Geological Metric Space Description by SVM Classification - Turbidite Reservoir Case Study with Multiple Training Images

Alexandra Kuznetsova, Vasily Demyanov, Michael Andrew Christie

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

Abstract

This paper shows the challenges related to handling multiple training images for reservoir prediction. We have identified two of the main challenges in handling multiple geological scenarios by creating a lower dimensional representation of the ensemble of model realizations: (i) how to relate geological knowledge to the metric space; and (ii) how to navigate in the metric space to facilitate in model update. In this work we demonstrate how to solve the classification problem in the metric space accounting for geological knowledge from a variety of prior geological concepts. In this paper we established geological relations in the metric space by making the links to the space of geologically interpretable parameters. These results would allow us to enhance geological realism of the new models obtained through the update process in the metric space.

Original languageEnglish
Title of host publicationPetroleum Geostatistics 2015
PublisherEAGE Publishing BV
Pages283-287
Number of pages5
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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