US shale: outlook and forecasts?

US shale production forecasts by basin

What outlook for US shale in the energy transition? This model sets out our US shale production forecasts by basin. It covers the Permian, Bakken and Eagle Ford, as a function of the rig count, drilling productivity, completion rates, well productivity and type curves. US shale likely adds +1Mbpd/year of production growth from 2023-2030, albeit flatlining in 2024, then re-accelerating on higher oil prices. Our shale outlook is also summarized below.

What outlook for shale in energy transition?

Shale is a technology paradigm where well productivity has risen by 3-7x over the past decade, through ever greater digitization. Shale economics are very strong, with 20% IRRs at $50/bbl oil on shale oil (model here) or at $2.8/mcf on shale gas (model here). We think 100bn bbls of recoverable shale resources remain in the US and ultimately, liquids production could be ramped up from 10Mbpd in 2023 to 17Mbpd by 2030 (note here), and most of this will be needed as energy shortages loom.

However the US shale industry has shifted its focus towards capital discipline and ESG. US shale averages 10kg/boe on a Scope 1 upstream basis (data here), shale oil averages 25kg/boe on a full Scope 1&2 basis running up to the refinery gate (data here) and 55kg/boe on a refined basis running up to the point of combustion (data here). The spread is wide, after comparing and contrasting 425 companies here and here. The best decarbonization opportunities for shale are mitigating flaring and methane leaks followed by electrification. Ultimately, we think the best operators could reach CO2 neutrality.

The most important questions on shale are how the resource base and well productivity will trend. This has been the topic of our shale research, and our latest views are covered in our 2024 shale outlook. Historically, we have also undertaken large reviews of the pace of shale technology progress, based on technical papers (examples here and here). There are fifty variables to optimize. And we are most excited about big data techniques, fiber optics and shale-EOR.

Modelling US shale production by basin?

Our model for US shale production looks at each of the main basins, using a factor breakdown. Total production in month T1 = Total production in month T0 + new additions – base declines. To calculate new monthly additions, we multiply (a) number of rigs running (b) wells drilled per rig per month (c) wells completed per well drilled (d) initial production of newly completed wells (IP30). And to calculate the base declines, we fit a best-fit type curve onto the new additions from past months. This model has worked quite smoothly for 6-years now, including history going back to 2011 and forecasts going out through 2030.

The Permian basin is the largest US shale oil basin, with 8Mbpd of total liquids production in 2023. Over the past six years from 2017-2023, the Permian basin has seen an average of 340 rigs running, drilling an average of 1.2 wells per rig per month, completing 1.06 wells for every well drilled (DUC drawdown) at an initial production rate of 780bpd (IP30 basis), adding +850kbpd/year of new supply to global oil markets. We still see strong growth potential, and the Permian could reach 14Mbpd of total liquids production by 2030, amidst higher activity and oil prices. All of these variables can be stress-tested in the model.

US shale production forecasts by basin
Permian production rigs productivity and drilling activity

The Bakken is the second largest US shale oil basin, with 1.3Mbpd of total liquids production in 2023. Over the past six years from 2017-2023, the Bakken has seen an average of 40 rigs running, drilling an average of 1.9 wells per rig per month, completing 1.15 wells for every well drilled (DUC drawdown) at an initial production rate of 780bpd (IP30 basis), adding +20kbpd/year of new supply to global oil markets. We see a decline in 2024, a recovery in 2025-26 and a plateau through 2030.

US shale production forecasts by basin
Bakken production rigs productivity and drilling activity

The Eagle Ford is the third largest US shale oil basin, with 1.1Mbpd of liquids production in 2023. Over the past six years from 2017-2023, the Eagle Ford has seen an average of 60 liquids-focused rigs running, drilling an average of 2.1 wells per rig per month, completing 1.22 wells for every well drilled (DUC drawdown) at an initial production rate of 680bpd (IP30 basis), but liquids production has actually declined, especially during the volatility of the COVID years. We see a decline in 2024, a recovery in 2025-26 and a plateau through 2030.

US shale production forecasts by basin
Eagle Ford production rigs productivity and drilling activity

Challenges and controversies for US shale?

The main revisions to our shale production models have been because of lower activity, as capital discipline has entrenched through the shale industry. The chart below shows our forecasts for activity levels at different, prior publication dates of this model. We have compiled similar charts for all of the different variables and basins, in the ‘revisions’ tab, to show how our shale numbers have changed.

