Impact of groundwater flow on methane gas migration and retention in unconsolidated aquifers.

Methane leaking at depth from hydrocarbon wells poses an environmental and safety hazard. However, determining the occurrence and magnitude of gas migration at ground surface is challenging, as part of the leaking gas is retained during upward migration. We investigated migration through unconsolidated sedimentary aquifers using a two-phase, two-component (water and methane) flow and transport model constructed in DuMux. A sensitivity analysis for migration through a 60 m thick sandy aquifer showed that retention by dissolution can be significant even with low groundwater Darcy velocities of 1 m.yr-1. Retention was negligible in the absence of groundwater flow. Besides groundwater velocity, both hydrogeological (permeability, entry pressure, pore-size distribution, and residual gas saturation) and leakage conditions (depth, magnitude and spatial dimensions) determined model outcomes. Additional simulations with interbedded finer grained sediments resulted in substantial lateral spreading of migrating gas. This delayed upward migration and enhanced retention in overlying sandy units where groundwater velocities are highest. Overall, the results of this study show that for unconsolidated aquifer systems and the most commonly observed leakage rates (0.1-10 m3.d-1), significant amounts of migrating methane can be retained due to dissolution into laterally flowing groundwater. Consequently, resulting atmospheric methane emissions above such leaks may be delayed with decades after the onset of leakage, significantly reduced, or prevented entirely.

A multiscale subvoxel perfusion model to estimate diffusive capillary wall conductivity in multiple sclerosis lesions from perfusion MRI data.

We propose a new mathematical model to learn capillary leakage coefficients from dynamic susceptibility contrast MRI data. To this end, we derive an embedded mixed-dimension flow and transport model for brain tissue perfusion on a subvoxel scale. This model is used to obtain the contrast agent concentration distribution in a single MRI voxel during a perfusion MRI sequence. We further present a magnetic resonance signal model for the considered sequence including a model for local susceptibility effects. This allows modeling MR signal-time curves that can be compared with clinical MRI data. The proposed model can be used as a forward model in the inverse modeling problem of inferring model parameters such as the diffusive capillary wall conductivity. Acute multiple sclerosis lesions are associated with a breach in the integrity of the blood-brain barrier. Applying the model to perfusion MR data of a patient with acute multiple sclerosis lesions, we conclude that diffusive capillary wall conductivity is a good indicator for characterizing activity of lesions, even if other patient-specific model parameters are not well-known.

Numerical modelling of a peripheral arterial stenosis using dimensionally reduced models and kernel methods.

In this work, we consider 2 kinds of model reduction techniques to simulate blood flow through the largest systemic arteries, where a stenosis is located in a peripheral artery, i.e., in an artery that is located far away from the heart. For our simulations, we place the stenosis in one of the tibial arteries belonging to the right lower leg (right posterior tibial artery). The model reduction techniques that are used are on the one hand dimensionally reduced models (1-D and 0-D models, the so-called mixed-dimension model) and on the other hand surrogate models produced by kernel methods. Both methods are combined in such a way that the mixed-dimension models yield training data for the surrogate model, where the surrogate model is parametrised by the degree of narrowing of the peripheral stenosis. By means of a well-trained surrogate model, we show that simulation data can be reproduced with a satisfactory accuracy and that parameter optimisation or state estimation problems can be solved in a very efficient way. Furthermore, it is demonstrated that a surrogate model enables us to present after a very short simulation time the impact of a varying degree of stenosis on blood flow, obtaining a speedup of several orders over the full model.

A coupled discrete/continuum model for describing cancer-therapeutic transport in the lung.

We propose a computational simulation framework for describing cancer-therapeutic transport in the lung. A discrete vascular graph model (VGM) is coupled to a double-continuum model (DCM) to determine the amount of administered therapeutic agent that will reach the cancer cells. An alveolar cell carcinoma is considered. The processes in the bigger blood vessels (arteries, arterioles, venules and veins) are described by the VGM. The processes in the alveolar capillaries and the surrounding tissue are represented by a continuum approach for porous media. The system of equations of the coupled discrete/continuum model contains terms that account for degradation processes of the therapeutic agent, the reduction of the number of drug molecules by the lymphatic system and the interaction of the drug with the tissue cells. The functionality of the coupled discrete/continuum model is demonstrated in example simulations using simplified pulmonary vascular networks, which are designed to show-off the capabilities of the model rather than being physiologically accurate.

Estimation of effective parameters for a two-phase flow problem in non-Gaussian heterogeneous porous media.

In this paper we discuss estimates of effective parameters for an upscaled model for buoyant counter flow of DNAPL and water in a closed box filled with heterogeneous porous material. The upscaling procedure is based on the assumption that the flow is dominated by capillary forces on the small scale and that the fluids are segregated. The upscaled model has the same form as the usual two-phase flow model with an effective capillary pressure function and an effective mobility function Λ. Effective parameters are then estimated in two different ways. Stochastic theory can be applied to calculate the effective parameters to first order in the parameter fluctuations. This approach does not take into account that different parameter ranges of the heterogeneous field may be connected or isolated, yielding very different macroscopic residual saturations. Therefore, the second estimate of effective parameters takes connectivity of parameter ranges into account. In this case, the univariate parameter distribution of the heterogeneous field and the values that mark connected materials are the only information about heterogeneity that is used. Effective parameters are then estimated using mean field theory (the Maxwell approach). The upscaled model and the estimation of effective parameters are applied to a numerical test case. Buoyant counter flow in heterogeneous parameter fields with different structures is simulated numerically and compared to the solutions of the quasi-1d upscaled model with differently estimated parameters. It is demonstrated that connectivity of the different parameter ranges is an important information that determines typical time scales for the flow process and the macroscopic residual saturation. Even simple estimates of effective parameters based on little information may capture the typical time scales, provided that information about connected parameter ranges is taken into account.

Macro-scale effective constitutive relationships for two-phase flow processes in heterogeneous porous media with emphasis on the relative permeability-saturation relationship.

Macro-scale simulations often play an important role in the assessment and remediation of contamination by dense non-aqueous phase liquids (DNAPLs) in the subsurface. Effective parameters for the macro scale are required for these simulations in order to avoid a detailed discretisation of the geological structures. Starting from the observed influence of heterogeneities on multiphase flow processes at the macro scale, we present an upscaling procedure from the local to the macro scale for the derivation of constitutive relationships for multiphase flow processes. The approach is based on the assumption of an equilibrium of (capillary) forces, which allows the application of a percolation model. This results in saturation distributions for different capillary pressures. Averaging these distributions gives rise to a macroscopic capillary pressure-saturation relationship. For the saturation distribution, relative permeabilities and effective conductivities are computed depending on the structure and the flow direction. These are averaged with the help of the renormalisation method. The evolving relative permeability-saturation relationship for the macro scale shows a saturation-dependent anisotropy and pronounced residual saturations of the nonwetting phase (which were not assumed for the local scale). The anisotropy reflects the underlying structure of the considered system that needs not to be known in detail.