Laboratory Investigation and Empirical Modelling of Polymer Solution Viscosity

Yazan Firas Fuad Hussein, Altamish Ahmed Pakeer, R. Muneer, M. Hashmet, Younes Alblooshi
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引用次数: 0

Abstract

Polymer flooding, an improved oil recovery technique, is a well-established method for improving oil production. Although numerous polymers have been suggested in the literature, identifying suitable ones for applications in high-temperature and high-salinity carbonate reservoirs remains a challenging task. The viscosity of polymer solutions depends on various parameters, including polymer concentration, temperature, and shear rate. Therefore, screening polymers for these demanding conditions necessitates meticulous experimental work. In this experimental study, polymer solutions' physical property—viscosity—was measured as a function of polymer concentration, temperature, and shear rate. Subsequently, the generated rheological data were used to develop empirical models capable of predicting the viscosity of polymer solutions under various conditions. An acceptable match was achieved between the experimental and model-predicted data. Furthermore, a validation was conducted for the empirical models, yielding a root mean square error of 1.75. These models will significantly reduce the number of experiments required for the screening process and prove valuable for optimization procedures.
聚合物溶液粘度的实验室研究与经验建模
聚合物淹没是一种改进的采油技术,是一种行之有效的提高石油产量的方法。尽管文献中已经提出了许多聚合物,但确定适合应用于高温、高盐度碳酸盐油藏的聚合物仍然是一项具有挑战性的任务。聚合物溶液的粘度取决于各种参数,包括聚合物浓度、温度和剪切速率。因此,要针对这些苛刻条件筛选聚合物,就必须开展细致的实验工作。在本实验研究中,聚合物溶液的物理性质--粘度--被测量为聚合物浓度、温度和剪切速率的函数。随后,利用生成的流变数据建立了经验模型,该模型能够预测聚合物溶液在各种条件下的粘度。实验数据和模型预测数据之间实现了可接受的匹配。此外,还对经验模型进行了验证,得出的均方根误差为 1.75。这些模型将大大减少筛选过程中所需的实验次数,并证明对优化程序很有价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
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