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Analysis of Injection and Production Data for Open and Large Reservoirs

机译:大型和大型油藏注入和生产数据分析

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Numerous studies have concluded that connectivity is one of the most important factors controlling the success of improved oil recovery processes. Interwell connectivity evaluation can help identify flow barriers and conduits and provide tools for reservoir management and production optimization. The multiwell productivity index (MPI)-based method provides the connectivity indices between well pairs based on injection/production data. By decoupling the effects of well locations, skin factors, injection rates, and the producers’ bottomhole pressures from the calculated connectivity, the heterogeneity matrix obtained by this method solely represents the heterogeneity and possible anisotropy of the formation. Previously, the MPI method was developed for bounded reservoirs with limited numbers of wells. In this paper, we extend the MPI method to deal with cases of large numbers of wells and open reservoirs. To handle open reservoirs, we applied some modifications to the MPI method by adding a virtual well to the system. In cases with large numbers of wells, we applied a model reduction strategy based on the location of the wells, called windowing. Integration of these approaches with the MPI method can quickly and efficiently model field data to optimize well patterns and flood parameters.
机译:大量研究得出结论,连通性是控制改进采油工艺成功与否的最重要因素之一。井间连通性评估可以帮助确定流量障碍和管道,并为油藏管理和生产优化提供工具。基于多井生产率指数(MPI)的方法基于注入/生产数据提供了各对井之间的连通性指数。通过将井眼位置,表皮因子,注入速率和生产商的井底压力与计算出的连通性分离开,通过这种方法获得的非均质性矩阵仅代表了地层的非均质性和可能的​​各向异性。以前,MPI方法是针对井数有限的有界油藏开发的。在本文中,我们将MPI方法扩展为处理大量井和裸露油藏的情况。为了处理露天油藏,我们通过向系统中添加虚拟井对MPI方法进行了一些修改。在有大量井的情况下,我们基于井的位置应用了模型缩减策略,称为窗口化。这些方法与MPI方法的集成可以快速有效地对现场数据进行建模,以优化井眼模式和洪水参数。

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