Alexandre Poisson, Charles A. S. Hall. 2013. Time Series EROI for Canadian Oil and Gas. Energies 2013, 6, 5940-5959
[ This is an extract from this 20-page paper. Tar sands are the hope offered by techno-optimists that a great deal of oil remains. Since I leave out tables, charts, and so much of the text, do read the published paper if this interests you. ]
Modern economies are dependent on fossil energy, yet as conventional resources are depleted, an increasing fraction of that energy is coming from unconventional resources such as tar sands. These resources usually require more energy for extraction and upgrading, leaving a smaller fraction available to society, and at a higher cost.
Here we present a calculation of the energy return on investment (EROI) of:
- All Canadian oil and gas (including tar sands) 1990–2008. Since the mid-1990s, total energy used (invested) in the Canadian oil and gas sector increased about 63%, while the energy production (return) increased only 18%, resulting in a decrease in total EROI from roughly 16:1 to 11:1.
- Tar sands alone (1994–2008). We found (with less certainty) that the EROI for tar sands has been around 4:1 since 1994, with only a slight increasing trend.
My comment: Later on in this paper it states only mined tar sand EROI was calculated (not in situ). Brandt  found that mined oil sands have the highest EROI of 5.5 to 6. Poisson and Hall cite 4:1. I think their lower result is because Brandt didn’t include the EROI of refining tar sand oil into usable syncrude oil). Brandt et al found that in situ EROI is only 3.5 to 4. Later, Poisson and Hall imply that in situ may not be viable energetically, but that mined may be possible. The last I looked, mined was perhaps 10% of the tar sands, which would leave 90% as unexploitable resources (though there are plans to put in nuclear reactors to make in situ possible when the natural gas runs out).
We used energy production and energy use data from Statistics Canada’s Material and Energy Flow Accounts (MEFA). We were able to quantify both direct and indirect energy use, the latter from Statistics Canada’s energy input-output model.
Finally, we analyzed underlying factors possibly influencing these trends.
Production of unconventional oil (diluted bitumen and synthetic crude from tar sands) has grown rapidly, almost tripling between 2000 and 2011, from 0.6 mbbl/d to 1.6 mbbl/d , and now even surpassing that of conventional oil
Originally, tar sands production (which began in 1967) was restricted to surface mining and upgrading operations. Since approximately the year 2000, recovery of tar sands from deeper layers using underground (in situ) extraction techniques has expanded, and now represents ~50% of total tar sands production
From the perspective of energy systems analysis, the shift in energy resources from conventional to unconventional oil and gas can be described as a decrease in natural resource quality . It can be quantified empirically in part by using the metric of energy return on investment (EROI), the ratio of energy output (returned) over energy input (invested) in an extraction process [8,9]. EROI captures the idea that society has to divert some portion of its existing or immediately available energy resources away from production to meet final demand, and instead invest it to extract more of the same (or an equivalent) energy resource, such as a coal deposit or an oil and gas reservoir. As such it is one index of the quality of that resource. This ratio of energy output over energy input may vary over space and time, based on many geological, technical and economic factors, including:
The initial concentration and total size of a resource, ease of access, efficiency of further conversions (e.g., chemical refining or electricity production) and depletion of the resource. As conventional oil and gas resources are increasingly depleted around the globe, the EROI of these resources are showing declining trends.
Recently, Brandt et al.  published the most detailed and complete energy analysis of tar sands. It uses high quality data from the Alberta government in physical units. Their data and analysis covered both in situ and surface mining, was disaggregated in terms of tracking the different types of fuel used, and spanned a wide period of time (from 1970 to 2010), at high temporal resolution (per month). It included good data on the energy used directly but did not include indirect energy uses, that is energy used off site to generate materials used on site.
Freise  calculated a preliminary time series EROI for conventional Canadian oil and gas from 1950 to 2010 using a monetary technique that we believe can be improved upon. Thus more accurate estimates of the EROI of Canadian oil and gas are needed to detect important trends in time, compare the extraction efficiency of Canadian oil and gas with that of other countries and compare the EROI of conventional with unconventional oil.
In this paper we present a calculation of the energy return on investment (EROI) for all Canadian oil and gas combined (including conventional oil, natural gas, natural gas liquids and tar sands) from 1990 to 2008, and similarly for that of tar sands alone, from 1994 to 2008. We compare these two results, detect any significant trends, and discuss possible underlying factors which may explain the temporal trends. Due to the high quality of the energy data derived from Statistics Canada’s database (in energy units), our study allowed for independently testing the validity of some common methodological assumptions employed in estimating energy expenditures at the national level over time. We discuss this in more detail below, and make some
Energy return (outputs, or production) data for hydrocarbons is easily available through various organizations and at different scales. However, it is usually much harder to get data on energy inputs, both direct and indirect, especially in energy units covering long periods of time . In this context, direct energy is defined as the energy commodities (e.g., diesel, gas, electricity) used on sites owned by the industry for its own production . In the case of oil and gas extraction, direct energy use includes the sum of energy commodities used at the site of extraction, up to the point of shipment from the producing property, during all activities in the exploration and preparation of natural gas, crude oil, natural gas liquids, and synthetic crude oil and bitumen (both surface mining and in situ extraction of tar sands). Indirect energy is defined as the energy used elsewhere in the economy for the production of the goods and services that are used by the industry in the production of that resource [7,20,21].
