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Oil Fragmentation, Interfacial Surface Transport and Flow Structure Maps for Two-Phase Flow in Model Pore Networks. Predictions Based on Extensive, DeProF Model Simulations

机译:模型孔隙网络中两相流的油碎裂,界面表面传输和流结构图。基于广泛的DeProF模型仿真的预测

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In general, macroscopic two-phase flows in porous media form mixtures of connected- and disconnected-oil flows. The latter are classified as oil ganglion dynamics and drop traffic flow, depending on the characteristic size of the constituent fluidic elements of the non-wetting phase, namely, ganglia and droplets. These flow modes have been systematically observed during flow within model pore networks as well as real porous media. Depending on the flow conditions and on the physicochemical, size and network configuration of the system (fluids and porous medium), these flow modes occupy different volume fractions of the pore network. Extensive simulations implementing the DeProF mechanistic model for steady-state, one-dimensional, immiscible two-phase flow in typical 3D model pore networks have been carried out to derive maps describing the dependence of the flow structure on capillary number, Ca , and flow rate ratio, r . The model is based on the concept of decomposition into prototype flows. Implementation of the DeProF algorithm, predicts key bulk and interfacial physical quantities, fully describing the interstitial flow structure: ganglion size and ganglion velocity distributions, fractions of mobilized/stranded oil, specific surface area of oil/water interfaces, velocity and volume fractions of mobilized and stranded interfaces, oil fragmentation, etc. The simulations span 5 orders of magnitude in Ca and r . Systems with various viscosity ratios and intermediate wettability have been examined. Flow of the non-wetting phase in disconnected form is significant and in certain cases of flow conditions the dominant flow mode. Systematic flow structure mutations with changing flow conditions have been identified. Some of them surface-up on the macroscopic scale and can be measured e.g. the reduced pressure gradient. Other remain in latency within the interstitial flow structure e.g. the volume fractions of ? or fractional flows of oil through ? connected-disconnected flows. Deeper within the disconnected-oil flow, the mutations between ganglion dynamics and drop traffic flow prevail. Mutations shift and/or become pronounced with viscosity disparity. They are more evident over variables describing the interstitial transport properties of process than variables describing volume fractions. Τhis characteristic behavior is attributed to the interstitial balance between capillarity and bulk viscosity.
机译:通常,多孔介质中的宏观两相流形成连通油流和不连通油流的混合物。后者根据非润湿阶段的组成流体元素(即神经节和液滴)的特征尺寸,被分类为油神经节动力学和液滴交通流。这些流动模式已在模型孔网以及实际的多孔介质内流动期间被系统地观察到。根据流动条件和系统的物理化学性质,大小和网络结构(流体和多孔介质),这些流动模式占据孔隙网络的不同体积分数。已经进行了广泛的模拟,以典型的3D模型孔网络中的稳态,一维,不混溶两相流动的DeProF力学模型实施,以得出描述流动结构对毛细管数,Ca和流速的依赖性的图。比r该模型基于分解为原型流的概念。 DeProF算法的实现可预测关键的体积和界面物理量,充分描述间隙流动结构:神经节大小和神经节速度分布,动/绞油比例,油/水界面比表面积,动量速度和体积分数以及Ca和r的模拟跨越5个数量级。已经研究了具有各种粘度比和中间润湿性的体系。断开状态的非润湿相的流动是重要的,在某些流动情况下,主要的流动模式是流动的。已经确定了随着流动条件的变化而发生的系统性流动结构突变。它们中的一些在宏观尺度上浮出水面,并且可以例如被测量。降低的压力梯度。其他残留在间隙流结构内的等待时间中,例如?的体积分数或部分油流通过?连接断开流。在不连续油流的更深处,神经节动力学和下降的交通流之间的突变占主导。突变移动和/或变得与粘度差异显着。它们比描述过程的间隙传输特性的变量比描述体积分数的变量更明显。他的特征行为归因于毛细作用和本体粘度之间的间隙平衡。

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