Lifetime optimisation of multiple deep closed-loop geothermal wells

在当前的能源和环境危机中，作为解决方案组合的一部分，全世界对地热能的兴趣正在增加。与太阳能和风能相比，地热能具有较高的容量系数，因此能够提供可预测的全天候热能供应，可用于满足直接供热应用中的基本负载，或与例如有机朗肯循环工厂一起生产电力。此外，地热足迹(m2/产生的能量)比风能和太阳能装置所需的要小得多，这使得它在土地利用和社会可接受性方面都是更有吸引力的选择。这里提出的闭环式地热系统的好处是可以在任何地方使用/安装，并消除了与开式环路系统相关的勘探风险。因此，在这样的系统中，流体包含在同轴热交换器中，消除了与抽水和注水(水力压裂)相关的任何环境/社会问题。最近，除了开发新油气井的计划外，还根据利用废弃油气井的需要，改进了使用地热能的选择和财务可行性。一口地热井对岩层和井筒之间的热能转移具有径向影响。这受到地质的热扩散率和传导性以及提取的能量的数量的影响。随着时间的推移，热提取会导致油井的热输出减少。因此，本PHD计划第一部分建议研究的多井开发可以帮助使用间歇性满负荷时间(可变热通量和瞬时热传导)对井进行热补充，或在井之间切换以允许原始地层(在井的影响半径范围内)恢复井筒的热量。地热融资的一个反复出现的问题是与新钻孔相关的前期成本。然而，地热能提供较低的能源成本(LCOE)。通过使用多口井来延长给定地点/应用的整体地热发电厂寿命将改善LCOE，使地热成为净零化进程中更具吸引力的选择。机器学习可以在没有人为干预的情况下帮助优化和控制过程的决策和问题解决。在工作的第二部分，研究方案将包括开发深度学习优化框架，以控制地热生产，确定最佳间歇时间、工作流速和目标返回液温度，以延长寿命和提高产量，同时还调查不同油井结构和地质对热降解的影响。

Worldwide interest in geothermal energy is increasing as part of the solution mix in the prevailing energy and environmental crisis. Geothermal energy has a high capacity factor compared to solar and wind, hence being able to provide a predictable 24/7 supply of thermal energy that can be used to meet the base load in direct heat to heat applications or produce electrical power in conjunction with, for example, Organic Rankine Cycle plants. In addition, the geothermal footprint (m2/energy produced) is significantly smaller than that required by wind and solar installations making it a more attractive option both in terms of land use and social acceptability. Closed-loop geothermal systems as proposed here have the benefit of being available/installed anywhere and remove the exploration risks associated with open loop systems. In such systems the fluid is contained in coaxial heat exchangers hence, removing any environmental/societal concerns associated with water extraction and injection (fracking). Recently, the option and financial viability of using geothermal energy has been improved in line with the need to utilise abandoned oil and gas well, in addition to the plans for new ones.A single geothermal well has a radial area of influence for the transfer of thermal energy between the rock formation and the well bore. This is affected by the thermal diffusivity and conductivity of the geology and the amount of energy extracted. Over time, heat extraction leads to a reduced thermal output of a well. Therefore, multi-well developments as the ones proposed to be studied in the first part of this PhD programme can aid in thermally recharging wells using intermittent full load hours (variable heat flux and transient heat conduction) or switching between wells to allow for the original formation (to the extent of the radius of influence of the well) to recuperate heat at the well bore. One recurrent issue with geothermal funding is the upfront cost associated with new bore holes. However, geothermal energy offers a lower cost of energy (LCOE). Increasing the overall geothermal plant lifetime for a given site/application by using multiple wells will improve the LCOE, making geothermal an even more attractive option in the drive to net-zero.Machine learning can aid in decision-making and problem-solving for optimisation and control processes without human interference. In the second part of the work, the research programme will include the development of a deep learning optimisation framework to control geothermal production and to determine optimum intermittency, working flow rates and targeted return fluid temperatures for increased lifetime and production, while also investigating the effects on thermal degradation due to different well architectures and geologies.