| 講演名 | 2018-11-05 A Study on a Feature Based Clustering and Decision Tree Regressions for Estimating the Bubble Point Pressure of Crude Oils Meshal Almashan(東大), Yoshiaki Narusue(東大), Hiroyuki Morikawa(東大), |
|---|---|
| PDFダウンロードページ |  PDFダウンロードページへ |
| 抄録(和) | Bubble point pressure (Pb) is one of the most important Pressure-Volume-Temperature (PVT) properties of any crude oil system, it is required in calculations used in production and reservoir engineering. By laboratory experiments, the PVT properties can accurately be determined. However, laboratory experiments require applying special tests on the oil samples which need to be handled with care. As alternative approaches, researchers have developed equations of state (EOS) and empirical correlations for estimating the PVT properties. However, these alternative solutions have some limitations. With the introduction of the machine learning applications in the petroleum industry, other researchers have studied the predictive power of several machine learning models for estimating Pb. One of the most commonly tested and applied modeling schemes are the artificial neural networks (ANNs). However, ANNs suffer from the ?black-box? problem and there is no direct and heuristic way of determining the importance of the input parameters in predicting the PVT properties. In the present study, a Boosted Decision Tree Regression (BDTR) predictive model with K-means clustering is built and evaluated in the estimation of Pb. |
| 抄録(英) | Bubble point pressure (Pb) is one of the most important Pressure-Volume-Temperature (PVT) properties of any crude oil system, it is required in calculations used in production and reservoir engineering. By laboratory experiments, the PVT properties can accurately be determined. However, laboratory experiments require applying special tests on the oil samples which need to be handled with care. As alternative approaches, researchers have developed equations of state (EOS) and empirical correlations for estimating the PVT properties. However, these alternative solutions have some limitations. With the introduction of the machine learning applications in the petroleum industry, other researchers have studied the predictive power of several machine learning models for estimating Pb. One of the most commonly tested and applied modeling schemes are the artificial neural networks (ANNs). However, ANNs suffer from the ?black-box? problem and there is no direct and heuristic way of determining the importance of the input parameters in predicting the PVT properties. In the present study, a Boosted Decision Tree Regression (BDTR) predictive model with K-means clustering is built and evaluated in the estimation of Pb. |
| キーワード(和) | Predictive model / PVT / Oil and gas / Reservoir characterization |
| キーワード(英) | Predictive model / PVT / Oil and gas / Reservoir characterization |
| 資料番号 | ASN2018-68 |
| 発行日 | 2018-10-29 (ASN) |
| 研究会情報 | |
| 研究会 | ASN / SRW |
|---|---|
| 開催期間 | 2018/11/5(から2日開催) |
| 開催地（和） | 東京電機大学 東京千住キャンパス |
| 開催地（英） | Tokyo Denki University, Tokyo Senju Campus |
| テーマ（和） | IoTワークショップ， 技術展示， ポスターセッション， 及び一般 SICEスマートセンシングシステム部会併催 |
| テーマ（英） | Poster session, etc. |
| 委員長氏名（和） | 岡田 啓(名大) / 中川 匡夫(鳥取大) |
| 委員長氏名（英） | Hiraku Okada(Nagoya Univ.) / Tadao Nakagawa(Tottori Univ.) |
| 副委員長氏名（和） | 山本 高至(京大) / 中澤 仁(慶大) / 門田 和也(日立) / 田野 哲(岡山大) / 濱湊 真(富士通研) |
| 副委員長氏名（英） | Koji Yamamoto(Kyoto Univ.) / Jin Nakazawa(Keio Univ.) / Kazuya Monden(Hitachi) / Satoshi Denno(Okayama Univ.) / Makoto Hamaminato(Fujitsu labs.) |
| 幹事氏名（和） | 大和田 泰伯(NICT) / 萬代 雅希(上智大) / 川喜田 佑介(神奈川工科大) / 水谷 圭一(京大) / 斎藤 健太郎(東工大) |
| 幹事氏名（英） | Yasunori Owada(NICT) / Masaki Bandai(Sophia Univ.) / Yusuke Kawakita(Kanagawa Inst. of Tech.) / Keiichi Mizutani(Kyoto Univ.) / Kentaro Saito(Tokyo Inst. of Tech.) |
| 幹事補佐氏名（和） | 橋本 匡史(阪大) / 大田 知行(広島市立大) / 菊月 達也(富士通研) / 中野 亮(日立) / 堀田 善文(三菱電機) / 山内 宏真(富士通研) / 野田 華子(アンリツ) |
| 幹事補佐氏名（英） | Masafumi Hashimoto(Osaka Univ.) / Tomoyuki Ota(Hiroshima City Univ.) / Tatsuya Kikuzuki(Fujitu Lab.) / Ryo Nakano(HITACHI) / Yoshifumi Hotta(Mitsubishi Electric) / Hiromasa Yamauchi(Fujitsu labs.) / Hanako Noda(Anritsu) |
| 講演論文情報詳細 | |
| 申込み研究会 | Technical Committee on Ambient intelligence and Sensor Networks / Technical Committee on Short Range Wireless Communications |
|---|---|
| 本文の言語 | ENG |
| タイトル（和） | |
| サブタイトル（和） | |
| タイトル（英） | A Study on a Feature Based Clustering and Decision Tree Regressions for Estimating the Bubble Point Pressure of Crude Oils |
| サブタイトル（和） | |
| キーワード(1)（和/英） | Predictive model / Predictive model |
| キーワード(2)（和/英） | PVT / PVT |
| キーワード(3)（和/英） | Oil and gas / Oil and gas |
| キーワード(4)（和/英） | Reservoir characterization / Reservoir characterization |
| 第 1 著者 氏名（和/英） | Meshal Almashan / Meshal Almashan |
| 第 1 著者 所属（和/英） | The University of Tokyo(略称：東大) The University of Tokyo(略称：The Univ. of Tokyo) |
| 第 2 著者 氏名（和/英） | Yoshiaki Narusue / Yoshiaki Narusue |
| 第 2 著者 所属（和/英） | The University of Tokyo(略称：東大) The University of Tokyo(略称：The Univ. of Tokyo) |
| 第 3 著者 氏名（和/英） | Hiroyuki Morikawa / Hiroyuki Morikawa |
| 第 3 著者 所属（和/英） | The University of Tokyo(略称：東大) The University of Tokyo(略称：The Univ. of Tokyo) |
| 発表年月日 | 2018-11-05 |
| 資料番号 | ASN2018-68 |
| 巻番号（vol） | vol.118 |
| 号番号（no） | ASN-282 |
| ページ範囲 | pp.75-80(ASN), |
| ページ数 | 6 |
| 発行日 | 2018-10-29 (ASN) |