OI L F IEL D AN A LY T I C S
The E&P industry has
become so data-driven
that the limitations of
piecemeal adoption are all
too evident. The resulting
data fragmentation
makes data management
less efficient and more
expensive than necessary.
In addition, it prevents
the company from
analyzing its data in a
comprehensive, unified and
forward-looking manner.
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national boundaries or oil-producing regions.
Such fragmentation reflects the way IT has
typically been adopted: in piecemeal fashion, by
local or departmental managers, to address a
specific problem. Today, however, the E&P industry (like many others) has become so data-driven
that the limitations of piecemeal adoption are all
too evident. For one thing, the resulting data fragmentation makes data management less efficient
and more expensive than necessary. In addition,
it prevents the company from analyzing its data
in a comprehensive, unified and forward-looking
manner. This, in turn, poses two main problems.
One is that fragmentation reduces the value of
each type of data – exploration, development and
production – individually. The other is that fragmentation makes it impossible to analyze the
three types holistically, denying the company the
insights that can come only from an integrated
approach.
The best path forward can vary considerably
from one enterprise to another, because piecemeal adoption means that no two companies
start from the same place. One E&P company
might be getting suboptimal results from its seismic tests and exploration drilling, but not realize
it because it lacks the tools to analyze historical
data and identify the factors degrading its results
(to say nothing of predictive tools). Another company might be drilling too many or too few development wells, or not siting them correctly – again,
due to lack of analytical insight. Yet another company might be deferring too much or too little production because it lacks the predictive analytics
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