Development of analytical equations for void fraction in biphasic systems using gamma radiation and MCNP6 code.

This study presents the development of mathematical equations for calculating void fractions in pipes using gamma densitometry. A traditional measurement setup, consisting of a137Cs point source and a NaI(Tl) scintillator detector, was simulated using the Monte Carlo method via the MCNP6 code. To validate the proposed equations, water-gas biphasic models were simulated in tubes with square and cylindrical cross-sections, varying diameters, and radiation sources (241Am, 137Cs, 60Co) through gamma-ray transmission. A comparative analysis with existing equations from the literature was conducted. The void fractions, determined from the transmission photopeak, were in close agreement with the actual values. The proposed equations demonstrated a maximum mean relative error of 0.21% for cylindrical tubes in stratified and annular flow regimes.

Volume fraction detection in multiphase systems using neutron activation analysis and artificial neural network.

This study presents an application of an Artificial Neural Network (ANN) to detect fluids in an annular flow regime using Prompt-Gamma Neutron Activation Analysis (PGNAA). The ANN was trained using gamma-ray spectra resulting from neutron interactions with chemical elements found in fluids typical of multiphase flow in oil exploration. These spectra were generated through mathematical simulation using the MCNP6 Monte Carlo computer code to model nuclear particle transport. A241Am-Be polyenergetic neutron source was simulated for these calculations. Several combinations of fluid fractions were developed to create a dataset used for both training and evaluation of the ANN. The ANN demonstrated robust generalization capabilities by accurately predicting the volume fraction of the three investigated fluids (saltwater, oil, and gas), even for cases not included in the training phase. The combination of ANN and PGNAA proved effective for analyzing multiphase systems, with over 92% of all showing errors of less than 5%.

Use of gamma radiation and artificial neural network techniques to monitor characteristics of polyduct transport of petroleum by-products.

This study presents a methodology based on the dual-mode gamma densitometry technique in combination with artificial neural networks to simultaneously determine type and quantity of four different fluids (Gasoline, Glycerol, Kerosene and Fuel Oil) to assist operators of a fluid transport system in pipelines commonly found in the petrochemical industry, as it is necessary to continuously monitor information about the fluids being transferred. The detection system is composed of a 661.657 keV (137Cs) gamma-ray emitting source and two NaI(Tl) scintillation detectors to record transmitted and scattered photons. The information recorded in both detectors was directly applied as input data for the artificial neural networks. The proposed intelligent system consists of three artificial neural networks capable of predicting the fluid volume percentages (purity level) with 94.6% of all data with errors less than 5% and MRE of 1.12%, as well as identifying the pair of fluids moving in the pipeline with 95.9% accuracy.

Optimization of a flow regime identification system and prediction of volume fractions in three-phase systems using gamma-rays and artificial neural network.

This study presents a method based on gamma-ray densitometry using only one multilayer perceptron artificial neural network (ANN) to identify flow regime and predict volume fraction of gas, water, and oil in multiphase flow, simultaneously, making the prediction independent of the flow regime. Two NaI(Tl) detectors to record the transmission and scattering beams and a source with two gamma-ray energies comprise the detection geometry. The spectra of gamma-ray recorded by both detectors were chosen as ANN input data. Stratified, homogeneous, and annular flow regimes with (5 to 95%) various volume fractions were simulated by the MCNP6 code, in order to obtain an adequate data set for training and assessing the generalization capacity of ANN. All three regimes were correctly distinguished for 98% of the investigated patterns and the volume fraction in multiphase systems was predicted with a relative error of less than 5% for the gas and water phases.

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%.

Density prediction for petroleum and derivatives by gamma-ray attenuation and artificial neural networks.

This work presents a new methodology for density prediction of petroleum and derivatives for products' monitoring application. The approach is based on pulse height distribution pattern recognition by means of an artificial neural network (ANN). The detection system uses appropriate broad beam geometry, comprised of a (137)Cs gamma-ray source and a NaI(Tl) detector diametrically positioned on the other side of the pipe in order measure the transmitted beam. Theoretical models for different materials have been developed using MCNP-X code, which was also used to provide training, test and validation data for the ANN. 88 simulations have been carried out, with density ranging from 0.55 to 1.26gcm(-3) in order to cover the most practical situations. Validation tests have included different patterns from those used in the ANN training phase. The results show that the proposed approach may be successfully applied for prediction of density for these types of materials. The density can be automatically predicted without a prior knowledge of the actual material composition.