Conférence : Uncertainty reduction through geologically conditioned petrophysical constraints...

La Société géophysique de Québec (chapitre étudiant de la SEG de la ville de Québec) vous invite à participer à la conférence intitulée : 

"Uncertainty reduction through geologically conditioned petrophysical constraints in joint inversion"

Présentée par Jérémie Giraud (PhD of University of Western Australia, Centre for Exploration Targeting (School of Earth Sciences), Crawley, Australia)

Quand : vendredi 27 octobre à 14h00

Où : salle 2422 de l'INRS

La présentation, donnée en français, est basée sur l'article du même titre publié dans la revue Geophysics (vol. 82, no. 6, p.1-16, doi:10.1190/geo2016-0615.1), dont le résumé (en anglais) est reproduit ci-dessous. 

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ABSTRACT
We have developed a joint geophysical inversion workflow that aims to improve subsurface imaging and decrease uncertainty by integrating petrophysical constraints and geologic data. In this framework, probabilistic geologic modeling is used as a source of information to condition the petrophysical constraints spatially and to derive starting models. The workflow then uses petrophysical measurements to constrain the values retrieved by geophysical joint inversion. The different sources of constraints are integrated into a least-squares framework to capture and integrate information related to geophysical, petrophysical, and geologic data. This allows us to quantify the posterior state of knowledge and to calculate posterior statistical indicators. To test this workflow, using geologic field data, we have generated a set of geologic models, which we used to derive a probabilistic geologic model. In this synthetic case study, we found that the integration of geologic information and petrophysical constraints in geophysical joint inversion could reduce uncertainty and improve imaging. In particular, the use of petrophysical constraints retrieves sharper boundaries and better reproduces the statistics of the observed petrophysical measurements. The integration of probabilistic geologic modeling permits more accurate retrieval of model geometry, and it better constrains the solution while still satisfying the statistics derived from geologic data. The analysis of statistical indicators at each step of the workflow indicates that (1) the inversion methodology is effective when applied to complex geology and (2) the integration of prior information and constraints from geology and petrophysics significantly improves the inversion results while decreasing uncertainty. Finally, the analysis of uncertainty to the integration of the conditioned petrophysical constraints also indicates that, for this example, the best results are obtained for joint inversion using petrophysical constraints spatially conditioned by geologic modeling.