Barium sulfate scale thickness prediction using MCNPX code and an artificial neural network

Abstract

This report presents a method to predict the barium sulfate scales (BaSO₄) thickness in pipelines of multiphase systems (oil, gas and water) found in the petroleum industry. The technique is based in gamma-ray densitometry which uses a transmission measurement of gamma-ray beam to determine the density of the materials. In this study, an artificial neural network (ANN) is training to solve problems related with the measurement’s conditions from this technique. The data to training, test and validation of the ANN was obtained through the Monte Carlo N-Particle 6 (MCNP6) code