Mickaele Le Ravalec is Director of the Economics and Technology Intelligence Division at IFPEN since 2023. She holds an engineering degree in geophysics from EOST (Strasbourg, 1992), a PhD in rock physics from the University of Rennes (1995), an HDR (Habilitation to Supervise Research) from Louis Pasteur University of Strasbourg (2002), and an Executive Master’s in Corporate Finance from Paris‑Dauphine University (2021). After a postdoctoral fellowship at Stanford, she joined IFPEN in 1997, where she developed work in geomodelling (geostatistics, inverse problems, multi‑source data integration, and uncertainty management) applied to petroleum engineering and hydrology. She worked as a reservoir engineer at TotalEnergies (2007–2008), led the Geology (2014–2016), Georesources (2016–2021) and Sciences for Soils and Subsoils (2021–2022) departments at IFPEN, and held scientific expert and supervisory roles. Involved in numerous bodies (CNE2, Hcéres, UNESCO IGCP, Observatoire de Lyon), she has supervised seven PhD students, taught at leading institutions, and contributed to the organization of several international conferences. Author of 122 articles, holder of 27 patents and author/co‑editor of three books, she received the Schlumberger Prize from the French Academy of Sciences (2011) and was made a Chevalier of the Legion of Honour (2014).
M. Le Ravalec, A. Rambaud, V. Blum, Taking climate change seriously: Time to credibly communicate on coporate climate performance, Ecological Economics, 200(7), 2022, DOI:/10.1016/j.ecolecon.2022.107542.
L. de Figueiredo, D. Grana, M. Le Ravalec, Revisited formulation and applications of FFT Moving Average, Mathematical Geosciences, 2019, DOI: 10.1007/s11004-019-09826-4.
C. Gardet, M. Le Ravalec, E. Gloaguen, Pattern-based conditional simulation with a raster path: a few techniques to make it more efficient, Stoch. Environ. Res. Risk Assess., 2016, DOI: 10.1007/s00477-015-1207-1.
Thenon, A., Gervais V., Le Ravalec M., Multi-fidelity meta-modeling for reservoir engineering – application to history-matching, Computational Geosciences, 2016, DOI: 10.1007/s10596-016-9587-y.
M. Le Ravalec, B. Noetinger, and L.-Y. Hu, The FFT moving average (FFT-MA) generator: an efficient numerical method for generating and conditioning Gaussian simulations, Mathematical Geology, 32(6), 701-723, 2000.