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Title: Applicability of aquifer impact models to support decisions at CO 2 sequestration sites

Abstract

The National Risk Assessment Partnership has developed a suite of tools to assess and manage risk at CO2 sequestration sites (www.netldoe.gov/nrap). This capability includes polynomial or look-up table based reduced-order models (ROMs) that predict the impact of CO2 and brine leaks on overlying aquifers. The development of these computationally-efficient models and the underlying reactive transport simulations they emulate has been documented elsewhere (Carroll et al., 2014, Dai et al., 2014, Keating et al., 2015). The ROMs reproduce the ensemble behavior of large numbers of simulations and are well-suited to applications that consider a large number of scenarios to understand parameter sensitivity and uncertainty on the risk of CO2 leakage to groundwater quality. In this paper, we seek to demonstrate applicability of ROM-based ensemble analysis by considering what types of decisions and aquifer types would benefit from the ROM analysis. We present four hypothetical four examples where applying ROMs, in ensemble mode, could support decisions in the early stages in a geologic CO2 sequestration project. These decisions pertain to site selection, site characterization, monitoring network evaluation, and health impacts. In all cases, we consider potential brine/CO2 leak rates at the base of the aquifer to be uncertain. We show that derivedmore » probabilities provide information relevant to the decision at hand. Although the ROMs were developed using site-specific data from two aquifers (High Plains and Edwards), the models accept aquifer characteristics as variable inputs and so they may have more broad applicability. We conclude that pH and TDS predictions are the most transferable to other aquifers based on the analysis of the nine water quality metrics (pH, TDS, 4 trace metals, 3 organic compounds). Guidelines are presented for determining the aquifer types for which the ROMs should be applicable.« less

Authors:
; ; ; ; ; ; ;
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Pacific Northwest National Lab. (PNNL), Richland, WA (United States); National Energy Technology Laboratory (NETL), Pittsburgh, PA, Morgantown, WV (United States); Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE Office of Fossil Energy (FE)
OSTI Identifier:
1342308
Report Number(s):
LA-UR-15-28985; PNNL-SA-114632
Journal ID: ISSN 1750-5836; AA7020000
DOE Contract Number:  
AC52-06NA25396; AC05-76RL01830; AC02-05CH11231
Resource Type:
Journal Article
Journal Name:
International Journal of Greenhouse Gas Control
Additional Journal Information:
Journal Volume: 52; Journal Issue: C; Journal ID: ISSN 1750-5836
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; carbon sequestration; groundwater impacts; risk assessment; reduced-order modeling

Citation Formats

Keating, Elizabeth, Bacon, Diana, Carroll, Susan, Mansoor, Kayyum, Sun, Yunwei, Zheng, Liange, Harp, Dylan, and Dai, Zhenxue. Applicability of aquifer impact models to support decisions at CO 2 sequestration sites. United States: N. p., 2016. Web. doi:10.1016/j.ijggc.2016.07.001.
Keating, Elizabeth, Bacon, Diana, Carroll, Susan, Mansoor, Kayyum, Sun, Yunwei, Zheng, Liange, Harp, Dylan, & Dai, Zhenxue. Applicability of aquifer impact models to support decisions at CO 2 sequestration sites. United States. doi:10.1016/j.ijggc.2016.07.001.
Keating, Elizabeth, Bacon, Diana, Carroll, Susan, Mansoor, Kayyum, Sun, Yunwei, Zheng, Liange, Harp, Dylan, and Dai, Zhenxue. Mon . "Applicability of aquifer impact models to support decisions at CO 2 sequestration sites". United States. doi:10.1016/j.ijggc.2016.07.001.
@article{osti_1342308,
title = {Applicability of aquifer impact models to support decisions at CO 2 sequestration sites},
author = {Keating, Elizabeth and Bacon, Diana and Carroll, Susan and Mansoor, Kayyum and Sun, Yunwei and Zheng, Liange and Harp, Dylan and Dai, Zhenxue},
abstractNote = {The National Risk Assessment Partnership has developed a suite of tools to assess and manage risk at CO2 sequestration sites (www.netldoe.gov/nrap). This capability includes polynomial or look-up table based reduced-order models (ROMs) that predict the impact of CO2 and brine leaks on overlying aquifers. The development of these computationally-efficient models and the underlying reactive transport simulations they emulate has been documented elsewhere (Carroll et al., 2014, Dai et al., 2014, Keating et al., 2015). The ROMs reproduce the ensemble behavior of large numbers of simulations and are well-suited to applications that consider a large number of scenarios to understand parameter sensitivity and uncertainty on the risk of CO2 leakage to groundwater quality. In this paper, we seek to demonstrate applicability of ROM-based ensemble analysis by considering what types of decisions and aquifer types would benefit from the ROM analysis. We present four hypothetical four examples where applying ROMs, in ensemble mode, could support decisions in the early stages in a geologic CO2 sequestration project. These decisions pertain to site selection, site characterization, monitoring network evaluation, and health impacts. In all cases, we consider potential brine/CO2 leak rates at the base of the aquifer to be uncertain. We show that derived probabilities provide information relevant to the decision at hand. Although the ROMs were developed using site-specific data from two aquifers (High Plains and Edwards), the models accept aquifer characteristics as variable inputs and so they may have more broad applicability. We conclude that pH and TDS predictions are the most transferable to other aquifers based on the analysis of the nine water quality metrics (pH, TDS, 4 trace metals, 3 organic compounds). Guidelines are presented for determining the aquifer types for which the ROMs should be applicable.},
doi = {10.1016/j.ijggc.2016.07.001},
journal = {International Journal of Greenhouse Gas Control},
issn = {1750-5836},
number = C,
volume = 52,
place = {United States},
year = {2016},
month = {7}
}