以某碳酸盐岩油藏注水开发为例,提出针对流线模拟结果的流场识别方法。在流线模拟计算完成后,利用基于Ocean平台自行编写的插件将流线数据导出,并通过Python编程语言进行后续数据处理及聚类分析,直观反映不同开发阶段水驱油藏流场分布。采用密度峰值聚类算法作为流线聚类主要算法,以轮廓系数算法作为聚类评价算法,选取合理的聚类数,并对不同聚类算法结果进行对比。当聚类数相同时,密度峰值聚类算法比K-means、层次聚类、谱聚类算法对不同类型流线区分能力更强且轮廓系数较高,说明了算法的有效性。依据流线聚类结果可对流场进行量化处理,有效识别油藏中无效注水循环通道以及具有开发潜力的区域,同时可对同一注采井间流线进行细分,描述注采井间水相驱动能力大小分布,为注水优化、井网层系调整、深部调剖等方案决策提供依据。图10表3参23
For the case of carbonate reservoir water flooding development, the flow field identification method based on streamline modeling result was proposed. The Ocean for Petrel platform was used to build the plug-in that exported the streamline data, and the subsequent data was processed and clustered through Python programming, to display the flow field with different water flooding efficiencies at different time in the reservoir. We used density peak clustering as primary streamline cluster algorithm, and Silhouette algorithm as the cluster validation algorithm to select reasonable cluster number, and the results of different clustering algorithms were compared. The results showed that the density peak clustering algorithm could provide better identified capacity and higher Silhouette coefficient than K-means, hierachical clustering and spectral clustering algorithms when clustering coefficients are the same. Based on the results of streamline clustering method, the reservoir engineers can easily identify the flow area with quantification treatment, the inefficient water injection channels and area with developing potential in reservoirs can be identified. Meanwhile, streamlines between the same injector and productor can be subdivided to describe driving capacity distribution in water phase, providing useful information for the decision making of water flooding optimization, well pattern adjustment and deep profile.
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