Method for Stochastic Inverse Modeling of Fault Geometry and Connectivity Using Flow Data |
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Authors: | Nicolas Cherpeau Guillaume Caumon Jef Caers Bruno Lévy |
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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 |
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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. |
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