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

• 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

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.

用于化学强化采油（cEOR）工艺的合成聚合物溶液表现出比从剪切粘度预期的高得多的表观粘度。使用稳态剪切流变学会高估注入性。粘弹性聚合物的开发依赖于岩心驱油实验来预测聚合物的注入能力。 相对于各种参数（诸如盐度、浓度）执行岩心驱替是一个繁琐的过程。定量聚合物溶液的增稠能力可以摆脱岩心驱替依赖性。此外，用于表征EOR聚合物的粘弹性的振荡流变学低估了残余油饱和度（Sor）降低潜力。因此，用剪切粘度和表观粘度计算的毛管数不能解释粘弹性聚合物溶液所表现出的各种残余油采收率。用拉伸粘度代替剪切粘度或表观粘度，可以改善毛管数的预测。因此，研究提高采收率聚合物的拉伸性能已成为一个关键问题。Azad和Trivedi最近开发了一种粘弹性模型，称为粘弹性模型，可以预测合成聚合物在多孔介质中的粘弹性行为，而无需岩心驱油实验。该模型已被验证用于预测储层渗透率（200 mD至37 D）、孔隙度（0.22至0.395）、盐水盐度（0 ppm至25，200 ppm）、浓度（200 ppm至2500 ppm）和聚合物分子量（6 MDa至26 MDa）的变化范围的粘弹性起始和剪切增稠状态。由于模型验证是在室温下进行的，因此必须将该技术扩展到代表原位储层条件的更宽温度范围，从而创建用于实施到商业工具中的数据库。此外，该模型需要在商业模拟器中进行测试和实施，以预测注入能力和石油采收率。