过程(计算)
工艺工程
石油工程
环境科学
材料科学
工程类
机械工程
计算机科学
操作系统
作者
Qun Lei,Jiarui Zhang,Xiaohua Zhu
摘要
Under the principle of “heat extraction without water extraction,” U-shaped wells offer greater advantages over coaxial heat exchange systems and serve as a critical method for efficient exploitation of medium-deep geothermal energy. However, most existing research has primarily focused on optimizing single U-shaped wells, which exhibit relatively low heat extraction efficiency and fail to achieve continuous heating (≥35 °C) for users throughout a heating season. To this end, this study proposes an innovative geothermal heat extraction system using multiple U-shaped wells. Centered around a production well, a distributed multi-well heat exchange system is constructed by uniformly arranging the injection wells around it. Under an inlet flow Q0 = 30 m3/h and inlet temperature Tb = 15 °C, a minimum of seven-injection wells are required to circulate to maintain heating temperatures ≥35 °C. However, increasing the number of injection wells significantly increases the engineering cost and operational complexity. Therefore, by optimizing the process parameters, this study reveals the coupled effects of inlet flow rate and inlet temperature on heat extraction power and the number of wells. The findings indicate that increasing the inlet flow rate not only significantly enhances both heat extraction power and the effective heating area but also increases the demand for the number of injection wells. In contrast, increasing the inlet temperature decreases the demand for wells but reduces the heat extraction power. Based on these results, this study establishes a correlation chart between different process parameters (flow rate and temperature) and the number of injection wells and heating capacity. This provides a scientific basis for balancing cost and efficiency in engineering applications and introduces a novel system design approach for the optimization of medium-deep geothermal energy exploitation.
科研通智能强力驱动
Strongly Powered by AbleSci AI