Enhancing the evaluation of Oil & Gas Investments with EROI

Enhancing the evaluation of Oil & Gas Investments with EROI
– Contrasting DCF analysis with Biophysical Economics analysis of two investment opportunities in Oil Sands versus Bacterially Enhanced EOR

Jan-Pieter Oosterom, Charles A. S. Hall

Abstract:

Energy companies have to make investment decisions by comparing alternative investment projects that impact how they source energy for society. In the face of the uncertainty of the current energy transition, traditional economic tools, such as discounted cash flow analysis that depends on long term cash forecasting, offer limited insights. We introduce Biophysical Economics (BPE),  an approach to economics based on the natural sciences, as an alternative to the exclusively social science-based approach of traditional economics.   The most immediately useful tool within BPE is the concept of Energy Return on Energy Invested (EROI). This paper compares an investment case in oil sands with one in microbial-enhanced oil recovery, applying the two methodologies in parallel. Results weakly favor the oil sands from a traditional economic perspective, whereas biophysical economics strongly favors the microbial case due to is significantly lower energy required to produce the energy that it yields. A close examination indicates that EROI can be used effectively and practically next to DCF to provide better insights and identify cases that are fundamentally less sustainable for society and therefore likely to result in additional regulation or curtailment and thus risk over time. We also show EROI to be an effective tool to pierce through investments that might appear “green” and sustainable but are not. This approach also holds promise in investment evaluation by financial markets that want to compare the sustainability of companies beyond simply CO2 criteria,  thereby yielding a  much more explicit, repeatable, comprehensive and useful tool to ESG investors who often depend primarily on qualitative factors.

  1. Introduction

We live in a world where energy needs have been increasing, and are likely to continue to increase significantly, on the back of population growth and desires for an increase in standards of living in developing countries. At the same time, there is a global drive to combat climate change, accelerating the potential for an energy transition away from fossil fuels, which in itself also requires very large investments (Heun & Brockway, 2019; Capellan Perez et al. 2019).  Aside from the real and serious challenge posed to climate by man-made CO2, it is clear that there are already tensions between the planet’s finite resources and the continuous growth in energy use, with symptoms showing in terms of degraded natural environments and wild populations, soil erosion, depletion of high-quality fuels, water scarcity and other pressures on natural resources.

It is necessary to be deliberate and thoughtful in where we invest the planet’s scarce remaining resources for meeting its energy needs.  Large quantities of high-quality energy underlie society’s wellbeing (Lambert et al. 2014).  But for society to be wealthy, as ours is today, it is necessary to have a relatively large surplus energy.  In other words, society must generate significantly more energy than it takes to get that energy.  England from 1300 to 1750 required about 40 % of all its economic activity to produce the food, fodder and wood necessary to run that 40 % plus the other 60 % (King et al.  2015). Starting in 1750 and with the industrial revolution that ratio went to 25 and 75 %, and today with petroleum to 10 and 90 %.  The consequence is that as society is left with more and more energy than it needs to procure that energy, it can expend that excess energy to engage in other pursuits.  Energy companies have traditionally played a major role in meeting the world’s energy needs, mostly by securing traditional hydrocarbon-based forms of energy.  They are increasingly active also in other, lower-carbon forms of energy.  A typical large multinational energy company makes thousands of individual investment decisions every year to allocate its scarce investment dollars, ranging from investing in maintaining legacy but declining on-shore oil fields to multi-billion deepwater oil development projects or a wind farm to increasing the energy efficiency of a refinery.  These decisions impact and determine future activities of the corporation directly and hence actual physical activities in the world.  Good economic decisions are probably mostly synonymous with increasing the net energy returned to society which in turn generate social welfare through its wise use.

Decision tools traditionally employed by the industry are typically economic, and generally rely on Discounted Cash Flow (DCF) analysis, based on Neo-Classical Economics (NCE).  Such approaches include “break-even price”, “pay-back time”, “unit technical cost” and other methods that rely on projecting future cash flows relative to investments today.  Increasingly, oil and gas companies enhance these cash flow projections with CO2 price assumptions significantly above currently prevailing levels as an attempt to anticipate future increases in CO2 pricing,  and this also signals to the public and stockholders a desire to discourage projects with high CO2 intensity. The net effect is that environmental, political and economic developments are generating a highly uncertain financial and energy future that especially energy companies will need to navigate carefully if they are to survive economically while maintaining their license to operate in society.

