The Shock Model (Part II)

This story was initially published on TheOilDrum.

This post is the second part of a review of the Shock Model that was introduced in part I. The shock model was developed by WebHubbleTelecsope and aims at modeling oil production based on the backdated oil discovery data. In the first part, we proposed to apply a bootstrap filter in order to estimate the shock function that was previously manually set by the user. We also observed that the predictive ability was limited because of a too conservative projection of future extraction rate values.

In this second part, I propose a modification of the extraction rate function in order to improve the predictive ability of the model. This modification is based on the observation that the extraction rate function is linearly dependent to the ratio of the cumulative production to the cumulative shifted discovery. The new formulation is similar to the logistic differential equation at the difference that the Ultimate Recoverable Resource (URR) is replaced by the cumulative shifted discovery.

I look also at the modelisation of reserve growth which is an important aspect of modern oil production that is often overlooked in the peak oil community.

The code in R language is provided at the end of this post.

Printer friendly version in pdf.

Unification of the Logistic Model and the Shock Model

The shock model can be summarized by the following equation:

where Q_T(t) is the cumulative filtered oil discovery, Q(t) is the cumulative production and E(t) the extraction rate function (or shock function). I note t₁ the last year of our baseline (i.e. the last year of available production data). One issue with this formulation is that E(t) remains constant and equals to E(t₁) in predictive mode when t>t₁. We have shown last time (see Figure 8) that this behaviour makes the predicted production follow the reserve profile R(t) leading to an immediate peak most of the time.

For the logistic model, the extraction rate is proportional to the cumulative production and all the oil is available for extraction at time t₀:

In equation (2) there is no notion of reserves, it implicitly assumes that all the available oil has been discovered and brought online during the first year (t=t₀). By combining equations (1) and (2), we can derive a hybrid shock model by modifying the extraction rate:

The extraction rate (or shock function) is then linearly dependent on the cumulative production as a fraction of the cumulative gross reserve addition:

Note that K is no longer a constant as in the logistic model (2) but becomes the shock function. In predictive mode ( t>t₁), E(t) will still evolve thanks to the ratio Q(t)/Q_T(t) giving a logistic-like behaviour to the forecast.

Is the relation (4) observed in reality? the response is yes as shown on Figure 1 for the years 1985-2006.

Fig 1. Estimated Shock function E(t) versus the cumulative production as a fraction of the cumulative gross reserve additions.
World production for crude oil + condensate from 1985 to 2006. The red line has a slope equals to K=0.0598.

I call Hybrid Shock Model (HSM) this model defined by the relation (3). If I repeat my last time experiment, we can see that predictions are better behaved. In particular, the forecast based on the 1900-1990 baseline gives a 2006 production close to the actual number.

Fig 2. Comparison of the two models for different baselines.

Compared to logistic curves derived from the Hubbert Linearization for different time ranges (1983-1990,1983-2000,1983-2005). We can see that the HSM is better behaved mainly because the logistic curve is unable to account for the 1970-1980 production bump.

Fig 3. Comparison of the logistic curves and the HSM for different baselines.
On Figure 4, the estimated HSM shock function for the baseline 1900-2006 is close to the one given by the SM except in the predictive part where we can see clearly the convergence of E(t) toward K(t).


Fig 4. Estimated shock functions for the HSM and SM using the 1900-2006 baseline .

Modeling Reserve Growth

Reserve growth is probably one of the most contentious and complex aspect of peak oil (see Rembrandt's posts for a great overview). A quick definition from Verma et al. [2]:

Reserve growth is a term used to refer to estimated increases in the total technically and economically recoverable petroleum reserves of a field that commonly occur through time because (1) additional reservoir and geologic information leads to increases in estimates of hydrocarbons in-place of existing reservoirs or pools; (2) new reservoirs or pools are discovered in existing fields; and (3) improvements take place in the hydrocarbon recovery factor owing to better understanding of reservoir characteristics and behavior through use of 3D/4D seismic interpretation, better geophysical logging tools, and improved reservoir simulation techniques. Additionally, application of horizontal-well drilling technology and enhanced recovery methods improve the hydrocarbon recovery factors significantly, resulting in increased estimates of reserves, particularly in oil reservoirs.

