Reservoir characterization using stochastic initializations and the level set method

Authors:
Rossmary Villegas;Oliver Dorn;Miguel Moscoso;Manuel Kindelan
Affiliations:
Modelling and Numerical Simulation Group, Universidad Carlos III de Madrid, Avda. de la Universidad 30, 28911 Leganés, Spain;Modelling and Numerical Simulation Group, Universidad Carlos III de Madrid, Avda. de la Universidad 30, 28911 Leganés, Spain;Modelling and Numerical Simulation Group, Universidad Carlos III de Madrid, Avda. de la Universidad 30, 28911 Leganés, Spain;Modelling and Numerical Simulation Group, Universidad Carlos III de Madrid, Avda. de la Universidad 30, 28911 Leganés, Spain
Venue:
Computers & Mathematics with Applications
Year:
2008

Citing 3
Cited 1

Control of singular problem via differentiation of a min-max

Systems & Control Letters
Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations

Journal of Computational Physics
Regularization of ill-posed problems via the level set approach

SIAM Journal on Applied Mathematics

History matching of facies distribution with the EnKF and level set parameterization

Journal of Computational Physics

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Abstract

In this work we present a novel level set technique for shape reconstruction in history matching for reservoirs with two or more kinds of rocks (the so-called lithofacies) using stochastic initializations. In the paper we discuss the use of sequential Gaussian simulation for the creation of geostatistical initial guesses which will then be applied to an earlier introduced level set based shape reconstruction algorithm for the history matching problem in reservoir characterization. The shapes or regions with sharp interfaces to each other are represented by a level set function which needs to be determined from the production data gathered at few locations of the reservoir. We present numerical results in 2D which demonstrate that our method is able to provide reliable estimates of this structure from these relatively few production data even though the topology of the unknown regions is a priori unknown.