Calcium carbonate scale thickness prediction in annular three-phase flow using gamma-ray densitometry and artificial neural networks

Abstract

This study presents a methodology based on gamma-ray densitometry for predicting calcium carbonate (CaCO3) scale thickness in pipelines of three-phase systems (oil, salty water, and gas) in the petroleum industry. Both concentric and eccentric scale thicknesses were calculated using analytical equations and artificial neural networks. The artificial neural networks were trained using a backpropagation algorithm with data acquired from the Monte Carlo N-Particle (MCNP6) computer code. The model utilized to achieve the supervised training of the network consisted of three 1¼ × ¾″ NaI(Tl) detectors positioned 120° apart around the pipe. Results demonstrate that the networks successfully predicted over 96 % of both concentric and eccentric cases of CaCO3 scale thickness with a relative error within 10%.