Topological characteristics of digital models of geological core

Abstract

We discuss the possibility of applying stochastic approaches to core modeling by using tools of topology. The study demonstrates the prospects of applying topological characteristics for the description of the core and the search for its analogs. Moreover application of topological characteristics (for example, in conjunction with machine learning methods) in the long term will make it possible to obtain petrophysical properties of the core samples without carrying out expensive and long-term filtration experiments.

Original language	English
Title of host publication	Machine Learning and Knowledge Extraction - Second IFIP TC 5, TC 8/WG 8.4, 8.9, TC 12/WG 12.9 International Cross-Domain Conference, CD-MAKE 2018, Proceedings
Editors	Peter Kieseberg, Edgar Weippl, Andreas Holzinger, A Min Tjoa
Publisher	Springer Verlag
Pages	273-281
Number of pages	9
ISBN (Print)	9783319997391
DOIs	https://doi.org/10.1007/978-3-319-99740-7_19
Publication status	Published - 1 Jan 2018
Externally published	Yes
Event	2nd International Cross-Domain Conference for Machine Learning and Knowledge Extraction, CD-MAKE 2018 - Hamburg, Germany Duration: 27 Aug 2018 → 30 Aug 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11015 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	2nd International Cross-Domain Conference for Machine Learning and Knowledge Extraction, CD-MAKE 2018
Country	Germany
City	Hamburg
Period	27.8.18 → 30.8.18

Keywords

Betti numbers
Digital core
Geological modeling
Topological characteristics

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-319-99740-7_19

Fingerprint Dive into the research topics of 'Topological characteristics of digital models of geological core'. Together they form a unique fingerprint.

Cite this

Gilmanov, R. R., Kalyuzhnyuk, A. V., Taimanov, I. A., & Yakovlev, A. A. (2018). Topological characteristics of digital models of geological core. In P. Kieseberg, E. Weippl, A. Holzinger, & A. M. Tjoa (Eds.), Machine Learning and Knowledge Extraction - Second IFIP TC 5, TC 8/WG 8.4, 8.9, TC 12/WG 12.9 International Cross-Domain Conference, CD-MAKE 2018, Proceedings (pp. 273-281). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11015 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-99740-7_19

Topological characteristics of digital models of geological core. / Gilmanov, Rustem R.; Kalyuzhnyuk, Alexander V.; Taimanov, Iskander A.; Yakovlev, Andrey A.

Machine Learning and Knowledge Extraction - Second IFIP TC 5, TC 8/WG 8.4, 8.9, TC 12/WG 12.9 International Cross-Domain Conference, CD-MAKE 2018, Proceedings. ed. / Peter Kieseberg; Edgar Weippl; Andreas Holzinger; A Min Tjoa. Springer Verlag, 2018. p. 273-281 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11015 LNCS).

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

Gilmanov, RR, Kalyuzhnyuk, AV, Taimanov, IA & Yakovlev, AA 2018, Topological characteristics of digital models of geological core. in P Kieseberg, E Weippl, A Holzinger & AM Tjoa (eds), Machine Learning and Knowledge Extraction - Second IFIP TC 5, TC 8/WG 8.4, 8.9, TC 12/WG 12.9 International Cross-Domain Conference, CD-MAKE 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11015 LNCS, Springer Verlag, pp. 273-281, 2nd International Cross-Domain Conference for Machine Learning and Knowledge Extraction, CD-MAKE 2018, Hamburg, Germany, 27.8.18. https://doi.org/10.1007/978-3-319-99740-7_19

Gilmanov RR, Kalyuzhnyuk AV, Taimanov IA, Yakovlev AA. Topological characteristics of digital models of geological core. In Kieseberg P, Weippl E, Holzinger A, Tjoa AM, editors, Machine Learning and Knowledge Extraction - Second IFIP TC 5, TC 8/WG 8.4, 8.9, TC 12/WG 12.9 International Cross-Domain Conference, CD-MAKE 2018, Proceedings. Springer Verlag. 2018. p. 273-281. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-99740-7_19

