LØRN case C0245 -

Bjørn Erik Dale


Solution Seeker

AI for oil and gas

In this episode of #LØRN, Silvija speaks with Co-Founder and Executive Chairman at Solution Seeker, Bjørn-Erik Dale. Solution Seeker has developed the first artificial intelligence for upstream oil & gas production optimization. In the episode, Bjørn-Erik tells about this solution and how they utilize AI to consult production engineers in the oil & gas industry about how to optimize their production. With their ProductionCompass, Solution Seeker aims to revolutionize some of the most complex production systems out there Bjørn-Erik tells in the episode.
LØRN case C0245 -

Bjørn Erik Dale


Solution Seeker

AI for oil and gas

In this episode of #LØRN, Silvija speaks with Co-Founder and Executive Chairman at Solution Seeker, Bjørn-Erik Dale. Solution Seeker has developed the first artificial intelligence for upstream oil & gas production optimization. In the episode, Bjørn-Erik tells about this solution and how they utilize AI to consult production engineers in the oil & gas industry about how to optimize their production. With their ProductionCompass, Solution Seeker aims to revolutionize some of the most complex production systems out there Bjørn-Erik tells in the episode.

17 min

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SS: Hello and welcome to podcast by ONA's and Learn. My name is Silvija Seres. The topic today is energy technology. And my guest is Bjorn Erik Dale. co-founder and executive chairman of Solution Seeker. Welcome.

BED: Thank you.

SS: Bjorn Erik, you were a partner at a consulting company that was involved in this particular company that you're leading now, and decided at some point that you really wanted to be involved in building up this technology company in the energy sector. And I'm really curious about the transition. Before we go there, would you mind telling us a little bit about who you are and what drives you?

BED: Yeah. So my name is Bjorn Erik Dale. I have a background from the technical university in Trondheim, computer science and finance. As you said, I worked in consulting for many years advicing energy technology companies. So big corporations and also private equities. Doing a lot of work in the domain of oil and gas and general energy. A kind of college student from the university, he took a different path. He took his PhD on production optimization in the oil and gas industry, worked at the university and made some nice discoveries on inventions that were really early on in the now machine learning age. And they developed technologies that were different from the things we saw in the industry. And so together with him at the university, we founded Solution Seeker, a few years back. I was certain I was still working as a consultant, but they had a lot of advances.

SS: You couldn't resist them anymore.

BED: No, I couldn't. And it started to become more and more kind of commercially ready.

So I entered as chairman, and it grew on me to a passion project. And then it became a kind of an executive chairman position. So noow I spend quite a lot of my time with the company.

SS: So a Solution Seeker has this product production compass AI. And it's very simply put, an artificial intelligence solution for managing the extraction of oil and gas, optimizing it real time.

BED: Yeah, yeah. So it's for the real time optimization of your system to maximize throughput through kind of all the, all the infrastructure that you are very...

SS: But very basically, for somebody that doesn't know how this extraction works.

There is an oil field somewhere far below the surface of the ocean. And then there is some oil or gas down there and it's in different pockets. And sometimes you have to apply some water or some gas. And this you need to calculate based on the sensors that you've put in the field. Is that sort of a very naive but…

BED: No, it's very accurate. So I think that is the thing. You have this reservoir. It's it's huge. And you really don't know exactly how it works. But you have some ideas. You have drilled our wells. You start producing. And then you start observing what is actually happening. And this we can observe through sensors, and from those sensors, we can learn the behavior and the dynamics in the fields and we can optimize how we then choose to drain from the different wells, how we would choose to allocate gas injection or water injection or other things that we can kind of..

SS: To push the gas out, or the oil?

BED: Oh, yeah. Yeah.. So it doesn't help having a camera in a dark place like this. So what you have are pressure sensors, or what kind of information?

SS: Yeah. So it's pressure and temperatures. It's a choke position. So openings of valves, it's injection rates on the gas or water. It's basically everything that has to do with the production and the beauty is that this is information that the oil companies have had and collected for years. But it's been really hard to extract learning from it. And that's where it kind of where advances in data driven modelling plays a nice role now.

SS: So now you look for patterns in these pressure changes and temperature changes, and you know that they might be signaling more or less oil or gas somewhere?

BED: Yeah. So. So, yes. So we try to then figure out an estimate of what is actually going on. So what flow comes from what parts of the system? How do the different parts of the system interact and influence each other? And the challenging thing is that this evolves over time. The reservoir changes as we drain the system. So we have to also be able to to predict the changes in the reservoir.

