## 3 Key Processes for Deep Learning-Based Seepage Field Prediction  
As a typical data-driven model, the performance of Deep Learning is highly dependent on the quality, quantity, and distribution characteristics of the training samples. In the prediction of reservoir seepage fields in high WCPs, the input data often come from multiple heterogeneous information sources, such as geology, well logging, injection and extraction, monitoring, etc., which show the characteristics of multi-source heterogeneity, multi-scale, high-dimensionality, and spatial-temporal inhomogeneous distribution. Although the Deep Learning model is capable of automatic feature extraction, under the concept of integrated geological engineering modeling, if the physical relevance and semantic structure of the data are neglected it can easily lead to network learning bias and generalization performance degradation. Therefore, before constructing the Deep Learning-based seepage field prediction model, it is still necessary to carry out targeted data preprocessing and feature engineering, such as feature screening, dimensional approximation, spatial-temporal normalization, and physical quantity conversion, in order to ensure that the input of the model has sufficient information-expression ability and physical consistency and to provide a reliable data basis for subsequent high-precision prediction. Deep Learning-based seepage field prediction research in high WC reservoirs usually includes key steps such as data preprocessing, model construction, model training, validation, and prediction (see Figure 4) [17]. In the data preprocessing stage, it is first necessary to integrate multi-source data, including geological attributes, logging parameters, injection history, and production response to establish a unified training data system. Subsequently, the original data should be quality-checked to deal with missing values, outliers, and inconsistent data, and normalization or standardization methods should be used to eliminate the influence of the scale, so as to ensure the comparability of various variables [11]. On this basis, the physical mechanism of seepage is combined with feature screening and engineering processing to extract the input factors that have a significant impact on the response variables, such as pressure, saturation, WC, etc., and that reasonably divide the training set and test set [12]. In the model construction stage, appropriate Deep Learning models, such as DNNs, CNNs, LSTMs, PINNs, GNNs, etc. (see Table 3), need to be selected according to the prediction objectives (e.g., multivariate joint prediction, time-varying modeling, and spatial distribution recovery, etc.) and data characteristics (e.g., small samples, spatial-temporal non-stationarity) [2,5,16]. The model training phase can be combined with optimization algorithms (e.g., Adam, SGD, learning rate scheduling, etc.) to improve the training efficiency and performance stability. In the model validation and testing stage, the model prediction effect is evaluated by using quantitative metrics such as Mean Square Error (MSE), coefficient of determination (R\({}^{2}\)), and Mean Absolute Error (MAE). In recent years, in order to improve the accuracy and physical consistency of seepage field prediction, research has gradually focused on feature engineering optimization and network structure enhancement, especially in the embedding of physical information, multiscale modeling, and spatio-temporal fusion, on which significant progress has been made [14,18-20].