Page 106 - Well Logging and Formation Evaluation

96 Well Logging and Formation Evaluation

Exercise 5.4. Thermal Decay Neutron Example

Consider a formation with the following properties:
S sh = 25c.u.
S matrix = 8c.u.
S w = 60c.u.
S o = 8c.u.
f= 0.25
V sh = 0.2
S measured by the tool is 15.

1. What is S w?
2. Suppose that the value of S sh can only be estimated to an accuracy of
±5. What is the uncertainty in S w resulting from this?

5.8 ERROR ANALYSES

In an ideal world, the net/gross, porosity, and saturation would be accu-
rately known in all parts of the reservoir. In practice, one is trying to deter-
mine the properties based on measurements performed in a number of
wells in the field, each subject to measurement error. Hence it is impor-
tant to realize that there are two completely different and independent
sources of error in petrophysical properties across a field. Firstly, there are
errors arising from tool accuracy, sampling, and the petrophysical model,
which will affect zonal averages as measured in individual wells.
Secondly, there are errors arising from the fact that these properties are
only “sampled” at discrete points in the field. Whether or not properties
such as porosity and net/gross are mapped over the structure, or if the well
data are used to make an estimate of the mean values, the result is uncer-
tainty, which in some cases can be huge.
We will first deal with errors in the zonal average properties as
measured in a particular well. I believe the most rigorous way of dealing
with measurement error is through the use of Monte Carlo analysis. This
method has the advantage of not requiring any difficult mathematics and
is easily implemented in a spreadsheet. In this example, we will attempt
to estimate the error in the average properties for a simple sand that is
assumed to follow an Archie model. The basic principle is that instead of
choosing point values for all the input parameters, we will allow them all