Projects|Department of Ocean Technology,Policy, and Environment Graduate School,The University of Tokyo

Department of Ocean Technology, Policy, and Environment, Graduate School, The University of Tokyo
JA
The University of Tokyo
Projects

Projects

Japan’s Methane Hydrate R&D Program

Methane hydrate is a solid crystal which consists of water and methane, and it is an important potential source of natural gas. Because methane hydrate is stable at low temperatures and high pressures, to extract the gas the temperature must be increased or the pressure must be decreased. To make this procedure commercially viable, it is necessary to predict its productivity, thus an accurate simulation tool is required. The permeability of gas and water in hydrate bearing sediments is important for this purpose. Equations for modeling the absolute permeability change were proposed as a function of hydrate saturation. Laboratory experiments revealed that hydrate saturation cannot solely determine permeability reduction caused by the hydrate existence. This is due to the hydrate distribution, which describes the shape of the hydrate in the pore spaces of the sand grains.

It is assumed that the initial location of the water determines the hydrate distribution in the sediment. One says that hydrates do not form bridging or floating distributions, but that the initial hydrate nucleation in the pores may take place on the surface of the sand grains and the hydrate grows outwards into the pore space. However, it seems that the mechanism of hydrate distribution is still not clear. In this study, we propose a numerical model for estimating the distribution of methane hydrate in porous media from the physical properties of the sediment. The formation of the methane hydrate is numerically simulated in a microscale computational domain, using classical nucleation theory and the phase-field model.

In addition, we develop enhanced gas recovery for methane hydrate reservoirs. To determine a promising gas production method, gas production behavior is numerically predicted in a reservoir scale using reservoir models reflecting real petrophysical properties.



Online Briefing Session
Access
For current students only
(In preparation)