Conventional neoclassical economic analysis, despite its wide level of adoption, comes with some serious deficiencies.  This includes a poor track record of predicting future cash flows and a disconnect with the biophysical reality that is becoming increasingly clear (Hall et al. 2001; Stiglitz 2009; Keen 2020 references…).  A fairly well-developed alternative to conventional economics is BioPhysical Economics, the study, using the natural as well as social sciences, of the ways and means by which human societies procure and use energy and other biological and physical resources to produce, distribute, consume and exchange goods and services, while generating various types of waste and environmental impacts.  This paper explores the application of biophysical economics in conjunction with traditional DCF analysis to compare two typical oil and gas investment projects.  Our hypothesis is that the use of the two approaches and a comparison of their results enables the user to navigate limitations inherent in each one, enabling the selection of more robust and sustainable investments.  In addition to comparing their effectiveness as a decision tool, this paper also attempts to explore ways of incorporating EROI in a fit-for-purpose way in day-to-day investment analysis.   This limited approach, while very useful for understanding BPE, does not, in our opinion, come close to understanding or appreciating its long term potential for greatly enhancing our understanding of real economic systems and their future.

The use of methods beyond DCF and its ilk becomes increasingly important in the new investment environment where Environmental Social and Governance (ESG) considerations have become increasingly relevant to investors.  The ESG investment pool accounts for close to 1/3 of total investment flows or 12 trillion $ (Marketwatch, 2019).  One can argue that their ascent is in part a reaction to shortcomings of DCF as an exclusive indicator of value because they identify practices that while not costed in monetary terms are genuine costs of production and may lead eventually to unsustainability for the firm or eventual loss of a license to operate.  DCF generally does not factor these real issues in explicitly.  A current shortcoming of the ESG assessments (or “ratings”) of companies, however, is their largely qualitative nature, lack of consistency and the absence of explicit criteria or standards, where the same company might be rated wildly differently in ESG terms depending on the rating agency, with evidence of rater bias (Berg et al, 2020).  This makes it harder for investors to assess companies consistently and for companies to respond to legitimate investor concerns.  EROI is the first, possibly most important, step in applying “hard” biophysical criteria to the currently “softer” criteria existing for ESG concerns.

By contrasting these two investment cases, this paper also explores whether BPE can ultimately offer a theoretical yet practical basis for part of the critical “E” of ESG considerations, enhancing their consistency and allowing the considerations to become applied and cascaded to below the corporate strategy level to the day to day investment decision level where ultimately the bulk of decisions are made in companies.  We think it also possible that BPE can help address a number of social and governmental issues in part by understanding the limitations of the present carbon-only focus.

  1. Current Methods of Evaluation: DCF and BPE

  • Current economic valuation methods

Economic evaluation is a key consideration for companies to choose between competing investment project opportunities in the face of scarce capital investment budgets.  This is an important activity in any company, especially for companies involved in construction of large assets in the physical world. Economic evaluation is typically centered on DCF analysis, which comes down to estimating the money an investor would receive from an investment, adjusted for costs and the time value of money.  The time value of money assumes that a dollar today is worth more than a dollar tomorrow because otherwise it could be invested and receive a return.  DCF analysis finds the “present value”, in which future costs and gains of expected future cash flows are adjusted by a discount rate. Investors can use the concept of the present value of money to determine whether future cash flows of an investment or project are equal to or greater than the value of the initial investment.  If the value calculated through DCF is higher than the current cost of the investment, the opportunity should be considered.

This involves making estimates of future cash flows as well as the discount rate.  The discount rate is typically the Weighted Average Cost of Capital (WACC) of a company, consisting of its cost of debt and cost of equity.  The cost of equity can be derived with the Capital Asset Pricing Model (CAPM), which is centered on the relationship between expected return and unavoidable risk and the valuation of securities.  All of the above is generally based on Neoclassical Economics.  In practice corporations usually use a discount rate of ~10% with actual capital allocation being based on the availability of alternative investment options for the company.

The use of the above approaches have been and still are widely applied by financial practitioners and are essentially the de facto standard for project valuation, company valuation (assuming a company is a portfolio of projects), and implicitly share price valuation as well as management compensation.

There are, however, a number of well-documented shortcomings with this method that are becoming increasingly apparent.  This includes its frequent failure to generate accurate results even over the short term, its limited ability to forecast further out into the future and it reliance on an implicit expectation of continuity of past performance. In addition, it assumes that all issues we might be interested in are captured in monetary assessments. Empirical evidence indicates that this predictability is often flawed. In the face of the energy transition, where society presumably shifts away from carbon-intensive sources of energy, and its relative predictive certainty, this lack of predictability is particularly problematic now for the energy sector.  Other factors include the absence of attaching value to the withdrawal of “goods” from the environment, including finite natural resources, making the projections in aggregate unsustainable.  Is it realistic that all companies grow in perpetuity, drawing finite resources from the environment, without hitting limitations at some point? That implies ignoring some of the most empirically tested laws in the universe including the laws of thermodynamics and conservation of matter (e.g.  Hall 2001).

