In the more recent EURO-CORDEX RCM ensemble 27 driven by ERA-Interim reanalysis, a standard evaluation over Europe shows a wide range of precipitation biases across the different members. This behaviour has been documented over the course of multi-model ensemble projects, such as PRUDENCE 23, 24, ENSEMBLES 25, or NARCCAP 26.
Mme effect water drivers#
For example, RCMs have been used as drivers for hydrological models assessing projected future evolution of river discharge 10, 11, 12, to assess water budgets in catchment hydrology 13, 14, as input to groundwater models 15, 16, 17, in hydropower modelling 18, 19, 20, or to assess water resources 21, 22.ĭespite continuous progress over the years 4, RCMs often show precipitation biases over land areas, that significantly affect the water cycle (Fig. Over the years, many water cycle-related questions have been addressed with RCMs. In addition, RCMs are used to answer a variety of research questions 4, and generate climate change projections, e.g., as part of large multi model ensembles experiments, that form the basis for vulnerability, impact and adaptation studies 7, 8 the latest of which is the ongoing COordinated Regional Downscaling EXperiment (CORDEX) 9. In dynamical downscaling setups, RCMs with their higher spatial resolution can represent small-scale surface heterogeneities due to orography, coastlines, lakes, or land cover as well as mesoscale dynamical processes or extreme events in more detail than global climate models (GCMs) 6. 1, 2, 3, RCMs have undergone many advancements towards Earth system modelling in climate research, as summarized by Refs. The results also suggest that extremely small grid blocks in the lateral direction may not be required to realistically assess the improvement in recovery with continued gas enrichment if the mixing from the viscous crossflow caused by alternate water and gas (WAG) cycles dominates the numerical mixing caused by moderate-size grid blocks.Since the first regional climate model (RCM) simulations by Refs. The results of this study suggest that recovery may continue to improve significantly with enrichment above the MME and that this effect should not be overlooked when optimizing the enrichment of an enriched gas-drive flood. It also attempts to provide insight into the error large grid blocks may cause when gas enrichment is studied with reservoir models. It does this in simplified reservoir-scale cross sections by 1) minimizing numerical dispersion relative to the input physical dispersion by using two-point upstream weighting and a large number of grid blocks, and 2) by approximating longitudinal physical dispersion with numerical dispersion in single-point weighting simulations by making the grid blocks small enough to mimic the physical dispersivity that might prevail in a reservoir. This paper investigates the effect of realistic levels of physical dispersion on any increase in recovery resulting from continued enrichment of hydrocarbon gas above the MME. Unfortunately, a concern with commercial simulators is that numerical mixing or dispersion, which is artificial and non-physical, may dwarf the mixing from physical mechanisms and cause an unrealistic prediction of the effect of continued gas enrichment on additional recovery even if physical mechanisms such as molecular diffusion and dispersion are accounted for in the formulation of the simulator. Richer gases may mix less deeply with oil into the multiphase region leaving less miscible flood residual oil behind.Īll of these mechanisms are influenced by the physical gas-oil mixing that occurs in a reservoir flood. The gas becomes more viscous and more dense from mixing with oil, which improves sweepout,Ī smaller lean-gas bank develops, which also may improve sweepout, and However, there are physical reasons why oil recovery might continue to improve with increasing gas enrichment above the MME in a reservoir flood: Slimtube experiments often show that oil recovery increases sharply up to the minimum miscibility enrichment (MME) and thereafter increases very little with further enrichment. Gas enrichment can be an important optimization variable in hydrocarbon enriched gas drive floods.