Big Digital Energy

00:00:00:00 - 00:00:19:22
Unknown
Hello everybody. Welcome to Pro frag. This is so cool out here. All right guys. As an ex finance pro they would never let me out in the field because the finance guy would always walk up push a button. Hey what is this Stu. And I'd mess something up. So this has been cool that y'all actually have me out here.

00:00:20:00 - 00:00:46:02
Unknown
Tell me what you two guys are doing together. I got the demo in there and thought it was really cool. Matt. What's up? Yeah, well, pro frag, probably 7 or 8 years ago, we started working on frac automation and, you know, we put ProPilot 1.0 out, about two years ago and, you know, eventually evolved into ProPilot 2.0, where it's fully automated.

00:00:46:04 - 00:01:19:22
Unknown
And what we've been looking for is, is a partner for real time data. We want to know more about what's going on down hole and, and looking at, you know, what is some real time data that we can, collaborate with and provide automated responses and really take completion designs forward from where, for the last, you know, since the show revolution started, it's been a, a cookie cutter approach where every stage is the same, very little variability from one stage to the next.

00:01:20:00 - 00:01:46:08
Unknown
And with frac automation, you've got the ability to adapt in real time and make make real time changes that are predefined based on the information and the real time data that you're collecting. But at pro frac we don't we don't collect as much real time data, outside of our pump performance, pump rate, pressures. But, what's what's unique about size most.

00:01:46:08 - 00:02:11:16
Unknown
And I'll let the panel talk about it here. You know, he knows so much better than I do, but, what you can see from a uniformity index, from perf efficiency. And how good is the frack? How well is each perf cluster taking fluid and sand? And how how much better can it be if we specifically target these parameters and make real time changes to to deliver this?

00:02:11:18 - 00:02:40:02
Unknown
And more importantly, how do we give the tools to the customers so that they can define it and predefine what they want to happen? Rather than coming to them and saying, we know this better than you do, like we've got it, we'll fix your problems. Like, it's that's not what we are. We, we want these tools to be fully utilized by our customers so that they can come in and, and push completion design to the next level.

00:02:40:04 - 00:03:02:06
Unknown
And it's I think that capability now exists and it's, it's it hasn't been something that was available previously. I mean, I'm old enough to remember you'd send out on a fracked and you waited two days to get the report to find out what happened? Or are you telling me that doesn't happen anymore? We can do stuff in real time.

00:03:02:08 - 00:03:37:13
Unknown
Yeah. Good point. So let me let me say a couple of words about size. So, what we bring to the mix. So we are a technology company focused on acoustic sensing and, essentially, you know, we, enable real time data, you know, for the subsurface and, and, you know, for the, for the business. We always had this vision of, of, you know, the pumping being automated of, of machines being self learning and essentially been in an adaptive mode and, you know, constantly learning more and more as a job goes.

00:03:37:15 - 00:04:02:08
Unknown
The partnership that we announced has to do with, you know, closed loop fracturing is how we call it. And essentially it is correcting a paradox. You know, we had surface based companies, you know, the pumping companies performing a subsurface task that was not even being measured. So essentially, we're closing the loop by enabling real time data feedback from the subsurface.

00:04:02:08 - 00:04:39:08
Unknown
And it and it integrates smoothly with all the frack automation that pro frack has enabled on the surface side. And what a closed loop means is we have now a situation where a pumping company is performing its task. We, you know, with all the automation that comes along with you can see in real time the performance of the frack that data feeds, the completion logic and that logic, you know, which can be pre-configured by the user as always, essentially dictates some of the changes that essentially have to be done on the pumping side so that you can optimize performance.

00:04:39:10 - 00:05:09:02
Unknown
So now that I can do things in real time with real data, do we have enough evidence to know, am I saving money on the frack? Am I increasing performance of the reserves as frack? Or am I doing both? So, most of the work that we've done for over the last two years with frac automation has been really focusing on, efficiencies, utilization and cost structure.

00:05:09:02 - 00:05:30:22
Unknown
Let's optimize the equipment for best performance. Let's get these components to last longer. But now that we've got this partnership with size Moz, now we have the ability to go in and target well, performance. And and that's that's for me. That's where it gets really exciting. I think, you know, why? Why do you innovate? Why do you build stuff?

00:05:31:00 - 00:05:56:01
Unknown
And I think I think the big reason is, like, my customers really, really don't care how much it costs me or how much, my expenses are or how much it saves me. They want to know what is okay, what is, why do I need this? What does this do for me? And and I think the answer is we can go in and and and work with our customers to advance completion design to get better results.

00:05:56:03 - 00:06:27:13
Unknown
And, you know, this is everything from uniformity, index, perf efficiency, as well as, you know, getting real time data from like disposal wells and, you know, allowing for us to delineate the third bones spring. There's, there's a lot of risks with these additional benches. And if you've got real time data and the ability to act on that real time data, we can start opening up new benches and helping operators delineate these, these formations.

00:06:27:13 - 00:06:51:04
Unknown
So as far as an uplift in, well, results, I don't want to get in and make promises. But I'll tell you this. You give the customer and their engineers better tools. They will figure it out. And that's that's our goal here is let's give them better tools, better execution and and, you know, work with them on how to maximize the result.

