American Oil and Gas Reporter - March 2016 - 72

SpecialReport: Unconventional Resource Science
FIGURE 3
Predicted versus Actual Production for 21 Input Wells (Left)
And Predicted Input and Blind Wells versus Actual Production (Right)

pletion interval and averaged for the next
stage of analysis.
The geoscience/engineering integration
included individually correlating each attribute with various production metrics
to determine the best response or predictor
variable for multivariate analysis. We
also evaluated how changing these production metrics over time affected the
correlations with each attribute. Based
on strong linear and nonlinear correlations,
three-month oil production scaled per
foot of completion length was selected
as the variable to predict. Using a nonlinear
multivariate regression method, we identified three primary performance indicators
out of a wide range of attributes, and
tested various transformations of each
one.
Key Performance Indicators
The most important KPIs driving the
statistical predictions of three-month oil
per foot, along with the reservoir properties
or conditions they represented, were:
* Root mean square curvature and
dip curvature, by themselves, were not
highly correlated with cumulative production. However, combined with other
variables, they made a significant contribution to well performance. This indicated
that structural features associated with
faulting and fracturing, seals and migration
pathways were impacting production.
* Relative amplitude change and 2472 THE AMERICAN OIL & GAS REPORTER

28 hertz spectral decomposition frequency
volumes, were used interchangeably, since
relative amplitude change had a high
linear relationship with midfrequency
volumes. The lower values of relative
amplitude change indicated an increase
in both water and oil production, which
was verified with hydrocarbon and water
saturation logs in several wells across
the field. Significantly higher fracture
porosity in these zones of higher fluid

saturation also was noticed.
* 10-14 Hz spectral decomposition
attributes correlated with both total organic
carbon and the brittle/ductile quality that
was calculated at several wells. Correlations of nine-month gas production versus
low-frequency volumes also supported
this relationship. As the 10-14 Hz component increased, so did both gas production and TOC.
Next, these top performance drivers
were modeled to production using the
optimal transformations for each, which
then were summed to form the production
prediction for various models. Using no
more than three seismic attributes for
each model, three unique 3-D models of
three-month oil per foot were generated.
The same extraction parameters were
used to extract the predictions along each
wellbore. The average correlation coefficient between actual versus predicted
production for the three models was 92
percent for the 21 input wells (left panel
in Figure 3).
To test how effectively the model
could predict cumulative production performance, the 25 producing wells that
had been excluded from the modeling
workflow were introduced. Adding actual
versus predicted production from 17 blind
horizontal wells and eight blind vertical
wells with single-zone completions to
the 21 input wells achieved a correlation
coefficient of 83 percent (right panel in
Figure 3). Looking only at the eight
vertical wells, the model showed a 90

FIGURE 4
Arbitrary Cross-Section through Six Vertical Wells
With Single-Zone Completions

American Oil and Gas Reporter - March 2016

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