Prospectivity evaluation by seismic trace form classification

V. V. Demyanov; V. B. Belozerov; V. E. Baranov

Prospectivity evaluation by seismic trace form classification

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

^*Corresponding author for this work

School of Energy, Geoscience, Infrastructure and Society

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	Proceedings of IAMG 2015
Publisher	International Association for Mathematical Geology (IAMG)
Pages	626-632
Number of pages	7
ISBN (Electronic)	9783000503375
Publication status	Published - 2015
Event	17th Annual Conference of the International Association for Mathematical Geosciences 2015 - Freiberg, Germany Duration: 5 Sept 2015 → 13 Sept 2015

Conference

Conference	17th Annual Conference of the International Association for Mathematical Geosciences 2015
Country/Territory	Germany
City	Freiberg
Period	5/09/15 → 13/09/15

ASJC Scopus subject areas

Mathematics (miscellaneous)
General Earth and Planetary Sciences

Cite this

@inproceedings{3f694a7b7a2e41aeae4db4d4f8db768c,

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",

language = "English",

pages = "626--632",

booktitle = "Proceedings of IAMG 2015",

publisher = "International Association for Mathematical Geology (IAMG)",

note = "17th Annual Conference of the International Association for Mathematical Geosciences 2015 ; Conference date: 05-09-2015 Through 13-09-2015",

}

TY - GEN

T1 - Prospectivity evaluation by seismic trace form classification

AU - Demyanov, V. V.

AU - Belozerov, V. B.

AU - Baranov, V. E.

PY - 2015

Y1 - 2015

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

M3 - Conference contribution

AN - SCOPUS:84961285292

SP - 626

EP - 632

BT - Proceedings of IAMG 2015

PB - International Association for Mathematical Geology (IAMG)

T2 - 17th Annual Conference of the International Association for Mathematical Geosciences 2015

Y2 - 5 September 2015 through 13 September 2015

ER -

Prospectivity evaluation by seismic trace form classification

Abstract

Conference

ASJC Scopus subject areas

Other files and links

Fingerprint

Cite this