数据物理驱动的图连接元智能注采模型OA

To address the challenges of connectivity characterization,dynamic prediction efficiency,and real-time optimization in complex reservoir injection-production systems,this study proposes a physics-and deep learning-integrated intelligent injection-production modeling framework based on the graph connection element method.The method adopts the connection element method as the physical foundation and constructs a non-Euclidean graph representation to describe interwell connectivity,enabling characterization of the physical topology and dynamic interactions within the well pattern system.By incorporating an adaptive attention mechanism into a graph convolutional network and embedding time-dependent node attributes,a physics-consistent reservoir performance prediction model is developed.Furthermore,a hybrid optimization strategy integrating differential evolution and particle swarm optimization is employed to establish an intelligent optimization framework taking the economic net present value as the objective.Based on rapid prediction of injection and production behaviors,the proposed approach enables optimization of injection-production parameters and maximization of exploitation economics.Field applications demonstrate that the proposed intelligent injection-production model based on graph connection element accurately reproduces water-cut behavior of producers and provides quantitative uncertainty estimation.It achieves rapid history matching and dynamic response forecasting for complex injection-production systems,exhibiting high accuracy and stability.It enables global optimization of production strategies under economic constraints,demonstrating strong engineering applicability and scalability.