Speaker: Marius Rapenne

Date: Thursday 13th of June 2024, 1:15pm.

Abstract:

With the constant increase of computational power, the numerical modelling of lithological site effects can now handle 3D, geologically complex settings. However, a computational overburden is reached when, e.g., uncertainties have to be quantified. A possible pathway towards decreasing the cost of seismic wave simulations in complex media is the non-periodic homogenization. This method is known to provide accurate effective media for wave propagation. In this work, we apply it to 2D sedimentary basins and explore its efficiency and accuracy in terms of amplification simulation. Two homogenization strategies are investigated: the Backus’ one, which considers the geological medium as a juxtaposition of 1D profiles, and the more general 2D homogenization, which involves the resolution of a partial differential equation. Using various velocity contrasts and geometries, we emphasize cases which require the general homogenization for an accurate modelling of amplification effects.

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Category: Seminar

Speaker: Matthieu Lusseyran

Date: Thursday 4th of April 2024, 1:15pm.

Abstract:

Understanding the damage processes in clay-bearing rocks is a decisive factor in geological engineering, and for instance considering nuclear waste deep geological repositories. But, more generally they may also contribute to localized deformation, and thus the rupture of fault gauges in seismic zones. However, owing to their complex mineralogy, multiscale microstructures and anisotropy, the mechanisms of clay-rich rock damage and their chronology are not yet well understood. Here we focus on the impact of micro-damage on ultrasonic wave propagation velocity, which is confronted with the corresponding full deformation fields calculated by digital image correlation (DIC). The aim is to associate the acoustic signature with the active deformation mechanisms identified by DIC. To this end, an integrated experimental approach is proposed to characterize localization and to identify the related deformation micro-mechanisms during uniaxial compression of natural clayey rock samples (Tournemire shales) with two simultaneous measurements: 1) the evolution of P-wave velocity within the sample by active acoustics, 2) the development of the 2D mechanical full field by digital image correlation. Both experimental techniques are well known, but the innovation of our approach is to combine simultaneously both measurements. Deformation localization is a multiscale problem, which obviously occurs at the sample scale, but also at the fines scales of the microstructure. Therefore, we developed two different experimental setups. On the one hand, during uniaxial compression with a standard MTS loading frame the macro-scale localization patterns are characterized by optical observations, which image resolution is well suited to the cm sample scale (sample diameter: 3.6 cm and double in length). On the other hand, in order to characterize the initiation of micro-damage at the microstructure scale of the composite type of rock, the same loading protocol is reproduced (while keeping the acoustic diagnosis) on smaller scale mm-sized specimens (sample diameter : 8 mm, double in length), using a home-designed miniature loading frame fit for an environmental scanning electron microscope (ESEM). The latter analysis is carried out under controlled relative humidity of RH = 80%, hence preventing the samples to dry out due to the high vacuum. A similar acoustic signature is identified at both scales of observation, in spite of the variations of experimental conditions imposed by the environmental SEM. We are therefore confident to be able to understand the fracturing process from micro-cracking initiation (microscale) to sample failure (macroscale), and to assess its impact on ultrasonic wave propagation.

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Category: Seminar

Speaker: Julien Herrero

Date: Thursday 6th of June 2024, 1:15pm.

Abstract:

We introduce a new method for post-stack seismic inversion using Bayesian transdimensional methods in layered media. We employ a reversible jump Markov chain Monte Carlo algorithm, defined within a parsimonious Bayesian framework, to infer not only the values of the model parameters but also the optimal number of layers required to describe the data. The parameterization includes a layer inclination angle to locally infer the stratigraphy from a group of adjacent seismic traces. The forward method generates a set of synthetic seismic traces using a classical convolution model. These synthetic traces are then compared with actual seismic traces to infer the geological model parameters that are layer acoustic impedances, interface depths, and dipping angles. We run the inversion on synthetic data obtained from a two-dimensional reference geometry and show its ability to recover the reference model parameters. This suggests the potential of the method to discover stratigraphic gaps in seismic reservoir characterization tasks. More generally, this demonstrates the capability of transdimensional inversion in quantitative interpretation tasks. It opens opportunities for inversion of entire post-stack seismic sections, accommodating lateral variability with relatively low computational time.

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Category: Seminar

Speaker: Janne T. Yliharyu

Date: Friday 22nd of March 2024, 1:15pm.

