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Tuesday, February 4, 2014
Oil and Gas: How Little Is Left
“If we’re doing things like fracking, it just shows how little is left of all this stuff, and how desperate we are to get at it.” — Anonymous
Global production of conventional oil is past its peak and is now beginning its decline. A mixed bag of unconventional fuels (shale oil, tar-sands oil, natural-gas-liquids, etc.) is keeping the total on a slight rise or a rough plateau.
The hottest discussion in the US over the last few years has involved the fracturing (“fracking”) of shale to extract both oil and gas, but production by this method is already slowing or in decline. The costs of fracking are considerable, and so is the environmental damage.
The price of oil is still about $100 a barrel, far above that of the 1990s, in terms of both nominal and real dollars. The failure of the price to go down is an embarrassment to those who think unconventional oil is really solving any problems. But the high price is due not just to increased demand or to geopolitical risk. It is because of trying to squeeze oil out of places where it makes little sense to be squeezing.
The following data are “annual” and “global” and are from BP’s 2013 report unless described otherwise.
Laherrère: “The plots of these data start flattening in 2005, followed by a bumpy plateau. The post-2010 increase is mainly caused by the increase of liquids from US shale gas and US shale oil.”
Hughes: “. . . Politicians and industry leaders alike now hail ‘one hundred years of gas’ and anticipate the U.S. regaining its crown as the world’s foremost oil producer. . . . The much-heralded reduction of oil imports in the past few years has in fact been just as much a story of reduced consumption, primarily related to the Great Recession, as it has been a story of increased production.”
RATE OF SUPPLY; NET ENERGY
Hughes: “The metric most commonly cited to suggest a new age of fossil fuels is the estimate of in situ unconventional resources and the purported fraction that can be recovered. These estimates are then divided by current consumption rates to produce many decades or centuries of future consumption. In fact, two other metrics are critically important in determining the viability of an energy resource:
“• The rate of energy supply — that is, the rate at which the resource can be produced. A large in situ resource does society little good if it cannot be produced consistently and in large enough quantities. . . . Tar sands . . . have yielded production of less than two percent of world oil requirements.
“• The net energy yield of the resource. . . . The net energy . . . of unconventional resources is generally much lower than for conventional resources. . . .”
GLOBAL OIL PRODUCTION
For conventional oil, the peak annual global production was about 27 billion barrels, or about 73 million barrels per day. The peak date of production was about 2010.
BP shows global oil production still increasing in 2012, although much more slowly than before — an annual increase of about 1 percent between 2002 and 2012, as opposed to about 9 percent annually between 1930 and 2001. Laherrère’s Figure 10, on the other hand, shows an actual peak at 2010. The difference is due to the fact that the BP figures include unconventional oil (shale oil, tar-sands oil, natural-gas-liquids, etc.).
According to most studies, the likely average rate of decline of oil production after the peak date is about 3 or 4 percent, resulting in a fall from peak production to half that amount about 20 years after the peak. However, there is also evidence (Höök et al., June 2009; Simmons, 2006) to suggest that the decline rate might be closer to 6 percent, i.e. reaching the halfway point about 10 years after the peak.
Per capita, the peak date of oil production was 1979, when there were 5.5 barrels of oil per person annually, as opposed to 4.4 in 2012.
Laherrère: “The confidential technical data on [mean values of proven + probable reserves] is only available from expensive and very large scout databases. . . .
“There is a huge difference between the political/financial proved reserves [so-called], and the confidential technical [proven + probable] reserves. Most economists do not believe in peak oil. They rely only on the proved reserves coming from [the Oil and Gas Journal, the US Energy Information Administration], BP and OPEC data, which are wrong; they have no access to the confidential technical data. . . .
“The last [International Energy Agency] forecasts report an increase in oil production from 2012 to 2018 of 8% for Non-OPEC (+30% for the US) and of 7% for OPEC, which is doubtful. . . .”
US OIL PRODUCTION peaked in 1970 at 9,637 thousand barrels daily, declined in 2008 to 5,000, and rose in 2013 to 6,488.
NATURAL GAS PRODUCTION
GLOBAL GAS PRODUCTION rose from 2,524 billion cubic meters in 2002 to 3,370 billion cubic meters (95 trillion cubic feet) in 2012, an average annual increase of 3%.
Laherrère: “. . . [Global] production will peak around 2020 at more than [100 trillion cubic feet per year].” [emphasis added]
“Outside the US, the potential of shale gas is very uncertain because the ‘Not In My Back Yard’ effect is much stronger when the gas belongs to the country and not to the landowners. . . . Up to now, there is no example of economical shale gas production outside the US. The hype on shale gas will probably fall like the hype on bio-fuels a few years ago. . . .
