Article

# A high performance gas–liquid two-phase flow meter based on gamma-ray attenuation and scattering

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## Abstract

The ability to precisely estimate the void fraction of multiphase flow in a pipe is very important in the petroleum industry. In this paper, an approach based on our previous works is proposed for predicting the void fraction independent of flow regime and liquid phase density changes in gas–liquid two-phase flows. Implemented technique is a combination of dual modality densitometry and multi-beam gamma-ray attenuation techniques. The detection system is comprised of a single energy fan beam, two transmission detectors, and one scattering detector. In this work, artificial neural network (ANN) was also implemented to predict the void fraction percentage independent of the flow regime and liquid phase density changes. Registered counts in three detectors and void fraction percentage were utilized as the inputs and output of ANN, respectively. By applying the proposed methodology, the void fraction was estimated with a mean relative error of less than just 1.2480%.

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... Using the above mentioned methodology, the authors succeeded to recognize all the flow patterns and also to determine volume fractions with mean absolute error of less than 5.68%. Further researches in this field of study can be found in [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. ...
... Different volume fractions were achieved by altering density of the mixture as well as the mass fraction of each component. Although the modelled homogenous flow pattern in this investigation is an ideal case and is slightly different from the real homogenous pattern that occurs in multiphase flows, this system is easy and suitable for simulation because of its symmetry; other researchers adopted this model to simulate the homogenous regime [2,10,24]. Different volume fractions in the range of 10-80% with steps of 10% were replicated for all of the three flow patterns. Thirty-six modelled combinations of gas, oil, and water volume fractions for each flow pattern are shown in Fig. 3 which presents a graphical representation called ternary. ...
Article
In this investigation, a fan-beam photon attenuation based system, including one X-ray tube and two sodium iodide crystal detectors, combined with group method of data handling (GMDH) neural network is proposed to recognize type of flow regime and predict gas-oil-water volume fractions of a three phase flow. One GMDH neural network was considered for recognizing flow patterns and two GMDH networks were implemented to predict the volume fractions. The recorded photon energy spectra from the two sodium iodide detectors were defined as the inputs of the three GMDH neural networks. The type of flow pattern and volume fractions were the output obtained from the first and the other two GMDH neural networks, respectively. Through the application of the proposed methodology, all of the flow patterns were recognized correctly except one single case. The volume fraction was also predicted with RMS error of less than 3.1.
... They used a 137 Cs source and one detector located next to the emitter to measure backscattered gamma rays. Roshani et al. implemented structures in order to determine the void fraction in twophase flows independent of the flow regime and liquid phase density changes [8]. Hanus et al. presented many time and frequency domain features to recognize the flow regimes in dynamic conditions [9][10]. ...
Article
Determining the type of flow pattern and gas volumetric percentage with high precision is one of the vital topics for researchers in this field. For this, in this paper, three different types of liquid-gas two-phase flow regimes, namely annular, stratified, and homogenous were simulated in various gas volumetric percentages ranging from 5% to 90%. Simulations were performed by Monte Carlo N Particle (MCNP) code. Metering system includes one 137 Cs sources, one Pyrex glass, and two NaI detectors to register the transmitted photons. Because the signals which are received from the MCNP simulations contain high-frequency noises, the Savitzky-Golay filter has been applied to solve this problem. Then, thirteen characteristics in time domain were extracted from the recorded data of both detectors. Since none of features were capable of completely separating the flow regimes, two methods as "extracting two different features from the recorded data of both detectors" and "extracting three features from the recorded data of both detectors" were proposed. Using these methods, many different separator cases were found and the best separator cases were distinguished via S parameter. Finally, two artificial neural network (ANN) models of multilayer perceptron (MLP) were implemented for each method to identify the flow regimes and approximate the gas volumetric percentages. The proposed methodology and networks could diagnose all flow patterns properly, and also predict the volumetric percentage with a root mean square error (RMSE) of less than 0.60. Increasing the precision of two-phase flow meter by extracting time-domain features and signal processing techniques is the most important advantage of this study.
... Another advantage of this method is its low cost of implementation and operation. In combination with other measuring methods, described for example in the articles [7][8][9][10][11], the orifice-based measurement can be used to control multiphase flows. Orifice flowmeters also have disadvantages, the main ones being high pressure drop and high sensitivity to the orifice inlet profile [12]. ...
