Luther Birdzell

It was $160 million, which is the size of many IPOs.

So just real quick, my background is electrical engineering, as you noted.

And for the past 20 years, I've been building software to make data more valuable to subject matter experts.

Coming out of my last company in 2013,

it was very clear to me that AI and machine learning were the most valuable technologies for increasing the value of existing data.

Uh, oil and gas was the fastest growing area of the U S economy at the time.

Oh, uh, the oil and gas industry was the fastest growing area.

What year was that?

Oh, excuse me, 2013.

So, um, you know, quickly came into focus.

So that, that helped bring the op, those just kind of high level economics helped bring this opportunity into focus and,

As we started to peel back the onion a little bit, well-planning optimization and forecasting the wells pre-drill was the area that we thought we could affect the most change, the most benefit with AI and machine learning.

And if we look at the oil and gas industry, Nathan, about $500 billion of cash is spent every year in the upstream part of oil and gas.

Right.

And 90% of that $500 billion is spent 30 days pre-drill to 30 days post-drill.

So it's essentially, you know, it's the planning and the execution of these wells.

And then the remaining 10% is spent over the remaining 20 to 30 year life of the well on various things with production.

So we started in the most capitally intensive part of a very capitally intensive industry and are consistently finding over 10% optimization opportunities in less than three months with the companies we're working with.

So on a per well basis, these are ways to change the wells

uh, to either reduce costs and get the same amount of oil out or keep costs the same and get more oil out.