Unfortunately, reserve growth is also very sensitive to the reserve booking and reporting requirements in each country. For the US, reserve growth has been spectacular and is believed to follow a kind of parabolic curve spanning over 95 years. The recent modified Arrington Cumulative Growth Function (CGF) proposed by Verma [2] is forecasting an increase of 0.42% per year for oil fields (onshore) between 1996 and 2030 for the US onshore fields:

with α=1.75752 and β=0.30050 and t in the 1-95 years range (CGF=1 for t=0). This reserve growth model has also been observed on West Siberia by Vermak and Ulmishek [1] with the parameters α=1.56639 and β=0.36060. I'm proposing to model reserve growth using also a linear filtering derived from the above parabolic curve, the filter is the following:

where t_Start is the initial reference year where we assume growth is taking place (i.e. the initial discovery number is including some reserve growth, up to t_Start years of equivalent growth). The growth filter impulsional response (6) is shown on Figure 5 for t_Start= 20 years.

Fig 5. Proposed linear filter response (bottom) for the modeling of reserve growth derived from the empirical CGF function (top).

Because reserve growth is generally occurring as soon as the first producing well is put in place, the reserve growth G(t) is the result of the convolution of h_Fallow,h_Build and h_Growth:

This linear filtering of initial discoveries simulates the response of the oil production infrastructure to new oil discovery D(t). Last time we have shown that this impulsional response was approximated by a Gamma function (in green on Figure 6) therefore simulating lagging effect and spreading reserve additions over time. The fourth convolution by h_Growth makes the impulsional response larger with a heavier tail as shown in blue on Figure 6. The area under the blue curve is greater than one hence simulating reserve growth.

Fig 6. Effect of the reserve growth on the system impulsional response. The Modified Arrington CGF is used here.
The tricky part is to find an appropriate value for t_Start. It seems logical that t_Start should depend on the discovery age because the discovery curve already includes an unknown amount of reserve growth. The chart below is taken from Robelius PhD thesis [3] and is showing how much reserve growth we have experienced in the 1994-2005 period and how it has affected the shape of the discovery curve. It's pretty obvious that reserve growth cannot be neglected and that a static view of oil production will underestimate future production levels. Also, there is no obvious correlation between discovery age and the amount of reserve growth. The amount of reserve growth between 1994 and 2005 is an astonishing 427 billion barrels (Gb). However, only 170-190 Gb seems to be genuine reserve growth (see Rembrandt post).


Fig 7. Global annual discoveries of both oil and condensate, as reported in
1994 and 2005, together with oil production in billion of barrels (Gb) The difference
reported discoveries is the reserve growth. Source: Based on data from IHS Energy,
ASPO and Oil & Gas Journal (from Robelius [3], page 71).
I assume the following model for t_Start:
where t_Ref is the reference year for which the backdated discovery curve has been issued and t_Init is a general offset that gives us a better control on the model. Below are a few claims that may help us calibrate our algorithm:

1. The USGS is forecasting 612 billion barrels (mean estimate) for conventional oil between 1996 and 2025.
2. Albrandt et al. (USGS) conclude that approximately 28% percent or 171 billion barrels of the forecasted 612 billion barrels for conventional oil had been added to the reserve pool between 1996 and 2003.
3. 2005 resource growth in pre-2005 discoveries was only 8 Gb.

In the simulations below, I made a few assumptions:

1. discovery data is the ASPO backdated (1932-2004) so t_Ref =2004.
2. pre-1932 total discovery is 30 Gb (mainly US)
3. post-2004 discovery forecast is based on a logistic decline.
4. lambda= 3 years.

