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在油田开发中,储层流体组分的性质划分和空间分布变化是导致预测与实际情况产生显著差异的两个重要因素。为了获取具有广泛代表性的储层流体样品,而非只是代表局部流体性质的样品,认识和掌握地下流体组分的变化情况是非常必要的。作为电缆测试中的一个新概念,井下流体分析(DFA)技术对这种认知的获取具有重要意义。过去DFA技术产生之前,在缺乏试验流体复杂特性证据的情况下,获取充足样品物质进行实验室分析研究存在很大难度。本文证明DFA技术作为“不可或缺的一环”为优化取样程序和实时确定取样位置提供了必要的信息。大量的DFA测点结合若干DFA指导下的流体取样点数据有效地利用了相关流体分析资源。因此DFA技术对于“连续井下流体测试”的推进和发展具有重要的意义。DFA测试时还能够根据流体特性复杂程度的需要实时增加测点而无需增添逻辑指令。另外由于不同层段可能充填不同的流体,所以流体组分的变化情况可用来划分储层。特别是DFA技术可通过流体密度反演来划分储层。这是一项提高产能预测水平的新型技术。
In the development of oilfields, the nature of reservoir fluid components and the spatial distribution of the changes are the two factors that lead to significant differences between the prediction and the actual situation. In order to obtain a broad representation of reservoir fluid samples rather than just samples that represent local fluid properties, it is necessary to know and grasp the changes of subsurface fluid components. As a new concept in cable testing, downhole fluid analysis (DFA) technology is of great importance to this acquisition of knowledge. Before the DFA technology was invented in the past, in the absence of evidence of complex fluid properties of the test fluid, it was difficult to obtain sufficient sample material for laboratory analysis. This paper demonstrates that DFA technology as an “integral part” provides the necessary information for optimizing the sampling procedure and for determining the sampling location in real time. A large number of DFA points combined with several DFA-directed fluid sampling point data make efficient use of the associated fluid analysis resources. Therefore, DFA technology is of great significance to the progress and development of “continuous downhole fluid test”. DFA testing also increases the number of points in real time without the need for additional logic, depending on the complexity of fluid characteristics. In addition, because different layers may be filled with different fluids, changes in fluid composition can be used to divide the reservoir. DFA in particular can be used to delineate reservoirs by fluid density inversion. This is a new technology that increases the level of capacity forecast.