Since the introduction of net energy (and EROI) analysis in the late 1970s, there has been considerable debate as to the most appropriate method to use for estimating indirect energy costs, particularly the energy embedded in materials and services [7,9,19,22–26].
Traditionally, two methods have existed to estimate the indirect energy embodied in goods and services: process-analysis and input-output analysis [7,22]. Process analysis is a micro-level technique which involves tracking, at a very detailed level, all individual materials and energy flows needed to manufacture a unit of product of interest, through many stages of a complex production and supply chain. It carries the advantage of being quite precise and specific. But due to the complexity and interconnectedness of the industrial system, the analysis must eventually be truncated  resulting in a systematic underestimation of the energy costs by an unknown factor. The second method, energy input-output analysis, is a more comprehensive and macro-level approach. An input-output model is a complex matrix of all financial transactions in a society, aggregated in sector categories, and organized by government agencies into national input-output accounts [7,24,28]. It can be used to identify how much activity (e.g., energy commodity inputs) from all other sectors of the economy (coal, iron, paper, business services) were necessary to generate a commodity of interest (e.g., steel output).
Although it lacks precision because of data aggregation, it benefits from being very comprehensive as the boundary of analysis is essentially infinite, encompassing all upstream stages of production and supply [28,30]. Early on, Bullard et al.  developed a procedure to combine the advantages of both process-analysis and input-output analysis, which they termed the hybrid approach. Increasingly, a hybrid approach is being recommended to provide sufficient precision and accuracy for robust results in both net energy analyses and greenhouse gas emissions inventories .
Along these lines, Murphy et al.  provide guidelines for evaluating EROI (including time-series EROI), combining direct energy use data in energy units and information derived from industry expenditure or sales data and national energy input-output tables. We essentially follow their description of “standard” EROIstnd at the “mine-mouth”.
Energy Return: Production of Canadian Oil and Gas
We used data on production of Canadian hydrocarbons from Statistic Canada’s Socioeconomic Information Management (CANSIM) database for oil, natural gas and natural gas liquids [5,31,32]. The CANSIM production data covers the period from 1985 to 2010 (although we use only data from 1990 to 2008, to match energy use data), and provides detailed production data by province and by fuel type (in units of volume per year) (see Table 1). We converted these annual production volumes into energy units using energy content factors (heat values) from the Alberta Government (see Table 2) . These numbers differ only slightly from those from other sources, such as from Canada’s National Energy Board . We chose the ones provided by the Alberta government because they were more complete, including values for synthetic crude and bitumen.
EROI of Canadian Oil and Gas
We calculated the EROI time series for Canadian oil and gas in two ways, first by dividing the annual energy production (energy return) by the annual direct (only) energy used (energy invested) and second by both direct and indirect energy used (see Section 2.2). The difference in the two EROI time series shows the sensitivity of the results to a change in the boundary of analysis; from accounting only for the direct consumption of energy commodities (e.g., diesel, gas, etc.), to also including the indirect energy embodied in the equipment and services used in the oil and gas extraction sector.
EROI for Tar Sands
Because of data limitations and study scope, we restricted our EROI calculation of tar sands to surface mining and upgrading operations, and to direct energy use only (thus excluding in situ extraction and indirect energy use).
The end product of surface mining is synthethic crude oil. Bitumen from the mines is upgraded to produce a substance chemically similar to conventional crude oil (named synthetic crude, or syncrude). Our EROI analysis includes the energy required to extract the mixture of bitumen and sand from the ground, separate it, and upgrade it to syncrude oil.
For our EROI calculation, we paired the output energy data from Statistics Canada’s CANSIM dataset (1994–2008)  to the energy input data from CIEEDAC (1994–2001)  and from Natural Resource Canada’s CIPEC report (2002–2008) , as shown in Table 5. We also include the energy production data (million barrels of syncrude) provided in the CIPEC report for the year 2000–2008 (Table 5)  to illustrate uncertainties associated with combining these datasets. Unfortunately, these energy production values differ by as much as 60%, which is unusual for energy production data. This results in a high and low estimate for the EROI of tar sands from surface mining for the years 2002 until 2008. We use the average of these two EROI calculations for our final estimate, but also present the high and low estimates in the results section below.