	Peak	URR (Gb)	Total Reserve Growth	1996-2025 Reserve Growth	1996-2003 Reserve Growth	2005 Reserve Growth	Pre-2005 Reserve Growth
Logistic	2005 @ 25.2 Gb	2023	0	0	0	0	0
SHM	2005 @ 26.9 Gb	1962	0	0	0	0	0
Modified Arrington tInit= 1	2036 @ 36.8 Gb	3937	1976	704	146	21	427
Modified Arrington tInit= 15	2017 @ 28.7 Gb	2616	655	230.8	59	7.7	236
Modified Arrington tInit= 20	2015 @ 28.1 Gb	2500	538	191	50	6.5	204
West Siberia Growth tInit= 1	2018 @ 29.3 Gb	2603	640	293	40	10.6	144

Table I. Peak estimates for crude oil + condensate derived from various model. The logistic fit was obtained using the Hubbert Linearization technique (1983-2006).
The result of the SHM + Modified Arrington with t_Init=1 year (third row in Table I) is shown on Figure 8 and replicates closely the rosy USGS/DOE/CERA view of future production with a large amount of reserve growth to come. We can see clearly the effect of the relation (8) with a ramp up of reserve growth prior to 2004 and a huge input of reserve growth on new discoveries post 2004. The SHM + Modified Arrington with t_Init= 15 years and the SHM + Russian growth (t_Init=1) give similar results with a peak in 2017-2018 and a URR around 2.6 trillion barrels. For the peak date to be before 2015, reserve growth should be around 6-7 Gb for 2006 and decrease afterward.


Fig 8. World producion forecast (C&C) produced by the HSM assuming the modified Arrington model for the reserve growth .


Fig 9.World producion forecast (C&C) produced by the HSM assuming Verma's model (West Siberia) for the reserve growth.
On Figure 10, we can see that the HSM + Modified Arrington (in orange) fits the proven reserves from BP but does not fit with the recent record prices. The HSM + West Siberia CGF (in green) is closed to the corrected BP reserve numbers except since 2001. However the green curve is going down precisely when prices started their climb which makes me think that the proven reserve increases for 2001 and 2002 are probably bogus.


Fig 10. Reserves to production ratio values. Proven reserves are from BP. The corrected reserves account for anomalous Middle-East reserve revisions.
HSM (US) and HSM (West Siberai) are the production curve shown on Figures 8 and 9.

Conclusions

About the Hybrid Shock Model:

1. The interest of the Shock Model approach resides in its capacity to exploit the discovery data, the production profile and the reserve growth models.
2. The URR is not an output of the model as it is the case for the Hubbert Linearization but results directly from the discovery curves and the application of reserve growth models. The HSM is a nice way to inject prior information about the URR.
3. The method can potentially deal with difficult multi-modal production profiles such as Saudi Arabia.
4. The logistic case can be seen as a particular case of the HSM when the extraction of total resource (URR) is instantaneous.

About reserve growth:

1. Using a fourth convolution function derived from empirical Cumulative Growth Factors, I was able to derive an estimate close to the USGS forecast on reserve growth.
2. Note that we don't know how much true reserve growth is included in the 171 Gb figure. However, the reserve growth in 2005 for pre-2005 discoveries was only 8 Gb, it should have been 21.5 Gb (171/8). If we assume that 8 Gb per year is the true reserve annual addition we get 64 Gb for 1996-2003.
3. In my opinion, peak oil proponents should pay more attention to reserve growth issues. Very often, the argument is only focus on new discoveries but reserve growth is poorly understood and may have a significant contribution especially within a high oil prices environment.
4. Using the West Siberia reserve growth factor and a decreasing number of new discoveries, I estimate the peak to be at most in 2018 for conventional oil. The interesting thing is that it seems to match the 8 Gb in 2005. This result assumes that reserve growth related technologies will be applied aggressively and extensively. Also, the two CGFs that I used are for onshore fields and they are probably very different for offshore fields (new discoveries to come will be increasingly offshore). Therefore, I consider this result as being an upper bound on conventional production.
5. It will be very important to watch reserve growth estimates for the year 2006 in order to confirm (or infirm) a decrease in reserve growth that was observed in 2005 (8 Gb). In particular, a collapse in reserve growth (2-3 Gb) could indicate that the peak for crude oil + condensate is likely to be in 2005-2006.
6. It's important to note that the CGF model (5) is significantly different between large fields and small fields [1]. Because new discoveries are likely to be small fields, reserve growth post-2005 is likely to be smaller.