00:06:51:06 - 00:07:15:19
Unknown
Yeah. If I may add a point or two. So I think another way you put your question is there's real time data matter when it comes to savings or production. And there's been sufficient evidence, you know, in the, in the industry has, for example, published a paper of the recent Euro tech conference where they show that if you can monitor fluid distribution and just marginally improve it, let's say by 10%, you get an extra NPV of 200,000 for a well.

00:07:15:21 - 00:07:32:03
Unknown
So by all means, you know, knowing how many customers are taking fluid, by all means it does correlate to production. So and that's one thing, how you approach the situation of course may vary according to the customer strategy. You may have a stage, for example, where 50 of the clusters or 50% of the clusters are taking fluid.

00:07:32:05 - 00:07:49:13
Unknown
What are you going to do? Are you going to fight it with hoping that you're going to add some more clusters? Are you going to cut volumes a little bit? I mean, you don't want to over capitalize and you know, essentially inject the same design volume, but to 50% of the clusters, it's going to lead to problems in fact.

00:07:49:15 - 00:08:13:06
Unknown
So and that would lead to significant savings like just saving, for example, 10% of volumes at a given frack job is going to save you hundred and 100,000 ramping up, some faster and save you another 50 60,000. Adjusting friction reducer can save you another 20 K. So so depending on the stage of the operator, you can have both benefits on the production side but also on the cost savings side.

00:08:13:09 - 00:08:38:12
Unknown
And I think what we are enabling now with the closed loop fracturing is for the first time, you have visibility on the millisecond. So you can see that during the stage and then it's on you how you act on them or how you act on the data, on which strategy you want to follow. Interesting. Because one of the things I always thought we had an issue with is completions of reservoir would be in their own silos and not talk to each other.

00:08:38:13 - 00:09:09:12
Unknown
And a lot of times this was your analogy completion recipe. And you'd come over here and you'd optimize it for cost and use. You know, I saved $200,000 on the frack and reservoirs going, but we needed that much water to get the reserves and stuff. And so is there an ability for potentially the reservoir person to be working in real time, along with the completion people?

00:09:09:14 - 00:09:45:03
Unknown
Yeah, I believe so. I think I think one of the one of the things is, is, when you get prescriptive on a per lateral foot standpoint, you're, you're adding in complexity, you're adding in, a lot of variability from one stage to the next. And you're opening up opportunities for, for risk, for failure, for, for, for issues and, you know, so when you, when you come in and you start introducing this complexity, it, it may it usually comes at the cost of efficiency efficiencies that the industry has fought so hard for.

00:09:45:05 - 00:10:10:07
Unknown
And that's where automation in real time data and a and a closed loop, you know, usability of that data allows you to have that complexity without sacrificing the efficiencies that the industry has fought so hard for. If I may add to this, I think you do have those engineering silos. We still have them today, and we're doing our best to bring them together.

00:10:10:09 - 00:10:32:08
Unknown
But I think as we're moving to tier to acreage, this is no more just an engineers discussion. The CFOs come in, the CEOs come in. You know, the whole strategy needs to change a little bit. Whereas optimization before was was not necessarily the, the, you know, the the first priority thing as we're moving to that tier to acreage, it's no more an engineers decision.

00:10:32:08 - 00:10:54:18
Unknown
That's what I'm saying. It's not forgiven anymore. So really being able to have real time data and acting on it is really becoming a sea level now type of decision. And and that's the approach we're taking with, the closed loop structure. So, Panos, when I was getting the demo in the, in the data van there, somebody brought up the concept of an independent audit.

00:10:54:20 - 00:11:19:23
Unknown
What does that mean? Yeah, yeah, that the head. First of all, I need to congratulate Pro Frack for being open to the idea of an audit because despite the partnership, we as a company or in the measurements business was the thing of it. So regardless how these guys perform, we're going to read what the performance is. We're going to benchmark it, we're going to provide a number and maybe good and maybe less good.

00:11:20:01 - 00:11:44:14
Unknown
So, so I think I think kudos to pro frack being open in that level of transparency because for the first time now you have a pumping company that is brave enough to say, you know what, I can do my work, I'm confident enough and I can be measured and I'm okay to situations where I'm not perfect, but at least, you know, so that loop, I can make an adjustment and I can correct for that imperfection.

00:11:44:16 - 00:12:23:14
Unknown
Well, Matt, the other thing I heard when I was in the, data van is I heard supervised and unsupervised closed loop. What are those two concepts? Yeah. So unsupervised is, you know, we take the completion schedule and then we also have the dynamic configuration so that whatever feedback we see while we're fracking from and whatever real time data we see, we execute, set of, of, of orders and we start executing based on what the customer is, is, configured and what they've required of us.

00:12:23:14 - 00:12:51:17
Unknown
And so there's a before a job starts, we go in, we work with them, we program everything, we walk through it. There's a customer configuration tool that allows them to go in and pre configure how they want us to respond based on what events or what data we see in real time. Now, of course, on the supervised side, under no circumstances do we want to take control away from our customer.

