Bazhenov Fm Classification Based on Wireline Logs; IOP Conference Series: Earth and Environmental Science; Vol. 33 : Contemporary Issues of Hydrogeology, Engineering Geology and Hydrogeoecology in Eurasia
| Parent link: | IOP Conference Series: Earth and Environmental Science Vol. 33 : Contemporary Issues of Hydrogeology, Engineering Geology and Hydrogeoecology in Eurasia.— 2016.— [012034, 5 p.] |
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| Zusammenfassung: | Title screen This paper considers the main aspects of Bazhenov Formation interpretation and application of machine learning algorithms for the Kolpashev type section of the Bazhenov Formation, application of automatic classification algorithms that would change the scale of research from small to large. Machine learning algorithms help interpret the Bazhenov Formation in a reference well and in other wells. During this study, unsupervised and supervised machine learning algorithms were applied to interpret lithology and reservoir properties. This greatly simplifies the routine problem of manual interpretation and has an economic effect on the cost of laboratory analysis. |
| Sprache: | Englisch |
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2016
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| Schriftenreihe: | Well drilling |
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| Online-Zugang: | http://dx.doi.org/10.1088/1755-1315/33/1/012034 http://earchive.tpu.ru/handle/11683/33992 |
| Format: | Elektronisch Buchkapitel |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=649495 |
| Zusammenfassung: | Title screen This paper considers the main aspects of Bazhenov Formation interpretation and application of machine learning algorithms for the Kolpashev type section of the Bazhenov Formation, application of automatic classification algorithms that would change the scale of research from small to large. Machine learning algorithms help interpret the Bazhenov Formation in a reference well and in other wells. During this study, unsupervised and supervised machine learning algorithms were applied to interpret lithology and reservoir properties. This greatly simplifies the routine problem of manual interpretation and has an economic effect on the cost of laboratory analysis. |
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| DOI: | 10.1088/1755-1315/33/1/012034 |