## 3 Key Processes for Deep Learning-Based Seepage Field Prediction
### Comprehensive Example  
[PERSON] et al. [47] constructed a Deep Learning model for the prediction of seepage response variables in reservoirs with high WC based on the strong nonlinear characteristics of production dynamics and on injection and extraction conditions during reservoir \begin{table}
\begin{tabular}{c c c c c} \hline
**Year** & **Author(s)** & **Application Target** & **Problem Addressed** & **Model Used** \\ \hline
2021 & [PERSON] et al. [12] & Production forecast & Oil production sequence modeling & RNN–SVM \\
2023 & [PERSON] et al. [13] & Production forecast & Dam seepage sequence prediction & AutoML–LSTM \\
2024 & [PERSON] et al. [4] & Production forecast & Coupled spatio–temporal modeling & CSTN (CNN–LSTM) \\ \hline
2020 & [PERSON] et al. [5] & Pressure field & Reconstruction from velocity data & PINNs \\
2022 & [PERSON] et al. [2] & Pressure field & Prediction in heterogeneous media & Physics-Reg. DNN \\
2023 & [PERSON] et al. [19] & Pressure field & Learning in sinusoidal domain & Sinusoidal PINN \\ \hline
2022 & [PERSON] et al. [21] & Saturation field & 3D multiphase saturation modeling & CNN + PDE loss \\ \hline
2023 & [PERSON] et al. [23] & Saturation field & Two-phase prediction in digital core & 3D CNN \\
2023 & [PERSON] et al. [3] & Saturation field & Fault block saturation mapping & CNN–Attention Fusion \\ \hline
2017 & [PERSON] et al. [11] & Porosity/permeability & Log-based property modeling & Ensemble ANN \\
2021 & [PERSON] et al. [10] & Permeability & Core-based prediction & MLP \\
2023 & [PERSON] et al. [20] & Pore-throat radius & Prediction from image data & DNN \\ \hline
2021 & [PERSON] et al. [24] & Microscale seepage & Mud invasion in tight sandstone & CNN + Regression \\
2023 & [PERSON] et al. [22] & Microscale seepage & Micro gas–water flow in cleats & LSTM \\
2024 & [PERSON] et al. [16] & Microscale seepage & Multiscale nonlinear flow & Graph-based CNN \\ \hline
2022 & [PERSON] et al. [15] & Well network modeling & Optimization of injection–production layout & Graph NN \\
2023 & [PERSON] et al. [18] & Well network modeling & Flow reconstruction via mesh graph & GNN with topology \\
2024 & [PERSON] et al. [11] & Connectivity classification & Classification by optimization & RF–SBO Hybrid \\ \hline
2023 & [PERSON] et al. [10] & Multimodal permeability & Core–log data fusion prediction & ANN–RF \\
2023 & [PERSON] et al. [3] & Multimodal saturation & Seismic–log fusion for saturation & Sample Pool + CNN \\
2024 & [PERSON] et al. [4] & Multimodal proxy modeling & Dynamic modeling from mixed input & CSTN + Fusion Block \\ \hline \end{tabular}
\end{table} 
Table 4: Applications of Deep Learning in seepage field prediction by task type. development, which effectively solved the coupled prediction problem of production, WC, and pressure changes among different wells [4]. The research process was consistent with the Deep Learning application process in Figure 4, covering key steps, such as feature engineering, sample construction, and network structure construction, as follows:  
1. Feature extraction and sample construction. Aiming at the data non-uniformity and production dynamic differences between injection and extraction well sites, [PERSON] et al. designed a temporal-spatial fusion sample organization strategy to standardize the injection and extraction data (e.g., injection intensity, WC, daily oil production, etc.) between wells and construct the sample sequence based on the temporal sliding window in order to maintain the temporal continuity of the data and the spatial consistency of the well group. The Dynamic Sample Pool design enhances the adaptability of the model to data from different production stages and alleviates the problem of \"data drift\" in high WC areas. 2. Construct neural network. In order to better extract the temporal and spatial coupling features in the seepage response variables, [PERSON] et al. proposed a Convolutional Spatio-temporal Network (CSN) structure that integrates a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) network.