<p>Natural gas hydrates are considered as one of the most promising alternatives to conventional fossil energy sources, and are thus subject to world-wide research activities for decades. Hydrate formation from methane dissolved in brine is a geogenic process, resulting in the accumulation of gas hydrates in sedimentary formations below the seabed or overlain by permafrost. The LArge scale Reservoir Simulator (LARS) has been developed (Schicks et al., 2011, 2013; Spangenberg et al., 2015) to investigate the formation and dissociation of gas hydrates under simulated in-situ conditions of hydrate deposits. Experimental measurements of the temperatures and bulk saturation of methane hydrates by electrical resistivity tomography have been used to determine the key parameters, describing and characterising methane hydrate formation dynamics in LARS. In the present study, a framework of equations of state to simulate equilibrium methane hydrate formation in LARS has been developed and coupled with the TRANsport Simulation Environment (Kempka, 2020) to study the dynamics of methane hydrate formation and quantify changes in the porous medium properties in LARS. We present our model implementation, its validation against TOUGH-HYDRATE (Gamwo & Liu, 2010) and the findings of the model comparison against the hydrate formation experiments undertaken by Priegnitz et al. (2015). The latter demonstrates that our numerical model implementation is capable of reproducing the main processes of hydrate formation in LARS, and thus may be applied for experiment design as well as to investigate the process of hydrate formation at specific geological settings.</p><p>Key words: dissolved methane; hydrate formation; hydration; python; permeability.</p><p>References</p><p>Schicks, J. M., Spangenberg, E., Giese, R., Steinhauer, B., Klump, J., & Luzi, M. (2011). New approaches for the production of hydrocarbons from hydrate bearing sediments. Energies, 4(1), 151-172, https://doi.org/10.3390/en4010151</p><p>Schicks, J. M., Spangenberg, E., Giese, R., Luzi-Helbing, M., Priegnitz, M., & Beeskow-Strauch, B. (2013). A counter-current heat-exchange reactor for the thermal stimulation of hydrate-bearing sediments. Energies, 6(6), 3002-3016, https://doi.org/10.3390/en6063002</p><p>Spangenberg, E., Priegnitz, M., Heeschen, K., & Schicks, J. M. (2015). Are laboratory-formed hydrate-bearing systems analogous to those in nature?. Journal of Chemical & Engineering Data, 60(2), 258-268, https://doi.org/10.1021/je5005609</p><p>Kempka, T. (2020) Verification of a Python-based TRANsport Simulation Environment for density-driven fluid flow and coupled transport of heat and chemical species. Adv. Geosci., 54, 67&#8211;77, https://doi.org/10.5194/adgeo-54-67-2020</p><p>Gamwo, I. K., & Liu, Y. (2010). Mathematical modeling and numerical simulation of methane production in a hydrate reservoir. Industrial & Engineering Chemistry Research, 49(11), 5231-5245, https://doi.org/10.1021/ie901452v</p><p>Priegnitz, M., Thaler, J., Spangenberg, E., Schicks, J. M., Schr&#246;tter, J., & Abendroth, S. (2015). Characterizing electrical properties and permeability changes of hydrate bearing sediments using ERT data. Geophysical Journal International, 202(3), 1599-1612, https://doi.org/10.1093/gji/ggv245</p>
An Efficient Simulation Environment for Modeling Large-Scale Cortical Processing
