基于图神经网络的井间注采动态响应研究

Research on dynamic response of interwell injection-production based on graph neural network

注采井井间注采动态响应关系是油藏开发过程中的重要参数,对井间注采动态响应的正确评价可为油藏开发后期流场调控的工艺措施优化提供理论基础。注采井网可以等效成一种图结构,且井点间具有强相关性,为此基于图神经网络开展井间注采动态响应研究。通过图注意力网络结合多个时间节点注水井的单位时间注水量变化量和生产井的单位时间产液量变化量,以及渗流物理过程信息中的井底压力和井位数据等参数,对生产井产液量进行预测并反向传播学习,进而定量表征不同时刻的井间注采动态响应关系。结果表明,采用的新方法适用于井数较多且开关井频繁的实际油藏,具有成本低、动静参数结合和适用性广的优点。

The dynamic response of interwell injection-production in injection and production wells is an important parameter in the process of reservoir development.The correct evaluation of the dynamic response of interwell injection-production can provide a theoretical basis for optimizing process measures of flow field control in the later stage of reservoir development.Injection-production well pattern can be equivalent to a graph structure,and there is a strong correlation between well points.Therefore,the dynamic response of interwell injection-production is studied based on the graph neural network.The fluid production of producing wells is predicted,and backpropagation learning is carried out,so as to quantitatively characterize the dynamic response relationship of interwell injection-production at different times based on the graph attention network,the variation amount of water injection per unit time in injection wells,the variation amount of liquid production per unit time in producing wells at multiple time nodes,and the parameters such as bottom hole pressure and well location data in the information of physical flow process.The results show that the new method is suitable for the actual reservoir with a large number of wells and frequent well opening and shutting operations.The method has the advantages of low cost and has combined dynamic and static parameters,so it can be widely applied.