Mohammad S. Masnadi, Hassan M. El-Houjeiri, Dominik Schunack, Yunpo Li, Jacob G. Englander, Alhassan Badahdah,Jean-Christophe Monfort, James E. Anderson, Timothy J.Wallington, Joule A. Bergerson, Deborah Gordon, Jonathan Koomey, Steven Przesmitzki, Inês L. Azevedo, Xiaotao T. Bi, James E. Duffy,Garvin A. Heath, Gregory A. Keoleian, Christophe McGlade, D. Nathan Meehan, Sonia Yeh, Fengqi You, Michael Wang, Adam R. Brandt
ABSTRACT: 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 (1). 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 (2). Yet, while regulations are beginning to address petroleum sector GHG emissions (3–5), and private investors are beginning to consider climate-related risk in oil investments (6), 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 (7, 8), supplementary materials (SM) 1.1]. Here, 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.
in Encyclopedia of Sustainable Technologies
Deborah Gordon, Jeffrey Feldman, Joule Bergerson, Adam Brandt, Jonathan Koomey
ABSTRACT: Global oils are diversifying. This underscores the need to compare the greenhouse gas (GHG) implications of different petroleum sources and their pathways. The Oil-Climate Index (OCI) conducts such a “crude-centric” lifecycle analysis of GHGs from a barrel of oil through the value chain—from production, transport, refining, all the way to complete product end-use combustion. The 75 global oils modeled through the OCI to date reveal GHG variations that are large enough to matter. This approach focuses attention on oil sector innovations, climate risks to investors, design of smarter public policies, and new strategies to reduce total GHG emissions.
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 of oil, with volume-weighted average emissions of 8.4 g CO2eq/MJ. 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.
in Nature Energy
Mohammad S. Masnadi, Hassan M. El-Houjeiri, Dominik Schunack, Yunpo Li, Samori O. Roberts, Steven Przesmitzki, Adam R. Brandt & Michael Wang
in Energy & Environmental Science
Clea Kolster,Mohammad S. Masnadi, Samuel Krevor, Niall Mac Dowell, Adam R. Brandt
ABSTRACT: 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. Carbon dioxide enhanced oil recovery could provide revenues for CO2 capture projects in the absence of strong carbon taxes, providing a means for technological learning and economies of scale to reduce the cost of CCS. We develop an open-source technoeconomic Model of Iterative Investment in CCS with CO2-EOR (MIICE), using dynamic technology deployment modeling to assess the impact of CO2-EOR on the deployment of CCS. Synthetic sets of potential CCS with EOR projects are created with typical field characteristics and dynamic oil and CO2 production profiles. Investment decisions are made iteratively over a 35 year simulation period, and long-term changes to technology cost and revenues are tracked. Installed capacity at 2050 is used as an indicator, with 1 gigatonne per year of CO2 capture used as a benchmark for successful large-scale CCS deployment. Results show that current CO2 tax and oil price conditions do not incentivize gigatonne-scale investment in CCS. For current oil prices ($45 per bbl–$55 per bbl), the final CO2 tax must reach $70 per tCO2 for gigatonne-scale deployment. If oil price alone is expected to induce CCS deployment and learning, oil prices above $85 per bbl are required to promote the development of a gigatonne-scale CCS industry. Nonlinear feedbacks between early deployment and learning result in large changes in final state due to small changes in initial conditions. We investigate the future of CCS in five potential ‘states of the world’: an optimistic ‘Base Case’ with a low CO2 tax and low oil price, a ‘Climate Action’ world with high CO2 tax, a ‘High Oil’ world with high oil prices, a ‘Depleting Resources’ world with an increasing deficit in oil supply, and a ‘Forward Learning’ world where mechanisms are in place to drive down the cost of CCS at rates similar to other clean energy technologies. Through multidimensional sensitivity analysis we outline combinations of conditions that result in gigatonne-scale CCS. This study provides insight levels of taxes, learning rates, and oil prices required for successful scale-up of the CCS industry .
