Dual-energy Gamma-ray Research Articles

Three-Phase Flow Measurement by Dual-Energy Gamma ray Technique and Static-Equivalent Multi-Phase Flow Simulator

There is a strong desire to use three-phase flowmeters in upstream operations because of their small size, portability, and cost-effectiveness. Traditional three-phase flowmeterstypically employ two main strategies: fully separating the flow into liquid and gas streams and measuring them using common two- and single-phase meters, or simplifying the direct measurement requirements by homogenization.In order to achieve accurate measurements with these meters, it is necessary to first create a suitable simulator for generating various multiphase flow regimes with minimal systematic error. Developing such a simulator on a laboratory scale, rather than using expensive test loops is a crucial and practical option.In this research, SEMPF as a Static-Equivalent Multi-Phase Flow with flexibility in creating different multi-phase flow regimes is introduced. In the following, the mechanism of action is validated for both homogenous two- and three-phase mixtures by using dual-energy gamma ray attenuation technique in the Monte Carlo simulator environment. Finally, the three-phase component fraction measurement accuracy by dual-energy gamma meter in three different modes of gasoil-, water-, and air-continuous mixtures were investigated. The experimental results show the maximum accuracy of 2.03%, 7.23%, and 5.09% for gasoil phase measurement in three different conditions that the carrier phase is air, gasoil and water respectively.

A flow rate measurement method for horizontal oil-gas-water three-phase flows based on Venturi meter, blind tee, and gamma-ray attenuation

Online horizontal oil-gas-water three-phase flow rate monitoring is essential for reliable operations during industrial production. A flow rate measurement method is developed in horizontal oil-gas-water three-phase flows by combining a blind tee, a Venturi meter, and a gamma-ray densitometer. The blind tee is installed at the test section entrance to homogenize the mixture by transforming the horizontal flow to a vertical upward flow. The Venturi meter is used for the total flow rate measurement. The dual-energy gamma-ray densitometer is used for phase holdup measurement. In the present method, blind-tee mixing effects and oil-water mixture slip behavior is essential, which were experimentally analyzed in this work. The phase inversion was found in the oil-water mixture with the increasing of the oil volume fraction. Besides, the addition of the gas reduces the oil-water slip ratio. For the range of 0–35% and 65–100% oil fraction in the oil-water liquid, the oil-water mixture can be well treated as a pseudo homogenous liquid with a slip ratio of 0.9–1.1. A three-phase flow rate model is then established for these conditions. The method was validated by horizontal oil-gas-water three-phase flows with average relative errors of 3.2% for the total flow rates, 4.3% for the gas flow rates, 11.5% for the oil flow rates, and 7.8% for the water flow rates.

The comparison of different multilayer perceptron and General Regression Neural Networks for volume fraction prediction using MCNPX code

This research presents a methodology for volume fraction predictions in water-gas-oil multiphase systems based on gamma-ray densitometry and artificial neural networks. The simulated geometry uses a dual-energy gamma-ray source and dual-modality (transmitted and scattered beams). The Am-241 and Cs-137 sources and two NaI(Tl) detectors have been used in this methodology. Different data from the pulse height distribution were used to train the artificial neural network to evaluate the volume fraction prediction. The MCNPX code has been used to develop the theoretical model for stratified regime and to provide data for the artificial neural network. 5-layers feed-forward multilayer perceptron using backpropagation training algorithm and General Regression Neural Networks has been used with different designs. The artificial neural network design that presented the best results of volume fraction prediction has a mean relative error below 2.0%.

Determination of the ash content of coal samples by nuclear techniques with bismuth germanate detectors

ABSTRACT A new promising nuclear technique to determine the coal-ash content was investigated using a bismuth germinate (BGO) detector instead of a sodium iodide-thallium [NaI(Tl)] detector for the dual-energy gamma-ray transmission (DEGT) measurements at 59.6 and 661.7 keV. The peak detection efficiency of the high-energy gamma-ray radiation was about two- to three-fold higher when using a BGO than a NaI(Tl) detector. This would increase the potential operation safety by allowing the use of a lower radioactivity level or a shorter measurement time. To analyze the coal-ash content using DEGT, the relationship between the transmittance ratios and the coal-ash content was successfully achieved as a linear equation, while the coal-ash contents determined by DEGT were in a very good agreement with those determined by X-ray fluorescence spectroscopy and standard chemical ASTM3174 methods, showing a highly linear relationship. In brief, the DEGT at 59.6 and 661.7 keV using a BGO detector could be developed to be a new promising approach to determine the coal-ash content as a real-time and on-belt analysis or a portable device in further investigation.