Article
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The article analyses the impact of the Reynolds number on the estimated uncertainty of the mass flow rate measurement using an orifice plate. The objects of the research were two types of orifices: centric (ISA) and eccentric, with the diameter ratio β = 0.5. Studies were performed by Monte Carlo simulation and experiments for Reynolds numbers in the range 10,000 ≤ Re ≤ 20,000. The obtained results have shown that for both orifice types, the results obtained from the experiments and from the Monte Carlo simulation are similar. The nature of changes in the expanded uncertainty of the flow measurement is very similar for each type. For the both types of orifices, the value of the expanded uncertainty of the flow measurement increases linearly with the increasing Reynolds number. (The final version of the article is available free of charge at: https://www.sciencedirect.com/science/article/pii/S0263224120303894?via%3Dihub )
... A variety of typical methods, including capacitance sensors [6,7], wire-mesh sensors [8,9], radiation attenuation techniques [10,11], magnetic resonance [12,13] and ultrasonic techniques [14,15] have been applied to measure the gas void fraction of the gas-liquid two-phase flow. The sensing principle of the capacitance sensors makes use of different electrical permittivities of the phases. ...
Article
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The carbon capture and storage (CCS) system has the potential to reduce CO2 emissions from traditional energy industries. In order to monitor and control the CCS process, it is essential to achieve an accurate measurement of the gas void fraction in a two-phase CO2 flow in transportation pipelines. This paper presents a novel instrumentation system based on the laser attenuation technique for the gas void fraction measurement of the two-phase CO2 flow. The system includes an infrared laser source and a photodiode sensor array. Experiments were conducted on the horizontal and vertical test sections. Two Coriolis mass flowmeters are respectively installed on the single-phase pipelines to obtain the reference gas void fraction. The experimental results obtained show that the proposed method is effective. In the horizontal test section, the relative errors of the stratified flow are within ±8.3%, while those of the bubble flow are within ±10.6%. In the vertical test section, the proposed method performs slightly less well, with relative errors under ±12.2%. The obtained results show that the measurement system is capable of providing an accurate measurement of the gas void fraction of the two-phase CO2 flow and a useful reference for other industrial applications.
... The simulation configuration is shown in Fig. 1. It should be noted that the simulation geometry has been benchmarked with the laboratory experiments in our previous works [2,14,15]. Air with a density of 0.00125 g/cm 3 was used as the air phase in all simulations. Also, gasoil with a density of 0.826 g/cm 3 and the chemical formula C 12 H 23 was used as the liquid phase. ...
Article
Full-text available
The void fraction is one of the most important parameters characterizing a multiphase flow. The prediction of the performance of any system operating with more than single phase relies on our knowledge and ability to measure the void fraction. In this work, a validated simulation study was performed in order to predict the void fraction independent of the flow pattern in gas-liquid two-phase flows using a gamma ray⁶⁰Co source and just one scintillation detector with the help of an artificial neural network (ANN) model of radial basis function (RBF). Three used inputs of ANN include a registered count under Compton continuum and counts under full energy peaks of 1173 and 1333 keV. The output is a void fraction percentage. Applying this methodology, the percentage of void fraction independent of the flow pattern of a gas-liquid two-phase flow was estimated with a mean relative error less than 1.17%. Although the error obtained in this study is almost close to those obtained in other similar works, only one detector was used, while in the previous studies at least two detectors were employed. Advantages of using fewer detectors are: cost reduction and system simplification.
... One of the most important issues in this regard is the best combination of gamma-ray sources; however, there is no study about the best combination until now. Roshani and coworkers used 241 Am & 137 Cs, 60 Co and 152 Eu & 137 Cs in their works [2][3][4][5][6][7][8][9][10][11][12]. Hanus and coworkers used 133 Ba and 241 Am in some works [13][14][15][16]. ...
Article
Full-text available
The used metering technique in this paper is based on the multienergy (at least dual) gamma-ray attenuation. The aim of the current study is investigation of different combinations of sources in order to find the best combination for precise metering gas, oil and water percentages in annular three-phase flows. The required data were generated numerically using Monte Carlo N Particle extended (MCNPX) code. As a matter of fact, the current investigation devotes to predict the volume fractions in the annular three-phase flow, on the basis of a multienergy metering system including different radiation sources and one sodium iodide detector, using the hybrid model. Since the summation of volume fractions is constant, a constraint modeling problem exists, meaning that the hybrid model must predict only two volume fractions. Six hybrid models associated with the number of applied radiation sources are employed. The models are applied to predict the oil and gas volume fractions. For the next step, the hybrid models are trained based on numerically obtained data from the MCNPX code. The results show that the best prediction results are obtained for the oil and gas volume fractions of a system with the (241Am & 137Cs) radiation sources.