  • Biophysical Economics in more detail

An alternative to conventional economics is biophysical economics, an approach to economics based on the natural, as well as social, sciences. Biophysical economics uses as its conceptual base and fundamental model the natural science associated with the structures and processes of real economic systems.  It acknowledges that the basis for nearly all wealth starts with nature, and views most human economic activity as a means to increase (directly or indirectly) the exploitation of nature to generate more wealth. Nevertheless, it often considers the relation of this biophysical structure and function to human welfare and to the money (i.e. dollar) flows that tend to go in the opposite direction to energy.  From a biophysical perspective, one’s job is viewed as trading one’s time at work (the monetary value of which is related to the energy flows of society controlled by the individual) for access through wages to the energy flows of the general economy.  This “general economy” contains goods and services created from the extraction of energy from the earth in anticipation of some demand for them. On average there are about 5 Megajoules used in the United States for each dollar of GDP generated.  Hence money can be considered a lien on energy that has or will be used to provide the good or service it is spent on.

Natural energies drive geological, biological and chemical cycles that maintain ecosystems,  and by so doing produce natural resources. They also maintain a livable environment for humans and their economic and other activities.  Extractive industries use economic energies to exploit natural resources and convert them to raw materials. Raw materials are used by manufacturing and other intermediate sectors to produce final goods and services.  These final goods and services are distributed by the commercial sector to final demand. Each step requires energy, often a lot of energy.  Eventually, the goods and energy return to the environment as waste materials and waste heat.

Biophysical economics recognizes that energy does the work of producing wealth, and is essential for its distribution as well, whether that energy is derived from land, labor or capital-assisted fossil fuels.  Ayres (e.g. , Ayres and Warr, 2005), Kuemmel (e.g., in Hall et al, 2001) and Hall and Ko (2004) have shown that the production of wealth in industrial and developing societies is almost perfectly a linear function of the energy use in those societies and that the correlation gets tighter and tighter when proper corrections are made for the quality of the energy used (e.g., coal vs. electricity) and for the amount of energy actually applied to the process (e.g., electric arc vs. Bessemer furnaces) and for imports and exports.  Much, perhaps most, technology is ultimately about these things. It may seem obvious now that wealth is generated by the application of energy by human society to the exploitation of natural resources.  Nature generates the raw materials with solar and geological energies, and human-directed “work processes” are used to bring those materials into the economy as goods and services.  These processes have been made enormously more powerful over time through technologies and capital investments that are mostly ways to use more or higher quality energies to do the job.

Thus we view BPE as a supplement, or even an alternative, to DCF.  There are many ways that BPE can be used to understand, manage or make decisions about economic matters.   Perhaps the most directly useful component is EROI and its use:

EROI =  Energy delivered by an energy gathering process

Energy used (or diverted from society) to get that energy

Energy return on investment (EROI, sometimes EROEI) is a tool (or metric) that avoids some of the problems with financial analysis while generating additional insight into the factors that influence present prices and future availabilities.  EROI is simply the energy delivered from an energy extraction process divided by the energy required to get it.  A lower EROI means that society must divert more of its total economic activity to get the energy to run the rest of the economy.  EROI integrates the counteracting effects of depletion and technological improvements.  The use of EROI has evolved over the past 40 years and yielded a broad set of  relative energy gain analyses of energy technologies ranging from different methods of oil extraction, to wind energy and photovoltaics (PVs) to corn-based ethanol.  These studies yielded insights into a range of societal challenges, often predicting what people knew to be true but somehow didn’t come out through other more prevalent approaches.  For example, it has found that corn-based ethanol has an EROI that approximates 1:1, i.e.  it uses as much fossil energy as it produces as alcohol, conventional oil extraction usually varies from about 30:1 to 12:1  and is generally decreasing, whereas photovoltaic energy is generally considerably less than 10:1 unless some quality factor is applied to the electricity so produced (Prieto and Hall 2012; Raugei et al 2017).

