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| 講演名 | 2018-11-05 14:20 A Study on a Feature Based Clustering and Decision Tree Regressions for Estimating the Bubble Point Pressure of Crude Oils ○Meshal Almashan・Yoshiaki Narusue・Hiroyuki Morikawa（The Univ. of Tokyo）  ASN2018-68 |
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| 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. |
| （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 / / / / |
| （英） | Predictive model / PVT / Oil and gas / Reservoir characterization / / / / | |
| 文献情報 | 信学技報, vol. 118, no. 282, ASN2018-68, pp. 75-80, 2018年11月. | |
| 資料番号 | ASN2018-68 | |
| 発行日 | 2018-10-29 (ASN) | |
| ISSN | Online edition: ISSN 2432-6380 | |
| 著作権に ついて |
技術研究報告に掲載された論文の著作権は電子情報通信学会に帰属します．(許諾番号：10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) | |
| PDFダウンロード | ASN2018-68 |
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| 研究会情報 | |
| 研究会 | ASN SRW |
| 開催期間 | 2018-11-05 - 2018-11-06 |
| 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. |
| 講演論文情報の詳細 | |
| 申込み研究会 | ASN |
| 会議コード | 2018-11-ASN-SRW |
| 本文の言語 | 英語 |
| 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（英） | |
| 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 |
| キーワード(5)（和/英） | / |
| キーワード(6)（和/英） | / |
| キーワード(7)（和/英） | / |
| キーワード(8)（和/英） | / |
| 第1著者 氏名（和/英/ヨミ） | 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/ヨミ） | 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/ヨミ） | 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) |
| 講演者 | 第1著者 |
| 発表日時 | 2018-11-05 14:20:00 |
| 発表時間 | 25分 |
| 申込先研究会 | ASN |
| 資料番号 | ASN2018-68 |
| 巻番号（vol） | vol.118 |
| 号番号（no） | no.282 |
| ページ範囲 | pp.75-80 |
| ページ数 | 6 |
| 発行日 | 2018-10-29 (ASN) |
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