IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i5p1807-d761490.html
   My bibliography  Save this article

Predicting the Compressibility Factor of Natural Gas by Using Statistical Modeling and Neural Network

Author

Listed:
  • Alaa Ghanem

    (PVT-Lab, Production Department, Egyptian Petroleum Research Institute, Nasr City, Cairo 11727, Egypt)

  • Mohammed F. Gouda

    (Atef H. Rizk & Company, Cairo 11331, Egypt)

  • Rima D. Alharthy

    (Department of Chemistry, Science & Arts College, Rabigh Branch, King Abdulaziz University, Rabigh 21911, Saudi Arabia)

  • Saad M. Desouky

    (PVT-Lab, Production Department, Egyptian Petroleum Research Institute, Nasr City, Cairo 11727, Egypt)

Abstract

Simulating the phase behavior of a reservoir fluid requires the determination of many parameters, such as gas–oil ratio and formation volume factor. The determination of such parameters requires knowledge of the critical properties and compressibility factor (Z factor). There are many techniques to determine the compressibility factor, such as experimental pressure, volume, and temperature (PVT) tests, empirical correlations, and artificial intelligence approaches. In this work, two different models based on statistical regression and multi-layer-feedforward neural network (MLFN) were developed to predict the Z factor of natural gas by utilizing the experimental data of 1079 samples with a wide range of pseudo-reduced pressure (0.12–25.8) and pseudo reduced temperature (1.3–2.4). The statistical regression model was proposed and trained in R using the “rjags” package and Markov chain Monte Carlo simulation, while the multi-layer-feedforward neural network model was postulated and trained using the “neural net” package. The neural network consists of one input layer with two anodes, three hidden layers, and one output layer. The input parameters are the ratio of pseudo-reduced pressure and the pseudo-reduced temperature of the natural hydrocarbon gas, while the output is the Z factor. The proposed statistical and MLFN models showed a positive correlation between the actual and predicted values of the Z factor, with a correlation coefficient of 0.967 and 0.979, respectively. The results from the present study show that the MLFN can lead to accurate and reliable prediction of the natural gas compressibility factor.

Suggested Citation

  • Alaa Ghanem & Mohammed F. Gouda & Rima D. Alharthy & Saad M. Desouky, 2022. "Predicting the Compressibility Factor of Natural Gas by Using Statistical Modeling and Neural Network," Energies, MDPI, vol. 15(5), pages 1-15, March.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:5:p:1807-:d:761490
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/5/1807/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/5/1807/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Vassilis Gaganis & Dirar Homouz & Maher Maalouf & Naji Khoury & Kyriaki Polychronopoulou, 2019. "An Efficient Method to Predict Compressibility Factor of Natural Gas Streams," Energies, MDPI, vol. 12(13), pages 1-20, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yun Xia & Wenpeng Bai & Zhipeng Xiang & Wanbin Wang & Qiao Guo & Yang Wang & Shiqing Cheng, 2022. "Improvement of Gas Compressibility Factor and Bottom-Hole Pressure Calculation Method for HTHP Reservoirs: A Field Case in Junggar Basin, China," Energies, MDPI, vol. 15(22), pages 1-20, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Cai, Mingyu & Su, Yuliang & Elsworth, Derek & Li, Lei & Fan, Liyao, 2021. "Hydro-mechanical-chemical modeling of sub-nanopore capillary-confinement on CO2-CCUS-EOR," Energy, Elsevier, vol. 225(C).
    2. George Truc & Nejat Rahmanian & Mahboubeh Pishnamazi, 2021. "Assessment of Cubic Equations of State: Machine Learning for Rich Carbon-Dioxide Systems," Sustainability, MDPI, vol. 13(5), pages 1-18, February.
    3. Xiaoping Li & Shudong Liu & Ji Li & Xiaohua Tan & Yilong Li & Feng Wu, 2020. "Apparent Permeability Model for Gas Transport in Multiscale Shale Matrix Coupling Multiple Mechanisms," Energies, MDPI, vol. 13(23), pages 1-24, November.
    4. Anna Samnioti & Vassilis Gaganis, 2023. "Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part I," Energies, MDPI, vol. 16(16), pages 1-43, August.
    5. Anna Samnioti & Vassiliki Anastasiadou & Vassilis Gaganis, 2022. "Application of Machine Learning to Accelerate Gas Condensate Reservoir Simulation," Clean Technol., MDPI, vol. 4(1), pages 1-21, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:5:p:1807-:d:761490. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.