Postdoctoral Fellow

The petitioner proposes to leverage artificial intelligence and machine learning to enhance the analysis and interpretation of formation testing data in carbon capture and storage (CCS) projects. This work involves evaluating reservoir properties and fluid characteristics to ensure accurate assessment of CO2 storage capabilities and injection safety to optimize data processing and operational performance within the clean energy sector.

The petitioner proposes to optimize carbon dioxide (CO2) gas injection methods to reduce oil viscosity and improve oil recovery efficiency in heavy oil reservoirs. This work involves applying advanced simulation and CO2-based methods to support reservoir optimization and extended production performance. The goal is to develop novel approaches for enhanced oil recovery (EOR) to extend the life of existing reserves.

The petitioner proposes to develop machine-learning-based methodologies to accelerate diverse workflows supporting geological characterization, carbon capture and utilization, risk management, and reservoir management. Her work focuses on using ML to improve various workflows within the energy and data science sectors.

The petitioner proposes to employ advanced numerical simulation models and engineering workflows to optimize enhanced oil recovery (EOR) processes using thermal and solvent methods. This research focuses on maximizing domestic oil production from existing reservoirs while reducing greenhouse gas emissions through methods like CO2-EOR. The work aims to meet increasing energy demands through sustainable and environmentally responsible oil production practices.