To increase confidence in the long-term security of CO2 geologic storage, reliable predictions of the level of post-injection CO2 residual trapping are needed. In this study, we conduct CO2/water coreflooding experiments at reservoir conditions on nine core samples with different degrees and types of heterogeneity to find the best petrophysical properties for predicting sandstone CO2 residual trapping ability. We are able to extract petrophysical properties such as porosity, permeability, degree of mesoscale heterogeneity, and spatial correlation lengths of petrophysical property fields in different directions using a CT scanner. Experimental results show that CO2 residual trapping ability decreases with porosity and increases with the degree of heterogeneity. A number of metrics for heterogeneity are evaluated, including the Dykstra-Parsons coefficient and the variance in voxel-level CO2 drainage saturation fields as well as the porosity and permeability fields. The variance of the saturation distribution during drainage provides the best predictor of residual gas trapping. By extrapolating the relationship between the degree of heterogeneity and the linear trapping coefficient, we show that pore-scale trapping mechanisms account for 46–97% of the residually trapped CO2 and the mesoscale capillary heterogeneity trapping mechanism accounts for 3–54% of the residually trapped CO2 for the nine sandstone samples tested.
%0 Journal Article
%1 Ni2019
%A Ni, Hailun
%A Boon, Maartje
%A Garing, Charlotte
%A Benson, Sally M
%D 2019
%J International Journal of Greenhouse Gas Control
%K curated EXC2075 PN1-13
%P 158-176
%R 10.1016/j.ijggc.2019.04.024
%T Predicting CO2 residual trapping ability based on experimental petrophysical properties for different sandstone types
%U https://www.sciencedirect.com/science/article/pii/S1750583618308181
%V 86
%X To increase confidence in the long-term security of CO2 geologic storage, reliable predictions of the level of post-injection CO2 residual trapping are needed. In this study, we conduct CO2/water coreflooding experiments at reservoir conditions on nine core samples with different degrees and types of heterogeneity to find the best petrophysical properties for predicting sandstone CO2 residual trapping ability. We are able to extract petrophysical properties such as porosity, permeability, degree of mesoscale heterogeneity, and spatial correlation lengths of petrophysical property fields in different directions using a CT scanner. Experimental results show that CO2 residual trapping ability decreases with porosity and increases with the degree of heterogeneity. A number of metrics for heterogeneity are evaluated, including the Dykstra-Parsons coefficient and the variance in voxel-level CO2 drainage saturation fields as well as the porosity and permeability fields. The variance of the saturation distribution during drainage provides the best predictor of residual gas trapping. By extrapolating the relationship between the degree of heterogeneity and the linear trapping coefficient, we show that pore-scale trapping mechanisms account for 46–97% of the residually trapped CO2 and the mesoscale capillary heterogeneity trapping mechanism accounts for 3–54% of the residually trapped CO2 for the nine sandstone samples tested.
@article{Ni2019,
abstract = {To increase confidence in the long-term security of CO2 geologic storage, reliable predictions of the level of post-injection CO2 residual trapping are needed. In this study, we conduct CO2/water coreflooding experiments at reservoir conditions on nine core samples with different degrees and types of heterogeneity to find the best petrophysical properties for predicting sandstone CO2 residual trapping ability. We are able to extract petrophysical properties such as porosity, permeability, degree of mesoscale heterogeneity, and spatial correlation lengths of petrophysical property fields in different directions using a CT scanner. Experimental results show that CO2 residual trapping ability decreases with porosity and increases with the degree of heterogeneity. A number of metrics for heterogeneity are evaluated, including the Dykstra-Parsons coefficient and the variance in voxel-level CO2 drainage saturation fields as well as the porosity and permeability fields. The variance of the saturation distribution during drainage provides the best predictor of residual gas trapping. By extrapolating the relationship between the degree of heterogeneity and the linear trapping coefficient, we show that pore-scale trapping mechanisms account for 46–97% of the residually trapped CO2 and the mesoscale capillary heterogeneity trapping mechanism accounts for 3–54% of the residually trapped CO2 for the nine sandstone samples tested.},
added-at = {2024-09-13T09:44:53.000+0200},
author = {Ni, Hailun and Boon, Maartje and Garing, Charlotte and Benson, Sally M},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2bc82261ba4094e40044edc1e576e79c3/simtech},
doi = {10.1016/j.ijggc.2019.04.024},
interhash = {208fa874c2bc9c4621159933c3747139},
intrahash = {bc82261ba4094e40044edc1e576e79c3},
issn = {1750-5836},
journal = {International Journal of Greenhouse Gas Control},
keywords = {curated EXC2075 PN1-13},
pages = {158-176},
timestamp = {2024-09-17T12:25:52.000+0200},
title = {Predicting CO2 residual trapping ability based on experimental petrophysical properties for different sandstone types},
url = {https://www.sciencedirect.com/science/article/pii/S1750583618308181},
volume = 86,
year = 2019
}
