氟碳化合物
润湿
肺表面活性物质
纳米颗粒
材料科学
化学工程
分子动力学
纳米技术
化学
复合材料
计算化学
工程类
作者
Iván Moncayo-Riascos,Camilo A. Franco,Farid B. Cortés
标识
DOI:10.1021/acs.jced.8b00910
摘要
Fluorocarbon surfactants have been widely used to promote gas-wetting alteration of sandstones with the objective of inhibiting the formation damage due to condensate banking and hence increasing the oil recovery. This study is focused on understanding the behavior of the wettability alteration from a liquid-wet state to gas-wettability by SiO2 nanoparticles functionalized with 20 wt % of a commercial fluorocarbon surfactant Silnyl FSJ (SY) using molecular dynamics simulations and experimental approaches. The SY-functionalized SiO2 nanoparticles were synthesized by an incipient wetness method. Then, three nanofluids were obtained by dispersing the modified SiO2 nanoparticles at 0.3, 0.5, and 0.7 wt % in a KCl brine (2 wt %) and were employed for wettability alteration of the oil-wet sandstone samples under room conditions. Changes of the samples' wettability were estimated by experimental contact angle measurements in brine/rock/air and n-decane/rock/air systems. The theoretical evaluation was made using molecular dynamics for reproducing the coating of the sandstone samples with the SY-functionalized nanoparticles and the contact angles measured experimentally. Further, the wettability alteration to a gas-wet system is described from a physical insight based on the thermodynamic analysis of the interaction energies of nanoparticles–liquid, surfactant–liquid, and liquid–liquid. The molecular model developed in this study allows predictive calculations of the contact angle of liquid droplets, with deviations lower than 7% regarding the experimental value. The theoretical approach allows optimizing the use of surfactant-functionalized nanoparticles for promoting the wettability alteration from liquid-wet to gas-wet state, which can lead to cost savings and increase the performance of improved and enhanced oil recovery processes.
科研通智能强力驱动
Strongly Powered by AbleSci AI