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- W4313461918 abstract "Numerical simulation is the most common method to predict reservoir production temperatures during geothermal energy extraction. Considering the principle of numerical modeling, the numerical simulation establishment process requires a large amount of good exploration data. In addition, it is heavily influenced by subsurface heterogeneity. Also, despite the superior performance of deep learning models, sparse data is a critical challenge in the training process. Therefore, we propose a one-dimensional-convolutional neural network (1D-CNN) model and use data augmentation techniques to build a large-scale multiscale production temperature data set. The network learns the nonlinear relationship between boundary conditions and production temperature from the data set and reaches the production temperature prediction for a three-well geothermal system. The maximum difference in production temperature is 1.8181 °C and the generalization performance is improved by 59.6%. It is worth noting that the excellent generalization capability indicates that the data-driven concept behind the model is an easily interpretable one. As a new data processing concept, the “data-guided approach” is a key step in establishing a universal approach for application in the geothermal field." @default.
- W4313461918 created "2023-01-06" @default.
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- W4313461918 date "2023-02-01" @default.
- W4313461918 modified "2023-10-06" @default.
- W4313461918 title "Using one-dimensional convolutional neural networks and data augmentation to predict thermal production in geothermal fields" @default.
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- W4313461918 doi "https://doi.org/10.1016/j.jclepro.2023.135879" @default.
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