Research Article | | Peer-Reviewed

A Method for Solving the Generalized Weng Model

Received: 2 April 2024     Accepted: 22 April 2024     Published: 17 May 2024
Views:       Downloads:
Abstract

The Generalized Weng Model is one of the basic models for oil production forecasting. Professor Chen Yuanqian first proposed the linear iterative trial-and-error method to solve the generalized Weng Model, and scholar Zhao Lin proposed the method to solve the Weng model based on binary regression. In this paper, a new method for solving Weng Model is put forward. Taking Liaohe Oilfield in China as an example, the process and results of the three methods are compared, and the advantages and disadvantages of the three methods are analyzed. The results show that when the original linear iterative trial and error method solves the model, it needs to simulate the value of parameter b with computer software, and then select a judgment criterion to find the optimal b value. In this paper, a method based on binary regression is proposed which can directly calculate parameter b. The new method can directly calculate the parameter b better than the method based on binary regression. The method in this paper is to fit all the data at one time, avoiding the above two kinds of uncertainties, and the calculation workload is small and can be realized by EXCEL, which is convenient for technical personnel.

Published in American Journal of Computer Science and Technology (Volume 7, Issue 2)
DOI 10.11648/j.ajcst.20240702.12
Page(s) 38-42
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Parameter, Prediction, Generalized Weng Model

References
[1] Weng Wenbo. Fundamentals of Prediction Theory [M], Beijing: Petroleum Industry Press, 2020. 67-86.
[2] Weng Wenbo. Theory of Forecasting [M]. International Academic Publishers: A Pergamon-CNPIEC Joint Venture, 1991.
[3] Zhao Xudong. Methods for predicting oil and gas field production and final recoverable reserves [J]. Petroleum Exploration and Development, 2019, 13(2): 72-78.
[4] Zhao Xudong. Prediction of finite life systems using the Weng cycle model [J]. Chinese Science Bulletin, 2018, 32(18): 1406-1409.
[5] Zhao Xudong. Quantitative evaluation of petroleum resources [M]. Beijing: Geological Publishing House, 2917.
[6] Zhao Xudong. Introduction to Petroleum Mathematical Geology [M]. Beijing: Petroleum Industry Press, 2006.
[7] Chen Yuanqian, Hu Jianguo, Zhang Dongjie. Logistic Model derivation and autoregressive methods [J]. Xinjiang Petroleum Geology, 2016, 17(2): 150-155.
[8] Chen Yuanqian. Derivation and Application of Generalized Weng's Prediction Model [J]. Natural Gas Industry, 2019, 16(2): 22-26.
[9] Chen Yuanqian, Hu Jianguo. Review and new derivation of the original modeling of Weng's model [J]. China Offshore Oil and Gas (Geology), 2020, 10(5): 317-324.
[10] Chen Yuanqian, Li Xuan. Modern reservoir engineering [M]. Beijing: Petroleum Industry Press, 2018.
[11] Chen Yuanqian. Practical methods for oil and gas reservoir engineering [M]. Beijing: Petroleum Industry Press, 2020, 1(6).
[12] Chen Yuanqian. Derivation of the water drive curve relation [J]. Acta PetroleiSinica, 2021, 6(2), 69-78.
[13] Chen Yuanqian. Theoretical Derivation and Application of Nazarov's Empirical Formula for Determining Dead Reserves, Petroleum Exploration and Development [J], 2020, 22(3), 63-68.
[14] Chen Yuanqian. Derivation and linear solution of dimensionless IPR curves [J]. Acta Petrolei Sinica, 2018, 7(2): 63-73.
[15] Zhao Lin, Feng Lianyong, Lu Xiagan, Tong Xiaoguang. Comparison of two methods for solving the generalized Weng model [J]. Xinjiang Petroleum Geology, 2017, 30(5): 658-660.
Cite This Article
  • APA Style

    Hongwu, Z., Qiuyu, X. (2024). A Method for Solving the Generalized Weng Model. American Journal of Computer Science and Technology, 7(2), 38-42. https://doi.org/10.11648/j.ajcst.20240702.12

    Copy | Download

    ACS Style

    Hongwu, Z.; Qiuyu, X. A Method for Solving the Generalized Weng Model. Am. J. Comput. Sci. Technol. 2024, 7(2), 38-42. doi: 10.11648/j.ajcst.20240702.12

    Copy | Download

    AMA Style

    Hongwu Z, Qiuyu X. A Method for Solving the Generalized Weng Model. Am J Comput Sci Technol. 2024;7(2):38-42. doi: 10.11648/j.ajcst.20240702.12

    Copy | Download

  • @article{10.11648/j.ajcst.20240702.12,
      author = {Zeng Hongwu and Xu Qiuyu},
      title = {A Method for Solving the Generalized Weng Model
    },
      journal = {American Journal of Computer Science and Technology},
      volume = {7},
      number = {2},
      pages = {38-42},
      doi = {10.11648/j.ajcst.20240702.12},
      url = {https://doi.org/10.11648/j.ajcst.20240702.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcst.20240702.12},
      abstract = {The Generalized Weng Model is one of the basic models for oil production forecasting. Professor Chen Yuanqian first proposed the linear iterative trial-and-error method to solve the generalized Weng Model, and scholar Zhao Lin proposed the method to solve the Weng model based on binary regression. In this paper, a new method for solving Weng Model is put forward. Taking Liaohe Oilfield in China as an example, the process and results of the three methods are compared, and the advantages and disadvantages of the three methods are analyzed. The results show that when the original linear iterative trial and error method solves the model, it needs to simulate the value of parameter b with computer software, and then select a judgment criterion to find the optimal b value. In this paper, a method based on binary regression is proposed which can directly calculate parameter b. The new method can directly calculate the parameter b better than the method based on binary regression. The method in this paper is to fit all the data at one time, avoiding the above two kinds of uncertainties, and the calculation workload is small and can be realized by EXCEL, which is convenient for technical personnel.
    },
     year = {2024}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - A Method for Solving the Generalized Weng Model
    
    AU  - Zeng Hongwu
    AU  - Xu Qiuyu
    Y1  - 2024/05/17
    PY  - 2024
    N1  - https://doi.org/10.11648/j.ajcst.20240702.12
    DO  - 10.11648/j.ajcst.20240702.12
    T2  - American Journal of Computer Science and Technology
    JF  - American Journal of Computer Science and Technology
    JO  - American Journal of Computer Science and Technology
    SP  - 38
    EP  - 42
    PB  - Science Publishing Group
    SN  - 2640-012X
    UR  - https://doi.org/10.11648/j.ajcst.20240702.12
    AB  - The Generalized Weng Model is one of the basic models for oil production forecasting. Professor Chen Yuanqian first proposed the linear iterative trial-and-error method to solve the generalized Weng Model, and scholar Zhao Lin proposed the method to solve the Weng model based on binary regression. In this paper, a new method for solving Weng Model is put forward. Taking Liaohe Oilfield in China as an example, the process and results of the three methods are compared, and the advantages and disadvantages of the three methods are analyzed. The results show that when the original linear iterative trial and error method solves the model, it needs to simulate the value of parameter b with computer software, and then select a judgment criterion to find the optimal b value. In this paper, a method based on binary regression is proposed which can directly calculate parameter b. The new method can directly calculate the parameter b better than the method based on binary regression. The method in this paper is to fit all the data at one time, avoiding the above two kinds of uncertainties, and the calculation workload is small and can be realized by EXCEL, which is convenient for technical personnel.
    
    VL  - 7
    IS  - 2
    ER  - 

    Copy | Download

Author Information
  • Sections