eprintid: 9093
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/90/93
datestamp: 2023-11-09 16:21:02
lastmod: 2023-11-09 16:21:02
status_changed: 2023-11-09 16:14:15
type: conference_item
metadata_visibility: show
creators_name: Al-Qutami, T.A.
creators_name: Ibrahim, R.
creators_name: Ismail, I.
title: Hybrid neural network and regression tree ensemble pruned by simulated annealing for virtual flow metering application
ispublished: pub
keywords: Cost effectiveness; Flow measurement; Flow of water; Flowmeters; Forestry; Multiphase flow; Neural networks; Oil wells; Petroleum industry; Regression analysis; Simulated annealing; Well testing, Ensemble learning; Flow metering; Heterogeneous ensembles; Regression trees; Soft sensors, Image processing
note: cited By 13; Conference of 5th IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2017 ; Conference Date: 12 September 2017 Through 14 September 2017; Conference Code:132915
abstract: Virtual flow metering (VFM) is an attractive and cost-effective solution to meet the rising multiphase flow monitoring demands in the petroleum industry. It can also augment and backup physical multiphase flow metering. In this study, a heterogeneous ensemble of neural networks and regression trees is proposed to develop a VFM model utilizing bootstrapping and parameter perturbation to generate diversity among learners. The ensemble is pruned using simulated annealing optimization to further ensure accuracy and reduce ensemble complexity. The proposed VFM model is validated using five years well-Test data from eight production wells. Results show improved performance over homogeneous ensemble techniques. Average errors achieved are 1.5, 6.5, and 4.7 for gas, oil, and, water flow rate estimations. The developed VFM provides accurate flow rate estimations across a wide range of gas volume fractions and water cuts and is anticipated to be a step forward towards the vision of completely integrated operations. © 2017 IEEE.
date: 2017
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041397800&doi=10.1109%2fICSIPA.2017.8120626&partnerID=40&md5=b2954f477274d15acf004e3eaaee49c9
id_number: 10.1109/ICSIPA.2017.8120626
full_text_status: none
publication: Proceedings of the 2017 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2017
pagerange: 304-309
refereed: TRUE
isbn: 9781509055593
citation:   Al-Qutami, T.A. and Ibrahim, R. and Ismail, I.  (2017) Hybrid neural network and regression tree ensemble pruned by simulated annealing for virtual flow metering application.  In: UNSPECIFIED.