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

Abstract

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.