By Neanda Salvaterra
Amid a growing push to cut operating costs, big oil is looking
to artificial intelligence for help with automating functions,
predicting equipment problems and increasing the output of oil and
gas.
AI tools can quickly find solutions for the costly problems that
can disrupt the business of searching for and extracting
hydrocarbons. For example, a faulty well pump at an unmanned
platform in the North Sea repeatedly disrupted production earlier
this year for Aker BP, a Norwegian oil company in which BP owns a
stake. The company finally fixed the problem by installing an AI
program that monitors data from sensors attached to the pump and
flags glitches before they cause a shutdown, says Lars Atle
Andersen, vice president of operations for technology and
digitalization. Engineers now can fly in to fix such problems ahead
of time and avert a shutdown, Mr. Andersen says.
While Aker BP got the help it needed from a small Austin,
Texas-based AI software firm, SparkCognition, some bigger
oil-and-gas companies are working with giants in the tech industry.
Exxon Mobil Corp. in February started a partnership with Microsoft
Corp. to deploy AI programs to optimize its operations in the
Permian, or West Texas Basin. The oil giant also recently installed
an AI program to gain insights from data coming from millions of
sensors that monitor its global refineries. Total SA, meanwhile, is
linking up with Google Inc. to better interpret seismic data, and
is set to increase its investment in AI to squeeze more
hydrocarbons from existing assets.
Royal Dutch Shell PLC, for its part, has tested an AI program
that monitors sensors on equipment at its Rotterdam refinery, the
largest in Europe, to help figure out where to better direct
maintenance staff and dollars. And through a subsidiary in
California, Shell has an AI program that helps drivers of electric
vehicles shift their charging times to when electricity is less
costly.
Advances in machine learning and the falling cost of storing
data are key factors in big oil's motivation to harness the
potential of AI. Since 2017, there has been an industrywide drive
to move data such as geological information into digital formats
that in turn has created vast troves of information that companies
can mine for insights using powerful data-crunching programs.
"When you mention data at this scale to data scientists, you can
see them start salivating," says Sarah Karthigan, data science
manager at Exxon Mobil, which says it has a database consisting of
about five trillion data points. "The intent here is that we can
run our plants more efficiently, more safely and potentially with
fewer emissions."
The company deployed an AI program in January to comb through
all of the data generated by its 42 refineries and chemical
processing plants around the globe. Feeding into what the company
calls its "data lake," all of its refineries now have sensors
monitoring things like how much oil is flowing through the system.
Exxon uses a machine-learning algorithm to mine its data for
problems and solutions such as how to best blend hydrocarbons to
get different types of petroleum products. The insights are
available to teams of human experts throughout the company.
The drive by oil-and-gas companies to cut costs has grown in
importance since oil prices plummeted in mid-2014, disrupting the
viability of upstream exploration projects that were planned when
crude was still fetching $100 a barrel. While the price of Brent,
the global benchmark, is up roughly 6% in the past year, companies
are being pressured by investors to keep up capital discipline and
find additional cost savings.
AI can help find those cost reductions by tackling a range of
problems. Its deployment in upstream operations could yield
collective savings in capital and operating expenditures of $100
billion to $1 trillion by 2025, according to a 2018 report by
PricewaterhouseCoopers. Most companies declined to discuss their
exact spending on AI.
"Combining data and analytics can create new business models,"
says Martin Kelly, head of corporate analysis at the consulting
firm Wood Mackenzie, who adds, "AI is a component of a broader
digital transformation that the oil-and-gas industry is
undergoing."
Exxon, the world's largest listed oil-and-gas company, is
plowing about $1 billion a year into research where machine
learning is included and says it intends to increase its spending
on AI in the future.
Another factor driving the introduction of AI is its promise in
helping capture the knowledge of retiring workers as the industry's
workforce ages. U.K.-based BP has a project called Hands, in which
experts in different fields such as water or sand management are
training an algorithm that will be able to dispense advice in the
future. Researchers at Exxon are also considering creating an
algorithm to retain the expertise of its workers, Ms. Karthigan
says.
"We have experts in many areas, and they will retire someday,"
says Ms. Karthigan. "A lot of their knowledge has been gained
through experience, which isn't captured anywhere."
France's Total this year is investing about EUR200 million ($219
million), or 30% of its research and development budget, into
digital technology, of which AI is an increasing part, says
Philippe Cordier, a scientific computing program director at the
company. In its partnership with Google, Total is testing an AI
program in the Gulf of Guinea, off western Africa, that will help
interpret data from three-dimensional images of the subsurface and
any potential hydrocarbon reservoirs found there.
Finding oil and gas, especially offshore, is a costly process
that can sometimes take years. Geologists spend a lot of time
looking at seismic graphics to learn about the geological
composition of areas they are exploring. The AI program Total is
using will help organize such data and identify imagery like fault
lines, Mr. Cordier says. The company will continue to rely on
humans to identify patterns that might lead to oil and gas
discoveries, he says. But by using the technology, Total hopes to
get faster, more reliable production forecasts and boost the
productivity of its engineers by freeing them from repetitive
tasks.
Ms. Salvaterra is a reporter for The Wall Street Journal in
London. She can be reached at neanda.salvaterra@wsj.com.
(END) Dow Jones Newswires
October 13, 2019 22:15 ET (02:15 GMT)
Copyright (c) 2019 Dow Jones & Company, Inc.
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