00:12:51:19 - 00:13:19:03
Unknown
You know, if if our customer wants their consultant to have, the ability to get in and control this and make adjustments to it, they still have control over the fact, they can still step in and make adjustments to, you know, for, for whatever reason, for any reason that they want. But with the pro Pilot is we've got full documentation of what was changed, why it was changed, and full visibility.

00:13:19:03 - 00:13:58:11
Unknown
So an unsupervised think of it like, like a Tesla with the full self-driving. You know, these cars will pretty much do the whole job for you. Now. They'll, like, drive you home. But, you know, if you need to step in and take the steering wheel, you still can. So, yeah. Yeah. So we live in a world now of AI and, and my running joke is I'm old enough to remember when we called that statistics, but, I, I'm told my kids that I've lived through three game changers.

00:13:58:11 - 00:14:33:15
Unknown
The internet and the reach of, the, the net to connect the world. I lived through the shale revolution, which totally changed geopolitics, the world, our economy, etc. and I think AI is actually going to be bigger than those two put together. How are y'all using AI? How are you thinking about AI? So I think I think when you go in and you start looking at, you know, automating a lot of your back office, I mean, I think everybody's working on that or should be working on it.

00:14:33:15 - 00:15:00:05
Unknown
And I think it's the amount of data that you can process, you know, and, and the number of processes you can automate is, is just incredible. But what I'm really excited about is we we collect so much data. It's it's nuts. I know the size most is collecting 20,000 data points per second. Is there are. And then, on our side, we're collecting 4000 data points per month.

00:15:00:07 - 00:15:21:05
Unknown
Per second. You know, we go up to Silicon Valley to try to raise money. And I tell people out there, we use less than 1% of the data we collect as an industry, and it blows their minds. It's just so much data that, like you, you've got a team of engineers, at your corporate office, in your maintenance department, and they're processing data.

00:15:21:05 - 00:15:57:22
Unknown
They're looking at reports, they're looking at all kinds of things from different angles. But you also don't want to rob your operations team of like, the tried and true, operational experience that they have. And there's a lot of tribal knowledge in this industry. Some of it's good, some of it's not. And I think but by taking a fresh look at it, processing as much data as you can and and what I allows you to do is to get organized with the way that you process that data for, for example, one of the things we've seen with, with automation, we had a fleet in the Utica that was pumping 110 barrels a minute.

00:15:58:03 - 00:16:22:20
Unknown
We had 22 pumps in line. You know, for, for redundancy, we had an extra six pumps. The problem was, is that the, you know, the way that your pump operators are trained is they balance that load across all 22 pumps. And so pumping five barrels a minute with 22 pumps to get your 110 rate, they consumed 1500 gallons of diesel an hour.

00:16:22:22 - 00:16:46:23
Unknown
But if you optimize it to 16 pumps and you pump, you know, little under seven barrels minute, what you what you end up with is only consuming 1100 gallons of diesel. And you don't put engine hours on those other six pumps. You don't put hours on your transmissions on your other six pumps. I'm not changing valves and seats or packing on the other six pumps.

00:16:47:00 - 00:17:10:03
Unknown
So it reduces the cost structure for us and it saves the customer 400 gallons an hour, which on an annualized basis is about $7.2 million a year. Just just because we ran 16 pumps at seven barrels minute instead of 22 at five. And so this is challenging. A lot of, you know, the way that you, train your your pump operators.

00:17:10:05 - 00:17:30:15
Unknown
I can do a software update now and target specific outcomes on this equipment and run it a specific way because it saves the operator money. And on top of that, with with the AI, we can come back in and do the proper analysis to actually prove it, because that was that was a there's a lot of noise. There's so much data.

00:17:30:17 - 00:17:57:11
Unknown
There's a lot of field tickets that the operators have to sort through. And the way that the industry calculates substitution is like, well, how much diesel did we consume, you know? Well, how much gas did we pay for? And it's the analysis on measuring this stuff in real time. It's just the industry's overloaded with data. We have too much data and we don't have the ability to process at all.

00:17:57:12 - 00:18:17:19
Unknown
And that's that's what's so great about AI is it's it's changing the way that we think about all the data that we're collecting. If I may add a few points, and I'm going to try and make it a little bit more specific to the closed loop fracturing that we're offering. So these guys pump, then come seismic, we take the real time subsurface measurements.

00:18:17:21 - 00:18:39:22
Unknown
One key differentiation is those are physics based. This is really important because it's not no more something that is inferring on an outcome. It's a direct measurement of the outcome. Those direct measurements are feeding a completion logic. And that's where I comes in, because you now have some conventional logic that says, okay, I read this close to efficiency 6%.

00:18:40:00 - 00:19:02:19
Unknown
What should I do? Right? Should I add friction reducer? Should I increase rates? And I do this. And that's where the AI component comes. Because as you frack more stages, that specific acreage for that specific operator, taking into account knowledge, maybe from other wells, you build knowledge. So you pass now. So the completion logic incorporates high pass feedback to the pumps again to the pro frac pumps.

