碳酸盐
离子
吸附
电化学
盐度
化学
提高采收率
环境科学
石油工程
地质学
物理化学
电极
海洋学
有机化学
作者
Hongna Ding,Sheik S. Rahman
出处
期刊:Energy & Fuels
[American Chemical Society]
日期:2018-08-09
卷期号:32 (9): 9314-9321
被引量:33
标识
DOI:10.1021/acs.energyfuels.8b02131
摘要
Recently, low salinity water flooding and ion tuned water flooding have gained a lot of attention in the petroleum industry for their promising applications in enhancing the oil recovery of carbonate reservoirs. The fundamental mechanism for low salinity waters (LSW) to enhance the oil recovery is most possibly due to an expansion of electrical double layer whereas it could be attributed to surface charge change/modification for ion tuned waters (ITW). However, the simulation studies in investigating the electrochemical interactions between the ions in the injection water and the minerals are simplified to double layer model (DLM) which is physically not appropriate in the cases of ITW. Therefore, a more sophisticated model-basic Stern model (BSM) is evaluated in this paper. The feasibility of DLM and BSM in reproducing the experimental ζ-potential results from our measurements and literatures are discussed. Furthermore, the influences of potential determining ions (PDI, Ca2+, Mg2+, SO42–) on the electrochemical interactions are evaluated based on the surface complexation results. Our results suggest that the DLM can be applied to reproduce or predict the ζ-potential results of LSW whereas the BSM can be employed to reproduce or predict the ζ-potential results of ITW. Moreover, the modeling results show a parallel change in the adsorption of Mg2+ and SO42–, a competitive relationship between Ca2+ and Mg2+ and a compensation relationship between Ca2+ and SO42–. Consequently, it is possible to choose the most appropriate LSW or ITW for water flooding program in carbonate reservoirs by predicting the ζ-potential results with corresponding model and as well estimate the magnitude of the electrical double expansion and the surface charge change in the presence of LSW and ITW, respectively.
科研通智能强力驱动
Strongly Powered by AbleSci AI