Of course, this is a work in progress and more tests are needed. The US, Norway and the UK should constitute a nice benchmark for the HSM (maybe in a part III). By the way, if anyone has discovery datasets, please contact me.

References:

[1] M. K. Verma and G. F. Ulmishek, Reserve growth in Oil fields of West Siberian Basin, Russia. Ulmishek of the United States Geological Survey. pdf.
[2] M.K. Verma, Modified Arrington Method for Calculating Reserve Growth—A New Model for United States Oil and Gas Fields, U.S. Geological Survey Bulletin 2172-D, pdf.
[3] Giant oil fields and their importance for future oil production, Fredrik Robelius, PhD Thesis.

The data for the world production of crude oil + condensate is composed of:

• 1900-1959: API Facts and Figures Centennial edition 1959.
• 1960-2006: EIA data (includes tar sands production from Canada and Venezuela).

Code:

The code is available in R language, the windows version of R software is available here. You will need to install the Matlab package (go in "Packages/Install Package(s)" then choose a CRAN site and select Matlab from the package list). To execute the program, open the file (ShockModel_Part2.txt) and click on "Edit/Run all".

Pickens Tells CNBC Oil Is Heading Higher

In an interview with On the Money's Melissa Francis, Pickens tells CNBC "I think the world is short of energy. There will be dips, but the trend is up." Pickens expects oil will "test the highs" around $78 before the end of the year. If there's a serious disruption of supply from Iran, "the sky's the limit." In late March, Pickens told us he expects oil will reach $75 per barrel before $55. The bottom line, according to Pickens, is that demand is growing as the global economy expands while the supply of oil remains fixed.
CNBC

The Shock Model: A Review (Part I)

This story was initially published on TheOilDrum.

WebHubbleTelescope, a long time TOD poster, has been one of the most active in the blogosphere in the area of oil production modeling. He has advocated a more physically based approach instead of a heuristic curve fitting approach such as the Hubbert Linearization. He proposed an original method, the so called Shock Model, that has a clear physical interpretation and that is making use of both the production profile and the discovery data. I think that a review of the Shock Model is long overdue.

I also propose three modifications or extensions:

1. Originally, the instantaneous extraction rate function E(t) has to be provided by the user. I propose a method to estimate E(t) directly from the observed production profile.
   
2. Reserve growth is modeled as a fourth convolution function based on an empirical parabolic cumulative growth functions (this will be detailed in part II).
3. A new way to project future extraction rate (in part II).

In summary, the shock model is a simple and intuitive model that is making use of both the production profile and the discovery curve. In this essay, the method is applied on the world conventional crude oil production (crude oil + condensate) and the ASPO backdated discovery data. Interestingly, the derived Reserve to Production ratio (R/P) seems to match the values obtained when using the proven reserve numbers (BP) once corrected for Middle-East spurious reserve revisions (in 1985, 1988 and 1990). In addition, R/P values are presently at a record low levels and below what have been observed during the previous oil shocks.

The code in R language is provided at the end of this post.

From Discovery to Reserves

The Shock Model is described in details on Mobjectivist. It is based on the following three equations:

where:

D	Discovery (backdated)
T	Instantaneous gross reserve addition
R	Oil reserves
E	Extraction rate or shock function
P	Annual oil production
hBuild,hFallow,hMature	Exponential functions

The first equation will create reserve additions from the initial discovery numbers (backdated). The second equation is the reserve evolution equation and dictates how reserves will change over time from the cumulative addition of new reserves and consumption. The three filtering functions involved in the computation of the instantaneous reserve addition T(t) are exponential distribution functions:

where λ is the mean time period implied by each process. The main reason behind this filtering process is to simulate the different steps (Fallow, Build and Maturation) involved in developing an oil field as shown on Figure 1.

Fig 1. Timeframe for the development of an oil field^1.

The result of the three convolution operations is equivalent to one convolution with a Gamma distribution:

On Figure 2, we can see an original discovery impulse is being spread out on a large time period.

Fig 2. The filtering operation by three successive Exponential
distributions is equivalent to a Gamma distribution

The convolution by a Gamma function will smooth and shift the original discovery curve by 2λ. Using the ASPO backdated discovery curve (Figure 3), we get a shifted and smooth reserve curve.

