AI 中文总结
本研究利用物理信息神经网络（PINN）建模闭式环路地热系统（CLGS），结合有机朗肯循环（ORC），评估废弃油井改造的技术性能与经济潜力，为地热能开发提供可靠工具。
AI 中文摘要
为实现2050年净零目标，多元化可再生能源至关重要。目前水电、风能和太阳能占据主导，地热能仍未得到充分利用。常规增强型地热系统（EGS）依赖水力压裂，存在诱发地震等风险。为解决这一问题，闭式环路地热系统（CLGS）在密封管道内循环工作流体，避免与储层直接接触，成为改造闲置油井且无环境危害的潜在方案。本研究开发了一种物理信息神经网络（PINN）来建模CLGS性能。与传统神经网络不同，PINN在学习过程中明确嵌入控制物理方程，如热传导和对流。这种集成使模型即使在训练数据稀疏的情况下，也能准确预测25年寿命内的井筒温度和流动特性。模拟结果证实了稳定的长期预测。当与有机朗肯循环（ORC）模型耦合时，系统的热力学效率达9.5%。关键是，通过计算多个经济指标（如动态投资回收期（DPP）、净现值（NPV）和平准化度电成本（LCOE）），评估了所提出的基于CLGS的发电系统的投资可行性和经济潜力。该框架为评估技术性能和经济回报提供了可扩展、符合物理规律的工具，为加速地热能应用提供了可靠途径。
英文摘要
To achieve net-zero targets by 2050, it is critical to diversify renewable energy. Hydropower, wind, and solar energy dominate; geothermal energy remains underutilized. Conventional Enhanced Geothermal Systems (EGS) rely on hydraulic stimulation, which poses risks such as induced seismicity. To address this, Closed-Loop Geothermal Systems (CLGS) circulate working fluids in sealed tubing to avoid direct reservoir contact, making them a potential solution for repurposing idle oil wells without environmental hazards. This study developed a Physics-Informed Neural Network (PINN) to model the CLGS performance. Unlike traditional neural networks, PINN explicitly embeds governing physical equations into their learning processes, such as heat conduction and convection. This integration enabled the model to accurately predict the wellbore temperature and flow characteristics over a 25-year lifespan, even with sparse training data. The simulation results confirmed stable long-term predictions. When coupled with an Organic Rankine Cycle (ORC) model, the system yielded a thermodynamic efficiency of 9.5%. Crucially, several economic indicators (e.g., DPP, NPV, and LCOE) are conducted to evaluate the investment feasibility and economic potential of the proposed CLGS-based power generation system. This proposed framework provides a scalable, physics-consistent tool for evaluating both technical performance and economic returns, offering a robust pathway to accelerate geothermal adoption.
Comments22 pages, 17 figures. Presented at the ASME 2026 20th International Conference on Energy Sustainability, Bellevue, WA, USA, July 26 to 29, 2026. Paper No. ES2026 184639