Gilmanov, Rustem R. ; Kalyuzhnyuk, Alexander V. ; Taimanov, Iskander A. ; Yakovlev, Andrey A. / Topological characteristics of digital models of geological core. Machine Learning and Knowledge Extraction - Second IFIP TC 5, TC 8/WG 8.4, 8.9, TC 12/WG 12.9 International Cross-Domain Conference, CD-MAKE 2018, Proceedings. editor / Peter Kieseberg ; Edgar Weippl ; Andreas Holzinger ; A Min Tjoa. Springer Verlag, 2018. pp. 273-281 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{d09557f069334b9d91d00879f8b3f677,

title = "Topological characteristics of digital models of geological core",

abstract = "We discuss the possibility of applying stochastic approaches to core modeling by using tools of topology. The study demonstrates the prospects of applying topological characteristics for the description of the core and the search for its analogs. Moreover application of topological characteristics (for example, in conjunction with machine learning methods) in the long term will make it possible to obtain petrophysical properties of the core samples without carrying out expensive and long-term filtration experiments.",

keywords = "Betti numbers, Digital core, Geological modeling, Topological characteristics",

author = "Gilmanov, {Rustem R.} and Kalyuzhnyuk, {Alexander V.} and Taimanov, {Iskander A.} and Yakovlev, {Andrey A.}",

year = "2018",

month = jan,

day = "1",

doi = "10.1007/978-3-319-99740-7_19",

language = "English",

isbn = "9783319997391",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "273--281",

editor = "Peter Kieseberg and Edgar Weippl and Andreas Holzinger and Tjoa, {A Min}",

booktitle = "Machine Learning and Knowledge Extraction - Second IFIP TC 5, TC 8/WG 8.4, 8.9, TC 12/WG 12.9 International Cross-Domain Conference, CD-MAKE 2018, Proceedings",

address = "Germany",

note = "2nd International Cross-Domain Conference for Machine Learning and Knowledge Extraction, CD-MAKE 2018 ; Conference date: 27-08-2018 Through 30-08-2018",

}

TY - GEN

T1 - Topological characteristics of digital models of geological core

AU - Gilmanov, Rustem R.

AU - Kalyuzhnyuk, Alexander V.

AU - Taimanov, Iskander A.

AU - Yakovlev, Andrey A.

PY - 2018/1/1

Y1 - 2018/1/1

N2 - We discuss the possibility of applying stochastic approaches to core modeling by using tools of topology. The study demonstrates the prospects of applying topological characteristics for the description of the core and the search for its analogs. Moreover application of topological characteristics (for example, in conjunction with machine learning methods) in the long term will make it possible to obtain petrophysical properties of the core samples without carrying out expensive and long-term filtration experiments.

AB - We discuss the possibility of applying stochastic approaches to core modeling by using tools of topology. The study demonstrates the prospects of applying topological characteristics for the description of the core and the search for its analogs. Moreover application of topological characteristics (for example, in conjunction with machine learning methods) in the long term will make it possible to obtain petrophysical properties of the core samples without carrying out expensive and long-term filtration experiments.

KW - Betti numbers

KW - Digital core

KW - Geological modeling

KW - Topological characteristics

UR - http://www.scopus.com/inward/record.url?scp=85053205337&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85053205337&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-99740-7_19

DO - 10.1007/978-3-319-99740-7_19

M3 - Conference contribution

AN - SCOPUS:85053205337

SN - 9783319997391

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 273

EP - 281

BT - Machine Learning and Knowledge Extraction - Second IFIP TC 5, TC 8/WG 8.4, 8.9, TC 12/WG 12.9 International Cross-Domain Conference, CD-MAKE 2018, Proceedings

A2 - Kieseberg, Peter

A2 - Weippl, Edgar

A2 - Holzinger, Andreas

A2 - Tjoa, A Min

PB - Springer Verlag

T2 - 2nd International Cross-Domain Conference for Machine Learning and Knowledge Extraction, CD-MAKE 2018

Y2 - 27 August 2018 through 30 August 2018

ER -