Underground Storage Engineering Research Center (USE)
Research
Machine learning
Machine learning is rapidly revolutionizing the field of subsurface storage, allowing for more efficient and accurate prediction and optimization of storage systems. By analyzing large amounts of data generated from subsurface storage operations and simulations, machine learning algorithms can identify patterns and relationships that are not immediately apparent to human analysts. This information can then be used to improve the characterization and operation of subsurface storage systems, resulting in more efficient storage, increased storage capacity, and improved safety. Machine learning can also be used to analyze and predict the behavior of subsurface fluids, allowing for a better understanding of the movement and interaction of various fluids with rocks in storage formations. The application of machine learning to underground storage has the potential to greatly improve the efficiency and effectiveness of these important systems.
Monitoring, management and risk modeling
Subsurface storage systems are an increasingly important technology for the safe and efficient storage of substances such as natural gas, carbon dioxide, and hydrogen. However, these systems also carry certain risks, such as leakage, migration, and potential impacts on groundwater quality. To minimize these risks, monitoring, and management of subsurface storage systems are critical. This includes ongoing monitoring of pressure and flow rates and real-time analysis of potential leaks. Risk modeling is also essential, to ensure that potential risks are identified and managed proactively. This involves using advanced modeling tools to simulate potential scenarios and evaluate the likelihood of various outcomes. By combining effective monitoring, management, and risk modeling, subsurface storage systems can be operated safely and effectively, while minimizing the potential impacts on the environment and public health.
Subsurface characterization
Subsurface characterization is critical for subsurface storage systems because it provides important information about the geology and physical properties of the subsurface rock formations that will be used to store materials. By characterizing the subsurface, engineers, and scientists can identify potential risks and develop strategies to mitigate them. Subsurface characterization can help identify potential pathways for leakage or migration of stored fluids, which can inform decisions about where to place monitoring wells and injection wells. Characterization can also help identify potential geohazards, such as faults or fractures, that could impact the integrity of the storage system. Additionally, subsurface characterization can inform decisions about the design and operation of the storage system, such as determining the optimal injection pressure and rate.
Fluid Flow in Porous Media
Fluid flow in porous media is a fundamental process that plays a critical role in all subsurface problems. Porous media, such as rock formations, contain interconnected pores and fractures that allow fluids to flow through them. Understanding how fluids flow through these media is essential for predicting the behavior of subsurface storage systems, including the injection, storage, and extraction of fluids. Knowledge of fluid flow can allow us to quantify how fluids interact with each other and how they flow in complex geometries. We cannot accurately characterize the behaviors of subsurface systems without monitoring fluid flow in such systems. This part of our research is fully connected to all other sections.
Rock-Fluid Interactions
Fluid-rock interactions are a critical aspect of underground storage systems, where fluids are stored within subsurface formations. These interactions occur as the stored fluid comes into contact with the surrounding rock/fluid, leading to changes in the physical and chemical properties of both the fluid and the rock. The stored fluid can cause changes in the rock’s porosity and permeability, which can affect fluid flow and storage capacity. Additionally, chemical reactions between the fluid and the rock can alter the fluid’s composition, potentially leading to changes in its properties such as viscosity and reactivity. Understanding fluid-rock interactions is essential for the long-term safe and effective operation of underground storage systems. It enables engineers and scientists to anticipate and mitigate any adverse effects on the system, such as changes in fluid quality or the potential for well clogging.
Geomechanics
Geomechanics is the study of the mechanical behavior of rocks, including their response to stresses and strains. In underground storage systems, geomechanical considerations include the stability of the rock formation, the potential for subsidence, faulting, or fracturing, and the potential impact of fluid injection or extraction on the rock’s mechanical properties. Geomechanical modeling is crucial for predicting the behavior of the system over time and ensuring its long-term safety and effectiveness. By accurately modeling the geomechanical response to fluid injection or extraction, we can identify potential risks and develop strategies to mitigate them. Understanding the potential for subsidence, fault reactivating, and fracturing can inform decisions about where to place injection wells and how to control injection rates.