Natural gas hydrate has emerged in recent decades as a promising alternative energy resource, attracting worldwide attention in the petroleum and natural gas industry. Hydrate-based technologies have demonstrated considerable potential in engineering applications including natural gas exploitation, transportation, gas sequestration, and seawater desalination. However, the industrial application of these technologies is severely constrained by an insufficient understanding of hydrate formation kinetics, especially in complex multiphase systems where gas, liquid, and solid phases coexist.

The new work is built upon published experimental data and aims to characterize the driving force for hydrate growth by accounting for temperature and pressure. On this basis, the authors propose a kinetic model for hydrate growth driven by chemical potential. The model also incorporates the factors that govern how quickly material reaches a growing crystal: diffusion, temperature, and the hydrate surface area available for growth.

To verify the formulation, the team compared the model's calculated growth rates against experimental data reported for hydrate growth in both oil–water suspensions and gas–water systems. The results indicate that the model can effectively predict hydrate growth rates in both settings with high reliability, according to the authors. The verification numbers are specific.

For hydrate growth in oil–water dispersed emulsion systems, the deviation between the model's calculations and the experimental data ranges from 2.7 percent to 7.6 percent. For gas–water systems, the calculated deviation is 2.35 percent to 2.89 percent. These deviations are reported by the authors as evidence of the model's accuracy across the two distinct multiphase environments.

The authors position the work as both an interpretive and an engineering contribution. On the interpretive side, expressing growth in terms of the chemical potential driving force provides a reference for interpreting hydrate growth mechanisms. On the engineering side, the model is intended to offer technical support for the optimization of hydrate-based engineering applications in the petroleum and natural gas industry, where formation kinetics have been a limiting uncertainty.

The scope of the study is defined by the data it draws on. Because the model is built and verified against published experimental measurements rather than new laboratory tests, its reliability reflects the quality and range of the underlying data, and the reported deviations apply to the oil–water emulsion and gas–water conditions for which verification was performed. The authors do not report validation beyond these system types in the present work.

Within that scope, the reported accuracy in two physically distinct systems—oil–water dispersed emulsions and gas–water contacts—suggests that the chemical-potential framework captures the relevant growth physics across both settings. For a field in which hydrate formation kinetics has been identified as insufficiently understood, a model that predicts growth rates to within a few percent in both environments offers a step toward more dependable hydrate-based engineering practice, from exploitation and transportation to sequestration and desalination applications.

Subject of Research: A chemical-potential-based kinetic model for natural gas hydrate growth kinetics

Article Title: A kinetic model for the growth of natural gas hydrates based on chemical potential driving force

Article References: Liang, H., Jiang, J., & He, J. (2026). A kinetic model for the growth of natural gas hydrates based on chemical potential driving force. Frontiers in Chemical Engineering, 8, Article 1934897. https://doi.org/10.3389/fceng.2026.1934897

Image Credits: AI Generated

DOI: 10.3389/fceng.2026.1934897

Keywords: natural gas hydrates, chemical potential, kinetic model, hydrate growth rate, gas–water systems, oil–water emulsions, multiphase systems, petroleum industry, gas sequestration, seawater desalination, thermodynamics, Frontiers in Chemical Engineering