00:19:03:00 - 00:19:22:01
Unknown
And those are making an adjustment. But guess what? The whole system gets wiser and wiser and wiser. So we move to the next stage would pump again. We take a measurements physics based, really important, big differentiation and then it feeds again the AI powered completion logic. But now it's more ways. It's one stage. Why is than before you understand what I'm saying.

00:19:22:01 - 00:19:46:00
Unknown
And it keeps moving on. And that's the whole purpose of an adaptive system. So we're training the machines of tomorrow. That's what I like saying. Like we have a pump that it's getting wiser and wiser at the pumps. I make two points about AI because I'm spending a lot of time doing it. One, I think it's very important that you understand that the human being is still the subject matter expert.

00:19:46:00 - 00:20:08:18
Unknown
AI can identify correlation. The human needs to tell you causation on stuff. I think that's really important. And I think the other thing that's really important, and here's what I want to get your take on, is we have the tribal knowledge. You talk about the old crusty guy just put his hand on the machine and say, well, it's broken because of this.

00:20:08:20 - 00:20:45:03
Unknown
He got there because of all the grunt work he did throughout his career to build that tribal knowledge. I worry with AI doing a lot of the work and the war front work, how do we create the old crusty guy or gal that we need in the future? Get thoughts on that? Yeah, that's an interesting question. And there's going to be both like, if we think of the oil field of the future, I mean, we all kind of agree that it's navigating towards a state of autonomy, almost human less.

00:20:45:05 - 00:21:08:16
Unknown
But but we cannot take it to that extreme. There's always a need for the expert. Prof. Rag has been very vocal, like the power and the decision making needs to be left in the hands of the customer. So the way we've designed this closed loop fracturing, whether it's supervised or unsupervised, the end decision is, is stays with the customer even in the unsupervised mode, which kind of runs automatically.

00:21:08:16 - 00:21:25:13
Unknown
Again, it's on the customer because it's the customer who comes and reconfigures the completion logic and think of it as a real time decision tree that says, if I read this, I'm going to do this. If I read something else, I'm going to do something different. So by all means, it's controlled by the expert. You're talking about. Yeah.

00:21:25:13 - 00:22:01:07
Unknown
And, you know, just to touch on that, I don't think I don't think I is going to replace, it is coming for, for, or for jobs who say AI is going to, you know, replace humans. I think humans with AI are going to replace humans without AI or, but when you look at the tribal knowledge, you need people who who've seen, you know, failure modes before that they understand that, like, I know what this data says here, but this there's a root cause that's being missed here.

00:22:01:09 - 00:22:24:20
Unknown
All the some of the data isn't going to be given. Like there's no magic bullet. There's no silver bullet here. This is to help make use of as much data as you possibly can, but you're still going to need guys that are coming in and saying, hey, I've seen this before. You know, I know that, like, I think with, with Elon, he talks about trying to optimize workflows.

00:22:24:20 - 00:22:44:03
Unknown
They shouldn't exist. There's problems that come from symptoms that shouldn't exist. You know, where the root cause happened? Way upstream. And I think that's where you're going to need the tribal knowledge guys to come in and say, hey, I know that this is saying this and we need to like, fix that if that's how we're going to do it.

00:22:44:08 - 00:23:08:19
Unknown
But we shouldn't be doing it that way anyway. Interesting. So how did this deal come together? What was kind of this is, y'all are on a dating site, and, and and, hook up was a first about blind date. How did this happen? Yeah. So we've been working on automation for a long time. We've had pro pilot out in the field for a couple of years.

00:23:08:21 - 00:23:29:03
Unknown
And you know, as we looked at the development path, it was all about fixing our own needs and fixing cost. And utilization and how can we be better at what we're doing. But this whole time we've had we've been, you know, really looking what can we do for the customer? What can we do besides going faster and charging less?

00:23:29:05 - 00:23:48:19
Unknown
What can we do for, well, performance. And, you know, so we knew that we needed to find a good data partner to work with. And, you know, Allen Smith made a recommendation to, to Panos as well as to, to us, like, hey, you guys need to talk. These guys are working on some amazing stuff.

00:23:48:21 - 00:24:09:14
Unknown
If you had the ability to, act on that stuff in real time, I think you would have a real differentiated offering that could substantially improve, well, results. This would be a powerhouse of a combination. And I think he's exactly right. Yeah. So, so, Yeah, by all means. It was a common board member that enabled introduction.

00:24:09:16 - 00:24:27:12
Unknown
But at the same time, we as a size was because we've been growing very fast. We realized we were at a time where one plus one can be five, right? So we were seeking the right partner. And and there's very specific things that we like to improve track. And it's not random that we that we are collaborating with project.

00:24:27:12 - 00:24:46:06
Unknown
So we like we're like their attitude. We like that they're aggressive in the market. They're their go getters. We also like the fact that they're understanding that the industry is moving towards a certain direction, and they want to be ahead of the industry in doing things. So it's and most important, it's the thing we mentioned before. They're they're transparent.

