(92g) Massively Parallel Simulation of Field-Scale Oceanic Gas Hydrate Deposits | AIChE

(92g) Massively Parallel Simulation of Field-Scale Oceanic Gas Hydrate Deposits

Authors 

Reagan, M. T. - Presenter, Lawrence Berkeley National Laboratory
Moridis, G. J. - Presenter, Lawrence Berkeley National Laboratory
Johnson, J. N. - Presenter, Lawrence Berkeley National Laboratory
Pan, L. - Presenter, Lawrence Berkeley National Laboratory
Boyle, K. L. - Presenter, Lawrence Berkeley National Laboratory
Freeman, C. M. - Presenter, Lawrence Berkeley National Laboratory


The quantity of hydrocarbon gases trapped in natural hydrate accumulations is enormous, leading to significant interest in the evaluation of their potential as an energy source. It has been shown that large volumes of gas can be readily produced at high rates for long times from some types of methane hydrate accumulations by means of depressurization-induced dissociation, and using conventional technologies with horizontal or vertical well configurations. However, these systems are currently assessed using simplified or reduced-scale 3D or even 2D production simulations. In this study, we use the massively parallel TOUGH+HYDRATE code (pT+H) to assess the production potential of a large, deep-ocean hydrate reservoir and develop strategies for effective production. The simulations model a full 3D system of over 24 km2 extent, examining the productivity of vertical and horizontal wells, single or multiple wells, and variations in reservoir properties. Systems of up to 2.7M gridblocks, running on hundreds of supercomputing nodes, are required to simulate such large systems at the highest level of detail. The simulations reveal the challenges inherent in producing from deep, relatively cold systems with extensive water-bearing channels and connectivity to large aquifers. Also highlighted are new frontiers in large-scale reservoir simulation of coupled flow, transport, thermodynamics, and phase behavior, including mesh construction, parallel numerical solvers, and large-scale visualization.