Fig 3. Effect of the succesive convolutions on the original backdated
discovery curves (ASPO) .

The question is how to choose a correct value for λ. WebHubbleTelescope is proposing λ=8 years. Higher values for λ will increase the degree of smoothing and the time lag between peak discovery and peak reserves. If you compare the simulated reserve curve with the actual proven reserve numbers (Figure 4), you can see that for λ=3 the reserve curve is matching closely the proven reserve numbers (once corrected for Middle-East increase).

Fig 4. Simulated oil reserves R for different values of λ.

Estimating the shock function

In reality, the extraction rate E is not a constant and presents large fluctuations especially during oil shocks. In WHT's original method, the shock function E(t) is set manually which is not an easy task in my opinion. I propose to estimate the shock function by literally tracking the instantaneous oil production using a bootstrap filter. I have previously applied this technique with good results. In a nutshell, the boostrap filter find the optimal value for E(t) at each time t by stochastically exploring a set of possible values and weighting them according to how well observed production levels are predicted (the technical term is Sequential Importance Resampling or SIR). A result of this approach for λ=3 years is shown on Figure 5, we can see how closely the production levels are reproduced by the model.

Fig 5. World production for crude oil + condensate².

The figure below is the estimated shock function E(t) with the proposed technique. The shock function is assumed constant in the future and equals to the last estimated value.

Fig 6. Shock function E(t) estimated by the bootstrap filter (with λ= 3 years).
Click to enlarge.

Forecasting ability

In my opinion, the model has limited predictive abilities because it cannot predict correctly future extraction rate values (i.e. the shock function E is unknown in the future). WHT has made the following prediction for crude oil + NGL.

Fig 7. World production forecast (crude oil + NGL) .

This forecast includes NGL production which I don't think is appropriate because the model is based on conventional oil discoveries. On Figure 8, we can see the application of the shock model for different time intervals assuming that the extraction rate remains constant in the future and equals to the last estimated value. Predicted production levels are falling immediately because E(t) should increase in order to maintain or increase production. In part II, I will propose a simple solution to deal with this problem.

Fig 8. Different world production forecasts for crude oil + condensate .

It's interesting to compare the inverse of the estimated extraction rate (1/E(t)), which is equal to the reserve to production ratio (R/P), and fluctuations in nominal crude oil prices:

1. When R/P values are below 30 years, prices are increasing. This was the case in 1970 at the time when demand for oil was very strong and energy efficiency not part of the vocabulary. A lowering of consumption after 1985 (mainly a combination of economic slowdown, energy conservation, drilling frenzy,...) has contributed in an increase in the R/P values.
2. The recent increase in price since 2003 corresponds also to current R/P values below the 30 years threshold minimum (27.6 years) observed in the 70s.
3. The ASPO discovery data is probably underestimating some significant amount of reserve growth that has occured in the late 90s hence the premature fall of the simulated R/P curve around 2000.
4. The R/P values given by the model are surprisingly closed to the R/P computed from proven reserves (BP) once corrected from large reserve additions in middle-east countries (red circles).
5. In order for the R/P value to come back above 30 years, either reserves need to increase significantly or crude oil consumption needs to decrease significantly.
   

Fig 9. Crude oil prices are inflation adjusted (nominal prices). The red dotted vertical line is the year
2007.

In Part II, I will look at the modelisation of reserve growth and a new way to improve the predictive ability of the shock model.

Footnotes:

¹Giant oil fields and their importance for future oil production, Fredrik Robelius, PhD Thesis.
²The data for the world production of crude oil + condensate is composed of:

• 1900-1959: API Facts and Figures Centennial edition 1959.
• 1960-2006: EIA data.

Code:

The code is available in R language, the windows version of R software is available here. You will need to install the Matlab package (go in "Packages/Install Package(s)" then choose a CRAN site and select Matlab from the package list). To execute the program, open the file given below and click on "Edit/Run all".