... Nazemi et al. (2015) applied a method to eliminate the dependency of multiphase flow meter on liquid phase density in two stratified phase horizontal flows. Further investigations in this field and also using ANN in nuclear engineering problems can be found in these references (Hanus 2015;Hanus et al. 2014a, b;Nazemi et al. 2014Nazemi et al. , 2016bMosorov et al. 2016;Roshani et al. 2014Roshani et al. , 2017bRoshani and Nazemi 2017;Yadollahi et al. 2016a, b;Zych et al. 2014). ...
Article
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The use of adaptive neuro-fuzzy inference system (ANFIS) has been reported for predicting the volume fractions in a gas–oil–water multiphase system. In fact, the volume fractions in the annular three-phase flow are measured based on a dual energy metering system consisting of $$^{152}\hbox {Eu}$$ and $$^{137}\hbox {Cs}$$ and one NaI detector using ANFIS. Since the summation of volume fractions is constant, therefore ANFIS must predict only two volume fractions. In this study, three ANFIS networks are applied. The first is utilized to predict the gas and water volume fractions. The next one is applied to predict the gas and oil, and the last one is used to predict the water and oil volume fractions. In the next step, ANFIS networks must be trained based on numerically obtained data from MCNP-X code. Then, the average testing errors of these three networks are computed and compared. The network with the least error has been selected as the best predictor model.
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In this work, the air and water flow-rates were accurately predicted within a two-phase flow loop using features extracted from a simple detector spectrum, independently of the changes in the flow regime. In this regard, a new method based on a single beam-single detector using single-energy of gamma-rays was proposed. The gamma-ray attenuation setup combined with Artificial Neural Network (ANN) was used to predict the flow-rates in various regime of gas-liquid two-phase flows such as bubble, plug, slug, annular and dispersed regimes. Moreover, the ANN was developed based on four features extracted from the recorded pulse height spectrum in the dynamic condition of the fluids. The results showed that the air and water flow-rates can be measured with an average of Mean Relative Error (MRE) less than 4.5%. Overall results revealed that using the proposed method, prediction of the flow-rates can be successfully carried out independent of their regimes.
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It is important for operators of poly-pipelines in petroleum industry to continuously monitor characteristics of transferred fluid such as its type and amount. To achieve this aim, in this study a dual energy gamma attenuation technique in combination with artificial neural network (ANN) is proposed to simultaneously determine type and amount of four different petroleum by-products. The detection system is composed of a dual energy gamma source, including americium-241 and barium-133 radioisotopes, and one 2.54 cm × 2.54 cm sodium iodide detector for recording the transmitted photons. Two signals recorded in transmission detector, namely the counts under photo peak of Americium-241 with energy of 59.5 keV and the counts under photo peak of Barium-133 with energy of 356 keV, were applied to the ANN as the two inputs and volume percentages of petroleum by-products were assigned as the outputs.
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In this paper, X-ray tube is introduced as a potential alternative for radioisotope sources used in radiation based liquid-gas two-phase flowmeters. X-ray tubes have lots of advantages over the radioisotope sources such as having an adjustable emitting photon's energy, being safer from point of view of radiation health physics during the transportation of the source, having ability to generate a high flux photon beam, and etc. The proposed radiation based system in this study composes an X-ray tube with a tube voltage of 150 kV and a 2.5 mm aluminum filter as the radiation source and one sodium iodide crystal as the photon detector. A pipe was positioned between the X-ray tube and the detector. Two main flow regimes of annular and stratified with different void fractions were modelled inside the pipe. Artificial neural network model of multi-layer perceptron (MLP) was also used in this study for analyzing the obtained data. The output spectrum of sodium iodide detector with 150 samples was applied as the input of multi-layer perceptron network and void fraction was considered as its output. The root mean squared error of proposed measuring system was 4.13 which shows the X-ray tube can be implemented as a promising alternative for radioisotope in radiation based two phase flow meters.
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Void fraction is an important parameter in the oil industry. This quantity is necessary for volume rate measurement in multiphase flows. In this study, the void fraction percentage was estimated precisely, independent of the flow regime in gas–liquid two-phase flows by using γ-ray attenuation and a multilayer perceptron neural network. In all previous studies that implemented a multibeam γ-ray attenuation technique to determine void fraction independent of the flow regime in two-phase flows, three or more detectors were used while in this study just two NaI detectors were used. Using fewer detectors is of advantage in industrial nuclear gauges because of reduced expense and improved simplicity. In this work, an artificial neural network is also implemented to predict the void fraction percentage independent of the flow regime. To do this, a multilayer perceptron neural network is used for developing the artificial neural network model in MATLAB. The required data for training and testing the network in three different regimes (annular, stratified, and bubbly) were obtained using an experimental setup. Using the technique developed in this work, void fraction percentages were predicted with mean relative error of <1.4%.