A simple and inexpensive design for volume fraction prediction in three-phase flow meter: Single source-single detector

The problem of how to correctly predict the volume fraction in three-phase flows in sensitive operational conditions is an important parameter in the oil and petroleum industry. This research presents a new methodology for volume fraction percentage prediction in water-gasoil-air multiphase systems based on transmitted gamma ray method and multilayer perceptron (MLP). The detection system uses an appropriate single source-single detector set-up, consists of a dual-energy gamma-ray source and one NaI(Tl) detector. The experimental setup provides the required data for training and testing the network. Using this proposed method, the volume fraction was predicted in water-gasoil-air three-phase flows with mean relative error percentage (MRE%) less than 4.64% and root mean square error (RMSE) of 1.49. MER% quantities for each phase of water, gasoil and air were calculated 3.72, 4.54 and 5.65, respectively. Also, the RMSE quantities were measured 0.79, 1.46 and 1.98 for water, gasoil and air, respectively. The results revealed that a good agreement is between the predicted volume fraction percentage and actual data due to the comparison of the performance of the proposed method (MLP) and the relationship derived from empirical data. The old systems used were adequately complicated whereas the new design is certainly very simple. Also, radiation safety and shielding conditions ans electronic requirements are optimized. So the cost is minimized as low as possible.

Enhancing the performance of a dual-energy gamma ray based three-phase flow meter with the help of grey wolf optimization algorithm

In recent years, there has been an increasing interest in implementing artificial intelligence in radiation based multiphase flow meter systems. This study revolves around an approach in which the grey wolf optimization (GWO) algorithm was employed to train the artificial neural network (ANN), and a hybrid network called as the GWO-trained ANN was introduced to predict the volume fractions in a gas-oil-water multiphase flow system. After that, the obtained GWO-based neural network was employed to measure the volume fractions in the stratified three-phase flow, on the basis of a dual energy metering system including the 152Eu and 137Cs and one NaI detector. The first network was utilized to predict the oil and gas, the next one to predict the gas and water, and the last one to predict the water and oil volume fractions. In the next step, the GWO-based networks were trained based on numerically obtained data from MCNP-X code. The accuracy of the nets were evaluated and compared with each other. Based on the results, the best GWO-based net could predict the oil and gas volume fractions with the mean absolute percentage error of less than 0.8% and 0.4% for the testing and checking data, respectively.

Simulation of a Gamma-Ray Computed Tomography System Using Two Radioisotopes for Structural Inspections: A Preliminary Study

Gamma-ray computed tomography (CT) has widely been used to inspect cracks, obstructions, and fluid flow in pipes. Previous studies have shown that the inspection accuracy is limited when materials in the pipe have a similar density. To overcome this drawback, we investigated a method to discriminate small density differences using a dual-energy gamma-ray CT. In this paper, the performance of a gamma-ray CT system was simulated using Geant4 Application for Tomographic Emission with three types of sources (Am-241, Co-57, and Cs-137). A polyvinyl chloride (PVC) pipe containing oil and sludge was modeled as a phantom. Sparsely acquired data were reconstructed using filtered back-projection and iterative total variation (TV) denoising algorithms. Each material discrimination procedure was performed with a postreconstruction technique. Our results clearly showed that the gamma-ray CT imaging with Am-241 and Co-57 sources and the TV denoising reconstruction algorithm can be used to inspect the internal structures based on discriminated images. The calculated areas of discriminated oil and sludge were within an error margin of 2% (oil: 78.4% and sludge: 21.6%) compared with the simulated phantom (oil: 80% and sludge: 20%). For both gamma sources, the calculated contrast-to-noise ratio (CNR) values with the TV denoising algorithm were the highest values for all structures (Am-241/Co-57: 18.03/28.95 for oil versus sludge, 41.67/31.53 for oil versus PVC pipe, and 23.64/31.53 for sludge versus PVC pipe). The feasibility of our approach and results indicates it usefulness for industrial fields that require structural inspections.

