PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Bayesian Petrophysical Decision Support System for Estimation of Reservoir Compositions
Willem Burgers, Wim Wigerinck, Bert Kappen and Mirano Spalburg
Proceeding BNAIC 2010 Volume 22, pp. 1-2, 2010.

Abstract

In this paper we describe a Bayesian approach for obtaining compositional estimates that combines expert knowledge with information obtained from measurements. We define a prior model for the compositional volume fractions and observation models for each of the measurement tools. Both prior and observation models are based on domain expertise. These models are combined in a joint probability model. To deal with the nonlinearities in the model, Bayesian inference is implemented by using the hybrid Monte Carlo algorithm.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
ID Code:7039
Deposited By:Bert Kappen
Deposited On:03 February 2011