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Method for Stochastic Inverse Modeling of Fault Geometry and Connectivity Using Flow Data
Authors:Nicolas Cherpeau  Guillaume Caumon  Jef Caers  Bruno Lévy
Institution:(1) Centre de Recherches P?trographiques et G?ochimiques, Universit? de Lorraine, ?cole Nationale Sup?rieure de G?ologie, Rue du doyen Marcel Roubault, 54501 Vandoeuvre-l?s-Nancy, France;(2) Department of Energy Resources Engineering, Stanford University, Stanford, CA 94305, USA;(3) Centre INRIA Nancy Grand-Est, Campus scientifique, 615 rue du Jardin Botanique, 54600 Villers les Nancy, France
Abstract:This paper focuses on fault-related uncertainties in the subsurface, which can significantly affect the numerical simulation of physical processes. Our goal is to use dynamic data and process-based simulation to update structural uncertainty in a Bayesian inverse approach. We propose a stochastic fault model where the number and features of faults are made variable. In particular, this model samples uncertainties about connectivity between the faults. The stochastic three dimensional fault model is integrated within a stochastic inversion scheme in order to reduce uncertainties about fault characteristics and fault zone layout, by minimizing the mismatch between observed and simulated data.
Keywords:
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