SS: So I assume this has quite a big commercial effect on how much of the reservoir you're able to extract in the region for a reasonable cost.

BED: Yeah. So. So it differs across the field, some are quite trivial to operate. Others are extremely complex. So, but they expected increased production to be somewhere between one to four or two to five percent.

SS: And that's a lot of money.

BED: That's a lot of money. It's a huge amount of money. And especially considering kind of only having software as your input. It's not a huge redesign or a rebuild of a field, a big expensive modification project. This is just software and working smarter.

SS: So this is a part of this digitalization wave in oil and gas. What more do people do to digitize?

BED: Yeah, so this has really come on the agenda the last couple of years. The digitalization wave is hitting the oil and gas industry, coming out of a kind of cost cutting phase, now into the digitization phase. And it means a lot of the oil companies are now really reorganising, organizing the way they work, and building new capabilities internally, working with the external ecosystem in a different way, I think, to really adopt these technologies and also change the way they work. A lot of this can improve how we work. So we can work smarter, make better decisions and make them faster.

SS: Because one thing is, you know, having the data, people say that the oil and gas companies have tons of data. It's more of being able to use the data for something that really is useful. But then the other thing is changing their processes.

BED: Yeah. So I think it's both. It's on the data side, it's two folded. So some data they have always had, and been available. Most of that data for us, most of that data has always been available, it has just not been utilized in the way that we do. Other parts of the oil and gas, they have not had that data available. It's been just stored in some safe place and unavailable for the ones that actually need the data. So that's a big difference for us. It's always been available, but now it's how to really exploit the value that is in there. And it's not trivial, because an oil and gas field is quite different from any other process system. So you have to take that into account that this is a completely different system with a lot of noise, less kind of, you have basically no clue about the real physics. And it's constantly changing, so that's creating hurdles for applying machine learning. And I think that some of the things that we have been able to solve, is how this can actually be applied in the oil and gas environment.

SS: You were saying that Norway has a very exciting ecosystem. What do you mean by that?

BED: I think some of the Norwegian oil companies are kind of really on the forefront of the digitalization wave, coupled with a lot of infrastructure that they have in place in the North Sea with the fiber optics, et cetera, that really makes real time data available and accessible. Coupled then with our supplier industry that is growing, emerging now with players like ourselves or the coordinates or the kind of incumbents, the seamen and ABB and everyone, there's a lot of competence on the infrastructure in place in Norway. And there is a collaborative spirit. So we work with many of them at the same time, and in the same projects, and I think that's pretty unique. The level of trust is high. Many of the people know each other from before, and I think that creates a good opportunity for Norway to develop a supplier industry in the digital space.

SS: And then there is also this timing issue where you say that, you know, we are moving from the hype into industrial relevance. Why is that happening now? You said we're out of cost doldrums, but there's something else?

BED: Moving from the hype, so I think there's clearly been and still is kind of hype in general, of what machine learning can do and cannot, et cetera. But I think now people have tried it, and tried different ways, angles to it and starting to get real results and create real value. And that also makes it possible to start to operationalize it. It's not just kind of research or pilots or simple mockups. It's it's real applications on real value being created and captured. So yeah.

SS: So I was asking you about some other favorites or inspiring examples of energy technology. And there are quite a lot of interesting things coming out of your university, and also coming out of the some of your customers that you mentioned that collaborate on these things. But you mentioned also that you might find other applications in your company for a sort of a reverse. So now you're emptying these reservoirs, but you might find ways to fill them with a CCS kind of content or, and there might be things in NLG or solar or wind that might be relevant for optimized extraction. I mean, what other things do you think are interesting?

BED: Um, those I think most energy technologies are interesting. I think. Well, what's really amazing now, is that we are in this transition phase.

When I started working with oil and gas, everyone was talking about peak oil, that we would run out of oil and gas. The opposite has happened. We have found extreme amounts and with shale gas, the shale oil in the US and everything. So now we have an abundance of oil and gas. At the same time, we have made huge advances within renewables. So we kind of have this parallel track. And I think that kind of solves the short term and the long term energy outlook, or demand. But I think you in particular, I think the LNG is really a short term, interesting technology, where Norway has always played a role, both on asset owner side and on technology side. So that that kind of solves, or allows us to utilize these vast gas reserves. Solar and wind are always in the media, so people kind of know the advances there. The question is really, can we also make oil and gas sustainable in the long run? Then I think we would have kind of the best of all the world, kind of both the efficient fuels of oil and gas, combined with renewables.

