Standard

Facies classification from well logs using machine learning methods : A survey. / Erzikova, Julia; Grafeeva, Natalia.

Education and Accreditation in Geosciences; Environmental Legislation, Multilateral Relations and Funding Opportunities. 2.1. ed. International Multidisciplinary Scientific Geoconference, 2019. p. 281-288 (International Multidisciplinary Scientific GeoConference Surveying Geology and Mining Ecology Management, SGEM; Vol. 19, No. 2.1).

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

Harvard

Erzikova, J & Grafeeva, N 2019, Facies classification from well logs using machine learning methods: A survey. in Education and Accreditation in Geosciences; Environmental Legislation, Multilateral Relations and Funding Opportunities. 2.1 edn, International Multidisciplinary Scientific GeoConference Surveying Geology and Mining Ecology Management, SGEM, no. 2.1, vol. 19, International Multidisciplinary Scientific Geoconference, pp. 281-288, 19th International Multidisciplinary Scientific Geoconference, SGEM 2019, Albena, Bulgaria, 9/12/19.

APA

Erzikova, J., & Grafeeva, N. (2019). Facies classification from well logs using machine learning methods: A survey. In Education and Accreditation in Geosciences; Environmental Legislation, Multilateral Relations and Funding Opportunities (2.1 ed., pp. 281-288). (International Multidisciplinary Scientific GeoConference Surveying Geology and Mining Ecology Management, SGEM; Vol. 19, No. 2.1). International Multidisciplinary Scientific Geoconference.

Vancouver

Erzikova J, Grafeeva N. Facies classification from well logs using machine learning methods: A survey. In Education and Accreditation in Geosciences; Environmental Legislation, Multilateral Relations and Funding Opportunities. 2.1 ed. International Multidisciplinary Scientific Geoconference. 2019. p. 281-288. (International Multidisciplinary Scientific GeoConference Surveying Geology and Mining Ecology Management, SGEM; 2.1).

Author

Erzikova, Julia ; Grafeeva, Natalia. / Facies classification from well logs using machine learning methods : A survey. Education and Accreditation in Geosciences; Environmental Legislation, Multilateral Relations and Funding Opportunities. 2.1. ed. International Multidisciplinary Scientific Geoconference, 2019. pp. 281-288 (International Multidisciplinary Scientific GeoConference Surveying Geology and Mining Ecology Management, SGEM; 2.1).

BibTeX

@inproceedings{4fe80792a2cb4feca675f23c8656f4d0,
title = "Facies classification from well logs using machine learning methods: A survey",
abstract = "The problem of automatic geophysical facies classification from well logs has played a crucial role in the mining industry from the 1980s to the present day. During this period, many different approaches were proposed to cope with this task; they were based on the methods of machine learning and deep learning. This paper gives a systematic survey of modern effective solutions to the assigned problem. The comparison of the approaches to the solution which are described in the works of various researchers is presented. The analysis of the available results is given. In addition, this paper provides a detailed description of the set of qualitative input well log data suitable for research.",
keywords = "Core, Facies classification, Machine learning, Supervised multiclass classification problem, Well log data",
author = "Julia Erzikova and Natalia Grafeeva",
year = "2019",
month = jan,
day = "1",
language = "English",
isbn = "9786197408768",
series = "International Multidisciplinary Scientific GeoConference Surveying Geology and Mining Ecology Management, SGEM",
publisher = "International Multidisciplinary Scientific Geoconference",
number = "2.1",
pages = "281--288",
booktitle = "Education and Accreditation in Geosciences; Environmental Legislation, Multilateral Relations and Funding Opportunities",
address = "Bulgaria",
edition = "2.1",
note = "19th International Multidisciplinary Scientific Geoconference, SGEM 2019, SGEM2019 ; Conference date: 09-12-2019 Through 11-12-2019",

}

RIS

TY - GEN

T1 - Facies classification from well logs using machine learning methods

T2 - 19th International Multidisciplinary Scientific Geoconference, SGEM 2019

AU - Erzikova, Julia

AU - Grafeeva, Natalia

N1 - Conference code: 19

PY - 2019/1/1

Y1 - 2019/1/1

N2 - The problem of automatic geophysical facies classification from well logs has played a crucial role in the mining industry from the 1980s to the present day. During this period, many different approaches were proposed to cope with this task; they were based on the methods of machine learning and deep learning. This paper gives a systematic survey of modern effective solutions to the assigned problem. The comparison of the approaches to the solution which are described in the works of various researchers is presented. The analysis of the available results is given. In addition, this paper provides a detailed description of the set of qualitative input well log data suitable for research.

AB - The problem of automatic geophysical facies classification from well logs has played a crucial role in the mining industry from the 1980s to the present day. During this period, many different approaches were proposed to cope with this task; they were based on the methods of machine learning and deep learning. This paper gives a systematic survey of modern effective solutions to the assigned problem. The comparison of the approaches to the solution which are described in the works of various researchers is presented. The analysis of the available results is given. In addition, this paper provides a detailed description of the set of qualitative input well log data suitable for research.

KW - Core

KW - Facies classification

KW - Machine learning

KW - Supervised multiclass classification problem

KW - Well log data

UR - http://www.scopus.com/inward/record.url?scp=85073322107&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:85073322107

SN - 9786197408768

T3 - International Multidisciplinary Scientific GeoConference Surveying Geology and Mining Ecology Management, SGEM

SP - 281

EP - 288

BT - Education and Accreditation in Geosciences; Environmental Legislation, Multilateral Relations and Funding Opportunities

PB - International Multidisciplinary Scientific Geoconference

Y2 - 9 December 2019 through 11 December 2019

ER -

ID: 48946897