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Title: Global carbon intensity of crude oil production

Journal Article · · Science
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  1. Stanford Univ., Stanford, CA (United States)
  2. Aramco Services Company, Detroit, MI (United States)
  3. Ford Motor Company, Detroit, MI (United States)
  4. Univ. of Calgary, AB (Canada)
  5. Carnegie Endowment for International Peace, Washington, D.C. (United States)
  6. Carnegie Mellon Univ., Pittsburgh, PA (United States)
  7. Univ. of British Columbia, Vancouver, BC (Canada)
  8. California Environmental Protection Agency, Sacramento, CA (United States)
  9. National Renewable Energy Lab. (NREL), Golden, CO (United States)
  10. Univ. of Michigan, Ann Arbor, MI (United States)
  11. International Energy Agency, Paris (France)
  12. Baker Hughes, a GE Company, Houston, TX (United States)
  13. Chalmers Univ. of Technology, Gothenburg (Sweden)
  14. Cornell Univ., Ithaca, NY (United States)
  15. Argonne National Lab. (ANL), Argonne, IL (United States)

Producing, transporting, and refining crude oil into fuels such as gasoline and diesel accounts for ~15 to 40% of the “well-to-wheels” life-cycle greenhouse gas (GHG) emissions of transport fuels. Reducing emissions from petroleum production is of particular importance, as current transport fleets are almost entirely dependent on liquid petroleum products, and many uses of petroleum have limited prospects for near-term substitution (e.g., air travel). Better understanding of crude oil GHG emissions can help to quantify the benefits of alternative fuels and identify the most cost-effective opportunities for oil-sector emissions reductions. Yet, while regulations are beginning to address petroleum sector GHG emissions, and private investors are beginning to consider climate-related risk in oil investments, such efforts have generally struggled with methodological and data challenges. First, no single method exists for measuring the carbon intensity (CI) of oils. Second, there is a lack of comprehensive geographically rich datasets that would allow evaluation and monitoring of life-cycle emissions from oils. We have previously worked to address the first challenge by developing open-source oil-sector CI modeling tools [OPGEE, supplementary materials (SM) 1.1]. Furthermore, we address the second challenge by using these tools to model well-to-refinery CI of all major active oil fields globally—and to identify major drivers of these emissions.

Research Organization:
Argonne National Laboratory (ANL), Argonne, IL (United States)
Sponsoring Organization:
Natural Sciences and Engineering Research Council of Canada (NSERC); Ford Motor Company; Aramco Services Company; USDOE
Grant/Contract Number:
AC02-06CH11357
OSTI ID:
1485127
Journal Information:
Science, Vol. 361, Issue 6405; ISSN 0036-8075
Publisher:
AAASCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 108 works
Citation information provided by
Web of Science

References (5)

Well-to-refinery emissions and net-energy analysis of China’s crude-oil supply journal February 2018
Open-Source LCA Tool for Estimating Greenhouse Gas Emissions from Crude Oil Production Using Field Characteristics journal May 2013
Climate impacts of oil extraction increase significantly with oilfield age journal July 2017
Climate-wise choices in a world of oil abundance journal April 2018
Potential solar energy use in the global petroleum sector journal January 2017

Cited By (4)

Synergies in offshore wind and oil industry for carbon capture and utilization journal August 2019
Life-cycle production optimization of hydrocarbon fields: thermoeconomics perspective journal January 2019
Emissions in the stream: estimating the greenhouse gas impacts of an oil and gas boom journal January 2020
Discovering Energy Consumption Patterns with Unsupervised Machine Learning for Canadian In Situ Oil Sands Operations journal February 2021

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