## 2 Development of Seepage Field Prediction Methods
### A Machine Learning-Based Method for Seepage Field Prediction  
With the development of Artificial Intelligence and the increase of digitization of oil and gas fields, oil fields have accumulated a large amount of oil and gas development data, and data-driven methods have become an important trend. Machine Learning methods are data-driven, capable of learning the hidden laws between seepage field data and related engineering parameters and analyzing the cyclical changes and trends of seepage flow fields. Currently, the Machine Learning methods applied in oil and gas production prediction are Support Vector Machine (SVM), Gaussian Process Regression (GPR), and other regression algorithms, as well as Random Forest (RF), eXtreme Gradient Boosting Tree (eXtreme Gradient Boosting Tree), and other methods that can be used to predict oil and gas production, including eXtreme Gradient Boosting, Light Gradient Boosting Machine, XGBoost, and LightGBM [10]. [PERSON] et al. [11] systematically evaluated the potential application of integrated learning methods in reservoir seepage parameter prediction and proposed that integrated models such as Random Forest (RF), Adaboost, and Gradient Boosting be used for modeling porosity and permeability, as they have higher prediction accuracy and robustness compared to traditional single models [PERSON] et al. [12] applied Support Vector Machines (SVMs) and Artificial Neural Networks (ANNs) to predictive modeling of permeability and porosity, and they constructed a supervised learning-driven framework for reservoir parameter prediction based on geological logging and logging data. The study conducted a systematic comparison between different models and found that SVMs showed stronger robustness and generalization ability under small sample conditions, while ANNs had a slight advantage in dealing with nonlinear relationships but were more prone to overfitting, which emphasized that different Machine Learning models should be targeted to be selected according to the sample size, feature complexity, and distribution of the target variables in the prediction of seepage parameters [12]. [PERSON] et al. [13], based on a review of seepage-prediction methods for earth and rock dams, proposed the use of Automatic Machine Learning (AutoML), clustering algorithms, and probabilistic graphical models for seepage parameter modeling and risk identification driven by multi-source monitoring data. They emphasize the advantages of AI technology in automatic modeling under the condition of unstructured, high-dimensional, and heterogeneous data, and they pointed out that traditional Machine Learning methods can play an important role in the path of \"seepage coefficient estimation-risk classification-dynamic change monitoring\" [13]. Although this study mainly focused on hydraulic engineering, its methodological framework is highly transferable to reservoir injection and recovery block delineation and to data-driven seepage field prediction [13]. [PERSON] et al. [10] used a variety of mainstream Machine Learning models (including XGBoost, Random Forest, SVM, and ANN) for systematic modeling and prediction of reservoir permeability, and the training data were obtained from the results of \begin{table}
\begin{tabular}{c c c} \hline
**Model Type** & **Applicable Scenarios** & \begin{tabular}{c} **Numerical Method** \\ **Examples** \\ \end{tabular} \\ \hline Black oil model & \begin{tabular}{c} Conventional three-phase \\ drive systems \\ \end{tabular} & FDM, FVM \\ \
\begin{tabular}{c} Multi-component scenarios \\ such as gas drive/volatile \\ reservoirs \\ \end{tabular} & FVM, FEM \\
\begin{tabular}{c} Unconventional reservoirs \\ such as shale and tight oil \\ reservoirs \\ \end{tabular} & Forchheimer–FEM \\ \hline \end{tabular}
\end{table} 
Table 1: Conventional seepage field prediction models. core physical properties analysis.