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

• An approach to salinity independent volume fraction prediction in water–gas–oil multiphase flows has been proposed.
• The methodology is based on pulse height distributions pattern recognition by an Artificial Neural Network (ANN).
• The dual-mode densitometry using NaI(Tl) real detectors has been modeled using MCNP-X to produce training patterns for the ANN.
• Results show that the proposed approach can make predictions with errors smaller than 3.05% for water, gas and oil.

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.