IDEAS home Printed from https://ideas.repec.org/a/ibn/masjnl/v14y2020i10p20.html
   My bibliography  Save this article

Product Mix Optimization for an Oil Field Operating Company

Author

Listed:
  • Najla Abdulaziz Khonji
  • Saad M. A. Suliman

Abstract

In this study, a mathematical model is formulated to select the optimal product mix of wells in terms of numbers and types of wells that helps to maximize profit. The optimization model comprises two main components, the first component is revenue which includes forecasting of production and oil price, and the second component is cost which includes capital and operating costs. In addition, the model considers all related constraints such as budget, production targets, surface facility limitations, drilling rigs availability and others. Time has influence on the model, since its output is not limited only to the types and numbers of wells to be drilled during the planned period, but also when each well to be drilled for the same plan. Actual planning data for three consecutive years is used for model testing. The results show that 42% to 47% cost saving can be achieved by using the model. The analysis shows that with every 10% increase in oil price, the profit increases by about 6%. Also, it shows that the number of rigs and the rig daily cost affect the profit tremendously, where by reducing these two parameters by 50% an increase of 66% in oil profit can be achieved. The study confirms that oil field operating companies can stand a better chance of maximizing their profit by using product mix optimization model to define the optimum schedule for the number of wells, type of wells and time of drilling.

Suggested Citation

  • Najla Abdulaziz Khonji & Saad M. A. Suliman, 2020. "Product Mix Optimization for an Oil Field Operating Company," Modern Applied Science, Canadian Center of Science and Education, vol. 14(10), pages 1-20, October.
    • Handle: RePEc:ibn:masjnl:v:14:y:2020:i:10:p:20
    as

    Download full text from publisher

    File URL: https://ccsenet.org/journal/index.php/mas/article/download/0/0/43772/46086
    Download Restriction: no
    File URL: https://ccsenet.org/journal/index.php/mas/article/view/0/43772
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, Jianliang & Feng, Lianyong & Zhao, Lin & Snowden, Simon & Wang, Xu, 2011. "A comparison of two typical multicyclic models used to forecast the world's conventional oil production," Energy Policy, Elsevier, vol. 39(12), pages 7616-7621.
    Full references (including those not matched with items on IDEAS)