Practical challenges with applying EROI include the difficulty of obtaining complete and consistent energy data, especially using appropriate boundaries.  Developments in the field have facilitated its consistent application (see Hall 2011; Hall 2017), including aligning definitions to enable consistent comparison as well as the development of using proxies for the often—hard—to– obtain exact energy data by using CO2 (Celi, 2019) or money spent on fuel (Court and Fizzaine 2018).  Interestingly these three different approaches tend to generate similar EROI values when applied to the same boundaries (Hall and Celi, in preparation).  Another inherent limitation of EROI is that by focusing explicitly on the biophysical, it doesn’t focus on the financials, hence ignores topics such as subsidies, taxes and other aspects of commercial arrangements.  There are some EROI analyses that factor it in by making assumptions on where these taxes or commercial rents are spent but these are assumptions with limitations that come with that

  1. Introducing Two Investment Cases: Producing Oil With Steam Versus Microorganisms

This paper examines two investment cases for two very different technologies to produce additional oil.  The world currently produces some 80m barrels of oil a day, representing a major source of energy and materials to society. Notwithstanding the CO2 footprint in its end-usage, it has a range of applications that make our current society work, many of which currently don’t have viable alternatives at scale, including fueling agriculture, aviation, shipping and trucking, and also providing feedstocks for plastics, synthetic materials, coatings and paints, asphalt, etc. Oil wells once drilled and hooked up, deplete continuously; meaning that to just maintain current production levels requires continuous investment in dollars and energy and therefore investment decisions such as the ones we explore here.  Conventional oil seems to be at more or less a peak in production (Fig. 1.)  and alternatives are required if a flow of  80m barrels of oil a day is to be maintained.  We examine two investment projects to get more oil from a DCF and an EROI perspective.

Fig.1 Oil production by region.  The data show that peak oil has occurred for some 6 of 8 continents.  (From Mushalak 2019).

Oil Sands (SAGD) Case

Oil sands, prevalent primarily in Canada and Venezuela, represents some of the world’s largest known oil reserves (165 billion barrels in Alberta, Canada, alone).  Extracting the oil is done through mining or “in situ” production, the latter relying on steam.  Steam is required for these in situ “tar sands” because the “oil” is about the consistency of asphalt and does not flow on its own.

This case looks at an in situ development project of a top quartile oil sands reservoir, with high permeability and relatively low viscosity.  The oil will be extracted using a Steam Assisted Gravity Drainage (SAGD) development in Alberta, Canada.  This implies injecting steam into the reservoir through a number of wells, making the oil flow and producing it through separate production wells.

Fig. 2. Two diagrams of a SAGD project in the oil sands of Alberta.

The project is assumed to produce 50,000 barrels of oil per day for the coming 30 years (a total of 548 million barrels) and requires a central processing facility, boiler and infrastructure of $1bln, wells and drilling spending of $100m/annum and eventual abandonment cost at the end of the project.  Natural Gas purchases are necessary for the steam production.  Note that natural gas prices in Alberta, linked to the nearby Aeco hub, are cheap as Alberta is a major natural gas production area, with local demand vastly outstripped by supply.  On top of that, the project uses water from a nearby river.  Heating the water for the steam by burning natural gas also emits significant CO2.  Alberta, Canada has currently a relatively low royalty regime and low CO2 pricing.

Biologically Enhanced EOR Case:

Our alternative investment project would be to extract more oil from existing, although faltering, wells:

Enhanced Oil Recovery (EOR), focuses on taking one or a set of actions to increase the recovery of an existing oil reservoir beyond its normal level of production through drilling, which typically limits recovery to ~30% of total reserves in place.  Oil in the ground usually is not like oil in an oil can but more like an oil-soaked brick.  Traditional (not enhanced) oil recovery usually means pumping water or natural gas behind the oil to pressurize the field and push it toward collecting wells.  There are a variety of techniques including thermal, gas and chemical injection to get an extra ~10 to (rarely) 20% of recovery from the same field.  The methods used have included injecting various substances (such as nitrogen, CO2 or microbes) to either replace the oil or make the oil less viscous and ease its movement through the pores of the substrate.

Fig.  3.   Schematic of traditional oil recovery from an oil field where water or natural gas is injected behind the oil to push it toward a collecting well.  Note : if drawn to scale the oil field would be much deeper.   b/) Traditional rocker arm or “grasshopper” collecting well, which uses electricity to pump a two-valved device up and down to move oil up a collecting pipe.

There are studies that indicate that it is possible to increase the yield of small oil fields using a relatively cheap bacterial process, which reduces the size of the oil globules so that they can fit more easily through the pores of the substrate (i.e. reduce the viscosity to enhance migration).  This technique is still relatively narrow in its application but is gathering momentum (Nikolova and Gutierrez, 2020).  This case focused on using specialized microbial nutrient injection to enhance migration of oil to the wellhead at an “average” mature onshore field in the Netherlands.  This is done by analyzing the composition of the in-situ microbes to determine what nutrients are necessary to make them grow.

