| Title | Research Status and Trends of Smart Geothermal System |
|---|---|
| Authors | Xianzhi Song, Gensheng Li, Gaosheng Wang, Shuang Li, Jialin Zhao, Chengcheng Zheng, Junlin Yi, Yifan Zhou, Haobin Xia, Tianjian Li, Yang Shi, Pei Wu |
| Year | 2026 |
| Conference | World Geothermal Congress |
| Keywords | |
| Abstract | As the global shift toward clean energy accelerates, smart geothermal systems are becoming a leading example of integrating geothermal development with advanced digital technologies. This trend is driving the industry toward more intelligent and low-carbon practices, marking a shift from experience-based to data-driven approaches. This paper focuses on the core technological framework of smart geothermal systems—intelligent characterization, intelligent prediction, and intelligent optimization—and reviews recent advances and challenges. Intelligent characterization integrates multi-source data and artificial intelligence to enable precise modeling and dynamic inversion of geothermal reservoirs, overcoming the limitations of traditional methods. Intelligent prediction, based on digital twins and multi-physics simulation, supports accurate forecasting of production trends and production decision-making. Intelligent optimization uses real-time data and closed-loop feedback to continuously refine development strategies, enhancing efficiency and sustainability. Addressing issues such as data scarcity and rigid models will require closer integration of AI and domain knowledge. The hybrid approach combining data-driven and physics-based methods is key to building accurate reservoir models and rapidly optimizing production strategies. Together, these elements form the foundation for the intelligent transformation of geothermal development. |