Viscoelastic Polymer Modelling for Accurate Predictions of In-Situ Rheology

用于准确预测原位流变学的粘弹性聚合物建模

基本信息

批准号：
544482-2019
负责人：
Trivedi, Japan
金额：
$ 9.11万
依托单位：
University of Alberta
依托单位国家：
加拿大
项目类别：
Idea to Innovation
财政年份：
2019
资助国家：
加拿大
起止时间：
2019-01-01 至 2020-12-31
项目状态：
已结题

来源：
https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=693394
关键词：
Viscoelastic Polymer Modelling Accurate Predictions

项目摘要

The synthetic polymer solutions used for chemically enhanced oil recovery (cEOR) processes exhibit apparent viscosity much higher than expected from the shear viscosity. Injectivity will be overestimated with the usage of steady shear rheology. Viscoelastic polymers developed so far relied on the core flood experiments to predict the injectivity of EOR polymers. Performing core flooding with respect to various parameters such as salinity, concentration is a cumbersome process. Quantifying the thickening ability of the polymer solutions may get rid of core flood dependency. Moreoever, the oscillatory rheology used to characterize the viscoelastic properties of the EOR polymers under-predicts the residual oil saturation (Sor) reduction potential. Therefore, the capillary number calculated using the shear viscosity and apparent viscosity does not explain the various residual oil recovery shown by the viscoelastic polymer solutions. Employing the extensional viscosity in the place of shear or apparent viscosity may improve the predictions of capillary number. Thus, it has become a critical issue to investigate the extensional properties of EOR polymers. Azad and Trivedi recently developed a viscoelastic model named a viscoelastic model that can predict the viscoelastic behavior of the synthetic polymer in porous media without core flood experiments. The model has been validated for predicting the viscoelastic onset and the shear thickening regime for varying range of reservoir permeability (200 mD to 37 D), porosity (0.22 to 0.395), brine salinity (0 ppm to 25,200 ppm), concentration (200 ppm to 2500 ppm) and polymer molecular weight (6 MDa to 26 MDa). Since the model validation is performed at room temperature, it is imperative to expand this technology to a wider range temperature that represents in-situ reservoir conditions and thereby creating a database for implementation into commercial tool. Moreover, the model needs to be tested and implemented in a commercial simulator for injectivity and oil recovery predictions.

用于化学增强石油回收（CEO）工艺的合成聚合物溶液表现出的粘度明显高于剪切粘度所预期的。使用稳定的剪切流变学的使用将高估注射率。迄今为止，粘弹性聚合物依靠核心洪水实验来预测EOR聚合物的注入性。相对于盐度等各种参数进行核心洪水，浓度是一个麻烦的过程。量化聚合物溶液的增厚能力可能会消除核心洪水的依赖性。更重要的是，用于表征EOR聚合物的粘弹性不足以预测的残留油饱和度（SOR）还原电位的振荡性流变学。因此，使用剪切粘度和明显的粘度计算得出的毛细血管数不能解释粘弹性聚合物溶液显示的各种残余油回收率。在剪切或明显的粘度代替延伸粘度的情况下，可以改善毛细血管数的预测。因此，研究EOR聚合物的扩展特性已成为一个关键问题。 Azad和Trivedi最近开发了一种名为粘弹性模型的粘弹性模型，该模型可以预测多孔介质中合成聚合物的粘弹性行为，而无需核心洪水实验。该模型已经过验证，用于预测各种储层渗透率（200 md至37 d），孔隙率（0.22至0.395），盐水盐度（0 ppm至25,200 ppm），浓度（200 ppm至2500 ppm）和26 md rymerecular sorecult and Morecular sorecult consmerecult（200 ppm）（200 ppm至25,200 ppm），孔隙率（0.22至0.395），盐度盐度（0 ppm至25,200 ppm），孔隙率（0.22至0.395）和剪切厚度。由于模型验证是在室温下执行的，因此必须将该技术扩展到代表原位储层条件的更广泛的范围温度，从而创建一个数据库，以实现在商业工具中。此外，需要在商业模拟器中测试和实施该模型，以进行注射率和石油回收预测。