Controls of subsurface fractures on mine water storage efficiency.

这个博士学位是一个机会，有助于改变游戏规则的绿色能源供应的基础上，地热水能源储存和发电能力。应对气候变化和满足废弃、被淹煤矿附近社会贫困社区和小型工业的能源需求的一个创新解决方案是投资于矿井水热能和热能储存。废弃的矿山，特别是在苏格兰，可以重新利用和重新设计，以生产可持续能源。然而，由于长期不活动而造成的地质力学完整性的任何变化，都引起人们对矿山是否有能力进行预期的可持续能源生产的关切。格拉斯哥天文台研究如何绕过这一障碍，收集相关观测数据，以探索低温、矿井水热和储热资源能力，并促进从这一独特的地下研究设施获取样本。这个博士提案旨在了解矿井水地热能生产中的循环载荷如何影响地下裂缝的生长和随后被淹竖井的完整性，特别是当岩体受到临界应力时。科学为了更好地了解矿井水热生产如何影响断裂岩石的性质和运动学，将进行动态载荷实验，再加上大量的X射线CT图像采集。机器学习变化检测方法将用于分析大量数字图像，以检测由于负载/温度变化而导致的岩石结构和纹理变化。变化检测将能够推断出安全运行的矿井水地热情景，并最大限度地提高矿井水产热和热储存。该博士学位的最终目标将是提供一个基于物理学的数据驱动的工作流程，相关的软件工具，以及一个可公开访问的数字图像库，以帮助解释由于矿井水产热和储存而导致的岩石属性变化，通过将实验数据与非破坏性全场方法和机器学习建模相结合。

This PhD is an opportunity to contribute to the game changer in green energy supply based on the geothermal capacity of mine water energy storage and generation. An innovative solution to tackle climate change and cover the energy needs of socially deprived communities and small-scale industries close to abandoned, flooded coal mines is to invest into mine water heat and heat storage. Abandoned mines, particularly in Scotland, can be repurposed and re-engineered to produce sustainable energy. However, any changes in their geomechanical integrity due to their prolonged inactivity raise concerns about the mine competence for the anticipated sustainable energy production. The Glasgow Observatory looks into ways around this obstacle to collect relevant observations to explore low temperature, mine water heat, and heat storage resource capacity and facilitate access to samples from this unique underground research facility. This PhD proposal aims to understand how cyclic loading in mine water geothermal energy production affects the growth of subsurface fractures and the subsequent integrity of the flooded shafts, especially when the rock mass is critically stressed. The ScienceTo better understand how mine water heat production may affect the properties and kinematics of the fractured rock, dynamic load experiments will be performed, coupled with large volumes of x-ray CT image acquisition. Machine learning change detection approach will be used to analyse the bulk of digital images to detect changes in rock structure and texture due to the change in load/temperature. Change detection will enable to infer mine water geothermal scenarios that are safe to operate and maximise the mine water heat production and heat storage.The end-goal of this PhD will be to provide a physics-based, data-driven workflow, the associated software tools, and a publicly accessible library of digital images to help explain changes in rock properties due to mine water heat production and storage, by combining experimental data with non-destructive full-field methods and machine learning modelling.