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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. [Copyright &y& Elsevier]

The methodology presented in this study is based on a 149.5 keV X-ray beam and two planar germanium detectors for X-ray transmission and scattering measurements for prediction of volume fractions in a three-phase system. Fluid volume fractions have been modeled using the MCNP6 code for an annular flow regime. A mathematical algorithm based on an artificial neural network was used to correlate the energy spectra from both detectors with the fluids volume fractions. The pulse height distributions obtained by the detectors are used as input data of the network that outputs the volume fractions of gas and water. The mean relative error, using the procedure presented here, for all data, was below 2.5% for both phases investigated. These results show that the methodology based on an X-ray beam has the potential to be used with flow meters. • An artificial neural network was used to predict volume fraction of gas, water, and oil. • X-ray beam and two planar germanium detectors for X-ray transmission and scattering measurements for prediction of volume fractions. • Fluid volume fractions have been modeled using the MCNP6 code for an annular flow regime. • The spectra obtained by the detectors are used as input data of the network that outputs gas and water volume fractions. [ABSTRACT FROM AUTHOR]

Metal alloys are widely used in the aerospace, biological, civil and automotive industries, thus being very important to develop techniques to identify these alloys. Nuclear technique based on gamma densitometry is a non-invasive technique that is able to identify metal alloys using a radiation source and a scintillator detector. The measurement geometry and the dataset for training an artificial neural network were developed using the MCNP6 code. Therefore, this study proposes the thickness prediction of five aluminum alloys (2024-O, 2090-T83, 3003, 5086-O and 7075-O), a titanium alloy, and two steel carbon alloys (stainless 302 and stainless 316) ranging from 2 to 50 mm for four different gamma-ray radiation energies using gamma transmission and artificial neural network. A study to evaluate the reliability of the results was performed by analyzing the uncertainties in the data from the simulation with the MCNP6 code and the data predicted by ANN. The results indicate that it was possible to predict all the alloys thicknesses using the energy of 137Cs radiation source, in which more than 96% of the cases with 5% of relative error, even for a group of alloys with very close densities values as aluminum alloys. • Prediction of metal alloys' thicknesses using gamma transmission and ANN. • Application of neural network using backpropagation algorithm and cross-validation. • The proposed geometry investigates the use of one detector and four radiation sources. • A total of 1040 simulations with different thicknesses and metal alloys were performed. • Investigations and comparisons among different ANN designs and training strategies. [ABSTRACT FROM AUTHOR]

Scale formation is one of the major problems in the oil industry as it can accumulate on the surface of the pipelines, which could even fully block the fluids' passage. It was developed a methodology to detect and quantify the maximum thickness of eccentric scale inside pipelines using nuclear techniques and an artificial neural network. The measurement procedure is based on gamma-ray scattering using NaI(Tl) detectors and a137Cs radiation source that emits gamma-rays of 662 keV. The simulations considered an annular flow regime composed of barium sulfate scale, oil, saltwater and gas, and three percentages of these fluids were used. In the present investigation, a study of detectors configuration was carried out to improve the measurement geometry and the simulations were made using the MCNP6 code, which is a mathematical code based on the Monte Carlo method. The counts registered in the detectors were used as input data to train a deep neural network (DNN) that uses rectifier activation functions instead of the usually sigmoid-based ones. In addition, a hyperparameters search was made using open software to develop the final DNN architecture. Results showed that the best detector configuration was able to predict 100% of the patterns with the maximum relative error of 5%. Moreover, the achieved mean absolute percentage error was 0.42% and the regression coefficient was 0.99996 for all data. The results are promising and encourage the use of DNN to calculate inorganic scale regardless of the fluids volume fraction inside pipelines. • Prediction of the maximum eccentric scale thickness using gamma-ray scattering. • Application of deep neural network using hyperparameters search software. • The prediction is made regardless of the fluids volume fraction in the multiphase system. • Measurement geometry is composed of two detectors and a137Cs radiation source. • Patterns of the eccentric scale thickness were simulated using the MCNP6 code. [ABSTRACT FROM AUTHOR]

A multiphase flow is defined as the transport of two or more fluids with different properties flowing together inside a pipeline. After offshore oil production, it is necessary to control the amount of transported fluids based on flow rate measurements. Therefore, in this study, we developed a simulation method for predicting the volume fraction and calculating the superficial velocity for a two-phase flow based on radioactive particle tracking, which involves using a sealed radiation source inside the pipeline in order to obtain volume fraction measurements. The test section for the multiphase flow comprised oil and saltwater under a stratified flow regime, with a polyvinyl chloride pipe, four NaI(Tl) detectors, and a137Cs radioactive particle that emitted gamma-rays at 662 keV. Simulations were conducted using the MCNP6 code, which is a mathematical code based on the Monte Carlo method. Volume fraction predictions were obtained using a multilayer perceptron neural network with a backpropagation algorithm. The novel feature of this method is the combination of radioactive particle tracking with an artificial neural network in order to predict volume fractions in multiphase flows. The results showed that 91.65% of the predicted patterns were within 5% of the relative error. In addition, the time delay was determined using the cross-correlation function to obtain the superficial velocity in three different volume fractions, which allowed each phase flow rate to be calculated in these cases. • Application of radioactive particle tracking to multiphase flows. • Volume fraction predicted using an artificial neural network. • Flow velocity calculated using a cross-correlation function. • Flow rate calculated in a stratified flow regime. [ABSTRACT FROM AUTHOR]

A recent investigation proposed a simulated radioactive particle tracking (RPT) system using eight scintillator detectors in order to predict instantaneous positions of a radioactive particle inside a concrete mixer using an artificial neural network as a location algorithm. In the context of RPT, the aim of the present study is to propose an optimization in the number of detectors in a single-phase flow RPT system. The new detection geometry consists of an array of six NaI(Tl) detectors, a 137Cs point source with isotropic emission of gamma-rays (radioactive particle) and a polyvinyl chloride mixer filled with concrete made with Portland cement as a homogenous flow regime. Another feature of this study is the use of MCNP6 code, which is based on Monte Carlo Method. In addition, three feed-forward multilayer perceptron networks with different configuration are tested as a location algorithm. All three networks showed good statistical results and the root mean square error is 1.18 in the worst scenario. The results also showed an agreement with previous study, which indicates that this methodology reducing two detectors works satisfactorily and maintain a good accuracy in position prediction. • Optimization of the detection system reducing two detectors. • Radioactive Particle Tracking methodology developed using MCNP6 code. • An artificial neural network predicts the position of the radioactive particle. • System based on RPT to evaluate industrial agitators in a homogeneous flow regime. [ABSTRACT FROM AUTHOR]

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.