Table 5. Energy use and production for tar sands from surface mining
The EROI for Canadian oil and gas combined using both direct and indirect energy, was about 16:1 in 1997 and has declined to about 11:1 in 2008, whereas when calculated using only direct energy, it was 18:1 in 1998, and decreased to about 13:1 in 2008. The EROI for tar sands alone (from surface mining only, and considering only direct energy inputs) averaged about 4:1 throughout the period analyzed, with only a slight increasing trend.
Freise’s EROI estimates were derived by estimating energy use (investment) in the oil and gas extraction sector from financial data alone and using a constant energy intensity factor (24 MJ/$US, 2005) for the entire 60 year period of his study (see below for further discussion). We believe that the direct and indirect energy use data from Statistics Canada (in energy units) have allowed us to get a more accurate estimate of energy use and hence EROI. This allows us to test the accuracy of Freise’s EROI estimates for the period where our studies (and reported data) directly overlap (1993–2008).
There are five approaches used by Freise that we believe can be improved upon (1) he used financial data alone to estimate both direct and indirect energy use; he also (2) multiplied the annual monetary expenditure for the industry (with some correction for inflation) by a single money-to-energy conversion factor for the entire 60-year study period. This assumes that the energy use intensity (i.e., MJ per dollar of expenditure, or dollar of production) of the Canadian oil and gas industry stayed constant over more than half a century, regardless of any technology change. Furthermore, his study also (3) used a money-to-energy conversion factor (24MJ/$US 2005) from a different country than the one under study (from the US instead of Canada); (4) used a single correction factor for currency fluctuations between the US and Canada for the entire 60-year study period; and (5) used a general consumer price index for inflation correction of the monetary expenditure, instead of a sector-specific producer price index (prices of commodities in specific industry sectors vary more from year to year than the average national inflation rate, especially in the oil and gas industry).
Our EROI estimates for tar sands fall within the range of previously published studies. Brandt et al. provide the most detailed analysis of tar sands yet. They find EROI values for tar sands (from both surface mining and in situ extraction, with direct energy only) fluctuating between 2.5:1 and 4:1 during the period from 1990 to 2003, very similar to our results.
After 2003, the EROI of tar sands from surface mining increases to around 6:1, showing a gain in extraction efficiency. Our results for surface mining show less fluctuation than Brandt’s. We also detect a similar (but very small) upward trend in EROI during this same period. The data used by Brandt is much more detailed (disaggregated) than ours, and we believe their more precise EROI values are more accurate and rich for interpretation. For example Brandt et al. are able to distinguish energy investment coming from the resource itself (coke and process gas) from external purchased energy (natural gas), and with this calculate a general EROI (low, around 6:1) and an external EROI (larger, around 15:1) .
Thus while we find low EROI values for tar sands, Brandt et al. show that for surface mining, much of the energy invested is from the resource being exploited, not after being processed through society. And therefore, in this regard, the extraction may be expensive, but possible. The fact that we both have similar results gives confidence to our analysis, and the general conclusions we derive from it.
For oil and gas extraction, Grandell et al.  found a temporal pattern quite similar to ours, in the case of Norway: an increase in EROI from 1991 to 1996, and then a decline until 2008. On the other hand the absolute values, ranged between 40:1 and 60:1, are much higher than our range of between 16:1 and 10:1. Gagnon et al.  estimated an EROI time series for global oil and gas between 1992 and 2006, and also found an increase in EROI until 1999, flowed by a decline (with a range in values between 18:1 and 35:1). Guilford et al.  examined the EROI of US oil and gas over a longer period: at five year intervals since 1972, and with more sparse estimates going back to 1919. Again, they found an increase in the EROI for oil and gas from 7:1 in 1982 to 16:1 in 1992, followed by a decline to approximately 11:1 in 2007. However, the problem in comparing and interpreting these studies directly is that the quality of the data and assumptions employed (to fill data gaps) differ, with large but generally unknown uncertainties in the EROI estimates.
Interpretation and Implications
The authors of the above studies for Norway, the US, Canada and at a global scale, tend to conclude that recent declines in EROI observed globally are likely due to the depletion of the highest quality conventional oil reserves internationally, and in some cases to an increase in drilling effort not associated with an increase in output [10–16]. As easily accessible oil and gas becomes more scarce, and the international price of oil rises, investments flow to resources which are more costly to exploit, both energetically and financially. Our preliminary analysis of underlying factors in Canada seems to support this interpretation, although more in depth time-series statistical modeling is required to test the accuracy of these ideas further.
The general concern in this field is that if the EROI of our major fuels continue to decline, and if the replacement “green” energy sources (with their backups) have as low an EROI as appears to be the case at this time, there is likely to continue to be a decline in the economic surplus and economic growth that previous generations had taken for granted and that seems to be increasingly characteristic of OECD countries. Will declining EROI further stress governments increasingly unable to meet legal financial commitments such as schools and pensions?