Data science and AI
Machine learning is having an impact across industries. Oil and
gas operations are moving toward predictive analytics with automated corrective actions. This leverages the production operations
Edge computing in the field
Edge computing analytics aids production operations by collecting
data and performing analysis in real time. Automated analytical
computation is performed on well sensor data. Instead of waiting
for the data to be sent back to the cloud for processing, corrective
actions can be applied immediately and securely in the field.
Cloud computing is a scalable, powerful capability that allows
field management from centralized
locations and can be extended as
Visualization techniques present
the information from the field so
that operators can focus on the
most important issue or
As with all automation of work, new
roles and organizations develop.
The transition from current practices will require attention to change
to develop a successful service.
IIo T-enabled instruments
Key to the impending changes is
the arrival of IIo T enabled devices.
The information provided by these instruments is processed
through edge computing with predictive machine learning.
Technology located in the field requires deployment expertise for
outdoor environments. This includes knowledge of automation
and communication equipment.
These closed loop systems need to be protected against incursions
by cyber criminals or terrorists. Protecting the production operation
systems should be built-in from the design start.
Production goals help maintain production and control costs in
a safe environment. A majority percent of asset lifetime costs and
time are related to production operations. This is the business
environment for which POaaS is designed. The assets are not
owned by the service provider. The commercial environment is
not static because of declining production rates and product prices.
Such variables need to be considered in the contractual service
agreement. Tracking an agreed upon decline curve and adjusting
payments to an oil price moving average can share uncontrolled
operational risks between the ser-
vice company and asset owner.
POaaS is a natural consequence of
the significant changes and opportunities currently impacting the oil
industry. The introduction of POaaS
will be disruptive to the participating organizations. Asset owners
need to reduce operational costs,
and POaaS providers need to organize around the expertise framework. These changes are the greatest barriers to implementing POaaS
in traditional organizations.
For this reason, the POaaS model
might instead emerge from consortiums that exist outside of current
operators and service companies.
These POaaS providers could develop the new integrated operations without the difficulties resulting from the destruction of solution silos in current service
companies. New asset owners can view producing assets as an
annuity and focus primarily on the financial agreements. They do
not have to transition from organizations focused on
This approach could begin in unconventional assets, which
operate with a manufacturing operational style. For such resources,
the POaaS approach could increase their value and market accessibility.
ABOUT THE AUTHOR
Jeff Pferd, PhD, is a senior member of the Halliburton
Information Management team with more than 25
years’ experience in the oil and gas industry. His
background combines scientific domain knowledge
and advanced technology.
F1: POAAS EXPERTISE FRAMEWORK
“The business concept presented here assembles
the fragmented production automation and
optimization services into a single solution, allowing
service companies to accept responsibility for
operational risks in exchange for a growing share
of the benefits provided by their services.”