Flow regime independent volume fraction estimation in three-phase flows using dual-energy broad beam technique and artificial neural network

In this paper, based on dual-energy broad beam gamma ray attenuation technique (using two transmission 1-inch NaI detectors and a dual-energy gamma ray source), an artificial neural network (ANN) model was used in order to predict the volume fraction of gas, oil and water in three-phase flows independent of the flow regime. A multilayer perceptron (MLP) neural network was used for developing the ANN model in MATLAB 8.1.0.604 software. The input parameters of the MLP model were registered counts under first and second full energy peaks of the both transmission NaI detectors, and the outputs were gas and oil percentage. The volume fractions were obtained precisely independent of flow regime using the presented model. Mean absolute error of the presented model was less than 2.24%.

Dual-modality and dual-energy gamma ray densitometry of petroleum products using an artificial neural network

The prediction of volume fractions in order to measure the multiphase flow rate is a very important issue and is the key parameter of multi-phase flow meters (MPFMs). Currently, the gamma ray attenuation technique is known as one of the most precise methods for obtaining volume fractions. The gamma ray attenuation technique is based on the mass attenuation coefficient, which is sensitive to density changes; density is sensitive in turn to temperature and pressure fluctuations. Therefore, MPFM efficiency depends strongly on environmental conditions. The conventional solution to this problem is the periodical recalibration of MPFMs, which is a demanding task. In this study, a method based on dual-modality densitometry and artificial intelligence (AI) is presented, which offers the advantage of the measurement of the oil–gas–water volume fractions independent of density changes. For this purpose, several experiments were carried out and used to validate simulated dual modality densitometry results. The reference density point was established at a temperature of 20 °C and pressure of 1 bar. To cover the full range of likely density fluctuations, four additional density sets were defined (at changes of ±4% and ±8% from the reference point). An annular regime with different percentages of oil, gas and water at different densities was simulated. Four features were extracted from the transmission and scattered detectors and were applied to the artificial neural network (ANN) as inputs. The input parameters included the 241Am full energy peak, 137Cs Compton edge, 137Cs full energy peak and total scattered count, and the outputs were the oil and air percentages. A multi-layer perceptron (MLP) neural network was used to predict the volume fraction independent of the oil and water density changes. The obtained results show that the proposed ANN model achieved good agreement with the real data, with an estimated root mean square error (RMSE) of less than 3.

Salinity independent volume fraction prediction in annular and stratified (water–gas–oil) multiphase flows using artificial neural networks

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.

A fast neutron and dual-energy gamma-ray absorption method (NEUDEG) for investigating materials using a 252Cf source

DEXA (dual-energy X-ray absorption) is widely used in airport scanners, industrial scanners and bone densitometers. DEXA determines the properties of materials by measuring the absorption differences of X-rays from a bremsstrahlung tube source with and without filtering. Filtering creates a beam with a higher mean energy, which causes lower material absorption. The absorption difference between measurements (those with a filter subtracted from those without a filter) is a positive number that increases with the effective atomic number of the material. In this paper, the concept of using a filter to create a dual beam and an absorption difference in materials is applied to radiation from a 252Cf source, called NEUDEG (neutron and dual-energy gamma absorption). NEUDEG includes absorptions for fast neutrons as well as the dual photon beams and thus an incentive for developing the method is that, unlike DEXA, it is inherently sensitive to the hydrogen content of materials. In this paper, a model for the absorption difference and absorption sum in NEUDEG is presented using the combined gamma ray and fast neutron mass attenuation coefficients. Absorption differences can be either positive or negative in NEUDEG, increasing with increases in the effective atomic number and decreasing with increases in the hydrogen content. Sample sets of absorption difference curves are calculated for materials with typical gamma-ray and fast neutron mass attenuation coefficients. The model, which uses tabulated mass attenuated coefficients, agrees with experimental data for porcelain tiles and polyethylene sheets. The effects of “beam hardening” are also investigated.

Precise volume fraction prediction in oil–water–gas multiphase flows by means of gamma-ray attenuation and artificial neural networks using one detector

Artificial neural network (ANN) is an appropriate method used to handle the modeling, prediction and classification problems. In this study, based on nuclear technique in annular multiphase regime using only one detector and a dual energy gamma-ray source, a proposed ANN architecture is used to predict the oil, water and air percentage, precisely. A multi-layer perceptron (MLP) neural network is used to develop the ANN model in MATLAB 7.0.4 software. In this work, number of detectors and ANN input features were reduced to one and two, respectively. The input parameters of ANN are first and second full energy peaks of the detector output signal, and the outputs are oil and water percentage. The obtained results show that the proposed ANN model has achieved good agreement with the simulation data with a negligible error between the estimated and simulated values. Defined MAE% error was obtained less than 1%.