In this theoretical investment project, the assumption is that it is applied to a mature oil field in the Netherlands where it enhances ultimate reservoir recovery by 5%, representing 15,000 boe/day for 30 years.  Since it extends the life of the field, the project would require continued operations staff and part of the cost of the project would be to pay for the technology/licensing cost.  Also, since the field is in the Netherlands, it would be subject to Dutch tax, CO2 and government royalties on oil proceeds.  Since oil and gas companies generally regard this as more experimental, assumptions in this case include a slow (4 year) ramp-up profile and relatively modest incremental production.  Most applications so far have shown companies applying this technology have moved cautiously by going slowly well-by-well to ensure they understand the subsurface and reservoir dynamics well before involving the entire reservoir.  Published results have generally been limited due to commercial confidentiality restrictions but a recent study from application at a range of fields in China provides some insights in its typical application and scale (She at al, 2020).  Note that from a biophysical perspective, these nutrients, which are for our case assumed to be 20-20-20 NPK fertilizer, carry a significant energy cost.  With an assumed cost of $5/bbl for the process, of which 85% relates to nutrients, this implies that at $500/ton for NPK, each barrel of oil produced requires 8.5kg of NPK, which has an embodied energy of 32MJ per kg or 272 MJ per barrel.

Overview of Assumptions of the Two Cases:


Table 1: summary of key project assumptions

Results

Figure 4: comparison of the cash-in and cash-out over the life of the project

Overview of the Economics Results and Biophysical Results of the Two Projects:

Table 2: comparison of key economic indicators based on detailed calculations

If a sensitivity is applied to both by using $40/ton for CO2 the results look as follows:

 

  1. Discussion

To be able to assess the effectiveness of either type of analysis, it is necessary to first be clear on the goals of the company (vs society that might encourage it through regulation) that this investment aims to contribute to.  At the most fundamental level, the primary “purpose”  of a business is to maximize profits for its owners or stakeholders over time while maintaining corporate social responsibility.  For energy companies that typically engage in long term investment projects (i.e.  10-20+ years), the time dimension in conjunction with long term societal welfare through its supply of energy also becomes an important factor.  This means that while they are not immune to pressures of short term results, they tend to have a longer and sometimes more comprehensive horizon than most other companies.

Comparison of Assessment Outcomes

  • The Oil Sands case has higher Net Present Value ($1,068m vs $224m), albeit also the larger investment. Internal Rate of Return (20% vs 15%) than the M-EOR case, indicating that from a pure DCF perspective it is the more attractive opportunity. The unit technical cost, defined as total capital and operating cost divided by total production, shows the SAGD case at $13.5/bbl and the M-EOR case at $19.0/bbl.  Break-even price is $36/bbl for the SAGD case vs $40/bbl for the M-EOR case.
  • The two royalty and tax regimes seem relatively comparable in terms of tax and royalty rates at closer inspection. The Netherlands’ one is a bit higher, but a no-tax comparison of the two cases actually boosts the SAGD case’s returns most (NPV shifts to $3250m for SAGD vs $894m for M-EOR with the IRR yielding 34% vs 24%).  This is primarily due to the larger profit component of the SAGD case.
  • The primary difference between the two is the relatively low investment cost and high operating cost of the M-EOR case. The relatively high operational cost of the M-EOR project is driven by on the one hand the need to keep running (i.e., maintaining, monitoring, etc.) a mature oil field with its ageing facilities (originally designed for higher production and shorter life span). The SAGD case also carries operating cost, which includes pumping, operators to monitor and maintenance, but those are for newer and more optimized facilities. Another factor is e cost of microbes / ingredients and associated royalties to the M-EOR service company. Depending on whether the facilities and other operational costs are shared with non-M-EOR wells, one could allocate less of that fixed operational cost to this project, which would further boost the economic attractiveness of the M-EOR case.  This case is arguably relatively conservative by allocating the entirety of that cost to the M-EOR case as the assumed alternative is the closure of the field
  • The above conclusion is based on applying CO2 pricing as currently prevalent in Alberta, Canada, with a market consensus view for the projected increase. Note that many oil & gas companies are applying higher CO2 pricing than the forecast required for DCF with e.g.  BP and Shell having used $40/ton of CO2 for many years already for all projects even in places with no CO2 pricing and have increased that further in recent years.  Applying the artificial level of $40/ton worsens the attractiveness of the SAGD project yet still leaves it (barely) more attractive in NPV/IRR terms than the EOR project.  Depending on the CO2 assumption taken, the EOR opportunity can come out to be more attractive in DCF terms.  There is however no obvious basis for the level at which to set CO2 price assumption.
  • The outcomes of the EROI assessment strongly favors the EOR project which has an EROI of 17:1 as opposed to the SAGD project with 5:1. The underlying biophysical aspects driving that are evident on account of the high energy usage of the SAGD project, in terms of natural gas as well as additional construction required (including labor, steel, concrete, etc.).  Note that this is despite the M-EOR case also coming with a need for high energy content nutrients.
  • Our outcome in EROI terms of the SAGD case aligns well with earlier research on oil sands EROI (Poisson and Hall, 2012) based on analysis of the Canadian government on energy used, showing that extracting this oil takes about one-quarter of the energy extracted.  This 4:1 ratio of their overall sector compares well to the 5:1 ratio we find here for a top quartile reservoir.
  • As a decision-maker, looking at this set of contradictory results from the two methodologies should trigger a red flag and hence a desire to examine more closely the underlying issues here and perhaps become suspicious of using only the DCF method of making decisions that have a long time horizon. The highly adverse EROI rating in the case of the SAGD project indicates that from a biophysical perspective it is not preferred as a society and that the DCF analysis might in fact be misleading.  Over time, which is a real consideration in a 20+ year project, societal pressures could very well result in aligning activities more with what makes sense from a biophysical perspective.  This could mean increases in CO2 and natural gas pricing, which this project would be impacted by. Alternatively, it could result in curtailments or restrictions such as on water usage, which in this particular case is not analyzed in EROI. Likewise, the EOR project, whilst still subject to regulatory pressures, is with its relatively high EROI rating essentially one that is actually a logical project to pursue from a biophysical perspective for a society that still needs oil. We do not entirely understand the reasons for the very different results but that the strong results of the EROI analysis are a very red flag for us to examine the DCF (and the EROI) assessments much more carefully.

