IDEAS home Printed from https://ideas.repec.org/a/eee/enepol/v105y2017icp263-268.html
   My bibliography  Save this article

Experience curve for natural gas production by hydraulic fracturing

Author

Listed:
  • Fukui, Rokuhei
  • Greenfield, Carl
  • Pogue, Katie
  • van der Zwaan, Bob

Abstract

From 2007 to 2012 shale gas production in the US expanded at an astounding average growth rate of over 50%/yr, and thereby increased nearly tenfold over this short time period alone. Hydraulic fracturing technology, or “fracking”, as well as new directional drilling techniques, played key roles in this shale gas revolution, by allowing for extraction of natural gas from previously unviable shale resources. Although hydraulic fracturing technology had been around for decades, it only recently became commercially attractive for large-scale implementation. As the production of shale gas rapidly increased in the US over the past decade, the wellhead price of natural gas dropped substantially. In this paper we express the relationship between wellhead price and cumulative natural gas output in terms of an experience curve, and obtain a learning rate of 13% for the industry using hydraulic fracturing technology. This learning rate represents a measure for the know-how and skills accumulated thus far by the US shale gas industry. The use of experience curves for renewable energy options such as solar and wind power has allowed analysts, practitioners, and policy makers to assess potential price reductions, and underlying cost decreases, for these technologies in the future. The reasons for price reductions of hydraulic fracturing are fundamentally different from those behind renewable energy technologies – hence they cannot be directly compared – and hydraulic fracturing may soon reach, or maybe has already attained, a lower bound for further price reductions, for instance as a result of its water requirements or environmental footprint. Yet, understanding learning-by-doing phenomena as expressed by an industry-wide experience curve for shale gas production can be useful for strategic planning in the gas sector, as well as assist environmental policy design, and serve more broadly as input for projections of energy system developments.

Suggested Citation

  • Fukui, Rokuhei & Greenfield, Carl & Pogue, Katie & van der Zwaan, Bob, 2017. "Experience curve for natural gas production by hydraulic fracturing," Energy Policy, Elsevier, vol. 105(C), pages 263-268.
  • Handle: RePEc:eee:enepol:v:105:y:2017:i:c:p:263-268
    DOI: 10.1016/j.enpol.2017.02.027
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0301421517301027
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.enpol.2017.02.027?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. Greaker, Mads & Lund Sagen, Eirik, 2008. "Explaining experience curves for new energy technologies: A case study of liquefied natural gas," Energy Economics, Elsevier, vol. 30(6), pages 2899-2911, November.
    2. Neij, Lena, 2008. "Cost development of future technologies for power generation--A study based on experience curves and complementary bottom-up assessments," Energy Policy, Elsevier, vol. 36(6), pages 2200-2211, June.
    3. Yeh, Sonia & Rubin, Edward S., 2012. "A review of uncertainties in technology experience curves," Energy Economics, Elsevier, vol. 34(3), pages 762-771.
    4. Arthur van Benthem & Kenneth Gillingham & James Sweeney, 2008. "Learning-by-Doing and the Optimal Solar Policy in California," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 131-152.
    5. Schoots, K. & Kramer, G.J. & van der Zwaan, B.C.C., 2010. "Technology learning for fuel cells: An assessment of past and potential cost reductions," Energy Policy, Elsevier, vol. 38(6), pages 2887-2897, June.
    6. Nemet, Gregory F., 2006. "Beyond the learning curve: factors influencing cost reductions in photovoltaics," Energy Policy, Elsevier, vol. 34(17), pages 3218-3232, November.
    7. Ferioli, F. & Schoots, K. & van der Zwaan, B.C.C., 2009. "Use and limitations of learning curves for energy technology policy: A component-learning hypothesis," Energy Policy, Elsevier, vol. 37(7), pages 2525-2535, 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. Langer, Jannis & Quist, Jaco & Blok, Kornelis, 2020. "Recent progress in the economics of ocean thermal energy conversion: Critical review and research agenda," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    2. Dalla Longa, Francesco & Nogueira, Larissa P. & Limberger, Jon & Wees, Jan-Diederik van & van der Zwaan, Bob, 2020. "Scenarios for geothermal energy deployment in Europe," Energy, Elsevier, vol. 206(C).
    3. Jong-Hyun Kim & Yong-Gil Lee, 2017. "Analyzing the Learning Path of US Shale Players by Using the Learning Curve Method," Sustainability, MDPI, vol. 9(12), pages 1-8, December.
    4. Jong-Hyun Kim & Yong-Gil Lee, 2020. "Progress of Technological Innovation of the United States’ Shale Petroleum Industry Based on Patent Data Association Rules," Sustainability, MDPI, vol. 12(16), pages 1-17, August.
    5. Reinhard Haas & Marlene Sayer & Amela Ajanovic & Hans Auer, 2023. "Technological learning: Lessons learned on energy technologies," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 12(2), March.
    6. Qian, Yuan & Scherer, Laura & Tukker, Arnold & Behrens, Paul, 2020. "China's potential SO2 emissions from coal by 2050," Energy Policy, Elsevier, vol. 147(C).
    7. Nyambuu, Unurjargal & Semmler, Willi, 2020. "Climate change and the transition to a low carbon economy – Carbon targets and the carbon budget," Economic Modelling, Elsevier, vol. 84(C), pages 367-376.
    8. Yang, Lin & Lv, Haodong & Wei, Ning & Li, Yiming & Zhang, Xian, 2023. "Dynamic optimization of carbon capture technology deployment targeting carbon neutrality, cost efficiency and water stress: Evidence from China's electric power sector," Energy Economics, Elsevier, vol. 125(C).
    9. Jong-Hyun Kim & Yong-Gil Lee, 2018. "Learning Curve, Change in Industrial Environment, and Dynamics of Production Activities in Unconventional Energy Resources," Sustainability, MDPI, vol. 10(9), pages 1-11, September.
    10. Girma T. Chala & Abd Rashid Abd Aziz & Ftwi Y. Hagos, 2018. "Natural Gas Engine Technologies: Challenges and Energy Sustainability Issue," Energies, MDPI, vol. 11(11), pages 1-44, October.

