American Oil and Gas Reporter - March 2016 - 69

SpecialReport: Unconventional Resource Science

Modeling Optimizes Asset Performance
By Chad Baillie
MISSOURI CITY, TX.-As more well
and completion data have become available
in unconventional U.S. oil and gas plays,
geoscientists and engineers have sought
to understand exactly which reservoir
properties have the greatest impact on
production performance in each formation.
Over time, operators also have come to
depend on seismic data both to map
overall structure and to delineate reservoir
properties away from well control.
However, it has become painfully clear
that strong well performance cannot be
correlated with individual reservoir properties in unconventional fields. Instead, a
unique combination of various properties
and conditions controls the productivity
of any particular well or reservoir. With
so many post-stack and prestack seismic
attributes available today (not to mention
well logs and engineering information),
operators are finding it increasingly difficult to sort through and quantify their
meanings, relationships, and relative contributions to reservoir behavior and production results.
Given that there are simply too many
static and dynamic variables and potential
outcomes for traditional analysis, interpretation and modeling, seismically-constrained multivariate statistical analysis
with integrated geoscience and engineering
data can provide far more reliable predictive models of cumulative production.
A case study from the Wolfcamp Shale
play in the Permian Basin shows how
operators can apply these models to optimize drilling, completion design and
execution, identify refracturing candidates,
and ultimately improve recovery in unconventional wells and fields.
The strength of multivariate statistics
lies in its ability to whittle down a potential
list of more than 100 attributes from 3-D
seismic and well data, and to identify the
top five or so attributes that have the
greatest impact on well performance. The
attributes that contribute to cumulative
production are known as the well's key
performance indicators (KPIs). Typical
KPIs include structural or geometric attributes, attributes related to seismic frequency or amplitude, mechanical and
elastic properties, and even completion
or engineering attributes such as proppant
mass and fluid volume.

KPIs that emerge from multivariate
analysis are correlated with other well
log, core and completion data to determine
which reservoir properties or conditions
they may represent. Once these indicators
are defined and understood, they can be
integrated into a single 3-D predictive
model of production.
Integrated Modeling Workflow
Figure 1 shows a generic overview of
a process dubbed well prospectivity and
productivity analysis (WPPA), in which
multivariate statistical analysis (orange
box in the lower right) plays such a
critical role in integrating geological and
geophysical data (green boxes) with engineering data (yellow boxes).
The first step is to load and quality
control all the available well information
needed for production modeling. Primary
well data typically consists of well logs
for geologic interpretation and petrophysical analysis, wells with velocity control
for seismic interpretation and velocity
modeling, and horizontal wells with production and completion data. Following
the QC (quality control) of the well in-

formation, a rigorous interpretation of
the formation tops is performed, making
sure the markers are consistently picked
at each well.
After defining reservoir zones and surrounding formations, the seismic is tied
to the wells using available sonic information. If sonic data are not available,
either sonic or density can be estimated
from the petrophysics. Obtaining accurate
well-ties is essential to further analysis,
since any errors that occur at this stage
will propagate, causing significant problems later in the workflow.
Following well-ties and error analysis,
formation tops, seismic time interpretations, and well velocity control are integrated to build the velocity model. In
parallel, a large number of both poststack and prestack seismic attributes are
computed. Finally, the integrated velocity
model is used to convert all seismic interpretations and attributes from time to
depth.
The first step on the engineering side
of the workflow is to load and QC all relevant deviation surveys, perforation locations, stages, completion and production

FIGURE 1
Generic Well Prospectivity and Productivity Analysis Workflow
HTI Processing
for Azimuthal
Anisotropy
Well Information
Logs Tops,
Culture Data

Import/QC
G&G Data

Ambient Seismic
(TFI™ and
Semblance)

Well Log
Preparation and
Petrophysics

Seismic
Interpretation and
Well Integration

Formation Top
Lithology/Facies
Estimation

Prestack Elastic
Inversion for
Rock Properties

Velocity Modeling

Generate Post-Stack
Attributes

Deviation Surveys, Perf
Locations, Stage Data,
Production Data and
Completion Data

Import/OC
Engineering Data

Depth Conversion

Geology & Geophysics Data
Engineering Data

Evaluation and Model
Validation

Updated Model
with New Wells

Geoscience Integration

Extract Seismic
Attributes and Assign to
Wellbore/Stage Intervals

Correlate Seismic
Extractions to Production
and Engineering Data

Create Wellbore and
Stage Intervals
Correct for Variability
in Completions
Create Production
Metrics

Multicollinearity Analysis

Build 3-D Production
Prediction Models
Create Prospectivity
and Probability Maps

Multivariate Statistical
Analysis

Engineering
"Best Practices"
Drilling Optimization
Analysis

Well Completion Analysis

MARCH 2016 69
American Oil and Gas Reporter - March 2016

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