American Oil and Gas Reporter - November 2020 - 40

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compounded by the fact that companies
must reshape their organizations to do
more with less, and define and emphasize
their particular operational niches like
never before.
An effective ERP system tightly integrates data across all departments and application areas. That integration is key to
efficiency. Most operators look for inefficiencies among their people and
processes, but an ERP system allows for
a systematic approach. Whether it is
called business process management,
continual improvement process or goes
by another name, the concept strives to
make organizations ultra-efficient in all
aspects. That means minimizing errors,
eliminating and preventing waste, continuously improving productivity, and rooting out inefficiencies from top to bottom.
Keep in mind that improving a
process is not only about improving its
efficiency. It is also about making it more
effective, generally by accelerating it, increasing the output or cutting the cost.
Companies are being forced to take a critical look at everything they do. If something is not working, a new process or
technology likely offers a solution.
Within data workflows, one way ERP
tightens efficiency and integration is automatic data entry and automated com-

munication that eliminates redundancies
and errors. For example, new well data or
a change in a well's status can be communicated automatically to everyone involved once it enters the system, and
users across different departments or locations can interact with it seamlessly.
Different types of communications and
interactions can occur throughout an organization in response to a single event,
and automating, simplifying and integrating workflows around all those processes
at the enterprise level can enable powerful efficiency gains.
So, what technological capabilities are
particularly crucial in taking the next
steps in efficiency? Foremost on the list
are artificial intelligence and machine
learning. These terms can be used generically to mean different things, but specific to oil and gas datasets, the
combination of these technologies allows
companies to quickly find patterns in
large amounts of information. With
AI/machine learning engines, users not
only can extract actionable intelligence
from massive datasets quickly, but also
apply predictive analytics to understand
potential outcomes. The ability to make
predictions on a well, field or vendor
product performance based on real-world
current and historical data helps drive

better business decisions.
The more data available, and the
richer and higher its quality, the greater
the value of the business intelligence that
can be extracted from it. The role of an
ERP system is to collect, store and deliver all the data a company uses in its operations. The challenge is learning how
to shape and feed data spanning all aspects of an asset's entire life cycle from
the ERP system into third-party AI/machine learning algorithms to enable data
analytics and predictive modeling.
Evolve Or Perish
When it comes to engineering innovation, this is an evolve-or-perish moment
for the oil and gas sector. The companies
that make it through will not emerge from
the downturn looking the same; they will
have adapted to survive, which means
they will have changed. In many cases,
that means replacing old, ineffective
technology with something better suited
to both the present and the future. An antiquated ERP, for instance, may not offer
automation or remote access, which
poses problems when people need or
want to work from home. In spite of the
imperative to reduce costs, this is an optimal time to consider investments in a
technology infrastructure.

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American Oil and Gas Reporter - November 2020

Table of Contents for the Digital Edition of American Oil and Gas Reporter - November 2020