Convolutional – recurrent neural network proxy for robust optimization and closed-loop reservoir management

Kim, Yong Do; Durlofsky, Louis J.

doi:10.1007/s10596-022-10189-9

Cited by 14 publications

(1 citation statement)

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“…(2020), which involve many simulation runs to generate training data. An alternative approach is to treat the geologic uncertainty as part of the inputs into a proxy model (Kim & Durlofsky, 2022), which will require the proxy model to have a more complex architecture with geologic maps as additional inputs. In this approach, one can train a single, and more complex, proxy model to represent different realizations instead of introducing multiple proxy models to capture the behavior of different realizations.…”

Section: Discussionmentioning

confidence: 99%

Efficient Optimization of Energy Recovery From Geothermal Reservoirs With Recurrent Neural Network Predictive Models

Qin

Jiang

Faulder³

et al. 2023

Water Resources Research

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Improving the long‐term energy production performance of geothermal reservoirs can be accomplished by optimizing field development and management plans. Reliable prediction models, however, are needed to evaluate and optimize the performance of the underlying reservoirs under various operation and development strategies. In traditional frameworks, physics‐based simulation models are used to predict the energy production performance of geothermal reservoirs. However, detailed simulation models are not trivial to construct, require a reliable description of the reservoir conditions and properties, and entail high computational complexity. Data‐driven predictive models can offer an efficient alternative for use in optimization workflows. This paper presents an optimization framework for net power generation in geothermal reservoirs using a variant of the recurrent neural network (RNN) as a data‐driven predictive model. The RNN architecture is developed and trained to replace the simulation model for computationally efficient prediction of the objective function and its gradients with respect to the well control variables. The net power generation performance of the field is optimized by automatically adjusting the mass flow rate of production and injection wells over 12 years, using a gradient‐based local search algorithm. Two field‐scale examples are presented to investigate the performance of the developed data‐driven prediction and optimization framework. The prediction and optimization results from the RNN model are evaluated through comparison with the results obtained by using a numerical simulation model of a real geothermal reservoir.

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Section: Discussionmentioning

confidence: 99%