Geophysical multiparameter fusion for temperature prediction and geothermal property characterization

Accurate estimation of the temperature distribution and thermal properties is essential for understanding the geothermal conditions and resource assessment, which provides fundamental information for subsequently efficient and sustainable utilization of geothermal energy resources. However, the estimation based on the temperature logs is limited to well locations and often suffers from inaccuracies in predicting the spatial distribution temperature beyond boreholes. In this study, we propose an efficient geothermometer for prediction of the 3D temperature distribution based on geophysical multiparameter fusion by integrating magnetotelluric (MT), gravity, thermal and well-logging data. The heat accumulation conditions at different depths were analyzed based on the internal relationship among electrical resistivity, temperature, and thermal conductivity of the formations. Furthermore, we develop a joint inversion procedure of the MT and thermal data for characterizing the thermal conductivity structure with the constraint of the measured temperature logs. The application to field data from Xiong’an New Area, China demonstrates the accuracy of the temperature prediction, and the estimated thermal conductivity structure helps us in characterizing the subsurface geothermal conditions and identifying potential geothermal resource areas.