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2023
Meeting Abstract
Title
Physics-based machine learning for modeling thermo-hydraulic processes in a coaxial deep borehole heat exchanger, considering an explicit reservoir-wellbore representation: A case study of Cornwall, UK
Abstract
Coaxial Deep Borehole Heat Exchanger (DBHE) provides an alternative way to extract geothermal energy by circulating a working fluid without producing geofluids or performing injection processes. It can be used to avoid induced seismicity issues caused by injection operations in hydrothermal doublets or to repurpose damaged or non-productive wells. A detailed numerical model is required to accurately capture as well the thermo-hydraulic processes within the DBHE and the cooling effects in the surrounding reservoir. This numerical model is often high dimensional. For a real-time monitoring purpose and optimization study, a direct numerical simulation with this model is computationally intractable.
In this study, we use a physics-based machine learning method to reduce the computational cost of the performed forward model run. The physics-based machine learning method here is based on the non-intrusive reduced-basis method which expresses a physical solution in a linear combination of basis functions and weights. It is a model-order reduction technique that is mathematically proven to produce physically consistent predictions. The structure of the physics is maintained in basis functions and a machine learning model is deployed to calculate the weight for each basis function.
We show the advantages of using the physics-based machine learning method by applying it to the planned coaxial DBHE in Eden (Cornwall, UK). The forward simulation is performed using the open-source simulator GOLEM, a finite-element (FE) based simulator that is built within the MOOSE framework. In this study we provide a running time comparison between the FE simulations and the physics-based machine learning simulations. We will also evaluate the accuracy of the physics-based machine learning predictions towards the FE solutions. Here, we would like to emphasize the significant computational speed-up that allow us to obtain new temperature and pressure state predictions in real-time context and to perform optimization with numerous iterations.
In this study, we use a physics-based machine learning method to reduce the computational cost of the performed forward model run. The physics-based machine learning method here is based on the non-intrusive reduced-basis method which expresses a physical solution in a linear combination of basis functions and weights. It is a model-order reduction technique that is mathematically proven to produce physically consistent predictions. The structure of the physics is maintained in basis functions and a machine learning model is deployed to calculate the weight for each basis function.
We show the advantages of using the physics-based machine learning method by applying it to the planned coaxial DBHE in Eden (Cornwall, UK). The forward simulation is performed using the open-source simulator GOLEM, a finite-element (FE) based simulator that is built within the MOOSE framework. In this study we provide a running time comparison between the FE simulations and the physics-based machine learning simulations. We will also evaluate the accuracy of the physics-based machine learning predictions towards the FE solutions. Here, we would like to emphasize the significant computational speed-up that allow us to obtain new temperature and pressure state predictions in real-time context and to perform optimization with numerous iterations.
Author(s)
