Insight Conversation: Jane Ren
Jane Ren, CEO of disruptive technology company Atomiton, talks to Paul Hickin about opportunities in the energy sector and the challenges faced by big oil
The mainstreaming of artificial intelligence and machine learning will have a profound impact on the industrial sphere, not least in energy industries.
Whether companies are active in the upstream, midstream or downstream segment, they face enormous pressures to control cost while living up to societal demands for environmental stewardship and a wholesale shift to cleaner forms of energy.
Energy companies are increasingly turning to digital solutions to achieve these goals. The Industrial Internet of Things (IIoT) brings into play a sensor- enabled network of interconnected devices applied to physical assets. It can help companies find operational efficiencies in the way they deploy assets, infrastructure, energy, products and, of course, their workforce.
This is part of a broader trend of reimagining operational processes and leveraging data that is familiar to S&P Global, Platts’ parent company, which has invested in next-generation tech company Kensho and fintech company Panjiva.
A former medical doctor with a career spanning multiple roles at US conglomerate GE and IT company Cisco, Jane Ren founded Atomiton in 2013. She explains how Atomiton is helping to drive the next wave of digitalization, using an IoT platform to connect operational systems and transform real-time data into operational models.
What do you see as the big opportunities for technology in the energy space?
We help oil and gas companies in predictive operations using data and intelligence gathered from the field. Our software stack extracts all the raw data coming from sensors, coming from machines, coming from the field. It is able to perform real-time analytics, generate actionable insights and then sometimes even help execute those actions into the field.
There are a few areas we see as [sources of] great gains of efficiency for the energy sector. The first is better and greater productivity of the equipment and assets in this domain. When I say assets, it includes wells that could be more productive using better analytics of their performance parameters. It could also indicate generators, machines, even drill pipes that could be better protected and maintained when we know their intelligence.
The second area to gain efficiency, surprisingly, and we see it as very immediate, is energy itself. It takes energy to transport, to generate and to transform energy. So for example in the downstream and midstream sector about 20% to 40% of operating costs is spending on burning fuel to generate heat and steam using water to drive processes. Using data and analytics people can better predict how they use energy and be more efficient.
The third area is the productivity of people. The oil and gas sector is a very people-heavy industry and the last thing you want is downtime. You don’t want people going onto the rigs or to the field and to be idle there. A lot of things lead to downtime: if you don’t coordinate the supply chain, they don’t have tools, they don’t have equipment. If you don’t have the logistics right it means you run out of fuel, you run out of battery, your machines aren’t working.
By having sensor data coming from the field, it is much better for operators to predict how to arrange their logistics, so people are much more productive. When you put all this together, one of the big opportunities in the mid-term is the whole range of supply chain and pricing. But without the visibility on these three factors it is hard to gain visibility in supply chain and pricing.
Some of the work we are already doing, in trying to be predictive about demand on the fuel and product and energy, and therefore respond better in supply chain and pricing structures to have better economic gains. Eventually we see all these changes driving much deeper transformation for the industry.
Is there an area in oil and gas where you see technology being of particular benefit?
Upstream, midstream and downstream all have a lot of [potential gains in] efficiencies but they are organized differently. It’s much easier to find very localised problems in midstream and downstream because they are not as fragmented as upstream. When you get to upstream there are operators who will outsource to contractors, so the gains may get segmented between different parties.
Let me give you an example. One of the biggest cost components for operating an upstream drilling project is the cost of maintaining, leasing and transporting equipment, and they often get lost and are not productive. Now who cares about that? It could be the operator or it could be the contractor, and that’s one area we see on the upstream side where there are efficiencies to be gained.
Secondly, the productivity of the well. A lot of companies have put their data science teams behind it and they claim to have much better resources, but it is yet to be seen how much productivity is to be gained by doing analytics on a well.