American Oil and Gas Reporter - December 2016 - 58

SpecialReport: Well Stimulation & Completion Technology
points with the highest potential, which
is indicated clearly by the yellow-colored
zones of higher production attribute values
in Figure 5. In this comparison, production
results from the earth model-optimized
location far exceed those of the standard
wellbore.
Over time, it was evident that including
completion design variables and examining
interwellbore distances both vertically
and horizontally allowed a higher degree
of calibration. Since 2015, Laredo has
been transitioning from a standardized
slickwater frac design to one that includes
higher proppant concentrations and tighter
perforation cluster spacing.
Figure 7 shows results from 21 wells
(11 Upper Wolfcamp, nine Middle Wolfcamp, and one Cline Shale) where completions with higher concentrations of
sand in combination with optimized landing points from the earth model demonstrate impressive early production behavior,
relative to published corporate type curves.
Production is scaled to a 10,000-foot
EUR type curve with nonproducing days
removed.

cesses using multivariate analytics and a
high-quality database established, Laredo
realized the opposite could be done. That
is, geological variables could be normalized using x and y coordinates and gamma
ray logs to model production behavior as
a result of the variance of engineering
and well design parameters.
A multivariate model was built for
six-month cumulative oil production with
97 wells, including x-coordinate, y-coordinate, wellbore completion length, and
the toe-up/toe-down factor (a derivative
of average wellbore inclination), along
with four other engineering input variables.
This model resulted in a correlation coefficient of 0.80.
Particular variables showed strong
ranked correlation coefficients and were
examined individually. The remaining
variables were held at mean values, and
a Monte Carlo simulation was run on
variables of interest.
When this was conducted on completed
wellbore length, no distinct degradation
was observed as wellbore lengths increased
from 7,500 to 10,000 feet. Being able to
recognize and understand this finding is
critical in optimizing development. Furthermore, in analyzing the toe-up/toedown factor, a negative result is attained
anytime the average wellbore inclination
is essentially off the horizontal.
Rapid, fact-based results then can be
reviewed in a multidisciplinary sense to

Engineering And Well Design
The basic concept of Laredo's initial
approach was to normalize engineering
variables during the multivariate workflow
to model the changeability of reservoir
quality, and geomechanical and natural
fracturing attributes. With growing sucFIGURE 7

Production Performance Utilizing Earth Model
In Combination with Higher Proppant Volumes
UWC

MWC
250

200
150

1.1 MMBOE
Type Curve

100
(11 Wells Avg. 1,595 #/ft Sand)
~129% of Type Curve

Cum. Production (MBOE)

250

150
1.0 MMBOE
Type Curve

100

(9 Wells Avg. 1,720 #/ft Sand)
~136% of Type Curve

50
0

0
0

120

180

240

300

360

120

180

240

300

Producing Days

Cline

250
Cum. Production (MBOE)

200

200
150

Consistent outperfomance of
average type curve across all
zones

1.0 MMBOE
Type Curve

100

(1 Well 1,635 #/ft Sand)
~128% of Type Curve

50
0
0

120

180
240
Producing Days

300

360

58 THE AMERICAN OIL & GAS REPORTER

360

determine appropriate actions. It is important to realize that multivariate analytics
is essentially the first step in recognizing
relationships. Considerable additional
studies based on scientific first principles
then can ensue to examine and study the
relationship in question. Results gained
from multivariate statistics alone are not
necessarily cause to immediately change
the status quo.
Fundamental Limitations
While multivariate statistics continue
to gain popularity for unlocking complex
relationships within unconventional tight
oil plays, there are fundamental limitations
intrinsically present in this approach.
Given that the methodology is a statistical forecasting technique, both Type
I and Type II errors are likely present
within the 3-D production attribute, which
can be easily misinterpreted. In short,
zones of both low and high potential illustrated by the production attribute can
be misleading, particularly when one is
testing new zones or stepping out long
distances from control points.
Early analysis of the multivariate relationship has revealed a good correlation,
yet there is still divergence from the absolute prediction. Furthermore, additional
field-level influences also can influence
look-back accuracy. Therefore, carefully
acknowledging specific test wells and
excluding those as necessary is an important step.
While operators of tight oil plays may
seek a simplified silver bullet answer to
what is fundamentally controlling production, the data, empirical observations,
and resulting interpretations suggest it is
a combination of variables that matter in
this instance.
So far, Laredo's 3-D production attribute has been implemented to plan and
drill 75 horizontal wells with a high correlation coefficient.
Keeping an open mind is crucial when
conducting this type of workflow, since
additional primary and secondary variables
continue to emerge as production drivers.
Building and maintaining a database that
includes log, seismic, completion, production, well design and drilling variables
is a crucial step toward unlocking hidden
production-driving mechanisms. Without
a diligent and deliberate focus of manpower

American Oil and Gas Reporter - December 2016

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