Online measuring density of oil products in annular regime of gas-liquid two phase flows

Abstract Gamma-ray densitometry is widely implemented in oil industry because it is an online technique and also has a good precision. If there is single phase flow in oil pipelines, measuring the density is possible just by using one source and one detector. But if in addition to oil, there is gas in oil pipelines and in fact there is a two-phase flow, conventional gamma ray densitometry (one source and one detector) could not be used for determining the density of liquid phase. In this study, a novel method is proposed for online measuring density of liquid phase in annular regime of liquid-gas two-phase flows using dual modality densitometry technique and artificial neural network (ANN). An experimental setup was designed in order to provide the required input data for training and testing the network. Registered counts in both scattering and transmission detectors were used as the inputs of the ANN and density of liquid phase was used as the output of the ANN. Using the proposed methodology, density of liquid phase was predicted with error of less than 0.031 g/cm −3 in annular regime of gas-liquid two phase flows for void fractions in the range of 10–70 percentages.

[1]  Roberto Schirru,et al.  Prediction of volume fractions in three-phase flows using nuclear technique and artificial neural network. , 2009, Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine.

[2]  Seyed Amir Hossein Feghhi,et al.  Flow regime identification and void fraction prediction in two-phase flows based on gamma ray attenuation , 2015 .

[3]  Qincheng Bi,et al.  Recognition and measurement in the flow pattern and void fraction of gas–liquid two-phase flow in vertical upward pipes using the gamma densitometer , 2013 .

[4]  Seyed Amir Hossein Feghhi,et al.  Precise Void Fraction Measurement in Two-phase Flows Independent of the Flow Regime Using Gamma-ray Attenuation , 2016 .

[5]  D. J. Cown,et al.  A wood densitometer using direct scanning with X-rays , 1983, Wood Science and Technology.

[6]  R. Schirru,et al.  Flow regime identification and volume fraction prediction in multiphase flows by means of gamma-ray attenuation and artificial neural networks , 2010 .

[7]  Bin Liu,et al.  Determination of Gas and Water Volume Fraction in Oil Water Gas Pipe Flow Using Neural Networks Based on Dual Modality Densitometry , 2006, ISNN.

[8]  César Marques Salgado,et al.  Salinity independent volume fraction prediction in annular and stratified (water–gas–oil) multiphase flows using artificial neural networks , 2014 .

[9]  Seyed Amir Hossein Feghhi,et al.  Design and construction of a prototype gamma-ray densitometer for petroleum products monitoring applications , 2011 .

[10]  Seyed Amir Hossein Feghhi,et al.  Precise volume fraction prediction in oil-water-gas multiphase flows by means of gamma-ray attenuation and artificial neural networks using one detector , 2014 .

[11]  A. Cheprasov,et al.  Measurements of the Density of Petroleum Products and Their Mixtures by the Ultrasonic Method , 2002 .

[12]  Seyed Amir Hossein Feghhi,et al.  Optimization of a method for identifying the flow regime and measuring void fraction in a broad beam gamma-ray attenuation technique , 2016 .

[13]  E. Nazemi,et al.  Intelligent densitometry of petroleum products in stratified regime of two phase flows using gamma ray and neural network , 2017 .

[14]  Guanghui Su,et al.  Analysis of CHF in saturated forced convective boiling on a heated surface with impinging jets using artificial neural network and genetic algorithm , 2011 .

[15]  Jing Chunguo,et al.  Flow regime identification of gas/liquid two-phase flow in vertical pipe using RBF neural networks , 2009, 2009 Chinese Control and Decision Conference.

[16]  Seyed Amir Hossein Feghhi,et al.  Void fraction prediction in two-phase flows independent of the liquid phase density changes , 2014 .

[17]  Geir Anton Johansen,et al.  Determination of void fraction and flow regime using a neural network trained on simulated data based on gamma-ray densitometry , 1999 .

[18]  Seyed Amir Hossein Feghhi,et al.  Application of adaptive neuro-fuzzy inference system in prediction of fluid density for a gamma ray densitometer in petroleum products monitoring , 2013 .

[19]  Halbert White,et al.  On learning the derivatives of an unknown mapping with multilayer feedforward networks , 1992, Neural Networks.

[20]  G. J. Taylor Neural Networks and Their Applications , 1996 .

[21]  A. El Abd Intercomparison of gamma ray scattering and transmission techniques for gas volume fraction measurements in two phase pipe flow , 2014 .

[22]  Seyed Amir Hossein Feghhi,et al.  Developing a gamma ray fluid densitometer in petroleum products monitoring applications using Artificial Neural Network , 2013 .

[23]  Volodymyr Mosorov,et al.  Density and velocity determination for single-phase flow based on radiotracer technique and neural networks , 2018, Flow Measurement and Instrumentation.