00:24:46:06 - 00:25:04:07
Unknown
So they're open open to the whole audit idea. Like they're going to frack and they're going to be measured and they're okay with that. And customers are going to see this. So there's very specific things. We like the project. So yes, it was enabled by, you know, a common acquaintance, but it's actually a partnership that we were very much seeking for.

00:25:04:09 - 00:25:27:18
Unknown
So Panos, I'm gonna put you on the spot first for kind of our wrap up question. Hopefully I haven't screwed up bad enough. You'll have me back in five years when we're here. What are we talking about? In five years that maybe no one's thinking about in the audience, particularly those guys. Rude over there talking. But. But what?

00:25:27:20 - 00:25:50:20
Unknown
What are we not talking about today? That we're going to be talking about the five years. Yeah. Not of the cost of people because we discussed this a little bit, oil and gas claiming that the new energy mix, it's fair share. Is it 30%, 50%, 80%. We can always debate on this, in that new energy mix, every source of energy, including oil and gas, has to be safe.

00:25:50:22 - 00:26:07:00
Unknown
And in some state of full automation and autonomy. So what I think is going to change in three years. You said five. It could be ten is the so-called fully autonomous field I don't think is going to come at the expense of people. There won't be any people at the field, but there's still going to be plenty of people in Houston.

00:26:07:00 - 00:26:34:20
Unknown
But that's the big change that I see happen, right? Yeah, I would I would be willing to go in and say, like, we'll be talking about this in ten years, that the industry still has ten years of inventory left in ten years because of the innovation that this this industry is known for. We're always finding solution solutions. There's benches that haven't been delineated because they've got technical challenges that they have to resolve before they can properly delineate it.

00:26:34:22 - 00:27:08:18
Unknown
And I think it's going to be solutions utilizing real time data, to solve real issues that allows you to do that. And I think that, you know, the what we've seen over the last three years, since 2021 is, productivity per foot. And, well, degradation from inventory quality has been coming down. But I think that, with, with dynamic completion designs and real time feedback and acting on that real time feedback, can turn the tide and actually start showing improved, well, results.

00:27:08:18 - 00:27:31:12
Unknown
We saw a step change in and, well, productivity when we went to slick water, when we went to invest in sand and increase the amount of fluid and sand is pumped per foot. There's so many innovations that that have happened through the years. I mean, going all the way back to the Barnett, we had a customer that stubbed the well at out at, 20 500ft instead of the 5000ft lateral.

00:27:31:14 - 00:27:54:16
Unknown
And we convinced him to let us go ahead and do the full frack that they were going to do on, on the one mile lateral. And it ended up being the best. Well, that they had, even though it was only 20 500ft. And so when we came back in and tried to convince them to do a one mile lateral, they thought we were trying to screw them the whole time, but they actually did it.

00:27:54:16 - 00:28:19:14
Unknown
They let us come in and pump twice as much sand, go from five stages to ten stages on this, this lateral, and you saw this massive improvement in and well results and it set off a massive change across the industry where it was chasing pounds per foot fluid per foot. And I think we've reached the diminishing returns of what you can get by just pumping more fluid, more sand.

00:28:19:16 - 00:28:49:17
Unknown
But when you started getting getting in and looking at what can you do in real time and how how let's let the formation tell us how different this stage needs to be than the last stage. And what does that do for, well, performance over time. And if you can, if you can change a statistical base and into a commercial or a statistical bench into a commercial bench, how how many more locations are there on that bench?

00:28:49:22 - 00:29:16:07
Unknown
How much like delineation can we actually do here? And I think there's so much, like opportunity that when you look at and you hear all the noise about there not being enough inventory, it's nonsense. Just like we did last year or the year before that. Every decade we will continue to innovate, and in ten years we're going to be talking about how operators only have ten years of inventory left.

00:29:16:09 - 00:29:39:05
Unknown
All right, guys, did y'all see the last podcast we did? Yes you did. So Matt and Panos are gone now. What did they mess up? You know, it's going to thing I think it's gonna be hard to follow up on that. But we're going to give it our shot. There we go. Yeah, I don't I wouldn't say they messed up a whole lot.

00:29:39:05 - 00:30:10:12
Unknown
I think they did a pretty good job. All right. Only because this is being recorded. All right. So tell me about the, the partnership. How do we measure what what? Let's get more granular or how do we measure what is good? What is bad? How are we thinking about that? I mean, that's a great question. I think back to what Matt was talking about, about how many data points we get and how much data we have to analyze.

00:30:10:14 - 00:30:31:15
Unknown
It's enormous. And so, you know, we've been at this for a long time. And so we we have a team of engineers that look at, analyze and assess the data points from, you know, screen out, screen out preventions to, you know, the type of chemistry that we're pumping in the water quality. I mean, it's it's all a big deal.

00:30:31:15 - 00:30:52:01
Unknown
And as super complex. And so when we say what's good and what's bad, I mean it's it's trying to filter out the bad and keep the good and, and use that to help train our systems. I mean, that's at least from a surface perspective from, from the track side. I think Steve will probably have, you know, some some additional comments on the down hall.

