Classification of the Bazhenov Formation Using Well Logs (R Field); Через интеграцию геонаук - к постижению гармонии недр
| Parent link: | Через интеграцию геонаук - к постижению гармонии недр.— 2016.— [5 c.] |
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| Erakunde egilea: | |
| Beste egile batzuk: | , , , |
| Gaia: | Title screen This paper consider the main aspects of the Bazhenov formation interpretation and applying of machine learning algorithms in cases a of Kolpashev type section of the Bazhenov formation. Application of automatic algorithms of classification which would transfer the scale of research from small to large. Machine learning algorithms help to interpret the Bazhenov formation in reference well and in the other wells. During this work the unsupervised and supervised machine learning algorithms were applied to interpret the lithology and reservoir properties. This greatly simplifies the routine problem which deals with manual interpretation and has an economic effect deal with cost of laboratory analysis. Режим доступа: по договору с организацией-держателем ресурса |
| Hizkuntza: | errusiera |
| Argitaratua: |
2016
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| Saila: | Exploration & Production in Unconventional Reservoirs |
| Gaiak: | |
| Sarrera elektronikoa: | http://dx.doi.org/10.3997/2214-4609.201600093 |
| Formatua: | Baliabide elektronikoa Liburu kapitulua |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=650140 |
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| 200 | 1 | |a Classification of the Bazhenov Formation Using Well Logs (R Field) |f D. A. Simonov [et al.] | |
| 203 | |a Текст |c электронный | ||
| 225 | 1 | |a Exploration & Production in Unconventional Reservoirs | |
| 300 | |a Title screen | ||
| 330 | |a This paper consider the main aspects of the Bazhenov formation interpretation and applying of machine learning algorithms in cases a of Kolpashev type section of the Bazhenov formation. Application of automatic algorithms of classification which would transfer the scale of research from small to large. Machine learning algorithms help to interpret the Bazhenov formation in reference well and in the other wells. During this work the unsupervised and supervised machine learning algorithms were applied to interpret the lithology and reservoir properties. This greatly simplifies the routine problem which deals with manual interpretation and has an economic effect deal with cost of laboratory analysis. | ||
| 333 | |a Режим доступа: по договору с организацией-держателем ресурса | ||
| 463 | |t Через интеграцию геонаук - к постижению гармонии недр |o материалы 7-ой Международной геолого-геофизической конференции, Санкт-Петербург, 11-14 апреля, 2016 г. |v [5 c.] |d 2016 | ||
| 610 | 1 | |a электронный ресурс | |
| 610 | 1 | |a труды учёных ТПУ | |
| 701 | 1 | |a Simonov |b D. A. |g Dmitry Arturovich | |
| 701 | 1 | |a Baranov |b V. E. |c geologist |c head of laboratory of Tomsk Polytechnic University |f 1976- |g Vitaliy Evgenievich |3 (RuTPU)RU\TPU\pers\34015 | |
| 701 | 1 | |a Bukhanov |b N. V. |c geologist |c engineer of Tomsk Polytechnic University |f 1986- |g Nikita Vladimirovich |3 (RuTPU)RU\TPU\pers\34016 | |
| 701 | 1 | |a Beschasova |b P. A. |c specialist in the field of oil and gas business |c engineer at Tomsk Polytechnic University |f 1990- |g Polina Anatoljevna |3 (RuTPU)RU\TPU\pers\37261 | |
| 712 | 0 | 2 | |a Национальный исследовательский Томский политехнический университет (ТПУ) |b Институт природных ресурсов (ИПР) |b Центр подготовки и переподготовки специалистов нефтегазового дела (ЦППС НД) |b Лаборатория геологии месторождений нефти и газа (ЛГМНГ) |3 (RuTPU)RU\TPU\col\19125 |
| 801 | 2 | |a RU |b 63413507 |c 20160914 |g RCR | |
| 856 | 4 | |u http://dx.doi.org/10.3997/2214-4609.201600093 | |
| 942 | |c CF | ||