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Impact of Alkaline-Surfactant-Polymer Flooding Model on Upscaled Recovery Predictions: Medium and Heavy Oils

机译:碱性表面活性剂 - 聚合物泛滥模型对升高恢复预测的影响:中等和重油

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Alkali-surfactant-polymer (ASP) flooding is a commercially viable enhanced oil recovery method. The complexity of chemical interactions, multi-phase flow, emulsification, capillary number changes and upscaling issues, especially in highly heterogeneous reservoir, make field designs difficult to extrapolate from coreflood measurements. In this work, two representaions of low interfacial tension conditions in chemical flooding were evaluated to determine the impact of model formulation on scaling-up from lab data to field situations. The first one is a mechanistic model based on interpolation of relative permeability curves parametrized with respect to the local capillary number. The second model requires tracking a thermodynamically stable phase known to exist at water-oil ultralow interfacial tension, namely a microemulsion. To perform this analysis, two sets of chemical coreflooding results were history matched and then the tuned models were utilized for field-scale predictions. For ASP flooding, a sensitivity analysis was implemented to show the importance of microemulsion phase on ASP upscaled (field scale) forecast. In this study, coreflooding experiments were performed using three different crude oils, case I: heavy oil with high acid number, case II: medium oil with high acid number and finally, case III: light oil with very low acid number. Predictions between the two modeling approaches are shown to diverge from each other upon upscaling of core-scale history matched models. This discrepancy is mostly attributed to the need to track a microemulsion phase behavior as well as its properties. Effects are more pronounced for heavier oil with high acid number. The results of this analysis should be useful to constrain field projections of any field design of surfactant-assisted EOR projects. Additionally, this study provides guidelines to understand existing uncertainties in current chemical flooding simulation regarding our ability to accurately predict the results of such a chemical flood design.
机译:碱表面活性剂聚合物(ASP)驱是一种商业上可行的强化油采收方法。该化学反应,多相流,乳化,毛细血管数量的变化和按比例放大的问题,尤其是在高度均质油藏的复杂性,制造商区域设计很难从岩心驱替量度可以推断。在这项工作中,在化学驱低界面张力条件的两个representaions进行评估,以确定模型公式的上定标,达到场情况造成的影响,从实验室数据。第一种是基于相对于所述局部毛细血管数目参数化相对渗透率曲线的内插的机械模型。第二个模型需要跟踪已知在水 - 油超低界面张力,即微乳液存在的热力学稳定相。为了进行这种分析,化学岩心驱替结果两组进行历史匹配,然后调整模型用于油田规模的预测。对于ASP驱,灵敏度分析被实施,以显示微乳液相的重要性上放大的ASP(字段标度)的预测。在这项研究中,使用三种不同的原油,情况进行岩心驱替实验I:重油具有高酸值,情况II:具有高酸值介质油,最后,壳体III:轻油具有非常低的酸值。两种建模方法之间的预测是在核心级历史拟合模型的升频出发散彼此。这种差异主要是由于需要跟踪的微乳液相行为以及其属性。效果更明显对于较重油具有高酸值。此分析的结果应该是表面活性剂辅助EOR项目的任一字段的设计约束字段突起是有用的。此外,这项研究提供指引,以了解关于我们的能力在目前的化学驱模拟现有的不确定性准确预测这种化学驱设计的结果。

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