US shale production forecasts by basin

Our shale outlook for 2023-2030 sees the potential for +1Mbpd of annual production growth as the industry also generates $150-200bn per year of annual free cash flow. You can stress test input variables such as oil prices in the model.

US shale production forecasts by basin
US shale cash flow and capex forecasts see potential for $150-200bn of free cash flow at $100 bbl oil

We have also modeled the Marcellus and Haynesville shale gas plays, using the same framework, in further tabs of the data-file. Amazingly, there is potential to underpin a 100-200MTpa US LNG expansion here, with just 20-50 additional rigs. Although recently we wonder whether the US blue hydrogen boom will absorb more gas and outcompete LNG, especially as the US Gulf Coast becomes the most powerful clean industrial hub on the planet (note here).

International shale? We have found it harder to get excited about international shale, but there is strong potential in other large hydrocarbon basins, if European shale is ever considered to rescue Europe from persistent gas shortages, and less so in China.

Please download the data-file to stress-test our US shale production forecasts by basin.

Offshore oilfields: development capex over time in Norway?

Across 130 offshore oil fields in Norway, going back to 1975, real development capex per flowing barrel of production has averaged $33M/kboed. Average costs have been 2x higher when building during a boom, when one-third of projects blew out to around $100M/kboed or higher. The data support countercyclical investment strategies in energy.

This data-file captures the development capex for 130 oilfields offshore Norway, from 1975-2023, in real 2023 USD per flowing barrel. Specifically, data on each field are publicly available from the Norwegian Offshore Directorate, in NOK, while we have cleaned the data, converted it into USD using historical exchange rates from the Bank of England and then translated the numbers in 2023$ real terms.

The average Norwegian offshore field cost $33M/kboed to develop, comprising $3.3bn of development capex, peaking at 100kboed of hydrocarbon production, of which two-thirds was liquids and one-third was gas. There was no material difference in the costs of oilfield or gas field developments (chart below).

Development capex of Norwegian offshore oil and gas fields. No significant difference between oil and gas fields in terms of capex/kboed of peak production.

Development costs can also be indexed over time, running at a relatively constant $30M/kboed in the 1980s, 1990s and early 2000s. Total development capex actually declined from 1970s levels over this time frame, in real terms, due to learning curve effects.

Activity levels have also varied. On average, there have been 12 fields in development across the Norwegian Continental Shelf. However, at peak, there were 20-25 fields under development in 2011-2015.

During this timeframe, the average development cost also doubled to $60-70M/kboed. The distribution of outcomes was also much wider during this timeframe of intense activity levels, with an intensified risk of ‘capex blow-outs’, as one-third of the projects exceeded $100M/kboed.

On the other side of the spectrum, low-cost developments can cost as little as $5-10M/kboed, especially using concepts such as tiebacks and unmanned platforms, and especially when the supply chain had slack capacity. A nice example is Johan Sverdrup. There was also a -16% correlation between field size and offshore development costs.

Our cleaned data-set is available for download below. Across all energy sub-sectors, there are benefits to counter-cyclical investment, whether we are considering oil, gas, LNG, nuclear, wind, solar or power grids.

Oil markets: rising volatility?

There have been a total of 80 oil market volatility events from 2003 to 2023, with an average magnitude of +/- 320kbpd. The largest drops in oil production were due to sanctions or unrest.

Oil markets endure 4 major volatility events per year, with a magnitude of +/- 320kbpd, on average. Their net impact detracts -100kbpd. OPEC and shale have historically buffered out the volatility, so annual oil output is 70% less volatile than renewables’ output. This 10-page note explores the numbers and the changes that lie ahead?

Global oil production by country?

Global oil production by country over time in Mbpd, correlates heavily with Brent crude oil prices in $/bbl

Global oil production by country by month is aggregated across 35 countries that produce >80kbpd of crude, NGLs and condensate, explaining >96% of the global oil market. Production has grown by almost +1Mbpd/year over the past two-decades, led by the US, Iraq, Russia, Canada. Oil market volatility is usually very low, at +/- 1.5% per year, of which two-thirds is down to conscious decisions over production levels.

Monthly global oil production by country is aggregated in this data-file, aggregating data from JODI, the International Energy Agency, the Energy Institute and individual countries’ national hydrocarbon registries, then extensively scrubbing and cleaning the data. This gives us month-by-month visibility on about 97% of the global oil market.

