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Structure-Property Model for Microemulsion Phase Behavior

机译:微乳液相位行为的结构性模型

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The objective of this research was to develop a model to predict the optimum phase behavior of chemical formulations for a given oil based on the molecular structure of the surfactants and co-solvents. The model is sufficiently accurate to provide a useful guide to an experimental testing program for the development of chemical EOR formulations. There are thousands of combinations of surfactants and co-solvents that could be tested for each oil, so even approximate predictions are very useful in terms of reducing the time and effort required for testing and for prioritizing the chemical combinations to test that are most likely to yield ultra-low IFT at reservoir conditions. The effects of changing molecular structures (e.g. swapping head groups, swapping hydrophobes, increasing the length of hydrophobes, increasing the number of PO and EO groups, adjusting the ratios of surfactants) are shown. The variables with the greatest impact on the optimum salinity and solubilization ratio were identified, and methods are proposed to shift the optimum salinity and the optimum solubilization ratios in any desired direction. The structure-property model was developed and tested using a large dataset consisting of 684 microemulsion phase behavior experiments using 24 oils. The chemical formulations used 85 surfactants and 18 co-solvents in various combinations. Both optimum salinity and optimum solubilization ratio (and thus IFT) are modeled whereas other models have focused almost exclusively on the optimum salinity. Predicting the optimum solubilization ratio is actually of more value because of its relationship to IFT. The models include the effects of co-solvent partitioning, soap formation and the molecular structure of both the surfactants and co-solvents.
机译:该研究的目的是开发一种模型,以预测基于表面活性剂和共溶剂的分子结构的给定油的化学制剂的最佳相行为。该模型足以准确,以为化学EOR制剂的发展的实验测试程序提供有用的指导。可以对每种油测试的表面活性剂和共同溶剂组合,因此甚至近似预测在减少测试所需的时间和精力方面非常有用,并优先考虑最有可能的测试的化学组合在储层条件下产量超低IFT。示出了改变分子结构的影响(例如,交换头部,交换疏水物,增加水化合物的长度,增加PO和EO基团的数量,调节表面活性剂的比率)。鉴定了对最佳盐度和溶解比的最大影响的变量,并提出了在任何所需方向上移动最佳盐度和最佳溶解比。使用由24个油组成的大型数据集进行结构 - 性能模型,使用大型数据集组成684微乳液相行为实验。化学制剂在各种组合中使用了85个表面活性剂和18个共溶剂。最佳盐度和最佳增溶率(以及因此IFT)被建模,而其他模型几乎专注于最佳盐度。由于其与IFT的关系,预测最佳溶解比实际上具有更多的价值。该模型包括共溶剂分配,肥皂形成和表面活性剂和共溶剂的分子结构的影响。

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