NSF Award
2436996
The geological sequestration of CO2 is considered one of the most effective strategies to mitigate the adverse effects of climate change due to the CO2 emitted from significant stationary anthropogenic sources. However, successful implementation of large-scale CO2 sequestration involves operational uncertainties and risks due to the complex flow behavior of CO2 in subsurface conditions. This proposed research will develop computationally efficient data-driven models to accurately predict and analyze the complex flow behavior of CO2 flow in geological formations by analyzing an extensive set of data collected from experimental and numerical studies. This project connects the fundamental topics covered in core chemical engineering courses at University of Texas (UT) Tyler to hypothesis-driven research, which promotes maximum participation of students equipped with the tools needed to make meaningful contributions to this work. Furthermore, this project involves outreach activities to recruit and attract students from under-represented populations, in collaboration with UT Tyler University Academy and Tyler Junior College, to the critical areas of climate change and sustainable energy production. <br/><br/>The proposed research activities are organized into three focus areas, the results from which will have a significant influence on the modeling of the diffusive, reactive, and convective transport of CO2 in porous media saturated with brine and consisting of geological uncertainties: (1) Quantify and predict the dissolution and precipitation of minerals such as calcite at different temperatures, pressures, and initial cation concentrations due to the interaction with CO2 using experimental data; (2) Evaluate the effect of porosity and permeability on the flow behavior of CO2 using high-resolution images of core samples collected from public data portals; (3) Visualize, and quantify the effect of discrete fractures and uncertain reservoir properties on channeling and plume migration of CO2 in porous media using images and data from public repositories. The generated or collected data will be analyzed in each case using robust statistical models to achieve the project objectives, which will lead to potentially transformative technologies to improve long-term carbon storage in deep saline aquifers. Finally, this research project will increase public awareness and enhance scientific literacy around energy use and CO2 emissions for a diverse audience through public lectures, hands-on demonstrations, and other outreach programs.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
