减速器
扩散
吸收(声学)
紫外可见光谱
粘度
光谱学
分析化学(期刊)
吸收光谱法
化学
材料科学
化学工程
色谱法
有机化学
光学
热力学
复合材料
物理
工程类
量子力学
作者
Jia‐Ming Li,Zhongxu Xie,Azizullah Shaikh,Ning Sun,Ning Yang,Guang Zhao,Caili Dai
出处
期刊:Energy & Fuels
[American Chemical Society]
日期:2023-01-23
卷期号:37 (3): 1998-2008
被引量:4
标识
DOI:10.1021/acs.energyfuels.2c03775
摘要
Chemical viscosity reduction and cold production technology has the characteristics of low cost, low energy consumption, and low emission, which makes it suitable for gradually replacing thermal production as the main development method of heavy oil reservoirs. Most of the existing evaluation processes of viscosity reducers used in chemical viscosity reduction and cold production adopt the dynamic method of preparing O/W emulsions by high-speed shearing. However, high-speed shearing cannot be achieved in the contact process between heavy oil and the viscosity reducer in porous media. There is only a weak seepage shear force, which makes heavy oil self-diffuse in the viscosity reducer solution. Therefore, the existing methods cannot accurately evaluate the self-diffusion performance of viscosity reducers under quasi-static conditions. In this work, a method for evaluating the self-diffusion performance of a heavy oil viscosity reducer based on UV–Vis absorption spectroscopy is proposed, and the influence of various factors on the viscosity reduction effect and self-diffusion performance of a viscosity reducer is investigated. The results show that there is a good correspondence between the viscosity reduction effect and the self-diffusion performance. The viscosity reducer concentration, oil content, temperature, and emulsification time have obvious effects on the self-diffusion performance of the viscosity reducer. Based on the results of the orthogonal experiment, a self-diffusion performance prediction model of the viscosity reducer is established. The verification experiment results show that the model has good applicability. This research provides a new idea for accurately evaluating the self-diffusion performance of viscosity reducers under quasi-static conditions.
科研通智能强力驱动
Strongly Powered by AbleSci AI