Flow rate determination in a two-phase system using radioactive particle tracking and deep learning.

In the oil industry, accurate flow rate determination and control in pipelines are critical for ensuring operational efficiency. However, most conventional flowmeters require direct contact with the transported fluids, which necessitates periodic maintenance and may cause system shutdowns, thereby increasing operational costs. To address these limitations, this study proposes a minimally intrusive methodology for predicting fluid volume fractions and calculating superficial velocities aimed at flow rate determination in two-phase systems using the radioactive particle tracking technique. This approach employs a sealed radiation source (137Cs, emitting 662 keV gamma-rays) inserted into the pipeline to obtain volume fraction data. The simulated setup consisted of a polyvinyl chloride (PVC) pipe, five NaI(Tl) scintillation detectors, and the radiation source, configured for a stratified saltwater-oil flow regime. Simulations were performed using the MCNP6 Monte Carlo code. Volume fractions were predicted using deep neural networks, while time delays for superficial velocity calculations were derived from the cross-correlation function applied to the oil-phase signals. The proposed method achieved a maximum mean absolute percentage error (MAPE) of 2.24% for the oil flow rate when compared with theoretical values.