## 2 Development of Seepage Field Prediction Methods  
With the deepening of oil and gas development and production, most of the main oil fields in China have entered the high WCP and the late stage of development, and the internal seepage behavior of the reservoir presents significant spatial and temporal non-homogeneity and complex evolution characteristics, which makes the traditional prediction model no longer applicable, and more refined and intelligent methods are needed to solve the complex problem of seepage field prediction.  
The concept of Artificial Intelligence (AI) was first proposed in 1956, marking the beginning of the era of intelligent computing. Since then, AI has gradually developed into an interdisciplinary field integrating computer science, mathematics, control theory, and other disciplines. As the core component of AI, Machine Learning (ML) has been evolving since it was proposed in 1959, and its core goal is to design algorithmic models that can learn autonomously from historical data, so as to realize the extraction, cognition, and prediction of data patterns. In recent years, Deep Learning (DL) Figure 1, as an important branch of Machine Learning, has made breakthroughs in many fields, such as image recognition, speech processing, natural language understanding, etc., by constructing multi-layer neural network structures to abstract features layer by layer and learn complex mapping relationships, and it has also injected new momentum into scientific computing and engineering simulation. Especially in the field of fluid dynamics and seepage simulation, AI technology is deeply integrated with traditional physical modeling to promote the formation of a new paradigm of \"data-driven-physical constraints-mixed modeling\" [5]. For example, in the task of visualizing flow field prediction, the Hidden Fluid Mechanics (HFM) model proposed by [PERSON] et al. (2020) breaks through the dependence of traditional methods on boundary conditions and observation data by introducing Physical Information Neural Networks (PINNs) to invert velocity and pressure distributions directly from flow field images [5]; in the field of reservoir seepage, [PERSON] et al. (2023) use CNNs to extract velocity and pressure distributions directly from flow field images, using CNNs to extract the spatial features of saturation in complex fracture blocks to achieve accurate modeling under multi-source data [3]. These studies show that Deep Learning not only improves the modeling accuracy of hydrodynamics, but also expands the boundary of its application in the field of seepage mechanics for unpredictable, nonlinear, and unstructured problems.  
The development of Artificial Intelligence provides new methods and tools for seepage field prediction in oil reservoirs. Seepage field prediction methods have gradually shifted from traditional methods to data-driven methods based on Machine Learning and Deep Learning, and they have gradually developed towards the combination of multiple data-driven methods and data-driven methods with traditional methods (Figure 2). The development process of seepage field prediction methods is mainly divided into four stages: the analytical method stage, the numerical simulation stage, Machine Learning modeling, and Deep Learning modeling.  
Figure 1: Artificial Intelligence, Machine Learning, and Deep Learning relationship.