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

被引：15

作者：

Salgado, Cesar Marques ^{[1
,3
]}

Salgado, William Luna ^{[1
,2
]}

Dam, Roos Sophia de Freitas ^{[1
,2
]}

Conti, Claudio Carvalho ^{[3
]}

机构：

[1] Inst Nucl Engn IEN, Div Radiopharmaceut DIRAD, Rua Helio de Almeida 75, BR-21941906 Rio De Janeiro, RJ, Brazil

[2] Fed Univ Rio de Janeiro UFRJ, Nucl Engn Program PEN COPPE, Ave Horacio Macedo 2030,G 206, BR-21941914 Rio De Janeiro, RJ, Brazil

[3] Fed Univ Rio de Janeiro UFRJ, Ctr Tecnol, Lab Nucl Instrumentat LIN COPPE, Bloco 1,Sala I-133, BR-21941972 Rio De Janeiro, RJ, Brazil

来源：

MEASUREMENT | 2021年 / 179卷

关键词：

Scale; Multiphase flow; Artificial neural network; Gamma-ray scattering; MCNP6; code; FLOW; PREDICTION; ABSORPTION; DENSITY; REGIME; MODEL;

D O I：

10.1016/j.measurement.2021.109455

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

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%.

引用

页数：9

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