Transport in heterogeneous sediments with multimodal conductivity and hierarchical organization across scales

Dai, Zhenxue; Ritzi, Robert W.; Huang, Cunping; Rubin, Yoram; Dominic, David F.

doi:10.1016/j.jhydrol.2003.10.024

Cited by 65 publications

(67 citation statements)

References 29 publications

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“…Well log data from 14 boreholes outside RSU but in the same sedimentary basin were used to obtain statistical parameters for porosity such as stratum thickness, vertical mean length and volumetric proportion (Table 1). Spatial random fields of porosity in four geological formations above were generated by transition probability based geostatiscal simulations (Carle and Foggs, 1996;Dai et al, 2004;Ye and Khaleel, 2008). Once spatial distributions of porosity in these rocks had been acquired, permeability spatial distributions were obtained on the basis of empirical correlation between porosity and permeability.…”

Section: Multi-scale Heterogeneity Characterizationmentioning

confidence: 99%

“…Assuming that both covariance function and transition probability are exponential (Dai et al, 2007), and that the cross-covariances are negligible (Lu and Zhang, 2002;Dai et al, 2004), the composite covariance function for porosity is expressed as (Dai et al, , 2007:…”

Section: Transition Probability and Covariance Of Porositymentioning

confidence: 99%

“…This is a reasonable assumption here and for many applications of transition probability in facies analysis for sedimentary rock (Carle, 1999;Carle and Fogg, 1997). We further assume that the horizontal correlation lengths for porosity and permeability in each geological formation are 50 times as large as the vertical ones (Carle and Fogg, 1997;Dai et al, 2004). With these assumptions, the transition rate matrix was computed through the analytical solution of Dai et al (2007).…”

Section: Characterization Of Reservoir Heterogeneitymentioning

confidence: 99%

“…Random fields are generated through sequential indicator simulation and simulated quenching algorithms. Readers interested in this method and T-PROGS can refer to the papers of Carle and others for further details (Carle and Foggs, 1996;Carle and Fogg, 1997;Carle, 1999;Dai et al, 2004Dai et al, , 2007Ye and Khaleel, 2008). With empirical correlations between porosity and permeability, the permeability facies-based fields are retrieved from the random porosity fields (Table 2 and Fig.…”

Section: Characterization Of Reservoir Heterogeneitymentioning

confidence: 99%

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Simulation of industrial-scale CO2 storage: Multi-scale heterogeneity and its impacts on storage capacity, injectivity and leakage

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Section: Multi-scale Heterogeneity Characterizationmentioning

confidence: 99%

Section: Transition Probability and Covariance Of Porositymentioning

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Section: Characterization Of Reservoir Heterogeneitymentioning

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Simulation of industrial-scale CO2 storage: Multi-scale heterogeneity and its impacts on storage capacity, injectivity and leakage

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“…Numerous studies have applied hierarchical modeling frameworks to model aquifer materials (e.g., Scheibe and Freyberg 1995;Weissmann and Fogg 1999;Barrash and Clemo 2002;Dai et al 2004;Ritzi et al 2004) using a variety of modeling approaches (Koltermann and Gorelick 1996). Object-based (or morphological) modeling approaches, which focus on the characteristic geometries of a sedimentary deposit, have been used in many hydrogeological studies (e.g., Jussel et al 1994;Scheibe and Freyberg 1995;Miller et al 2000).…”

Section: Introductionmentioning

confidence: 99%

An Open, Object‐Based Framework for Generating Anisotropy in Sedimentary Subsurface Models

Bennett¹,

Haslauer

Ross

et al. 2018

Groundwater

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The spatial distribution of hydraulic properties in the subsurface controls groundwater flow and solute transport. However, many approaches to modeling these distributions do not produce geologically realistic results and/or do not model the anisotropy of hydraulic conductivity caused by bedding structures in sedimentary deposits. We have developed a flexible object-based package for simulating hydraulic properties in the subsurface-the Hydrogeological Virtual Realities (HyVR) simulation package. This implements a hierarchical modeling framework that takes into account geological rules about stratigraphic bounding surfaces and the geometry of specific sedimentary structures to generate realistic aquifer models, including full hydraulic-conductivity tensors. The HyVR simulation package can create outputs suitable for standard groundwater modeling tools (e.g., MODFLOW), is written in Python, an open-source programming language, and is openly available at an online repository. This paper presents an overview of the underlying modeling principles and computational methods, as well as an example simulation based on the Macrodispersion Experiment site in Columbus, Mississippi. Our simulation package can currently simulate porous media that mimic geological conceptual models in fluvial depositional environments, and that include fine-scale heterogeneity in distributed hydraulic parameter fields. The simulation results allow qualitative geological conceptual models to be converted into digital subsurface models that can be used in quantitative numerical flow-and-transport simulations, with the aim of improving our understanding of the influence of geological realism on groundwater flow and solute transport.

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Transverse mixing in three-dimensional nonstationary anisotropic heterogeneous porous media

et al. 2015

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Groundwater plumes originating from continuously emitting sources are typically controlled by transverse mixing between the plume and reactants in the ambient solution. In two-dimensional domains, heterogeneity causes only weak enhancement of transverse mixing in steady-state flows. In threedimensional domains, more complex flow patterns are possible because streamlines can twist. In particular, spatially varying orientation of anisotropy can cause steady-state groundwater whirls. We analyze steadystate solute transport in three-dimensional locally isotropic heterogeneous porous media with blockwise anisotropic correlation structure, in which the principal directions of anisotropy differ from block to block. For this purpose, we propose a transport scheme that relies on advective transport along streamlines and transverse-dispersive mass exchange between them based on Voronoi tessellation. We compare flow and transport results obtained for a nonstationary anisotropic log-hydraulic conductivity field to an equivalent stationary field with identical mean, variance, and two-point correlation function disregarding the nonstationarity. The nonstationary anisotropic field is affected by mean secondary motion and causes neighboring streamlines to strongly diverge, which can be quantified by the two-particle semivariogram of lateral advective displacements. An equivalent kinematic descriptor of the flow field is the advective folding of plumes, which is more relevant as precursor of mixing than stretching. The separation of neighboring streamlines enhances transverse mixing when considering local dispersion. We quantify mixing by the flux-related dilution index, which is substantially larger for the nonstationary anisotropic conductivity field than for the stationary one. We conclude that nonstationary anisotropy in the correlation structure has a significant impact on transverse plume deformation and mixing. In natural sediments, contaminant plumes most likely mix more effectively in the transverse directions than predicted by models that neglect the nonstationarity of anisotropy.

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Transport in heterogeneous sediments with multimodal conductivity and hierarchical organization across scales

Cited by 65 publications

References 29 publications

Simulation of industrial-scale CO2 storage: Multi-scale heterogeneity and its impacts on storage capacity, injectivity and leakage

Simulation of industrial-scale CO2 storage: Multi-scale heterogeneity and its impacts on storage capacity, injectivity and leakage

An Open, Object‐Based Framework for Generating Anisotropy in Sedimentary Subsurface Models

Transverse mixing in three-dimensional nonstationary anisotropic heterogeneous porous media

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