American Oil and Gas Reporter - March 2016 - 73

SpecialReport: Unconventional Resource Science
percent correlation between predicted
and actual production. These high correlations demonstrate the predictive power
of multivariate statistical analysis and the
impact it can have on unconventional
field development.
Refrac Candidate Wells
The decision then was made to examine
the possibility of refracturing some or all
of the vertical wells in specific zones
where the model indicated high production
potential. This was accomplished by comparing the ratio of production from the
entire Wolfcamp zone with production
extracted at the actual perforation locations.
According to the analysis, three of the
eight vertical wells with single-zone completions had potential zones of high production not accessed by the original completions.
Figure 4 displays an arbitrary crosssection through six of the eight vertical
wells. The log displayed in color is the
extracted production model along the
vertical wells. The red disks show the
actual completion interval for these wells.
Wells 1, 3 and 4 (highlighted in yellow)
have the highest probability for refrac
success.
Looking at the models in 3-D showed
how much lateral variability existed between these wells in the target interval.
Figure 5 shows an arbitrary line through
vertical wells 1, 3 and 6 from Figure 4.
Note the lateral variations of the production prediction model. The seismic
in the background is co-rendered with
the fault probability, with the production
ribbon displayed in color over the reservoir
zone.
The best vertical producer in the survey
area-well 6 shown at the left in Figures
4 and 5-had 10,700 barrels of actual production over three months versus a predicted production of 10,900 barrels (98
percent accurate). Well 3 had 2,090 barrels
of actual versus 2,300 barrels of predicted
production.
Well 1 was one of the poorer performers. It produced 101 barrels, and its
predicted production was roughly the
same. However, wells 1 and 3 have the
highest probability for refracturing success
because the completions in both wells
missed the sweet spot revealed by multivariate analysis and predictive modeling.
Targeting these zones, each well should
be able to generate three-month production
values of about 10,000 barrels. It should

FIGURE 5
Arbitrary Line through Three Vertical Wells
Showing Lateral Variations in Predicted Production

be remembered that neither of these wells
was used in the initial modeling.
This Permian Basin case study clearly
demonstrates the potential value of using
WPPA workflows to improve well planning, optimize completions, and enhance
recovery, even without a full suite of
data.
The productivity of any well is a function of the interplay among many static
and dynamic characteristics that require
an integrated, multivariable solution. The
WPPA workflow represents a sophisticated
multidisciplinary integration of the geophysical, geological and engineering data.
By combining numerous diverse sets of
data, geoscientists and engineers can
identify the specific attributes essential
to hydrocarbon production in a particular
reservoir. Not only can they generate
quantitative estimates of the values that
impact production, but they also can identify the most prospective areas for drilling.
Properly applied, this workflow can reduce
drilling risk significantly and improve
drilling programs.
The Wolfcamp case study emphasizes
the value that multivariate statistical analysis of 3-D seismic, well and engineering
data can bring to unconventional reservoirs.
The 3-D seismic attributes applied in the
Permian Basin can predict well performance down to the stage level.
Similar results have been obtained in
successful projects in both conventional
and unconventional reservoirs across
North America. Results achieved in the
field have proven that WPPA models can
high-grade prospective drilling areas, en-

hance well planning and geosteering, optimize completion placement and frac
design, and boost the estimated ultimate
recoveries of individual wells.
❒
Editor's Note: The author acknowledges Global Geophysical colleagues Rohit Singh, a petroleum engineer, and
Leszek Bednarski, a petrophysicist/geologist, for their contributions to the Wolfcamp modeling project.

CHAD
BAILLIE

Chad Baillie is a lead reservoir
geophysicist at Global Geophysical
Services. Prior to joining the company
in 2012, he worked as a geophysicist
at Prism Seismic/Sigma3. Baillie has
extensive experience in interdisciplinary
reservoir characterization and interpretation, and his areas of expertise
include multivariate statistics by integrating 3-D seismic attributes, rock
mechanics, microseismic, engineering
and geological data to optimize completion and well performance. He
holds a B.S. in geophysics from the
Colorado School of Mines.
MARCH 2016 73

American Oil and Gas Reporter - March 2016

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