US GAS PRODUCTION rose from 536 billion cubic meters in 2002 to 681 in 2012, an average annual increase of 2.5%.
Laherrère: “Natural gas production in the US, which peaked in 1970 like oil, is showing a sharp increase since 2005 because of shale gas. In 2011 unconventional gas production ([coal bed methane], tight gas and shale gas . . . .) was higher than conventional gas production . . . .
“This . . . leads to a peak in 2020 at 22 [trillion cubic feet] and the decline thereafter of all natural gas in the US . . . should be quite sharp. [emphasis added] The goal of exporting US liquefied natural gas seems to be based on very optimistic views. . . .
“The gross monthly natural gas production in the US has been flat since October of 2011, after its sharp increase since 2003, with only shale gas production rising. . . .” [emphasis added]
“Some claim that the US can export its shale gas as [liquid natural gas] even though conventional gas . . . is declining fast and will be quite small in just a few years.”
Hughes: “Shale gas production has grown explosively to account for nearly 40 percent of U.S. natural gas production; nevertheless production has been on a plateau since December 2011. . . . The very high decline rates of shale gas wells require continuous inputs of capital — estimated at $42 billion per year. . . . In comparison, the value of shale gas produced in 2012 was just $32.5 billion.”
TIGHT OIL (SHALE OIL) PRODUCTION
Laherrère: “Shale oil is now called light tight oil because the production in Bakken is not from a shale reservoir, but a sandy dolomite reservoir between two shale formations. . . . In Montana, production from Bakken is mainly coming from the stratigraphic field called Elm Coulee, which is decline since 2008. In North Dakota, production from Bakken has sharply increased.”
Hughes: “Tight oil production has grown impressively and now makes up about 20 percent of U.S. oil production. . . .More than 80 percent of tight oil production is from two unique plays: the Bakken in North Dakota and Montana and the Eagle Ford in southern Texas. . . . Tight oil plays are characterized by high decline rates. . . . Tight oil production is projected to grow substantially from current levels to a peak in 2017. . . . [emphasis added]
TAR-SANDS OIL PRODUCTION
Hughes: “Tar sands oil is primarily imported to the U.S. from Canada. . . It is low-net-energy oil, requiring very high levels of capital inputs (with some estimates of over $100 per barrel required for mining with upgrading in Canada). . . . The economics of much of the vast purported remaining extractable resources are increasingly questionable. . . .
NATURAL GAS PLANT LIQUIDS (NGPL) PRODUCTION
Laherrère: “World NGPL production . . . may peak in 2030 at over 11 [million barrels per day]. . . .”
Hughes: “Other unconventional fossil fuel resources, such as oil shale [kerogen], coalbed methane, gas hydrates, and Arctic oil and gas — as well as technologies like coal- and gas-to-liquids, and in situ coal gasification — are also sometimes proclaimed to be the next great energy hope. But each of these is likely to be a small player. . . .
“Deepwater oil and gas production . . . would expand access to only relatively minor additional resources.”
Laherrère: “Peak oil deniers claim that peak oil is an unscientific theory, ignoring that peak oil has actually happened in several countries like France, UK, Norway. They confuse proved reserves with the [proven + probable] mean reserves. . . . It seems that world oil (all liquids) production will peak before 2020. . . The dream of the US becoming independent seems to be based on resources, but not on reserves.”
REFERENCES AND FURTHER READING
BP. (2013). Global statistical review of world energy. Retrieved fromhttp://www.bp.com/statisticalreview
Heinberg, R. (2013). Snake oil: How fracking’s false promise of plenty imperils our future. Santa Rosa, California: Post Carbon Institute.
Höök, M., Hirsch, R., & Aleklett, K. (2009, June). Giant oil field decline rates and their influence on world oil production. Energy Policy, Volume 37, Issue 6, pp. 2262-72. Retrieved fromhttp://dx.doi.org/10.1016/j.enpol.2009.02.020
Hughes, J. D. (2013, Feb.) Drill, baby, drill; Can unconventional fuels usher in a new era of energy abundance? Executive Summary. Post Carbon Institute. Retrieved fromhttp://www.postcarbon.org/reports/DBD-report-FINAL.pdf
Klare, M.T. (2012).The race for what’s left: The scramble for the world’s last resources. New York: Picador.
Laherrère, J. H. (2013, July 16). World oil and gas production forecasts up to 2100. The Oil Drum. Retrieved from www.theoildrum.com/node/10009
Simmons, M. R. (2006). Twilight in the desert: The coming Saudi oil shock and the world economy. Hoboken, New Jersey: John Wiley & Sons.
