In Silico Analysis of the Effect of Alkyl Tail Length on Antiagglomerant Adsorption to Natural Gas Hydrates in Brine Hadi Mehrabian, † Matthew R. Walsh, ‡ and Bernhardt L. Trout* ,† † Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States ‡ Chevron Energy Technology Company, Flow Assurance, Houston, Texas 77002, United States ABSTRACT: Antiagglomerant (AA) gas hydrate inhibitors can prevent blockages in oil and gas flowlines and could help enable safe and profitable production from deepwater environments. AAs, which are generally surfactants, often have an ionic head and one or more alkyl tails. Here, we use molecular dynamics simulations to investigate the mechanistic and energetic effects of alkyl tail length on adsorption to an sII methane/propane hydrate for well-known AA molecules (n-alkyl-tri(n-butyl)ammonium salt surfactants) of varying tail length. We consider alkyl tails from 8 to 16 carbon atoms and show that the dodecyl (12- carbon) tail has the highest percentage of the strongest binding configuration (i.e., the simultaneous head and tail binding configuration) and the lowest free energy of binding among the five molecules investigated. This maximum in strongest-configuration binding statistics and minimum in binding free energy at a tail length of 12 carbons may result from the appropriate size of the AA molecule with the dodecyl tail for spanning the most abundant binding sites on the hydrate surface. Furthermore, competition between the enthalpic gain of removing the hydrophobic tail from the aqueous solution and the entropic penalty of binding gives an optimum binding free energy for the dodecyl tail. Notably, experimental work on the same quaternary ammonium cations of varying tail length shows a maximum in performance for the dodecyl tail. This suggests that free energy of binding and more computationally efficient measures such as head-plus-tail binding probability are possible metrics for using molecular simulation to predict experimental and field performance of surfactant-type AAs, a capability which could accelerate the chemical innovation process. 1. INTRODUCTION Natural gas hydrates are icelike solids composed of hydrogen- bonded water cages, which trap light hydrocarbons such as methane, ethane, propane, and other natural gas components, including CO 2 and H 2 S. 1 Hydrocarbon flowlines in subsea environments can provide suitable pressure and temperature conditions for gas hydrate formation, and the safe and economical prevention of solid hydrate blockages remains the most significant flow assurance challenge for the oil and gas industry. Different chemical solutions have been used to prevent the plugging of flowlines due to hydrate formation, generally grouped into two categories: thermodynamic inhibitors, which shift the thermodynamic phase boundary of gas hydrates to prevent their formation, and the so-called low- dosage hydrate inhibitors, which rather than changing system thermodynamics can change the system’s nucleation and growth kinetics, morphology, and interactions to prevent hydrate plugging. The low-dosage hydrate inhibitors have two subcategories: kinetic inhibitors, which delay hydrate nuclea- tion and can slow hydrate growth, and antiagglomerants (AAs), which facilitate hydrate slurry transport by preventing hydrate particles from agglomerating into large blockages. 2 Antiagglomerants, which could be an enabling technology for major deepwater projects, are the focus of this study. Many existing AA molecules are quaternary ammonium salt surfactants, molecules about which much performance-based data exist, some of which is in the form of published literature. 3 Using molecular simulation to add mechanistic and energetic understanding about AA performance and AA-hydrate interaction to the existing bank of knowledge about quaternary ammonium-based inhibitors could allow for improved under- standing of why some inhibitors are more effective than others, as well as the identification of simulation-derived metrics, which are predictive of experimental and field performance. Such capabilities could reduce trial and error in the chemical innovation process and lead to more effective and possibly cheaper chemistries. Despite their common application, there is no consensus on the mechanism of AA’s action, i.e., how AA molecules prevent hydrate agglomeration, 4 and while there have been some comparisons to general experimental trends in performance, 5-9 a specific quantitative simulation-derived metric that can predict experimental/field performance (and benchmarked against a series of AAs of known performance) has remained elusive. Such a metric could streamline the innovation process by reducing costly experimental trial and error, and by virtue of being derived from molecular simulation data, would be complemented of course by detailed mechanistic information, Received: February 28, 2019 Revised: June 24, 2019 Published: June 24, 2019 Article pubs.acs.org/JPCC Cite This: J. Phys. Chem. C XXXX, XXX, XXX-XXX © XXXX American Chemical Society A DOI: 10.1021/acs.jpcc.9b01952 J. Phys. Chem. C XXXX, XXX, XXX-XXX Downloaded by UNIV OF TOLEDO at 18:31:17:867 on July 07, 2019 from https://pubs.acs.org/doi/10.1021/acs.jpcc.9b01952.