## 3 Key Processes for Deep Learning-Based Seepage Field Prediction
### Neural Network Construction Methods
[22] introduced the LSTM structure to investigate the gas-water two-phase microscopic seepage process in coal rock fissures, realizing the high-precision time-sequence portrayal of the fracture-matrix periodic response process and showing good results in the modeling of a microscale dynamic evolution process. From the current study, within the Deep Learning framework, LSTM, RNN, and GRU as the basic models have been widely used to solve time-series dynamic prediction problems, and experts and scholars have developed different variant models according to the actual needs of different scenarios, which provide a flexible and efficient technical path for modeling seepage behaviors in high WCPs [48; 49; 50]. 3. Convolutional Neural Network model: Convolutional Neural Networks (CNNs) have been widely used in reservoir seepage modeling by virtue of their advantages in spatial feature extraction and local pattern recognition, which are especially suitable for processing gridded distribution maps such as pressure and saturation fields, or three-dimensional structural data such as digital core images [22]. In complex seepage systems, CNNs can extract features such as inter-well interference, hyper-permeable channels, and non-homogeneous structures through a local sensing mechanism, and they capture the spatial variation patterns in the seepage field [51]. For example, [PERSON] et al. [2] used the Physical Difference Convolutional Neural Network (PD-CNN),which integrates the finite difference idea with the convolutional structure, and they embedded the residuals of the control equations as a convolution kernel into the CNN network to realize high-precision prediction of the distribution of the joint pressure-saturation field. [PERSON] et al. [3] constructed a deep convolutional network model based on U-Net for dynamic prediction of three-dimensional saturation fields in complex fault block reservoirs. By designing jump connections and a multi-scale structure, the model can reconstruct high-resolution oil saturation distribution maps in the low well control region, which improves the accuracy and spatial consistency of residual oil identification. [PERSON] et al. [23] applied 3D CNN to digital core image modeling; they extracted pore structure information through 3D convolution, and they predicted the pressure gradient and saturation evolution process of a two-phase flow in the core. This method can automatically learn microscopic seepage paths in complex pore channels, expanding the application space of CNN in microscale seepage modeling. Overall, the Convolutional Neural Network-like model has become one of the mainstream modeling tools in reservoir seepage field prediction by virtue of its powerful spatial structure perception capability and good scalability. Especially in complex flow patterns during high WCPs, CNN shows good ability to portray the response mechanism of non-homogeneous seepage by introducing multi-scale structures, physical constraints, and spatio-temporal fusion [24; 52]. * Graph Neural Network model: The Graph Neural Network (GNN) is a class of Deep Learning. GNN models are capable of handling non-Euclidean spatial structure data, which are especially suitable for describing data forms with irregular topology, such as pore networks, fracture systems, well network arrangements, and other complex spatial relationships. In reservoir seepage modeling, GNNs can regard pores or well points as \"nodes\" in the graph, and their connection relationships (such as flow paths, neighboring wells interference, etc.) as \"edges\", and they can extract spatial topological features through graph convolution, message passing, and other mechanisms, so as to model the propagation pattern of the fluid in the complex structure. The topological features are extracted through graph convolution and message passing to model the propagation of fluids in complex structures [53; 54; 55]. For example, [PERSON] et al.