CBET-EPSRC: Enhancing the CSMHyK fluid dynamics calculations via the inclusion of a stochastic model of hydrate nucleation, agglomeration and growth

Colorado School Of Mines, Golden CO

Investigators

Abstract

The abundance of natural gas hydrate deposits across the world could provide abundant energy resources for the future. Natural gas hydrates are solid structures formed by water and gas that can block oil & gas pipelines, which can lead to pipeline ruptures, causing spills and environmental disasters, production interruptions, and even loss of life. The ability to predict gas hydrate formation and fluid dynamics based on molecular-level models has the potential to revolutionize the strategies used for gas hydrate control in oil and gas pipelines, as well as other gas hydrate energy applications. The predictive capability of gas hydrate fluid dynamics models is currently lacking due to the inability to fundamentally model the timescales for hydrate formation. The principle aim of the project is to develop a kinetic molecular model to predict gas hydrate formation times based on a synergistic experimental and molecular modeling campaign, leading to the ability to predict quantitatively gas hydrate formation in pipelines. The project is also focused on inclusion of under-represented minority students in STEM disciplines, via outreach activities that target students at all stages of development, industry engagement, and state-of-the-art web-based materials. The goal of the project is to quantify the rate-limiting mechanisms in gas hydrate formation to provide a predictive fluid dynamics model for gas hydrate formation. Identification of which mechanisms of hydrate formation (i.e. nucleation, growth, agglomeration, adhesion) is rate-limiting under different scenarios remains unknown, and yet must be overcome in order to have a predictive fluid dynamics model. This knowledge gap will be filled by the four main aims of the project: (1) obtain free energy barriers representative of gas hydrate growth, agglomeration and adhesion; (2) assess the reliability of (1) with in-situ micromechanical force and film growth measurements; (3) develop a computationally efficient stochastic kinetic Monte Carlo model that incorporates information from (1) and (2); (4) incorporate the outcomes from (3) into the transient fluid dynamics model and validate this predictive stochastic model against flow-loop experiments. This approach will provide a potential game-changing improvement to the fluid dynamics simulation model and molecular-based predictions of hydrate formation in pipelines. This research was funded under the NSF Engineering – UKRI Engineering and Physical Sciences Research Council opportunity NSF 20-510.” Co-PIs included Alberto Striolo and Michail Stamatakis at the University College of London. 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.