17 MarEarth Science and Engineering Graduate SeminarZOOM WEBINAR: Geophysical monitoring of CO2 sequestration in deep saline aquifers
ZOOM WEBINAR: Geophysical monitoring of CO2 sequestration in deep saline aquifers
  • Prof. Dario Grana
  • University of Wyoming
  • Wednesday, March 17, 2021
  • 04:45 PM - 05:45 PM
2021-03-17T16:452021-03-17T17:45Asia/RiyadhZOOM WEBINAR: Geophysical monitoring of CO2 sequestration in deep saline aquifersKAUST, WEBINAR VIA ZOOMDr. Thomas Finkbeinerthomas.finkbeiner@kaust.edu.sa


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Abstract: Carbon dioxide sequestration in deep saline aquifers and depleted reservoirs relies on numerical models for the prediction of the spatial distribution of CO2 saturation during injection and migration. Due to the limited knowledge of the rock and fluid properties distribution, model predictions are often uncertain and must be updated when new measurements are available. The spatial distribution of CO2 saturation and the plume location can be monitored using time-lapse geophysical data, such as seismic and electromagnetic surveys. Geostatistical inversion methods provide a valid tool for the prediction of the time-dependent spatial distribution of CO2 saturation from geophysical data. The predicted models of CO2 saturation are obtained by updating an ensemble of geostatistically generated prior realizations, based on the misfit between geophysical model predictions and measured data. Stochastic methods allow estimating the posterior probability density function of the model variables conditioned by the observed data, hence providing a reliable estimate of the uncertainty, but are generally computationally demanding and often present numerical challenges in terms of convergence and acceptance ratio. Ensemble based methods represent a family of iterative algorithms that simultaneously update an ensemble of geostatistical realizations such that the model predictions match the measured data. This approach is efficient for non-linear inverse problems for which the computation of the conditional means and conditional covariance matrices of the model given the data cannot be analytically solved. The ensemble of posterior realizations is then used to predict the most likely model and its uncertainty. The method is illustrated through the application to the Johansen formation model, offshore Norway, using synthetic seismic and electromagnetic data.

Biography: Dario Grana is an associate professor in the Department of Geology and Geophysics at the University of Wyoming. He received a MS in Mathematics at University of Pavia (Italy) in 2005, a MS in Applied Mathematics at University of Milano Bicocca (Italy) in 2006, and a Ph.D. in Geophysics at Stanford University in 2013. He worked four years at Eni Exploration and Production in Milan. He joined the University of Wyoming in 2013. He is coauthor of the book ‘Seismic Reflections of Rock Properties’, published by Cambridge University Press in 2014. He is the recipient of the 2017 EAGE Van Weelden Award, the 2016 SEG Karcher Award, and the 2014 Eni award with Gary Mavko, Tapan Mukerji, and Jack Dvorkin for “pioneering innovations in theoretical and practical rock physics for seismic reservoir characterization”. His main research interests are rock physics, seismic reservoir characterization, geostatistics, data-assimilation, and inverse problems for subsurface modeling.


  • Dr. Thomas Finkbeiner
  • thomas.finkbeiner@kaust.edu.sa