Speaker
Description
Underground natural gas storage (UNGS) is a means to store energy temporarily for later recovery and use. In such storage operations, carbon dioxide (CO$_2$) can be injected as cushion gas to improve the operating efficiency of the working gas and then be permanently stored in the same reservoir. A potential obstacle for widespread use of this technology is that the mixing of the different gases can lead to undesired CO$_2$ production. Herein, we use a two-component flow model to simulate injection and withdrawal periods of methane (CH$_4$) in idealized reservoirs containing CO$_2$. First, we simulate cases with a single well for both CH$_4$ injection and production. From $1200$ simulations with systematic variation of reservoir temperature, porosity, permeability, height, and injection time, we find that the reservoir height and permeability have the most significant impact on the production time until the well stream reaches $1\%$ mole fraction of CO$_2$. In another set of simulations, we investigate the impact of well spacing in seasonal gas storage scenarios with separate wells for CH$_4$ injection and production, while CO$_2$ injection occurs from a third well. Based on the simulated data we construct artificial neural networks (ANNs) that describe the relations between the varied input parameters and the production time of CH$_4$, well-block mole fraction and pressure. We conclude that trained and validated ANN models are useful tools to optimize important parameters for UNGS operations, including well positioning, with the aim at reaching higher CH$_4$ production time and hence larger amounts of delivered gas.
| Conference Topic Areas | Track9: Smart Energy Storage, Integration and Utilization |
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