Predicting Scale Thickness in Oil Pipelines Using Frequency Characteristics and an Artificial Neural Network in a Stratified Flow Regime

Tzu-Chia Chen (), Abdullah M. Iliyasu (), Robert Hanus (), Ahmed S. Salama and Kaoru Hirota
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Tzu-Chia Chen: College of Management and Design, Ming Chi University of Technology, New Taipei City 243303, Taiwan
Abdullah M. Iliyasu: College of Engineering, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
Robert Hanus: Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, 35-959 Rzeszow, Poland
Ahmed S. Salama: Faculty of Engineering and Technology, Future University in Egypt, New Cairo 11835, Egypt
Kaoru Hirota: School of Computing, Tokyo Institute of Technology, Yokohama 226-8502, Japan

Energies, 2022, vol. 15, issue 20, 1-11

Abstract: One of the main problems in oil fields is the deposition of scale inside oil pipelines, which causes problems such as the reduction of the internal diameter of oil pipes, the need for more energy to transport oil products, and the waste of energy. For this purpose, the use of an accurate and reliable system for determining the amount of scale inside the pipes has always been one of the needs of the oil industry. In this research, a non-invasive, accurate, and reliable system is presented, which works based on the attenuation of gamma rays. A dual-energy gamma source ( 241 Am and 133 Ba radioisotopes), a sodium iodide detector, and a steel pipe are used in the structure of the detection system. The configuration of the detection structure is such that the dual-energy source and the detector are directly opposite each other and on both sides of the steel pipe. In the steel pipe, a stratified flow regime consisting of gas, water, and oil in different volume percentages was simulated using Monte Carlo N Particle (MCNP) code. Seven scale thicknesses between 0 and 3 cm were simulated inside the tube. After the end of the simulation process, the received signals were labeled and transferred to the frequency domain usage of fast Fourier transform (FFT). Frequency domain signals were processed, and four frequency characteristics were extracted from them. The multilayer perceptron (MLP) neural network was used to obtain the relationship between the extracted frequency characteristics and the scale thickness. Frequency characteristics were defined as inputs and scale thickness in cm as the output of the neural network. The prediction of scale thickness with an RMSE of 0.13 and the use of only one detector in the structure of the detection system are among the advantages of this research.

Keywords: scale thickness; frequency characteristics; fast Fourier transform; multilayer perceptron neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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