Downstream Research and Data

Storage tank costs: storing oil, energy, water and chemicals?

Storage tank costs are tabulated in this data-file, averaging $100-300/m3 for storage systems of 10-10,000 m3 capacity. Costs are 2-10x higher for corrosive chemicals, cryogenic storage, or very large/small storage facilities. Some rules of thumb are outlined below with underlying data available in the Excel.

This data-file tabulates 80 data-points into the costs of storage tanks for water, oil products, chemicals, LNG, natural gas and hydrogen. In both $/m3 terms and $/ton terms.

This matters as storage tanks are used in downstream industry, materials value chains, and in several types of new energies such as redox flow batteries or pumped hydro.

We also think that some industrial facilities may be able to benefit from increasingly volatile power prices, amidst the build out of renewables, by demand shifting, which means timing their electrical loads to the times when renewables are generating. In some cases, this requires increasing the sizes of storage tanks to increase flexibility.

Volatility is also growing in the global energy system, which may allow owners of midstream infrastructure to generate excess returns in years of deep shortages, per our overview of energy market volatility.

A good rule of thumb is that the storage tank costs for storing fluid commodities will average around $100-300/m3 of capacity, at capacities of 10m3 to 10,000 m3, for relatively simple and non-hazardous commodities such as water and fuel.

Generally tank costs fall (in $/m3 terms) as tank capacities rise. Bigger tanks benefit from economies of scale, and this is visible in the chart above for all categories. Although some mega-sized terminals re-inflate.

Costs are typically 3-5x higher for corrosive chemicals that can require double tanks, stainless steel or specialized tank linings. Maybe $1,000/ton is fair here.

Costs are also typically 3-5x higher for storing cryogenic liquids, which can require specialized nickel steel and insulation.

Costs are also 2-3x higher for very small tanks (below 10m3, lacking economies of scale) or very large tanks (on the magnitude of 100,000m3, so large that they need to be stick-built rather than simply purchased as finished modular units).

LNG storage tanks thus come in as some of the most expensive storage facilities in the data-file, because they are very large and cryogenic. Higher capex may be worthwhile to install higher grade tanks that minimize boil-off and improve energy efficiency.

Large-scale hydrogen storage would likely be higher cost than LNG storage, in our view, and the median small-scale facility for cryogenic or ultra-compressed hydrogen storage is estimated to cost $8,000/m3. Please see our hydrogen storage model and broader hydrogen research.

Storage costs are lowest for underground gas storage, with a median $0.4/m3 of storage capacity. The key reason is scale. The average facility in our database can store over 1bcm of gas.

Methodology. Mainly we have aimed to capture tank costs in the data-file, while excluding the costs of their foundations, pumps, valves and installation; but the lines get a little bit blurry, especially for some of the very large tanks.

Context matters. Some of the data-points are supplier quotes, some are estimates for technical papers, and some are disclosed data-points from specific projects. Please download the data-file for the additional context.

Crude to chemicals: there will be naphtha?

Oil markets are transitioning, with electric vehicles displacing 20Mbpd of gasoline by 2050, while petrochemical demand rises by almost 10Mbpd. So it is often said oil refiners should ‘become chemicals companies’. It depends. This 18-page report charts petrochemical pathways and sees greater opportunity in chemicals that can absorb surplus BTX.

Global oil demand forecasts: by end use, by product, by region?

This model forecasts long-run oil demand to 2050, by end use, by year, and by region; across the US, the OECD and the non-OECD. We see demand gently rising through the 2020s, peaking at 105Mbpd in 2026-28, then gently falling to 85Mbpd by 2050 in the energy transition.

The model forecasts long-term global oil demand, based off of 25 input variables, such as GDP growth, electric vehicle penetration, energy efficiency initiatives, and substitution effects. You can flex these input assumptions, in order to assess long-term global oil demand out through 2050, and as part of the energy transition.

Our own scenario foresees oil demand gently increasing from c100Mbpd in 2022 towards a plateau of 105Mbpd in 2026-28. This peak for global oil demand is followed by a gradual decline to 85Mbpd in 2050. This can be consistent with decarbonization of the global energy system, in our roadmap to net zero.

The biggest shift for global oil demand is the disruption of c35Mbpd of oil demand for light vehicles, which is based on all of our vehicle research.

Our base case forecast captures a ramp up to producing over 65M electric vehicles in 2030, and almost 200M electric vehicles in 2050, while accelerating the retirement rates of today’s 1.7bn ICE fleet by several percentage points, to bludgeon down the total ICE fleet to around 1bn units by 2050. Underlying numbers are discussed in our recent research note on this topic and modelled here (see below).

Electric vehicles: breaking the ICE?

Even these 1bn remaining ICE units must be driven 20% less than they are on average today (the average global vehicle is driven 8,000 miles per year, build-up in the data-file). While these vehicles also need to become 20% more fuel efficient on average (reaching an effective on-road fuel efficiency of over 30mpg, build-up also in the data-file).

Oil demand forecasts. Light vehicles per capita, their annual mileage, and fuel economy for OECD, non-OECD, and world average. — Light vehicle fleet ownership rises from 0.2 to 0.3 units per global version while miles driven falls 20 percent and fuel economy rises 20 percent

How will long-term oil demand vary by region? Another notable trend in the model is that it embeds very different trajectories for oil demand in the developed versus the emerging worlds (charts below).

Oil demand forecasts for OECD and non-OECD by category. — Developed world oil demand falls from 50Mbpd to 20Mbpd while emerging world oil demand climbs steadily to 60Mbpd

Oil demand by region. OECD oil demand has already peaked, at 50Mbpd in 2005, had softened to 48Mbpd in 2019 and is seen falling to 20Mbpd by 2050, effectively all for planes and freight, which are hard to decarbonize due to limits on the energy density of lithium ion batteries and myriad issues with hydrogen-based fuels.

Non-OECD oil demand has risen from 35Mbpd in 2005 to 52Mbpd in 2019 and is seen rising to 60Mbpd by 2050. This is even after two-thirds of future EM passenger cars are assumed to be electric, and some very stark assumptions about fuel economy.

How will long-term oil demand vary by product? The balance of oil product demand through 2050 is also changing starkly in our model (chart below). Gasoline demand declines from 30% of the total oil market to <10%. Jet fuel rises from 8% to over 20%. Materials rise from 13% to over 20%. And a few pp of share continues shifting from fuel oil (<10%) to diesel (>30%).

Oil demand forecasts — Long term oil demand by product is shifting away from gasoline towards clean diesel, jet fuel and materials

How will the primary-to-useful efficiency of global oil consumption change? A typical gallon of oil products contains 38kWh-th of primary energy. As a blended average, the combustion of oil products in 2023 is 35% efficient in converting primary to useful energy, albeit with sharp variations by category. Primary to useful efficiency of oil rises from 35% to 46% by 2050, of which two-thirds is due to mix-shifts in the oil market, and one-third is underlying efficiency gains of prime movers.

Without delivering the technology improvements in our energy transition research, total global oil demand would most likely keep growing to 130Mbpd by 2050, due to global population growth and greater economic development in the emerging world.

The model has been updated in June-2024, to reflect 2023 data, and our latest outlook into changing vehicle fleets and long-term plastic demand. Underlying workings are shown in seven subsequent tabs, covering light vehicles, trucks, jet fuel, plastics, shipping, other products and demographics.

Oil demand: making millions?

For more granular, month-by-month data, tabulating oil demand by region, we also have a tracker file based on JODI data, which is linked here.

Please download the data-file to stress test your own long-run oil demand forecasts, and evaluate oil demand by category, oil demand by region and oil demand by end use.

US Refinery Database: CO2 intensity by facility?

This US refinery database covers 125 US refining facilities, with an average capacity of 150kbpd, and an average CO2 intensity of 33 kg/bbl. Upper quartile performers emitted less than 20 kg/bbl, while lower quartile performers emitted over 40 kg/bbl. The goal of this refinery database is to disaggregate US refining CO2 intensity by company and by facility.

Every year, the c125 core refineries in the US, with c18Mbpd of throughput capacity report granular emissions data to the US EPA. The individual disclosures are something of a minefield, and annoyingly lagged. But this refinery database is our best attempt to tabulate them, clean the data and draw meaningful conclusions.

Some of the larger companies assessed in the data-file include Aramco, BP, Chevron, Citgo, Delek, ExxonMobil, Koch, HF Sinclair, Marathon, Phillips66, PBF, Shell and Valero.

The average US refinery emits 33kg of direct CO2 per barrel of throughputs, we estimate, with a 10x range running from sub-10 kg/bbl to around 100 kg/bbl (chart below).

Breakdown of direct US refinery emissions? The 33 kg/bbl average CO2 intensity of US refineries comprises 20 kg/bbl of stationary combustion, 8 kg/bbl of other refining processes, 3 kg/bbl of on-site hydrogen generation, 1 kg/bbl of cogeneration, 0.2 kg/bbl associated with methane leaks.

Some care is needed in interpreting the data. Refineries that are more complex, make cleaner fuels, make their own hydrogen (rather than buying merchant hydrogen) and also make petrochemicals are clearly going to have higher CO2 intensities than simple topping refineries. There is a 50% correlation between different refineries’ CO2 intensity (in kg/bbl) and their Nelson Complexity Index.

Correlation between the CO2 intensity of US refiners and their Nelson Complexity Index

Which refiners make their own hydrogen versus purchasing merchant hydrogen from industrial gas companies? This question matters, as hydrogen value chains come into focus. Those who control the Steam Methane Reformers may be readily able to capture CO2 in order to earn $85/ton cash incentives under the IRA’s reformed 45Q program, as discussed in our recent research note into SMRs vs ATRs. One SuperMajor and two pure play refiners stand out as major hydrogen producers, each generating 250-300kTpa of H2.

How has the CO2 intensity of US refineries changed over the past 3-years? The overall CO2 intensity is unchanged. However, some of the most improved refineries have lowered their CO2 intensities by 2-10 kg/bbl (chart below). Conversely, some Majors have seen their CO2 intensities rise by 2-7 kg/bbl.

For further context and ideas, we have also published summaries of our key conclusions into downstream, vehicles and long-term oil demand. All of our hydrocarbon research is summarized here.

The full refinery database contains a granular breakdown, facility-by-facility, showing each refinery, its owner, its capacity, throughput, utilisation rate and CO2 emissions across six categories: combustion, refining, hydrogen, CoGen, methane emissions and NOx (chart below). The data-file was last updated in 2023 and covers the full US refinery landscape in 2018, 2019 and 2021, going facility by facility, and operator by operator.

Scope 4 emissions: avoided CO2 has value?

Scope 4 CO2 reflects the CO2 avoided by an activity. This 11-page note argues the metric warrants more attention. It yields an ‘all of the above’ approach to energy transition, shows where each investment dollar achieves most decarbonization and maximizes the impact of renewables.

Electric vehicles: chargers of the light brigade?

This 14-page note compares the economics of EV charging stations with conventional fuel retail stations. They are fundamentally different. Our main question is whether EV chargers will ultimately get over-built, as retailers look to improve their footfall and accelerate the energy transition. This means prospects may be best for charging equipment and component manufacturers.

Fuel retail: economics of a petrol station?

This data-file captures fuel retail economics covering the costs and margins of a typical fuel-retailing petrol station to earn a 10% unlevered IRR, based on data from companies in the space, covering capex, opex, margins and costs.

The typical EBIT margin of a fuel retail station is around 17c/gallon. This is derived from a c6% margin on direct fuel sales; but in addition, around 10-20% of revenues are from selling convenience retail products at a higher, c25-30% margin.

Economics are more attractive at larger service stations with higher throughput volumes, which in turn, allows for lower fuel retail margins. Please download the data-file to stress test the economics.

Fuel retail largely about volumes and competition. In our base case numbers, we have
assumed that our fuel retail station sells 0.75M gallons of fuel each year. This is the
average across Europe. But it varies by country.

The capex costs of a new fuel retail station will average $1.75M, and these costs are broken down across a dozen cost lines in the data-file.

Our recent 14-page note compares the economics of EV charging stations with conventional fuel retail stations. They are fundamentally different. Our main question is whether EV chargers will ultimately get over-built, as retailers look to improve their footfall and accelerate the energy transition.

All of our downstream oil sector research is summarized here, from refining, to petrochemicals to fuel retail, to decarbonization using nature based solutions.

Hydroprocessing: the economics?

The data-file captures the economics of hydroprocessing at an oil refinery, such as hydrotreating or hydrocracking, to remove impurities such as sulphur, and upgrade heavier product into lighter product.

Our base case model requires a $7.5/bbl upgrade spread to earn a 10% IRR across a new unit. CO2 emissions are quantified from hydrogen production. Input assumptions are based on past projects and technical papers, including capex costs (in $M/kbpd) and hydrogen utilization (in scf/bbl).

It is possible to decarbonize hydroprocessing by using green hydrogen instead of grey hydrogen, but the result is a 3x increase in the upgrading spread required for economical running of the unit.

Methanol production: the economics?

This model captures the economics and CO2 intensity of methanol production in different chemical pathways.

Different tabs of the model cover grey methanol production from gas reforming, blue methanol from blue hydrogen and industrially captured CO2, green methanol from green hydrogen and direct air capture CO2, and finally bio-methanol.

Inputs are taken from a wide survey of technical papers, cost breakdowns and energy intensity data. These are also broken down in the data-file.

Based on the analysis, we see interesting potential for bio-methanol and blue methanol as liquid fuels with lower carbon intensity than conventional oil products. You can stress-test input assumptions in the underlying model tabs.

Methanol: leading companies?

This data-file tabulates the details of companies in the methanol value chain. For incumbents, we have quantified market shares. For technology providers, we have simply tabulated the numbers of patents filed into methanol production since the year 2000. For new, lower-carbon methanol producers, we have compiled a screen, noting each company’s size, patent library and a short description (chart above).

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