Research for Integrative Numerical Geology

Deep seated gravitational slope deformations (DSGSDs) are a commonly observed type of slope instability process found in mountain ranges all over the world. Their rates of movement differ fundamentally from more superficial rapid mass movements known in high relief terrain, with displacements characterized by long periods of dubious activity or time spans of inactivity. If some DSGSDs do not induce catastrophic failures but instead cease activity, others might be in a preparatory stage before a large volume catastrophic landslide. This last evolutionary stage could be favored by geological and fracturing conditions, as well as topographical, climatic, or seismo-tectonic conditions. However, due to the scarcity of very large rockslides, this possible catastrophic evolution and the reasons for it are still poorly documented and understood.

In order to explore the conditions that lead or not to an unfavorable catastrophic failure, we propose to use discrete element models to investigate DSGSDs from a mechanical viewpoint. After validating our modeling approach based on comparisons with Limit Equilibrium Analysis of simple slope geometries, we explore preconditions and mechanisms acting towards failure considering the influence of mechanical rock properties, slope topography, and pre-existing structures. Furthermore, we can implement our method on real existing case studies by shaping our simulations with digital terrain models (DTMs).

Comparison of our modeling results with those DSGSDs for which long-term and recent activity has been documented (e.g. La Clapière case in the South Western Alps of France) provides insight into their prevailing patterns of deformation (continuous or stepwise over time) and the conditions (geological, fracturing or topographic) for a catastrophic landslide or progressive stabilization. This may lead to better understanding and prediction of behavior for large scale deep seated deformation in mountainous areas, especially relevant for estimating natural hazards.

Coal beds are dual permeability systems characterized by a porous matrix enclosed within sets of orthogonal fractures known as cleats. Production of coalbed methane (CBM) consists of desorbing methane from the low permeable coal matrix to the high permeable cleat system. Unlike in conventional reservoir exploitation, sorption mechanisms cause shrinkage and swelling of the matrix which increase the complexity of the phenomena at stake, leading to complex reservoir behaviors in terms of production. A 3D discrete element method (DEM) coupled to a pore scale finite volume method (PFVM) is used here to better understand the different mechanisms at stake. The model, implemented in the open-source software Yade Open DEM (Smilauer et al., 2015), is an offspring of the hydro-mechanical model proposed by Catalano et al. (2014). The coal matrix is treated as an assembly of bonded particles interacting one with another through elastic-brittle contact laws. The pore space is discretized into tetrahedra, generated from a regular triangulation of the particle assembly. Both Knudsen and surface diffusion, as well as sorption processes, are modeled considering the coal matrix as a microporous material. The method is hydro-mechanically coupled in the sense that changes in pore pressure produce hydrostatic forces that deform the solid skeleton, while deformation of the pore space induces pore pressure changes that promote interporal flow. In addition, sorption induced deformations are taken into account by considering an additional pressure term related to the concentration of gas within the medium (the so-called solvation pressure). In this work, we first present the model and its constitutive equations. We assess its capabilities by comparing its predictions to well established solutions describing diffusive flow in porous media as well as to classic poroelasticity concepts. In particular, we focus on the influence of sorption induced deformations on the Biot coefficient estimation. Finally, we compare the model predictions to swelling test data from the literature to illustrate its consistency.