Assessing the optimal placement and design of a large-scale high temperature energy storage system in crystalline bedrock is a challenging task. This study applies and evaluates various methods and strategies for pre-site investigation for a potential high temperature borehole thermal energy storage (HT-BTES) system at Linköping in Sweden. The storage is required to shift approximately 70 GWh of excess heat generated from a waste incineration plant during the summer to the winter season. Ideally, the site for the HT-BTES system should be able to accommodate up to 1400 wells to 300 m depth. The presence of major fracture zones, high groundwater flow, anisotropic thermal properties, and thick Quaternary overburden are all factors that play an important role in the performance of an HT-BTES system. Inadequate input data to the modeling and design increases the risk of unsatisfactory performance, unwanted thermal impact on the surroundings, and suboptimal placement of the HT-BTES system, especially in a complex crystalline bedrock setting. Hence, it is crucial that the subsurface geological conditions and associated thermal properties are suitably characterized as part of pre-investigation work. In this study, we utilize a range of methods for pre-site investigation in the greater Distorp area, in the vicinity of Linköping. Ground geophysical methods, including magnetic and Very Low-Frequency (VLF) measurements, are collected across the study area together with outcrop observations and lab analysis on rock samples. Borehole investigations are conducted, including Thermal Response Test (TRT) and Distributed Thermal Response Test (DTRT) measurements, as well as geophysical wireline logging. Drone-based photogrammetry is also applied to characterize the fracture distribution and orientation in outcrops. In the case of the Distorp site, these methods have proven to give useful information to optimize the placement of the HT-BTES system and to inform design and modeling work. Furthermore, many of the methods applied in the study have proven to require only a fraction of the resources required to drill a single well, and hence, can be considered relatively efficient.
Bayesian inference for thermal response test parameter estimation and uncertainty assessment