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Paper Abstract and Keywords
Presentation 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
Abstract (in Japanese) (See Japanese page) 
(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) 
(in English) Predictive model / PVT / Oil and gas / Reservoir characterization / / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 282, ASN2018-68, pp. 75-80, Nov. 2018.
Paper # ASN2018-68 
Date of Issue 2018-10-29 (ASN) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
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Conference Information
Committee ASN SRW  
Conference Date 2018-11-05 - 2018-11-06 
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. 
Paper Information
Registration To ASN 
Conference Code 2018-11-ASN-SRW 
Language English 
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  
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Keyword(6)  
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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)
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Speaker Author-1 
Date Time 2018-11-05 14:20:00 
Presentation Time 25 minutes 
Registration for ASN 
Paper # ASN2018-68 
Volume (vol) vol.118 
Number (no) no.282 
Page pp.75-80 
#Pages 6 
Date of Issue 2018-10-29 (ASN) 

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