Speaker: Nour Hachem

Date: Thursday 3rd of December 2026, 1:15pm

Abstract:

The Data-Driven Identification (DDI) method estimates the mechanical response of material samples without assuming a predefined constitutive model. DDI solves an inverse problem based on the principle that similar strains should correspond to similar stresses, while ensuring mechanical equilibrium. This work focuses on the development and theoretical validation of the DDI method. First, a rigorous mathematical and numerical analysis is provided, including the study of the uniqueness criterion, a new implementation with a proof of convergence, and an assessment of conditioning and convergence rates. Second, the case of ill-posed DDI problems is addressed, and corrective strategies are introduced to obtain valid solutions. Finally, essential tools and practical guidelines for reliable implementation are developed, providing a reference for future research and applications. These contributions establish a rigorous theoretical foundation for the DDI method, enhancing its robustness and applicability while enabling safer use through essential tools and a clearer understanding of its practical limitations.

Speaker: Pedram Massoudi

Date: Monday 23rd of November 2026, 1:15pm

Abstract:

Prise en compte de la forte proportion de valeurs nulles dans les données horaires de précipitations

Prise en compte de la non-stationnarité latitudinale des précipitations et des températures

Corégionalisation avec l’altitude pour les températures et avec les données satellitaires pour les précipitations

Speaker: Théo Chamarande

Date: Thursday 19th of November 2026, 1:15pm

Speaker: Charlie Garayt

Date: Tuesday 17th of November 2026, 1:15pm

Speaker: Hasnain Khan

Date: Friday 6th of November 2026, 1:15pm

Abstract:

Artificial intelligence (AI) and machine learning are increasingly used to model complex
geotechnical and subsurface systems, where the subsurface material behaviour depends on multiple
interacting factors and available experimental data are often limited or heterogeneous. This
presentation will introduce my research at the intersection of AI, data-driven modelling, and
geomechanics, highlighting applications to the prediction and interpretation of soil behaviour and
subsurface properties.
My doctoral research focused on predicting and analyzing the strength development of cement-
treated soils using machine learning and explainable AI approaches. My current postdoctoral
research extends these approaches towards the transfer properties of compacted sand–clay
mixtures, particularly hydraulic conductivity and gas permeability in the context of deep geological
waste disposal. These studies investigate how data-driven models can predict material behaviour
from experimental data, while addressing challenges related to data availability and model
interpretability.
Building on this work, I will discuss future research perspectives involving interpretable and
physically informed machine learning, uncertainty assessment, and the integration of data-driven
approaches with numerical modelling. I will also explore potential synergies between these
directions and the research activities of the RING team and GeoRessources.

Speaker: Marc Benziane

Date: Thursday 15th of October 2026, 1:15pm

Speaker: Clémence Pavageau

Date: Thursday 8th of October 2026, 1:15pm

Abstract:

Low-enthalpy geothermal energy, characterized by temperatures below 90°C and exploitation at shallow depths (i.e. normally less than 500m), represents a sustainable energy alternative for residential and industrial heating and cooling, while offering a reduced environmental impact. However, the sustainable exploitation of these resources requires a thorough understanding of reservoir behavior and accurate monitoring of the thermal evolution of aquifers. Electrical geophysics provides valuable insights into subsurface thermal processes; therefore, understanding the coupled evolution of heat and electrical charge across multiple scales is of significant interest.

This study, first, presents an experimental approach to establish the relationship between the electrical resistivity of water-saturated rocks and temperature variations with simple yet realistic assumptions and then presents a numerical counterpart. The experimental study was conducted in the laboratory using a cylindrical water-saturated sandstone sample containing a central axial conduit through which water was circulated in a closed loop. The experimental protocol consisted of injecting either hot or cold water through the conduit to induce heating and/or cooling scenarios within the sample. Temperature was monitored using thermocouples positioned at the inlet and outlet of the water circuit, as well as at two radial locations within the rock sample. Electrical resistivity measurements were acquired using a Syscal Junior resistivity meter with a Wenner–Schlumberger electrode array. Measurements were recorded over roughly 30-minute periods, enabling the analysis of the coupled evolution of fluid temperature and the electrical conductivity of the saturated porous medium during both heating and cooling experiments. Through this, bulk resistivity evolution of the quadrupole was measured directly and verified later with the model. 

The experimental scenarios were simulated using direct numerical simulation of coupled flow and heat transfer in porous media with electrical current density distribution. The petrophysical model of Waxman and Smits (1968), applied to saturated porous media, in which temperature primarily affects the electrical conductivity of the pore water is used to link temperature effects to electrical conductivity of the DC current injected. We assume and show that the temperature dependence primarily resulted from temperature-induced changes in ionic mobility and fluid viscosity, resulting in the evolution of water electrical conductivity measured directly in the experimental phase. Within the temperature range characteristic of low-enthalpy geothermal systems, this relationship can be considered approximately linear. The surface conductivity effects have secondary importance and can be expressed through the temperature dependence of equivalent conductance of exchange cations (i.e. parameter B).

