Calculation of scales in oil pipeline using gamma-ray scattering and artificial intelligence

This study investigates a methodology to study the deposition of barium sulfate scales (BaSO4) commonly found in the oil industry; it causes an internal diameter decrease, making it difficult for the flow. A measurement procedure was elaborated on gamma-ray scattering with three NaI(Tl) detectors and a 137Cs gamma-ray source to detect and quantify the maximum thickness of eccentric scale. The detectors data were used to train the artificial neural network for the prediction of the maximum scale thickness values regardless of oil, saltwater, gas and scale inside the tube. A data subset for training and evaluation of the artificial neural network generalization capability was generated using the MCNP6 code. Different thicknesses and positions of the maximum scale value were considered. The results show that more than 90% of the patterns presented relative errors lower than +/- 10%.

Automated summary

This study investigates a methodology using gamma-ray scattering to study barium sulfate scales in the oil industry. An artificial neural network was trained to predict maximum scale thickness values with over 90% accuracy.