Abstract:

In many applications, it is essential to know what happens to the properties of a material if the water content is changed, i.e., if the material is wetted or dried [1]. One such example is bentonite clay, which is planned to be used as a buffer material in the geological final disposal of radioactive waste. Safety assessment of the multibarrier system, including the bentonite buffer, requires modelling and investigating the release barrier over long periods. These models benefit from information on the local water content of the bentonite layer. To facilitate modelling, we have developed X-ray tomographic methods to measure the time evolution of the local water content and applied the methods to bentonite clay samples in various conditions. This approach provides direct experimental data on the wetting and hydromechanical behavior of small-scale compacted bentonite samples, which can be used to validate material models. We present two approaches for measuring local water content. The first one is based on comparing successive tomographic images obtained during the wetting of the bentonite clay sample. This method involves measurement of the internal deformations of the sample, which is vital because, in general, materials will deform if the water content is changed. The second method is based on dual-energy imaging. Energy information is utilized to obtain quantitative information about the spatial distributions of bentonite and water. In both cases, we have applied a nonstandard beam hardening correction and careful calibrations of the mass attenuation coefficients. The deformation measurement method has been applied extensively to bentonite samples with different dry densities. Also, the influence of temperature on water transport has been studied. Based on the observations, the evolution of the water distribution is qualitatively reminiscent of diffusive transport. The first results from the dual-energy CT are promising, but further studies and validations are required before more profound conclusions can be drawn. These results suggest that deformation measurement enables more accurate water content estimates, but dual-energy CT can be applied in a broader range of material combinations.

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Category: Seminar

Speaker: Paul Marchal

Date: Thursday 23th of May 2024, 1:15pm.

Abstract:

In mining, assessing uncertainties is an essential step throughout the resource development cycle, from exploration campaigns to remediation and development strategy planning. Indeed, geological data are only partially covering the subsurface and are subject to two main types of uncertainty:  i) sampling and measurement uncertainties, and ii) epistemic or conceptual uncertainties related to data interpretation. This paper focuses on the second ones. It aims to evaluate the diversity of conceptual interpretations that specialists and non-specialists have on data, and the potential impact this can have on the estimation of  uranium deposit geometries. For this purpose, a case study was carried out in the context of an unconformity-associated uranium deposit in the Athabasca basin. Based on a reference section from this area, a cross-section with synthetic drillcores was produced and given to 30 people to correlate and interpret. Our objectives are multiple: defining metrics for comparing data interpretations, assessing the differences in interpretations between expert and non-expert uranium geologists. We defined a set of mathematical criteria (50) based on 4 key characteristics: i) mineralized zones, ii) associated altered zones, iii) associated structural network, and iv) interpretation glyphs and annotations. Individual and group analysis of the defined criteria (t-SNE, MDS) were performed. Digital Leapfrog models are also compared with hand-drawn models. Primary results show that the group of uranium experts is less dispersed overall in terms of property variance. They  tend to propose mineralization zones that are more impacted by the influence of  faults and unconformities. They are finally prone to produce less parsimonious interpretations, incorporating more geological concepts.

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Category: Seminar

Speaker: Claire Bossennec

Date: Thursday 21st of March 2024, 1:15pm.

Abstract:

La caractérisation et la modélisation précises des milieux géologiques hétérogènes sont essentielles pour optimiser l'exploration et l'exploitation des ressources énergétiques, telles que la géothermie, le stockage thermique en profondeur et les processus de co-production d'énergie (lithium, hydrocarbures). Cette présentation propose une approche intégrant des analyses multi-échelles et multi-méthodes, depuis l'observation de terrain jusqu'à la modélisation numérique, incluant également des expérimentations de laboratoire. La combinaison avec de l’imagerie géophysique et l’analyse des propriétés thermo-physiques et pétrophysiques permet de couvrir un spectre allant du microscopique au macroscopique, et d'englober des techniques avancées pour examiner en détail les structures et mécanismes régissant les transferts de chaleur et de matière dans des milieux géologiques complexes à différentes échelles temporelles. La caractérisation des propriétés de transfert des grès et de leur évolution par diagénèse et déformation constitue la base des travaux de thèse et sont une première étape des travaux présentés.

Les études de cas variées illustrant l’approche développée, issues de projets tels que INTERREG DGE-ROLLOUT, MEET, SKEWS, PotAMMO, PUSH-IT et AMPEDEK, complètent et explorent des contextes géologiques divers (sédimentaires, volcano-sédimentaires et cristallins), permettant une meilleure compréhension de la complexité et de l'hétérogénéité des formations géologiques visées. Ceci facilite une évaluation plus précise du potentiel géothermique et de stockage. Les résultats obtenus jouent également un rôle clé dans l'amélioration de la production d'énergie géothermique, le perfectionnement des stratégies de stockage thermique et l'évaluation des opportunités de co-production d'énergie, tout en respectant les contraintes environnementales et économiques.