The experimental and numerical results validate the assumption on the evolution of the electrical conductivity of the saturated Vosges sandstone as a function of temperature. Laboratory validation and calibration of this model provide the basis for its integration into a numerical framework capable of reproducing the experimental observations. At a larger scale, this approach could be extended to Electrical Resistivity Tomography (ERT) surveys, providing a non-invasive tool for in situ thermal monitoring of geothermal reservoirs.

Speaker: Kurama Okubo

Date: Thursday 1st of October 2026, 1:15pm

Speaker: Alan Lelièvre

Date: Thursday 24th of September 2026, 1:15pm

Speaker: Mohamadhasan Sarvandani

Date: Thursday 9th of April 2026, 1:15pm

Speaker: Guillaume Caumon

Date: Thursday 26th of March 2026, 1:15pm

Abstract:

This seminar will present a digest of two external presentations I gave last fall. The first (by Simon Lopez and I) was given at the PEPR Digital Earth days at BRGM, and defines some views on the needs of the data and modeling platform which is being implemented in the project. The second part (given at the decision-based reservoir modeling workshop) shows the value of probabilistic Bayesian geosteering in depositional space (resulting from the MSc projects of Guillaume Peyssard, Julien Herrero and Leo Letellier). It discusses how the model updating approaches of Ayoub (and previously Capucine) could be used in this context, and how uncertainty could further be reduced using dehomogenization (Giusi's work).   

Speaker: Louisa Barlatier

Date: Thursday 19th of March 2026, 1:15pm

Abstract:

The In Situ Recovery technique has been used for over 20 years to extract uranium from sedimentary deposits (unconsolidated sands) with low U content (hundreds of ppm). This technique is based on injecting and pumping a leaching solution through the deposit. This solubilizes uranium in the circulating fluid through oxidizing conditions, obtained by strong acid. Alternative methods involve leaching at very low acid concentrations (neutral pH) and in the presence of an oxidizing agent such as O2. A series of autoclave leaching tests (under pressure) has been launched at GeoRessources to reproduce in situ conditions and analyze the influence of operating conditions on acid consumption, kinetics, and yield. The initial results presented here are part of tests conducted at low acidity with the addition of O2 (aq). Some geochimical modeling has also been carried out.

Speaker: Jorge Guizar

Date: Thursday 12th of March 2026, 1:15pm

Abstract:

Seismic data processing plays a fundamental role in determining the accuracy, resolution, and reliability of results obtained from full-waveform inversion (FWI), seismic imaging, and seismic interpretation. High-fidelity processing workflows—including noise attenuation, multiple suppression, deghosting, deconvolution, amplitude and phase corrections, and velocity model building—directly influence the quality of the wavefield used in advanced inversion and imaging techniques. Furthermore, the careful design of the processing sequence impacts the final seismic image by shaping its frequency content, reflector continuity, and structural reliability. Recent advances in AI and machine-learning approaches have demonstrated strong potential for seismic interpretation—supporting applications such as automatic fault extraction, salt boundary interpretation, gas-chimney detection, and stratigraphic classification. Nonetheless, the performance and reliability of these models remain fundamentally dependent on the quality of the input seismic data, and therefore on the robustness of the underlying processing workflow.

Speaker: Joseph Delbreil

Date: Thursday 5th of March 2026, 1:15pm

Speaker: Bérénice Vallier

Date: Thursday 5th of February 2026, 10am

Abstract:

Numerous numerical models have been developed to study natural hydrothermal circulation and its interaction with industrial processes in various geothermal contexts. The main part of this presentation numerically studies deep circulation in the Upper Rhine Graben (URG) to investigate the origin of the thermal anomaly and the impact of fluid injection on induced seismicity. First, a thermo-hydro-mechanical (THM) model is developed to provide new insights into hydrothermal convection. Secondly, a numerical study based on the diffusion of overpressures from fluid injection is conducted to determine an appropriate pumping strategy that will reduce the risk of induced seismicity. Fluid injection with an oscillating flow rate is an alternative that has the potential to significantly reduce induced seismicity on distant faults while enhancing permeability in the near-well region. Drawing on my knowledge of geomodelling and deep circulation in the URG, I have conducted a preliminary study on fluid natural circulation in the Taupō Volcanic Zone (TVZ, New Zealand). This study aims to investigate the impact of rock anisotropy on flow paths for deep geothermal fluids in fractured, highly metamorphic volcanic reservoirs in the TVZ. This preliminary study demonstrates that permeability anisotropy significantly alters fluid flow in the crust, favouring vertical flow but reducing its intensity. In future, the thermo-hydro-mechanical and chemical effects will be studied in the TVZ. Understanding these processes is crucial for identifying the mechanisms that control the opening or sealing of fractures through hydrothermal circulation in TVZ geothermal reservoirs.