American Oil and Gas Reporter - April 2016 - 74

SpecialReport: Drilling Technology

Modeling Methods Optimize Production
By Jared Schuetter,
Srikanta Mishra,
Ming Zhong
and Randy LaFollette
HOUSTON-Modeling unconventional
reservoirs is an extremely complicated
task that requires simulating multiphase
production flow in a network of natural
and induced fractures, coupled with geomechanical effects and processes such as
water blocking, nondarcy flow in nanoscale
pores, and adsorption/desorption.
Data-driven statistical modeling can
help operators understand the behavior
of unconventional reservoirs and optimize
production performance. Compared with
equivalent mechanistic models, such as
physics-based simulators, data-driven
models provide an easier pathway to realtime optimization, and require less time
to set up, execute and interpret. The goal
is to extract important patterns and trends,
and to discern "what the data say."
A range of data mining/analytical techniques is being applied to assess unconventional reservoirs, including nonparametric regression, tree-based modeling,

classification tree analysis, fuzzy clustering, and time-series analysis. Common
regression modeling approaches include
ordinary least squares (OLS), random
forest (RF), gradient boosting machine
(GBM), support vector regression (SVR),
and kriging modeling (KM). Each approach has relative strengths and weaknesses, and deciding which model to use
is not always straightforward. Different
modeling strategies are intended for different situations.
Statistical analysis software is readily
available to engineers and geoscientists,
but there remain the critical issues of
choosing the right algorithm for the problem at hand, applying the algorithm with
the proper choice of user-defined parameters, avoiding data "overfitting" and its
resulting bias in fitted-model predictions,
and ensuring that data-driven models
make physical sense in terms of variable
selection and parameter importance.
Using data from 476 horizontal wells
completed in the Wolfcamp Shale play
in the Delaware Basin, this article focuses
on building robust predictive models and
developing decision rules that help identify

FIGURE 1
Pair-Wise Scatterplot of Predictors
And First-Year Cumulative Production

74 THE AMERICAN OIL & GAS REPORTER

factors that separate good wells from
poor performers. Data categories used in
the study include well location and assorted
operational metrics that capture various
aspects of well design and production.
Twelve specific predictor variables relate to completion date, geographic location, azimuth angle, true vertical depth,
drift angle, lateral length, number of frac
stages, amount of frac fluid, amount of
proppant, and proppant concentration.
The response of primary interest was cumulative production over the first 12
months (M12CO).
First Step
Although 319 of the 476 wells in the
dataset had M12CO values, only 171
wells had complete data for the response
and all 12 predictors. To avoid losing
nearly half of the wells to missing data, a
random forest imputation method was
used to fill in missing entries using a
proximity score between each pair of observations. For each predictor, missing
values were assigned using a weighted
average of all nonmissing values across
the dataset, with the weights proportional
to proximity scores.
A typical first step in exploratory data
analysis is to examine a pair-wise scatter-plot of the response against the predictors to determine whether any predictors
have strong marginal effects on the response. This matrix of scatter-plots shows
the relationships between all possible
input variables (12 predictors) and the
output variable (M12CO), along with the
empirical histogram for all variables along
the diagonal. The plot also reveals correlations between pairs of predictors.
In this case, the aim was to predict
cumulative first-year production for new
wells, given operational characteristics.
Figure 1 shows the pair-wise scatter-plot
for the dataset. The top row and first column show the relationship between the
response (M12CO) and each of the predictors individually.
None of these predictors shows a
strong relationship with the response.
Lateral length (LATLEN) potentially has
a positive association with the response,
but the correlation is fairly weak. Frac
fluid volume also appears to have some
correlation with LATLEN, although the
outlier obscures this fact in the scatterplot.



American Oil and Gas Reporter - April 2016

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American Oil and Gas Reporter - April 2016 - Cover2
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