Optimizing Carbon Capture, Transport, and Storage: Overcoming Challenges with Machine Learning and Cost-Benefit Analysis
Year: 2024
Type: conference-paper
Abstract: Map of 314 saline reservoirs in CO2_S_COM geologicdatabase. Energy Information Administration's (EIA) National Energy Modeling System (NEMS)• Uses reduced order models of pipeline transport costs and CO 2 saline storage costs based on CO2_T_COM and CO2_S_COM output.• Calculates oil production and CO 2 storage from CO 2 EOR for oil fields in EIA's database using CO2_Prophet. National Renewable Energy Laboratory's Regional Energy Deployment System (ReEDS) Model• Uses reduced order model of pipeline transport costs based on CO2_T_COM output.• Uses break-even CO 2 saline storage costs for storage formations from CO2_S_COM. Great Plains Institute's CO 2 Transportation Infrastructure Study• Used CO2_T_COM to estimate pipeline transport costs. Los Alamos National Laboratory's (LANL) SimCCS• Uses reduced order model of transport pipeline costs based on output from CO2_T_COM Princeton University's Net-Zero America: Potential Pathways, Infrastructure, and Impacts report• Used CO2_T_COM to calculate pipeline transport costs. DOE Policy's CCUS supply chain study• Estimated nationwide CCS infrastructure material requirements using CO2_T_COM and CO2_S_COM.
DOI: 10.2172/2480776
Authors: Chung Yan Shih, David Morgan, Guoxiang Liu, Luciane Cunha
Institution: National Energy Technology Laboratory
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4405395848