Efficient Optimization of Energy Recovery from Geothermal Reservoirs with Recurrent Neural Network Predictive Models

The paper proposes the use of recurrent neural network (RNN) architectures for capturing the dynamics of historical well response data as a function of input control variables. A trained RNN is then used as an efficient input-output dynamical model for optimization of energy recovery from geothermal reservoirs. Results from time-consuming simulation-based and fast RNN prediction models are presented and evaluated to compare the optimization strategies of the two approaches, indicating their consistency. The results suggest that RNN can be used as an efficient dynamic prediction tool for decision support and management of geothermal reservoir operations and development.

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