Machine Learning/Climate Action Alert

One of my PhD students, Lyra ( @Lyra_Wang) proposed an idea to use machine learning to significantly reduce the cost of #methane emissions mitigation.

I didn't think it would work. She still wanted to try.

And here's what happened.

As background, #methane leakage from O&G is a big issue. They are also hard to find:

1) Leaks cannot be predicted
2) There are super-emitters - the top 5% of sites account for *half* of all emissions (see fig.)
3) We couldn't predict which sites will become super-emitters

So what did governments do? They required operators to check *every* single site for leaks. These are called leak detection and repair programs. So, operators go site by site, in random order, to find leaks.

They work.

But, they're also expensive. https://iopscience.iop.org/article/10.1088/1748-9326/ab6ae1/meta

. @Lyra_Wang asked if we can predict which sites become super-emitters using public data. Consider this:

You are an operator with 200 sites (20/200 sites being super-emitters). In random order, it will take 40 days to finish the survey. ~20 days to find 50% of emissions.

Turns out, you can use machine learning to identify a survey order instead of randomly going to all sites. You can do this by asking the model to estimate probability that a site is likely to be a super-emitter and survey in order of highest probabilities.

Here's what we found:

In the ML predicted order, you mitigate 50% of emissions by day 7. If you survey randomly, it takes 12 days to achieve 50% mitigation. If you survey based on production volumes, it takes 10 days.

Cost to achieve 50% mitigation reduces from $82/tCO2e to $49/tCO2e. That's huge!

Obviously, this is preliminary. We're trying to extend the model to all of the US, improve predictive power, & add other variables (e.g, time since last survey).

ML is powerful, but whether it will be useful in this context remains to be seen.

Feedback & new ideas are welcome!