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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
Published: September 1, 2020
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 sensorenabled 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.
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 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 Insight Conversation: Jane Ren September 2019 Insight 17 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.
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.
That resistance is assumed. When we decided to look at the oil and gas industry as an opportunity area, we knew very well that the industry has often claimed to be in a race to be second. I see a couple of reasons for that. The first level is the mindset. I think the industry’s process is designed to be people-heavy and scarce on information, so the processes are the hardest things to change – how people work. So if my work process includes writing on a piece of paper every day information I need to report, I need to verify, I need to make decisions based on my intuition and experience without having to rely on information or intelligence because it wasn’t there, this is the way I work. I don’t want to be disrupted. So the resistance often comes from the field level.
The second part of that friction is the technology is coming from a different industry, so in the past it was much more Schlumberger, Haliburton etc, who provided the best and newest technology to this industry. Now with the importance of data science and AI there are a different group of companies. There is a cultural mismatch between the companies that do data science and the ones that do hard physical engineering science. There is a resistance between those groups. However, I think the whole sector is trying and they have made a lot of progress in the last few years.
There was quite a bit of introspection around 2015 when oil prices really did nosedive, and at the same time [there was a] wake up call about the future of energy and where are the new sources of business revenue coming from. Fear about the future of energy is far greater than the tangible gain in ROI that’s been checked on the books and I believe it’s there, it’s very complex to get there but the whole ecosystem has to stay in the game because there are no other options.
The fears come as the industry realises it has very little control of the oil price. The fear is, if we don’t change things now while we still can, what if it happens again and the price continues to drop down? That’s the short-term fear. Then there’s the long-term fear, the prediction that in 20-30 years the use of fossil fuels will dramatically decrease, with EVs and an entire change in the energy value chain, drives major companies, the likes of Shell and Chevron, to think of the future of their companies if they don’t change. Of course they have examples of companies that didn’t transform and that drives them from the board level to make their business more intelligent.
Since the oil price drop, the industry has, one, been talking about how to get more efficient for every barrel of oil and two, we need to look at alternatives, diversify, go to wind, go to other renewables so the risk is not so exposed.
"There is a cultural mismatch between the companies that do data science and the ones that do hard physical engineering science"
I will compare three geographies that we have interactions with – they have different characteristics. A lot of the interactions we have are in the US with oilfield service companies, also with midstream and downstream companies. The benefit is that Houston, an oil hub, is fairly close to California which has mushroomed with a lot of AI.
In terms of looking at the future for renewables and how we adapt our energy strategy, Europe seems to be more progressive than the US. So we helped one of the midstream operators there save 15-20% energy, a corporate mandate. So yes, we reduce operating costs but we must reduce our carbon footprint and this is a most important priority.
I talk to quite a few national oil companies in Asia, including Thailand and Malaysia – they want to catch up in technology. So tech is a big driver for them because they feel they have been a little behind the Western world and it’s an opportunity to leap forward, so they want to adopt and learn fast. But in general they are still early.
I agree with you, both industries have been behind in female representation at a senior level. But this area is new and when an area is new nobody is putting a claim on this kind of profile. The winners are the ones that outperform others and make an impact. The perception of knowledge with engineering is associated with a male type of engineer and that perception needs to be changed, and that’s what I’m trying to do through though leadership.