Using the Dual Energy Gamma-Ray Transmission Technique to Measure Soil Bulk Density and Water Content of Central Southwestern Nigerian Soils

In this work, a radiological technique which simultaneously measures soil bulk density and water contents accurately and quickly in a non-destructive manner at different depths of the soil profile of the major soil series of Central Southwestern Nigeria is developed. Undisturbed samples from Iwo, Ondo, Egbeda, Itagunmodi, Okemessi, Mamu, Origo and Jago soil series of Southwestern Nigeria were collected on monthly intervals from June 2006 to May 2007. Using gamma-ray energy pairs of 122 and 1112 keV; and 344 and 1408 keV obtained from europium-152 (152Eu) radionuclide, the attenuation coefficients for soil and water, μs, μw and consequently ρs and θv at varying soil depths of these series were calculated. Comparative gravimetric measurements of these soil parameters were carried out. Using the XCOM computer algorithm with the soil elemental concentrations obtained using the Energy Dispersive X-Ray Fluorescence analysis (EDXRF) technique, theoretical estimates of μs for the various soil series were obtained and compared with the experimental values. The result of the developed dual energy gamma-ray transmission technique was compared with the gravimetric measurement method and Pearson correlation coefficient (0.987, p s and (0.996, p θv. The results of the measurements showed that θv minima with values 0.1931, 0.1987, 0.2377, 0.2111, 0.1738, 0.1701, 0.2334, 0.2341 and minima ρs values of 1.21 g/cm3, 1.20 g/cm3, 1.02 g/cm3, 1.38 g/cm3, 1.12 g/cm3, 1.14 g/cm3, 0.64 g/cm3, 1.33 g/cm3 were obtained respectively for the soil series stated above in January. The maxima θv with values 0.3621, 0.4212, 0.3962, 0.3794, 0.3049, 0.2811, 0.4415, 0.4416 and maxima ρs values of 1.52 g/cm3, 1.67 g/cm3, 1.74 g/cm3, 1.69 g/cm3, 1.70 g/cm3, 1.68 g/cm3, 1.08 g/cm3, 1.54 g/cm3 occurred for these soil series respectively in July except for Mamu soil series which occurred in May. These correlated very well with the occurrence of the Dry and Rainy seasons in the study area. The results of the EDXRF analysis showed that maxima Iron, Fe concentrations of 5.0890 ppm at the depth of 90 - 120 cm into the soil profile, was obtained for Iwo series, 4.4691 ppm at 50 - 70 cm for Ondo, 6.3438 ppm at 15 - 30 cm for Egbeda, 6.6845 ppm at 30 - 50 cm for Itagunmodi, 5.0252 ppm at 90 - 120 cm for Okemessi, 3.4996 ppm at 30 - 50 cm for Mamu, 5.1191 ppm at 50 - 70 cm for Jago and 4.7193 ppm at 90 - 120 cm for Origo. Maxima Potassium, K concentrations of 2.9613 ppm at the depth of 50 - 70 cm, was obtained for Iwo soil series, 2.3315 ppm at 0 - 15 cm for Egbeda, 2.2763 ppm at 70 - 90 cm for Okemessi and 3.3636 ppm at 50 - 70 cm for Mamu. Maxima Ti concentrations of 1.4822 ppm at 0 - 15 cm was obtained for Ondo soil series, 2.5159 ppm at 15 - 30 cm for Egbeda, 1.8690 ppm at 90 - 120 cm for Itagunmodi, 2.2975 ppm at 15-30 cm for Okemessi, 1.6453 ppm at 0 - 15 cm for Jago and 1.0513 ppm at 30 - 50 cm for Origo.

Flow regime identification and volume fraction prediction in multiphase flows by means of gamma-ray attenuation and artificial neural networks

Abstract 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.

