It's a very good idea!! Maybe I could start an Open Source project on sourceforge.net an put all the codes and dataset online!

any idea for a name? Open Peak Oil Project?

Any language preference! R looks good to me

I've seen a couple of mentions that field size has a Zipf distribution. FWIW...

I think it is pretty well agreed amoungst the folks who make a living at this (Pete Rose and Robert Megill come to mind) that field size distributions are fairly well described by a lognormal distribution. The thought being that anything that is a net result of multiplication of a number of random variables will result in an approximate lognormal distribution. Given all the other uncertainties probably not a bad approach.

Let me know if tables, graphs, source-code spreads, etc. need formatted display here. There might be a couple options in phpBB that allow things we haven't tried before.

thanks for asking! formatted display for tables and source-code spreads could be interesting.

I finally found some info in Simmons's book in the appendix B (p. 374 and 375):

the last line is taken from Figure B.1 p. 374 but there are no information on the time of discovery distribution. The production figures are for 2000.

Here the corresponding bar chart (without the small fields):


Figure 8

I appreciate your take and expertise on data regression. But like Rockdoc, I have heartburn with the logistic model for more fundamental reasons. Why should it work at all for this class of behaviors? When did the strong non-linear component of the basic "predator/prey" get applied to oil depletion?

I have been playing around a lot with the stochastic formulation staying away from the non-linear terms, apart from the forcing function which corresponds to newly tapped discoveries.

Symmetric Triangular discovery window


Welch (upside down parabola) discovery window -- "logistic"-like


Khebab has got some curves with some of the same characteristics, but again, I just do not like the fact that we are solving the Verhulst equation to provide the parametric fits.

I have time to work this out some more. I have a post up where I list the posts I have done in the last few months.

http://mobjectivist.blogspot.com/2005/1 ... posts.html

Recently, I have tried to account for the oil shocks and even more recently have tried to understand the mathematical and physical underpinnings of the logistic curve model. Like I said, a bit frustrating that last bit. On the other hand, a stochastic model works brilliantly to account for oil shocks, i.e. the dips in the production curves.

I'm looking forward to reading about it.

One suggestion: if you present results, make sure you specify what type of numbers you're using: conventional only, "all liquids", future non-conventional, which types, etc...

=====

I've commented in a couple of places about WHT's efforts - good stuff no doubt. (@TOD and Mobjectivist) I think WHT is totally right that relying on unsupported logistic models is VERY bad for PO.

I'll confess that I was initially interested in PO because I saw the numbers and the Hubbert analysis - and the Scientific American article. I read about Simmon's new book and his concerns about the Saudis. I assumed there was a sound mathematical economic or geologic model under it that led to the imminent PO conclusions. I went to find out more...

Note to the unwary: under NO circumstances should you confuse friend-of-the-White-House Matt Simmons with LATOC-paranoid Matt Savinar.

When I found out there really wasn't much of a model, I was a little dismayed. I expected a bunch of folks calling themselves "Environmental Economists" (e.g., Heinberg) to actually engage in some economics: price effects, elasticities, market behavior models, etc. I expected the geologists to have a bit more understanding of economics. Moreover, every time I tried to find something related to the subject, I'd find something that suggested that price was VERY important and that the PO folks were flat out wrong about a LOT of things...

So, in short, I think this is a great effort.

- Silent E, aka "Eroei van Tanstaafl" @ TOD

It looks like the last two lines are mucked up. The "total" production of 32,350 is actually a sum for the fields of size 100-1000+. It obviously does not include the larger number for fields size 0-100. Also, the "total" No. of Fields is 61 - but this is the same as the number of fields size 100-200. The total for 200-1000+ is 55, so if this "total" actually excludes the 0-100 size, it should still be 116. But the table could be incorrect in other ways as well (or instead).

Q1: what if you model future discoveries in the 100-300 range? Looks like there should be perhaps another 200-300 and perhaps 30+ more 100-200 size fields? We could compare to the 2000-2005 period to check the model?

Q2: what about discoveries under 100? They are more than half of the production, if the 36K is correct. We should expect that a growing percentage of production will come from the smaller fields in the future. What does that imply for a production forecast that ignores smaller fields?

Here's some thoughts from someone who doesn't understand half the words in this thread .

Are you guys intending to do a special statistical method for calculating the production increase and subsequent decline of fields with EOR techniques and waterflooding (based on fields that show this trend?)

If you need a data gatherer i am willing to spend some hours looking for production data.

Good suggestion, unfortunately available data on the web is not always that clear.

The logisitc is more an historical model and not a state of the art approach. Parameteric methods are usually the first think you try on a new problem because they are easy and quick (even Excel does it now). A lot of posters (WHT, EnergySpin, etc.) have many skills in advanced statistical techniques. Why not using this potential? I intend to put the code in an Open Source framework (R software) so that anybody can experiment and improve it!

That's the big challenge! how to include price fluctuations in a depletion model! the first to have proposed something is Guseo with the GBM model (Peak prediction based on the Riccati equation: 2007!).

It's my mistake, the original table from Simmons does not include the small fields (0 to 0.1 mbpd). I took it from another graph in his book (Figure B.1). Unfortunately, it does not give the distribution of discovery per decades.

This class of field is rather broad but I have no data to make it more refiend. In order to project future discoveries, I just took the last distribution of field size (for the 90s) and assumed that it was similar for the years 2000s and 2010s.

VERY GOOD QUESTION! I have no data on the distribution of the discovery for small fields (0-100 tbpd). Simmons is just saying that there are 4,000+ fields that have been discovered up to the year 2000 which are providing 53% of the world production!. That'it, that's all! In statistics, when we have no knowledge, you usually assume a uniform distribution which gives about 700 discoveries per decades. However, since discoveries have been declining since the 80s I assumed a uniform loss of 100 new fields per decades.




I'm thinking about it, but it's rather complex to model because it's a nonlinear behavior. Probably, some EOR techniques were not available before the 80s. I was thinking about adding a kind of shock function that could boost production past the peak.


YES, I need data! in particular on small fields (0-100 tbpd) which constitute the majority of the world production.

Khebab,
I checked on the Ricatti. It looks like you did an excellent job reproducing the math in some of your supporting posts. Weird how the URR seems really low (~1600) which causes the curve to do a nose-dive between 2010-2030. However, if you push up the URR to 2500, the curve flips upward crazily. You said something about adding another control function to suppress this, but this non-linear stuff sometimes has a mind of its own.



I don't have any non-linearities in my formulation apart from the forcing function (which relates to the discovery curve). This makes it well-behaved for all parameter inputs, so that you don't get those unexpected swings.

the 4039 curve (red line) is a plausible scenario if you believe the US-EIA and others who predict demand will continue to grow at a current rate, and we have the reserves to pump.

But, do you really want to be around between 2030 and 2040? The backside of this curve will be really painful.