Numerical Investigations of Geologic CO2 Sequestration Using Physics-Based and Machine Learning Modeling Strategies

• Main Author: Wu, Hao
• Other Authors: Geosciences
• Format: Others
• Published: Virginia Tech 2020
• Subjects: CO2 sequestration; capillary pressure; relative permeability; reactive transport; machine learning
• Online Access: http://hdl.handle.net/10919/99604

Carbon capture and sequestration (CCS) is an engineering-based approach for mitigating excess anthropogenic CO2 emissions. Deep brine aquifers and basalt reservoirs have shown outstanding performance in CO2 storage based on their global widespread distribution and large storage capacity. Capillary trapping and mineral trapping are the two dominant mechanisms controlling the distribution, migration, and transportation of CO2 in deep brine aquifers and basalt reservoirs. Understanding the behavior of CO2 in a storage reservoir under realistic conditions is important for risk management and storage efficiency improvement. As a result, numerical simulations have been implemented to understand the relationship between fluid properties and multi-phase fluid dynamics. However, the physics-based simulations that focus on the uncertainties of fluid flow dynamics are complicated and computationally expensive. Machine learning method provides immense potential for improving computational efficiency for subsurface simulations, particularly in the context of parametric sensitivity. This work focuses on parametric uncertainty associated with multi-phase fluid dynamics that govern geologic CO2 storage. The effects of this uncertainty are interrogated through ensemble simulation methods that implement both physics-based and machine learning modeling strategies. This dissertation is a culmination of three projects: (1) a parametric analysis of capillary pressure variability effects on CO2 migration, (2) a reactive transport simulation in a basalt fracture system investigating the effects of carbon mineralization on CO2 migration, and (3) a parametric analysis based on machine learning methods of simultaneous effects of capillary pressure and relative permeability on CO2 migration. === Doctor of Philosophy === Carbon capture and sequestration (CCS) has been proposed as a technological approach to mitigate the deleterious effects of anthropogenic CO2 emissions. During CCS, CO2 is captured from power plants and then pumped in deep geologic reservoirs to isolate it from the atmosphere. Deep sedimentary formations and fractured basalt reservoirs are two options for CO2 storage. In sedimentary systems, CO2 is immobilized largely by physical processes, such as capillary and solubility trapping, while in basalt reservoirs, CO2 is transformed into carbonate minerals, thus rendering it fully immobilized. This research focuses on how a large range of capillary pressure variabilities and how CO2-basalt reactions affect CO2 migration. Specifically, the work presented utilizes numerical simulation and machine learning methods to study the relationship between capillary trapping and buoyancy in a sandstone formation, as well as the combined effects of capillary pressure and relative permeability on CO2 migration. In addition, the work also identifies a new reinforcing feedback between mineralization and relative permeability during reactive CO2 flow in a basalt fracture network. In aggregate, the whole of this work presents a new, multi-dimensional perspective on the multi-phase fluid dynamics that govern CCS efficacy in a range of geologic formations.