Oil in the energy transition

US shale: outlook and forecasts?

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 15Mbpd by 2030 (note here).

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 12Mbpd of total liquids production by 2030, amidst higher activity and oil prices. All of these variables can be stress-tested in the model.

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 plateau through 2030.

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 plateau through 2030.

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.

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.

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.

Commodity intensity of global GDP in 30 key charts?

Intensities of oil and other materials for the global GDP have fallen over time, but electricity intensity has increased.

The commodity intensity of global GDP has fallen at -1.2% over the past half-century, as incremental GDP is more services-oriented. So is this effect adequately reflected in our commodity outlooks? This 4-page report plots past, present and forecasted GDP intensity factors, for 30 commodities, from 1973->2050. The -1.5% pa decline in the oil intensity of global GDP is anomalous and could even slow from here. While surprisingly many other commodities show demand increasing in line with, or above GDP growth.

Oil demand: making millions?

What are the best pathways for decarbonization and reducing global oil demand?

What does it take to move global oil demand by 1Mbpd? This 22-page note ranks fifteen themes, based on their costs and possible impacts. We still think oil demand plateaus around 105Mbpd mid-late in the 2020s, before declining to 85Mbpd by 2050. But the risks now lie to the upside?

Japan oil demand: breakdown over time?

Japan's oil demand from 1990 to 2023. Japan's oil demand peaked in 1996 at 5.8Mbpd and has since declined to 3.4Mbpd by 2023.

Japan’s oil demand peaked at 5.8Mbpd in 1996, and has since declined at -2.0% per year to 3.4Mbpd in 2023. To some, this trajectory may be a harbinger of events to come in broader global oil markets? While to others, Japan has unique features that will not generalize?

The 7-page report, linked via the first button below, contains our own observations into Japan’s oil demand, which does not generalize globally.

The data-file, linked via the second button below, contains all of the underlying data, to interrogate Japanese oil demand over time.

Our roadmap to net zero sees global oil demand rising to 105Mbpd in the mid-late 2020s, then declining at a rate of -1%pa to 85Mbpd by 2050. But does Japan’s decline in oil demand, set a precedent for steeper declines ahead?

This 7-page note argues that there are key features of Japan’s energy mix, which mean its history cannot be generalized more broadly: including Japan’s reliance on imports motivating efficiency gains across the board (pages 2-3), declines in manufacturing activity (pages 4-5) and the underlying structure of Japan’s oil market, which has always been weighted to easy-to-substitute categories (pages 6-7).

The underlying data-file breaks down Japan’s oil demand over time, based on data from METI, across Passenger Vehicles, Commercial Vehicles, Motorcycles, Taxis, Buses, Trucking, Rail, Aviation, Shipping, Agriculture, Mining, Construction, Steel, Chemical Feedstock, Chemicals Heat, Materials, Food, Industrial Heat, Industrial Steam, Retail, Hotels, Restaurants, Hospitals, Schools, Waste Collection, Commercial, Power Generation, Residential Heat, Refineries, Lubricants, Asphalts, Petcoke, annually, from 1990 to 2023.

The underlying data-file also breaks down Japan’s oil demand across all of these categories, for different oil products: total oil products, gasoline, distillates, jet fuel and fuel oil.

Further data is available on the TSE site into Japan’s gas and power demand, energy security, population and GDP, and other commodities supply-demand.

Commodity price volatility: energy, metals and ags?

Commodity prices are distributed lognormally, so the average price will tend to be higher than the median price.

Commodity price volatility tends to be lognormally distributed, based on the data from twelve commodities, over the past 50-years. Means are 20% higher than medians. Skew factors average +1.5x. Standard errors average 50%, while more volatile prices have more upside skew.

This data-file contains data plotting the statistical distributions of volatility for twelve major commodities, ranging across energy commodities such as oil, gas and coal; industrial metals such as iron ore, copper and aluminium; precious metals such as gold and silver; and agricultural commodities such as sugar, soybeans and palm oil.

Commodity price volatility tends to be lognormally distributed, based on starting with the charts shown below, then smoothing all of these statistical distributions together, for the title chart shown above. This statistical distribution is intuitive, as prices are effectively uncapped to the upside during commodity shocks, but they are effectively capped to the downside, as commodities cannot sustainedly trade below zero.

A fascinating finding is that when commodities are more volatile overall (e.g., as indexed by standard error) then this is 75% correlated with skew in the commodity, or in other words, the mean tends to run further above the median. In other words, this is another indicator that commodities illustrate more upside volatility than downside volatility.

The positive skew (mean to median ratio) and standard error of commodity prices. These measures turn out to be 75% positively correlated, so rising volatility drives the average price further from the median.

If base case forecasts are thought of as the median price levels of commodities (e.g., $65/bbl for oil over the past 50-years, $8/mcf for global gas, etc), then the data imply that mean average prices will tend to run 1.1 – 1.5x above median expectations.

The final two tabs of the data-file model the lognormal volatility of commodities, illustrating how the value of commodity marketing and trading is likely to rise during the energy transition, as volatility grows on an absolute basis, but also inter-regional volatility is growing due to the ascent of renewables such as wind, solar and hydro.

Fantastic underlying data that helped to build this data-file came from the World Bank pink sheets, which we recommend to anyone looking for free monthly or annual commodity price data.

For more recent and more detailed pricing across a wider range of commodities, please see our commodity price database, for time series that go further back in time to the 1800s, please see our database of very long-term commodity prices, while we also have analysis into the performance of commodities during conflicts and performance of commodities during recessions.

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 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?

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).

What outlook for shale in energy transition?

Modelling US shale production by basin?

Challenges and controversies for US shale?

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