Oil depletion is seriously all around us. Not many of us have much talent with technical analysis and so we rely on experts for a lot of what we can act as if we understand. Depletion is one of those things that people can grasp for a second but then life continues on around them and it drifts to the background. We don't have much of a choice any longer; FWIW I have always advocated arguments based on the knowledge that lurks right beneath the surface.
I spent quite a bit of time writing this book, based on over 5 years of blog posts on http://mobjectivist.blogspot.com and http://TheOilDrum.com. I haven't written much since because I wanted to let the document sink in for awhile.
This is a good spot to pull together a few other thoughts.
The oil geologists (including Hubbert) misinterpreted the origin of the Hubbert curve. It doesn't operate as a single rate that applies to depleting a geological volume but a dispersion of rates amongst several operating regions that aggregate to model the curve.
If we segregate the rates into intervals the curves look like the following, with the aggregate in this case following the derivative of the logistic sigmoid:
Figure 1 : Dispersive discovery split into rate intervals.
This is essentially the way that discoveries accumulate, with the most technologically advanced regions (such as the USA) exploiting the dispersed regions first, and then the slower regions following.
Dispersion creates the long fat-tails and if we want to make the fat-tails thinner we aggressively exploit (both in search and extraction) of the resources. If we don't apply this accelerated pressure, natural dispersion will reveal these tails.
I find it useful to have a few non-heuristic models that explain the behaviors collectively. Geologists and earth science types have never seen the elegance of this interpretation. That describes the book "The Oil ConunDrum" in a nutshell.
Topic #2
Lots of interesting statistical data show increases in USA oil production the last two years. From a chart on the TOD post here it says that 181,000 active wells existed in Texas in 2005. This table from the EIA http://www.eia.doe.gov/... gives the distribution of production rates from existing oil rigs in the entire USA. The total number is 363,459, and integrating the rates for 2009 gives a total production of 4.7 million barrels per day for 2009. The other EIA data from http://www.eia.gov/... gives 5.361 million barrels per day. The missing oil from the smaller number results from crude oil collected from wells classified as mainly producing natural gas. You can see that on the EIA table as we find the extra 660 thousand barrels coming from something other than a tally of oil-specific wells.
From the previous year (2008) we find about a 400,000 barrels per day increase. Trying to correlate this with the oil well data, 300,000 barrels per day difference comes from an extra 17 high rate wells that produce on average 18,000 barrels per day. These form the top of the end producers from the histogram below:
Figure 2 : Producing well distribution in the USA for the year 2009.
All the rest of the wells constitute so-called stripper wells because they average less than 10 barrels a day. It looks like this historically for the entire USA
Figure 3 : Historical USA reservoir size fit to a dispersive aggregation model (described in "The Oil ConunDrum").
The two curves have the same basis and relate just by the assumption of an average drawdown. When you start with a distribution of reservoirs initially and a given drawdown rate, then some years later the same distribution would remain as an invariant. The same relative rates would remain while all the reservoirs shrink in size proportionately. That is the beauty of a proportional drawdown model and why should never get too greedy on extracting the oil -- as a natural proportional rate exists that the reservoirs can maintain without overdrawing.
As a bottom-line, the weird uptick in USA crude production from 2008 to 2009 arises due to an extra 17 wells that started producing likely in the Gulf. (These aren't Bakken oil fields as these wouldn't be individually large producers)
2005 |
2006 |
2007 |
2008 |
2009 |
2010
|
5,178 |
5,102 |
5,064 |
4,950 |
5,361 |
5,512 |
(in thousands of barrels/day)
So the fluctuations in the data accounted from just the high end producers. The number of high-rate wells more than doubled from 2008 to 2009 going from 13 to 30. Reservoir size distributions are very fat tailed so anything added to the mix will show up in the bottom-line numbers.
2009 data
production rate bracket |
#wells |
%wells |
Total annual |
%production |
Rate/well (bpd)
|
> 12800 |
30 |
0.0 |
176.5 MB |
10.7 |
18,162.2 |
2008 data
production rate bracket |
#wells |
%wells |
Total annual |
%production |
Rate/well (bpd)
|
> 12800 |
13 |
0.0 |
60.8 MB |
4.0 |
15,461.2 |
Again, this increase likely constitutes all the Gulf of Mexico (GoM) wells. Look at the %production and one can see how important these act in temporarily slowing the decline. They also have a higher extraction rate; I plotted this curve last year from data off the historical (back to 1975) Maximum Production Rate numbers from the former MMS (
http://www.gomr.boemre.gov/...). This is a tough site to get data from as the data is embedded in PDF's.
Notice that I plot this in terms of a
maximum production rate and the median of 200 barrels per day reflects the fact that companies will extract from a GoM well if it can provide a high initial return. Still it shows how few wells produce at over 10,000 barrels/day (maybe 3% of them). Of course, although all these off-shore wells can produce at a high rate, they do not last too long and the operators tend to maximize the depletion so they don't have to pay for long-term maintenance. The on-shore strippers go for a long time, but the volume does not exist. The new Bakken fields occupy the worst of both worlds: they don't last long and they don't have great rates individually (same goes for most fracture fields).
