Professor of geological sciences at the Stanford Doerr School of Sustainability Jef Caers has argued Artificial Intelligence (AI) is the key for ensuring countries hit their net zero targets.
Multiple countries have pledged to achieve net zero emissions by 2050 as part of the Paris Agreement, but achieving that goal will require complicated feats of engineering, argues Caers. “Doing this successfully will require AI,” said Caers. “We can’t figure it out on our own.”
Caers team uses a type of statistical reasoning called “sequential planning under uncertainty.” A planning and designing system where each move affects the ensuing move, in order to decipher the best choices possible.
At its early stages, Caers and his colleagues collaborated with the company KoBold Metals to develop an AI system for efficiently deciding where to drill for minerals that are essential for making electric vehicle batteries. “It’s a way of prospecting in an intelligent way by deciding what information needs to be gathered in order to reduce uncertainty most,” Caers says.
Caers’ team has started using the same approach to determine where and how to inject carbon dioxide (CO2) into the earth to safely store CO2 produced by steel or cement factories.
This has led to a $5.15 million collaboration with the Austrian energy company OMV and professor Mykel Kochenderfer (Stanford Engineering) that Caers’ team will use to develop intelligent agents to help Europe transition from oil to sustainable energy sources for residential and industrial heat.
“The software we use for all of these projects is very similar,” said Caers. “Whether you’re planning to drill wells for geothermal energy or to monitor a CO2 injection site over time, there’s a single formulation of this problem mathematically. This means, he says, that there will be many other instances in which AI can contribute to planning for Net Zero 2050.”
Net Zero 2050 and CO2 Sequestration
Caers argues the toughest challenge in hitting net zero will be dealing with industrial heat production. “Fossil fuels burn hot and today are relatively cheap. In the long run, other solutions are emerging, such as hydrogen or ammonia, but today, the only way to mitigate the effects of burning fossil fuels for industrial heat production is to take the CO2 produced and put it underground.”
Sequestering CO2 in the earth involves injecting it into a location where porous rock lies beneath a non-porous rock such as shale that is capable of containing a substantial quantity of CO2 without leaking it into the groundwater.
Knowing where and how much to inject is one planning problem Caers’ team will help to address. Another is safety: Researchers know that injecting water into the earth for geothermal energy production can trigger earthquakes. And because CO2 becomes a liquid at great depths, the same risk exists for CO2 sequestration. The future of Net Zero 2050 depends on precise planning to ensure the risks are minimised.
Prototyping an AI Decision-Making Tool
Knowing where to inject fluids into porous rock and understanding or predicting how it will bubble up is incredibly difficult. To tackle this, Caers’ team recently developed a system to determine how many CO2 wells to inject in what order/rate at a particular site using AI.
To take their intelligent agent to the next step Caers team has collaborated with OMV to implement this into depleted gas reservoirs in Europe.
Investment for the Future
As well as a moral imperative in achieving net zero by 2050 companies also need a business incentive. Investors have responded positively to the use of AI for mining cobalt, a key mineral in electric vehicle batteries.
Caers thinks investors will be drawn to the way AI will take some of the guesswork out of carbon sequestration. The recent $5.15 million investment from OMV signals as much and means Caers and Kochernderfer’s team will double from 10 to about 20 graduate students working on the various components of the AI system.
“One of the challenges with Net Zero 2050 is the speed at which things have to happen,” Caers says. “We need to do complex planning and engineering on a massive scale at a very significant speed. And for that, we need smart solutions that use AI.”
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