Global carbon intensity of crude oil production
- Stanford Univ., Stanford, CA (United States)
- Aramco Services Company, Detroit, MI (United States)
- Ford Motor Company, Detroit, MI (United States)
- Univ. of Calgary, AB (Canada)
- Carnegie Endowment for International Peace, Washington, D.C. (United States)
- Carnegie Mellon Univ., Pittsburgh, PA (United States)
- Univ. of British Columbia, Vancouver, BC (Canada)
- California Environmental Protection Agency, Sacramento, CA (United States)
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- Univ. of Michigan, Ann Arbor, MI (United States)
- International Energy Agency, Paris (France)
- Baker Hughes, a GE Company, Houston, TX (United States)
- Chalmers Univ. of Technology, Gothenburg (Sweden)
- Cornell Univ., Ithaca, NY (United States)
- 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
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