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Title: Geologic CO 2 sequestration monitoring design: A machine learning and uncertainty quantification based approach

Monitoring is a crucial aspect of geologic carbon dioxide (CO 2) sequestration risk management. Effective monitoring is critical to ensure CO 2 is safely and permanently stored throughout the life-cycle of a geologic CO 2 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, CO 2 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 CO 2 injection in saline aquifer and subsequent multi-phase fluid flow. We use example scenarios of CO 2 leakage through legacy wellbore and demonstrate a monitoring strategy can be selected with the aim of reducing uncertainty in metrics related to CO 2 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 developingmore » monitoring approaches that take into consideration uncertainties.« less
Authors:
ORCiD logo [1] ;  [1] ; ORCiD logo [1] ;  [1] ;  [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Report Number(s):
LA-UR-18-22683
Journal ID: ISSN 0306-2619
Grant/Contract Number:
AC52-06NA25396
Type:
Accepted Manuscript
Journal Name:
Applied Energy
Additional Journal Information:
Journal Volume: 225; Journal Issue: C; Journal ID: ISSN 0306-2619
Publisher:
Elsevier
Research Org:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org:
USDOE Office of Fossil Energy (FE). Clean Coal (FE-20); USDOE
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
OSTI Identifier:
1463506
Alternate Identifier(s):
OSTI ID: 1495299

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., Web. doi:10.1016/j.apenergy.2018.05.044.
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. doi:10.1016/j.apenergy.2018.05.044.
Chen, Bailian, Harp, Dylan R., Lin, Youzuo, Keating, Elizabeth H., and Pawar, Rajesh J.. 2018. "Geologic CO2 sequestration monitoring design: A machine learning and uncertainty quantification based approach". United States. doi:10.1016/j.apenergy.2018.05.044.
@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 = {2018},
month = {5}
}