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- W4220742635 abstract "Accurate estimation of reservoir parameters (e.g., permeability and porosity) helps to understand the movement of underground fluids. However, reservoir parameters are usually expensive and time-consuming to obtain through petrophysical experiments of core samples, which makes a fast and reliable prediction method highly demanded. In this article, we propose a deep learning model that combines the 1-D convo- lutional layer and the bidirectional long short-term memory network to predict reservoir permeability and porosity. The mapping relationship between logging data and reservoir parameters is established by training a network with a combination of nonlinear and linear modules. Optimization algorithms, such as layer normalization, recurrent dropout, and early stopping, can help obtain a more accurate training model. Besides, the self-attention mechanism enables the network to better allocate weights to improve the prediction accuracy. The testing results of the well-trained network in blind wells of three different regions show that our proposed method is accurate and robust in the reservoir parameters prediction task." @default.
- W4220742635 created "2022-04-03" @default.
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- W4220742635 date "2023-07-01" @default.
- W4220742635 modified "2023-10-11" @default.
- W4220742635 title "High-Fidelity Permeability and Porosity Prediction Using Deep Learning With the Self-Attention Mechanism" @default.
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- W4220742635 doi "https://doi.org/10.1109/tnnls.2022.3157765" @default.
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- W4220742635 hasPublicationYear "2023" @default.
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