In particular, the data cover 35 countries with over 80kbpd of production (crude, NGL and condensate), which comprise 96% of the global oil market. Of this sample, 25 countries with over 600kbpd of production comprise 93% of the global oil market; 10 countries with over 2.5Mbpd of production comprise 75% of the global oil market; and 4 countries with over 5Mbpd of production comprise 50% of the global oil market (the United States, Saudi Arabia, Russia and Canada).

Global oil production has grown by almost +1Mbpd per annum over the past 20-years, matching the trend in global oil demand by country.

The largest increases in oil production have come from the United States (+0.6Mbpd/year, due to US shale growth), Iraq (>0.1Mbpd/yr), Russia (>0.1Mbpd), Canada (>0.1Mbpd), Brazil (0.1Mbpd), UAE (<0.1Mbpd), Saudi Arabia (<0.1Mbpd), Kazakhstan (<0.1Mbpd).

Conversely, the largest declines in oil production by country have come from Venezuela, Mexico, the UK, Norway (all <0.1Mbpd/year).

The volatility of global oil markets is low compared to new energies. Across the 20-year period from 2003-2023, the standard deviation of YoY monthly oil production is 3Mbpd, for a standard error of 3.4%. However, excluding the volatility during the COVID-19 pandemic from 2020 onwards, the standard deviation of YoY monthly oil production is 1.8Mbpd, for a standard error of 2%. And after smoothing out over a TTM basis, this falls even further to 1.2Mbpd, for a 1.5% standard error.

Volatility or voluntary? Countries such as Saudi Arabia, Kuwait, UAE, the US, Canada and Russia very clearly adapt their growth/output to market pricing signals, which actually dampens down supply volatility. Countries with the highest volatility in their production are Libya (standard error of +/- 35% of average output, on a TTM basis), Iran, Iraq, Venezuela and Nigeria (all around +/- 10%). Full details in the data-file.

Combustion fuels: density, ignition temperature and flame speed?

Combustion properties

The quality of a combustion fuel comes down to its physical and chemical properties. Hence the purpose of this data-file is to aggregate data into different fuels’ combustion properties, such as their energy content (kg/m3), energy density (kWh/kg, kWh/gal), flash point (ºC), auto-ignition point (ºC) and flame speed (m/s, cm/s). Conclusions about high quality fuels follow.

Gasoline is an excellent transportation fuel. A high energy density of 36 kWh/gal yields a high vehicle range. A low flash point of -40ºC means it is easy to start an engine, even in the dead of winter. A low auto-ignition point of 250ºC means near-complete combustion will occur in an engine cylinder, even one with cold spots. And finally, a high flame speed (0.4 m/s at STP) enables high-RPM engine performance.

Other hydrocarbons have similar properties to gasoline, with high energy densities, low auto-ignition temperatures and high flame speeds.

Natural gas (methane) has the lowest flash point, at -188ºC but one of the higher auto-ignition temperatures of 540ºC, making it well suited to stationary power generation, with fast ramp rates.

Conversely, marine fuel oil has a high flash point, around 85ºC, which limits fire risk. But combustion slip can be an issue in marine engines. Diesel is similar, famously ignited not by a spark plug, but by high pressures in the Otto Cycle.

Solid fuels generally have slower combustion. And more variable combustion conditions, depending on the degree to which they are dried and pulverized.

A typical coal grade might need to be heated above 400ºC to ignite, auto-ignition is at 500ºC, and flame speeds will be 50% lower than hydrocarbons. This makes it slower to ramp up steam engines and steam power plants.

Lower carbon fuels have lower energy density and more variable combustion qualities. Lithium ion batteries have an effective energy density 80-90% below hydrocarbons.

Hydrogen also has low energy density, even when ultra-compressed to 700-bar, while hydrogen also has the lowest flash point of any gas, at -250ºC, explaining a very heavy focus on safety, when we have reviewed hydrogen patent libraries (e.g., NEL).

Ammonia is a possible candidate for a low-carbon fuel, as the combustion of NH3 emits no CO2. Ammonia can be liquid so that its energy density is only 50% below hydrocarbons. But it has one of the highest flash points (130ºC) of any fuel, and one of the lowest flame speeds (80% below hydrocarbons). This creates risks of combustion slip, lower engine responsiveness and the need for a pilot fuel to start up ammonia burners.

