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We propose a methodology, called multilevel local–global (MLLG) upscaling, for generating accurate upscaled models of permeabilities
or transmissibilities for flow simulation on adapted grids in heterogeneous subsurface formations. The method generates an
initial adapted grid based on the given fine-scale reservoir heterogeneity and potential flow paths. It then applies local–global
(LG) upscaling for permeability or transmissibility [7], along with adaptivity, in an iterative manner. In each iteration of MLLG, the grid can be adapted where needed to reduce
flow solver and upscaling errors. The adaptivity is controlled with a flow-based indicator. The iterative process is continued
until consistency between the global solve on the adapted grid and the local solves is obtained. While each application of
LG upscaling is also an iterative process, this inner iteration generally takes only one or two iterations to converge. Furthermore,
the number of outer iterations is bounded above, and hence, the computational costs of this approach are low. We design a
new flow-based weighting of transmissibility values in LG upscaling that significantly improves the accuracy of LG and MLLG
over traditional local transmissibility calculations. For highly heterogeneous (e.g., channelized) systems, the integration
of grid adaptivity and LG upscaling is shown to consistently provide more accurate coarse-scale models for global flow, relative
to reference fine-scale results, than do existing upscaling techniques applied to uniform grids of similar densities. Another
attractive property of the integration of upscaling and adaptivity is that process dependency is strongly reduced, that is,
the approach computes accurate global flow results also for flows driven by boundary conditions different from the generic
boundary conditions used to compute the upscaled parameters. The method is demonstrated on Cartesian cell-based anisotropic
refinement (CCAR) grids, but it can be applied to other adaptation strategies for structured grids and extended to unstructured
grids. 相似文献
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James V. Lambers Margot G. Gerritsen Bradley T. Mallison 《Computational Geosciences》2008,12(3):399-416
We propose a new single-phase local upscaling method that uses spatially varying multipoint transmissibility calculations.
The method is demonstrated on two-dimensional Cartesian and adaptive Cartesian grids. For each cell face in the coarse upscaled
grid, we create a local fine grid region surrounding the face on which we solve two generic local flow problems. The multipoint
stencils used to calculate the fluxes across coarse grid cell faces involve the six neighboring pressure values. They are
required to honor the two generic flow problems. The remaining degrees of freedom are used to maximize compactness and to
ensure that the flux approximation is as close as possible to being two-point. The resulting multipoint flux approximations
are spatially varying (a subset of the six neighbors is adaptively chosen) and reduce to two-point expressions in cases without
full-tensor anisotropy. Numerical tests show that the method significantly improves upscaling accuracy as compared to commonly
used local methods and also compares favorably with a local–global upscaling method. 相似文献
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While carbon pricing is widely seen as a crucial element of climate policy and has been implemented in many countries, it also has met with strong resistance. We provide a comprehensive overview of public perceptions of the fairness of carbon pricing and how these affect policy acceptability. To this end, we review evidence from empirical studies on how individuals judge personal, distributional and procedural aspects of carbon taxes and cap-and-trade. In addition, we examine preferences for particular redistributive and other uses of revenues generated by carbon pricing and their role in instrument acceptability. Our results indicate a high concern over distributional effects, particularly in relation to policy impacts on poor people, in turn reducing policy acceptability. In addition, people show little trust in the capacities of governments to put the revenues of carbon pricing to good use. Somewhat surprisingly, most studies do not indicate clear public preferences for using revenues to ensure fairer policy outcomes, notably by reducing its regressive effects. Instead, many people prefer using revenues for ‘environmental projects’ of various kinds. We end by providing recommendations for improving public acceptability of carbon pricing. One suggestion to increase policy acceptability is combining the redistribution of revenue to vulnerable groups with the funding for environmental projects, such as on renewable energy.
Key policy insights
If people perceive carbon pricing instruments as fair, this increases policy acceptability and support.
People’s satisfaction with information provided by the government about the policy instrument increases acceptability.
While people express high concern over uneven distribution of the policy burden, they often prefer using carbon pricing revenues for environmental projects instead of compensation for inequitable outcomes.
Recent studies find that people’s preferences shift to using revenues for making policy fairer if they better understand the functioning of carbon pricing, notably that relatively high prices of CO2-intensive goods and services reduce their consumption.
Combining the redistribution of revenue to support both vulnerable groups and environmental projects, such as on renewable energy, seems to most increase policy acceptability.
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