Using one-dimensional convolutional neural networks and data augmentation to predict thermal production in geothermal fields

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.