文献
J-GLOBAL ID:202202219646689113
整理番号:22A0327146
チョークの間隙率を予測するための機械学習技術の比較【JST・京大機械翻訳】
Comparison of machine learning techniques for predicting porosity of chalk
著者 (6件):
Nourani Meysam
(Reservoir Geology Department, Geological Survey of Denmark and Greenland (GEUS), Copenhagen, Denmark)
,
Alali Najeh
(College of Petroleum Engineering, Al-Ayen University, Thi-Gar, 64001, Iraq)
,
Samadianfard Saeed
(Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran)
,
Band Shahab S.
(Future Technology Research Center, National Yunlin University of Science and Technology, Douliou, Yunlin, 64002, Taiwan, ROC)
,
Chau Kwok-wing
(Department of Civil and Environmental Engineering, Hong Kong Polytechnic University, Hong Kong, China)
,
Shu Chi-Min
(Graduate School of Engineering Science and Technology, National Yunlin University of Science and Technology, Yunlin, 64002, Taiwan, ROC)
資料名:
Journal of Petroleum Science and Engineering
(Journal of Petroleum Science and Engineering)
巻:
209
ページ:
Null
発行年:
2022年
JST資料番号:
T0412A
ISSN:
0920-4105
資料種別:
逐次刊行物 (A)
記事区分:
原著論文
発行国:
オランダ (NLD)
言語:
英語 (EN)