Informing Geologic CO2 Storage Site Management Decisions under Uncertainty: Demonstration of NRAP's Integrated Assessment Model (NRAP-IAM-CS) Application

Pawar, Rajesh; Dilmore, Robert; Chu, Shaoping; Zhang, Yingqi; Oldenburg, Curtis; Stauffer, Philip; Guthrie, George; Bromhal, Grant

doi:10.1016/j.egypro.2017.03.1582

Title: Informing Geologic CO₂ Storage Site Management Decisions under Uncertainty: Demonstration of NRAP's Integrated Assessment Model (NRAP-IAM-CS) Application

Abstract

Managing risks at geologic CO₂ storage sites requires making decisions based on the quantitative predictions of long-term performance that not only take into account various subsurface hydrologic, geochemical processes and interactions but also the related uncertainties. The US DOE (United States Department of Energy) funded National Risk Assessment Partnership (NRAP) has developed a quantitative approach and a model to predict the long-term behavior of GCS sites. The approach uses an integrated assessment model (NRAP-IAM-CS) that couples reduced order models (ROMs) for various GCS components (such as storage reservoir, wells, seals, and groundwater aquifer) in a stochastic modeling framework that allows for rapid simulations of entire system behavior over thousands of years while probing uncertainties. We demonstrate application of NRAP-IAM-CS using two examples that focus on CO₂ leakage and resulting impacts. The examples are used to demonstrate how different uncertain parameters affect overall performance objectives.

Authors:

Pawar, Rajesh ^[1]; Dilmore, Robert ^[2]; Chu, Shaoping ^[1]; Zhang, Yingqi ^[3]; Oldenburg, Curtis ^[3]; Stauffer, Philip ^[1]; Guthrie, George ^[1]; Bromhal, Grant ^[4]

Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
National Energy Technology Lab. (NETL), Pittsburgh, PA, (United States)
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
National Energy Technology Lab. (NETL), Morgantown, WV (United States)

Publication Date:: Fri Aug 18 00:00:00 EDT 2017

Research Org.:: Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Sponsoring Org.:: USDOE Office of Fossil Energy (FE), Clean Coal and Carbon Management

OSTI Identifier:: 1542822

Report Number(s):: LA-UR-18-26256
Journal ID: ISSN 1876-6102

Grant/Contract Number:: 89233218CNA000001

Resource Type:: Accepted Manuscript

Journal Name:: Energy Procedia (Online)

Additional Journal Information:: Journal Name: Energy Procedia (Online); Journal Volume: 114; Journal Issue: C; Journal ID: ISSN 1876-6102

Publisher:: Elsevier

Country of Publication:: United States

Language:: English

Subject:: 03 NATURAL GAS; Earth Sciences; Risk assessment; Risk management; Uncertainty quantification; Integrated assessment model; NRAP-IAM-CS; NRAP

Citation Formats


                    Pawar, Rajesh, Dilmore, Robert, Chu, Shaoping, Zhang, Yingqi, Oldenburg, Curtis, Stauffer, Philip, Guthrie, George, and Bromhal, Grant. Informing Geologic CO2 Storage Site Management Decisions under Uncertainty: Demonstration of NRAP's Integrated Assessment Model (NRAP-IAM-CS) Application.  United States: N. p., 2017. 
Web.  doi:10.1016/j.egypro.2017.03.1582.

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                    Pawar, Rajesh, Dilmore, Robert, Chu, Shaoping, Zhang, Yingqi, Oldenburg, Curtis, Stauffer, Philip, Guthrie, George, & Bromhal, Grant. Informing Geologic CO2 Storage Site Management Decisions under Uncertainty: Demonstration of NRAP's Integrated Assessment Model (NRAP-IAM-CS) Application.  United States.  https://doi.org/10.1016/j.egypro.2017.03.1582

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                    Pawar, Rajesh, Dilmore, Robert, Chu, Shaoping, Zhang, Yingqi, Oldenburg, Curtis, Stauffer, Philip, Guthrie, George, and Bromhal, Grant. Fri .  
"Informing Geologic CO2 Storage Site Management Decisions under Uncertainty: Demonstration of NRAP's Integrated Assessment Model (NRAP-IAM-CS) Application".  United States.  https://doi.org/10.1016/j.egypro.2017.03.1582.  https://www.osti.gov/servlets/purl/1542822.

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@article{osti_1542822,

  title        = {Informing Geologic CO2 Storage Site Management Decisions under Uncertainty: Demonstration of NRAP's Integrated Assessment Model (NRAP-IAM-CS) Application},

  author       = {Pawar, Rajesh and Dilmore, Robert and Chu, Shaoping and Zhang, Yingqi and Oldenburg, Curtis and Stauffer, Philip and Guthrie, George and Bromhal, Grant},

  abstractNote = {Managing risks at geologic CO2 storage sites requires making decisions based on the quantitative predictions of long-term performance that not only take into account various subsurface hydrologic, geochemical processes and interactions but also the related uncertainties. The US DOE (United States Department of Energy) funded National Risk Assessment Partnership (NRAP) has developed a quantitative approach and a model to predict the long-term behavior of GCS sites. The approach uses an integrated assessment model (NRAP-IAM-CS) that couples reduced order models (ROMs) for various GCS components (such as storage reservoir, wells, seals, and groundwater aquifer) in a stochastic modeling framework that allows for rapid simulations of entire system behavior over thousands of years while probing uncertainties. We demonstrate application of NRAP-IAM-CS using two examples that focus on CO2 leakage and resulting impacts. The examples are used to demonstrate how different uncertain parameters affect overall performance objectives.},

  doi          = {10.1016/j.egypro.2017.03.1582},

  journal      = {Energy Procedia (Online)},

  number       = C,

  volume       = 114,

  place        = {United States},

  year         = {Fri Aug 18 00:00:00 EDT 2017},

  month        = {Fri Aug 18 00:00:00 EDT 2017}

}

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https://doi.org/10.1016/j.egypro.2017.03.1582

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Fig. 1: Predicted maximum CO₂ flow rate through a wellbore at a hypothetical CO₂ storage site as a function of wellbore quality (effective cement permeability).

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Title: Informing Geologic CO2 Storage Site Management Decisions under Uncertainty: Demonstration of NRAP's Integrated Assessment Model (NRAP-IAM-CS) Application

Abstract

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Title: Informing Geologic CO₂ Storage Site Management Decisions under Uncertainty: Demonstration of NRAP's Integrated Assessment Model (NRAP-IAM-CS) Application