| 講演名 | 2021-03-01 An Experimental Study on Gas-Liquid Two-Phase Flow-Pattern Classification in Gas Wells Using Machine Learning Techniques ミシェル アルマシャン(東大), 成末 義哲(東大), 森川 博之(東大), |
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| 抄録(和) | |
| 抄録(英) | When gas and liquid are flowing simultaneously inside a transport conduit, their spatial distribution is referred to as the flow pattern of a multi-phase flow. In the industry of petroleum engineering, multiphase flow characterization is required in many applications. Therefore, an accurate gas-liquid flow pattern predictive model is what the industry is aiming for in this field. Flow patterns change from one to another based on the gas and the liquid flow rates, the properties of fluid and gas, the diameter and the incline of the pipeline, and based on other fluid mechanical properties. The datasets used in this study were collected from a natural gas facility in Niigata in Japan, by Japan Oil, Gas and Metals National Corporation (JOGMEC). The experimentally derived datasets include the superficial gas velocity, the superficial liquid velocity, the flow pattern, the pressure, the temperature and the liquid holdup. The data acquisition was performed with a pipeline of a large diameter (106.3 mm) and it was inclined at three different angles (0°, 1°, and 3°). The pressure was set at two different rates (592 and 2060 kPa) and the liquid and gas flow rates were covering a wide range of flow rates. In this study, a machine learning based model is trained and tested in predicting gas-liquid two-phase flow patterns. The multiclass decision jungle algorithm was used in building the predictive model. The dataset is imbalanced. Therefore, over-sampling and under-sampling techniques were used as a preprocessing step before training the predictive model, in order to increase the prediction accuracy. A comparative study has been carried out in this research to compare the performance of the multiclass decision jungle algorithm with other classification machine learning algorithms. Results show that the classified two-phase gas-liquid flow-patterns are in a good agreement with the experimental ones. Furthermore, the feature importance of the trained models is discussed in this study. |
| キーワード(和) | |
| キーワード(英) | Predictive modelClassificationOil and gasReservoir characterization |
| 資料番号 | SeMI2020-60 |
| 発行日 | 2021-02-22 (SeMI) |
| 研究会情報 | |
| 研究会 | SeMI / IPSJ-MBL / IPSJ-UBI |
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| 開催期間 | 2021/3/1(から2日開催) |
| 開催地（和） | オンライン開催 |
| 開催地（英） | Online |
| テーマ（和） | 一般, モバイルコンピューティング, ユビキタスコンピューティング |
| テーマ（英） | Mobile Computing, Ubiquitous Computing, etc. |
| 委員長氏名（和） | 石原 進(静岡大) |
| 委員長氏名（英） | Susumu Ishihara(Shizuoka Univ.) |
| 副委員長氏名（和） | 門田 和也(日立) / 山本 高至(京大) |
| 副委員長氏名（英） | Kazuya Monden(Hitachi) / Koji Yamamoto(Kyoto Univ.) |
| 幹事氏名（和） | 西尾 理志(京大) / 橋本 匡史(サイバー大) / 五十嵐 悠一(日立) / 金井 謙治(早大) |
| 幹事氏名（英） | Takayuki Nishio(Kyoto Univ.) / Masafumi Hashimoto(Cyber Univ.) / Yuichi Igarashi(Hitachi) / Kenji Kanai(Waseda Univ.) |
| 幹事補佐氏名（和） | 勝間田 優樹(NTTドコモ) / 中山 悠(東京農工大) / 内山 彰(阪大) |
| 幹事補佐氏名（英） | Yuki Katsumata(NTT DOCOMO) / Yu Nakayama(Tokyo Univ. of Agri. and Tech.) / Akira Uchiyama(Osaka Univ.) |
| 講演論文情報詳細 | |
| 申込み研究会 | Technical Committee on Sensor Network and Mobile Intelligence / Special Interest Group on Mobile Computing and Pervasive Systems / Special Interest Group on Ubiquitous Computing System |
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| 本文の言語 | ENG |
| タイトル（和） | |
| サブタイトル（和） | |
| タイトル（英） | An Experimental Study on Gas-Liquid Two-Phase Flow-Pattern Classification in Gas Wells Using Machine Learning Techniques |
| サブタイトル（和） | |
| キーワード(1)（和/英） | / Predictive modelClassificationOil and gasReservoir characterization |
| 第 1 著者 氏名（和/英） | ミシェル アルマシャン / Meshal Almashan |
| 第 1 著者 所属（和/英） | 東京大学(略称：東大) The University of Tokyo(略称：UTokyo) |
| 第 2 著者 氏名（和/英） | 成末 義哲 / Yoshiaki Narusue |
| 第 2 著者 所属（和/英） | 東京大学(略称：東大) The University of Tokyo(略称：UTokyo) |
| 第 3 著者 氏名（和/英） | 森川 博之 / Hiroyuki Morikawa |
| 第 3 著者 所属（和/英） | 東京大学(略称：東大) The University of Tokyo(略称：UTokyo) |
| 発表年月日 | 2021-03-01 |
| 資料番号 | SeMI2020-60 |
| 巻番号（vol） | vol.120 |
| 号番号（no） | SeMI-382 |
| ページ範囲 | pp.13-18(SeMI), |
| ページ数 | 6 |
| 発行日 | 2021-02-22 (SeMI) |