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The main purpose of this study is experimental and numerical void fraction measurement in two-phase flow inside a vertical pipe by using gamma-ray. Three types of flow regimes including homogenous, stratified and annular were modeled in a vertical pipe by using polyethylene phantoms. These three flow regimes are basis regimes in two-phase flow and the other flow regimes are incorporated of these patterns. For all three modeled flow regimes all transmitted and scattered gamma rays in all directions were measured by setting a gamma ray source and detector around the pipe. Numerical modeling was done by using MCNP code to improve the accuracy and validation of experimental results. Finally, innovative correlations to predict the void fraction in two-phase flow in a vertical pipe was presented.
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The fluid properties strongly affect the performance of radiation-based multiphase flow meter. By changing the fluid properties (especially density), recalibration is necessary. In this study, a method was presented to eliminate the dependency of multiphase flow meter on liquid phase density in stratified two phase horizontal flows. At the first step the position of the scattering detector was optimized in order to achieve highest sensitivity. Several experiments in optimized position were done. Counts under the full energy peak of transmission detector and total counts of scattering detector were applied to the Radial Basis Function neural network and the void fraction percentage was considered as the neural network output. Using this method, the void fraction was predicted independent of the liquid phase density change in stratified regime of gas-liquid two-phase flows with mean relative error percentage less than 1.2%.
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This paper presents the application of adaptive neuro-fuzzy inference system (ANFIS) for prediction of fluid density in a previously designed and constructed gamma ray densitometer for pipes of various diameters and different fluids densities. The input parameters of the proposed ANFIS model are the pipe diameter and the number of the counted photons and the output is the density of the considered material. The required data for training and testing the ANFIS model has been obtained based on simulations using MCNP4C Monte Carlo code. A hybrid learning algorithm consists of back-propagation and least-squares estimation is used for training the proposed ANFIS model. Simulations for 4-in. polyethylene pipe had been validated with the experimental data previously. The proposed ANFIS model has achieved good agreement with the experimental results and has a small error between the estimated and experimental values. The obtained results show that the mean relative error percentage (MRE%) for training and testing data are less than 2.14% and 2.64%, respectively.
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Flow regime information can be used to improve measurement accuracy on gas volume fractions and as complementary information for other types of flow instrumentation in order to enhance their accuracy. In this study a method based on dual modality densitometry using artificial neural network (ANN) was presented to first identify the flow regime and then predict the void fraction in two-phase flows. The full energy peak (transmission count), photon counts of Compton edge in transmission detector and total count in the scattering detector, were chosen as the three inputs of the ANN. The stratified, homogenous and annular regimes with various void fractions were simulated by the Monte Carlo N-Particle (MCNP) code, version X, in order to obtain adequate data set used for training and testing the ANN. To validate the simulated results, several experiments were carried out in the annular regime of two-phase flow. Experimental results were in good agreement with the simulated data. The maximum difference between experimental and simulated results for the transmission, Compton edge and scattered counts, is 3.4%, 3.8% and 3.6%, respectively. By applying this method, all the three regimes were correctly distinguished and void fraction was predicted in the range of 5-95% with error of less than 1.1%.
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Artificial Neural Networks (ANNs) have been applied to deal with flow and heat transfer problems over the past two decades. In the present paper, recent work on the applications of ANNs for predicting the flow regime, pressure drop, void fraction, critical heat flux, onset of nucleate boiling, heat transfer coefficient and boiling curve has been reviewed, respectively. As can be noted in this review work, various types of ANNs can be employed as predictors with acceptable precisions. At the end of this review, methods to improve performance of ANNs and further applications of ANNs in flow and heat transfer problems were introduced.
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This work investigates the response of attenuation gamma-rays in volume fraction prediction system for water–gas–oil multiphase flows considering variations in salinity of water. The approach is based on pulse height distributions pattern recognition by artificial neural network. The detection system uses fan beam geometry, comprised of a dual-energy gamma-ray source and two NaI(Tl) detectors in order calculate transmitted and scattered beams. Theoretical models for annular and stratified flow regimes have been developed using MCNP-X code to provide data for the network.
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Void fraction simulating stratified air–water flow in cylindrical tubes of different radii was measured using transmission and scattering of gamma rays. A simple experimental set-up using 137Cs γ-ray point source of 10 μCi and NaI(Tl) detector was used. The void fractions determined from Compton–Compton scattering and transmission peaks were found in good agreement with the real void fractions. However, deviations were noticed between the results obtained from traditional Compton scattered gamma rays and real void fraction. It was shown that sensitivity of gamma ray scattering is better than the transmission measurements. The set-up used in the present work is simpler than those existing in literature and radiation safety and shielding requirements are minimized.
