Geologic CO2 sequestration monitoring design: A machine learning and uncertainty quantification based approach

Chen, Bailian; Harp, Dylan R.; Lin, Youzuo; Keating, Elizabeth H.; Pawar, Rajesh J.

doi:10.1016/j.apenergy.2018.05.044

Title: Geologic CO₂ sequestration monitoring design: A machine learning and uncertainty quantification based approach

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Abstract

Monitoring is a crucial aspect of geologic carbon dioxide (CO₂) sequestration risk management. Effective monitoring is critical to ensure CO₂ is safely and permanently stored throughout the life-cycle of a geologic CO₂ sequestration project. Effective monitoring involves deciding: (i) where is the optimal location to place the monitoring well(s), and (ii) what type of data (pressure, temperature, CO₂ saturation, etc.) should be measured taking into consideration the uncertainties at geologic sequestration sites. We have developed a filtering-based data assimilation procedure to design effective monitoring approaches. To reduce the computational cost of the filtering-based data assimilation process, a machine-learning algorithm: Multivariate Adaptive Regression Splines is used to derive computationally effcient reduced order models from results of full-physics numerical simulations of CO₂ injection in saline aquifer and subsequent multi-phase fluid flow. We use example scenarios of CO₂ leakage through legacy wellbore and demonstrate a monitoring strategy can be selected with the aim of reducing uncertainty in metrics related to CO₂ leakage. We demonstrate the proposed framework with two synthetic examples: a simple validation case and a more complicated case including multiple monitoring wells. The examples demonstrate that the proposed approach can be effective in developing monitoring approaches that take into consideration uncertainties.

Authors:

^[1]; Harp, Dylan R. ^[1];

^[1]; Keating, Elizabeth H. ^[1]; Pawar, Rajesh J. ^[1]

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

Publication Date:: Tue May 15 00:00:00 EDT 2018

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

Sponsoring Org.:: USDOE Office of Fossil Energy (FE). Clean Coal (FE-20); USDOE Office of Fossil Energy and Carbon Management (FECM)

OSTI Identifier:: 1463506

Alternate Identifier(s):: OSTI ID: 1495299

Report Number(s):: LA-UR-18-22683
Journal ID: ISSN 0306-2619

Grant/Contract Number:: AC52-06NA25396

Resource Type:: Accepted Manuscript

Journal Name:: Applied Energy

Additional Journal Information:: Journal Volume: 225; Journal Issue: C; Journal ID: ISSN 0306-2619

Publisher:: Elsevier

Country of Publication:: United States

Language:: English

Subject:: 58 GEOSCIENCES; Earth Sciences; Geologic carbon sequestration, monitoring design, machine learning, reduced order model, data assimilation, uncertainty reduction

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                    Chen, Bailian, Harp, Dylan R., Lin, Youzuo, Keating, Elizabeth H., and Pawar, Rajesh J. Geologic CO2 sequestration monitoring design: A machine learning and uncertainty quantification based approach.  United States: N. p., 2018. 
Web.  doi:10.1016/j.apenergy.2018.05.044.

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                    Chen, Bailian, Harp, Dylan R., Lin, Youzuo, Keating, Elizabeth H., & Pawar, Rajesh J. Geologic CO2 sequestration monitoring design: A machine learning and uncertainty quantification based approach.  United States.  https://doi.org/10.1016/j.apenergy.2018.05.044

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                    Chen, Bailian, Harp, Dylan R., Lin, Youzuo, Keating, Elizabeth H., and Pawar, Rajesh J. Tue .  
"Geologic CO2 sequestration monitoring design: A machine learning and uncertainty quantification based approach".  United States.  https://doi.org/10.1016/j.apenergy.2018.05.044.  https://www.osti.gov/servlets/purl/1463506.

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@article{osti_1463506,

  title        = {Geologic CO2 sequestration monitoring design: A machine learning and uncertainty quantification based approach},

  author       = {Chen, Bailian and Harp, Dylan R. and Lin, Youzuo and Keating, Elizabeth H. and Pawar, Rajesh J.},

  abstractNote = {Monitoring is a crucial aspect of geologic carbon dioxide (CO2) sequestration risk management. Effective monitoring is critical to ensure CO2 is safely and permanently stored throughout the life-cycle of a geologic CO2 sequestration project. Effective monitoring involves deciding: (i) where is the optimal location to place the monitoring well(s), and (ii) what type of data (pressure, temperature, CO2 saturation, etc.) should be measured taking into consideration the uncertainties at geologic sequestration sites. We have developed a filtering-based data assimilation procedure to design effective monitoring approaches. To reduce the computational cost of the filtering-based data assimilation process, a machine-learning algorithm: Multivariate Adaptive Regression Splines is used to derive computationally effcient reduced order models from results of full-physics numerical simulations of CO2 injection in saline aquifer and subsequent multi-phase fluid flow. We use example scenarios of CO2 leakage through legacy wellbore and demonstrate a monitoring strategy can be selected with the aim of reducing uncertainty in metrics related to CO2 leakage. We demonstrate the proposed framework with two synthetic examples: a simple validation case and a more complicated case including multiple monitoring wells. The examples demonstrate that the proposed approach can be effective in developing monitoring approaches that take into consideration uncertainties.},

  doi          = {10.1016/j.apenergy.2018.05.044},

  journal      = {Applied Energy},

  number       = C,

  volume       = 225,

  place        = {United States},

  year         = {Tue May 15 00:00:00 EDT 2018},

  month        = {Tue May 15 00:00:00 EDT 2018}

}

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Title: Geologic CO2 sequestration monitoring design: A machine learning and uncertainty quantification based approach

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Title: Geologic CO₂ sequestration monitoring design: A machine learning and uncertainty quantification based approach