  To maximize the economic benefits of geothermal energy production, it is
essential to optimize geothermal reservoir management strategies, in which
geologic uncertainty should be considered. In this work, we propose a
closed-loop optimization framework, based on deep learning surrogates, for the
well control optimization of geothermal reservoirs. In this framework, we
construct a hybrid convolution-recurrent neural network surrogate, which
combines the convolution neural network (CNN) and long short-term memory (LSTM)
recurrent network. The convolution structure can extract spatial information of
geologic parameter fields and the recurrent structure can approximate
sequence-to-sequence mapping. The trained model can predict time-varying
production responses (rate, temperature, etc.) for cases with different
permeability fields and well control sequences. In the closed-loop optimization
framework, production optimization based on the differential evolution (DE)
algorithm, and data assimilation based on the iterative ensemble smoother
(IES), are performed alternately to achieve real-time well control optimization
and geologic parameter estimation as the production proceeds. In addition, the
averaged objective function over the ensemble of geologic parameter estimations
is adopted to consider geologic uncertainty in the optimization process.
Several geothermal reservoir development cases are designed to test the
performance of the proposed production optimization framework. The results show
that the proposed framework can achieve efficient and effective real-time
optimization and data assimilation in the geothermal reservoir production
process.
