the value of synergies between large-scale equipment manufactured
to provide real-time data and the big data analytics services that
might provide valuable insights and improved decision-making in
oil and gas field development. It seems to us that the benefits can
be achieved through contractual means, specifying the data formats
required from the equipment to enable data analytics to be performed. Even better would be industry standards adopted to allow
multiple data bases to be interrogated by artificial intelligence-en-hanced algorithms.
Moreover, there are risks in combining unlike companies. In
previous research, UH Bauer teams observed differences in the
drivers of shareholder value for the equipment manufacturing
segment from those driving shareholder value of the major service
companies and concluded that different business models and
different capabilities are rewarded in the two sectors. Putting them
together in a single organization may weaken both parties.
The team concluded that substantial value will be created from
innovation and deployment of big data analytics in the oil field, but
there does not seem to be a strong business case for a fundamental
restructuring of the OFS sector. Indeed, consolidation of verticals
to provide integrated services risks alienation of oil and gas company
customers as threatening their fundamental value proposition of
finding, developing and operating oil and gas fields based on their
expert reservoir understanding. Less threatening and with more
evident synergies is the acquisition by Technip of FMC Technologies,
which mainly designs and engineers subsea and production
However, there seems a strong likelihood of attrition in the
number of companies in each segment. The three OFS majors are
limited in their growth potential in their existing verticals, and they
have opportunities to add new verticals or strengthen their few
weak segments by acquisition at a time when valuation multiples
are low. Also, cutbacks in capital spending have led to less work
available in many sectors than the total capacity available, particularly in deepwater segments. A roll-up possibly funded by private
equity of two to four companies in several segments would allow
capacity to be removed to match available demand without leading
to high Herfindahl indices. Overall, the team expects there to be
significantly fewer public OFS companies in five years’ time than
today still competing in the same “vertical” segments.
A second conclusion addresses which companies will capture
the value created by advances in big data analytics. Our view is:
• Innovators in data acquisition technologies should do well. Af-
fordable sensors, chemical tracers, and controls to provide mas-
sive 4D data sets tracking reservoir properties and production
metrics over time will provide the foundation for better field
development plans and allow for increasing automation and
• Geophysical service companies with existing “spec” data sets
will be able to transform existing 2D seismic libraries to approx-
imate 3D images and sell useful, albeit low, resolution images.
Improved geophones and improved processing will lead to higher
resolution subsurface images.
• Oil companies will be able to upgrade their existing reservoir
models to incorporate new relationships derived from big data
analytics. New reservoir models will become more accurate more
quickly through machine learning algorithms.
• Creators of successful new artificial intelligence enhanced data
analytics algorithms will prosper. Many of these may be small
“garage” companies with a single powerful idea and the capabilities to bring it to market. Others will be oil and OFS companies
that nurture the new capabilities internally to become early
• The major OFS companies will compete with oil companies as
predators seeking to acquire early successful software entrants
and integrate their capabilities into their current models while
being Darwinian in rethinking their product portfolios to align
with the new technologies.
• The providers of platforms hosting massive data sets to enable
“Cloud Computing” as well as basic analytic tools will be able to
charge usage and service fees. These may include new oilfield
entrants such as Microsoft, Amazon, and Google/Alphabet as
well as existing OFS companies.
Big data analytics will form a new fiercely competitive ecosystem,
driving down costs and increasing capabilities. This fierce competition will over time award most of the benefits to lease-holders
who harness the resulting innovation most effectively to become
the low-cost producer of what had previously been thought to be
high-cost resources. Ultimately, competition among the oil and gas
companies deploying big data analytics innovations will lead to
lower oil and gas prices than would otherwise have occurred, so
the true beneficiary will be the oil and gas consumer.
ABOUT THE AUTHORS
Chris Ross ( firstname.lastname@example.org) is an executive professor of finance at the CT Bauer College of Business
of the University of Houston. He has authored numerous articles on the oil and gas industry and is co-author
of Terra Incognita: A Navigation Aid for Energy Leaders.
He chairs the Oil and Gas Policy Subcommittee of the
Greater Houston Partnership, and sits on the Program Committee
of the Offshore Technology Conference. Ross began his career with
BP in London. In 1973 he joined Arthur D. Little, and moved to
Algeria where he managed a large project office assisting SONA-TRACH with commercial challenges in oil and LNG and advising
on OPEC issues such as price coordination, price indexation, and
production quotas. In 1978, he moved to the ADL headquarters in
Cambridge, MA and on to Houston, where he opened the ADL
office in 1982. From then until 2010 he led the Houston energy
consulting practice which was acquired by Charles River Associates
in 2002. As a consultant, Ross works with senior oil and gas executives to develop and implement value creating strategies.
Chris Chapin, David Woods, and Henry Xi are students at the CT
Bauer College of Business of the University of Houston.