Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 1512.04581

Testing a coupled hydro-thermo-chemo-geomechanical model for gas hydrate bearing sediments using triaxial compression lab experiments

Shubhangi Gupta·Christian Deusner·Matthias Haeckel·Rainer Helmig·Barbara Wohlmuth
View original source ↗Read source document ↗Export metadata ↓

Abstract

Natural gas hydrates are considered a potential resource for gas production on industrial scales. Gas hydrates contribute to the strength and stiffness of the hydrate-bearing sediments. During gas production, the geomechanical stability of the sediment is compromised. Due to the potential geotechnical risks and process management issues, the mechanical behavior of the gas hydrate-bearing sediments needs to be carefully considered. In this study, we describe a coupling concept that simplifies the mathematical description of the complex interactions occuring during gas production by isolating the effects of sediment deformation and hydrate phase changes. Central to this coupling concept is the assumption that the soil grains form the load-bearing solid skeleton, while the gas hydrate enhances the mechanical properties of this skeleton. We focus on testing this coupling concept in capturing the overall impact of geomechanics on gas production behavior though numerical simulation of a high-pressure isotropic compression experiment combined with methane hydrate formation and dissociation. We consider a linear-elastic stress-strain relationship because it is uniquely defined and easy to calibrate. Since, in reality, the geomechanical response of the hydrate bearing sediment is typically inelastic and is characterized by a significant shear-volumetric coupling, we control the experiment very carefully in order to keep the sample deformations small and well within the assumptions of poro-elasticity. The closely co-ordinated experimental and numerical procedures enable us to validate the proposed simplified geomechanics-to-flow coupling, and set an important precursor towards enhancing our coupled hydro-geomechanical hydrate reservoir simulator with more suitable elasto-plastic constitutive models.

Explore related subjects

math.NAcs.NAmath.DSphysics.geo-ph

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Shubhangi Gupta, Christian Deusner, Matthias Haeckel, Rainer Helmig, Barbara Wohlmuth. 2015-12-15. Testing a coupled hydro-thermo-chemo-geomechanical model for gas hydrate bearing sediments using triaxial compression lab experiments. https://doi.org/10.1002/2017gc006901

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Stress-divergence, Laplacian, and rotational forms of the incompressible Navier--Stokes equations with variable viscosity

In the Navier--Stokes equations, incompressibility allows rewriting the viscous term in various forms leading to distinct numerical properties and flow descriptions. Furthermore, models accounting for non-Newtonian, thermal or turbulent effects often break the constant-viscosity assumption, thereby producing additional consistency terms. In this context, the present work compares the classical symmetric-gradient diffusion term with more recent variable-viscosity generalizations of the Laplacian and rotational forms. We discuss, analyze and test their differences with respect to implementation, efficiency, numerical stability and outflow boundary conditions. With a focus on time-dependent flows, we consider second-order implicit-explicit (IMEX) temporal discretizations aimed at improving efficiency and numerical stability. Through a rigorous stability analysis, we show how selected explicit treatments can bypass algorithmic nonlinearities without inducing CFL conditions. Our numerical results highlight important differences between the three viscous formulations---especially in the presence of outflow boundaries, for which the generalized Laplacian form proves more suitable in diffusion-dominated regimes. %(as widely known for constant viscosity).

math.NA↗
arXiv2026

Full-window branch discovery and loss-selected EnKF continuation for data assimilation

We develop a framework for offline full-window branch discovery, optionally followed by online continuation with an ensemble Kalman filter (EnKF). Three mechanisms drive the branch search: adjoint path-kernel (APK) differentiation balances kernel differentiation and correction-stabilized path perturbation, shifting the optimization from exploration to exploitation; an optimized Gaussian initial law broadens the search over initial-state basins; and loss-weighted mixing across independent runs recombines successful path components. We may then select an interior state using a local loss and continue online with an EnKF. In 40-dimensional Lorenz-96 experiments, the mean offline path RMSE of APK is 4.3 times smaller than that of population weak-$\mathrm{4D\text{-}Var}_x$. The resulting APK-EnKF method has a mean online RMSE 64 times smaller than that of ordinary EnKF.

math.NA↗
arXiv2026

A variational physics-informed graph neural network for heterogeneous solid mechanics

Stress localization in heterogeneous solids is governed by the bimaterial interface, where the displacement field remains $C^0$-continuous, while in-plane stresses jump due to the stiffness mismatch. Coordinate-based physics-informed neural networks (PINNs) represent this jump via a prescribed regularization width or a weighted interface penalty, making their accuracy sensitive to how phase-contrast changes are handled. This work presents a variational, label-free physics-informed graph neural network (PI-GNN) in which the heterogeneity is carried by the discretization rather than by the trial field. The solver operates on a conforming adaptive mesh graph, assigns constitutive behavior per element, and minimizes the discrete total potential energy as a single unweighted objective in which only first derivatives appear. The discrete energy on piecewise-linear elements coincides with the finite element (FE) Ritz functional. Dirichlet conditions are enforced by construction, with no penalty term, no interface weight, and no prescribed transition width. Using one fixed architecture, optimizer, and loss across small-strain elasticity and finite-strain Neo-Hookean hyperelasticity in two and three dimensions, the von Mises error remains below $3.58\%$ across a stiffness-contrast sweep spanning $(E_{\mathrm{inc}}/E_{\mathrm{mat}}\in[10^{-2},10^{2}])$, where a strong-form PINN degrades to $5.58\%$, and its displacement error reaches $7.66\%$ against $0.49\%$ for the PI-GNN. A trained network halves the ($\sigma_{xx}$) error of an energy-based PINN ($5.01\%$ versus $10.94\%$). Training cost exceeds a single FE solve by more than an order of magnitude, so the construction is a variationally consistent, penalty-free interface representation for parametric surrogates and inverse identification rather than a replacement for a one-off FE analysis.

math.NA↗