Developing meshing workflows for Geologic Uncertainty Assessment in High-Temperature Aquifer Thermal Energy Storage

Abstract. Evaluating uncertainties of geological features on temperature and pressure changes in reservoir’s fluids are crucial for a safe and sustainable operation of the High-Temperature Aquifer Thermal Energy Storage (HT-ATES). This study uses a new automated surface fitting function in Python API of GMSH (v. 4.11) to model the impact of arbitrary structural barriers and variations of roof and floor geometries on temperature and pressure in heat storage application. A workflow is developed in Python to implement an automated mesh generation routine for varying geological scenarios that cannot be predicted by surface-based exploration methods. This way, the geologic models and their underlying uncertainties are transferred into reservoir simulations. We applied our modelling approach on two case studies: 1) Greater Geneva Basin with the Upper Jurassic (“Malm”) limestone reservoir of 100 m thickness and 2) the DeepStor project in the Upper Rhine Graben with an Oligocene sandstone reservoir of 10 m thickness. In the Greater Geneva Basin showcase, the upper and lower surfaces of the reservoir are shifted ± 8 and ± 10 m, respectively to perturb topology of the thick reservoir. Independence of the heat plume from reservoir's topology indicates the limited propagation of the induced thermal regime in thick reservoirs and redundancy of the advanced exploration campaigns like 3D seismic. In DeepStor, an arbitrary sub-seismic fault juxtaposing the permeable sandstone layers against low-permeable clay-marl units is introduced to the base case model. The arbitrary fault is located in distances varying 4 m to 118 m from the borehole and resulted in a ~10 % difference in the pressure field of the cases. Modelling the pressure and temperature in the tilted reservoir reveals that heat tends to accumulate updip while pressure values are higher in the downdip side.