Jingfan Wang, John O'Donnell, Adam R. Brandt
ABSTRACT: We examine the potential for solar energy in global oil operations, including both extraction and transport (“upstream”) and refining (“downstream”). Two open-source oil-sector GHG models are applied to a set of 83 representative global oil fields and 75 refinery crude oil streams (representing ∼25% of global production). Results from these models are used to estimate per-barrel energy intensities (power, heat), which are scaled to generate country-level demand for heat and power. Multiple solar resource quality cutoff criteria are used to determine which regions may profitably use solar. Potential solar thermal capacity ranges from 19 to 44 GWth in upstream operations, and from 21 to 95 GWth in downstream operations. Potential PV deployment ranges from 6 to 11 GWe in upstream operations and 17–91 GWe in downstream operations. The ranges above are due to both per-bbl variation in energy intensity, as well as uncertainty in solar resource quality criteria. Potential solar deployment in upstream operations would displace a much smaller fraction of upstream energy use because a large fraction of global upstream energy use is are either offshore or in high latitude regions (e.g. Russia, Canada, Central Asia).
Energy Return on Investment (EROI) for Forty Global Oilfields Using a Detailed Engineering-Based Model of Oil Production
in PLOS One
ABSTRACT: Studies of the energy return on investment (EROI) for oil production generally rely on aggregated statistics for large regions or countries. In order to better understand the drivers of the energy productivity of oil production, we use a novel approach that applies a detailed field-level engineering model of oil and gas production to estimate energy requirements of drilling, producing, processing, and transporting crude oil. We examine 40 global oilfields, utilizing detailed data for each field from hundreds of technical and scientific data sources. Resulting net energy return (NER) ratios for studied oil fields range from ≈2 to ≈100 MJ crude oil produced per MJ of total fuels consumed. External energy return (EER) ratios, which compare energy produced to energy consumed from external sources, exceed 1000:1 for fields that are largely self-sufficient. The lowest energy returns are found to come from thermally-enhanced oil recovery technologies. Results are generally insensitive to reasonable ranges of assumptions explored in sensitivity analysis. Fields with very large associated gas production are sensitive to assumptions about surface fluids processing due to the shifts in energy consumed under different gas treatment configurations. This model does not currently include energy invested in building oilfield capital equipment (e.g., drilling rigs), nor does it include other indirect energy uses such as labor or services.
in Nature Climate Change
Mohammad S. Masnadi & Adam R. Brandt
ABSTRACT: Record-breaking temperatures have induced governments to implement targets for reducing future greenhouse gas (GHG) emissions. Use of oil products contributes ∼35% of global GHG emissions, 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 data. 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 modeling, as well as for climate policy.
GHG emissions impact of shifts in ratio of gasoline to diesel production at U.S. refineries: A PADD level analysis
in Environmental Science and Technology
Kavan Motazedi, I. Daniel Posen, Joule Bergerson
ABSTRACT: Fuel economy standards, driver behavior, and biofuel mandates, are driving a decline in the Gasoline-to-Diesel ratio (G:D) in U.S. refineries. This paper investigates the GHG implications associated with two methods available to shift refinery output: 1) refinery operational changes and 2) input crude slate variation. This analysis uses an open-source refinery GHG emissions model, PRELIM. Newly developed modeling capabilities, and publicly available data are used to present Petroleum Administration for Defense District (PADD) level results (energy consumption and GHG emissions) for U.S. refineries. The results are indicative of negligible changes in the U.S. refining GHG emissions on a country level (~3%) while variations up to 8% are observed within individual regions. Meeting the 2040 national G:D projections may require drastic changes to the current U.S. crude mix (e.g., more than 30% shift from the current U.S. crude mix), which could increase the U.S. refining GHG emissions by 25%. The analysis provides insights about future changes in refining GHG emissions due to a shift in product demand, and a framework for additional analyses such as evaluation of crude market changes or biofuel blending on refining GHG emissions that can inform development of environmental regulations such as low carbon fuel standards.