The effect of lignite quality variation on the efficiency of on-line ash analyzers

Abstract A sustainable coal industry needs to achieve a number of objectives including improvements in utilization of deposits, energy efficiency, and environmental performance during combustion. The “Clean Coal Concept” promises to combine the secure and low cost electricity supply offered by hard coal and lignite with adequate environmental precautions. In this context, modern quality control systems, which are greatly based on the on-line analyzers, play an important role in achieving sustainability targets. This paper examines the possibility of using commercial on-line analysis systems for monitoring the mineral matter content of low quality lignite mined from the lignite basin of Megalopolis, Peloponnese, Greece. The effect of the accuracy of the on-line analyzers to process control, when used for the characterization of low rank coals with complex and variable composition, is investigated by carrying out numerous bench- and pilot-scale trials. Pilot-scale trials were based on a dual energy gamma-ray transmission analyzer, which was installed on the conveyor belt that transports lignite from the mine pit to the homogenization stockyard. All measurement data were compared to data gathered during the realisation of similar trials in the lignite mines of Ptolemais Basin, Northern Greece. Results indicated that the accuracy of the on-line measurements was not satisfactory and did not allow lignite quality monitoring in real time. The achieved inferior accuracy of the on-line measurement's accuracy, compared to previous applications at other mining sites, was related to the intense variation of the mineral matter content of lignite and lignite composition, which distorted the calibration of the analyzer. The latter is based on certain assumptions regarding the average mass absorption coefficient of the organic and mineral matter contained in the lignite. Further experimental work is needed to investigate solutions for successful implementation of this method to low grade lignites that exhibit large variation in mineral matter content and composition.

Prediction of volume fractions in three-phase flows using nuclear technique and artificial neural network

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.

Volumetric fraction measurement in oil-water-gas multiphase flow with dual energy gamma-ray system

Volumetric fraction distribution measurement is a constituent part of process tomography system in oil-water-gas multiphase flow. With the technological development of nuclear radial inspection, dual-energy γ-ray techniques make it possible to investigate the concentration of the different components on the cross-section of oil-water-gas multiphase pipe-flow. The dual-energy gamma-ray technique is based on materials attenuation coefficients measurement comprised of two radioactive isotopes of241Am and241Cs which have emission energies at 59.5 keV and 662 keV in this project. Nuclear instruments and data acquisition system were designed to measure the material's attenuation dose rate and a number of static tests were conducted at the Multiphase Laboratory, Institute of Mechanics, Chinese Academy of Sciences. Three phases of oil-water-gas media were investigated for their possible use to simulate different media volumetric fraction distributions in experimental vessels. Attenuation intensities were measured, and the arithmetic of linear attenuation coefficients and the equations of volumetric fractions were studied. Investigation of an unexpected measurement error from attenuation equations revealed that a modified arithmetic was involved and finally the system achieved acceptable accuracy in experimental research.

Study on absorption coefficients of dual-energy γ-rays in determining phase fractions of multiphase flows

This paper discusses the principle and mathematical method to measure the phase fractions of multiphase flows by using a dual-energy gamma-ray system. The dual-energy gamma-ray device is composed of radioactive isotopes of241Am and137Cs with emission energies of 59.5 keV and 662 keV respectively. A rational method to calibrate the absorption coefficient was introduced in detail. The statistical error has been analyzed on the basis of the accurate absorption coefficient which enables determination phrase fractions almost independent of the flow regime. Improvement has been achieved on the measurement accuracy of phase fractions.

Volumetric fraction measurement in oil-water-gas multiphase flow with dual energy gamma-ray system

Volumetric fraction measurement in oil-water-gas multiphase flow with dual energy gamma-ray system

Dual-energy gamma-ray technique for quantitative measurement of coal ash in the Shahroud mine, Iran

Three typical coal fields of the Shahroud, Zirab ,and Lushan from the well-known geological setting of Alborz coal basin in north of Iran, chosen to cover a range of rank, were used to compare and contrast conventional ash measurement method to improving the newly made dual-energy gamma-ray device in Iran. The conventional method is based on the measurement of the ash percent by analytical balance. The dual-energy gamma-ray device method is based on the dual-energy gamma-ray transmission isotopes of 241Am and 137Cs. Conventional methods (combustion method) for the measurement of coal ash are time consuming. Nuclear (isotopic) devices are a new technique that provides rapid and sensitive coal ash measurement of various coal types. We investigated the use of this technique (dual-energy gamma ray) for control of coal quality in Iran. The applications of conventional and nuclear devices for coal ash measurements in different coal types of the Shahroud mine show that the use of the nuclear technique is advantageous.