more is better
The Shock Model: A Review (Part I)
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.
Just noticed this post by TOD contributor Khebab, and I am glad to see more perspectives on production modeling brought into the foreground at a site which for all intents and purposes is the hub of peak oil blogs and discussion forums. Deffeyes linearization has a certain home spun charm but I think we all own a protractor.
The issues around peak oil are interesting and important. I do what I do with the expectation that everyone engaged in this topic do the best they can, in the manner they are accustomed, be they scientist or artist to acknowledge and integrate this crisis in a positive way into our daily lives.
The work is noticed. And the notice of the work gets noticed, as when one A.M. Samsam Bakhtiari presents a paper where he states:
“Having seen the results of Prof. Guseo's GBM model, it became clear that the modeling phase of 'Peak Oil' had come to an abrupt close and that henceforward 'Peak Modeling'
should be shelved once and for all. Some experts still seem unconvinced as they continue to compare and weigh results generated by all types of available models --- as, for example, 'The Oil Drum' [3] and 'TrendLines' [4] websites.”
I’ll bet Prof. Guseo is a swell guy but just because the globe appears to have edged beyond the production peak doesn’t mean critical analysis stops.
So cheers to work completed, cheers for more to come, and cheers for open and transparent disagreements. The water level in the pool rises and everyone smartens up. Even us so-called doomer types.
1 Comments:
The GBM model looks like an extension of the Logistic. Unfortunately instead of a couple of parameters that have no meaning, it has about twice as many fudge factors.
I had seen this paper before, thanks for bringing it up again.
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