00:30:52:01 - 00:31:21:07
Unknown
But yeah, and in terms of how do we define what's good versus bad, I think in a lot of ways we're starting at ground zero, because a lot of what we're trying to do today, is not something that's being done on a consistent basis. And so it's about raising the standards a bit. That's what this whole partnership is aim to do is how do we get away from looking at things from a price per stage perspective to focus on outcomes.

00:31:21:09 - 00:31:47:10
Unknown
And so this little things about how do we get better outcomes out of the work that we're doing on location. So I'm an oil and gas company, I'm the client and we're talking about this. How much are we doing on the front end in terms of level setting expectations? Walking through these things is it's still early days and we're figuring that out.

00:31:47:10 - 00:32:09:08
Unknown
But, what are those discussions look like? I mean, from from our side, we've we've already been on some work together already to where you already kind of tested the waters. There is an appetite for the automation plus the real time insights. I think the industry is still absorbing a lot of it. And so we're at the front and center of it.

00:32:09:08 - 00:32:30:01
Unknown
So you will see some building the bridge where it's going to take some time for it to have mass adoption. And again, more case studies, the more success stories you have through that process, you're going to start to see that, what it's going to boil down to is I, you know, I kind of led with that in my last answer, but we're creating this new standard.

00:32:30:01 - 00:32:56:08
Unknown
And for those that aren't doing it, they're going to be left behind. And so it's you're either going to be the odd man out or you're gonna be playing catch up. Yeah. There's a there's a lot on the front end. There's a tremendous amount of work that, that we do. You know, whether it's analyzing the historical performance or, you know, putting together a completions design, whether it's, you know, I think Matt alluded it to, alluded to it earlier, like, why are we doing this?

00:32:56:08 - 00:33:37:23
Unknown
I mean, completions designs have changed drastically since the show revolution, and they've. Yeah. Whether it's more profit per foot or fluid volume per foot. We've we've optimized it to a point where we are today. And now we, we need to take a prescriptive approach. And so that prescriptive approach is, is very complex. And so what, what we, we can do is take all of our data that we have accumulated, know since inception of the company, put all that together to help, build the mattresses for our customers, to help make those decisions, not to make them for them, but to give them some, background of what we know and

00:33:37:23 - 00:34:19:10
Unknown
what we've seen. So then they can make, better, more informed decisions with our tools. So I'm the client. We're going to talk a lot on the front end. How does the discussion go between a supervised and an unsupervised closed loop? What do I need to be thinking about? Pros, cons. Good question. In both cases, the the goal here is to leave the final decision to the customer whether it's them making the decision on the spot versus then building some predetermined predefined logic that follows a sequence of, checks before it makes a decision.

00:34:19:12 - 00:34:41:21
Unknown
At the end of the day, it it's putting the power within the customer for for that workflow. But it's a better defined closed closed loop, supervised versus unsupervised. I look at it a lot in the way of cars. So close loop when you think of what is an unsupervised, what does that look like? You can kind of think like a self-driving car.

00:34:41:23 - 00:34:57:12
Unknown
You know, there's sensors on that on that car that's kind of guiding you and telling you what to do or even in some cases with adaptive cruise control. You know, it's alerting you that you're about to smash into the person in front of you, right? And then when we look at supervise, you can think of that as just regular cruise control.

00:34:57:13 - 00:35:22:04
Unknown
Like if you didn't take some action, then you would likely have an issue, whether it's braking, etc.. And so you can think of frack in the same way. Yeah, I think I think it depends, on the customer and their comfort level and their knowledge of the, the tools that we have. And so the more we can educate and, and show and open up and, and give them the tools to make the decisions.

00:35:22:04 - 00:35:45:21
Unknown
I mean, I think the more comfortable that they'll get or they will be. And so, you know that when we when we think about supervised versus unsupervised, it maybe, you know, we start one way and we work our way into the, you know, into the unsupervised. But yeah, not not really. We I guess I'll back up as the customer who's, who's building in designing it and making those decisions to, you know, get to that point and that level for their wells.

00:35:45:23 - 00:36:11:03
Unknown
So and to further add to that, you know, there's there's two things that we're accomplishing here. One is we're we're taking the the automation from the pro frac side, which is going to help with a lot of the consistency on the execution part of it. And then it's we're taking the, the, the subsurface intelligence part. And so instead of sitting in a fragment and looking at treating pressure and asking yourself like, am I losing perforations?

00:36:11:03 - 00:36:33:14
Unknown
Is is my pipe friction higher than it needs to be having these measurements, having this insight to kind of help you guide you through that process on like what's what's the right knobs to turn is is what we're after today. So going back to my days as, as a finance pro, we used to do early stage assets.

00:36:33:16 - 00:37:06:11
Unknown
And so I talk about I've done 125 leasing drills in my career, and we would often drill the first horizontal well in accounting. Those are the kind of assets we, we, we like to do. And so one of the things that I found really important in doing those type projects was making sure we measured, measured something and isolated a variable one at a time when we went forward.