Code

The ELP Plan: Economize; Localize & Produce

By: Jeffrey J. Brown

In this article I will further expound on my reasoning behind the ELP plan, otherwise known as “Cut thy spending and get thee to the non-discretionary side of the economy.”

I have been advising for anyone who would listen to voluntarily cut back on their consumption, based on the premise that we were probably headed, in a post-Peak Oil environment, for a prolonged period of deflation in the auto/housing/finance sectors and inflation in food and energy prices.

To put our current rate of worldwide crude oil consumption in perspective, during George W. Bush’s first term, the world used about 10% of all crude oil that has been consumed to date, and based on our mathematical models, the world will use about 10% of our remaining conventional crude oil reserves during George W. Bush’s second term.

First, a discussion of our current economy.

The Current Economy, “The Iron Triangle” & The Mortgage Meltdown

Author Thom Hartmann, in his book, “The Last Hours of Ancient Sunlight,” described a high tech company that he consulted for that went through several rounds of start up financing, and then collapsed, without ever delivering a real product. At the peak of their activity, that had several employees and lavish office space--until they ran out of capital. His point was that this company was analogous to a large portion of the US economy, which has the appearance of considerable activity and uses vast amounts of energy, but how much of this economic activity delivers essential goods and services?

I have read, and it seems reasonable, that the majority of Americans live off the discretionary income of other Americans. We are therefore facing a wrenching transformation of the US economy--from an economy focused on meeting “wants” to an economy focused on meeting needs--and the jobs of a vast number of Americans are thereby directly threatened in a post-Peak Oil environment.

I have described three segments of what I call the Iron Triangle: (1) The auto/housing/finance group (the “Debt” group); (2) The mainstream media group (the “MSM” group) and (3) Some major oil companies, some major oil exporters and some energy analysts (the “Energy” Group).

The Debt Group wants Americans to keep buying and financing large SUV’s and houses. The MSM Group wants to keep selling advertising to the Debt Group. The Energy Group provides the intellectual ammunition for the Debt Group and the MSM Group, i.e., we have trillions and trillions of barrels of remaining oil reserves, and Peak Oil is something that we don’t have to worry about for decades.

Unfortunately, the net effect of the efforts of the Iron Triangle is to encourage Americans to continue buying and financing large SUV’s and houses at great distances from their jobs, because higher oil production, and thus lower fuel prices, are right around the corner.

The US Mortgage Meltdown was inevitable, but in my opinion, the trigger for the meltdown was the increase in oil prices in the second quarter of 2005. The US Personal Saving Rate metric is not perfect, but it is a consistent measurement, and in recent years it was positive--until the second quarter of 2005. It has been negative ever since the second quarter (April, May, June) of 2005 .

The average monthly Brent spot crude oil price, in the 20 months prior to May, 2005 (the middle of the second quarter) was $38 per barrel. The average price after May, 2005 has been about $62, within a range of $54 to $74. I believe that this increase in energy prices was the final straw that pushed many US households into a negative saving rate, triggering the current wave upon wave of foreclosures.

Daniel Yergin, chairman of Cambridge Energy Research Associates (CERA), in 2004 predicted that the long term oil price would be $38 per barrel, because rising crude oil production would force oil prices down in order to equalize supply and demand. In reality, flat to declining crude oil production since May, 2005 has forced prices up in order to equalize supply and demand.

Those who listened to the false promises of energy abundance made by CERA, et al, have had considerable reason to regret it.

What have I and others been advocating? Let’s start with Economize.

ELP: Economize

For some time, I have suggested a thought experiment. Assume that your income dropped by 50%. How would you change your lifestyle?

Many employees of Circuit City don’t have to imagine such a scenario. Many higher paid employees at Circuit City have been fired and then been told that they are welcome to apply for their old jobs, subject to about a 50% pay cut.

In my opinion, the unfortunate new reality is that we are going to see a growing labor surplus--against the backdrop of deflation in the auto/housing/finance sectors and inflation in food and energy prices. By reducing your expenses now, while you can do it voluntarily, you will at least be better prepared for whatever the future may bring.

A key way to Economize is to Localize.

ELP: Localize

I recommend that you try to reduce the distance between work and home to as close to zero as possible, and furthermore, that you live in smaller, much more energy efficient housing, preferably close to mass transit lines.

