Projects
Publications
- Crude Oil Density Prediction Improved by Multiblock Analysis of Fourier Transform Ion Cyclotron Resonance Mass Spectrometry, Fourier Transform Infrared, and Near-Infrared Spectroscopy Data
- 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 Read publication
- Using machine learning-based variable selection to identify hydrate related components from FT-ICR MS spectra Read publication
- Phase Transitions and Separation Time Scales of CO2-Crude Oil Fluid Systems: Wheel Flow Loop Experiments and Modelling Read publication
- Study on how oil type and weathering of crude oils affect interaction with sea ice and polyethylene skimmer material Read publication
- Oil-water dispersion formation, development and stability studied in a wheel-shaped flow loop
- Experimental Study of the Relative Effect of Pressure Drop and Flow Rate on the Droplet Size Downstream a Pipe Restriction
- MultiFlow JIP – Campaign 2 3-phase Gas Dominated Flows in 8-inch Diameter Pipe
- Dense Packed Layer Modeling in Oil-Water Dispersions: Model Description, Experimental Verification, and Code Demonstration
Other
- Exploring the possibilities of a regression model for the prediction of wetting index from crude oils
- 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
- SINTEF Webinar "Bridging the gap between the lab and the field- Session2: Hydrate Management"
- Evaluation of Gas Hydrates Operation Zone to Establish an Optimal Hydrate Management Strategy
