Topological characteristics of oil and gas reservoirs and their applications

Abstract

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

Original language	English
Title of host publication	Towards Integrative Machine Learning and Knowledge Extraction - BIRS Workshop, Revised Selected Papers
Editors	Andreas Holzinger, Randy Goebel, Massimo Ferri, Vasile Palade
Publisher	Springer Verlag
Pages	182-193
Number of pages	12
ISBN (Print)	9783319697741
DOIs	https://doi.org/10.1007/978-3-319-69775-8_11
Publication status	Published - 1 Jan 2017
Externally published	Yes
Event	BIRS Workshop on Advances in Interactive Knowledge Discovery and Data Mining in Complex and Big Data Sets, 2015 - Banff, Canada Duration: 24 Jul 2015 → 26 Jul 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10344 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	BIRS Workshop on Advances in Interactive Knowledge Discovery and Data Mining in Complex and Big Data Sets, 2015
Country	Canada
City	Banff
Period	24.7.15 → 26.7.15

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)

Access to Document

10.1007/978-3-319-69775-8_11

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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

Topological characteristics of oil and gas reservoirs and their applications. / Baikov, V. A.; Gilmanov, R. R.; Taimanov, I. A.; Yakovlev, A. A.

Towards Integrative Machine Learning and Knowledge Extraction - BIRS Workshop, Revised Selected Papers. ed. / Andreas Holzinger; Randy Goebel; Massimo Ferri; Vasile Palade. Springer Verlag, 2017. p. 182-193 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10344 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Baikov, VA, Gilmanov, RR, Taimanov, IA & Yakovlev, AA 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. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10344 LNAI, Springer Verlag, pp. 182-193, BIRS Workshop on Advances in Interactive Knowledge Discovery and Data Mining in Complex and Big Data Sets, 2015, Banff, Canada, 24.7.15. https://doi.org/10.1007/978-3-319-69775-8_11

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

Baikov, V. A. ; Gilmanov, R. R. ; Taimanov, I. A. ; Yakovlev, A. A. / Topological characteristics of oil and gas reservoirs and their applications. Towards Integrative Machine Learning and Knowledge Extraction - BIRS Workshop, Revised Selected Papers. editor / Andreas Holzinger ; Randy Goebel ; Massimo Ferri ; Vasile Palade. Springer Verlag, 2017. pp. 182-193 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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