油页岩
吸附
石油工程
解吸
页岩气
等温过程
地质学
化学
热力学
有机化学
物理
古生物学
作者
Hong-Bin Liang,Qi Zhou,Shuai Wang,Xiaoliang Huang,Wende Yan,Yingzhong Yuan,Zhiqiang Li
出处
期刊:Energy & Fuels
[American Chemical Society]
日期:2022-10-12
卷期号:36 (21): 12946-12960
被引量:11
标识
DOI:10.1021/acs.energyfuels.2c02885
摘要
Clarifying shale gas adsorption mechanism and establishing a reliable shale gas adsorption model are the basis of evaluating shale gas geological reserve and studying flow theory, so rich research on shale gas adsorption has been reported recently. However, with the development of shale gas reservoirs, the reservoir depth is deeper, and the environment is more complicated, especially the characteristic of high temperature and high pressure. These bring a new challenge to reveal the deep shale gas adsorption characteristics by using existing shale gas adsorption theoretical models established based on the middle and shallow shale gas reservoirs. Therefore, the popular methods of experiments, simulation, and models on shale gas adsorption are summarized in detail, and their advantages and disadvantages are clarified. The results show that the isothermal adsorption experiment of shale gas is still the basis method to study shale gas adsorption, but the experimental pressure should be further increased to meet the conditions of deep shale gas reservoir. Molecular simulation can reveal the shale gas adsorption microcosmic mechanisms, but the simulation results have multiple solutions, and the simulation scale is small, difficult to truly reflect the adsorption characteristics of shale gas in a larger scale. Shale gas adsorption models are established mostly based on classical adsorption models or their modifications, and hence they are difficult to reflect shale reservoir characteristics, such as total organic carbon (TOC), primary water, and desorption hysteresis. Therefore, it is important to establish a set of shale gas adsorption–desorption model which can fully consider the shale reservoir characteristics in the future.
科研通智能强力驱动
Strongly Powered by AbleSci AI