Point-to-point connectivity, an abstract concept or a key issue for risk assessment studies?

Trinchero, Paolo; Sánchez-Vila, Xavier; Fernàndez-García, Daniel

doi:10.1016/j.advwatres.2008.09.001

Cited by 56 publications

(74 citation statements)

References 43 publications

(74 reference statements)

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“…Trinchero et al [58] introduce a localized connectivity metric for radial flow conditions around a well. Their technique is based on previous studies by Meier et al [126] who have shown on field data ( Fig.…”

Section: Apparent Storativitymentioning

confidence: 99%

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Connectivity metrics for subsurface flow and transport

Renard

Allard

2013

Advances in Water Resources

337

258

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a b s t r a c tUnderstanding the role of connectivity for the characterization of heterogeneous porous aquifers or reservoirs is a very active and new field of research. In that framework, connectivity metrics are becoming important tools to describe a reservoir. In this paper, we provide a review of the various metrics that were proposed so far, and we classify them in four main groups. We define first the static connectivity metrics which depend only on the connectivity structure of the parameter fields (hydraulic conductivity or geological facies). By contrast, dynamic connectivity metrics are related to physical processes such as flow or transport. The dynamic metrics depend on the problem configuration and on the specific physics that is considered. Most dynamic connectivity metrics are directly expressed as a function of an upscaled physical parameter describing the overall behavior of the media. Another important distinction is that connectivity metrics can either be global or localized. The global metrics are not related to a specific location while the localized metrics relate to one or several specific points in the field. Using these metrics to characterize a given aquifer requires the possibility to measure dynamic connectivity metrics in the field, to relate them with static connectivity metrics, and to constrain models with those information. Some tools are already available for these different steps and reviewed here, but they are not yet routinely integrated in practical applications. This is why new steps should be added in hydrogeological studies to infer the connectivity structure and to better constrain the models. These steps must include specific field methodologies, interpretation techniques, and modeling tools to provide more realistic and more reliable forecasts in a broad range of applications.

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Section: Apparent Storativitymentioning

confidence: 99%

“…It implies that the dynamic metrics can vary with time. Dynamic and static connectivity metrics are linked through complex relations [29,42,[56][57][58][59]. Dynamic metrics are easier to measure in the field.…”

Section: Introductionmentioning

confidence: 99%

Connectivity metrics for subsurface flow and transport

Renard

Allard

2013

Advances in Water Resources

337

258

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“…Both aspects are likely related to spatial correlations and interactions between fractures [14]. The capacity of DFN models to reproduce flow channeling is essential for a good estimation of hydraulic properties [15][16][17].…”

Section: Discrete Fracture Network Modelmentioning

confidence: 99%

Topological impact of constrained fracture growth

et al. 2015

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The topology of two discrete fracture network models is compared to investigate the impact of constrained fracture growth. In the Poissonian discrete fracture network model the fractures are assigned length, position and orientation independent of all other fractures, while in the mechanical discrete fracture network model the fractures grow and the growth can be limited by the presence of other fractures. The topology is found to be impacted by both the choice of model, as well as the choice of rules for the mechanical model. A significant difference is the degree mixing. In two dimensions the Poissonian model results in assortative networks, while the mechanical model results in disassortative networks. In three dimensions both models produce disassortative networks, but the disassortative mixing is strongest for the mechanical model.

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“…Thus, the hydraulic conductivity can vary several orders of magnitude even in relatively mild heterogeneous aquifers [Gelhar, 1993;Rubin, 2003]. This typically leads to geological structures with preferential channels [Sanchez-Vila et al, 1996;Gomez-Hernandez and Wen, 1998;Trinchero et al, 2008] and low permeability areas where contaminants can be trapped and slowly released in time [Stroo et al, 2012;de Barros et al, 2013]. Moreover, degradation rates of contaminants in aquifers can vary substantially in space [Allen-King et al, 2006] due to, for instance, changes in the bacteria activity responsible for biodegradation [e.g., Fennell et al, 2001;Sandrin et al, 2004].…”

Section: Introductionmentioning

confidence: 99%

Toward efficiency in heterogeneous multispecies reactive transport modeling: A particle‐tracking solution for first‐order network reactions

Henri

Fernàndez-García

2014

Water Resources Research

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Modeling multispecies reactive transport in natural systems with strong heterogeneities and complex biochemical reactions is a major challenge for assessing groundwater polluted sites with organic and inorganic contaminants. A large variety of these contaminants react according to serial-parallel reaction networks commonly simplified by a combination of first-order kinetic reactions. In this context, a random-walk particle tracking method is presented. This method is capable of efficiently simulating the motion of particles affected by first-order network reactions in three-dimensional systems, which are represented by spatially variable physical and biochemical coefficients described at high resolution. The approach is based on the development of transition probabilities that describe the likelihood that particles belonging to a given species and location at a given time will be transformed into and moved to another species and location afterward. These probabilities are derived from the solution matrix of the spatial moments governing equations. The method is fully coupled with reactions, free of numerical dispersion and overcomes the inherent numerical problems stemming from the incorporation of heterogeneities to reactive transport codes. In doing this, we demonstrate that the motion of particles follows a standard random walk with time-dependent effective retardation and dispersion parameters that depend on the initial and final chemical state of the particle. The behavior of effective parameters develops as a result of differential retardation effects among species. Moreover, explicit analytic solutions of the transition probability matrix and related particle motions are provided for serial reactions. An example of the effect of heterogeneity on the dechlorination of organic solvents in a threedimensional random porous media shows that the power-law behavior typically observed in conservative tracers breakthrough curves can be largely compromised by the effect of biochemical reactions.

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Point-to-point connectivity, an abstract concept or a key issue for risk assessment studies?

Cited by 56 publications

References 43 publications

Connectivity metrics for subsurface flow and transport

Connectivity metrics for subsurface flow and transport

Topological impact of constrained fracture growth

Toward efficiency in heterogeneous multispecies reactive transport modeling: A particle‐tracking solution for first‐order network reactions

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