Clean methanol is suggested as a better blending alternative to ammonia, as it has a flash point closer to 10ºC and a flame speed similar to liquid fuels (TSE research here).

Diesel power generation: levelized costs?

Levelized costs of diesel power generation

A multi-MW scale diesel generator requires an effective power price of 20c/kWh, in order to earn a 10% IRR, on c$700/kW capex, assuming $70 oil prices and c150km trucking of oil products to the facility. Levelized costs of diesel power generation can be stress-tested in this economic model.

A diesel genset includes an engine, power generator, switchgear, control systems, fuel supply systems, coolant and lubrication systems, a foundation, powerhouse civil works and wiring towards the connected load.

In the fuel cycle, air is drawn into the cylinder, compressed by 14-25x so its temperature reaches 700-900ºC, then a metered quantity of injected diesel spontaneously ignites, which provides the power to turn a rotating shaft, usually at 1,500-3,000 rpm (gas comparison here).

Total CO2 intensity is 0.6 kg/kWh for a diesel generator, at 40% average electrical efficiency, and including Scope 1, Scope 2 and Scope 3. This creates a rationale for expanding power grids and hybridizing diesel generation with solar and wind.

Some sensitivities are that each $10/bbl on the oil price translates into a 2c/kWh variation in power costs. For remote locations, each 100km of trucking distance adds another 0.2 c/kWh to the power price.

Capex costs can vary +/- 50%, especially depending on the emissions clean-up downstream of the generator (e.g., Diesel generators tend to be Tier 4, which emit 94% less NOx and 91% less particulate than Tier 2).

Another context where diesel generators are used is as a back-up power solution. Federal regulations require critical infrastructure, such as hospitals, care homes, airports, to have backup generators with 48-96 hours of fuel supplies. While facilities with risks of product spoilage might also have on-site generators to protect against grid failures, hence a typical super-market maintains a 250kW generator with 36 hours of fuel. When regulators talk of banning fossil fuels, it is not entirely clear what alternative is envisaged for these contexts.

The effective power price can be calculated for back-up generation systems, and might translate into around 100-200 c/kWh, depending on how frequently they are used. Although strictly, back-up generators exist to avoid much larger costs associated with power failures, rather than connoting a general willingness to pay 100-200c/kWh for electricity.

Companies with leading market share in diesel generators include Caterpillar, Generac, Cummins, Atlas Copco, AKSA, Aggreko.

Please download the economic model, to stress test the levelized costs of diesel power generation. The model allows for some easy flexing of power prices (c/kWh), capex costs ($/kW), oil prices ($/bbl), delivered diesel costs ($/gal), O&M costs ($/kW/yr) and CO2 prices ($/ton).

Commercial aviation: fuel economy of planes?

This data-file calculates the fuel economy of planes from first principles, using physics to calculate lift and drag, and comparing with actual data from aircraft manufacturers. The typical fuel economy of a plane is 80 passenger-mpg to carry 400 passengers, 8,000km at 900kmph, using jet fuel with 12,000 Wh/kg energy density. What sensitivities and decarbonization opportunities?

This data-file captures the fuel economy of planes, i.e., passenger jets in commercial aviation, starting from first principles in a flexible model, which can be stress-tested, and contrasted with forty years of actual data. You can flex the fuel mass, fuel density, efficiency, passenger count and flight velocity, to derive different ranges and fuel economies.

As a rule of thumb, a passenger jet that takes off with 25% of its weight in jet fuel can travel 8,000km at 900kmph, with a fuel economy of 80 passenger-mpge. Taking off with a larger weight of fuel, 35-45% of the total take-off mass, extends the range to 12,000-14,000km.

How does the fuel economy of planes vary as a function of key input variables? For example, larger planes tend to be more efficient per passenger (chart below). But they also tend to travel further. And each +/- 1,000km of range tends to increase or decrease the fuel economy by around 1.2 passenger mpg-e (chart above).

Each 100kmph increase in velocity also degrades fuel economy by around 4 passenger mpg-e (chart below). The key reason is that the thrust needed to overcome air resistance increases as a cube function of velocity. Concorde had a top speed of 2,179 kmph, which all else equal, hurt its fuel economy by -70%, and if we also reflect that Concorde only carried 100 passengers, then its fuel economy would be -92% lower than today’s planes. Conversely, at low velocities, fuel economy degrades because more energy is expended on lift, to keep the plane in the air for longer.

