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Coupled Capillary Pressure and Relative Permeability Using an Equation-of-State Approach

机译:使用状态方程耦合毛细管压力和相对渗透率

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Accurate and continuous capillary pressure (Pc) and relative permeability (kr) models are key relations in modeling of enhanced oil recovery (EOR) processes. Current commercial reservoir simulators tune empirical models for relative permeabilities and capillary pressures to experimental data based solely on a limited set of data under immiscible conditions. These empirical models attempt to represent very complex compositional processes, even though they are only a function of phase saturation and type. Thus, "fully" compositional models that use these empirical relations are not fully composition and discontinuities in compositions and saturations result. These discontinuities lead to failed simulations, significant inaccuracies and increased computational time. This paper develops a coupled equation-of-state (EoS) kr-Pc model that can reproduce important features of the current empirical models, but also yield physically consistent predictions that cannot generate discontinuities. The model parameters use the same inputs for both relative permeability and capillary pressure and are tuned simultaneously. We focus here on capillary hysteresis and understanding the components of the EoS from measured data using saturation, phase distribution (Euler characteristic or phase contact area), and wettability as inputs. The new EoS Pc model maintains a similar functional form as the common Brooks-Corey correlation, and can predict capillary pressure away from the tuned experimental data. The results using CT scans of imbibition and drainage processes show excellent agreement once contact angle hysteresis is included. A quadratic response surface is used to understand better the functional form of the EoS, i.e. partial derivative expressions. The new coupled kr-Pc approach could improve compositional simulation by making it faster, more robust, and accurate since these key parameters are more continuous and physical.
机译:准确和连续的毛细管压力(PC)和相对渗透率(KR)模型是建模增强的储油(EOR)过程的关键关系。目前的商业储层模拟器对实验数据的实证模型仅基于在不混溶的条件下的有限数据上的实验数据。这些经验模型试图代表非常复杂的组合过程,即使它们只是相位饱和度和类型的函数。因此,使用这些实证关系的“完全”的组成模型并不完全组成和组成和饱和度的不连续性。这些不连续性导致模拟失败,显着的不准确性和增加的计算时间。本文开发了一种耦合方程式(EOS)KR-PC模型,可以重现当前经验模型的重要特征,但也产生无法产生不连续性的物理上一致的预测。模型参数使用相同的输入,用于相对渗透性和毛细管压力,并同时进行调谐。我们在这里专注于毛细血管滞后,并使用饱和度,相位分布(欧拉特征或相位接触面积)和润湿性作为输入的测量数据的EOS的组件。新的EOS PC模型将类似的功能形式保持为常见的Brooks-Corey相关性,并且可以从调谐的实验数据预测毛细管压力。一旦包括接触角滞后,使用CT扫描的结果显示出优异的一致性。二次响应表面用于了解EOS的功能形式,即部分衍生表达。新耦合的KR-PC方法可以通过使得这些关键参数更加连续和物理,从而使其更快,更强大,准确地改善组成模拟。

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