This analysis works out nicely because all the models fit together like interlocking jigsaw puzzle pieces and if we find one one piece missing you can reconstruct the missing data from the pieces around it. What makes it difficult for most analysts to deal with is appreciating the fact that the jigsaw pieces are statistical in nature and the fitting is done in terms of probability distributions of various observables.
Topic #3
The JODI/EIA divergence, started by Ron (known as Darwinian) at TheOilDrum.com who noticed that EIA data collected primarily from the (possibly nefarious) private consultants at CERA differed from the JODI database which consists of a consortium of oil producers who voluntarily contribute to the set. Sam Foucher tried to compare these more directly by using some of the EIA data to fill in the missing gaps in the JODI database.
I took a different tact and started to deconstruct the individual countries with this chart.
Figure 4: Yearly JODI data is interspersed with the more frequent EIA data with countries stacked as layers. By looking carefully at the layers you can see where the data diverges.
The bottom set tends to balance out then we see:
1. Canada is high in EIA compared to JODI
2. Angola is high in EIA compared to JODI
3. Algeria is high in EIA compared to JODI
Then you look at 2008 and you notice similar erratic deviations in the country-by-country, yet they all seem to balance out in the end.
The following countries account for 2 of the 2.25 total difference in the year 2010.
|
JODI |
EIA |
%Change |
Change |
Saudi Arabia |
8.16 |
8.9 |
9 |
0.74 |
Canada |
2.03 |
2.73 |
34.34 |
0.7 |
Venezuala |
2.78 |
2.15 |
-22.7 |
-0.63 |
Iran |
3.54 |
4.08 |
15.14 |
0.54 |
Algeria |
1.2 |
1.73 |
43.58 |
0.52 |
Russia |
10.17 |
9.67 |
-4.89 |
-0.5 |
Qatar |
0.73 |
1.13 |
53.66 |
0.39 |
Angola |
1.7 |
1.94 |
14.13 |
0.24
|
Saudi Arabia and Russia weren't off a lot percentage wise but they tended to balance each other out, so it was hard to sense the shift in the stacked bar chart. That looks like the full story; just a few countries account for most of the difference between JODI and EIA totals.
For much of the data between 2005 and 2009, the JODI and EIA (besides lining up) have similar fine structure in their totals. This still happens even though many of the individual countries don't match, by quite a large margin! This normally can't happen by random chance, as countries that go + / - will leave lots of fluctuation noise in their wake. See the figure below for the part I am talking about.
I would call this basic forensic statistics and I use the same kind of sleuthing as the government to determine if you cheat on your income taxes. Some of the similarities arise because the reconstruction of JODI uses some of the EIA data, but the main + / - deviations come from JODI and reflect as variable noise at the top. Strong seasonal variations or economic shocks could propagate through every country. Or it could arise from strong variations from a single country that drawn out the noise from the smaller countries.
Apparently the US government has started to shut down their collection and interpretation of international oil statistics -- this just means we will have to go with JODI and fill in the blanks on our own in the future.
Final topic
I love taking on the professional oil guys on TOD. Today one said
my industry is constantly turning the other cheek and as a consequence the public is very uneducated about oil and natural gas,
We remain uneducated about oil and gas because the oil and gas industry has never sponsored a comprehensive study of fossil fuel resource limitations. They must not have a conscience. I grew up understanding the electronics industry and science by watching all those educational films sponsored through Bell Labs.
http://www.youtube.com/...
That's right 1958, Dr. Frank C. Baxter of Bell Labs talking about climate change.
There are a bunch of ways to engender antagonism by talking tech. Here is my list:
- You can antagonize a few of the Luddites who hate even the thought of seeing math in some analysis. Math=bad and a sign of incipient technology.
- You can antagonize some of the science fiction types who have flighty ideas just by keeping the ideas grounded in physics and applying some critical thinking. Corollary, be careful about saying anything bad about Azimuth or any other SciFi author.
- You don't get doomers on your side if you don't write anything about doom and try to remain objective based on what you discover.
- You can antagonize people that don't believe in modeling and think data drives everything. They twist Box's quote of "All models are wrong, some are useful" into a meaning completely divorced from his original context.
- You can antagonize the in-crowd of geologists and earth science types by encroaching on their territory and calling them on something.
- You can antagonize people by challenging commenters who spout some nonsense.
- You can antagonize the occasional cornucopians who show up. Big whoop.
- You can antagonize a few people by using some big words and potentially talk over their heads. They think we need to raise Carl Sagan from the dead to do the interpretation.
- You can antagonize people by annoying repetition. Counter: that's how Faux News operates
- You can antagonize people by appearing to possess some intelligence, who then sense that you have a superiority complex.
- You can antagonize people by writing in an active voice, which often gets misinterpreted as an egocentric form of conversation. See #10
- You can antagonize people that don't like lists by putting a dozen things together in a list
Above all else consider this quote from the ace journalist George Monbiot:
"Tell people something they know already and they will thank you for it. Tell them something new and they will hate you for it."