Other Assessment Methods—An Overview
171
where t is equal to time, R w is the ultimate reserve, R t is the reserve at
time t, and b is the coeffi cient of exploration maturity.
The assumption is made that future additions of resources will
increase according to this equation (Eq. 7.23). Examples of the application of the regression method to resource evaluation are shown in
Figure 7.4, which demonstrates that these data sets can be approximated by the method. The advantages are that the method (1) fi ts data
acquired over a long time range, as well as aggregated data; and (2) it is
simple to apply. The disadvantages are (1) the statistical assumptions
required by the regression might not be valid for future prediction from
the current data, (2) the method is not suitable for predicting individual
pool sizes, and (3) results from this kind of assessment are inadequate
for economic study.
An example of this type of approach is found in Lee and Price
(1991). A prediction of total petroleum resources by extrapolation of
past exploration performance is a procedure commonly used in wellexplored basins (Bettini, 1987). Finding rates over a long period of
time are fi tted by curves, and the area under the curve is integrated and
interpreted as the ultimate reserve.
In addition, Dolton (1984) provides an example of this method by
fi tting the historical data from the Illinois Basin using both exponential
and hyperbolic curves, which yielded different estimates. The exponential curve indicated that there were 38 MMbbls of recoverable oil,
whereas the hyperbolic curve indicated that there were 115 MMbbls.
This demonstrates that different mathematical functions used in the
curve-fi tting process can yield different estimates.
The Fractal Method
Lee and Lee (1994) demonstrate that distributions of some objects can
be characterized by fractal properties. Fractal self-similarity is one of
such properties incorporated in the power function, y = x
b . This function is versatile and has been used to describe other types of distributions worldwide, such as continental populations, areas of a continent,
and river lengths.
Unlike Zipf’s law, the fractal method does not require a constant
ratio as stated in Equation 7.19, but does require knowledge of the size
of the largest member in the population. Examples from the Leduc–
Bashaw oil play and the Slave Point reef complexes–Cranberry gas
play (Table 7.4) demonstrate that the fractal method has merit. From
the predictions studied, we can assume that the few largest members
171
where t is equal to time, R w is the ultimate reserve, R t is the reserve at
time t, and b is the coeffi cient of exploration maturity.
The assumption is made that future additions of resources will
increase according to this equation (Eq. 7.23). Examples of the application of the regression method to resource evaluation are shown in
Figure 7.4, which demonstrates that these data sets can be approximated by the method. The advantages are that the method (1) fi ts data
acquired over a long time range, as well as aggregated data; and (2) it is
simple to apply. The disadvantages are (1) the statistical assumptions
required by the regression might not be valid for future prediction from
the current data, (2) the method is not suitable for predicting individual
pool sizes, and (3) results from this kind of assessment are inadequate
for economic study.
An example of this type of approach is found in Lee and Price
(1991). A prediction of total petroleum resources by extrapolation of
past exploration performance is a procedure commonly used in wellexplored basins (Bettini, 1987). Finding rates over a long period of
time are fi tted by curves, and the area under the curve is integrated and
interpreted as the ultimate reserve.
In addition, Dolton (1984) provides an example of this method by
fi tting the historical data from the Illinois Basin using both exponential
and hyperbolic curves, which yielded different estimates. The exponential curve indicated that there were 38 MMbbls of recoverable oil,
whereas the hyperbolic curve indicated that there were 115 MMbbls.
This demonstrates that different mathematical functions used in the
curve-fi tting process can yield different estimates.
The Fractal Method
Lee and Lee (1994) demonstrate that distributions of some objects can
be characterized by fractal properties. Fractal self-similarity is one of
such properties incorporated in the power function, y = x
b . This function is versatile and has been used to describe other types of distributions worldwide, such as continental populations, areas of a continent,
and river lengths.
Unlike Zipf’s law, the fractal method does not require a constant
ratio as stated in Equation 7.19, but does require knowledge of the size
of the largest member in the population. Examples from the Leduc–
Bashaw oil play and the Slave Point reef complexes–Cranberry gas
play (Table 7.4) demonstrate that the fractal method has merit. From
the predictions studied, we can assume that the few largest members