Evaluation of the Usage of the Two Approaches

The original hypothesis is that a conjunction of the two approaches enables the user to navigate limitations and strengths inherent in each one, enabling sustainability of the investments from a number of perspectives, including the most basic one of the material world and also long term potential for profitability and for supplying society with net energy.  The outcome of these cases suggests that EROI effectively helps identify investments that are more robust in the long run in ways that DCF doesn’t necessarily.  DCF is typically based on the current outlook and regulatory environment where we believe that EROI identifies true value-added to society from a biophysical perspective and is as such a better predictor of what will in the long run likely be taxed, constrained or valued.  In this particular case, we essentially see artificially low natural gas prices (due to supply/demand considerations at the time) and CO2 pricing, making a low EROI project more financially appealing than the competing higher EROI project.

EROI might do a decent job in predicting what particular activities might (over time) get taxed more if they are particularly energy-intensive or trigger waste, but it doesn’t help much with overall corporate tax differences in a country.  Given that the stated aim of the company also includes maximizing profits, this is likely an aspect where DCF is more insightful.  The same applies arguably to the cost of the proprietary M-EOR technology used, which depends on the provider of the technology and has likely less of a link with biophysical reality, albeit that presumably the price is set in a competitive environment influenced by that.

Note that the application of EROI in this particular case was done by using a shortcut approach for estimating some of the indirect costs.  This made it easy and straightforward to apply by simply plugging in the assumptions typically available to the project team when doing an investment project (i.e., natural gas, major categories of cost) and then translating those into their energy equivalents. The similarity of the outcome with earlier research (Poisson, Hall and  2012 ) based on Canadian government Input-Output data on Alberta oil sands, provides comfort that despite its relatively easy application here, the results are likely to be sufficiently accurate for this example.

Companies are unlikely to apply it if the mechanics of adding EROI as an extra evaluation step is prohibitively onerous, making this particular proxy approach especially useful.  Note that the shortcut approach used here for EROI ignores the impact of water, which SAGD  utilizes a great deal of and is subject to further constraints (currently also not considered in DCF).  This shortcut approach could relatively easily be enhanced with e.g., the work of Mulder et al. 2010.

There are alternative approaches for improving capital allocation for achieving the company’s objectives that weren’t explored here.  This includes centrally constraining or blocking certain low EROI types of investments altogether (e.g., no more oil sands, which is essentially what Shell did).  The primary counterargument to that is that it would be a rather blunt instrument that captures the most egregious cases but precludes the careful optimization over many projects that over time achieves significant improvement.  For example, conventional oil investments cover a wide range of EROI outcomes, which has historically not been a decision consideration.  If EROI is considered as an additional factor in analysis to the point that it impacts decisions, more projects with a higher EROI could get selected, resulting in more robust investments with more energy for society as a whole. Although perhaps at the expense of lower EROI in the future.  Another alternative is to look at CO2 explicitly as part of investment opportunities or look at the “CO2 intensity” of a given investment opportunity.  In terms of looking at CO2 explicitly, it is unclear how exactly that helps in terms of comparing opportunities other than acknowledging that some have higher CO2 release than others. It wouldn’t give insights as to what level of CO2 is acceptable or might be more reasonable.  CO2 intensity does that to some extent but that is a derived metric without a broader framework behind it and focuses on the –important but singular- issue of CO2.  It is clear that CO2 is a relevant lens that would sit next to DCF but doesn’t yield the same depth of insights as EROI does.

One area where biophysical economics and DCF are clearly at odds with each other is in the concept of discounting.  DCF, derived off the CAPM and unburdened by physical or resource constraints, argues that getting income faster is preferable.  BPE would argue that petroleum resources represent some of our planet’s greatest assets that shouldn’t be squandered by withdrawing them in an energy inefficient fashion –implying negative discount rates (Hall 1986).  In light of ongoing technological development, boosting efficiency in extraction, it may be preferable to leave resources in the ground longer rather than extract them quicker and presumably less efficiently.  Day et al (in press) argue that extracting oil from Louisiana marshes quickly in past decades, probably in response to a high discount rate, has lowered eventual yield of these major oil resources as well as generated much greater than necessary environmental damage.

Note that studies in the field of biophysical economics concluded that as an economy expands, energy prices go up and EROI decreases as increasingly lower EROI resources are used to meet demand (as first put forthwith agricultural land by economist David Ricardo)  (e.g.  Murphy, 2011; King and Hall 2011).  Applying that finding to the SAGD case suggests that this development should be done further into the future rather than now as opposed to the EOR one that is competitive now.  By that time prices would be higher, perhaps technology more advanced and hence more efficient, making doing the SAGD project later in aggregate a better outcome.

Linking Individual Project Evaluation to Financial Markets – the Current Failure of ESG

From a biophysical perspective, the purpose of an oil company is to provide economies with petroleum, perhaps their most valuable resource, and the use of which is highly correlated with human wellbeing, whether measured by GDP or HDI (Lambert et al.  2014). While there are many negative aspects associated with the production and use of oil, and these should be minimized, it is necessary to understand that there is a tradeoff between generating human wellbeing with oil and losing some from its production and use.

Environmental Social and Governance (ESG) considerations are attracting increasing interest from investors, in particular in the energy sector (e.g., see the interview of outgoing CFO of BP, Brian Gilvary mentioning that he spent 50-100% of his time with investors talking about these issues).  However, on account of ESG evaluations being relatively new and still being developed, its application is relatively patchy or blunt.  The entire oil & gas sector is generally seen as being weak in terms of ESG attributes, which is taken as not sustainable as a whole, as evidenced as ESG investors generally avoiding oil & gas.  Within the sector, most of the interest of it seems to be focused on the E in ESG, which is in practice generally defined as exclusively CO2.  That creates a really narrow and often misleading brush for a wide variety of activities in terms of true underlying energy efficiency and sustainability. This is likely to result in relatively smaller valuation differences between companies within the oil & gas sector but also a discount on a potentially highly efficient and robust project from a biophysical sustainability perspective just because they are executed by an oil & gas company.  This limited perspective often causes the actions taken to not only not meet the objectives of reducing carbon but inflicting additional damage on a broad range of environmental, energy and social objectives. good  This lack of thorough investor scrutiny on these aspects also creates incentives for companies to engage in “greenwashing” where they would, typically with the help of a consultant, present the company in a greener light, finding ways to report less CO2, etc. without necessary addressing underlying activities in a biophysical sense.

Recent advances in non-financial reporting, including more companies signing up to the Global Reporting Initiative (GRI), has triggered increased disclosure with an expectation of further disclosure including on energy usage, CO2, water usage, etc.  This increased disclosure opens up the way for assessing energy companies truly as portfolios of projects with biophysical characteristics and assessing them accordingly.  By equity research analysts and fund managers increasingly being able to assess the EROI of energy companies and their underlying portfolios, they are able to distinguish between those that have more robust portfolios from those that have weaker ones.  Over time, as investors shift away from the weaker ones to the more robust ones, eliminating the current arbitrage opportunities, price differences will start to occur.  As it becomes an area of interest to investors with direct share price implications, CFOs and then CEOs of companies will take note and the stronger ones will overhaul their investment decision making process to incorporate EROI, similar to how leading ones have incorporated CO2 before.  This in itself will trigger a greater interest in applying EROI to investment opportunities and pull for the necessary skills and information.

Discussion of the Broader Implications for Society

If broadly applied and ultimately recognized by the investment community, applying EROI will value a portfolio more robustly thereby further enhancing companies’  incentives to shift further towards higher EROI opportunities and in aggregate maximize EROI and, we believe, social welfare for society.  As the world needs energy for switching to more renewable technologies, it cannot afford the unnecessary destruction of energy by picking sub-optimally in the sources provision of its energy (Capellan-Perez 2019). It also provides a better opportunity to help deal with the major challenge of abatement of climate change which will probably require all the net energy we can get our hands on as a society, reducing other aspects of societal well-being.  Scenarios of declining EROI, whether due to depletion of our best fuels or through a transition to renewable fuels are ugly for society as they tend to require lowering living standards and making choices that societies generally struggle to deal with (Ahmed 2018, Lambert et al.  2014).

