Estimation of void fraction for homogenous regime of two-phase flows in unstable operational conditions using gamma-ray and neural networks

Ehsan Nazemi; Gholam Hossein Roshani; Seyed Amir Hossein Feghhi; Reza Gholipour Peyvandi

Ehsan Nazemi 1.Nuclear Science and Technology Institute, Tehran, Iran 2.Shahid Beheshti University, Tehran, Iran http://orcid.org/0000-0001-5457-6943
Gholam Hossein Roshani Radiation Application Department, Shahid Beheshti University, Tehran, Iran
Seyed Amir Hossein Feghhi Radiation Application Department, Shahid Beheshti University, Tehran, Iran
Reza Gholipour Peyvandi Nuclear Science and Technology Research Institute, Tehran, Iran

Keywords: Gamma ray, Artificial Neural Network, Multi Layer Perceptron, Void fraction

Abstract

Almost all the multi-phase flow meters (MPFMs) using gamma-ray attenuation, are calibrated for liquid and gas phases with constant density and pressure. When operational conditions such as temperature and pressure change in pipelines, the radiation-based multi-phase flowmeters would measure the flow rate with error. Therefore, performance of MPFMs would be improved by eliminating any dependency on the fluid properties such as density. In this work, a method based on dual modality densitometry combined with Artificial Neural Network (ANN) is proposed in order to estimate the void fraction in homogenous regime of gas-liquid two-phase flows in unstable operational conditions (changeable temperature and pressure) in oil industry. An experimental setup was implemented to generate the optimum required input data for training the network. ANNs were trained on the registered counts of the transmission and scattering detectors in various liquid phase densities and void fractions. Void fractions were predicted by ANNs with mean relative error of less than 0.78% in density variations range of 0.735 up to 0.98 g/cm³

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References

Department of Trade and Industry (UK), Guidance Notes for Petroleum Measurement, Issue, December, 2003, pp. 8-9.

Williams, J., Status of Multiphase Flow Measurement Research, paper SPE 28515 presented at the 1994 SPE Annual Technical Conference and Exhibition, New Orleans, 25–28 September.

Ribeiro, A., Developments in Multiphase Metering, paper SPE 36197 presented at the 1996 Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, U.A.E., 13–16 October.

Thorn, R., Johansen, G.A., and Hammer, E.A., Recent Developments in Three-Phase Flow Measurement, Measurement Sci. Technology, 8 (1997) 691–701.

Thorn, R., Johansen, G.A, Hjertaker, Three-phase flow measurement in the petroleum industry, MEASUREMENT SCIENCE AND TECHNOLOGY, 24 (2013).

Hussein, E.M.A., Han, P., Phase volume-fraction measurement in oil-water-gas flow using fast neutrons, Nuclear Geophysics. 9(1995) 229-234.

WANG, J., GARDNER, F., LI, R.P., On the Use of Prompt Gamma-ray Neutron Activation Analysis for Determining Phase Amounts in Multiphase Flow, Meas. Sci. Technol. 19(2008).

Abouelwafa, M.S.A., Kendall, E.J.M., The measurement of component ratios in multiphase systems using gamma-ray attenuation, Journal of Physics E:Scientific Instruments. 13(1980)341-345.

Li, D. H., Wu, Y. X., Li, Z. B., Zhong, X. F., Volumetric Fraction Measurement in Oil-Water-Gas Multiphase Flow with Dual Energy Gamma-Ray System, J. Zhejiang Univ. SCI.6A, (12)(2005)1405 – 1411.

E. Nazemi, S.A.H. Feghhi, G.H. Roshani, void fraction prediction in two-phase flows independent of the liquid phase density changes, Radiation Measurements 68 (2014), pp. 49-54.

Roshani, G.H., Nazemi, E., Feghhi, S.A.H, Setayeshi, S., 2015. Flow regime identification and void fraction prediction in two-phase flows based on gamma ray attenuation. Measurement. 65, 25-32.

Roshani, G.H., Feghhi, S.A.H., Mahmoudi-Aznaveh, A., Nazemi, E., Adineh-Vand, A., 2014. Precise volume fraction prediction in oil-water-gas multiphase flows by means of gamma-ray attenuation and artificial neural networks using one detector. Measurement. 51, 34-41.

Alehizadeh, A., Shama, F., Nazemi, E., Roshani, G.H., Feghhi, S.A.H., 2014. Prediction of Optimum Design of Anode Shape for Obtain Highest Soft X-ray Yield in Plasma Focus Device Using Adaptive Neuro-Fuzzy Inference System. Global Perspectives on Artificial Intelligence (GPAI), Volume 2.

Roshani, G.H., Feghhi, S.A.H., Shama, F., Salehizadeh, A., Nazemi, E., 2014. Prediction of materials density according to number of scattered gamma photons using optimum artificial neural network. Computational.Methods in Physics. Article ID 305345.

Roshani, G. H., Feghhi, S. A. H., Shama, F., 2013. Some Applications of Artificial Neural Network in Nuclear Engineering, LAMBERT Academic Publishing.

Nazemi, E., Roshani, G.H., Feghhi, S.A.H, Gholipour Peyvandi, R., Setayeshi, S., 2015. Precise Void Fraction Measurement in Two-Phase Flows Independent of the Flow Regime using gamma-ray attenuation, Nuclear Engineering and Technology, In press.

Nazemi, E., Roshani, G.H., Feghhi, S.A.H, Setayeshi, S., Gholipour Peyvandi, R., 2015. A radiation-based hydrocarbon two-phase flow meter for estimating of phase fraction independent of liquid phase density in stratified regime, Flow Measurement and Instrumentations, 46, 25-32.

Taylor J G, Neural networks and their applications. West sussex (UK), John Wiley & Sons Ltd., 1996.

Gallant A R, White H, On learning the derivatives of an unknown mapping with multilayer feed forward networks, Neural Networks 1992; 5:129-138.

M. T. Hagan, and M. Menhaj, “Training Feedforward Networks with the Marquardt Algorithm”, IEEE Trans. Neural Networks, Vol. 5, No. 6, pp. 989-993, 1994.