L’ensemble des travaux souligne l'importance vitale d'une analyse détaillée des propriétés et processus régissant les milieux géologiques pour un développement responsable des ressources énergétiques souterraines. En ouvrant la voie vers des stratégies de gestion énergétique plus efficaces, ces travaux proposent plusieurs innovations concrètes pour répondre aux défis de la transition énergétique et à la nécessité d'une sécurité énergétique renforcée par une production plus locale et intégrée.

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Category: Seminar

Speaker: Amandine Fratani

Date: Thursday 25th of April 2024, 1:15pm.

Abstract:

Interpretation of faults is a requirement for a 3D geological modelling process. However, due to the incomplete observations caused by the gap between 2D seismic images or outcrops, the results of this stage can be ambiguous and uncertain. Recently, a proposition of solution based on a graph formalism has been expressed. In the graph, fault observations are represented as nodes, and edges carry the potential of pairwise associations computed from selected expert geological rules. Main limit of this work is that fault observations are not pairwise independent, therefore considering pair prevents the consideration of higher-order effects such as the distribution of the throw along several aligned nodes. We propose to consider a multiple-point likelihood computation to extend the graph where expert rules are replaced using machine learning on analog or partly observed data. The model is trained from a set of selected features such as the length of the fault trace or the throw value. Features are computed from fault traces extracted from 3D geological models. Association potential of k fault observations are then directly computed using the trained model. To prevent overfitting in our small geological models dataset, we propose to mimic a partly interpreted case: we split a 3D domain in two disjoint, contiguous sectors A and B, and use sector A as training and sector B for testing. This presentation will show first results on 2 and 3 points association using a Random Forest algorithm.

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Category: Seminar

Speaker: Charlie Garayt

Date: Thursday 14th of March 2024, 1:15pm.

Abstract:

This work aims to simulate 2D structural geological models, or geomodels, that respect given knowledge and data. By definition, geomodeling is an ill-posed problem due to the limited quantity and quality of available data. Current geomodeling methods struggle to both characterize uncertainties and produce realistic geomodels.To achieve this goal, a deep generative adversarial network (GAN) has been implemented. GANs, which are usually used in image generation, need a large training dataset. For instance the ImageNet dataset contains more than 1.5 million images. In geomodeling, such a large real dataset does not exist. Fortunately, geological structures are a consequence of physical and chemical processes, so creating a synthetic dataset is feasible from the simulation of these processes. The training dataset is created from Noddy, which can be viewed as an object-based simulator. The use of advanced GANs like Least-Square GAN (LSGAN) and Wasserstein GAN allows training a deep neural network called the Generator. The Generator defines an implicit distribution of geological models. This is a function that transforms a random vector into a geomodel similar to the training dataset. However, the Generator produces unconstrained geomodels. A Bayesian approach is used in order to generate geomodels that fulfill constraints, or conditioned geomodels. Thanks to the versatility of the Bayesian approach, constraints can be of different types and quality, for instance rock type, rock orientation or geophysical data. A modified Metropolis adjusted Langevin algorithm (MALA), which is a Markov chain Monte Carlo method, enables the acquisition of conditional geomodels. Finally, the combination of MALA and the GAN allows the generation of conditioned geomodels. In addition, this approach enables handling uncertainties and to perform computation, since the final results is an implicit distribution of conditioned geomodels.

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Category: Seminar

Speaker: Thomas Bodin

Date: Thursday 11th of April 2024, 1:15pm.

Abstract:

Earth sciences are by essence multidisciplinary as different fields study the same objects and processes at different scales. In seismology, the challenge is that tomographic models constructed from long period seismograms only “sees” the crust and the mantle as an effective medium, where the smallest scales are given by the resolution power of seismic waves. On the other hand, geodynamics and geochemistry “see” the Earth as a mixture of different chemical components, with the smallest scales given by chemical diffusion. In this presentation, I will show how modern tools from inverse theory, computational statistics, and data science can surpass qualitative interpretations of results obtained separately in different fields. Within a Bayesian framework, we will propose a fully probabilistic approach to explore the ensemble of small-scale earth models compatible with a set of observations and constraints.

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Category: Seminar