Global crude oil refining: A key potential contributor to climate targets

The carbon footprint of oil refining differs depending on crude oil quality and refinery configuration. Analysis of global oil refining in 2015 shows refining carbon intensity at crude, refinery and country levels and highlights potential for emissions reductions.

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The Covid-19 pandemic, coupled with the global economic downturn and an historic oil price collapse, presents an opportunity to rethink our current approach to combating climate change. The 2020 IEA special report, Oil and Gas Industry in Energy Transitions, stresses that the oil and gas industry must make meaningful contributions to reduce GHG emissions to achieve the goals of the Paris Agreement.

Petroleum refinery reconfiguration (Source: pixabay.com)

Petroleum refining is the lynchpin in the oil and gas sector, turning unusable crudes into a myriad of consumer products. As such, it plays a key role in maintaining a stable global energy market. At the same time, refining accounts for 40% of GHG emissions from the oil and gas supply chain and 6% of global industrial emissions. Refiners have the potential to reduce their GHG footprints in the short term by shifting crude slates and reconfiguring production processes. Nevertheless, sellers and buyers are often unaware of the carbon footprint of individual marketable crude because each has different characteristics and is processed in different destination markets. Lack of such key information hinders the industry’s efforts to decarbonize the oil supply chain.

To fill this knowledge gap, in this paper we estimate refining carbon intensities and mitigation potentials at the country, refinery, and crude levels. We assess GHG emissions for 93% of 2015 global refining throughput using an open-source assessment tool developed at the University of Calgary (the Petroleum Refinery Life Cycle Inventory Model, PRELIM) and commercial datasets representing 343 crude oils processed in 478 refineries located in 83 countries. This bottom-up approach also enabled us to investigate emissions reduction potential at a refinery process unit level, which can help refiners prioritize the most cost-effective emissions reductions. In addition, by coupling our results with previous research on mitigating GHG emissions in crude oil production and transportation, we calculated wide-ranging refining carbon intensities (10.1–72.1 kg CO2e per barrel) and significant GHG reduction potentials (56–79 GtCO2e by 2100) from the wellhead to refinery exit. Providing a better understanding of the entire crude oil supply chain is a critical step in guiding climate-wise refining choices and informing policies that incentivize investments in emissions mitigation technologies. This research also provides a scientific basis for transparently tracking emissions reduction progress from the oil supply chain as well as evaluating the potential role employing renewable energy inputs in the oil supply chain.

In an increasingly carbon-constrained world, the petroleum industry can contribute GHG reductions. Switching crude slates, improving energy efficiency, and implementing carbon capture are already plausible. And refiners have other opportunities to further reduce their carbon footprints through on-site integration with gasification, CO2-based synthetic fuel, power-to-liquids, renewable hydrogen, and process optimization. Our research team, including researchers at the University of Calgary, Aramco Americas, Brown University and Stanford University are working to evaluate commercial oil and gas technology options as we advance toward a carbon-constrained world.

 

Link to paper: https://www.nature.com/articles/s41558-020-0775-3

Liang Jing

Research Associate, University of Calgary

Dr. Liang Jing is currently a research associate at the University of Calgary. His research areas include modeling and techno-economic analysis for conventional and emerging energy technologies, energy systems simulation-optimization and decision making under uncertainty, and processes control and optimization by artificial intelligence and data analytics.

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