Abstract
In this paper, a Multilayer neural network has been developed to carry out the fusion of multi-sensor information for a new multiphase flow meter (MPFM) device. The velocity and density of each phase are determined using the fluid electrical and acoustic property signals which are combined with the physical models of multiphase fluids, in addition to the venturi, differential pressure, and absolute pressure sensors. Two rings of high and low frequency ultrasonic sensors are used to overcome the uncertainties of the electrical sensors in the range of 40-60% water-cut for low and high gas fractions respectively. Experimental results on a multiphase flow loop show that real-time classification of phase flow rates for up to 90% gas fraction can be achieved with less than 10% relative error.
| Original language | English |
|---|---|
| Title of host publication | 14th International Conference on Multiphase Production Technology |
| Pages | 65-78 |
| Number of pages | 14 |
| Publication status | Published - 2009 |
| Event | 14th International Conference on Multiphase Production Technology - Cannes, France Duration: Jun 17 2009 → Jun 19 2009 |
Other
| Other | 14th International Conference on Multiphase Production Technology |
|---|---|
| Country | France |
| City | Cannes |
| Period | 6/17/09 → 6/19/09 |
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ASJC Scopus subject areas
- Geochemistry and Petrology
- Environmental Chemistry
Cite this
A non-radioactive flow meter using a new hierarchical Neural network. / Meribout, M.; Al-Rawahi, N.; Al-Naamany, A.; Al-Bimani, A.; Al-Busaidi, K.; Meribout, A.
14th International Conference on Multiphase Production Technology. 2009. p. 65-78.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A non-radioactive flow meter using a new hierarchical Neural network
AU - Meribout, M.
AU - Al-Rawahi, N.
AU - Al-Naamany, A.
AU - Al-Bimani, A.
AU - Al-Busaidi, K.
AU - Meribout, A.
PY - 2009
Y1 - 2009
N2 - In this paper, a Multilayer neural network has been developed to carry out the fusion of multi-sensor information for a new multiphase flow meter (MPFM) device. The velocity and density of each phase are determined using the fluid electrical and acoustic property signals which are combined with the physical models of multiphase fluids, in addition to the venturi, differential pressure, and absolute pressure sensors. Two rings of high and low frequency ultrasonic sensors are used to overcome the uncertainties of the electrical sensors in the range of 40-60% water-cut for low and high gas fractions respectively. Experimental results on a multiphase flow loop show that real-time classification of phase flow rates for up to 90% gas fraction can be achieved with less than 10% relative error.
AB - In this paper, a Multilayer neural network has been developed to carry out the fusion of multi-sensor information for a new multiphase flow meter (MPFM) device. The velocity and density of each phase are determined using the fluid electrical and acoustic property signals which are combined with the physical models of multiphase fluids, in addition to the venturi, differential pressure, and absolute pressure sensors. Two rings of high and low frequency ultrasonic sensors are used to overcome the uncertainties of the electrical sensors in the range of 40-60% water-cut for low and high gas fractions respectively. Experimental results on a multiphase flow loop show that real-time classification of phase flow rates for up to 90% gas fraction can be achieved with less than 10% relative error.
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M3 - Conference contribution
SN - 9781855981119
SP - 65
EP - 78
BT - 14th International Conference on Multiphase Production Technology
ER -