## 5 Conclusions  
1. **Paradigm shift in seepage-prediction technology:** The evolution of reservoir seepage prediction has undergone three distinct phases: from physics-based analytical models to data-driven Machine Learning approaches, culminating in the current paradigm of physics-informed Deep Learning systems. This progression reflects the oil industry's growing demand for intelligent solutions to address the complex nonlinear dynamics of high-water cut reservoirs. Deep Learning architectures (CNNs, GNNs, PINNs) demonstrate superior capability in modeling multiphase flow heterogeneities compared to traditional numerical simulations (FDM/FVM), achieving >85% accuracy in saturation field reconstruction under low well-control conditions [3; 15]. The integration of Deep Learning mechanisms with governing equations (e.g., Darcy's law, mass conservation) enables these models to maintain physical consistency while capturing complex spatial-temporal patterns. 2. **Current implementation landscape:** Practical implementations reveal distinct architectural advantages: \(\bullet\)_CNN-based models_ achieve 92.4% accuracy in inter-well pressure gradient prediction through 3D convolutional feature extraction [2]. \(\bullet\)_PINN frameworks_ reduce pressure field inversion errors to <8% under sparse data conditions (n = 500 samples) [5]; \(\bullet\)_GNN solutions_ enable fracture network flow simulations with 15% faster convergence than conventional discrete fracture models [16]. Case studies in Daqing and Shengli oilfields demonstrated 20-35% improvement in remaining oil recovery rates through Deep Learning-driven injection optimization [4; 25]. 3. **Strategic development pathways:** Four critical frontiers emerge for next-generation intelligent systems: * _Real-time closed-loop control:_ Integrating edge-computing optimized CNNs (inference time < 50 ms) with IoT sensor networks enables dynamic waterflood adjustment. The DSP-XGBoost framework achieved 92.7% real-time decision accuracy in pilot tests [25]. * _Multimodal data fusion:_ Transformer-based architectures incorporating seismic attributes, production logs, and core CT images improve heterogeneity characterization accuracy by 40% compared to single-modality models [30]. * _Physics-guided explainability:_ Hybrid PINN architectures embedding modified black-oil equations reduce unphysical predictions by 78%, while attention mechanisms provide quantifiable feature importance metrics [2; 34]. * _Cross-domain adaptability:_ Meta-learning enhanced models maintain >80% prediction accuracy when transferred between carbonate and sandstone reservoirs, significantly reducing recalibration costs [35]. **Technological impact:** The convergence of differentiable programming and reservoir digital twins is reshaping development strategies--early adopters report 18-25% reductions in water injection costs and 30%-shorter simulation cycles. Future research should prioritize human-AI collaboration frameworks to bridge the gap between data scientists and reservoir engineers [68; 69]. T.W. : experimental, data curation, writing--original draft, review and editing, methodology. Q.L. : conceptualization, resources, writing--review and editing. Y.W. : resources, data curation, writing--review and editing. Y.X. : resources, supervision, writing--review and editing. J.S. : investigation, formal analysis, validation. Y.Y., Q.C., J.L., S.T. : experimental design, data analysis, data curation, review and editing. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was supported by the CNPC Science and Technology Innovation Fund (Grant No. 2023 ZZ04-08). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Not applicable. **Conflicts of Interest:** Authors [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] were employed by the company PetroChina.