ABSTRACT: Constrained oil supply has given way to abundance at a time when strong action on climate change is wavering. Recent innovation has pushed US oil production to all-time heights and driven oil prices lower. At the same time, attention to climate policy is wavering due to geopolitical upheaval. Nevertheless, climate-wise choices in the oil sector remain a priority, given oil's large role in modern economies. Here we use a set of open-source models along with a detailed dataset comprising 75 global crude oils (~25% of global production) to estimate the effects of carbon intensity and oil demand on decadal scale oil-sector emissions. We find that oil resources are abundant relative to all projections of 21st century demand, due to large light-tight oil (LTO) and heavy oil/bitumen (HOB) resources. We then investigate the 'barrel forward' emissions from producing, refining, and consuming all products from a barrel of crude. These oil resources have diverse life-cycle-greenhouse gas (LC-GHG) emissions impacts, and median per-barrel emissions for unconventional resources vary significantly. Median HOB life cycle emissions are 1.5 times those of median LTO emissions, exceeding them by 200 kgCO2eq./bbl. We show that reducing oil LC-GHGs is a mitigation opportunity worth 10–50 gigatonnes CO2 eq. cumulatively by 2050. We discuss means to reduce oil sector LC-GHGs. Results point to the need for policymakers to address both oil supply and oil demand when considering options to reduce LC-GHGs.
Techno-Economic Evaluation of Technologies to Mitigate Greenhouse Gas Emissions at North American Refineries
in Environmental Science and Technology
Kavan Motazedi, JESsica patricia abella, Joule A. Bergerson
ABSTRACT: A petroleum refinery model, Petroleum Refinery Life-cycle Inventory Model (PRELIM), that estimates energy use and CO2 emissions was modified to evaluate the environmental and economic performance of a set of technologies to reduce CO2 emissions at refineries. Cogeneration of heat and power (CHP), carbon capture at Fluid Catalytic Cracker (FCC) and Steam Methane Reformer (SMR) units as well as alternative hydrogen production technologies were considered in the analysis. The results indicate that a 3 to 44% reduction in total annual refinery CO2 emissions (2 to 24% reductions in the CO2 emissions on a per barrel of crude oil processed) can be achieved in a medium conversion refinery that processes a typical U.S. crude slate obtained by using the technologies considered. A sensitivity analysis of the quality of input crude to a refinery, refinery configuration, and prices of natural gas and electricity revealed how the magnitude of possible CO2 emissions reductions and the economic performance of the mitigation technologies can vary under different conditions. The analysis can help inform decision making related to investment decisions and CO2 emissions policy in the refining sector.
in Proceedings of the National Academy of Sciences
Ian J. Laurenzi, Kavan Motazedi, Joule A. Bergerson
ABSTRACT: In recent years, hydraulic fracturing and horizontal drilling have been applied to extract crude oil from tight reservoirs, including the Bakken formation. There is growing interest in understanding the greenhouse gas (GHG) emissions associated with the development of tight oil. We conducted a life cycle assessment of Bakken crude using data from operations throughout the supply chain, including drilling and completion, refining, and use of refined products. If associated gas is gathered throughout the Bakken well life cycle, then the well to wheel GHG emissions are estimated to be 89 g CO2eq/MJ (80% CI, 87-94) of Bakken-derived gasoline and 90 g CO2eq/MJ (80% CI, 88-94) of diesel. If associated gas is flared for the first 12 mo of production, then life cycle GHG emissions increase by 5% on average. Regardless of the level of flaring, the Bakken life cycle GHG emissions are comparable to those of other crudes refined in the United States because flaring GHG emissions are largely offset at the refinery due to the physical properties of this tight oil. We also assessed the life cycle freshwater consumptions of Bakken-derived gasoline and diesel to be 1.14 (80% CI, 0.67-2.15) and 1.22 barrel/barrel (80% CI, 0.71-2.29), respectively, 13% of which is associated with hydraulic fracturing.