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Title: Visualization and prediction of supercritical CO2 distribution in sandstones during drainage: An in situ synchrotron X-ray micro-computed tomography study

Abstract

Pore-scale distribution of supercritical CO2 (scCO2) exerts significant control on a variety of key hydrologic as well as geochemical processes, including residual trapping and dissolution. Despite such importance, only a small number of experiments have directly characterized the three-dimensional distribution of scCO2 in geologic materials during the invasion (drainage) process. Here, we present a study which couples dynamic high-resolution synchrotron X-ray micro-computed tomography imaging of a scCO2/brine system at in situ pressure/temperature conditions with quantitative pore-scale modeling to allow direct validation of a pore-scale description of scCO2 distribution. The experiment combines high-speed synchrotron radiography with tomography to characterize the brine saturated sample, the scCO2 breakthrough process, and the partially saturated state of a sandstone sample from the Domengine Formation, a regionally extensive unit within the Sacramento Basin (California, USA). The availability of a 3D dataset allowed us to examine correlations between grains and pores morphometric parameters and the actual distribution of scCO2 in the sample, including the examination of the role of small-scale sedimentary structure on CO2 distribution. The segmented scCO2/brine volume was also used to validate a simple computational model based on the local thickness concept, able to accurately simulate the distribution of scCO2 after drainage. The same methodmore » was also used to simulate Hg capillary pressure curves with satisfactory results when compared to the measured ones. Finally, this predictive approach, requiring only a tomographic scan of the dry sample, proved to be an effective route for studying processes related to CO2 invasion structure in geological samples at the pore scale.« less

Authors:
 [1];  [2];  [1]
  1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  2. Korea Advanced Inst. Science and Technology (KAIST), Daejeon (Korea, Republic of)
Publication Date:
Research Org.:
Energy Frontier Research Centers (EFRC) (United States). Center for Nanoscale Control of Geologic CO2 (NCGC); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES)
OSTI Identifier:
1420108
Grant/Contract Number:  
AC02-05CH11231
Resource Type:
Accepted Manuscript
Journal Name:
International Journal of Greenhouse Gas Control
Additional Journal Information:
Journal Volume: 66; Journal Issue: C; Journal ID: ISSN 1750-5836
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
58 GEOSCIENCES; Geological carbon sequestration; scCO2 drainage; In situ synchrotron X-ray micro computed tomography; 3D quantitative morphometric analysis; Drainage predictive model.

Citation Formats

Voltolini, Marco, Kwon, Tae-Hyuk, and Ajo-Franklin, Jonathan. Visualization and prediction of supercritical CO2 distribution in sandstones during drainage: An in situ synchrotron X-ray micro-computed tomography study. United States: N. p., 2017. Web. doi:10.1016/j.ijggc.2017.10.002.
Voltolini, Marco, Kwon, Tae-Hyuk, & Ajo-Franklin, Jonathan. Visualization and prediction of supercritical CO2 distribution in sandstones during drainage: An in situ synchrotron X-ray micro-computed tomography study. United States. https://doi.org/10.1016/j.ijggc.2017.10.002
Voltolini, Marco, Kwon, Tae-Hyuk, and Ajo-Franklin, Jonathan. Sat . "Visualization and prediction of supercritical CO2 distribution in sandstones during drainage: An in situ synchrotron X-ray micro-computed tomography study". United States. https://doi.org/10.1016/j.ijggc.2017.10.002. https://www.osti.gov/servlets/purl/1420108.
@article{osti_1420108,
title = {Visualization and prediction of supercritical CO2 distribution in sandstones during drainage: An in situ synchrotron X-ray micro-computed tomography study},
author = {Voltolini, Marco and Kwon, Tae-Hyuk and Ajo-Franklin, Jonathan},
abstractNote = {Pore-scale distribution of supercritical CO2 (scCO2) exerts significant control on a variety of key hydrologic as well as geochemical processes, including residual trapping and dissolution. Despite such importance, only a small number of experiments have directly characterized the three-dimensional distribution of scCO2 in geologic materials during the invasion (drainage) process. Here, we present a study which couples dynamic high-resolution synchrotron X-ray micro-computed tomography imaging of a scCO2/brine system at in situ pressure/temperature conditions with quantitative pore-scale modeling to allow direct validation of a pore-scale description of scCO2 distribution. The experiment combines high-speed synchrotron radiography with tomography to characterize the brine saturated sample, the scCO2 breakthrough process, and the partially saturated state of a sandstone sample from the Domengine Formation, a regionally extensive unit within the Sacramento Basin (California, USA). The availability of a 3D dataset allowed us to examine correlations between grains and pores morphometric parameters and the actual distribution of scCO2 in the sample, including the examination of the role of small-scale sedimentary structure on CO2 distribution. The segmented scCO2/brine volume was also used to validate a simple computational model based on the local thickness concept, able to accurately simulate the distribution of scCO2 after drainage. The same method was also used to simulate Hg capillary pressure curves with satisfactory results when compared to the measured ones. Finally, this predictive approach, requiring only a tomographic scan of the dry sample, proved to be an effective route for studying processes related to CO2 invasion structure in geological samples at the pore scale.},
doi = {10.1016/j.ijggc.2017.10.002},
journal = {International Journal of Greenhouse Gas Control},
number = C,
volume = 66,
place = {United States},
year = {Sat Oct 21 00:00:00 EDT 2017},
month = {Sat Oct 21 00:00:00 EDT 2017}
}

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