SS: You said something to me earlier that I think is a very important point I'd like to go back to. In the oil and gas industry, we have the principle of best available technology. So all oil companies have to demonstrate to the authorities that they operate in the best and safest way. And this, in turn, creates a market for technology adoption. For me, this is a really interesting way, you know, an application of incentivizing innovation, that then leads to world leading products, that then leads to new markets. How do we do more of this? You know, we did it in oil and gas. Should we do it for welfare and health?

BED: Yeah, it's very interesting.It's clearly worked for Norway in the oil and gas domain. There is a huge amount of innovation. Relatively few people here. So and so I think we can be proud of what we've done there. Rarely see that in other industries where we are more adopters of foreign technologies and standards. You mentioned health care. I can think of some kind of medicines, et cetera, that we kind of, where we often are challenged for being laggers. So I think those standards, at least, have really fostered innovation in Norway within the oil and gas industry. And we could probably adopt that for other industries going forward.

SS: Where do you go to learn more? Do you have some recommended reading?

BED: Yeah, so. So I think at least from my perspective, I think that since machine learning is such a topic, everyone should try to get a grasp of what it actually is and what it isn't, because it's not as dangerous as people think. But it will impact every industry and I think every workplace. Myself, I go just to YouTube or to some of these universities that publish their lectures online to learn. I think that's a place where people should go to learn and try to understand, start with the basics and advance.

SS: Do you have a favorite technology quote? You could leave us a little present for our listeners?

BED: Yes. So I think I like to quote our CTO. Bjarne Grimstad, he said that .

His motivation for doing what we do, is that no one has been able to do it before. And I think that's the real motivation people need in the energy technology space to really challenge the status quo and try to solve the impossible.

SS: If people have to remember one thing from our conversation, what would you like it to be?

BED: I think it is that we are now actually seeing tangible results of data driven applications or machine learning, A.I, in the oil and gas industry. So so we are moving from the early phase of experiments into real value capture.

SS: Where the real money is.

BED: Yeah.

SS: But also a huge effect, positive effect on the environment. I think we should also remember that optimization has a very, very important long term effect as well.

BED: Absolutely.

SS: Bjørn Erik Dale, from Solution Seeker. Thank you for coming here and helping us understand more about the digitalization in the oil and gas industry.

BED: Thank you.

SS: And thank you for listening.


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What are you doing at work?

At Solution Seeker we are developing the first artificial intelligence for real-time oil and gas production optimization.

What are the most important concepts in energy technology?

The digitalization wave that is hitting the oil and gas industry now is huge. It has so much data that is not being utilized and exploited, and combined with the recent advancements in machine learning and computing power, it is driving big changes in the industry.

Why is it exciting?

This enables new ways of working. New technologies and solutions radically change how we can interact with and learn from the field itself.

What do you think are the most interesting controversies?

The hype. When we started a few years back no one focused on digitalization – it was all about cost cutting in the oil and gas industry. Now the focus has changed, and there are so many with little or no clue that is trying to get a piece of the so-called “digital transformation”.

Your own favourite projects in energy technology?

ProductionCompass is pioneering advanced data analytics, machine learning and optimization for some of the world’s most complex production systems.

Your other favourite examples of energy technology internationally and nationally?

I am really optimistic about energy technologies in general, both providing the short-term and long-term solutions to supply the world with energy in a sustainable way.

What do we do particularly well in Norway of this?

We have always pushed for new technological developments. In the oil and gas industry we have the principle of “best available technology”, so all oil companies have to demonstrate to the authorities that they operate in the best and safest way.

A favourite energy technology quote?

My fundamental motivation lies in the fact that we are solving a problem no one has been able to solve before us.

Most important takeaway from our conversation?

There are real advances in AI for oil and gas, and those advances are being made by and between major oil companies and new start-ups.

Bjørn Erik Dale
Solution Seeker
CASE ID: C0245
DATE : 190206
DURATION : 17 min

David Silver from Google’s DeepMind Andrew Ng at Coursera


Avansert dataanalyseMaskinlæring
Olje og gass

"There are real advances in AI for oil and gas, and those advances are being made by and between major oil companies and new start-ups."
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