Geothermal energy has been recognized as an important clean renewable energy. Accurate assessment of geothermal resources is an essential foundation for their development and utilization. The North Jiangsu Basin (NJB), located in the Lower Yangtze Craton, is shaped like a wedge block of an ancient plate boundary and large-scale carbonate thermal reservoirs are developed in the deep NJB. Moreover, the NJB exhibits a high heat flow background because of its extensive extension since the Late Mesozoic. In this study, we used the Monte Carlo method to evaluate the geothermal resources of the main reservoir shallower than 10 km in the NJB. Compared with the volumetric method, the Monte Carlo method takes into account the variation mode and uncertainties of the input parameters. The simulation results show that the geothermal resources of the sandstone thermal reservoir in the shallow NJB are very rich, with capacities of (6.6–12) × 10²⁰ J (mean 8.6 × 10²⁰ J), (5.1–16) × 10²⁰ J (mean 9.1 × 10²⁰ J), and (3.2–11) × 10²⁰ J (mean 6.6 × 10²⁰ J) for the Yancheng, Sanduo and Dai’nan sandstone reservoir, respectively. In addition, the capacity of the geothermal resource of the carbonate thermal reservoir in the deep NJB is far greater than the former, reaching (9.9–15) × 10²¹ J (mean 12 × 10²¹ J). The results indicate capacities of a range value of (1.2–1.7) × 10²¹ J (mean 1.4 × 10²² J) for the whole NJB (<10 km).

Geothermal energy is an eco-friendly, renewable source of underground thermal energy that exists in the interior of the earth. By tapping into these formations, fluids can be channeled to heat the rock formations above, resulting in a significantly higher land surface temperature (LST). However, LST readings are influenced by various factors such as sun radiation, cyclical variations, and precipitation, which can mask the temperature anomalies caused by geothermal heat. To address these issues and highlight the LST anomalies caused by geothermal heat, this paper proposes a methodology to efficiently and quickly calculate the multi-temporal LST leveraging of the Google Earth Engine (GEE) in the Damxung–Yangbajain basin, Qinghai–Tibet Plateau. This method incorporates terrain correction, altitude correction, and multi-temporal series comparison to extract thermal anomaly signals. The existing geothermal manifestations are used as a benchmark to further refine the methodology. The results indicate that the annual mean winter LST is a sensitive indicator of geothermal anomaly signals. The annual mean winter LST between 2015 and 2020 varied from −14.7 °C to 26.7 °C, with an average of 8.6 °C in the study area. After altitude correction and water body removal, the annual mean winter LST varied from −22.1 °C to 23.3 °C, with an average of 6.2 °C. When combining the distribution of faults with the results of the annual mean winter LST, this study delineated the geothermal potential areas that are located predominantly around the fault zone at the southern foot of the Nyainqentanglha Mountains. Geothermal potential areas exhibited a higher LST, ranging from 12.6 °C to 23.3 °C. These potential areas extend to the northeast, and the thermal anomaly range reaches as high as 19.6%. The geothermal potential area makes up 8.2% of the entire study area. The results demonstrate that the approach successfully identified parts of known geothermal fields and indicates sweet spots for future research. This study highlights that utilizing the multi-temporal winter LST is an efficient and cost-effective method for prospecting geothermal resources in plateau environments.

The study of thermal conductivity anisotropy is of great importance for more accurate heat flow calculations, geodynamic studies, development and utilization of hot dry rock, and simulation of heat transfer in geological reservoirs of nuclear waste, and so on. To study the thermal conductivity anisotropy of rocks, 1158 cores from 60 boreholes in East China were tested for thermal conductivity, including thermal conductivity values parallel to (λ_∥) and perpendicular to (λ_⊥) structural planes of basalt, mudstones, gneisses, sandstones, carbonates, evaporites, and metamorphic rocks. The thermal conductivity anisotropy is not obvious for sand, clay, and evaporate, and the average anisotropic factors of 1.19 ± 0.22, 1.18 ± 0.17, and 1.18 ± 0.17 for tuff/breccia, granitoid and contact metamorphic rocks, respectively, indicate that these three rocks have strong anisotropy characteristics. Finally, the effect of thermal conductivity anisotropy on heat flow is studied and discussed in detail, showing that the results of thermal conductivity tests have a significant effect on the calculation of heat flow and thermal structure, and the data show that a deviation of about 10% in thermal conductivity causes a deviation of about 11% in heat flow, which may lead to a misperception of deep thermal structure studies. The regular and anisotropic characteristics of thermal conductivity of various rocks in Eastern China obtained in this paper can provide parameter support for projects such as heat flow calculations, thermal structure studies, and geothermal resource development and utilization.

Surface Heat flow (HF) can be calculated from the accumulation of radiogenic heat production (RHP) of arbitrarily subdivided thin layers of the crust and the residual HF. However, geothermal studies of scientific drilling projects around the world do not have clear vertical correspondence between HF and RHP, which has created a great controversy for the relationship between the two. For the first time, continuous temperature data, thermal conductivity and RHP measurements from the 3,008‐m‐deep Lujiang‐Zongyang Scientific Drilling in the northeastern Yangtze Craton, demonstrate that the abnormal HF is determined by high RHP rock (mean value, 10 μW/m³). The detailed spatial‐temporal study of magmatic activity in the Lujiang‐Zongyang basin shows the high RHP rock are concentrated in the early syenite‐monzonite period (133–131 Ma) in the northern basin. Two enrichment processes caused by late Neoproterozoic and Mesozoic subduction contribute to the formation.

The thermal conductivity of a stratum is a key factor to study the deep temperature distribution and the thermal structure of the basin. A huge expense of core sampling from boreholes, especially in offshore areas, makes it expensive to directly test stratum samples. Therefore, the use of well logging (the gamma-ray, the neutron porosity, and the temperature) to estimate the thermal conductivity of the samples obtained from boreholes could be a good alternative. In this study, we measured the thermal conductivity of 72 samples obtained from an offshore area as references. When the stratum is considered to be a shale–sand–fluid model, the thermal conductivity can be calculated based on the mixing models (the geometric mean and the square root mean). The contents of the shale and the sand were derived from the natural gamma-ray logs, and the content of the fluid (porosity) was derived from the neutron porosity logs. The temperature corrections of the thermal conductivity were performed for the solid component and the fluid component separately. By comparing with the measured data, the thermal conductivity predicted based on the square root model showed good consistency. This technique is low-cost and has great potential to be used as an application method to obtain the thermal conductivity for geothermal research.