Estimation of Void Fraction for Homogenous Regime of Two-Phase Flows in Unstable Operational Conditions Using Gamma-Ray and Neural Networks

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 flow meters 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.

Full text

Boson Journal of Modern Physics (BJMP)
ISSN: 2454-8413
Volume 2, Issue 1 available at www.scitecresearch.com/journals/index.php/bjmp/index 51
SCITECH Volume 2, Issue 1
RESEARCH ORGANISATION| October 23, 2015|
Boson Journal of Modern Physics
www.scitecresearch.com
Estimation of Void Fraction for Homogenous Regime of
Two-Phase Flows in Unstable Operational Conditions Using
Gamma-Ray and Neural Networks
Ehsan Nazemi 1, Gholam Hossein Roshani 2, Seyed Amir Hossein Feghhi2, Reza Gholipour Peyvandi3
1Young Researchers and Elite Club, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.
2Radiation Application Department, Shahid Beheshti University, Tehran, Iran.
3Nuclear Science and Technology Research Institute, Tehran, Iran.
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 flow meters 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.
Keywords: Gamma ray; Artificial neural network; Multi layer perceptron; Void fraction.
1. Introduction
During the last three deca des, development, evaluation, and use of multiphase-flow- measurement (MFM) systems
have been a major focus for the oil and gas industry worldwide. Within the oil and gas industries, it is recognized
that MFMs have several benefits in applications such as layout of production facilities, well testing, reservoir
management, production allocation, and production monitoring [1]. Conventional test separators have many
disadvantages such their large space for installing hard-wares, more capital and operating expenses, and requiring
much time to monitor each well‟s performance [2-4].
By determination of volume fraction of each phase coupled with flow velocity, the mass flow rate can be achieved;
which is one of the key parameters in the oil industry. In order to determine the gas, oil and water volume fractions,
there are some methods like nuclear techniques, electrical impedance, and microwave techniques [5]. Utilizing
nuclear techniques such as neutrons and gamma ray because of their ability for measuring volume fractions without
modifying the operational conditions and being non-invasive, is so useful [6-7]. Aboulwafa and Kendall were the
first that proposed a multi-energy gamma attenuation technique to resolve three-phase mixture component ratios
[8]. They examined various static mixture of oil-water-gas in a 0.1 m diameter pipe section using cobalt-57
(122KeV) and barium-133 (365 KeV) radioisotopes and a lithium-drifted germanium based detector. Li et al also
analyzed static mixtures of stratified regime in a cubic conduit using americium-241 (59.5 KeV) and cesium-137
(662 KeV) radioisotopes and a sodium iodide detector crystal [9].
Also, It has been shown that Artificial Neural Network (ANN) is an useful tool in nuclear engineering [10-17]. In
2014, Roshani et al. used a dual energy source consists of 241Am (59.5 keV ) and 137Cs (662 keV) with just one
transmission NaI detector to predict volume fraction in oil-water-gas three-phase flows [12]. By using ANN, they
predicted the volume fraction of oil, water and gas phases with Mean Absolute error (MAE%) of less than 1%.