    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. Wang, Jianliang & Feng, Lianyong & Steve, Mohr & Tang, Xu & Gail, Tverberg E. & Mikael, Höök, 2015. "China's unconventional oil: A review of its resources and outlook for long-term production," Energy, Elsevier, vol. 82(C), pages 31-42.
    2. Yang, Guangfei & Li, Xianneng & Wang, Jianliang & Lian, Lian & Ma, Tieju, 2015. "Modeling oil production based on symbolic regression," Energy Policy, Elsevier, vol. 82(C), pages 48-61.
    3. Wang, Ke & Feng, Lianyong & Wang, Jianliang & Xiong, Yi & Tverberg, Gail E., 2016. "An oil production forecast for China considering economic limits," Energy, Elsevier, vol. 113(C), pages 586-596.
    4. Zhang, Yujiang & Feng, Guorui & Zhang, Min & Ren, Hongrui & Bai, Jinwen & Guo, Yuxia & Jiang, Haina & Kang, Lixun, 2016. "Residual coal exploitation and its impact on sustainable development of the coal industry in China," Energy Policy, Elsevier, vol. 96(C), pages 534-541.
    5. Al Rawashdeh, Rami, 2020. "World peak potash: An analytical study," Resources Policy, Elsevier, vol. 69(C).
    6. Wang, Jianliang & Mohr, Steve & Feng, Lianyong & Liu, Huihui & Tverberg, Gail E., 2016. "Analysis of resource potential for China’s unconventional gas and forecast for its long-term production growth," Energy Policy, Elsevier, vol. 88(C), pages 389-401.
    7. Wang, Jianliang & Feng, Lianyong & Zhao, Lin & Snowden, Simon, 2013. "China's natural gas: Resources, production and its impacts," Energy Policy, Elsevier, vol. 55(C), pages 690-698.
    8. Chavez-Rodriguez, Mauro F. & Szklo, Alexandre & de Lucena, Andre Frossard Pereira, 2015. "Analysis of past and future oil production in Peru under a Hubbert approach," Energy Policy, Elsevier, vol. 77(C), pages 140-151.
    9. Wang, Jianliang & Feng, Lianyong & Tverberg, Gail E., 2013. "An analysis of China's coal supply and its impact on China's future economic growth," Energy Policy, Elsevier, vol. 57(C), pages 542-551.
    10. Delannoy, Louis & Longaretti, Pierre-Yves & Murphy, David J. & Prados, Emmanuel, 2021. "Peak oil and the low-carbon energy transition: A net-energy perspective," Applied Energy, Elsevier, vol. 304(C).
    11. Wang, Jianzhou & Jiang, Haiyan & Zhou, Qingping & Wu, Jie & Qin, Shanshan, 2016. "China’s natural gas production and consumption analysis based on the multicycle Hubbert model and rolling Grey model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1149-1167.
    12. Huang, Chen & Gu, Baihe & Chen, Yingchao & Tan, Xianchun & Feng, Lianyong, 2019. "Energy return on energy, carbon, and water investment in oil and gas resource extraction: Methods and applications to the Daqing and Shengli oilfields," Energy Policy, Elsevier, vol. 134(C).
    13. Zha, Wenshu & Liu, Yuping & Wan, Yujin & Luo, Ruilan & Li, Daolun & Yang, Shan & Xu, Yanmei, 2022. "Forecasting monthly gas field production based on the CNN-LSTM model," Energy, Elsevier, vol. 260(C).
    14. Wang, Xibo & Lei, Yalin & Ge, Jianping & Wu, Sanmang, 2015. "Production forecast of China׳s rare earths based on the Generalized Weng model and policy recommendations," Resources Policy, Elsevier, vol. 43(C), pages 11-18.
    15. Hu, Yan & Hall, Charles A.S. & Wang, Jianliang & Feng, Lianyong & Poisson, Alexandre, 2013. "Energy Return on Investment (EROI) of China's conventional fossil fuels: Historical and future trends," Energy, Elsevier, vol. 54(C), pages 352-364.
    16. Fang, Jianchun & Lau, Chi Keung Marco & Lu, Zhou & Wu, Wanshan, 2018. "Estimating Peak uranium production in China – Based on a Stella model," Energy Policy, Elsevier, vol. 120(C), pages 250-258.
    17. Zhou, Nan & Zhang, Jingjing & Khanna, Nina & Fridley, David & Jiang, Shan & Liu, Xu, 2019. "Intertwined impacts of water, energy development, and carbon emissions in China," Applied Energy, Elsevier, vol. 238(C), pages 78-91.
    18. Wang, Jianliang & Feng, Lianyong & Davidsson, Simon & Höök, Mikael, 2013. "Chinese coal supply and future production outlooks," Energy, Elsevier, vol. 60(C), pages 204-214.
    19. Keqiang Guo & Baosheng Zhang & Kjell Aleklett & Mikael Höök, 2016. "Production Patterns of Eagle Ford Shale Gas: Decline Curve Analysis Using 1084 Wells," Sustainability, MDPI, vol. 8(10), pages 1-13, September.
    20. Wang, Jianliang & Guo, Meiyu & Liu, Mingming & Wei, Xinqiang, 2020. "Long-term outlook for global rare earth production," Resources Policy, Elsevier, vol. 65(C).

    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

    Statistics

    Access and download statistics

    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:ibn:masjnl:v:14:y:2020:i:10:p:20. 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: Canadian Center of Science and Education (email available below). General contact details of provider: https://edirc.repec.org/data/cepflch.html .

    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.