Topological characteristics of oil and gas reservoirs and their applications

V. A. Baikov, R. R. Gilmanov, I. A. Taimanov, A. A. Yakovlev

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

3 Citations (Scopus)

Abstract

We demonstrate applications of topological characteristics of oil and gas reservoirs considered as three-dimensional bodies to geological modeling.

Original languageEnglish
Title of host publicationTowards Integrative Machine Learning and Knowledge Extraction - BIRS Workshop, Revised Selected Papers
EditorsAndreas Holzinger, Randy Goebel, Massimo Ferri, Vasile Palade
PublisherSpringer Verlag
Pages182-193
Number of pages12
ISBN (Print)9783319697741
DOIs
Publication statusPublished - 1 Jan 2017
Externally publishedYes
EventBIRS Workshop on Advances in Interactive Knowledge Discovery and Data Mining in Complex and Big Data Sets, 2015 - Banff, Canada
Duration: 24 Jul 201526 Jul 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10344 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceBIRS Workshop on Advances in Interactive Knowledge Discovery and Data Mining in Complex and Big Data Sets, 2015
CountryCanada
CityBanff
Period24.7.1526.7.15

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Keywords

  • Betti numbers
  • Bottleneck distance
  • Euler characteristic
  • Geological modeling
  • Oil and gas reservoirs
  • Persistent homology

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Baikov, V. A., Gilmanov, R. R., Taimanov, I. A., & Yakovlev, A. A. (2017). Topological characteristics of oil and gas reservoirs and their applications. In A. Holzinger, R. Goebel, M. Ferri, & V. Palade (Eds.), Towards Integrative Machine Learning and Knowledge Extraction - BIRS Workshop, Revised Selected Papers (pp. 182-193). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10344 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-69775-8_11