Ensemble-based assisted history matching of DTS monitoring data in HT-ATES systems: application to a real-life case study

Underground heat storage is an important element in accelerating the energy transition. It can significantly contribute to CO₂ emission reduction and cost savings since it is one of the cheapest forms of energy storage and it enables the seasonal storage of large energy surpluses from sustainable sources, e.g. wind, sun, geothermal. Numerical models are used for the prediction of thermal behavior important in establishing the high efficiency of the high temperature aquifer thermal energy storage (HT-ATES) systems. However, the lack of exact knowledge of the subsurface conditions introduces modeling uncertainty. It is therefore important to employ approaches that reduce subsurface uncertainty. History matching is a methodology where the numerical models are updated to match historical observations that will in turn not only increase understanding of the subsurface but also improve accuracy of the model predictability of future behavior. In this research, models of the first large-scale operational HT-ATES system in Middenmeer, the Netherlands, were used to evaluate the thermal evolution in the storage aquifer and the over- and underburden clay layers. The HT-ATES system, consisting of a hot and warm well, with a monitoring well inbetween, became operational in the summer of 2021. The extensive monitoring program implemented for the first few operational years provided an opportunity to study the performance of such a system from an environmental and operational point of view. A state-of-the-art assisted history matching approach was applied to the first storage cycle, using a coupling between history matching software and the thermal flow simulator. This approach was compared to a more traditional single-model manual history matching method. Rock properties of the aquifer and over- and underburden layers were updated in the randomly generated prior ensemble of models to fit the simulated temperature evolution measured down the monitoring well with the distributed temperature sensing (DTS) data. The observations gathered during the second year of operations were used to validate the accuracy of the prediction capabilities of the updated models. The obtained results indicate the value of history matching to improve understanding of the subsurface conditions for HT-ATES systems and obtain models with better predictability of the future behavior of heat in the storage reservoir and overburden formations. Such improved models are instrumental in providing engineers with a better quantitative grip on the environmentally responsible storage potential and heat deliverability of the target storage site, which is important to achieve cost-effective site-specific design (e.g. number of wells, well placement) and performing operational strategies (e.g. injection/production rates and temperatures) for new HT-ATES systems. Moreover, the benefits of the assisted history matching approach over manual method are highlighted and both approaches are validated where the assisted history matching method produced more accurate predictions than the manual approach.