• Energy Research

Changing market demand and increasing environmental regulations challenge the refining industry to shift crude slates and reconfigure production processes while reducing emissions. Yet sellers and buyers remain unaware of the carbon footprint of individual marketable networks, and each crude oil has different specifications and is processed in different destination markets. Here we show the global refining carbon intensity at country level and crude level are 13.9–62.1 kg of CO2-equivalent (CO2e) per barrel and 10.1–72.1 kgCO2e per barrel, respectively, with a volume-weighted average of 40.7 kgCO2e per barrel (equivalent to 7.3 gCO2e MJ−1) and energy use of 606 MJ per barrel. We used bottom-up engineering-based refinery modelling on crude oils representing 93% of 2015 global refining throughput. On the basis of projected oil consumption under 2 °C scenarios, the industry could save 56–79 GtCO2e to 2100 by targeting primary emission sources. These results provide guidance on climate-sensitive refining choices and future investment in emissions mitigation technologies. 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.

Abstract Bottom-up life-cycle assessment (LCA) based on engineering-based models is emerging as a way to model the energy consumption and greenhouse gas emissions from important segments of the energy sector. However, a major challenge that has hindered further application of this approach is data and computationally intensive thermodynamic modeling. In order to address this issue, we introduce a general data-driven framework to develop statistical reduced-order models (henceforth “proxy models”) from advanced thermodynamic or engineering simulations that can be utilized for bottom-up LCA and/or energetic assessment purposes of any energy system. To demonstrate the performance of the proposed framework, we simulate four important oil and gas process units with a commercial process simulation package. Using a combination of deterministic and random sampling strategy, >25,000 simulations are performed and quadratic proxy models are trained on the results to predict the energy consumption and product compositions across a wide ranges of independent variables. The simple proxy models have excellent predictive accuracy (R2 > 0.95 in most cases, R2 = 1 for pump power consumption prediction), solve nearly instantaneously, and require fewer input parameters (less than 10, varies based on the process unit). We also examine and prove the proposed methodology stability across training and validation datasets via 1,000 independent data splitting runs. We lastly implement the oil and gas proxy models in a LCA simulator, compare the results with textbook correlations, and demonstrate the improved flexibility.

Abstract Oil production choices are influenced by the interaction of oilfield production costs and the global price of oil. What are the characteristics of less economic oilfields, fields whose profitably is at the margin? These oilfields may differ from average fields in terms of geographical location, crude type, production practices, and carbon intensity (CI). Because these economically-marginal fields are the ones likely to respond to drop in demand (e.g., due to 2020 COVID-19 pandemic, or a rapid shift to alternatives), they represent the likely sources of oil that would be displaced. The present paper links the field-by-field costs of 1933 oilfields (representing ~ 90% of 2015 worldwide crude production) with their production environmental footprint. We show that many marginal fields also have high CI. We estimate that the fields at margin due to the 2020 COVID-19 pandemic demand reduction have upstream CI and marginal cost of production ~ 35% and 3 times higher than global average, respectively. The production termination of these fields could result in 181 Mtonne CO2Eq. annual reduction in upstream emissions in the short-term. The marginal producers in a generic small demand shock (5% or ~ 3.6 mmbbl/d drop) have an upstream CI 26% higher than average global oil producers, and at our larger generic demand shock (20% or ~ 14.3 mmbbl/d drop) have CI that is 5% higher than average. Heavy oilfields have the highest volume share in marginal crudes in all scenarios. These results further suggest that life cycle benefits of alternative fuels or vehicles or regulations that lead to reduced oil consumption are systematically larger than those typically estimated when displaced emissions are modeled using average crudes. The results only cover the upstream production CI and do not include synergistic impacts of differences in refining CI of different types of crudes, and thus could underestimate the total life cycle differences between marginal and average crudes.

Energy return on investment (EROI) dynamics probabilistic projections of global giant oil fields.

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.

The footprint of oil typically considers combustion emissions, neglecting extraction emissions. This study shows that production declines with depletion for 25 significant oil fields, whilst emissions increase through greater energy expenditure. Record-breaking temperatures1 have induced governments to implement targets for reducing future greenhouse gas (GHG) emissions2,3. Use of oil products contributes ∼35% of global GHG emissions4, and the oil industry itself consumes 3–4% of global primary energy. Because oil resources are becoming increasingly heterogeneous, requiring different extraction and processing methods, GHG studies should evaluate oil sources using detailed project-specific data5. Unfortunately, prior oil-sector GHG analysis has largely neglected the fact that the energy intensity of producing oil can change significantly over the life of a particular oil project. Here we use decades-long time-series data from twenty-five globally significant oil fields (>1 billion barrels ultimate recovery) to model GHG emissions from oil production as a function of time. We find that volumetric oil production declines with depletion, but this depletion is accompanied by significant growth—in some cases over tenfold—in per-MJ GHG emissions. Depletion requires increased energy expenditures in drilling, oil recovery, and oil processing. Using probabilistic simulation, we derive a relationship for estimating GHG increases over time, showing an expected doubling in average emissions over 25 years. These trends have implications for long-term emissions and climate modelling, as well as for climate policy.

Using carbon dioxide for enhanced oil recovery (CO2-EOR) has been widely cited as a potential catalyst for gigatonne-scale carbon capture and storage (CCS) deployment.

AbstractA pressing challenge facing the aviation industry is to aggressively reduce greenhouse gas emissions in the face of increasing demand for aviation fuels. Climate goals such as carbon-neutral growth from 2020 onwards require continuous improvements in technology, operations, infrastructure, and most importantly, reductions in aviation fuel life cycle emissions. The Carbon Offsetting Scheme for International Aviation of the International Civil Aviation Organization provides a global market-based measure to group all possible emissions reduction measures into a joint program. Using a bottom-up, engineering-based modeling approach, this study provides the first estimates of life cycle greenhouse gas emissions from petroleum jet fuel on regional and global scales. Here we show that not all petroleum jet fuels are the same as the country-level life cycle emissions of petroleum jet fuels range from 81.1 to 94.8 gCO2e MJ−1, with a global volume-weighted average of 88.7 gCO2e MJ−1. These findings provide a high-resolution baseline against which sustainable aviation fuel and other emissions reduction opportunities can be prioritized to achieve greater emissions reductions faster.

There has been a notable disagreement between life cycle GHG emission estimates reported by research communities and key energy sector stakeholders as many LCA models are not validated against real operation data. This is originated from lack of collaboration and knowledge exchange between model developers and company experts. We present a pragmatic procedure for engaging company experts to advance the assumptions, models, and information used in an open-source LCA simulator (OPGEE). Using real operation and local emission factor data, two oil sands SAGD fields GHG emissions are compared rigorously against the scope 1 and 2 reported emissions. By introducing consistent region-specific input data, system boundaries, and assumptions, OPGEE carbon intensity estimates are within 1%-5% of reported data by companies. The system boundary expansion (e.g., expanding from direct emissions to also include offsite emissions from natural gas co-production, diluent source emission) impacts the GHG intensities estimates for both fields.