00:37:06:13 - 00:37:35:16
Unknown
Because often what we do is we have a company that would want to over optimize the second well, and we got a vastly different result. And you have no idea because of the 3 or 4 or 5 or 12 different variables you changed. Yeah, we added more probably changed the sand type, etc.. And so it was incredibly important to literally just change a variable at a time so we could measure and monitor that.

00:37:35:16 - 00:38:13:01
Unknown
And so that's one of the things as we were talking about supervised versus unsupervised, I, I understand the importance of real time data, but at the same time, being able to have a certain prescription followed so that I can measure it would seem to be pretty important. Yeah. I mean, that that's that's really the reason that we, we've kind of come down to where we're at is, you know, prescriptive, completions design, you know, as an engineer, you know, you you only want to change one variable so you can control the, the system as a whole and know and measure what's going on.

00:38:13:01 - 00:38:36:09
Unknown
And, and turn the knobs, you know, the right way so you can measure the results. We don't always get to do that, like you were talking about. But when we get to prescriptive, we can, And we will and we are. And so, you know, historically, it's been. Okay, well, here's our completions design, here's our lateral, you know, it's not perfectly straight, you know, deviates.

00:38:36:09 - 00:38:59:09
Unknown
And so it changes, you know, and so we may not be able to get a great perf efficiency in a stage. And we need to be able to make a, make a call whether it's, you know, reduce the amount of resources for, for that stage or cut it off and add those resources to a different stage. So, you know, being able to measure and control the the variables is, is key.

00:38:59:11 - 00:39:18:09
Unknown
And Chuck, I think you said the right words, a lot of variables. I don't think we have a real good handle as an industry or what's going on behind pipe. It's a difficult challenge to really nail down. And not to mention, you know, as you transition from heel to toe, there's there's different rock mechanics going on or different things happening that are outside of our control.

00:39:18:11 - 00:39:50:09
Unknown
But if we can be surgical at surface with how we execute, if we can be, really constrain what's happening inside the wellbore, it puts us in a better position to at least get to the point where we can predict what the outcome is going to be, and I think that's what we're after. So walk me through maybe an anecdotal story or two on something we've seen in terms of saving cost and or increasing, performance from the partner Show.

00:39:50:11 - 00:40:14:14
Unknown
Yeah. It can be something as simple as, being able to identify if you have a plug issue, like a plug failure, you know, where is that fluid going? Or you over capitalizing the stage that you previously treated, even if you had the good isolation and you can tell that you're only treating, let's say, 50% of your clusters, do you still pump the same amount of fluid, the same amount of profit, when you're only stimulating half that interval?

00:40:14:16 - 00:40:40:13
Unknown
I mean, there's there's substantial cost savings that can come into play there. Whether it's taking the fluid that was designed for that stage and reallocating it to another stage that's performing well, you can think of it that way. When it comes to some of the other variables that you mentioned, whether it's getting a better handle on pipe friction per friction, look, you know, there's there's a lot of there's a lot of things that can happen even during the perforating process.

00:40:40:15 - 00:41:01:21
Unknown
Not to touch too much on perforating, but that's kind of the wild, wild less. And what goes into the perforating and how we get a handle on or my hole is being shot where, where I want them to or they the right size. How does that go into what I design for. How does that impacted you know, the better handle that we can get on those things, the better chances of us are in success.

00:41:01:23 - 00:41:27:12
Unknown
Have, you know, again, it's back to, giving the customer a tool to to make the right decisions so they can, enhance, or make their wells more efficient. And so whether that be through, you know, a perfect efficiency downhole tools, you know, real time water, water quality measurements to address chemistry. Yeah. Real time on the fly.

00:41:27:14 - 00:41:54:05
Unknown
You know, or, you know, like I alluded to earlier, reallocating resources to either less or or to a different stage. And, you know, at the surface, we've, we've got the pro pilot and, you know, on average, yeah, the past year we've seen that across our portfolio for on average of seven minutes, a stage savings, just on the automation of the equipment so we can get to rate faster and maintain and maintain the velocity throughout the stage.

00:41:54:07 - 00:42:21:00
Unknown
And so now with, with size, most we can enhance that performance by here bringing the, the loop closed system. So do we have some items on the roadmap that we'll see next year, two years from now, three years from now. And you know, you're a publicly traded company. So go ahead and just lay it all out there and we'll, we'll deal with the SEC later.

00:42:21:02 - 00:42:43:00
Unknown
I mean, I think, man, Pano, you know, had had some really good, responses to that question. You know, it's it's difficult to see, you know, when we're in the middle of it looking, looking right at it. But yeah, we know that we're going to continue to, to have technology and use technology to help advance and help our customers.

00:42:43:02 - 00:43:03:20
Unknown
And so, you know, what's down the road. I mean, look, we're we're right now looking at, at stage by stage, inner stage, efficiencies. And I think that's going to continue to get better and smarter and faster. As, as we use AI and, and machine learning and, you know, build these AI algorithms to help us continue to innovate.

00:43:03:20 - 00:43:25:09
Unknown
So, I mean, technology is just going to get, you know, it's going to continue to make us way more Fisher. Look, I think when we look back five years from now and you're going to see that frack is going to be very similar to how directional drilling operates now to where it's fairly, fairly automated. I talked about it previously where we're trying to raise that standard.

