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Transition From Viscous Fingering to Capillary Fingering: Application of GPU‐Based Fully Implicit Dynamic Pore Network Modeling
Water Resources Research ( IF 4.6 ) Pub Date : 2020-10-22 , DOI: 10.1029/2020wr028149
Senyou An 1 , Hamidreza Erfani 1 , Omar E. Godinez‐Brizuela 1 , Vahid Niasar 1
Affiliation  

Immiscible two‐phase flow through porous materials exhibits different invasion patterns controlled by dynamic conditions, competition between the viscous and capillary forces, and the contrast between the fluids viscosities. Two distinct invasion patterns are viscous and capillary fingering. While the first one happens under unfavorable viscosity ratios at high injection rates, the second one happens when the viscous forces are very small compared to the capillary forces. Depending on whether the invasion is under the capillary fingering or viscous fingering regime, the remaining oil saturation and the effective permeability of the fluids can significantly change. The contribution of the present work has two key aspects: (a) It addresses how the remaining saturation changes at different flow rates (i.e., capillary numbers) for different unfavorable viscosity ratios in a three‐dimensional system; (b) it presents a new dynamic pore network model using the fully implicit scheme which has been enhanced by the graphic processing unit (GPU) parallel computing. Additionally, the model has been carefully validated against micromodel experiments in both time and space, which to our best knowledge has not been reported in such detail in the literature. The results of the validated 3‐D dynamic pore network model demonstrate the remaining saturation at the breakthrough time as a nonmonotonic trend with the imposed capillary number.

中文翻译:

从粘性指法到毛细管指法的过渡:基于GPU的全隐式动态孔网络建模的应用

通过多孔材料的不混溶的两相流在动态条件,粘性力和毛细力之间的竞争以及流体粘度之间的反差的控制下表现出不同的侵入模式。两种不同的侵入方式是粘性和毛细管指法。第一个发生在高注射速率下在不利的粘度比下发生,而第二个发生在与毛细管作用力相比粘性作用力非常小时。取决于侵入是在毛细管指法还是粘性指法下,剩余的油饱和度和流体的有效渗透率会发生显着变化。当前工作的贡献有两个关键方面:a)解决了剩余饱和度如何在不同流速下变化的问题(即 毛细数)在三维系统中具有不同的不利粘度比; b)它使用完全隐式方案提出了一个新的动态孔网络模型,该模型已通过图形处理单元(GPU)并行计算得到了增强。此外,该模型已经在时空上针对微模型实验进行了仔细验证,据我们所知,文献中尚未对此细节进行过报道。经过验证的3D动态孔网络模型的结果表明,在突破时间时保留的饱和度是施加毛细管数的非单调趋势。该模型已经在时空上针对微模型实验进行了仔细验证,据我们所知,文献中尚未对此细节进行过报道。经过验证的3D动态孔网络模型的结果表明,在突破时间时保留的饱和度是施加毛细管数的非单调趋势。该模型已经在时空上针对微模型实验进行了仔细验证,据我们所知,文献中尚未对此细节进行过报道。经过验证的3D动态孔网络模型的结果表明,在突破时间时保留的饱和度是施加毛细管数的非单调趋势。
更新日期:2020-12-02
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