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.

Automated summary

This study proposes a method based on gamma-ray densitometry and a multilayer perceptron artificial neural network to identify flow regime and predict volume fraction of gas, water, and oil in multiphase flow. Through simulation and data training, the study successfully distinguished three flow regimes and accurately predicted the volume fraction of gas and water phases in multiphase systems.