“Robots
will steal HALF of all jobs in the world within 15 years creating unemployment
crisis for millions, AI expert warns”, is a real headline from a leading UK
national newspaper. This warning says jobs such as truckers, salespeople and
couriers will become obsolete as artificial intelligence takes over.
Contrast
this with another headline – “Robots and AI will actually create more jobs than
they take”, and who knows what to believe.
What
we do know is, Industry 4.0 and industrial digitalisation is creating a new way
of working across all sectors and it will change the way we work. While the oil
and gas industry has been slower than others to embrace big data and artificial
intelligence (AI), the pace of adoption in the industry has accelerated and is
now an area of innovation for most companies.
The
technology available today can provide operational insight at a new level, help
operators locate and develop reservoirs, enhance how operators produce and
refine crude oil, increase process automation and operational efficiency, and
even optimize business activities such as how companies market their products.
Using
AI applications in anomaly detection for example, can help to free up time for
engineers on site so their time can be invested in more value-added production
activities, making operations more efficient. AI can take some of the mundane,
but critical tasks, and with the ability to process data at speed, find
anomalies and understand a problem will happen, much earlier than any human
could ultimately leading to more uptime for customer assets.
But
underpinning all of this is data. We need big data to train the AI to know what
normal looks like and without it, AI won’t work.
Using
customer data to understand normal and adding in historic failure data into an Industrial
Internet of Things platform (IIoT), such as Siemens’ MindSphere, can provide
the algorithms to train AI in a structured way. If you add to this the
knowledge from operators and engineers and suddenly the AI becomes an integral
part of decision support within anomaly detection.
By
using the IIoT platform, data from multiple assets can be shared so that
machine learning is robust, and all customers benefit from the shared knowledge
of issues which may not necessarily have happened in their operation. This is
important because compared to say trying to get AI to drive a car, there are
only a finite number of situations where we can take the data needed to learn
from in the oil and gas industry. It also would alert operators working in
similar applications and environments of potential issues which need their
attention – in theory providing even more notice to take action.
This
isn’t pie in the sky. Siemens has real world applications of AI support where
issues have been detected before any fault occurred. Our pilot programme
centred on electric submersible pumps (ESP), which is used to lift fluids from
wellbores.
We
operate hundreds of thousands of units across the world, and by using the
shared data, have trained AI in anomaly detection. In one pilot use of this
application across a fleet of 30 ESPs, the failure of one was accurately predicted
two weeks before it occurred.
This
is a step-change in current field monitoring systems. ESP downtime can be
costly, but this predictive AI model allowed asset downtime to be reduced, and
remedial action to taken. With more time to prepare, a decision can also be
taken on whether to manage the issue until the next planned shutdown, or if the
failure isn’t avoided, a rig to replace the pump can be mobilised sooner.
The
AI works in tandem with a traditional supervisory control and data acquisition
(SCADA) system meaning if the AI misses a fault, the SCADA system will still
pick it up. But the AI takes the SCADA data and uses it to continuously learn
about faults and what normal looks like.
The
benefits of such a system are huge. As in the pilot, critical failures can be
avoided which could potentially impact the operation of an entire reservoir.
This means operational efficiency is improved for ESP availability and most
importantly, the reservoirs profitability.
Today,
Siemens has more than 200 data scientists working on AI research using decades
of oil and gas industry experience. While we’re just scratching the surface of
the potential of AI, there are many new applications expected which will
improve asset utilisation and profitability across the sector. ESP can also
mean extrasensory perception – or a sixth sense – and with AI, that’s what
you’re getting to run oil and gas assets.
Find out more at www.siemens.com/digital-oil-gas