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This work presents a new methodology for flow regimes identification and volume fraction predictions in water–gas–oil multiphase systems. The approach is based on gamma-ray pulse height distributions (PHDs) pattern recognition by means the artificial neural networks (ANNs). The detection system uses appropriate fan beam geometry, comprised of a dual-energy gamma-ray source and two NaI(Tl) detectors adequately positioned in order measure transmitted and scattered beams, which makes it less dependent on the regime flow. The PHDs are directly used by the ANNs without any parameterization of the measured signal. The system comprises four ANNs. The first identifies the flow regime and the other three ANNs are specialized in volume fraction predictions for each specific regime. The ideal and static theoretical models for annular, stratified and homogeneous regimes have been developed using MCNP-X mathematical code, which was used to provide training, test and validation data for the ANNs. The energy resolution of NaI(Tl) detectors is also considered on the mathematical model. The proposed ANNs could correctly identify all three different regimes with satisfactory prediction of the volume fraction in water–gas–oil multiphase system, demonstrating to be a promising approach for this purpose.
Article
Gamma-ray densitometry is a frequently used non-intrusive method for determining void fraction in two- and multi-phase gas liquid pipe flows. The traditional gamma-ray densitometer using a 137Cs source and a scintillation PMT detector has proved itself reliable and robust. This paper presents a method using a low energy source (241Am), which offers the advantages of reduced size due to reduced shielding requirements, compact detectors, and lesser dependence on flow regime, due to its multibeam measurement configuration. These are important aspects with regard to future subsea and down-hole fluid flow measurement applications. The performance of single-beam and the compact multi-beam low-energy gamma-ray measurement principles was compared. Consideration of the measurement volume, defined by the detector area and the radiation beam, demonstrated the flow regime dependency of single-beam gamma-ray measurement principles. With the multi-beam low-energy gamma-ray measurement principle, the dependence on flow regime is negligible when several detector responses are combined. Use of phantoms and one movable detector verified the multi-beam gamma-ray measurement principle. The detector responses at several positions around the pipe were obtained for different flow regimes and void fractions.
Conference Paper
The gamma ray scattering energy spectrum detected by one detector was presented to distinguish the gas liquid two-phase flow regime of vertical pipe. The simulation geometries of the gamma ray scattering measurement were built using Monte Carlo software Geant4. Computer simulations were carried out with homogeneous flow, annular flow and slug flow. The results show that the scattering energy characters of homogeneous flow and annular flow have significantly different. The scattering spectrum of slug flow is similar to annular flow for long gas slugs and similar to homogeneous flow for short gas slugs. The RBF neural networks were used to predict the flow regime. The results show that the homogeneous flow and annular flow can be completely distinguished and most of the slug flows were recognized by the neural network. It was demonstrated that the method of one detector scattering energy spectrum has the ability to identify the typical gas liquid flow regime of vertical pipe and fit the applications in engineering.
Conference Paper
The models of dual modality densitometry were developed. It can be used for measuring the gas volume fraction and water volume fraction in oil water gas pipe flow. The models are complex. In order to solve models, it often uses simplified models. This reduces measurement precision. The method of measuring gas and water volume fraction using neural networks was presented. The simulation data was gotten using Geant4. The radial basis function networks were trained and tested on computer simulation data. The results show that networks predicted gas volume fraction fit true gas fraction well and water volume fraction has some deviations.
Article
This work presents methodology based on nuclear technique and artificial neural network for volume fraction predictions in annular, stratified and homogeneous oil-water-gas regimes. Using principles of gamma-ray absorption and scattering together with an appropriate geometry, comprised of three detectors and a dual-energy gamma-ray source, it was possible to obtain data, which could be adequately correlated to the volume fractions of each phase by means of neural network. The MCNP-X code was used in order to provide the training data for the network.
Article
Dual mode densitometry is presented as a novel method of measuring the gas volume fraction in gas/oil/water pipe flows independent of the salinity of the water component. The different response in photoelectric attenuation and Compton scattering to changes in salinity is utilized. The total attenuation coefficient is found through traditional transmission measurements with a detector positioned outside the pipe wall diametrically opposite the source. The scatter response is measured with a second detector positioned somewhere between the source and the transmission detector. The feasibility of the method is demonstrated for homogeneously mixed flows.
Development of the multiphase meter using gamma densitometer concept
• M M Ibrahim
• Babelli