If you can walk or take mass transit to work, in many cases you can get by without a car, or least fewer cars--and save considerable amounts of money. Currently, it costs about $7,500 per year to drive the average late model US car about 15,000 miles per year. As gasoline prices increase, and as depreciation rates probably also increase, the cost per mile of driving cars will continue to increase.

I would further recommend that you integrate yourself into your local community. Get to know your neighbors. Become involved in local government, etc.

I would especially recommend support of local food producers, perhaps via Community Supported Agriculture, and support of local manufacturing and local businesses.

Finally, the Produce recommendation.

ELP: Produce

Jim Kunstler has suggested that we should not celebrate being largely a nation of consumers. I agree with Jim. We need to once again become a nation of producers. I recommend that you try to become, or work for, a provider of essential goods and services.

Key recommended sectors are obviously energy--conventional, non conventional and alternative energy production and energy conservation--as well as food production, especially local organic farming close to towns and cities.

Other sectors to consider are repair and maintenance, low cost energy efficient housing, low cost transportation, basic health care, etc.

The biggest risk to family finances is trying to maintain the SUV, suburban mortgage way of life in a period of contracting energy supplies. Beyond that, one of the next biggest risks in my opinion, is excessive and unwise spending--especially debt financed spending--on college education costs.

While we will desperately need engineers and many other technically qualified graduates, we are seeing wave upon wave of college graduates entering the work force with degrees that very poorly prepare them for work in a post-Peak Oil environment. We may ultimately see college graduates competing with illegal immigrants for agricultural jobs.

Perhaps the best education investment that many young people could make is a two year associate degree in some kind of repair/maintenance area, perhaps with summer jobs in the agricultural sector.

I would especially recommend that you consider buying, perhaps with a joint venture group, a small farm, either currently organic, or that can be converted to an organic farm. In the short term, if nothing else you could lease it out to an organic farmer. Longer term, you might consider building or moving a prefab, small energy efficient house to the farm. If nothing else, this plan may provide a place of work for your unemployed college graduate.

I think that “Tiny Houses” will become more popular, as larger homes are no longer viable. Where there are jobs nearby, many McMansions could be subdivided, but absent local job centers, I expect large swaths of American suburbia to be essentially abandoned. As Jim Kunstler warned, American suburbs represent the “Worst misallocation of capital in the history of the world.”

Very small (250 square feet or so), highly energy efficient, perhaps prefabricated housing makes a lot of sense, and this may become a growth sector.

I should confess that I in no way have a green thumb, but others certainly do, and there are some very encouraging case histories of Americans doing quite well with their own “Victory Gardens” so to speak, such as this case history: “Berkeley: Urban farmers produce nearly all their food with a sustainable garden in their backyard.”

How have people responded to these recommendations?

The Responses Thus Far

Two responses, from recent years, are illustrative.

First, the West Texan. After outlining my plan, a friend of mine from West Texas thought about it for a moment and then said, “But if we stop borrowing and spending, what will happen to the economy?”

Second, the Dallas socialite. Again after outlining my plan, this lady said, “You’re not from Dallas, are you?” I replied that I was not. To which she said, “No one raised in Dallas would ever talk about living below their means.”

So, living below one’s means, at least in years past, was somehow considered vaguely un-American and socially unacceptable.

However, recently people who have followed some version of the ELP plan, either because of my recommendations, or based on their own evaluation of the present environment, have had considerable reasons to be glad that they voluntarily downsized. So far, I have not heard any regrets from anyone who downsized.

Or, turn it around. Does anyone now wish that they had bought a large SUV and large suburban McMansion--all with 100% financing--on January 1, 2006?

Finally, if we are wrong about Peak Oil, and if you followed the ELP plan, you will have less--or no--debt, more money in the bank, and a lower stress way of life.

Please note that the next essay in this series probably won’t be posted until the week of April 16th. I will be doing ELP research, checking out post-Peak Oil locales.

Jeffrey J. Brown is an independent petroleum geologist in the Dallas, Texas area. His e-mail address is westexas@aol.com.