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FACIES CLASSIFICATION FROMWELL LOGS USING MACHINE LEARNING METHODS: A SURVEY

Julia Erzikova

First published: 2019-06-20https://doi.org/10.5593/sgem2019/2.1/s07.037View metrics

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.

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Publication details

Title
FACIES CLASSIFICATION FROMWELL LOGS USING MACHINE LEARNING METHODS: A SURVEY
Authors
Julia Erzikova
Proceedings
SGEM International Multidisciplinary Scientific GeoConference EXPO Proceedings; 19th International Multidisciplinary Scientific GeoConference SGEM2019, Informatics, Geoinformatics and Remote Sensing
Publisher
STEF92 Technology
Year
2019
Pages
281-288
SWS Citekey
Erzikova20197281288
ISSN
1314-2704
ISBN
978-619-7408-79-9
Language
en
Publication type
Conference Paper
Keywords
References8
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  2. Dorrington K.P., Link C.A., Genetic-algorithm/neural network approach to seismic attribute selection for well-log prediction, Geophysics 69, pp. 212–221, 2004.

  3. Dubois M.K., Bohling G.C., Chakrabarti S., Comparison of four approaches to a rock facies classification problem, Computers & Geosciences 33, pp. 599–617, 2007.

  4. Hall B., Facies classification using machine learning, The Leading Edge (Society of Exploration Geophysicists), 35(10), pp. 906–909, 2016.

  5. Hall B., Hall M., Distributed collaborative prediction: Results of the machine learning contest, The Leading Edge (Society of Exploration Geophysicists), 36(3), pp. 267–269, 2017.

  6. Huang Z., Williamson M.A., Artificial neural network modeling as an aid to source rock characterization, Marine and Petroleum Geology 13, pp. 227-290, 1996.

  7. Imamverdiyev Y., Sukhostat L., Lithological facies classification using deep convolutional neural network, Journal of Petroleum Science and Engineering 174, pp. 216 – 228, 2019.

  8. Total energy consumption. Acceleration in energy consumption in 2017. [Online]. Available: https://yearbook.enerdata.net/total-energy/world-consumption-statistics.html (last accessed : 12.03.2019)

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