    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. Rubin, Edward S. & Azevedo, Inês M.L. & Jaramillo, Paulina & Yeh, Sonia, 2015. "A review of learning rates for electricity supply technologies," Energy Policy, Elsevier, vol. 86(C), pages 198-218.
    2. Strupeit, Lars & Neij, Lena, 2017. "Cost dynamics in the deployment of photovoltaics: Insights from the German market for building-sited systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 948-960.
    3. Elia, A. & Taylor, M. & Ó Gallachóir, B. & Rogan, F., 2020. "Wind turbine cost reduction: A detailed bottom-up analysis of innovation drivers," Energy Policy, Elsevier, vol. 147(C).
    4. Mauleón, Ignacio, 2016. "Photovoltaic learning rate estimation: Issues and implications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 507-524.
    5. Zakerinia, Saleh, 2018. "Understanding the Role of Transportation in Meeting California’s Greenhouse Gas Emissions Reduction Target: A Focus on Technology Forcing Policies, Interactions with the Electric Sector and Mitigation," Institute of Transportation Studies, Working Paper Series qt0r69m651, Institute of Transportation Studies, UC Davis.
    6. Beck, Marisa & Rivers, Nicholas & Wigle, Randall, 2018. "How do learning externalities influence the evaluation of Ontario's renewables support policies?," Energy Policy, Elsevier, vol. 117(C), pages 86-99.
    7. Nicodemus, Julia Haltiwanger, 2018. "Technological learning and the future of solar H2: A component learning comparison of solar thermochemical cycles and electrolysis with solar PV," Energy Policy, Elsevier, vol. 120(C), pages 100-109.
    8. Candelise, Chiara & Winskel, Mark & Gross, Robert J.K., 2013. "The dynamics of solar PV costs and prices as a challenge for technology forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 26(C), pages 96-107.
    9. Heuberger, Clara F. & Rubin, Edward S. & Staffell, Iain & Shah, Nilay & Mac Dowell, Niall, 2017. "Power capacity expansion planning considering endogenous technology cost learning," Applied Energy, Elsevier, vol. 204(C), pages 831-845.
    10. Samadi, Sascha, 2018. "The experience curve theory and its application in the field of electricity generation technologies – A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2346-2364.
    11. Yao, Xilong & Liu, Yang & Qu, Shiyou, 2015. "When will wind energy achieve grid parity in China? – Connecting technological learning and climate finance," Applied Energy, Elsevier, vol. 160(C), pages 697-704.
    12. Strupeit, Lars, 2017. "An innovation system perspective on the drivers of soft cost reduction for photovoltaic deployment: The case of Germany," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 273-286.
    13. Paul Lehmann & Patrik Söderholm, 2018. "Can Technology-Specific Deployment Policies Be Cost-Effective? The Case of Renewable Energy Support Schemes," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 71(2), pages 475-505, October.
    14. Hernández-Moro, J. & Martínez-Duart, J.M., 2013. "Analytical model for solar PV and CSP electricity costs: Present LCOE values and their future evolution," Renewable and Sustainable Energy Reviews, Elsevier, vol. 20(C), pages 119-132.
    15. Hong, Sungjun & Chung, Yanghon & Woo, Chungwon, 2015. "Scenario analysis for estimating the learning rate of photovoltaic power generation based on learning curve theory in South Korea," Energy, Elsevier, vol. 79(C), pages 80-89.
    16. Sommerfeldt, Nelson & Madani, Hatef, 2017. "Revisiting the techno-economic analysis process for building-mounted, grid-connected solar photovoltaic systems: Part one – Review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 1379-1393.
    17. Reinhard Haas & Marlene Sayer & Amela Ajanovic & Hans Auer, 2023. "Technological learning: Lessons learned on energy technologies," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 12(2), March.
    18. Thomassen, Gwenny & Van Passel, Steven & Dewulf, Jo, 2020. "A review on learning effects in prospective technology assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    19. Odam, Neil & de Vries, Frans P., 2020. "Innovation modelling and multi-factor learning in wind energy technology," Energy Economics, Elsevier, vol. 85(C).
    20. 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.

    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:eee:enepol:v:105:y:2017:i:c:p:263-268. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/enpol .

    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.