1. Calculation of scales in oil pipeline using gamma-ray scattering and artificial intelligence.
- Author
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Salgado, César Marques, Salgado, William Luna, Dam, Roos Sophia de Freitas, and Conti, Claudio Carvalho
- Subjects
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GAMMA-ray scattering , *ARTIFICIAL intelligence , *PETROLEUM pipelines , *ARTIFICIAL neural networks , *ANNULAR flow , *GAMMA ray spectrometry , *CESIUM isotopes - Abstract
• The proposed geometry composes gamma-rays and two NaI(Tl) detectors. • Different thicknesses and relative positions of scale for the annular flow regime were modeled using the MCNP code. • A Backpropagation 5-layer perceptron network was used for scale prediction. • The gamma-ray scattering was used to quantify the maximum thickness of eccentric scales (BaSO 4). • The maximum scale was predicted independently of its position inside the tube and the presence of the fluids. This study investigates a methodology to study the deposition of barium sulfate scales (BaSO 4) 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%. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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