A physics-based machine learning for geothermal reservoir simulations:
Thermo-Hydro-Mechano-Chemical (THMC) simulations are important for describing fluid-flow, reactive transport, and geo-mechanical processes in geothermal reservoirs. They are to assess efficiency in heat production, ensure safe operations, and evaluate the associated-environmental impacts, such as induced seismicity. These simulations are numerically expensive (especially for large-scale simulations) since they require relatively fine meshes to accurately capture the physical processes and guarantee numerical stability. This aspect makes them difficult and challenging to perform detailed parameter estimation studies (e.g., uncertainty quantification and global sensitivity anaysis). Therefore, surrogate models are needed to significantly reduce the computational costs.
In this project, we introduce the use of physics-based machine learning as the surrogate to perform large-scale TH(M)C simulations. As our physics-based machine learning technique, we use the non-intrusive reduced-basis (RB) method (see Figure (a)). It has two components: the construction of basis functions for preserving the physics (obtained through the Proper Orthogonal Decomposition) and the prediction of the associated weights via machine learning (e.g., Gaussian Process Regression and Neural Networks). At the current stage, we managed to compare the performance of non-intrusive RB towards data-driven machine learning using a hydro-mechanical benchmark case. We found that the data-driven machine learning produces small-scale perturbances of the prediction for a small number of training samples which is physically unreasonable, while the non-intrusive RB gives a high-accuracy for a comparable number of training samples.