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Title: Prediction of CO2 leakage risk for wells in carbon sequestration fields with an optimal artificial neural network

Abstract

Not provided.

Authors:
; ; ; ; ; ;
Publication Date:
Research Org.:
Univ. of Louisville at Lafayette, Lafayette, KY (United States)
Sponsoring Org.:
USDOE Office of Fossil Energy (FE)
OSTI Identifier:
1538335
DOE Contract Number:  
FE0009284
Resource Type:
Journal Article
Journal Name:
International Journal of Greenhouse Gas Control
Additional Journal Information:
Journal Volume: 68; Journal Issue: C; Journal ID: ISSN 1750-5836
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
Science & Technology - Other Topics; Energy & Fuels; Engineering

Citation Formats

Li, Ben, Zhou, Fujian, Li, Hui, Duguid, Andrew, Que, Liyong, Xue, Yanpeng, and Tan, Yanxin. Prediction of CO2 leakage risk for wells in carbon sequestration fields with an optimal artificial neural network. United States: N. p., 2018. Web. doi:10.1016/j.ijggc.2017.11.004.
Li, Ben, Zhou, Fujian, Li, Hui, Duguid, Andrew, Que, Liyong, Xue, Yanpeng, & Tan, Yanxin. Prediction of CO2 leakage risk for wells in carbon sequestration fields with an optimal artificial neural network. United States. doi:10.1016/j.ijggc.2017.11.004.
Li, Ben, Zhou, Fujian, Li, Hui, Duguid, Andrew, Que, Liyong, Xue, Yanpeng, and Tan, Yanxin. Mon . "Prediction of CO2 leakage risk for wells in carbon sequestration fields with an optimal artificial neural network". United States. doi:10.1016/j.ijggc.2017.11.004.
@article{osti_1538335,
title = {Prediction of CO2 leakage risk for wells in carbon sequestration fields with an optimal artificial neural network},
author = {Li, Ben and Zhou, Fujian and Li, Hui and Duguid, Andrew and Que, Liyong and Xue, Yanpeng and Tan, Yanxin},
abstractNote = {Not provided.},
doi = {10.1016/j.ijggc.2017.11.004},
journal = {International Journal of Greenhouse Gas Control},
issn = {1750-5836},
number = C,
volume = 68,
place = {United States},
year = {2018},
month = {1}
}