Operational Safety With AI: Chevron’s Ellen Nielsen

**Sam Ransbotham** (0:02)
Digital Twins, Generative AI for Engineering. On today's episode, find out how one petrochemical company upskills its workforce to benefit from new tech like generative AI.

**Ellen Nielsen** (0:15)
I'm Ellen Nielsen from Chevron, and you're listening to Me, Myself and AI.

**Sam Ransbotham** (0:21)
Welcome to Me, Myself and AI, a podcast on artificial intelligence and business. Each episode, we introduce you to someone innovating with AI.
I'm Sam Ransbotham, Professor of Analytics at Boston College. I'm also the AI and Business Strategy guest editor at MIT Sloan Management Review.

**Shervin Khodabandeh** (0:40)
And I'm Shervin Khodabandeh, senior partner with BCG and one of the leaders of our AI business. Together, MIT SMR and BCG have been researching and publishing on AI since 2017, interviewing hundreds of practitioners and surveying thousands of companies on what it takes to build and to deploy and scale AI capabilities and really transform the way organizations operate.
Hi, everyone. Today, Sam and I are speaking with Ellen Nielsen, chief data officer at Chevron. Ellen, thanks for taking the time to talk to us. Welcome to the show.

**Ellen Nielsen** (1:16)
Thank you for having me. I'm really excited to have a very cool conversation today.

**Shervin Khodabandeh** (1:21)
Let's get started. I would imagine most of our listeners, in fact, all of them have heard about Chevron.
But what they may not know is the extent to which AI is prevalent across all of Chevron's value chain. So maybe tell us a little about your role and how AI is being used at Chevron.

**Ellen Nielsen** (1:39)
Maybe talking about my role, it started three years ago. I was the first data officer within Chevron. That doesn't mean that we deal with data since a long time. But the need to put more focus on the data was starting to emerge. And with that, I was tasked in evangelizing data-driven decisions. And that, of course, includes any kind of data science analytics along the way. And that was very, very interesting to see it growing over the time. We use AI in many places.
Some areas where we use robots, for example, in tank inspection today. You can imagine that was very cumbersome. Having the human involved, now we do this with robots.
And we take basically the human beings out of these confined spaces. And that's a combination of computer vision, taking images, comparing the images, and take predictions on what's the status of this tank and this equipment. Is it rusting? Does it need maintenance? Do we need to tackle it in a very predictive way? So that's operating in a much more reliable and safe way in the future.
The other example is when we talk about sensors in compressors or any kind of equipment. In the past, we were, of course, installing them. But the prices dropped so dramatically for those sensors and the data collection.
And I just saw recently, actually, it was a citizen development application which has been created because these sensors have to be installed. And when you install them, you basically take a QR code. And with one click, you can add the geospatial location to the sensor. And then you can see all these sensors you have installed in your facility in a map. So you see actually really actively happening what's going on and where are the things actually working and which sensors have been inventoried there. So we have a combination here of computer vision, of using citizen development, and then, of course, using this sensor in a machine learning, AI-based way to come to predictions and how they work.

**Shervin Khodabandeh** (3:48)
So one of the things I know that you do quite well is digital twin. Maybe you can comment a little bit about that example.

**Ellen Nielsen** (3:57)
Digital twin is one of many examples where we use that.
What triggers to do digital twin? One is you can imagine that we have people out in the field, so we want to make their life easier and safer. That means the more data and the more information we can gather about our field assets and how to operate them will serve the purpose for being more safe, more reliable in the operations. And that was one trigger.
The second trigger is that you collect a lot of information based on, let's say, Internet of Things, IOT devices, censoring, and that feeds into another pool of information where you can drive even predictive decisions in these assets. So with the digital twin, we want to basically serve both. We want to be safer, reliable, but also more predictive on what we do that speaks to efficiency and do the right thing at the right time.

**Sam Ransbotham** (4:54)