American Oil and Gas Reporter - March 2016 - 71

SpecialReport: Unconventional Resource Science
multivariate analysis and WPPA technologies, the integrated workflow was
applied to predict cumulative production
in the Wolfcamp formation in West Texas
using a unique dataset consisting of a
multiclient 3-D seismic survey and public
well and completion data. The objective
was to determine if the success of production models created with multivariate
analysis and WPPA for more detailed
projects elsewhere within the basin and
in other unconventional plays could be
reproduced using these data inputs.
The 3-D multiclient survey was acquired in 2012, targeting the Permian
section of the Wolfcamp. Figure 2 shows
the location of the University Lands 3-D
dataset and the major subdivisions of the
Permian Basin. The dataset covers 356
square miles in Reagan, Upton and Crockett counties, including the eastern edge
of the Central Basin Platform and part of
the geologically complex Ozona Arch
area.
The entire thickness of the Permian
section is roughly 4,000 feet, and is
shown in color in Figure 2 overlaid on

the seismic section. The survey was designed, based on a primary depth of
6,000-8,000 feet to allow proper imaging
of azimuthal anisotropy. This ensured
that stable amplitudes within the formation
were adequate for interpretation and integration. The survey's dense acquisition
geometry also resulted in an uplift in the
nominal fold, allowing an increase in the
signal-to-noise ratio and providing more
robust gathers for future inversion work.
The seismic attribute analysis began
with computing a suite of 45 post-stack
seismic attributes, which included a set
of incoherence volumes, several fault
probability volumes, 15 volumetric curvature attributes, several complex trace
attributes, and a set of spectral decomposition frequency volumes. All attributes
were analyzed over different intervals
within the Wolfcamp section for variations
in stratigraphy, thickness, faulting and
fracturing, fluid and lateral facies.
From an analytics standpoint, even
though the interpreter may be interested
only in the reservoir surrounding the producing wells, it is also important to un-

derstand how adjacent formations may
impact reservoir properties and conditions,
and how the seismic data may respond to
these changes. Therefore, all seismic attributes also were extracted and analyzed
over other horizons and intervals to see
how they varied laterally across the University Lands dataset.
The next step was integrating the seismic with the well data used for the modeling, which consisted of 21 horizontal
wells in the Wolfcamp with production
data and limited completion information.
In addition, 25 other producing wells
were left out of the analysis, reserving
them for blind testing to validate or revise
predications later. A single horizontal
section was created for the 21 input wells,
and defined both the completion interval
and the interval for the well-to-seismic
extractions.
Formation tops and the corresponding
time horizons were used with well velocities from two wells to convert all
seismic attributes and horizons to depth.
The depth-converted seismic attributes
then were sampled along the target com-



American Oil and Gas Reporter - March 2016

Table of Contents for the Digital Edition of American Oil and Gas Reporter - March 2016

Contents
American Oil and Gas Reporter - March 2016 - Cover1
American Oil and Gas Reporter - March 2016 - Cover2
American Oil and Gas Reporter - March 2016 - 3
American Oil and Gas Reporter - March 2016 - Contents
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