  1. Conclusion

The findings from exploring these two cases indicate that DCF alone does not provide the right answer for an energy company to achieve its objectives when comparing alternatives. The standard DCF practice to base its projections on current regulatory setup and rules may actually be misleading for a long term project and underplay the likely changes in that regulatory setup. By disregarding the inherent limitations of the biophysical environment, DCF analysis is prone to underestimating the very predictable risks that those create to the company if and as the future pans out differently.

EROI acts as an additional lens in this case, providing assurance in the case of the EOR opportunity and a red flag in the case of the SAGD one. This biophysical lens acts as an indicator of the sustainability of the project where a project that scores poorly in EROI terms effectively represents a case where the activities are not in synch with society’s needs from a biophysical perspective.  That, combined with a longer duration project, implies a higher likelihood that at some point society takes action to make an adjustment.  Those adjustments could happen in terms of extra taxes and restrictions that are arguably predictable by EROI analysis.  Another aspect could be natural gas prices in this case.  It is however also clear that certain aspects are not addressed by EROI, including the relative financial attractiveness of the project on account of taxes, costs, etc. where these don’t have a direct predictable biophysical component.

The practical application of EROI becomes a big factor in whether it will actually be used.  This particular case also provides credence to getting meaningful insights from estimating EROI by using a set of practical shortcuts on information typically easily available at companies when they do project decisions.  It also illustrates the risk that proxies inevitably mean that some aspects are then left out, such as the example of water and potential restrictions on that.  On balance, given the relevance of the proxies, it seems better to apply a proxy and then uses it for as many investment cases as possible to get some insights, rather than design a more perfect estimation of EROI and have companies decide that applying it is too onerous and not do it. This can be largely sidestepped with quality analysis tools that translates typically readily available project data to do a sufficiently accurate and consistent EROI analysis.

Overall, this paper presents a strong case for adding EROI (even through a proxy) as a complementary lens compared to sticking with only DCF (and CO2).  It provides a method of structurally differentiating investment opportunities that are robust in the long run versus those that are artificially attractive due to limitations of DCF analysis.  By applying this consistently over time, a company can make its portfolio more robust to these risks.

The same approach can also potentially be used for investments in “new energies” or other “CO2 light” investments by companies.  It is likely to be effective in differentiating cases that are fundamentally more sustainable from a biophysical perspective and thereby robust in the long run versus those that might look green/sustainable at first sight.

This approach can also be used to identify projects that may be currently politically popular, hence subsidized and attractive in DCF terms, yet not sustainable in EROI terms.  An EROI lens could pierce through that and separate the true sustainable projects from the “greenwashing” ones.  If projects don’t make sense to society in EROI terms, any such subsidies would not be reflecting true net environmental gain and hence would be at higher risk of disappearing over time.  There are a growing number of examples, including corn-based ethanol, which has an EROI of approximating 1:1 (Murphy et al.  2011), meaning it uses as much energy to produce as it yields to society, which is clearly not a sustainable activities for society even though  ethanol was  seen as “green”.  As society comes to terms with those, subsidies are likely to disappear.  At a more aggregate level, the approach can also be used to assess the sustainability of companies as a whole in terms of energy provided to society and hence provide a measure of robustness to investments. That, in term, could displace current more qualitative ESG criteria and allocate capital more effectively.

Areas for Future Research

When considering areas of future research, it is noted that the literature has a sizeable number of high-quality studies that look in-depth at the EROI of individual projects (e.g., solar, wind, unconventional oil) as well as some excellent aggregate/macroeconomic analyses (e.g.  Hall et al.  2014; Lambert et al.  2014).  There have been, however, few studies were done that compare projects in a way that is practical to implement for companies in day to day decision making.  Whilst this may change in the future, companies are typically unable/unwilling to do a systems analysis of the entire life cycle for all energy use of every investment opportunity they have.  Studies that test more practical applications of the concepts without a heavy burden of collecting detailed energy data and assess whether these still yield similar insights are critical to encourage practitioners to apply them to real life investment decisions.  Another area that can benefit from the further study is the application of these concepts in the market by identifying companies that have a superior portfolio from an EROI perspective and assessing whether their financial returns over time are superior as the above insights would expect given that they would be less likely to be impacted by some of the risks that companies with an inferior portfolio of projects in terms of EROI would be subject to.

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Appendix:

 

 

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