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Title: The cost of getting CCS wrong: Uncertainty, infrastructure design, and stranded CO 2

Carbon capture, and storage (CCS) infrastructure will require industry—such as fossil-fuel power, ethanol production, and oil and gas extraction—to make massive investment in infrastructure. The cost of getting these investments wrong will be substantial and will impact the success of CCS technology. Multiple factors can and will impact the success of commercial-scale CCS, including significant uncertainties regarding capture, transport, and injection-storage decisions. Uncertainties throughout the CCS supply chain include policy, technology, engineering performance, economics, and market forces. In particular, large uncertainties exist for the injection and storage of CO 2. Even taking into account upfront investment in site characterization, the final performance of the storage phase is largely unknown until commercial-scale injection has started. We explore and quantify the impact of getting CCS infrastructure decisions wrong based on uncertain injection rates and uncertain CO 2 storage capacities using a case study managing CO 2 emissions from the Canadian oil sands industry in Alberta. We use SimCCS, a widely used CCS infrastructure design framework, to develop multiple CCS infrastructure scenarios. Each scenario consists of a CCS infrastructure network that connects CO 2 sources (oil sands extraction and processing) with CO 2 storage reservoirs (acid gas storage reservoirs) using a dedicated COmore » 2 pipeline network. Each scenario is analyzed under a range of uncertain storage estimates and infrastructure performance is assessed and quantified in terms of cost to build additional infrastructure to store all CO 2. We also include the role of stranded CO 2, CO 2 that a source was expecting to but cannot capture due substandard performance in the transport and storage infrastructure. Results show that the cost of getting the original infrastructure design wrong are significant and that comprehensive planning will be required to ensure that CCS becomes a successful climate mitigation technology. Here, we show that the concept of stranded CO 2 can transform a seemingly high-performing infrastructure design into the worst case scenario.« less
Authors:
ORCiD logo [1] ; ORCiD logo [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Report Number(s):
LA-UR-17-30479
Journal ID: ISSN 1750-5836
Grant/Contract Number:
AC52-06NA25396; FE0029465
Type:
Accepted Manuscript
Journal Name:
International Journal of Greenhouse Gas Control
Additional Journal Information:
Journal Volume: 70; Journal Issue: C; Journal ID: ISSN 1750-5836
Publisher:
Elsevier
Research Org:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Southern States Energy Board, Norcross, GA (United States)
Sponsoring Org:
USDOE Office of Fossil Energy (FE), Clean Coal and Carbon (FE-20)
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; Earth Sciences
OSTI Identifier:
1417829
Alternate Identifier(s):
OSTI ID: 1469365

Middleton, Richard Stephen, and Yaw, Sean Patrick. The cost of getting CCS wrong: Uncertainty, infrastructure design, and stranded CO2. United States: N. p., Web. doi:10.1016/j.ijggc.2017.12.011.
Middleton, Richard Stephen, & Yaw, Sean Patrick. The cost of getting CCS wrong: Uncertainty, infrastructure design, and stranded CO2. United States. doi:10.1016/j.ijggc.2017.12.011.
Middleton, Richard Stephen, and Yaw, Sean Patrick. 2018. "The cost of getting CCS wrong: Uncertainty, infrastructure design, and stranded CO2". United States. doi:10.1016/j.ijggc.2017.12.011.
@article{osti_1417829,
title = {The cost of getting CCS wrong: Uncertainty, infrastructure design, and stranded CO2},
author = {Middleton, Richard Stephen and Yaw, Sean Patrick},
abstractNote = {Carbon capture, and storage (CCS) infrastructure will require industry—such as fossil-fuel power, ethanol production, and oil and gas extraction—to make massive investment in infrastructure. The cost of getting these investments wrong will be substantial and will impact the success of CCS technology. Multiple factors can and will impact the success of commercial-scale CCS, including significant uncertainties regarding capture, transport, and injection-storage decisions. Uncertainties throughout the CCS supply chain include policy, technology, engineering performance, economics, and market forces. In particular, large uncertainties exist for the injection and storage of CO2. Even taking into account upfront investment in site characterization, the final performance of the storage phase is largely unknown until commercial-scale injection has started. We explore and quantify the impact of getting CCS infrastructure decisions wrong based on uncertain injection rates and uncertain CO2 storage capacities using a case study managing CO2 emissions from the Canadian oil sands industry in Alberta. We use SimCCS, a widely used CCS infrastructure design framework, to develop multiple CCS infrastructure scenarios. Each scenario consists of a CCS infrastructure network that connects CO2 sources (oil sands extraction and processing) with CO2 storage reservoirs (acid gas storage reservoirs) using a dedicated CO2 pipeline network. Each scenario is analyzed under a range of uncertain storage estimates and infrastructure performance is assessed and quantified in terms of cost to build additional infrastructure to store all CO2. We also include the role of stranded CO2, CO2 that a source was expecting to but cannot capture due substandard performance in the transport and storage infrastructure. Results show that the cost of getting the original infrastructure design wrong are significant and that comprehensive planning will be required to ensure that CCS becomes a successful climate mitigation technology. Here, we show that the concept of stranded CO2 can transform a seemingly high-performing infrastructure design into the worst case scenario.},
doi = {10.1016/j.ijggc.2017.12.011},
journal = {International Journal of Greenhouse Gas Control},
number = C,
volume = 70,
place = {United States},
year = {2018},
month = {1}
}