April 2015
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Drillinginfo Graded Acreage Philosophy
Understanding Productive Quality of Reservoir Zones
in Unconventional Hydrocarbon Plays
subsurface composition. However, while the underlying
Summary
geology is critical to production outcomes, drilling and
As unconventional oil and gas play development con-
completion operations may also significantly influ-
tinues to expand across the United States, these plays
ence production, independent of the reservoir quality.
demonstrate significant heterogeneity between the
If an operator uses poor techniques or the wrong ap-
sweet spots and the fringe areas. Drawing on a rich data-
proach to drilling and completing a well, the well may
set of geologic parameters and production outcomes,
experience low production even if it is located in a high
Drillinginfo’s Geology and Analytics teams perform
quality area of the reservoir. Therefore, operators and
comprehensive subsurface analyses and advanced
investors must closely study these two primary drivers
statistical modeling to qualify the productivity of the res-
of productivity, the geologic variables and the opera-
ervoir rock and assign grades
tional variables, and determine
to the acreage for each pro-
the independent effects of each
ductive interval within a play.
The graded acreage provides
a guideline for strategic decisions around acquiring, consolidating and drilling lease
positions. In addition, graded
acreage enables benchmarking of operator performance
and
completion
techniques
“...operators and investors
must closely study these
two drivers of productivity,
the geologic variables and
the operational variables,
and determine the independent effects of each one
on production.”
one on production.
Drillinginfo’s Geology and Analytics teams solve this problem
by creating a Graded Acreage
map that defines the quality
of production for the reservoir zone independent of the
way the wells were (or will be)
within the context of similar res-
drilled and completed. Drilling-
ervoir quality.
info’s teams isolate the influence of the geologic variables by fixing the operational
Introduction
variables as though all wells were drilled and completed the same way. The resulting predicted production
As oil and gas producers evaluate assets, they need to
thus reflects only the variation in geologic conditions1,
identify the most productive drilling locations indicated
by the reservoir quality. Much time and money is
invested in developing a thorough understanding of the
1 The absolute values of the predicted production will be a
function of the fixed operational values chosen for the model, but
the relative values of the predicted production remain the same.
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enabling the Drillinginfo teams to quantify the relation-
variables (independent variables) to known produc-
ship and develop maps grading the acreage quality of a
tion (dependent variables). The Graded Acreage solu-
reservoir at a one square mile resolution.
tion utilizes the statistical relationship between input
variables and known production to predict production
across the play at one square mile resolution, and then
Methodology
categorizes results into letter grades A through J (“A” is
The Drillinginfo Geology and Analytics teams use
highest production and “J” is lowest).
established interpolation methods, combined with
multivariate statistical modeling and data valida-
Figure 1 shows the six steps to the grading solution,
tion techniques, to relate geologic and operational
which are described in more detail below.
DI Geology
Steps 1 - 3
1. Identify candidate data sources
- Production metrics
- Completion data
- Geologic data
2. Interpret geology at target and bounding
formations
DI Analytics
Steps 4 - 6
4. Build statistical model to predict production
--Production
Control for metrics
variation in completion methods
--Completion
data
Control for over-fitting
- Geologic data
5. Test and validate model
- Perform out of sample validations
- Refine LAS data
- Identify stratigraphic framework
3. Interpolate geology data and assign data to
producing wells
- Thin-plate spline, b-spline, kriging and
inverse distance weighting
6. Assign grades
- Bin predicted production
- Categorize into grades at one square mile
resolution
Figure 1: Graded Acreage Workflow
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(1) Identify Candidate Data Sources
only lateral lengths and azimuth, yet these serve as
For a given play, two datasets are required for the
reliable predictors of production outcomes and the model
analysis: production and geologic wells. Production
generates robust results because operational practices
wells provide gas and oil production volumes and op-
are more uniform. In the Bakken, lateral lengths are
erational variables as reported to state regulatory
clustered at 5,000 and 10,000 feet, and therefore the sta-
agencies. Geologic wells contribute information mea-
tistical modeling utilizes additional operational variables
sured via well logs submitted to state regulatory agen-
to accurately assess production, including proppant
cies. Drillinginfo digitizes the well logs into log ASCII
type and volume, number of stages, fluid type and
standard (LAS) files. Further details about the datasets
volume, and azimuths.
