## 4 Future Directions for Technology
### Real-Time Prediction and Closed-Loop Optimization Control  
The dynamic evolution of production capacity in high-water-bearing-period reservoirs is the result of the joint action of geological conditions, engineering intervention, and real-time regulation [61]. In terms of the causal mechanism, the influencing factors can be divided into two categories: one is static geological factors (such as reservoir non-homogeneity, original fluid distribution) and the other is dynamic human intervention factors (including injection and extraction system adjustments, frequency of well network  
Figure 5: Technology selection decision tree.  
perturbation, and production parameter changes, etc.) [62]. Currently, oilfield development has shifted from static scheme design to dynamic closed-loop optimization, and there is an urgent need to establish a prediction model that can couple real-time monitoring data (e.g., fluid production, WC fluctuation, injection and recovery response curves) with numerical simulation to support the precise regulation of seepage and flow field [25].  
The limitations of the existing studies are centered on the lack of data dynamics: traditional Deep Learning models are mostly trained based on static historical data (e.g., [PERSON] et al. [23]), and they lack the ability to learn real-time data streams online, leading to a significant disconnect between the prediction results and the dynamic regulation needs in the field. To break through this bottleneck, this study inherited and developed the theoretical system of multi-sample pool construction:  
1. Data level: Based on the Dynamic Sample Pool (DSP) architecture proposed by [PERSON] et al. [23], feature extraction and prioritization of high-frequency monitoring data streams are realized through the sliding time window mechanism. The migration learning framework developed by [PERSON] et al. [15] is further integrated to construct an incremental sample pool updating strategy to solve the cold-start and small-sample learning problems [25].
2. Algorithmic level: The sample space expansion method proposed by [PERSON] et al. [54] in Generative Adversarial Networks (GANs) is introduced to compensate for the missing data segments in the process of injection and mining adjustment through the generative adversarial mechanism, and, at the same time, coupling the production optimization algorithms (e.g., gradient descent method, reinforcement learning) with the feedback control theory, to establish a quantitative response function for the adjustment of injection and mining parameters [50].
3. System level: Drawing on the closed-loop optimization paradigm of [PERSON] et al. [4], the above method is embedded into the control chain of \"monitoring-prediction-decision-execution\", and the parameters of the seepage prediction model are dynamically corrected through the real-time WC feedback (error tolerance <5%), forming an adaptive optimization mechanism [4].  
Compared with the existing research, this method realizes breakthroughs in three aspects:  
1. Inheriting the real-time data capture capability of DSP architecture, and solving the generalization problem of cross-reservoir samples through migration learning.
2. Integrating the adversarial generation mechanism to effectively mitigate model oscillations caused by sudden changes in injection and recovery parameters.
3. Constructing a feedback control module with physical constraints to ensure that the optimization results comply with the seepage mechanics.  
This technical route not only solves the synergy problem between dynamic data and offline model but also provides methodological support for the implementation of a \"digital twin--real-time optimization\" system in the construction of intelligent oilfields through the organic integration of academic theoretical systems.