Integration of Multi-scale Uncertainty Assessment into Geostatistical Seismic Inversion

L. Azevedo, Vasily Demyanov, A. Soares

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Traditional geostatistical seismic inversion approaches are able to account for the uncertainty related with the stochastic simulation algorithms that are used as part of the inverse methodology for the model perturbation. However, they assume stationarity and no uncertainty related with large scale geological parameters represented for example by the spatial continuity pattern and the prior probability distribution of the property to invert as estimated from well-log data. We propose a multi-scale uncertainty assessment for traditional iterative geostatistical seismic methodologies by integrating stochastic adaptive sampling and Bayesian inference to tune the variogram ranges and the prior probability distribution of the property to invert within the inverse workflow. The application of the proposed methodology to a challenging synthetic dataset showed a good convergence of the inverted seismic towards the recorded one while the local and global uncertainty were jointly assessed.

Original languageEnglish
Title of host publicationPetroleum Geostatistics 2015
PublisherEAGE Publishing BV
Pages288-292
Number of pages5
Volume1
ISBN (Print)9781510814110
DOIs
Publication statusPublished - 7 Sep 2015
EventPetroleum Geostatistics 2015 - Biarritz, France
Duration: 7 Sep 201511 Sep 2015

Conference

ConferencePetroleum Geostatistics 2015
CountryFrance
CityBiarritz
Period7/09/1511/09/15

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology
  • Geophysics
  • Statistics, Probability and Uncertainty
  • Geology

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