are as follows:
(2) Interpret Geology
• Geologic wells: The datasets include approxi-
From the LAS files, the Drillinginfo Geology team interprets
mately 2,200, 1,070 and 1,950 LAS files for the Bak-
geologic parameters at the target and bounding formations:
ken, Barnett and Eagleford, respectively.
average and standard deviation resistivity, formation depth,
• Production wells: The datasets include horizontal,
formation thickness, rock properties, organic context, and
vertical and directional wells that have at least
other variables. As a specific example, the team interprets
three months of production reported from the
the following geologic parameters for the Eagleford shale:
pertinent reservoirs. In states like Texas, where
production is reported at the lease level, the
• Formation depth for Austin Chalk, Upper
wells are required to be one well on a lease to
Eagleford, Lower Eagleford, Buda and Edwards.
avoid concerns regarding allocated production
The formation surfaces are chosen using
methodologies. Well coverage includes 10,000
sequence stratigraphic techniques.
wells in the Bakken with first production as of January 2004 or later, 18,000 wells in the Barnett with
first production as of January 2004 or later, and
12,000 wells in the Eagleford with first production
as of January 2007 or later. Approximately 80% of
the production wells in each area are used for the
regression modeling, and the remaining wells are
used to cross-validate the model results.
• Formation thickness for the five formations
mentioned above.
• Gamma ray average and standard deviation
across the Upper and Lower Eagleford.
• Net feet having gamma ray greater than 75 for the
Upper and Lower Eagleford.
• Average and standard deviation resistivity measured by Deep Induction Log (ILD), neutron porosity (NPHI), and bulk density (RHOB) for the Upper
The operational variables employed in the model depend
and Lower Eagleford formations.
on the play and the variance of drilling and completion
techniques within a play. The predictive statistical mod-
The geologic parameters (g1, …, gN) are determined
eling for the Eagleford and the Barnett incorporates
for each well (well 1, well 2,…, well 3) at the target
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Well
Pmax
g1 (x,y,z)
g 2 (x,y,z)
...
g N (x,y,z)
e 1 (x,y,z)
e 2 (x,y,z)
...
e N (x,y,z)
(4) Build Multivariate Statistical Model
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The production wells contribute produc-
2
tion volumes that are used to compute
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the production measure, or dependent
variable, P, of a location of a possible
Select subset of wells
Remaining wells are out of
sample and used for testing
and validation
well. The Production Measure is any
specified function of a well’s production characteristics, such as initial rate,
maximum monthly rate, six month cu-
Perform regression using
b-spline
mulative production, etc. The Analytics team uses the maximum monthly
Optimize regression
variables
production, which typically occurs in
the second month of a well’s produc-
Resulting Model
Output P (predicted
production measure)
Figure 2: Regression Methodology
tion, as the predicted production measure. The model is run for three distinct
maximum rates: oil, gas and barrel of oil equivalent (BOE) using a
and bounding formations within the area of interest.
20:1 ratio of gas to oil. For BOE, Pmax
Once the geologic parameters are compiled, they un-
is defined as follows: Pmax = Omax + 20*Gmax, where
dergo statistical data exploration processes including
Omax and Gmax are, respectively, the maximum monthly
analysis of data quality, histograms, and outliers.
oil and gas production in any month for a well since the
well began producing.
(3) Interpolate Geologic Parameters
Using the geologic parameters described above for
each well, the Geology team interpolates geologic data
using thin-plate spline and B-spline approximations,
kriging and inverse distance weighting. This process
generates a set of functions Fg1(x,y,z), Fg2(x,y,z), …,
FgN(x,y,z) which estimates the geologic values across
geographic locations. In addition, the team employs
an algorithmic process to define where the formations pinch out, and ensure that physical constraints of
Using the geologic parameters described in section
(3) above, the Analytics team builds a robust advanced
linear regression model using b-spline basis functions2
to relate the Production Measure, P, of each production
well to the set of estimated geologic parameters.
The regression model also includes variables related
to operational data, such as lateral length, azimuth and
well spacing. Figure 2 illustrates the regression inputs,
methodology and outputs.
geological surfaces are honored (i.e., one geologic
surface never crosses another geologic surface
although two surfaces may touch).
2 This process is documented in Hastie, T.J. (1992) Generalized
Additive Models, Chapter 7 of Statistical Models in S eds J.M.
Chambers and T.J. Hastie, Wadsworth & Brooks/Cole.
