Speaker
Description
Accurate characterization of subsurface properties such as porosity and permeability is a central challenge in modeling flow and transport in porous media. These parameters play a decisive role in predicting plume migration and long-term storage security in applications such as CO$_2$ sequestration, groundwater hydrology, and subsurface energy systems. However, direct measurement of these properties at relevant spatial scales is often infeasible, and conventional inverse modeling approaches typically rely on computationally expensive history-matching or optimization procedures governed by partial differential equations.
While recent works have demonstrated estimation of efficient parameters from high resolution imaging on lab scale, these methods are highly sensitive to priors and initial conditions and comes with a huge computational cost [1].
In this work, we explore an alternative framework for parameter estimation in porous media that is inspired by tracer-based perfusion modeling techniques originally developed in medical imaging. In medical applications, dynamic contrast-enhanced imaging infer tissue properties from time-resolved concentration data using simplified transport models and conservation principles [2]. Despite differences in scale and application, fluid transport in biological tissue and geological formations is governed by similar physical laws, including Darcy flow and mass conservation, motivating a transfer of modeling concepts between these fields. The models comes with known, but well understood, errors and bias, but has the benefit of being highly efficient and scalable.
As a base case, we consider a single-phase flow model governed by Darcy’s law, coupled with an advection-dominated tracer transport equation. Rather than formulating a full inverse problem, we derive a direct estimates of effective parameters. We show that porosity estimates based on time-integrated tracer concentrations, under suitable assumptions are fairly acuate.
We validate the approach through numerical experiments where we generate synthetic flow images using finite element discretizations of Darcy flow and discontinuous Galerkin methods for tracer transport. For advection-dominated flow regimes, representative of tracer or CO$_2$ migration in porous formations, the proposed estimator accurately recovers porosity from synthetic concentration data. The method is computationally efficient and robust with respect to discretization effects, provided that the inflow signal is correctly accounted for.
Current and ongoing work extends this framework to simulation of more complex scenarios, including advection-diffusion transport, spatially varying and heterogeneous porosity fields, and partial tracer coverage of the domain. In addition, we are investigating the estimation of further subsurface parameters, such as permeability, concentration and effective transport coefficients, using related concepts from medical imaging and perfusion analysis [3]. By bridging methodologies from biomedical imaging and geoscience modeling, this work aims to provide fast approximations of efficient parameters. This may serve as direct input to reservoir models of various scales, or as a high quality prior for more complex history-matching methodology, with direct relevance to CO$_2$ storage, environmental monitoring, and subsurface flow applications.
| References | [1] Hanson, E. A., Sandmann, C., Malyshev, A., Lundervold, A., Modersitzki, J., and Hodneland, E. Estimating the discretization dependent accuracy of perfusion in coupled capillary flow measurements. Plos one 13, 7 (2018), e0200521. [2] Landa-Marbán, D., Sandve, T. H., Both, J. W., Nordbotten, J. M., and Gasda, S. E. Performance of an open-source image-based history matching framework for co2 storage, 2025. [3] Zhang, J., Winters, K., Kiser, K., Baboli, M., and Kim, S. G. Assessment of tumor treatment response using active contrast encoding (ace)-mri: Comparison with conventional dce-mri. PLOS ONE 15, 6 (06 2020), 1–13. |
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| Country | Norway |
| Green Housing & Porous Media Focused Abstracts | This abstract is related to Green Housing |
| Student Awards | I would like to submit this presentation into the Earth Energy Science (EES) and Capillarity Student Poster Awards. |
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