERSON] et al. [16] & 0.12 & 8\% & 8.7 & Low \\ PINNs & [PERSON] et al. [2] & 0.09 & \textless{}5\% & 12.5 & Very Low \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of representative model applications (partial data). \begin{table}
\begin{tabular}{l l l} \hline \hline
**Model Type** & **Typical Application** \\ \hline FFN & Single-point parameter prediction (Wellhead Pressure/WC) \\ CNN & 2D permeability field reconstruction \\ GNN & Simulation of multiphase flow in fractured reservoirs \\ PINNs & Inversion of PDE with continuous field reconstruction \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Typical application of Deep Learning models. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Model Type** & \begin{tabular}{l} **Spatio–Temporal** \\ **Processing** \\ \end{tabular} & **Physics Constraint** & \
\begin{tabular}{l} **Small Sample** \\ **Generalization** \\ \end{tabular} &
\begin{tabular}{l} **Computational** \\ **Efficiency** \\ \end{tabular} \\ \hline FFN & \(\times\) & \(\times\) & \(\times\) & \(\star\)\(\star\)\(\star\)\(\star\) \\ CNN & Spatial locality & \(\triangle\) (custom kernel) & \(\times\) & \(\star\)\(\star\)\(\star\)\(\star\) \\ GNN & Unstructured relationships & ✓ & ✓ & \(\star\)\(\star\) \\ PINNs & Spatio–temporal & ✓/✓ & ✓ & \(\star\) \\  & continuity & ✓/✓ & ✓ & \(\star\) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of Deep Learning models in permeability field prediction. Field continuity judgment: is the predicted target a spatio-temporally continuous field? Field continuity (Yes): Select PINNs (Physically Informed Neural Networks) Applicable Scenario: Dynamic evolution of pressure field ([PERSON] et al. [2]) Coupled multi-phase flow simulation ([PERSON] et al. [21]) Technical Advantage: PINNs guarantee physical consistency by embedding control equations (e.g., Darcy's law) and are suitable for mathematical modeling of continuous fields. Field discontinuity (no): Selection of GNN (Graph Neural Network) Applicable scenarios: Fracture network flow path identification ([PERSON] et al.