AL-Qutami, T.A. and Ibrahim, R. and Ismail, I. and Ishak, M.A. (2017) Radial basis function network to predict gas flow rate in multiphase flow. In: UNSPECIFIED.
Full text not available from this repository.Abstract
Estimation of individual phase flow rates in multiphase flow is of great significance to production optimization and reservoir management in oil and gas industry. This paper proposes radial basis function network to develop a virtual flow meter (VFM) that can estimate gas flow rate in multiphase flow production lines. The model is validated with actual well test measurements, and testing results reveal excellent performance and generalization capability of the developed VFM. The paper also discusses the significance of bottom-hole and choke valve measurements to attain accurate predictions. Proposed VFM model potentially of fers an attractive and cost-effective solution to meet real-time production monitoring demands, and reduces operational and maintenance costs. © 2017 ACM.
Item Type: | Conference or Workshop Item (UNSPECIFIED) |
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Additional Information: | cited By 23; Conference of 9th International Conference on Machine Learning and Computing, ICMLC 2017 ; Conference Date: 24 February 2017 Through 26 February 2017; Conference Code:128357 |
Uncontrolled Keywords: | Artificial intelligence; Education; Flow measurement; Flow of gases; Flowmeters; Functions; Gas industry; Learning algorithms; Learning systems; Multiphase flow; Neural networks; Oil wells; Petroleum reservoir engineering; Petroleum reservoir evaluation; Radial basis function networks; Well testing, Accurate prediction; Cost-effective solutions; Generalization capability; Oil and Gas Industry; Production optimization; Radial basis networks; Real-time production; Soft sensors, Reservoir management |
Depositing User: | Mr Ahmad Suhairi UTP |
Date Deposited: | 09 Nov 2023 16:20 |
Last Modified: | 09 Nov 2023 16:20 |
URI: | https://khub.utp.edu.my/scholars/id/eprint/8827 |