How underground lithologies respond to thermo-mechanical coupling during energy extraction/storage.

在我们寻求能源脱碳的过程中，地下热能储存是一个关键因素。然而，长期频繁的循环热机械（TM）应力变化的影响仍然知之甚少，并可能威胁系统的寿命。本项目旨在填补这一空白，通过在相关的循环TM加载条件下进行实验室和数值实验，以调查此类系统中目标岩性的稳定性。为了应对能源转型的挑战，新的地下解决方案将重点放在新的资源开发（地热）或存储（放射性废物，热和/或气体-地下气体存储，压缩空气储能，H，CO2）上。所有这些应用的共同点是引起新的、浅层的、周期性的、局部的热机械应力变化。这个博士项目的范围是使用不同尺度的观测来模拟和预测地下复杂系统中目标岩性的稳定性，当这些系统长时间受到周期性TM应力变化时。这项研究工作旨在了解颗粒尺度变形如何有助于地下系统的全球响应，以及如何控制这种响应以减少任何伴随的诱发灾害。一种创新的方法，结合实验室和数值实验，将被应用到扩展多孔岩石中的热敏脆性变形过程的理解。这些数据将提供信息以支持涉及周期性TM应力变化的现场规模操作条件，并揭示潜在的级联浅层地质灾害。目标：O 1：检查微结构变形和TM应力之间的关系。O2：收集、分析TM数据并一起建模。O3：将现有DEM代码从UDEC转移并改进为开源代码，并进行粒度敏感性分析。提供相关数据，以告知地下地质储存条件下与TM应力相关的风险。将在不同规模（从颗粒到样品规模）进行实验室实验。将在HWU对岩心样品进行X射线扫描，以了解其内部3D微观结构，并在TM实验前后（O 1）评估其输运特性（孔隙度和渗透率）。TM实验将在BGS进行，以模拟升高的环境条件。将研究几种变形方案，以涵盖不同的工业现场操作。该实验室规模的全球传感数据将结合岩相分析（O2），以将实验室引起的损伤在受试材料中的时空分布与微观结构演变相关联。此外，类似的晶粒尺度实验与同步变形监测（X射线）可能计划解开微观尺度的过程。数值模拟和机器学习技术现在更频繁地用于预测地下水的行为。TM耦合校准的Voronoi晶粒基模型（GBM）可以捕获微裂纹作为渐进损伤的机制，再现实验室试验的应力-应变行为。开发这样的模型将有助于了解TM脆性损伤如何跨尺度发展，从粒度开裂到岩体破裂，以及它的时间依赖性。该博士项目将建立在Woodman等人（2021）开发的DEM基础上，特别是在热机械模拟（O3）中进行粒度和分布敏感性分析，以进一步评估实验室数据到现场规模（O 4）的放大。

On our quest to decarbonise our energy resources, underground heat energy storage is a key player. However, the impact of frequent cyclic thermo-mechanical (TM) stress changes over prolonged periods remains poorly understood and may threatened the longevity of the systems. This project aims to fill this gap by performing laboratory and numerical experiments under relevant cyclic TM loading conditions to investigate the stability of targeted lithologies in such systems. To address the energy transition challenges, new subsurface solutions focus either on new resources exploitation (geothermal) or storage (radioactive waste, heat and/or gas -underground gas storage, compressed-air energy storage, H, CO2). All these applications have in common to induce new, shallow, periodic, local thermo-mechanical stress changes. The scope of this PhD project is to use different-scale observations to model and predict the stability of targeted lithologies in underground complex systems when those are subjected to cyclic TM stress changes over prolonged periods. This research work seeks to understand how grain-scale deformation can contribute to the global response of the underground systems and how this response can be controlled to reduce any accompanied induced hazards. An innovative methodology, combining laboratory and numerical experiments, will be applied to extend the understanding of the thermo-sensitive brittle deformation processes in porous rocks. The data will provide information to support field-scale operational conditions involving periodic TM stress changes as well as shed light on potential cascading shallow geohazards.Objectives:O1: Examine the relationship between microstructural deformation and TM stresses.O2: Collect, analyse and model TM data together.O3: Transfer and improve existing DEM code from UDEC to open source and undertake grain size sensitivity analysis.O4: Provide relevant data to inform on risks associated to TM stresses in underground geological storage conditions.Laboratory experiments will be performed at different scales (from grain to sample scales). Core samples will be x-ray scanned at HWU to understand their internal 3D microstructure and assess their transport properties (porosity and permeability) at pre- and post-TM experiments (O1). TM experiments will be performed at BGS to simulate elevated environmental conditions. Several deformation scenarios will be investigated to cover different industrial field operations. This lab-scale global sensing data will be combined to petrographical analysis (O2) to correlate the spatiotemporal distribution of the lab-induced damage within the tested materials with the microstructural evolution. Moreover, similar grain-scale experiments with syn-deformation monitoring (x-rays) are possibly planned to unravel the micro-scale processes. Numerical modelling and machine learning techniques are nowadays used more frequently to predict the subsurfacesystem behavior. TM coupling calibrated Voronoi Grain Based Modelling (GBM) can capture micro-cracking as a mechanism of progressive damage, reproducing the stress-strain behavior of laboratory tests. Developing such models will help to understand how TM brittle damage develops across the scales, from grain-size cracking to rock mass fracturing, and its time dependency. This PhD project will build on the DEM developed by Woodman et al. (2021) to undertake notably a grain size and distribution sensitivity analyses in thermo-mechanical simulations (O3) to further assess the up scaling of laboratory data to field scale (O4).