Evaluation Models
11
and Pine Point formations, are dolomitized and diagenetically altered,
resulting in enhanced reservoir development.
The Slave Point and Pine Point formations exhibit at least three types
of reef population (i.e., isolated reef, barrier reef, and platform reef).
The areal extent and net pay of these populations may be quite different. The effect of the geology on the accumulation of hydrocarbons
might also differ. Consequently, the Slave Point and Pine Point formations in northeastern British Columbia are divided into three plays with
respect to natural gas resource evaluation: the Yoyo isolated reef play,
the Clarke Lake barrier reef play, and the Adsett platform play.
The point to be emphasized here is that the fi rst step in any resource
evaluation is to identify properly the geological populations that will
serve as the framework for statistical evaluation. It is also important
to remember that a geological population is merely a working hypothesis that should be revised or redefi ned as new information becomes
available.
The next step in play identifi cation is to defi ne the minimum pool
size within a play at the time the assessments are performed. After the
minimum pool size is defi ned and the sample for the assessment has
been collected, the statistical models can predict the pool sizes within
the range represented by the sample with least uncertainty. Predictions
made beyond the sample bear larger uncertainty than those within
the sample range. This concept applies to all statistical estimation
methods.
It must be emphasized that the geological population adopted here is
a single and natural geological population—a play. On the other hand,
Drew (1990) adopted an entire basin truncated by depth boundaries.
The estimation method used for the pool-size distribution of a play
and of a basin should not be the same. This is discussed in Chapter 4.
What statistical and geological models entail and how they relate to
one another are topics of discussion in the following sections.
Statistical Models
Random variables of a geological model (e.g., net pay or porosity) can
be quantifi ed with a set of possible attainable values. If we take the
porosity values from a sandstone formation as an example, we fi nd that
some values occur more frequently than others. Thus, we can associate each porosity value with a real number or with a likelihood (the
likelihood that the value will occur—a large number for a likely
11
and Pine Point formations, are dolomitized and diagenetically altered,
resulting in enhanced reservoir development.
The Slave Point and Pine Point formations exhibit at least three types
of reef population (i.e., isolated reef, barrier reef, and platform reef).
The areal extent and net pay of these populations may be quite different. The effect of the geology on the accumulation of hydrocarbons
might also differ. Consequently, the Slave Point and Pine Point formations in northeastern British Columbia are divided into three plays with
respect to natural gas resource evaluation: the Yoyo isolated reef play,
the Clarke Lake barrier reef play, and the Adsett platform play.
The point to be emphasized here is that the fi rst step in any resource
evaluation is to identify properly the geological populations that will
serve as the framework for statistical evaluation. It is also important
to remember that a geological population is merely a working hypothesis that should be revised or redefi ned as new information becomes
available.
The next step in play identifi cation is to defi ne the minimum pool
size within a play at the time the assessments are performed. After the
minimum pool size is defi ned and the sample for the assessment has
been collected, the statistical models can predict the pool sizes within
the range represented by the sample with least uncertainty. Predictions
made beyond the sample bear larger uncertainty than those within
the sample range. This concept applies to all statistical estimation
methods.
It must be emphasized that the geological population adopted here is
a single and natural geological population—a play. On the other hand,
Drew (1990) adopted an entire basin truncated by depth boundaries.
The estimation method used for the pool-size distribution of a play
and of a basin should not be the same. This is discussed in Chapter 4.
What statistical and geological models entail and how they relate to
one another are topics of discussion in the following sections.
Statistical Models
Random variables of a geological model (e.g., net pay or porosity) can
be quantifi ed with a set of possible attainable values. If we take the
porosity values from a sandstone formation as an example, we fi nd that
some values occur more frequently than others. Thus, we can associate each porosity value with a real number or with a likelihood (the
likelihood that the value will occur—a large number for a likely