Stochastic Bubble Population (SBP) balance model has been widely applied on investigation of surfactant foam flow characteristics in porous media, while its application on predict nanoparticle (NP)-stabilized foam behavior has not been well refined. There are two empirical parameters in the SBP model, namely, the bubble generation rate K_g and the maximum bubble density n_max. To qualify the SBP model on prediction of the transient NP-stabilized foam propagation behavior, we make the efforts to calibrate the parameters of K_g and n_max based on the literature reported systematic experimental results. Through five sets of simulations in correspondence to the experimental works, it is found K_g has little effect on the pressure drop along the core, while the n_max increases from 200 mm^-3 to 8000 mm^-3 with the addition of NPs and the increment of NP concentration. Larger n_max values at higher NP concentrations indicates the foam stabilized by NPs has stronger foam structure and therefore leads to a better displacement result. It is concluded that based on the simple but carefully calibrated parameters, SBP model could provide accurate predictions of NP-stabilized foam flow characteristic in porous media and thus supply potential guidance for foam flooding processes in field applications.

随机气泡人口（SBP）平衡模型已广泛用于研究多孔介质中表面活性剂泡沫的流动特性，而在预测纳米颗粒（NP）稳定的泡沫行为方面的应用尚未得到很好的完善。SBP模型中有两个经验参数，即气泡产生率K _g和最大气泡密度n _max。为了使SBP模型能够预测NP稳定的瞬时泡沫传播行为，我们努力校准K _g和n _max的参数。根据文献报道系统的实验结果。通过五组与实验工作相对应的模拟，发现K _g对沿堆芯的压降影响很小，而n _max随着NP的增加而从200 mm ^-3增加到8000 mm ^-3。 NP浓度的增加。大ñ_最大NP浓度较高时的数值表明，由NP稳定的泡沫具有更强的泡沫结构，因此导致更好的置换结果。结论是，基于简单但经过仔细校准的参数，SBP模型可以为多孔介质中NP稳定的泡沫流动特性提供准确的预测，从而为现场应用中的泡沫驱替工艺提供潜在的指导。