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Employing GMDH-Type Neural Network and Signal Frequency Feature Extraction Approaches for Detection of Scale Thickness inside Oil Pipelines

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  • Abdullah M. Iliyasu

    (Electrical Engineering Department, College of Engineering, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
    School of Computing, Tokyo Institute of Technology, Yokohama 226-8502, Japan
    School of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China)

  • Abdulilah Mohammad Mayet

    (Electrical Engineering Department, King Khalid University, Abha 61411, Saudi Arabia)

  • Robert Hanus

    (Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, 35-959 Rzeszow, Poland)

  • Ahmed A. Abd El-Latif

    (EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
    Mathematics and Computer Science Department, Faculty of Science, Menoufia University, Shebin El-Koom 32511, Egypt)

  • Ahmed S. Salama

    (Electrical Engineering Department, Faculty of Engineering and Technology, Future University in Egypt, New Cairo 11835, Egypt)

Abstract

In this paper, gamma attenuation has been utilised as a veritable tool for non-invasive estimation of the thickness of scale deposits. By simulating flow regimes at six volume percentages and seven scale thicknesses of a two phase-flow in a pipe, our study utilised a dual-energy gamma source with Ba-133 and Cs-137 radioisotopes, a steel pipe, and a 2.54 cm × 2.54 cm sodium iodide (NaI) photon detector to analyse three different flow regimes. We employed Fourier transform and frequency characteristics (specifically, the amplitudes of the first to fourth dominant frequencies) to transform the received signals to the frequency domain, and subsequently to extract the various features of the signal. These features were then used as inputs for the group method for data Hiding (GMDH) neural network framework used to predict the scale thickness inside the pipe. Due to the use of appropriate features, our proposed technique recorded an average root mean square error (RMSE) of 0.22, which is a very good error compared to the detection systems presented in previous studies. Moreover, this performance is indicative of the utility of our GMDH neural network extraction process and its potential applications in determining parameters such as type of flow regime, volume percentage, etc. in multiphase flows and across other areas of the oil and gas industry.

Suggested Citation

  • Abdullah M. Iliyasu & Abdulilah Mohammad Mayet & Robert Hanus & Ahmed A. Abd El-Latif & Ahmed S. Salama, 2022. "Employing GMDH-Type Neural Network and Signal Frequency Feature Extraction Approaches for Detection of Scale Thickness inside Oil Pipelines," Energies, MDPI, vol. 15(12), pages 1-12, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:12:p:4500-:d:843469
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    References listed on IDEAS

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    1. Mohammed Balubaid & Mohammad Amir Sattari & Osman Taylan & Ahmed A. Bakhsh & Ehsan Nazemi, 2021. "Applications of Discrete Wavelet Transform for Feature Extraction to Increase the Accuracy of Monitoring Systems of Liquid Petroleum Products," Mathematics, MDPI, vol. 9(24), pages 1-14, December.
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    Cited by:

    1. Abdulilah Mohammad Mayet & Tzu-Chia Chen & Ijaz Ahmad & Elsayed Tag Eldin & Ali Awadh Al-Qahtani & Igor M. Narozhnyy & John William Grimaldo Guerrero & Hala H. Alhashim, 2022. "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," Mathematics, MDPI, vol. 10(19), pages 1-13, September.
    2. Abdulilah Mohammad Mayet & Karina Shamilyevna Nurgalieva & Ali Awadh Al-Qahtani & Igor M. Narozhnyy & Hala H. Alhashim & Ehsan Nazemi & Ilya M. Indrupskiy, 2022. "Proposing a High-Precision Petroleum Pipeline Monitoring System for Identifying the Type and Amount of Oil Products Using Extraction of Frequency Characteristics and a MLP Neural Network," Mathematics, MDPI, vol. 10(16), pages 1-20, August.

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