Mechanisms and effects of amphiphilic lamellar nanofluid for enhanced oil recovery in low permeability reservoirs

Tuo Liang, Huipeng Wang,Changhua Yang

JOURNAL OF MOLECULAR LIQUIDS(2024)

引用 0|浏览1
暂无评分
摘要
Amphiphilic nanosheets have attracted more attention for enhanced oil recovery (EOR) owing to their unique properties. Here, the amphiphilic molybdenum disulfide (MoS2) nanosheets (AP-MDN) were prepared by functionalizing octadecyl amine (ODA) molecules onto the molybdenum disulfide nanosheets (MDN) surfaces. The dynamic stability and interfacial tension (IFT) measurements of AP-MDN nanofluid were evaluated. Besides, the mechanisms and effects of AP-MDN nanofluid for EOR were also systematically investigated and studied. Experimental results indicate that the dynamic stability of AP-MDN nanofluid is improved when MDN is successfully functionalized with ODA molecules. The IFT is decreased to 10(-1) mN/m and micron-emulsions ranging from 1 mu m similar to 2 mu m can be formed. In addition, the contraction rate of oil film exposed in ultra-low concentration AP-MDN nanofluid (50 mg/L) experiences two stages: a steep stage (8.5817 x 10(-5) cm/s) and a gentle stage (0.6617 x 10(-5) cm/s) owing to the generation of structuring disjoining pressure. Comprehensive mechanisms of AP-MDN nanofluid for EOR are also proposed and revealed. Moreover, oil washing efficiency of AP-MDN nanofluid is as high as 95.7 % when the soaking time is 6 hrs and temperature is 75 degree celsius. Core flooding experimental results demonstrate that the increase in shut-down time and the injection mode of multiple rounds with small slugs can better promote the interaction between AP-MDN nanofluid and crude oil in porous media. The highest oil recovery contributed by AP-MDN nanofluid is 19.1 %. These results are expected to promise AP-MDN nanofluid as an effective candidate for EOR.
更多
查看译文
关键词
Surface-functionalizedMoS2 nanosheet,Interfacial adsorption,Structuring disjoining pressure,Amphiphilic lamellar nanofluid flooding,Enhanced oil recovery
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要