Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Well-to-refinery emissions and net-energy analysis of China’s crude-oil supply

Nature Energy volume 3, pages 220–226 (2018)Cite this article

Subjects

Abstract

Oil is China’s second-largest energy source, so it is essential to understand the country’s greenhouse gas emissions from crude-oil production. Chinese crude supply is sourced from numerous major global petroleum producers. Here, we use a per-barrel well-to-refinery life-cycle analysis model with data derived from hundreds of public and commercial sources to model the Chinese crude mix and the upstream carbon intensities and energetic productivity of China’s crude supply. We generate a carbon-denominated supply curve representing Chinese crude-oil supply from 146 oilfields in 20 countries. The selected fields are estimated to emit between ~1.5 and 46.9 g CO2eq MJ−1 of oil, with volume-weighted average emissions of 8.4 g CO2eq MJ−1. These estimates are higher than some existing databases, illustrating the importance of bottom-up models to support life-cycle analysis databases. This study provides quantitative insight into China’s energy policy and the economic and environmental implications of China’s oil consumption.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: OPGEE macroscopic structural flow diagram.
Fig. 2: 2015 China’s crude-oil carbon intensity supply curve.
Fig. 3: China’s 2015 crude-oil supply curve segment-by-segment emissions.
Fig. 4: Allocation versus displacement approaches to address the co-product energy use and emissions.
Fig. 5: 2015 China’s crude-oil supply map.
Fig. 6: Country-level production coverage by the studied basket of crudes.
Fig. 7: 2015 China’s crude-oil energetic productivity supply curves.

Similar content being viewed by others

References

  1. World Population Prospects: The 2015 Revision, Key Findings and Advance Tables Working Paper No. ESA/P/WP.241 (United Nations Department of Economic and Social Affairs and Population Division, 2015).

  2. Myers, J. Which are the world’s fastest-growing economies? World Economic Forum (18 April 2016); https://www.weforum.org/agenda/2016/04/worlds-fastest-growing-economies/

  3. Key World Energy Statistics (International Energy Agency, 2016); http://dx.doi.org/10.1787/key_energ_stat-2016-en

  4. Liu, Z. China’s Carbon Emissions Report 2015 (Belfer Center for Science and International Affairs, Harvard Kennedy School, 2015); http://www.belfercenter.org/sites/default/files/legacy/files/carbon-emissions-report-2015-final.pdf

  5. China’s Key Energy Statistics (US Energy Information Administration, 2015); https://www.eia.gov/beta/international/analysis.cfm?iso=CHN

  6. S&P Global Platts China oil analytics: China oil demand little changed year over year in June. S&P Global (11 August 2016); https://www.platts.com/pressreleases/2016/081116

  7. Statistical Database (National Bureau of Statistics of China, 2017); http://www.stats.gov.cn/english/Statisticaldata/AnnualData/

  8. Masnadi, M. S. & Brandt, A. R. Climate impacts of oil extraction increase significantly with oilfield age. Nat. Clim. Change 7, 551–556 (2017).

    Article  Google Scholar 

  9. Environmental Performance Indicators – 2013 Data Report No. 2013E (International Association of Oil & Gas Producers, 2014).

  10. Saving Energy in the Oil and Gas Industry (International Petroleum Industry Environmental Conservation Association, 2013); https://www.world-petroleum.org/docs/docs/socialres/saving_energy_6_feb_2013.pdf

  11. Energy Efficiency: Improving Energy Use from Production to Consumer (International Petroleum Industry Environmental Conservation Association, 2012); http://www.ipieca.org/resources/fact-sheet/energy-efficiency-improving-energy-use-from-production-to-consumer/

  12. Masnadi, M. S. & Brandt, A. R. Energetic productivity dynamics of global super-giant oilfields. Energy Environ. Sci. 10, 1493–1504 (2017).

    Article  Google Scholar 

  13. Gordon, D., Brandt, A. R. & Bergerson, J. Know Your Oil: Creating a Global Oil-Climate Index. (Carnegie, 2015).

  14. Höök, M., Xu, T., Xiongqi, P. & Aleklett, K. Development journey and outlook of Chinese giant oilfields. Pet. Explor. Dev. 37, 237–249 (2010).

