*Yutaka Shoji1, Takao Katsura1, Katsunori Nagano1
(1.Hokkaido University)
Keywords:shallow geothermal , soil effective thermal conductivity, data assimilation, ensemble Kalman filter, inversion analysis
Sustainable development is required to address global warming. In response to that, shallow geothermal system has been attracting attention as a kind of renewable energy heat source. The performance of shallow geothermal system is largely determined by the soil effective thermal conductivity. The soil effective thermal conductivity is usually estimated by in-situ test called thermal response test. However, due to the high cost of this test in terms of time and money, thermal response test is often not conducted in small-scale geothermal installations. In such places, geological-information-based or average reference values are used for design and control. Alternatively, several methods have been studied such as estimation from the geological structure and spatial correlation with points where the in-situ estimated values have already been obtained. In this study, we proposed a method for posteriori estimation of soil effective thermal conductivity from actual operation data of a shallow geothermal system using a kind of data assimilation method: ensemble Kalman filter. By assimilating the observed inlet/outlet temperature of the ground heat exchanger into the numerical simulation model, the soil effective thermal conductivity is modified. The data-assimilating estimation reproduced the value from the thermal response test at the same location. In addition, the prediction accuracy of the circulating fluid temperature in the ground heat exchanger was improved by this modification. This result will contribute to the optimal control of the shallow geothermal system by using the modified soil effective thermal conductivity, and will also improve the estimation accuracy of the shallow geothermal potential map, by increasing the in-situ estimation data of the soil effective thermal conductivity.