可靠地预测油藏特性需要一个精确的油藏模型。为进行油藏模拟 ,识别和预测流动单元主要取决于渗透率的分布。对于只具有极少的渗透率测量手段的油藏来说 ,例如阿帕拉契 (Appalachian)盆地的大多数油藏 ,以有限的岩心分析数据为基础 ,以小型渗透率仪测量手段、地质解释以及测井数据为补充的统计及人工智能技术能够识别流动单元。 Reliable prediction of reservoir properties requires an accurate reservoir model. For reservoir simulation, the identification and prediction of flow cells mainly depend on the distribution of permeability. For reservoirs with very few means of permeability measurement, most reservoirs, such as the Appalachian Basin, are based on limited core analysis data, small-scale permeability measurements, geologic interpretation As well as statistical and artificial intelligence techniques that complement well logging data to identify mobile units.