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Extraction of Time-Domain Characteristics and Selection of Effective Features Using Correlation Analysis to Increase the Accuracy of Petroleum Fluid Monitoring Systems

Author

Listed:
  • Abdulilah Mohammad Mayet

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

  • Seyed Mehdi Alizadeh

    (Petroleum Engineering Department, Australian College of Kuwait, Kuwait City 13015, Kuwait)

  • Karina Shamilyevna Nurgalieva

    (Department of Development and Operation of Oil and Gas Fields, Saint-Petersburg Mining University, 199106 Saint-Petersburg, Russia)

  • Robert Hanus

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

  • Ehsan Nazemi

    (Imec-Vision Laboratory, Department of Physics, University of Antwerp, 2610 Antwerp, Belgium)

  • 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)

Abstract

In the current paper, a novel technique is represented to control the liquid petrochemical and petroleum products passing through a transmitting pipe. A simulation setup, including an X-ray tube, a detector, and a pipe, was conducted by Monte Carlo N Particle-X version (MCNPX) code to examine a two-by-two mixture of four diverse petroleum products (ethylene glycol, crude oil, gasoline, and gasoil) in various volumetric ratios. As the feature extraction system, twelve time characteristics were extracted from the received signal, and the most effective ones were selected using correlation analysis to present reasonable inputs for neural network training. Three Multilayers perceptron (MLP) neural networks were applied to indicate the volume ratio of three kinds of petroleum products, and the volume ratio of the fourth product can be feasibly achieved through the results of the three aforementioned networks. In this study, increasing accuracy was placed on the agenda, and an RMSE < 1.21 indicates this high accuracy. Increasing the accuracy of predicting volume ratio, which is due to the use of appropriate characteristics as the neural network input, is the most important innovation in this study, which is why the proposed system can be used as an efficient method in the oil industry.

Suggested Citation

  • Abdulilah Mohammad Mayet & Seyed Mehdi Alizadeh & Karina Shamilyevna Nurgalieva & Robert Hanus & Ehsan Nazemi & Igor M. Narozhnyy, 2022. "Extraction of Time-Domain Characteristics and Selection of Effective Features Using Correlation Analysis to Increase the Accuracy of Petroleum Fluid Monitoring Systems," Energies, MDPI, vol. 15(6), pages 1-19, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:6:p:1986-:d:767033
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    Cited by:

    1. Aleksey Dengaev & Vladimir Shishulin & Elena Safiullina & Aleksandra Palyanitsina, 2022. "Modeling Results for the Real Horizontal Heavy-Oil-Production Well of Mechanical Solids," Energies, MDPI, vol. 15(14), pages 1-13, July.
    2. 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.
    3. Alexander Vitalevich Martirosyan & Yury Valerievich Ilyushin, 2022. "The Development of the Toxic and Flammable Gases Concentration Monitoring System for Coalmines," Energies, MDPI, vol. 15(23), pages 1-13, November.

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