Logging evaluation of favorable areas of a low porosity and permeability sandy conglomerate reservoir based on machine learning
Author:
Funder
Heilongjiang Provincial Postdoctoral Science Foundation
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Geophysics
Link
https://link.springer.com/content/pdf/10.1007/s11600-023-01130-4.pdf
Reference77 articles.
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3. Alkinani HH, Al-Hameedi ATT, Dunn-Norman S, Flori RE, Alsaba MT, Amer AS (2019) Applications of artificial neural networks in the petroleum industry: a review. In: SPE middle east oil and gas show and conference, MEOS, proceedings, 2019-March (1957). https://doi.org/10.2118/195072-ms
4. Alzubaidi F, Mostaghimi P, Swietojanski P, Clark SR, Armstrong RT (2021) Automated lithology classification from drill core images using convolutional neural networks. J Pet Sci Eng 197:107933. https://doi.org/10.1016/j.petrol.2020.107933
5. Ao Y, Zhu L, Guo S, Yang Z (2020) Probabilistic logging lithology characterization with random forest probability estimation. Comput Geosci 144:104556. https://doi.org/10.1016/j.cageo.2020.104556
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