Prospectivity evaluation by seismic trace form classification

V. V. Demyanov, V. B. Belozerov, V. E. Baranov

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

Abstract

The aim of this work is to evaluate the prospective areas with good hydro carbon reservoir quality by using machine learning approaches to classify seismic traces. Quality of reservoir described by lithological features is encoded in the shape of seismic trace. Detection of good reservoir units is difficult due to their small thicknesses. A traditional manual interpretation performed on a set of 2D seismic profiles, covering license block, has detected a region with good reservoir quality. Based on these results the automatic procedure is offered to get the quick-look evaluation of the most perspective areas for development drilling.

Original languageEnglish
Title of host publicationProceedings of IAMG 2015 - 17th Annual Conference of the International Association for Mathematical Geosciences
EditorsRaimon Tolosana Delgado, K. Gerald van den Boogaart, Regina van den Boogaart, Helmut Schaeben
PublisherInternational Association for Mathematical Geology (IAMG)
Pages626-632
Number of pages7
ISBN (Electronic)9783000503375
Publication statusPublished - 1 Jan 2015
Event17th Annual Conference of the International Association for Mathematical Geosciences, IAMG 2015 - Freiberg, Germany
Duration: 5 Sep 201513 Sep 2015

Publication series

NameProceedings of IAMG 2015 - 17th Annual Conference of the International Association for Mathematical Geosciences

Conference

Conference17th Annual Conference of the International Association for Mathematical Geosciences, IAMG 2015
CountryGermany
CityFreiberg
Period5.9.1513.9.15

Fingerprint

Trace
Evaluation
Drilling
Machine Learning
Carbon
Covering
Classify
drilling
Unit
Form
evaluation
Evaluate
carbon

ASJC Scopus subject areas

  • Mathematics (miscellaneous)
  • Earth and Planetary Sciences(all)

Cite this

Demyanov, V. V., Belozerov, V. B., & Baranov, V. E. (2015). Prospectivity evaluation by seismic trace form classification. In R. T. Delgado, K. G. van den Boogaart, R. van den Boogaart, & H. Schaeben (Eds.), Proceedings of IAMG 2015 - 17th Annual Conference of the International Association for Mathematical Geosciences (pp. 626-632). (Proceedings of IAMG 2015 - 17th Annual Conference of the International Association for Mathematical Geosciences). International Association for Mathematical Geology (IAMG).

Prospectivity evaluation by seismic trace form classification. / Demyanov, V. V.; Belozerov, V. B.; Baranov, V. E.

Proceedings of IAMG 2015 - 17th Annual Conference of the International Association for Mathematical Geosciences. ed. / Raimon Tolosana Delgado; K. Gerald van den Boogaart; Regina van den Boogaart; Helmut Schaeben. International Association for Mathematical Geology (IAMG), 2015. p. 626-632 (Proceedings of IAMG 2015 - 17th Annual Conference of the International Association for Mathematical Geosciences).

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

Demyanov, VV, Belozerov, VB & Baranov, VE 2015, Prospectivity evaluation by seismic trace form classification. in RT Delgado, KG van den Boogaart, R van den Boogaart & H Schaeben (eds), Proceedings of IAMG 2015 - 17th Annual Conference of the International Association for Mathematical Geosciences. Proceedings of IAMG 2015 - 17th Annual Conference of the International Association for Mathematical Geosciences, International Association for Mathematical Geology (IAMG), pp. 626-632, 17th Annual Conference of the International Association for Mathematical Geosciences, IAMG 2015, Freiberg, Germany, 5.9.15.
Demyanov VV, Belozerov VB, Baranov VE. Prospectivity evaluation by seismic trace form classification. In Delgado RT, van den Boogaart KG, van den Boogaart R, Schaeben H, editors, Proceedings of IAMG 2015 - 17th Annual Conference of the International Association for Mathematical Geosciences. International Association for Mathematical Geology (IAMG). 2015. p. 626-632. (Proceedings of IAMG 2015 - 17th Annual Conference of the International Association for Mathematical Geosciences).
Demyanov, V. V. ; Belozerov, V. B. ; Baranov, V. E. / Prospectivity evaluation by seismic trace form classification. Proceedings of IAMG 2015 - 17th Annual Conference of the International Association for Mathematical Geosciences. editor / Raimon Tolosana Delgado ; K. Gerald van den Boogaart ; Regina van den Boogaart ; Helmut Schaeben. International Association for Mathematical Geology (IAMG), 2015. pp. 626-632 (Proceedings of IAMG 2015 - 17th Annual Conference of the International Association for Mathematical Geosciences).
@inproceedings{7630711a7cdb4255b71a5901f6ff29ad,
title = "Prospectivity evaluation by seismic trace form classification",
abstract = "The aim of this work is to evaluate the prospective areas with good hydro carbon reservoir quality by using machine learning approaches to classify seismic traces. Quality of reservoir described by lithological features is encoded in the shape of seismic trace. Detection of good reservoir units is difficult due to their small thicknesses. A traditional manual interpretation performed on a set of 2D seismic profiles, covering license block, has detected a region with good reservoir quality. Based on these results the automatic procedure is offered to get the quick-look evaluation of the most perspective areas for development drilling.",
author = "Demyanov, {V. V.} and Belozerov, {V. B.} and Baranov, {V. E.}",
year = "2015",
month = "1",
day = "1",
language = "English",
series = "Proceedings of IAMG 2015 - 17th Annual Conference of the International Association for Mathematical Geosciences",
publisher = "International Association for Mathematical Geology (IAMG)",
pages = "626--632",
editor = "Delgado, {Raimon Tolosana} and {van den Boogaart}, {K. Gerald} and {van den Boogaart}, Regina and Helmut Schaeben",
booktitle = "Proceedings of IAMG 2015 - 17th Annual Conference of the International Association for Mathematical Geosciences",

}

TY - GEN

T1 - Prospectivity evaluation by seismic trace form classification

AU - Demyanov, V. V.

AU - Belozerov, V. B.

AU - Baranov, V. E.

PY - 2015/1/1

Y1 - 2015/1/1

N2 - The aim of this work is to evaluate the prospective areas with good hydro carbon reservoir quality by using machine learning approaches to classify seismic traces. Quality of reservoir described by lithological features is encoded in the shape of seismic trace. Detection of good reservoir units is difficult due to their small thicknesses. A traditional manual interpretation performed on a set of 2D seismic profiles, covering license block, has detected a region with good reservoir quality. Based on these results the automatic procedure is offered to get the quick-look evaluation of the most perspective areas for development drilling.

AB - The aim of this work is to evaluate the prospective areas with good hydro carbon reservoir quality by using machine learning approaches to classify seismic traces. Quality of reservoir described by lithological features is encoded in the shape of seismic trace. Detection of good reservoir units is difficult due to their small thicknesses. A traditional manual interpretation performed on a set of 2D seismic profiles, covering license block, has detected a region with good reservoir quality. Based on these results the automatic procedure is offered to get the quick-look evaluation of the most perspective areas for development drilling.

UR - http://www.scopus.com/inward/record.url?scp=84961285292&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84961285292&partnerID=8YFLogxK

M3 - Conference contribution

T3 - Proceedings of IAMG 2015 - 17th Annual Conference of the International Association for Mathematical Geosciences

SP - 626

EP - 632

BT - Proceedings of IAMG 2015 - 17th Annual Conference of the International Association for Mathematical Geosciences

A2 - Delgado, Raimon Tolosana

A2 - van den Boogaart, K. Gerald

A2 - van den Boogaart, Regina

A2 - Schaeben, Helmut

PB - International Association for Mathematical Geology (IAMG)

ER -