Over the past 40-years, commercial jets have become more efficient at a pace of 1% per annum, while Airbus and Boeing stand out as having made the most efficient aircraft. Data comparing different companies are available in the data-file.

For the future of the energy transition, there may be challenges to displacing jet fuel, which has an energy density of 12,000 Wh/kg. A large plane powered by a lithium ion battery, at 300 Wh/kg battery energy density simply cannot exceed a range beyond 600km, even if the batteries comprise one-half of its mass at take-off.

In theory, hydrogen has 3x higher energy density per unit mass than jet fuel. Hence a hydrogen-powered plane would need to carry less fuel to achieve the same range, which could improve fuel economy by 25%. This, at least, is one physics advantage for hydrogen-powered planes.

However, the practical challenges of storing liquefied or ultra-compressed fuel are the reason that the aviation industry has not already harnessed LNG as a potential fuel source, which is 25% more energy dense than jet fuel per unit mass and could have afforded a 10% improvement in fuel economy for the same range.

Across all of our research, we think aviation will be one of the last sectors to decarbonize, as reflected in our long-term oil demand models. We have also evaluated e-fuels (aka SAF), biogas to liquids, alcohol-to-jet and renewable jet fuel (upgraded renewable diesel). The lowest cost and most practical option is to offset the CO2 emissions of continued jet fuel consumption with high-quality nature-based solutions, or even next-gen DAC.

Development capex: long-term spending from Oil Majors?

This data-file tabulates the five ‘Big Oil’ Super-Majors’ development capex from the mid-1990s, in headline terms (billions of dollars) and in per-barrel terms ($/boe of production). Real development capex quadrupled from $6/boe in 1995-2000 to $24/boe in 2010-15, and has since collapsed to $10/boe.

The peer group of Super-Majors comprises ExxonMobil, Chevron, BP, Shell and TOTAL, which comprise c10% of the world’s oil production and 12% of the world’s gas production. As a good rule of thumb, this group can be thought of as c10% of global production.

Development capex by region: gaining share? The US has always been the most favored destination, attracting c25% of all development capex, both offshore (e.g., Gulf of Mexico) and increasingly for short-cycle shale. However, the share of these companies’ development capex in the US has averaged around 32% in the past three years.

Development capex by region: losing share? Development projects in Africa and Europe have fallen most out of favor. Development capex in Africa peaked at $17bn in 2009, almost 25% of the group’s total development capex, and has since fallen back to $5bn per year, or 8% of the group’s total development capex.

It is somewhat terrifying to consider that the industry needed to spend an average of $15/boe (real terms) on development capex in order to hold its organic production “flattish” (including some large acquisitions in 2014-17, such as Shell buying BG).

Another scary data-point is that this peer group of Super-Majors spent $18/boe (real) on development projects in the decade from 2004-14 (which is 80% more than recent levels of spending) yet its net production declined by 1.5% per year over this timeframe.

Similar data for the Super-Majors’ exploration capex over time is tabulated here.

Under-investment across the entire energy industry may foreshadow a sustained shortage of energy, especially if 50% lower-carbon gas is intended to replace coal as part of the energy transition, per our roadmap to net zero. Hence one cannot help but wonder about energy shortages, energy pragmatism and our fears of another up-cycle.

This data-file aggregates the Oil Majors’ development capex, across ExxonMobil, Chevron, BP, Shell and TOTAL disclosures, apples-to-apples, back to 1995, based on supplementary oil and gas disclosures, in the SEC’s EDGAR archives.

Offshore vessels: fuel consumption?

This database tabulates the typical fuel consumption of offshore vessels, in bpd and MWH/day. We think a typical offshore construction vessel will consume 300bpd, a typical rig consumes 200bpd, supply vessels consume 150bpd, cable-lay vessels consume 150bpd, dredging vessels consume 100bpd and medium-sized support vessels consume 50bpd. Examples are given in each category, with typical variations in the range of +/- 50%.

This data-file tabulates the typical fuel consumption for different types of offshore vesesel, across all of our research into the offshore and shipping industries.

Offshore construction vessels are especially used in the offshore wind industry, where installation costs for a large-scale wind project will average aroud $1,000/kW spread across 60-100 vessels during peak activity. The largest are offshore construction vessels which will tend to consume around 300bpd of fuel. This is also factored in our EROEI calculations for a wind turbine.

