We quantify the economic benefits of working remotely between $5-16k per employee per year, as a function of income levels, looking line-by-line across time savings, productivity gains, office costs and energy costs. The model allows you to flex these input assumptions and test your own scenarios.
Based on our research, we think the proportion of remote work could step up from 2009 and 2017 levels (quantified in the file) to displace 30% of all commutes by 2030. This conclusion is justified, by summarizing an excellent technical paper, and a granular breakdown of jobs around the US economy, looking profession-by-profession.
The impacts of COVID-19 on global oil demand are extremely uncertain. However, this model aims to help you bound the uncertainties, disaggregating 2020 oil demand in the developed and the developing world, as a function of some simplifying assumptions: GDP declines, flight cancellations, travel reductions and the pace of the crisis’s resolution.
Our base case forecastsees -11.5Mbpd of YoY demand destruction to be likely in 2Q20, averaging 6.5Mbpd of demand destruction in 2020. We can also construct scenarios where 2Q20 declines run past -20Mbpd YoY. To interrogate our assumptions, or stress-test your own scenarios, please download the model.
US gasoline is the largest component of global oil demand, at c9Mbpd, or c9% of the global market. Hence we have modelled how it could be impacted by COVID-19, looking line by line, across a granular, c100-line breakdown.
A -2Mbpd contraction is possible in 2Q20, if 34% of all US workplaces close temporarily and 50% of non-essential travel is cancelled. This is an extreme scenario, commensurate with a c5pp slowdown in US GDP, comparable to the “Great Recession” of 2008-09 in economic terms, but with 8x deeper demand destruction for gasoline.
Such steep declines are not inconceivable, from a modelling perspective. They could underpin a c10Mbpd YoY collapse in global oil demand.
How quickly could demand rebound? Very minimal long-term impacts persist from 2022 onwards, with demand destruction of just 60kbpd in 2023-24. We can even construct scenarios where US gasoline demand surprises to the upside, rising +0.5Mbpd, if COVID is brought under control. So when the oil market does turn, it may turn very quickly.
To run your own scenarios, please download the model.
Global oil demand is going through an unprecedented disruption. In the short-term, this is due to COVID-19. In the long-term, it is due to the rise of the internet and the energy transition. To contextualise how demand will change, we have aggregated granular data on travel-miles in the US and the UK.
This data-file breaks down all miles travelled by individuals in the US and UK, according to 20 different categorizations on 20 distinct tabs: by purpose, by vehicle type, by journey distance, by age, by income category, and by urban location; plus we assess remote working’s impact on commuter-miles, and internet retail’s impact on shopping-miles.
The dataare derived from the US National Household Travel Survey, which was last conducted in 2017, collecting a day’s data across 1M journeys from 250,000 individuals in the United States; and the UK Department of Transportation’s National Travel Surveys, which interviews and tabulates travel-diaries from 14,000 – 20.000 individuals each year since 2002.
For TSE clients, we will be happy to run further, bespoke data and charting requests. Please contact us if this would be useful.
Our oil price outlook is informed by a 45-line supply-demand model, running month-by-month out to 2025. This download contains both the model, and a 4-page summary of our outlook.
2020 markets have a 5% chance of recovering, but otherwise will run at $20-30/bbl, after reflecting the impacts of COVID19 and the breakdown of OPEC’s output accord. This detracts 2.5Mbpd from our shale models by 2021. The market comes back into balance in 2022, yielding a gradual recovery.
After ten years forecasting oil markets, our humble conclusion is that all oil models are wrong. Some are nevertheless useful. To be most useful, our model takes a Monte Carlo approach to the key uncertainties, to quantify the “risk” of positive and negative surprises (illustrative example below).
Please download the modelto see, and to flex our input assumptions. Included with the download is a PDF summary of our latest oil price thesis, plus country-by-country notes, which inform our longer-term forecasts.
This model contains our live, basin-by-basin shale forecasts. It covers the Permian, Bakken and Eagle Ford, as a function of the rig count, drilling productivity, completion rates, well productivity and type curves. Thus, we derive production and financial expectations.
For 2020, we model the impacts of a price collapse to $30/bbl. In this scenario, c270 rigs are shed YoY in 2H20, to keep the core shale basins running within cash flow. This slows supply growth from +1.2Mbpd YoY in 2019 to +0.4Mbpd 2020 and sets the stage for a -0.5Mbpd decline in 2021.
Our longer-term numbers hinge on the productivity gainsdescribed in our thematic research. Shale productivity trebled from 2012-2018. We think it can effectively double again by 2025. This would unleash c20Mbpd of US liquids production by 2025, within cash flow at a flat $50/bbl Brent input.
This model estimates the line-by-line costs of an FPSO project, across c45 distinct cost lines, in order to quantify the potential savings of a tieback or a ‘fully subsea’ development.
Our estimatesdrawing on four technical papers, as illustrated in the backup tabs of the model. For a full discussion, see our recent note ‘The future of offshore: fully subsea‘.
We estimate c$750M of cost savings for a tieback, and c$500M of cost savings for a fully subsea development, as compared against a traditional project with a traditional production facility. Please download the model to see the different cost drivers, line-by-line.
This data-model calculates the contribution of Platform Supply Vessels (PSVs) to an offshore oil and gas asset’s emissions profile, as measured in kg/boe.
Our base case estimate is 0.1kg/boefor a productive asset in a well-developed basin. The numbers can be increased c4x in a remote basin, or by another c4x for smaller fields, so emissions >1kg/boe are possible.
Initatives to lower these emissionsby 10-20% through LNG-fuelling or hybridization are described in the final tab. They will likely save 0.01-0.02kg/boe from most PSVs and other supply vessels.
This model presents the economic impactsof developing a typical, 625Mboe offshore gas condensate field using a fully subsea solution, compared against installing a new production facility.
Both projects are modelled out fully, to illstrate production profiles, per-barrel economics, capex metrics, NPVs, IRRs and sensitivity to oil and gas prices (e.g. breakevens).
The result of a fully offshore projectis lower capex, lower opex, faster development and higher uptime, generating a c4% uplift in IRRs, a 50% uplift in NPV6 (below) and a 33% reduction in the project’s gas-breakeven price.
Please download the modelto interrogate the numbers and input assumptions.
This model calculates the uplift in FCF and NPV for a fuel-retail station that offers CO2-offsets at the point of sale, alongside selling fuel. The rationale, and the different models that could be employed are outlined in our recent deep-dive research note.
In both models shown above, annual FCF can be uplifted by 15-30%, while fuel retail stations’ NPV can be uplifted by 15-25%, depending on the portion of consumer that purchase the carbon credits.
Gross profits from selling $50/ton carbon credits may be around 3x the typical EBIT margins of retail stations, hence we explore a particular sales model that can at least double fuel retail NPVs.
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