Artificial Neural Network to Predict the Thermal Drawdown of Enhanced Geothermal System

This work presents the prediction of thermal drawdown of an enhanced geothermal system (EGS) using artificial neural network (ANN). A three-dimensional numerical model of EGS was developed to generate the training and testing data sets for ANN. We have performed a quantitative study of geothermal energy production for various injection operating conditions and reservoir fracture aperture. Input parameters for ANN include temperature, mass flux, pressure, and fracture transmissivity, while the production well temperature is the output parameter. The Levenberg-Marquardt back-propagation learning algorithm, the tan-sigmoid, and the linear transfer function were used for the ANN optimization. The best results were obtained with an ANN architecture composed of eight hidden layers and 20 neurons in the hidden layer, which made it possible to predict the production temperature with a satisfactory range (R-2 > 0.99). An appropriate accuracy of the ANN model was obtained with a percentage error less than (+/- 4.5). The results from the numerical simulations suggest that fracture transmissivity has less effect on thermal drawdown than the injection mass flux and temperature. From our results, we confirm that ANN modeling may predict the thermal drawdown of an EGS system with high accuracy.

Automated summary

This study utilized artificial neural networks to predict the thermal drawdown of an enhanced geothermal system (EGS), revealing that fracture transmissivity has less impact on thermal drawdown compared to injection mass flux and temperature.