Search

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. [ABSTRACT FROM AUTHOR]

This paper presents a novel methodology for volume fraction predictions in multiphase flow meters in offshore petroleum industries using gamma-ray densitometry. The algorithm that interprets spectra recorded at detectors is based on the new architectures of deep neural networks (deep learning). In this study, an 8-layer deep rectifier neural network (DRNN) has been used, running on a GPU-based parallel framework in order to increase the measuring precision of volumetric percentage of gas-oil-water system. The adaptive learning rate optimization algorithm (Adam) was used for DRNN training. All spectra recorded at detectors were normalized and used as the inputs of the DRNN. The detection system uses two NaI(Tl) detectors and appropriate narrow beam geometry, comprised of a (59.54 and 662 keV) dual-energy gamma-ray source and a dual-modality in order measure transmitted and scattered beams. The theoretical models consider a static oil-water-gas annular flow regime that were developed using MCNP6 code, which was used to provide training, validation and test data for the DRNN. The response-function of a real NaI(Tl) detector is also considered on the mathematical model. The results showed the root mean square error below than 0.8 for the fluids investigated in this study. • The spectra interpreter algorithm is based on new deep learning architectures. • An 8-layer DRNN was used to increase the accuracy of the fluids volume fraction. • The detection system uses dual-energy gamma-ray source and a dual-modality. • Annular flow regime models were developed using MCNP6 to train the DRNN. [ABSTRACT FROM AUTHOR]

Knowing the volume fraction in a multiphase flow is of fundamental importance in predicting the performance of many systems and processes, it has been possible to model an experimental apparatus for volume fraction studies using Monte Carlo codes. Artificial neural networks have been applied for the recognition of the pulse height distributions in order to obtain the prediction of the volume fractions of the flow. In this sense, some researchers are unsure of which Monte Carlo code to use for volume fractions studies in two-phase flows. This work aims to model a biphasic flow (water and air) experiment in a stratified regime in two Monte Carlo-based codes (MCNP-X and Gate/Geant4), and to verify which one has the greatest benefits for researchers, focusing on volume fractions studies. • The artificial neural network training provided good accuracy on volume fraction prediction. • The artificial neural network training provided good accuracy on flow regime identification. • MCNPX presents a simpler input development methodology, compared to Gate/Geant4. • MCNPX has lower computational cost than Gate/Geant4. • The results showed the good viability of working with MCNPX in volume fractions studies. [ABSTRACT FROM AUTHOR]

Knowledge of the flow regime and the volume fraction in multiphase flow is of fundamental importance in predicting the performance of many systems and processes. This study is based on gamma-ray pulse height distribution pattern recognition by means of an artificial neural network. The detection system uses appropriate one narrow beam geometry, comprising a gamma-ray source and a NaI(Tl) detector. The models for annular and stratified flow regimes were developed using MCNPX code, in order to obtain adequate data sets for training and testing of the artificial neural network. Several experiments were carried out in the stratified flow regime to validate the simulated results. Finally, Ansys-CFX was used as computational fluid dynamics software to simulate two different volume fractions, which were modeled and transformed in voxels and transferred to MCNPX code. The use of computational fluid dynamics is of great importance, because it brings the studies closer to the reality. All flow regimes were correctly recognized and the volume fractions were appropriately predicted with relative errors less than 1.1%. • Artificial neural network (ANN) training led to accurate prediction of volume fraction. • ANN training provided good accuracy in flow regime identification. • Computational fluid dynamics (CFD) helped to make the studies closer to reality. • The use of CFD was of great importance for flow study. • The results showed the viability of working with Monte Carlo code and CFD. [ABSTRACT FROM AUTHOR]