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In total, the model tests over 100 variables, however,
process can be accurately graded to predict production,
in order to prevent over-fitting of the model, it is
and that the model does not over-fit the data.
desirable to limit the regression terms. The Analytics
team employs an algorithmic variable optimization
(6) Assign Grades
process to determine which variables to include. The
Once the production measure is generated for each
approach4 is as follows:
well location, the model estimates production on a
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one square mile resolution. Within a square mile, the
• Identify the single regression term that created
the highest r-squared in a linear model;
geologic parameters are relatively constant, so these
parameters are input into the statistical model to
• Identify the next variable which, when combined
generate an expected peak month production value
with the first variable, created the highest 2-
for that cell. Then the Analytics team uses equal width
variable r-squared linear model; and
binning5 to generate eight bins for the production wells,
• Continue this process until approximately ten
variables are chosen.
each one designated by a letter “grade”, A through J.
A corresponds to the highest production values, and J
corresponds to lowest production. These grades can
Once the variables are determined, the predictive model
be color-coded to create maps of shale plays, like the
generates the estimated maximum monthly production
Eagleford map in Figure 3 below.
for each location. An (x,y) well location is input into the
model, the process estimates geologic parameters using the interpolation functions described above (Fg1(x,y,z), Fg2(x,y,z), …, FgN(x,y,z), and then the advanced linear model estimates maximum production while fixing
operational variables to assume that all wells are drilled
and completed in an identical fashion.
(5) Test and Validate Model
The Analytics team cross-validates the model by running
it on the out of sample wells to compare how well the
predicted production matches actual production. Testing
results verify that wells excluded from the model creation
3 This algorithmic strategy is discussed in Das et al., “Algorithms
for Subset Selection in Linear Regression,” 40th ACM International
Symposium on Theory of Computing (STOC ’08), May 17-20, 2008,
pp. 45-54.
4 The optimization process includes advanced machine learning
techniques such as discriminant analysis, principal component
analysis, and forward/backward regression.
Figure 3: Graded Acreage Map of the Eagleford Shale Play
5 Equal width binning divides the data results into k intervals
of equal size. The width of intervals is: w = (max-min)/k and the
interval boundaries are min+w, min+2w, …, min+(k-1)w.
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With a grid of graded cells, a user can grade anything
By incorporating graded acreage into the Analytics
geographically related to the grid. For example, within
products, geology teams are able to evaluate assets by
the Analytics products, a permit to drill a new well is
reviewing an intuitive “heat map” to distinguish core
assigned a grade based on the grid cell correspond-
versus non-core holdings, rank the relative quality of
ing to the surface hole location of the proposed well.
each, and identify where the greatest potential value
Similarly, the Analytics team grades leased miner-
exists. In addition, categorizing acreage by reservoir
al tracts based on the grid cell corresponding to the
quality provides business development teams and fi-
center of the lease polygon.
nancial investors with a refined benchmarking process
to identify both outstanding and marginal performers, as
Application
well as the ability to quantify the impact of different operational practices (e.g., spacing, proppant, stages, and
Drillinginfo’s Graded Acreage identifies the locations of
lateral lengths) on production.
the most productive drilling opportunities. The methodology encompasses thorough understanding of subsurface composition, operational practices and well production, in order to generate a predictive, multivariate
Learn more at www.drillinginfo.com
statistical model that isolates the impact of reservoir
quality on production while controlling for operational
practices.
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APPENDICES
A. Map Leasehold Positions by Grade in the Bakken
Focus your acreage search by zooming
into a specific grade to view leasehold by
operator assignment.
Here we see lease polygons in a portion of
grade A acreage.
The next step is to filter the leases by
expiring and active flags to help identify
open acreage.
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B. Map Rig Locations by Grade in the Eagleford
Snapshot of rig locations on April 13, 2015
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C. Analyze Operator Lateral Lengths in the Eagleford
This scatterplot shows lateral lengths on
the x-axis and 6 month cum oil (bbls) on the
y-axis.
This is for a specific Operator in the
Eagleford for wells drilled since 1/2010.
Coloring the same scatterplot by grade
reveals a strategic approach to the
Operator’s lateral lengths. The Operator
drilled longer laterals in lower quality rock.
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D. Compare Operator Productivity: Creaming Curves in the Bakken
Despite these wells all being drilled in F grade acreage, we see a significant performance discrepancy across operators.
Operators A and B are performing very well, whereas Operators C and D have lower productivity from their wells.
Copyright © 2015, Drillinginfo, Inc.
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