Generic Aquifer Component Training Dataset

Bacon, Diana

doi:10.18141/2001068

Title: Generic Aquifer Component Training Dataset

Dataset
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Abstract

Training dataset for the Generic Aquifer Component of NRAP-Open-IAM. Consists of the results of 62,500 STOMP-CO2 simulations of brine and CO2 leakage of varying rates and salinity into aquifers of varying thickness, depth, porosity, permeability, anisotropy and initial salinity. Described in report https://www.pnnl.gov/main/publications/external/technical_reports/PNNL-32590.pdf

Authors:

Bacon, Diana

Pacific Northwest National Laboratory

Publication Date:: Thu Sep 21 00:00:00 EDT 2023

Other Number(s):: 5da4198f-f614-48a9-8c44-e50aeecee0f5

DOE Contract Number:: AC05-76RL01830

Research Org.:: National Energy Technology Laboratory - Energy Data eXchange; NETL

Sponsoring Org.:: USDOE Office of Fossil Energy (FE)

Subject:: Aquifer Impact Model; NRAP-Open-IAM; integrated assessment model

OSTI Identifier:: 2001068

DOI:: https://doi.org/10.18141/2001068

Citation Formats


                    Bacon, Diana. Generic Aquifer Component Training Dataset.  United States: N. p., 2023. 
        Web.  doi:10.18141/2001068.

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                    Bacon, Diana. Generic Aquifer Component Training Dataset.  United States.  doi:https://doi.org/10.18141/2001068

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                    Bacon, Diana. 2023.  
"Generic Aquifer Component Training Dataset".  United States.  doi:https://doi.org/10.18141/2001068.  https://www.osti.gov/servlets/purl/2001068. Pub date:Thu Sep 21 00:00:00 EDT 2023

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@article{osti_2001068,

  title        = {Generic Aquifer Component Training Dataset},

  author       = {Bacon, Diana},

  abstractNote = {Training dataset for the Generic Aquifer Component of NRAP-Open-IAM. Consists of the results of 62,500 STOMP-CO2 simulations of brine and CO2 leakage of varying rates and salinity into aquifers of varying thickness, depth, porosity, permeability, anisotropy and initial salinity. Described in report https://www.pnnl.gov/main/publications/external/technical_reports/PNNL-32590.pdf},

  doi          = {10.18141/2001068},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Thu Sep 21 00:00:00 EDT 2023},

  month        = {Thu Sep 21 00:00:00 EDT 2023}

}

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DOI: https://doi.org/10.18141/2001068

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