| Presentation | 2021-03-01 An Experimental Study on Gas-Liquid Two-Phase Flow-Pattern Classification in Gas Wells Using Machine Learning Techniques Meshal Almashan, Yoshiaki Narusue, Hiroyuki Morikawa, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | 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. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Predictive modelClassificationOil and gasReservoir characterization |
| Paper # | SeMI2020-60 |
| Date of Issue | 2021-02-22 (SeMI) |
| Conference Information | |
| Committee | SeMI / IPSJ-MBL / IPSJ-UBI |
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| Conference Date | 2021/3/1(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Mobile Computing, Ubiquitous Computing, etc. |
| Chair | Susumu Ishihara(Shizuoka Univ.) |
| Vice Chair | Kazuya Monden(Hitachi) / Koji Yamamoto(Kyoto Univ.) |
| Secretary | Kazuya Monden(Kyoto Univ.) / Koji Yamamoto(Cyber Univ.) / (Hitachi) / (Waseda Univ.) |
| Assistant | Yuki Katsumata(NTT DOCOMO) / Yu Nakayama(Tokyo Univ. of Agri. and Tech.) / Akira Uchiyama(Osaka Univ.) |
| Paper Information | |
| Registration To | 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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| Language | ENG |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | An Experimental Study on Gas-Liquid Two-Phase Flow-Pattern Classification in Gas Wells Using Machine Learning Techniques |
| Sub Title (in English) | |
| Keyword(1) | Predictive modelClassificationOil and gasReservoir characterization |
| 1st Author's Name | Meshal Almashan |
| 1st Author's Affiliation | The University of Tokyo(UTokyo) |
| 2nd Author's Name | Yoshiaki Narusue |
| 2nd Author's Affiliation | The University of Tokyo(UTokyo) |
| 3rd Author's Name | Hiroyuki Morikawa |
| 3rd Author's Affiliation | The University of Tokyo(UTokyo) |
| Date | 2021-03-01 |
| Paper # | SeMI2020-60 |
| Volume (vol) | vol.120 |
| Number (no) | SeMI-382 |
| Page | pp.pp.13-18(SeMI), |
| #Pages | 6 |
| Date of Issue | 2021-02-22 (SeMI) |