Quantitatively evaluating greenhouse gas leakage from CO2 enhanced oil recovery fields

被引:4
|
作者
Chen, Bailian [1 ]
Mehana, Mohamed Z. [1 ]
Pawar, Rajesh J. [1 ]
机构
[1] Los Alamos Natl Lab, Earth & Environm Sci Div, Los Alamos, NM 87545 USA
来源
ADVANCES IN GEO-ENERGY RESEARCH | 2023年 / 7卷 / 01期
关键词
Greenhouse gas; CO2 enhanced oil recovery; CO2 and CH4 Leakage; machine learning; UNCERTAINTY QUANTIFICATION; CARBON CAPTURE; SEQUESTRATION; STORAGE; SCALE;
D O I
10.46690/ager.2023.01.03
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Greenhouse gas (mainly CO2 and CH4) leakage from abandoned wells in CO2 enhanced oil recovery sites is a long-standing environmental concern and health hazard. Although multiple CO2 capture, utilization, and storage programs, e.g., CarbonSAFE and Regional Carbon Storage Partnerships, have been developed in the U.S. to reach the net-zero emission target by 2050, one cannot neglect the significant amount of CO2 and CH4 leakage from abandoned wells. This study will investigate the potential of CO2 and oil components leakages from the abandoned wellbore and develop the first-ever quantitative approach to evaluating CO2 and oil component leakage from a CO2 enhanced oil recovery field. Results show that in addition to a large amount of CO2 leakage, a significant amount of light and intermediate oil components leaked through the wellbore. In contrast, a minimal amount of heavy oil component leaked. Oil components' leakage is mainly through the gas phase rather than the liquid phase. CO2 leakage is positively correlated to reservoir depth, wellbore pressure, and permeability through sensitivity analysis. In contrast, it is negatively related to net-to-gross ratio, residual oil saturation, and mole fraction of CH4. On the other hand, oil component leakages are positively correlated to all uncertain parameters, except the net-to-gross ratio. Lastly, the reduced-order models generated using the machine learning technique have a relatively high fidelity.
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页码:20 / 27
页数:8
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