With low commodity prices, growing environmental concerns, and difficulties with market access and supply chains, the pressure is now on oil and gas companies to innovate. For the Alberta Machine Intelligence Institute (Amii), which has a mandate to accelerate AI adoption in industries such as oil and gas, the key is not to do the fishing for these companies but to help them fish for themselves.
It’s been five years since the Government of Canada launched the first phase of what was the first national AI strategy in the world. Canada remains at that leading edge – an outcome that, according to David Chan – Product Lead, Industry, Amii, is due in no small part to the bold thinking and insistence on leveraging the power of synergy of organizations like his.
“We run on two pillars,” he said. “Through leading-edge research we’re able to push the boundaries of what’s possible. This bleeds into industry adoption among oil and gas companies (among others), and, generally, to a growing base of AI and machine learning skills among Canadians.”
This synergy, said Chan, is bearing much fruit. “Research leads to corporate investment, which results in further research. The two pillars work as one, with the result being that innovation in the oil and gas sector is skyrocketing.”
Chan said helping oil and gas companies fish for themselves is done through training, general AI guidance, and through another key piece.
“Our connections to top-quality talent in areas like AI means we’re often a bridge between people of a specific skill-set and the companies who need them. If not for this, you might – and often do – have these individuals leaving the country to work for the Googles of the world.”
In any emerging technology there is the question of how to best use it – how to evolve it from “cool” to “practical” or even “difference-making.” Chan mentioned one startup company in the province of Alberta that is working with an oil and gas pipeline operator to monitor pipeline health, and coming up with accurate assessments of the probability of possible leaks in the future.
“This company is currently using manually collected data – collected by the operator – who will then, using AI and ML, come up with a forecast of what pipeline health is going to look like down the road,” he said. “From this, operators get a much better understanding of what to focus on and which sites to prioritize for predictive intervention to ensure no accidents and leaks occur.”
Download – Profiles in Innovation: Oil and Gas
Chan said that while the company draws upon mostly manually derived data at the moment, they are looking to move toward sensor data.
“At this point,” he said, “it’s a matter of walking before you run – of proving out what you can do first, clearly showing benefits, and then you can potentially use sensors for even more in-depth and accurate data. And make no mistake, there are benefits. I know one company that is actually using sensors in their work with clients. The results speak loud and clear – 30 per cent savings in pipeline maintenance costs and four times ROI.”
Real World Results
Profire Energy, billed as “the experts in burner management and combustion control,” has been able to move up the AI adoption spectrum by working closely with Amii through its Reducing Emissions through Machine Intelligence program.
“Profire came in wanting to explore possible opportunities around leveraging AI in their business,” said Chan. “We helped them assess opportunities specific to them, where some of the pieces were already in place for them to be able to move forward without too much difficulty.”
One area Chan said Profire is dialed into is the detection of out-of-tune burners. “They wanted to begin using sensors to predict when a burner is out of tune. This helps companies run their burners more efficiently, with less wastage in the form of unused fuel, which is not only cost-inefficient but also a potential environmental hazard.”
Know Thy Self
Chan refers back to his organization’s AI Adoption Spectrum and perhaps the biggest key for oil and gas companies looking to move into the brave new world of AI and ML.
“It’s absolutely essential that companies know where they are at the moment, and from that come up with a clear view of what steps they must take to get to the point where AI and ML becomes a true differentiator. We come in when companies commit to change.”
“The adoption spectrum has four dimensions – Strategy, Tools, Skills, and Data. Oil and gas companies looking to turn AI, for example, into a difference-maker will be made to look at each of these areas, asking key questions like ‘Do we have the right skill on board?’ and ‘Do we have the right tools?’ and ‘Do we have the right strategy set in stone?’ AI readiness comes when companies satisfy all areas.”
Download – Profiles in Innovation: Oil and Gas