Cable lay vessels are also used in offshore wind and more broadly amidst the expansion of power grids and HVDC interconnectors. We think a typical cable lay vesel will consume 150bpd of fuel.

Offshore rigs also see a continued role in our energy balances, in order to provide 85Mbpd of long-term oil demand and 800 bcfd of long-term gas demand in our roadmap to net zero. A typical offshore oil rig consumes 200bpd of fuel. The numbers are lower for jack-ups and ultra-efficient drillships, but can be higher for larger and older semi-subs.

Elsewhere in our shipping research, we see the typical fuel consumption of a large container ship at 1400bpd, a bulk tanker at 420bpd and a LNG carrier at 270bpd.

The fuel consumption of dredging vessels and the fuel consumption of platform supply vessels (PSVs) are also covered in the data-file of offshore vessels’ fuel consumption.

Please download the data-file for additional datapoints into the fuel consumption of different ships, and individual data-points that led us to these numbers.

Global oil demand: breakdown by product by country?

This data-file breaks down global oil demand, country-by-country, product-by-product, month-by-month, across 2017-2023. Global oil demand again hit new highs in 2023, driven by emerging world growth, especially across gasoline, jet fuel and naphtha. Although jet fuel use in 2023 was still 1Mbpd below pre-COVID levels.

This data-file is a breakdown of oil demand, month-by-month, across 120 countries/regions, and 12 oil products, from 2017 to 2023. We have compiled and cleaned the data as a reference for TSE clients.

Overall, global oil demand fell by -22Mbpd at trough in April-2020; and by -9Mbpd YoY in 2020 overall. In 2021, two thirds of the lost demand recovered, but global oil demand was still -3Mbpd below 2019 levels.

However, in 2022 and 2023, global oil demand hit new all-time highs. Global oil demand grew by +2.5Mbpd in 2023 to surpass 102Mbpd, a new all time peak, 1.5Mbpd higher than 2019 levels, prior to the COVID crisis.

Looking across countries and regions, 2023’s YoY growth in oil demand can be attributed to China (+1.5Mbpd to 16Mbpd), the US (+0.3Mbpd to 20.3Mbpd), India (+0.2Mbpd to 5.4Mbpd), Iraq (+0.1Mbpd to 0.9Mbpd), Saudi Arabia (+0.1Mbpd to 4.0Mbpd), Indonesia (+0.1Mbpd to 1.8Mbpd) and Africa (+0.1Mbpd to 4.3Mbpd). The full numbers are in the data-file. Continued demand from emerging world countries more than offsets declines from developed world countries, which is a source of growing geopolitical tension, per our note here.

Looking across products, jet fuel demand grew most in 2023, rising +1Mbpd to 7.0 Mbpd, yet jet fuel is the only major product category where demand still remains below 2019 levels, which were 8Mbpd.

Gasoline demand also grew by a full +1Mbpd YoY in 2023 to an all time high of 27Mbpd. LPG demand for heating grew +0.4Mbpd to a new all time high above 11Mbpd. Naphtha demand for plastics grew +0.4Mbpd to a new all time high above 6.5Mbpd.

Distillate demand for diesel and industrial use grew +0.2Mbpd to a new all-time high above 28Mbpd. Fuel oil demand for shipping grew +0.2Mbpd to a new all time high above 5.4Mbpd.

Even direct crude burning for power generation grew by +0.1Mbpd and averaged 0.85Mbpd (chart below).

Overall this data-set confirms our fears that renewables, EVs and other new energies would all need to ramp about 2-4x faster than their likely run-rate in the 2020s to stop oil demand (and even coal demand) from continuing to rise (note here).

This matters because in 2020, many commentators were stating that 2019 would have been the all-time peak for fossil fuels, that demand would never recover to pre-COVID levels, and that the world should therefore “stop investing” in hydrocarbons. Even today, we worry that some commentators are still materially over-estimating the pace of disruption in commodities (note here). A lack of pragmatism worries us (note here). Our LT oil demand model is here.

However, there is some uncertainty in this data-set, as the original data-source (JODI) only covers 80% of the oil market. We estimate the remaining countries by taking a proxy from “analogous countries” (the methodology is described in our original report here). There is always some guesswork involved, including for the ever-elusive “other products” category, which can be particularly erratic, especially in countries such as China. Full details are in the data-file.

Copyright: Thunder Said Energy, 2019-2024.