Geothermal energy, a renewable energy source with enormous reserves independent of the external environment, is essential for reducing carbon emissions. Spiral fin coaxial borehole heat exchanger (SFCBHE) is vital for geothermal energy extraction. Its heat extraction performance requires further improvements for efficient performance that consider the structural sizes and installation positions of the SFCBHE and the nonlinear coupling with respect to several factors. The heat extraction performance of SFCBHE is optimized using a combination of genetic algorithm–back-propagation neural network (GA–BPNN) and the Q-learning-based marine predator algorithm (QLMPA). This study analyzes and compares the effects of geothermal energy extraction of smooth pipe TY-1, structure before optimization TY-2, and optimized structure TY-3. Following optimization with GA–BPNN–QLMPA, the heat extraction performance of TY-3 is enhanced by 30.8% and 23.6%, respectively. The temperature of maximum extraction is improved by 26.8 K and 24.0 K, respectively. The power of maximum heat extraction is increased by 148.2% and 109.5%, respectively. The optimization method can quickly and accurately determine the heat extraction performance for different structural sizes and installation positions of the SFCBHE. These findings are crucial for developing high-performance SFCBHE and efficiently using geothermal energy.

The exploration and development of deep marine resources are faced with the problems of poor drill ability and serious wellbore instability in high temperature and high-pressure formations. The bottom hole dynamic drilling tool with low vibration characteristics is the best choice for deep well drilling. The output torque of the turbodrill is relatively small, which limits its application potential. In this study, intelligent optimization algorithms are used to improve the blade shape design to improve its output torque. Firstly, based on the moment of momentum theorem, the key blade profile parameters and range affecting the output characteristics of the turbodrill are analyzed and summarized. Subsequently, the five-order polynomial method and UG software (version 10.0) are used to complete the three-dimensional configuration of the bent-twisted blade. Then, based on the GA-LSSVM-MOPSO-TOPSIS intelligent optimization algorithm, the two-dimensional and three-dimensional modeling design parameters under the optimal hydraulic performance are optimized, and the accuracy of the intelligent optimization algorithm and parameters is verified by CFD simulation analysis. The results show that the hydraulic efficiency of only 4.9% is sacrificed, and the output torque is increased by 36.61%, which significantly improves the hydraulic performance of the turbodrill and provides guidance for the design of low-speed and high-torque turbodrills.