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- W4293090532 abstract "Summary Using neural network to predict reservoir parameters, we can map the relationship between logs and reservoir parameters as long as building a suitable model and have a large number of training data. With the help of neural network, we can map the unknown physical relationship without much geological expertise. The existing research of reservoir parameter prediction neural network with logs only focuses on one kind of reservoir parameter prediction modeling, ignoring the relationship between reservoir parameters. In this paper, relevance transfer learning is introduced, which using the knowledge of petrophysics to improve the performance of neural network reservoir parameters prediction. Constructing the transfer learning model based on correlation, and the experiment is carried out with the data of 64 wells in China oilfield. The experimental results show that transfer learning can effectively assist the training of permeability prediction model and water saturation prediction model in reservoir parameter prediction." @default.
- W4293090532 created "2022-08-26" @default.
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- W4293090532 date "2021-01-01" @default.
- W4293090532 modified "2023-09-28" @default.
- W4293090532 title "Relevance Based Transfer Learning for Reservoir Parameters Prediction with Logs" @default.
- W4293090532 doi "https://doi.org/10.3997/2214-4609.202112716" @default.
- W4293090532 hasPublicationYear "2021" @default.
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