IDEAS home Printed from https://ideas.repec.org/a/spr/empeco/v51y2016i3d10.1007_s00181-015-1032-x.html
   My bibliography  Save this article

Comment on: “Optimal dynamic production from a large oil field in Saudi Arabia”

Author

Listed:
  • Islam Rizvanoghlu

    (Zirve University, Kizilhisar Campus)

Abstract

This paper extends the study by Gao et al. (Empir Econ 37:153–184, 2009), which models the profit-maximizing dynamic oil production from a large oil field in Saudi Arabia by using an engineering model of oil extraction. Although it gives an important insight about the dynamics of oil production by examining and comparing different scenarios for exogenous variables, it assumes perfect knowledge and foresight about the future. However, the production decision might not be based on different scenarios, but rather on different expectations about the future. Therefore, we propose to extend the model by incorporating uncertainty arising from a random arrival date of a new backstop technology that will enable the production of a perfect substitute for oil. We find that the optimal production path has a different dynamic under this new specification that may explain the less aggressive extraction behavior of the producer before 2000, which was concluded to be economically irrational by Gao et al. (2009).

Suggested Citation

  • Islam Rizvanoghlu, 2016. "Comment on: “Optimal dynamic production from a large oil field in Saudi Arabia”," Empirical Economics, Springer, vol. 51(3), pages 1281-1288, November.
  • Handle: RePEc:spr:empeco:v:51:y:2016:i:3:d:10.1007_s00181-015-1032-x
    DOI: 10.1007/s00181-015-1032-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00181-015-1032-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s00181-015-1032-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hartley, Peter R., 1996. "Value function approximation in the presence of uncertainty and inequality constraints an application to the demand for credit cards," Journal of Economic Dynamics and Control, Elsevier, vol. 20(1-3), pages 63-92.
    2. Nemet, Gregory F., 2006. "Beyond the learning curve: factors influencing cost reductions in photovoltaics," Energy Policy, Elsevier, vol. 34(17), pages 3218-3232, November.
    3. Weiyu Gao & Peter Hartley & Robin Sickles, 2009. "Optimal dynamic production from a large oil field in Saudi Arabia," Empirical Economics, Springer, vol. 37(1), pages 153-184, September.
    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. Bosetti, Valentina & Catenacci, Michela & Fiorese, Giulia & Verdolini, Elena, 2012. "The future prospect of PV and CSP solar technologies: An expert elicitation survey," Energy Policy, Elsevier, vol. 49(C), pages 308-317.
    2. Bosetti, Valentina & Carraro, Carlo & Duval, Romain & Tavoni, Massimo, 2011. "What should we expect from innovation? A model-based assessment of the environmental and mitigation cost implications of climate-related R&D," Energy Economics, Elsevier, vol. 33(6), pages 1313-1320.
    3. Wilson, Charlie, 2012. "Up-scaling, formative phases, and learning in the historical diffusion of energy technologies," Energy Policy, Elsevier, vol. 50(C), pages 81-94.
    4. Andrew Chapman & Timothy Fraser & Melanie Dennis, 2019. "Investigating Ties between Energy Policy and Social Equity Research: A Citation Network Analysis," Social Sciences, MDPI, vol. 8(5), pages 1-18, April.
    5. Seel, Joachim & Barbose, Galen L. & Wiser, Ryan H., 2014. "An analysis of residential PV system price differences between the United States and Germany," Energy Policy, Elsevier, vol. 69(C), pages 216-226.
    6. Elizabeth Baldwin & Yongyang Cai & Karlygash Kuralbayeva, 2018. "To Build or Not to Build? Capital Stocks and Climate Policy," CESifo Working Paper Series 6884, CESifo.
    7. Kiriyama, Eriko & Kajikawa, Yuya & Fujita, Katsuhide & Iwata, Shuichi, 2013. "A lead for transvaluation of global nuclear energy research and funded projects in Japan," Applied Energy, Elsevier, vol. 109(C), pages 145-153.
    8. Trappey, Amy J.C. & Trappey, Charles V. & Liu, Penny H.Y. & Lin, Lee-Cheng & Ou, Jerry J.R., 2013. "A hierarchical cost learning model for developing wind energy infrastructures," International Journal of Production Economics, Elsevier, vol. 146(2), pages 386-391.
    9. Elofsson, Katarina, 2014. "International knowledge diffusion and its impact on the cost-effective clean-up of the Baltic Sea," Working Paper Series 2014:06, Swedish University of Agricultural Sciences, Department Economics.
    10. Lafond, François & Bailey, Aimee Gotway & Bakker, Jan David & Rebois, Dylan & Zadourian, Rubina & McSharry, Patrick & Farmer, J. Doyne, 2018. "How well do experience curves predict technological progress? A method for making distributional forecasts," Technological Forecasting and Social Change, Elsevier, vol. 128(C), pages 104-117.
    11. Johannes Urpelainen, 2014. "Sinking costs to increase participation: technology deployment agreements enhance climate cooperation," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 16(3), pages 229-240, July.
    12. Verdolini, Elena & Anadon, Laura Diaz & Lu, Jiaqi & Nemet, Gregory F., 2015. "The effects of expert selection, elicitation design, and R&D assumptions on experts' estimates of the future costs of photovoltaics," Energy Policy, Elsevier, vol. 80(C), pages 233-243.
    13. Harashima, Taiji, 2009. "A Theory of Total Factor Productivity and the Convergence Hypothesis: Workers’ Innovations as an Essential Element," MPRA Paper 15508, University Library of Munich, Germany.
    14. Matteson, Schuyler & Williams, Eric, 2015. "Residual learning rates in lead-acid batteries: Effects on emerging technologies," Energy Policy, Elsevier, vol. 85(C), pages 71-79.
    15. Witajewski-Baltvilks, Jan & Verdolini, Elena & Tavoni, Massimo, 2015. "Bending the learning curve," Energy Economics, Elsevier, vol. 52(S1), pages 86-99.
    16. Alves, Joana Duarte Ouro & Faria, Weslem Rodrigues, 2024. "Reserves, well drilling and production: Assessing the optimal trajectory of oil extraction for Brazil," Resources Policy, Elsevier, vol. 88(C).
    17. McNerney, James & Doyne Farmer, J. & Trancik, Jessika E., 2011. "Historical costs of coal-fired electricity and implications for the future," Energy Policy, Elsevier, vol. 39(6), pages 3042-3054, June.
    18. Polzin, Friedemann & Sanders, Mark & Serebriakova, Alexandra, 2021. "Finance in global transition scenarios: Mapping investments by technology into finance needs by source," Energy Economics, Elsevier, vol. 99(C).
    19. Mikkola, Jani & Lund, Peter D., 2016. "Modeling flexibility and optimal use of existing power plants with large-scale variable renewable power schemes," Energy, Elsevier, vol. 112(C), pages 364-375.
    20. Jenner, Steffen & Groba, Felix & Indvik, Joe, 2013. "Assessing the strength and effectiveness of renewable electricity feed-in tariffs in European Union countries," Energy Policy, Elsevier, vol. 52(C), pages 385-401.

    More about this item

    Keywords

    Optimal oil production; Dynamic programming; Value function approximation; Backstop technology;
    All these keywords.

    JEL classification:

    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • Q32 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation - - - Exhaustible Resources and Economic Development
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

    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:spr:empeco:v:51:y:2016:i:3:d:10.1007_s00181-015-1032-x. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.