The Snow Avalanche Image
During the good times the snow falls and slowly builds up. Without anyone noticing, the snow reaches a pre-collapse state. It is at this time that avalanches are born. The impossible becomes the inevitable.
The Arab Spring is an example of the snow avalanche concept as applied to societies.
Tarek al-Tayeb Mohamed Bouazizi was a Tunisian street vendor who set himself on fire on 17 December 2010, in protest of the confiscation of his wares and the harassment and humiliation that he reported was inflicted on him by a municipal official and her aides. His act became a catalyst for the Tunisian Revolution and the wider Arab Spring, inciting demonstrations and riots throughout Tunisia in protest of social and political issues in the country. Source: Wikipedia.
How is it possible that a Tunisian fruit vendor could bring down governments through one act of defiance? It simply is not possible unless the countries involved were already in a pre-collapse state. The snow was ready to avalanche and just needed a trigger. The fruit vendor provided the trigger.
The Avalanche Concept Applied to Societies
Stability is not your friend. Controlled instability is your friend. Compare democracies and dictatorships: One has controlled instability – elections, and the other has only stability. The dictatorship model is more stable, until there is a revolution and everything breaks. Democracies avoid the revolutions by voting out the bums. Systems with controlled instabilities avoid the big avalanches.
While democracies use controlled instability to avoid revolutions, the same is not true in economics. Typically democratic governments suppress recessions in order to get reelected. This suppression process seeks to enhance economic stability. The elimination of controlled instability in economics pushes societies to the point of economic avalanche – a depression.
When is an avalanche likely?
First rule, moving from a stable state to avalanche state takes time. Time of stability is the most important factor in determining when the next avalanche will occur. Looking back in history will give us an idea of how long it takes before things break. For the US, that time is 80 to 100 years since the beginning of the last crisis. The last crisis period ran from 1925 to 1945. The next crisis period runs from 2005 to 2025. These periods are based on the research by two historians as told in The Fourth Turning.
Second rule, problems or cracks start to appear in society after a long period of time. Experts start to warn about instabilities or dangers on the horizon. Societies become more sensitive, and there are protests and/or riots.
Third rule, there must be a triggering event. However, this event does not need to be big as we saw with the Tunisian fruit vendor. Causality is not linear. Linear causality is where small things can only have a small impact.
Avalanches, forest fires, economic crashes and wars work the same way. They all follow the same mathematical distribution in terms of collapses – the power law distribution. Who cares? Keep reading as I apply these concepts.
Mathematics of Collapse
Take a look at this little video about the mathematics of war.
Take a look at the graphs in the video. These are the same graphs as forest fires. Notice how the frequency (y-axis) versus size (x-axis) graph follows a straight line for both attacks in war and forest fires. Wars in total also follow the same graph.
The next graph shows attack frequency versus the size of the attack in the Iraq war.
The following graphs show forest fire frequency versus size of the fire.
The graphs between the Iraq war and forest fires look kind of similar, don’t they? They tend to form a straight line. Why is that?
Societies and forests move into the future in the same way. Each new day is heavily influenced by the past. And that is a positive feedback loop process. That feedback loop process causes collapses to be similar in both sets of graphs with both having a power-law distribution of collapses. You can treat societies and forests the same way in terms of collapses. If you suppress small collapses then you will get bigger collapses. If you suppress bigger collapses then you will get the mother of all collapses. If you suppress that collapse then you will sit on the edge of a cliff forever, or until you allow the collapse to happen. The probability of an extreme collapse (the black swan) is 10 to 20 times greater than you think. A society or forest becomes susceptible to a black swan (catastrophic fire, depression, major war, …) after a long period of stability. Use history to determine what “long period” means. For a snow avalanche, long period may mean months. For a forest or society, long period may mean 50 years or 100 years.
If societies follow a positive feedback loop process, then so do economies. Economic stability (suppressing collapses) leads to catastrophe. That’s why Japan has been stuck in the mud for the last 20 years. The West is now stuck with Japan at the edge of a cliff waiting for something to push them over.
Why is stability a bad thing?
During the good times, the bad stuff (bad ideas, bad decisions and corruption) grows along with the good. Small collapses help to eliminate some of the bad stuff before it gets too big. Suppressing all collapses means the bad stuff grows so big that only a huge crash will fix the problems. No crash equals no solution.
How can we tell when the bad stuff has become a real problem? In the next paragraph see how scientists figured out how to discover the rot developing in growing sandpiles until there was a complete collapse.
