The Hubbert Linearization is a way to plot production data to estimate two important parameters of a Hubbert curve; the logistic growth rate and the quantity of the resource that will be ultimately recovered. The Hubbert curve is the first derivative of a Logistic function, which has been used in modeling depletion of crude oil, predicting the Hubbert peak, population growth predictions[1] and the depletion of finite mineral resources[2]. The technique was introduced by Marion King Hubbert in his 1982 review paper [3]. The geologist Kenneth S. Deffeyes has recently applied this technique to make a prediction about the peak production of conventional oil [4].
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The first step of the Hubbert linearization consists of plotting the production data (P) as a fraction of the cumulative production (Q) on the vertical axis and the cumulative production on the horizontal axis. This representation exploits the linear property of the logistic differential equation:
where K and URR are the logistic growth rate and the Ultimate Recoverable Resource respectively. We can rewrite (1) as the following:
The above relation is a line equation in the P/Q versus Q plane. Consequently, a linear regression on the data points gives us an estimate of the slope and intercept from which we can derive the Hubbert curve parameters:
The chart on the right gives an example of the application of the Hubbert Linearization technique in the case of the US Lower-48 oil production. The fit of a line using the data points from 1956 to 2005 (in green) gives a URR of 199 Gb and a logistic growth rate of 6%.
The Hubbert linearization principle can be extended to the second derivatives[5] by computing the derivative of (2):
the left term is often called the decline rate.
This representation was proposed by Roberto Canogar[6] and applied to the oil depletion problem: