American Oil and Gas Reporter - November 2015 - 46

Organizations routinely rely on geostatistical methods to accurately quantify
heterogeneity, integrate scalable data,
and capture the scope of uncertainty. But
between 70 and 80 percent of allotted
time for any reservoir characterization
study worth its investment should be focused on exploratory data analysis (EDA).
In combination with spatial analysis, simulation and uncertainty quantification,
EDA ensures consistent integration, aggregation and management of data-underpinned by univariate, bivariate and
multivariate analysis.
To communicate the insights from
EDA, producing companies are turning

to advanced data visualization technologies. In particular, they seek solutions
that enable a distributed and mobile workforce to access and analyze information
while in the field. These solutions use an
in-memory analytic engine to perform
descriptive and inferential statistics on
upstream data.
Visual analytics discovers hidden patterns in multivariate big data (see Figure
1). A robust visual analytics approach
includes:
* Forecasting, scenario analysis, and
other analytic visualizations;
* Interactive dashboards and reporting;
* Integration with mapping technologies;
* Reports that are designed once and
published anywhere; and
* Feature-rich mobile applications
for iPad® and Android.

Predictive & Prescriptive Analytics
Technologies that enable a multidisciplinary control solution to remotely
manage every aspect of development and
production are at the crux of reservoir
development and management. Geoscientists and engineers must have automated
systems to analyze a continuous stream
of reservoir, well facility and pipeline
data to help formulate effective responses
to changing surface and subsurface conditions. Increasingly, those responses are
expected in near real time.
For example, in drilling, operators use
time-based performance metrics such as
cost per foot drilled or time to total depth.
However, these measures don't capture
the quality of the wellbore that is handed
to production teams, and often can incentivize poor well construction decisions
that lead to production problems later in
the asset's life.
Historical drilling data can be used to

quantitatively identify best and worst
practices that affect both the target and
the quality of wellbore construction, with
the intent to improve not only future
drilling operations, but also increase return
on investment from both conventional
and unconventional plays.
The future of drilling optimization requires these predictive models be enhanced
further to become prescriptive data models
that provide not only insight into what
will happen next, but that also provide
advisory or direct control recommendations to the drilling system that ensure
the optimal outcome for both cost and
quality.
Virtually all oil and gas companies
today are taking nontraditional approaches
to exploration and production. While the
approach to digital oil fields is different
for each company, successfully attaining
the value entails developing an analytical
solution capability that is at the core of
global upstream activities.

Probabilistic Forecasting
Analytical workflows also need to
quantify the future using probabilistic
solutions. One example of this is incorporating a decline curve analysis step to
implement an oil field production forecast.
This identifies short- and long-term forecasts for oil, gas and water production
(Figure 2).
Implementing mature forecasting models and first principles, such as Arp's
empirical algorithms, can help organizations estimate accurate well performance
and estimated ultimate recoveries, and to
measure the impact-positive or negative-of well remediation techniques.
When one oil and gas company took
this approach, it was able to estimate reserves with fewer data exceptions, more
time for strategic planning and what-if
analysis, and much more confidence in

FIGURE 1
Visual Analytics Reveal Hidden Patterns in Multivariate Data

46 THE AMERICAN OIL & GAS REPORTER

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American Oil and Gas Reporter - November 2015

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