“To find out why [such unpredictability] should show up in their sandpile game, Bak and colleagues next played a trick with their computer. Imagine peering down on the pile from above, and coloring it in according to its steepness. Where it is relatively flat and stable, color it green; where steep and, in avalanche terms, ‘ready to go,’ color it red. What do you see? They found that at the outset the pile looked mostly green, but that, as the pile grew, the green became infiltrated with ever more red. With more grains, the scattering of red danger spots grew until a dense skeleton of instability ran through the pile. Here then was a clue to its peculiar behavior: a grain falling on a red spot can, by domino-like action, cause sliding at other nearby red spots. If the red network was sparse, and all trouble spots were well isolated one from the other, then a single grain could have only limited repercussions. But when the red spots come to riddle the pile, the consequences of the next grain become fiendishly unpredictable. It might trigger only a few tumblings, or it might instead set off a cataclysmic chain reaction involving millions. The sandpile seemed to have configured itself into a hypersensitive and peculiarly unstable condition in which the next falling grain could trigger a response of any size whatsoever.”
Without color-coding it’s a lot harder to see the rot. We have to rely on clues. Extreme problems in one or more areas of society after a long period of stability probably indicate that that society is in trouble. 9/11 was one clue. The financial collapse in 2008 was another clue. There is rot in our military. The US nuclear arsenal has been gutted. So America appears to be in trouble at this time.
About the Power Law Distribution
Find out a little more about the power law distribution. Did you ever wonder where the 80-20 rule comes from? Please meet the power law distribution.
The power law – sometimes referred to as the Pareto distribution, Zipf’s law, or the 80-20 rule – has drawn a great deal of attention lately as an alternative to the ‘normal’ (Gaussian) distribution (i.e, the bell curve). The power law has gained in popularity among more numerate intellectuals, policy makers, and business people because it seems to fit better with common sense than what we were told in Statistics 101: Extreme and rare events have a greater than expected impact; a few products, people, and websites seem to have the bulk of market share, wealth, and mindshare; etc.
The power law distribution doesn’t fit everything which means outliers exist. However, it does a much better job than the normal distribution. For our purposes of trying to understand the real world better, the power law distribution provides a good foundation.
Collapse Framework for Societies
What follows is a framework for viewing collapses in society – economic collapse or war/revolution. I have essentially summarized the concepts I covered above.
1. Societies follow a positive feedback loop process. Each new day is heavily dependent on the past. This is similar to forests and sandpiles. The process never stops and collapses are impossible to prevent. One may only transform the size and timing of collapses.
2. Positive feedback loop processes are subject to self-organizing criticality. They will automatically move toward a pre-collapse state, then just collapse.
3. Collapses follow a power-law distribution. Outliers exist.
4. All collapses are the same. There is no difference (other than size) between a small collapse and a big collapse. Big collapses require longer to form and happen less often.
5. Collapse transformation: Collapse suppression will delay a collapse and make the resulting collapse bigger. Suppress small collapses and you will get bigger collapses. Suppress bigger collapses and you will get the mother of all collapses. Suppress that and you will sit on the edge of a cliff forever waiting to fall or be pushed over the edge. Think Japan.
6. Collapses are caused by the build-up of bad ideas, bad decisions and corruption. These things can spread to all corners of society.
7. War or revolution is just a collapse like an economic collapse. Only the form is different.
8. Collapse suppression leaves the original problems (bad ideas, bad decisions and corruption) in place giving them the ability to continue growing.
9. A collapse in one area could mean problems in other areas as well. For example, 9/11 could mean more than a terrorism problem. It could represent a sign of spreading problems into all corners of society.
10. The longer the time of stability means the longer (and bigger) the problems can grow. Time of relative stability is the most important criteria in determining when a large collapse is possible. History helps us determine which time frames are important.
11. When a system has reached a point where a small event can have a large impact then it is at a pre-collapse state or tipping point. Causality is not linear.
12. Big collapses (the outliers) may represent phase transitions where everything you know changes.
13. Black swans are outliers in a normal distribution which cause a phase transition.
14. Dragon-kings are outliers in a power-law distribution which cause a phase transition.
15. Examples of systems with a power-law distribution (outliers allowed): Forest fires, sandpile collapses, snow pile avalanches, earth quakes, financial market collapses, wealth, city size, serial killers, riots, attacks within war and wars.
16. In financial mathematics, the use of the normal distribution is forbidden. It assumes behavior is independent. In a crisis or collapse, market behavior is not independent as people start herding. Naturally this means in reality all financial mathematics uses the normal distribution. Were you wondering why these models blow up?
17. How to build a better economic model. The key here is to harness collapses.