Zobrazeno 1 - 3
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pro vyhledávání: '"Rongbo Shao"'
Autor:
Binsen Xu, Zhou Feng, Jun Zhou, Rongbo Shao, Hongliang Wu, Peng Liu, Han Tian, Weizhong Li, Lizhi Xiao
Publikováno v:
Artificial Intelligence in Geosciences, Vol 5, Iss , Pp 100091- (2024)
Machine learning has been widely applied in well logging formation evaluation studies. However, several challenges negatively impacted the generalization capabilities of machine learning models in practical implementations, such as the mismatch of da
Externí odkaz:
https://doaj.org/article/0f2b1679f65e4c41a63a7f0bffcfcbb3
Publikováno v:
Artificial Intelligence in Geosciences, Vol 5, Iss , Pp 100070- (2024)
We propose a novel machine learning approach to improve the formation evaluation from logs by integrating petrophysical information with neural networks using a loss function. The petrophysical information can either be specific logging response equa
Externí odkaz:
https://doaj.org/article/b679defeca5a4dcb9ce8275e279d6133
Publikováno v:
Journal of Magnetic Resonance. 346:107358
Nuclear magnetic resonance (NMR) is a powerful tool for formation evaluation in the oil industry to determine parameters, such as pore structure, fluid saturation, and permeability of porous materials, which are critical to reservoir engineering. The
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f428fb38109db36b5eef7c5780beb255
https://doi.org/10.1016/j.jmr.2022.107358
https://doi.org/10.1016/j.jmr.2022.107358