Application of Neural Network and Dual-Energy Radiation-Based Detection Techniques to Measure Scale Layer Thickness in Oil Pipelines Containing a Stratified Regime of Three-Phase Flow

Abdulilah Mohammad Mayet, Tzu-Chia Chen (), Ijaz Ahmad (), Elsayed Tag Eldin, Ali Awadh Al-Qahtani, Igor M. Narozhnyy, John William Grimaldo Guerrero and Hala H. Alhashim
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Abdulilah Mohammad Mayet: Electrical Engineering Department, King Khalid University, Abha 61411, Saudi Arabia
Tzu-Chia Chen: College of Management and Design, Ming Chi University of Technology, New Taipei City 243303, Taiwan
Ijaz Ahmad: Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences (UCAS), Shenzhen 518055, China
Elsayed Tag Eldin: Electrical Engineering Department, Faculty of Engineering & Technology, Future University in Egypt, New Cairo 11845, Egypt
Ali Awadh Al-Qahtani: Electrical Engineering Department, King Khalid University, Abha 61411, Saudi Arabia
Igor M. Narozhnyy: Department of Commercialization of Intellectual Activity Resultse Center for Technology Transfer of RUDN University, Mining Oil and Gas Department, RUDN University, 117198 Moscow, Russia
John William Grimaldo Guerrero: Department of Energy, Universidad de la Costa, Barranquilla 080001, Colombia
Hala H. Alhashim: Department of Physics, College of Science, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia

Mathematics, 2022, vol. 10, issue 19, 1-13

Abstract: Over time, oil pipes are scaled, which causes problems such as a reduction in the effective diameter of the oil pipe, an efficiency reduction, waste of energy, etc. Determining the exact value of the scale inside the pipe is very important in order to take timely action and to prevent the mentioned problems. One accurate detection methodology is the use of non-invasive systems based on gamma-ray attenuation. For this purpose, in this research, a scale thickness detection system consisting of a test pipe, a dual-energy gamma source ( 241 Am and 133 Ba radioisotopes), and two sodium iodide detectors were simulated using the Monte Carlo N Particle (MCNP) code. In the test pipe, three-phase flow consisting of water, gas, and oil was simulated in a stratified flow regime in volume percentages in the range from 10% to 80%. In addition, a scale with different thicknesses from 0 to 3 cm was placed inside the pipe, and gamma rays were irradiated onto the pipe; on the other side of the pipe, the photon intensity was recorded by the detectors. A total of 252 simulations were performed. From the signal received by the detectors, four characteristics were extracted, named the Photopeaks of 241 Am and 133 Ba for the first and second detectors. After training many different Multi-Layer Perceptron(MLP) neural networks with various architectures, it was found that a structure with two hidden layers could predict the connection between the input, extracted features, and the output, scale thickness, with a Root Mean Square Error (RMSE) of less than 0.06. This low error value guarantees the effectiveness of the proposed method and the usefulness of this method for the oil and petrochemical industry.

Keywords: three-phase flow; scale layer thickness; volume fraction independent; MLP neural network (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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