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Numerical Simulation of Hydrate Formation in the LArge-scale Reservoir Simulator (LARS)

Urheber*innen
/persons/resource/zli

Li,  Zhen
3.4 Fluid Systems Modelling, 3.0 Geochemistry, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

/persons/resource/erik

Spangenberg,  Erik
4.8 Geoenergy, 4.0 Geosystems, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

/persons/resource/schick

Schicks,  J
3.1 Inorganic and Isotope Geochemistry, 3.0 Geochemistry, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

/persons/resource/kempka

Kempka,  T.
3.4 Fluid Systems Modelling, 3.0 Geochemistry, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

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5010429.pdf
(Verlagsversion), 5MB

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Zitation

Li, Z., Spangenberg, E., Schicks, J., Kempka, T. (2022): Numerical Simulation of Hydrate Formation in the LArge-scale Reservoir Simulator (LARS). - Energies, 15, 6, 1974.
https://doi.org/10.3390/en15061974


Zitierlink: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5010429
Zusammenfassung
The LArge-scale Reservoir Simulator (LARS) has been previously developed to study hydrate dissociation in hydrate-bearing systems under in-situ conditions. In the present study, a numerical framework of equations of state describing hydrate formation at equilibrium conditions has been elaborated and integrated with a numerical flow and transport simulator to investigate a multi-stage hydrate formation experiment undertaken in LARS. A verification of the implemented modeling framework has been carried out by benchmarking it against another established numerical code. Three-dimensional (3D) model calibration has been performed based on laboratory data available from temperature sensors, fluid sampling, and electrical resistivity tomography. The simulation results demonstrate that temperature profiles, spatial hydrate distribution, and bulk hydrate saturation are consistent with the observations. Furthermore, our numerical framework can be applied to calibrate geophysical measurements, optimize post-processing workflows for monitoring data, improve the design of hydrate formation experiments, and investigate the temporal evolution of sub-permafrost methane hydrate reservoirs.