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 | |
| 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 |
Fingerprint
Keywords
- Betti numbers
- Digital core
- Geological modeling
- Topological characteristics
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)
Cite this
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
}
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
ER -