Suitability Determination of Machine Learning Techniques for the Operational Quality Assessment of Geophysical Survey Results-Reference-Cited by-同舟云学术

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

Published:2020-12-01 Issue:2 Volume:25 Page:153-162
ISSN:2255-8691
Container-title:Applied Computer Systems
language:en
Short-container-title:

Author:

Abramov Kirill¹^ORCID,Grundspenkis Janis²^ORCID

Affiliation:

1. Branch Office “Geotehnocenter” of JSC Volcovgeology , Almaty , Kazakhstan

2. Riga Technical University , Riga , Latvia

Abstract

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.

Publisher

Walter de Gruyter GmbH

Link

https://www.sciendo.com/pdf/10.2478/acss-2020-0017

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