Multiscale data integration using coarse-scale models

Yalchin Efendiev*, A. Datta-Gupta, I. Osako, B. Mallick

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

In this paper we combine a multiscale data integration technique introduced in [Lee SH, Malallah A, Datta-Gupta A, Hidgon D. Multiscale data integration using Markov Random Fields. SPE Reservoir Evaluat Eng 2002;5(1):68-78] with upscaling techniques for spatial modeling of permeability. The main goal of this paper is to find fine-scale permeability fields based on coarse-scale permeability measurements. The approach introduced in the paper is hierarchical and the conditional information from different length scales is incorporated into the posterior distribution using a Bayesian framework. Because of a complicated structure of the posterior distribution Markov chain Monte Carlo (MCMC) based approaches are used to draw samples of the fine-scale permeability field.

Original languageEnglish (US)
Pages (from-to)303-314
Number of pages12
JournalAdvances in Water Resources
Volume28
Issue number3
DOIs
StatePublished - Mar 1 2005

ASJC Scopus subject areas

  • Water Science and Technology

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