Publications
- Combined Approach to Evaluate Hydrate Slurry Transport Properties through Wetting and Flow Experiments
- Current overview and way forward for the use of machine learning in the field of petroleum gas hydrates
- Using machine learning-based variable selection to identify hydrate related components from FT-ICR MS spectra
- Utilization of machine learning on FT-ICR MS spectra for improved understanding and prediction of theproperties of hydrate-active components
- A new high pressure method for successive accumulation of hydrate active components
- Developing machine learning models for identifying chemical components from wide and short FT-ICR mass spectrometry data
- Identifying components related to hydrate formation by machine learning-based variable selection
- Towards a machine learning based produced for interpretation of mass spectra for better understanding of hydrate phenomena in oil systems
- Successive accumulation of naturally occurring hydrate active components and the effect on the wetting properties
- Machine learning as a basis for better understanding of flow assurance through FT-ICR-MS analysis of gas hydrates