| Presentation | 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, Yoshiaki Narusue, Hiroyuki Morikawa, |
|---|---|
| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | 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. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Predictive model / PVT / Oil and gas / Reservoir characterization |
| Paper # | ASN2018-68 |
| Date of Issue | 2018-10-29 (ASN) |
| Conference Information | |
| Committee | ASN / SRW |
|---|---|
| Conference Date | 2018/11/5(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Tokyo Denki University, Tokyo Senju Campus |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Poster session, etc. |
| Chair | Hiraku Okada(Nagoya Univ.) / Tadao Nakagawa(Tottori Univ.) |
| Vice Chair | Koji Yamamoto(Kyoto Univ.) / Jin Nakazawa(Keio Univ.) / Kazuya Monden(Hitachi) / Satoshi Denno(Okayama Univ.) / Makoto Hamaminato(Fujitsu labs.) |
| Secretary | Koji Yamamoto(NICT) / Jin Nakazawa(Sophia Univ.) / Kazuya Monden(Kanagawa Inst. of Tech.) / Satoshi Denno(Kyoto Univ.) / Makoto Hamaminato(Tokyo Inst. of Tech.) |
| Assistant | 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) |
| Paper Information | |
| Registration To | Technical Committee on Ambient intelligence and Sensor Networks / Technical Committee on Short Range Wireless Communications |
|---|---|
| Language | ENG |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A Study on a Feature Based Clustering and Decision Tree Regressions for Estimating the Bubble Point Pressure of Crude Oils |
| Sub Title (in English) | |
| Keyword(1) | Predictive model |
| Keyword(2) | PVT |
| Keyword(3) | Oil and gas |
| Keyword(4) | Reservoir characterization |
| 1st Author's Name | Meshal Almashan |
| 1st Author's Affiliation | The University of Tokyo(The Univ. of Tokyo) |
| 2nd Author's Name | Yoshiaki Narusue |
| 2nd Author's Affiliation | The University of Tokyo(The Univ. of Tokyo) |
| 3rd Author's Name | Hiroyuki Morikawa |
| 3rd Author's Affiliation | The University of Tokyo(The Univ. of Tokyo) |
| Date | 2018-11-05 |
| Paper # | ASN2018-68 |
| Volume (vol) | vol.118 |
| Number (no) | ASN-282 |
| Page | pp.pp.75-80(ASN), |
| #Pages | 6 |
| Date of Issue | 2018-10-29 (ASN) |