I used a simple model, not differentiating between proven and possible reserves, so the analysis would also be simpler. Maybe my analysis could also be applied to your reserve function, but the Shock model described in your link looks a lot more complicated than just using R(t)= CumulativeDiscovery(t) - Scaled_and_shifted_CumulativeDiscovery(t).

Are there models where possible reserves either change to proven reserves or disappear, possibly randomly?

Hi Dudley,

For U.S. oil, the simplest model for the EIA reserves is just 10 times the production in that year. That number is within 20% of the actual reserves for the last 50 years.

For coal, the pattern is different. Countries typically carry unrealistically large reserves, until someone in a resources agency decides that there are no good prospects for new mines. Then the reserves to collapse to the coal that is accessible at working mines. For example, German bituminous coal reserves were reported as 23Gt in the 2001 World Energy Council Survey, but in the next Survey in 2004, they dropped to 183Mt.

Dave

That's interesting. So for oil R/P is about 10 but for coal R/P jumps around. Does it jump around some median value in a white noise or Gaussian kind of way. In Deffeyes' book he uses anthracite coal (I think) as an example which does follow a Hubbert-type curve.

Hi Dudley,

Here is PA anthracite, with the cumulative and the gaussian fits. The reserves are indicated by circles. The reserves are too high, and they do not come down until too late.

Dave

DAve