As a simple example, start with the ubiquitous temperature gradient measurement.  Often these measurements pose difficulties for developers because they have low Browness.  However, this example will illustrate how to apply BNT to optimize brownness for a simple data set. The great advantage for the application of BNT is that temperature gradients are nearly always evaluated with linear functions.

Consider the temperature profile at the right. This type of profile is found in many geothermal locations such as the Carson Sink in Nevada, Hawthorne, Nevada and near the Fallon Naval Air Station.  The measured data show a strong surface gradient followed by a lesser gradient to the bottom of the hole.  The trend line for the entire data set is relatively low Brownness but is in violation of Law II in the direction of extrapolation. The low brownness lines follow the lower gradient only.  Using this gradient does not produce sufficient temperature at the target depth of 4000 feet to justify a power plant.  However, by shifting the overall gradient by using a starting point at the surface of the mean surface temperature, the gradient is sufficiently improved to produce a higher expected temperature at the same level of Br.  The  gradient from the upper data only should not be used because it conflicts with measured data in the holes (Law II).

This example illustrates the usefulness of BNT.  The data are unaltered, yet the possible gain has been dramatically increased.  If the flow rate if a future well is assumed at 500 gpm, the result of using the 50% Br gradient is 75% more electrical production.  Clearly, this approach could substantially change the economic picture for the project as well as attract more investment capital.

Because the data set is small and calculations are limited, Oder is high.  Situations such as this one do not lend themselves to extensive calculations and thus cannot be made into OBNs.