    Article  Google Scholar 

  15. Territorial Disputes in the South China Sea (Council on Foreign Relations, accessed 10 October 2015); https://www.cfr.org/interactives/global-conflict-tracker#!/conflict/territorial-disputes-in-the-south-china-sea

  16. Owen, N. A. & Schofield, C. H. Disputed South China sea hydrocarbons in perspective. Mar. Policy 36, 809–822 (2012).

    Article  Google Scholar 

  17. Wang, J. et al. China’s unconventional oil: A review of its resources and outlook for long-term production. Energy 82, 31–42 (2015).

    Article  Google Scholar 

  18. Gagnon, N., Hall, C. & Brinker, L. A preliminary investigation of energy return on energy investment for global oil and gas production. Energies 2, 490–503 (2009).

    Article  Google Scholar 

  19. Guilford, M., Hall, C., O’Connor, P. & Cleveland, C. A new long term assessment of energy return on investment (EROI) for US oil and gas discovery and production. Sustainability 3, 1866–1887 (2011).

    Article  Google Scholar 

  20. Cleveland, C. Net energy from the extraction of oil and gas in the United States. Energy 30, 769–782 (2005).

    Article  Google Scholar 

  21. Brandt, A. R., Sun, Y., Bharadwaj, S., Livingston, D. & Tan, E. Energy return on investment (EROI) for forty global oilfields using a detailed engineering-based model of oil production. PLoS One 10, e0144141 (2015).

    Article  Google Scholar 

  22. Court, V. & Fizaine, F. Long-term estimates of the global energy-return-on-investment (EROI) of coal, oil, and gas global productions. Ecol. Econ. 138, 145–159 (2017).

    Article  Google Scholar 

  23. Dale, M., Krumdieck, S. & Bodger, P. Net energy yield from production of conventional oil. Energy Policy 39, 7095–7102 (2011).

    Article  Google Scholar 

  24. Kopits, S. Oil and Economic Growth: A supply-Constrained View (Columbia University, 2014).

  25. Cleveland, C. Energy quality and energy surplus in the extraction of fossil fuels in the US. Ecol. Econ. 6, 139–162 (1992).

    Article  Google Scholar 

  26. Norgaard, R. Output, Input, and Productivity Change in US Petroleum Development: 1939-1968. PhD thesis, Univ. Chicago (1971).

  27. Tripathi, V. & Brandt, A. Estimating decades-long trends in petroleum field energy return on investment (EROI) with an engineering-based model. PLoS One 12, e0171083 (2017).

    Article  Google Scholar 

  28. Summary of Expansions, Updates, and Results in GREET®2016 Suite of Models (Systems Assessment Group, Energy Systems Division, ANL, 2016).

  29. El-Houjeiri, H. M., Masnadi, M. S., Vafi, K., Duffy, J. & Brandt, A. R. Oil Production Greenhouse Gas Emissions Estimator OPGEEv2.0a: User Guide & Technical Documentation (2017); https://pangea.stanford.edu/departments/ere/dropbox/EAO/OPGEE/OPGEE_documentation_v2.0a.pdf

  30. El-Houjeiri, H. M., Brandt, A. R. & Duffy, J. E. Open-source LCA tool for estimating greenhouse gas emissions from crude-oil production using field characteristics. Environ. Sci. 47, 5998–6006 (2013).

    Article  Google Scholar 

  31. Oil Climate Index (Carnegie, accessed 30 June 2017); http://oci.carnegieendowment.org/#analysis?opgee=run000&prelim=run01&showCoke=1&ratioSelect=perBarrel&xSelect=apiGravity&ySelect=downstream

  32. Pathways to an Energy and Carbon Efficient Russia (McKinsey & Company, 2009); http://www.mckinsey.com/business-functions/sustainability-and-resource-productivity/our-insights/pathways-to-an-energy-and-carbon-efficient-russia

  33. Global Gas Flaring Observed from Space (National Oceanic and Atmospheric Administration, 2017); https://www.ngdc.noaa.gov/eog/viirs/download_global_flare.html

  34. The GPC Platform of Abuzar Oil Field is Inaugurated (National Iranian Oil Company, 2017).

  35. Prevention from Flaring of over 95% of the Associated Gas Produced with Oil (Iran Petroleum Ministry, 2017).

  36. Brandt, A. Embodied energy and GHG emissions from material use in conventional and unconventional oil and gas operations. Environ. Sci. Technol. 49, 13059–13066 (2015).

    Article  Google Scholar 

  37. Portworld (S&P Global, accessed 1 February 2018); http://www.portworld.com/map.

  38. Refinery Benchmarking Tool (Wood Mackenzie, 2015).

  39. Total Petroleum and Other Liquids Production - 2016 (US Energy Information Administration, 2017); https://www.eia.gov/beta/international/

  40. Brandt, A. R., Sun, Y. & Vafi, K. Uncertainty in regional-average petroleum GHG intensities: countering information gaps with targeted data gathering. Environ. Sci. Technol. 49, 679–686 (2014).

    Article  Google Scholar 

  41. Wang, M., Huo, H. & Arora, S. Methods of dealing with co-products of biofuels in life-cycle analysis and consequent results within the US context. Energy Policy 39, 5726–5736 (2011).

    Article  Google Scholar 

  42. Hall, C. A. S. Energy Return on Investment: A Unifying Principle for Biology, Economics, and Sustainability, Vol. 36 (Springer, Switzerland, Cham, 2017).

  43. Worldwide Oil Field Production Survey 2015 (Oil & Gas Journal, PennWell Publishing, 2015); http://www.ogj.com/ogj-survey-downloads.html

  44. Upstream Oil & Gas (Wood Mackenzie, 2017); https://www.woodmac.com/our-expertise/capabilities/upstream-oil-and-gas/

Download references

Acknowledgements

The Natural Sciences and Engineering Research Council of Canada (NSERC) provided financial support to M.S.M.

Author information

Authors and Affiliations

  1. Department of Energy Resources Engineering, School of Earth, Energy & Environmental Sciences, Stanford University, Stanford, CA, USA

    Mohammad S. Masnadi & Adam R. Brandt

  2. Strategic Transport Analysis Team, Aramco Research Center - Detroit, Aramco Services Company, Novi, MI, USA

    Hassan M. El-Houjeiri & Steven Przesmitzki

  3. Department of Civil and Environmental Engineering, Stanford University, Stanford, CA, USA

    Dominik Schunack & Yunpo Li

  4. Department of Physics, Morehouse College, Atlanta, GA, USA

    Samori O. Roberts

  5. Systems Assessment Group, Energy Systems Division, Argonne National Laboratory, Lemont, IL, USA

    Michael Wang

Authors
  1. Mohammad S. Masnadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hassan M. El-Houjeiri
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dominik Schunack
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yunpo Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Samori O. Roberts
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Steven Przesmitzki
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Adam R. Brandt
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Michael Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

M.S.M., D.S., Y.L., A.R.B and H.M.E.-H. were involved in data gathering, processing and analysis of different fields. The final results were integrated by M.S.M. M.S.M. wrote the manuscript, and all authors contributed to revising the paper.

Corresponding author

Correspondence to Mohammad S. Masnadi.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figures 1–2, Supplementary Table 1 and Supplementary references.

Supplementary Data 1

The input data of each oilfield that have been collected from public domain

Supplementary Data 2

Field-level: the carbon intensities (CI), NER and EER results of all studied oilfields; country-level: the volume-weighted average results aggregated based on countries; production coverage: captured total oil production of each country; OPGEE versus Ecoinvent: here the OPGEE CI results are compared with the corresponding Ecoinvent database numbers; break-down of emissions: seven major sources of emissions from upstream processes

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Masnadi, M.S., El-Houjeiri, H.M., Schunack, D. et al. Well-to-refinery emissions and net-energy analysis of China’s crude-oil supply. Nat Energy 3, 220–226 (2018). https://doi.org/10.1038/s41560-018-0090-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41560-018-0090-7

This article is cited by

Search

Advanced search

Quick links

Nature Briefing Anthropocene

Sign up for the Nature Briefing: Anthropocene newsletter — what matters in anthropocene research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: Anthropocene