skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Geologic Carbon Sequestration Leakage Detection: Physics-Guided Machine Learning Approaches

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]
  1. Los Alamos National Laboratory
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1460670
Report Number(s):
LA-UR-18-26575
DOE Contract Number:  
AC52-06NA25396
Resource Type:
Conference
Resource Relation:
Conference: NRAP Technical Review Meeting ; 2018-04-25 - 2018-04-25 ; Los Alamos, New Mexico, United States
Country of Publication:
United States
Language:
English
Subject:
Computer Science

Citation Formats

Lin, Youzuo, Harp, Dylan Robert, Chen, Bailian, and Pawar, Rajesh J. Geologic Carbon Sequestration Leakage Detection: Physics-Guided Machine Learning Approaches. United States: N. p., 2018. Web.
Lin, Youzuo, Harp, Dylan Robert, Chen, Bailian, & Pawar, Rajesh J. Geologic Carbon Sequestration Leakage Detection: Physics-Guided Machine Learning Approaches. United States.
Lin, Youzuo, Harp, Dylan Robert, Chen, Bailian, and Pawar, Rajesh J. Tue . "Geologic Carbon Sequestration Leakage Detection: Physics-Guided Machine Learning Approaches". United States. https://www.osti.gov/servlets/purl/1460670.
@article{osti_1460670,
title = {Geologic Carbon Sequestration Leakage Detection: Physics-Guided Machine Learning Approaches},
author = {Lin, Youzuo and Harp, Dylan Robert and Chen, Bailian and Pawar, Rajesh J.},
abstractNote = {},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {7}
}

Conference:
Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

Save / Share: