<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Well logging, also known as a geophysical survey, is one of the main components of a nuclear fuel cycle. This survey follows directly after the drilling process, and the operational quality assessment of its results is a very serious problem. Any mistake in this survey can lead to the culling of the whole well. This paper examines the feasibility of applying machine learning techniques to quickly assess the well logging quality results. The studies were carried out by a reference well modelling for the selected uranium deposit of the Republic of Kazakhstan and further comparing it with the results of geophysical surveys recorded earlier. The parameters of the geophysical methods and the comparison rules for them were formulated after the reference well modelling process. The classification trees and the artificial neural networks were used during the research process and the results obtained for both methods were compared with each other. The results of this paper may be useful to the enterprises engaged in the geophysical well surveys and data processing obtained during the logging process.</p></abstract>

Well logging, also known as a geophysical survey, is one of the main components of a nuclear fuel cycle. This survey follows directly after the drilling process, and the operational quality assessment of its results is a very serious problem. Any mistake in this survey can lead to the culling of the whole well. This paper examines the feasibility of applying machine learning techniques to quickly assess the well logging quality results. The studies were carried out by a reference well modelling for the selected uranium deposit of the Republic of Kazakhstan and further comparing it with the results of geophysical surveys recorded earlier. The parameters of the geophysical methods and the comparison rules for them were formulated after the reference well modelling process. The classification trees and the artificial neural networks were used during the research process and the results obtained for both methods were compared with each other. The results of this paper may be useful to the enterprises engaged in the geophysical well surveys and data processing obtained during the logging process.

Suitability Determination of Machine Learning Techniques for the Operational Quality Assessment of Geophysical Survey Results

Applied Computer Systems

This work is licensed under the Creative Commons Attribution 4.0 International License.

Well logging, also known as a geophysical survey, is one of the main components of a nuclear fuel cycle. This survey follows directly after the drilling...

Suitability Determination of Machine Learning Techniques for the Operational Quality Assessment of Geophysical Survey Results

Online veröffentlicht: 28. Dez. 2020

Seitenbereich: 153 - 162

DOI: https://doi.org/10.2478/acss-2020-0017

Schlüsselwörter
Classification trees, machine learning, neural networks, well logging

© 2020 Kirill Abramov et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Suitability Determination of Machine Learning Techniques for the Operational Quality Assessment of Geophysical Survey Results

Online veröffentlicht: 28. Dez. 2020

Seitenbereich: 153 - 162

DOI: https://doi.org/10.2478/acss-2020-0017

SchlüsselwörterClassification trees, machine learning, neural networks, well logging

© 2020 Kirill Abramov et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Classification trees, machine learning, neural networks, well logging