Deep Temperature-Field Prediction Utilizing the Temperature–Pressure-Coupled Resistivity Model: A Case Study in the Xiong’an New Area, China

Accurate estimation of the Earth’s interior temperature is essential for solving fundamental scientific and applied geothermal problems. Currently, there is no universal method for determining deep temperature fields; however, such a method may be based on resistivity, a temperature-dependent proxy parameter. We propose an electromagnetic (EM) geothermometer based on the temperature–pressure coupled resistivity model (TPCRM). This geothermometer can accurately determine the relationship between the normalized resistivity, temperature, and pressure in deep formations based on well-logging, gravity, and EM data, thus allowing to visualize the temperature distribution. The TPCRM is utilized to predict the subsurface temperature in the Xiong’an New Area and shows an accuracy of 76.35%–96.58%. Sensitivity analysis of the critical variables of the TPCRM reveals that the TPCRM relatively weakly depends on the number of constraining boreholes and that the optimization of the subdivision spacing of the well-logging data can significantly improve temperature prediction accuracy. In addition, the effect of the spacing of inverted resistivity normalization grid nodes on the temperature prediction accuracy is relatively weak because the TPCRM considers the factor of the overburden pressure. The TPCRM is a promising tool for studying thermal genetic mechanisms, as well as fine evaluation of geothermal resources for their large-scale and efficient development and utilization.