机器学习在维库洛夫地层测井解释中的应用

IF 0.8 Q3 ENGINEERING, PETROLEUM Georesursy Pub Date : 2022-05-16 DOI:10.18599/grs.2022.2.21
Vladlen I. Sakhnyuk, E. V. Novikov, Alexander M. Sharifullin, Vasiliy S. Belokhin, A. Antonov, Mikhail U. Karpushin, M. Bolshakova, S. Afonin, R. Sautkin, A. Suslova
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引用次数: 1

摘要

目前,测井曲线是由地质学家解释的,他们为此目的对数据进行预处理并对曲线进行归一化。准备过程可能需要很长时间,特别是当涉及成百上千口井时。本文探讨了机器学习方法在地质任务中的适用性,特别是利用测井资料进行岩性解释的问题,并揭示了与专家解释相比,这种预测的质量问题。该文章的作者部署了三组机器学习算法:随机森林、梯度增强和神经网络,并开发了自己的度量标准,该度量标准考虑了研究区域的地质特征和基于对数曲线值的岩石类型的统计接近性。
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Machine learning applications for well-logging interpretation of the Vikulov Formation
Nowadays well logging curves are interpreted by geologists who preprocess the data and normalize the curves for this purpose. The preparation process can take a long time, especially when hundreds and thousands of wells are involved. This paper explores the applicability of Machine Learning methods to geology tasks, in particular the problem of lithology interpretation using well-logs, and also reveals the issue of the quality of such predictions in comparison with the interpretation of specialists. The authors of the article deployed three groups of Machine Learning algorithms: Random Forests, Gradient Boosting and Neural Networks, and also developed its own metric that takes into account the geological features of the study area and statistical proximity of lithotypes based on log curves values.
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来源期刊
Georesursy
Georesursy ENGINEERING, PETROLEUM-
CiteScore
1.50
自引率
25.00%
发文量
49
审稿时长
16 weeks
期刊最新文献
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