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Statistical framework for scale-up of dispersivity in multi-scale heterogeneous media
- Vishal, Vikrant, Leung, JulianaY.
- Environmental earth sciences 2017 v.76 no.17 pp. 624
- geostatistics, models, probability distribution, spatial variation, uncertainty, variance
- A hierarchical scale-up framework is formulated to study the scaling characteristics of reservoir attributes and input dispersivities at the transport modeling scale, where heterogeneity distribution exhibits both non-stationarity (trend) and sub-scale variability. The proposed method is flexible to handle heterogeneities occurring at multiple scales, without any explicit assumption regarding the multivariate distribution of the heterogeneity. This paper extends our previous work by incorporating the effects of non-stationarity into the modeling workflow. Rock property at a given location is modeled as a random variable, which is decomposed into the sum of a trend (available on the same resolution of the transport modeling scale) and a residual component (defined at a much smaller scale). First, to scale up the residual component to the transport modeling scale, the corresponding volume variance is computed; by sampling numerous sets of “conditioning data” via bootstrapping and constructing multiple realizations of the residual components at the transport modeling, uncertainty due to this scale-up process is captured. Next, to compute the input dispersivity at the transport modeling scale, a flow-based technique is adopted: multiple geostatistical realizations of the same physical size as the transport modeling scale are generated to describe the spatial heterogeneity below the modeling scale. Each realization is subjected to particle-tracking simulation. Effective longitudinal and transverse dispersivities are estimated by minimizing the difference in effluent history for each realization and that of an equivalent average medium. Probability distributions of effective dispersivities are established by aggregating results from all realizations. The results demonstrate that both large-scale non-stationarity and sub-scale variability are both contributing to anomalous non-Fickian behavior. In comparison with our previous work, which ignored large-scale non-stationarity, the non-Fickian characteristics observed in this study is dramatically more pronounced.