00:43:25:09 - 00:43:50:22
Unknown
And that's, that's the ultimate goal here. You're going to see that. Whereas although our industry is fairly slow to adopt changes and adopt new technology and adopt new ways, you're going to see that progression as far as the oil and gas goes. You know, we're pretty resilient as an industry. We've shown that we can that we can innovate when we need to, whether it's commodity pricing that's pressure, etc..

00:43:51:00 - 00:44:13:10
Unknown
But I like to think of us as a cockroach. You know, we always come out on top. We always find a way to survive. But that's what you can take. I think you can look forward to that. Yeah. No, it was interesting. One of the I forget which formation it was, but we literally found out as we were drilling a ten foot difference in where you landed.

00:44:13:10 - 00:44:42:02
Unknown
The lateral led to 35% differences in a year. And so geo steering became incredibly important in that play. And so being able to turn around and do that with Frax by interval is going to be pretty interesting. And and to add to that, I mean, we've been pretty fortunate with tier one acreage. I think partner I mentioned in his talk to where the there is room for error.

00:44:42:07 - 00:45:01:20
Unknown
You know, with tier one acreage you can maybe do not so good of a job and still still have a pretty good well. But as we start to get into that fringe acreage, the room for error is going to be far less. And for the economics for it to make sense is also going to be tighter. And so it's what we're doing now is extremely important for the success of that.

00:45:01:22 - 00:45:27:03
Unknown
You know, any time I talk to folks outside the industry, they don't believe me when I say, yeah, I don't know. We're getting 10% of the oil and place sell. I mean, they kind of have a view of an oil well, as a bloom. You pop it, the air comes out. I'm like nuts. It's 10%. Best case, does the partnership potentially have ramifications for refracts?

00:45:27:05 - 00:45:50:03
Unknown
I mean, at some point, no. We have to go back and frack every well we've ever frack before. Yeah. I mean look the possibilities are, you know, whatever we can dream up and however we think about it, I, you know, when you think about that, about the 10%. I mean, that's that's a staggering number considering how much we, we produce in the country versus just that.

00:45:50:03 - 00:46:15:11
Unknown
So, you know, the way that we get more and enhance that is, you know, stage by stage, getting very prescriptive and intentional about the decisions we make. And now now we're beginning to get, you know, basically get some eyes downhole to, to give us, you know, an idea of, what we're doing. And if we're with what we're doing is, is making a difference.

00:46:15:13 - 00:46:34:22
Unknown
Yeah, yeah. I mean, you touched on it. If you look at the the number of wells refracts year over year, the last couple of years, it's doubled. And so I do think people are catching on to that. Sometimes I ask myself why more wells aren't refract because the economics are there. I'm sure there's there's reasons for that. But you talk about having a whole separate a whole separate problem.

00:46:34:22 - 00:47:14:14
Unknown
Like now you're going from shooting through one casing, through shooting through to. And so the problem just becomes that much more difficult to make sure that you're, doing a good job at it. What is maybe a misperception that a client has that you would love to be able to convert some of. Interesting question. So I asked. I think everyone, anytime someone suggesting to do something different, obviously the first thing that comes to mind is what is the risk of doing the different thing?

00:47:14:16 - 00:47:36:16
Unknown
Like a lot of times what we're going to be focused on is the negative versus the positive. And so the biggest thing I would like to see overcome from some of the customer standpoint is in order for us to continue to innovate and push forward, we're going to have to take risk at some time. And our job is to minimize, you know, what the impact of that negative risk could be.

00:47:36:18 - 00:48:06:09
Unknown
Hundred. Yeah. So, you know, I think what our tools do, what ProPilot does, what we always size most in the closed loop, you know, it's it's fully customizable to the customer, because nobody knows the walls better than them. And so, you know, we're here to help provide. Yeah. Smart tools to help make their decisions faster, quicker and more efficient.

00:48:06:11 - 00:48:36:03
Unknown
I like a go ahead and say track. All the way to think about it is, you know, we're we're blessed as an industry to have a people with a lot of experience. But as you talk to some of these people, a lot of times you'll hear them say, like, I've I've forgotten more than, you know, like that's a, that's a common thing that you'll hear people say, you had brought up in your discussion with Bono and, Matt about I, one of the things about AI is that it doesn't forget and so is able to use that data, that learnings to continue to optimize the whole process.

00:48:36:05 - 00:48:58:01
Unknown
And so that's the one thing I see AI coming into play as we talk into how do we ease the burden on the how customers think of what we're trying to accomplish, what one of the things I'd love for, and maybe we can do it right here on stage. I'll represent AMP world, and y'all are the service companies is.

00:48:58:01 - 00:49:22:07
Unknown
I do wish we could take more of a partnership approach on things. Like you said. Let's share some risk on this. Because we both have the goal of us doing better because at the end of the day, if I drove better wells, I can afford to pay you more. And, I think too often we were way too adversarial when we had power.

00:49:22:09 - 00:49:46:03
Unknown
We'd stick it to you guys on pricing. When you guys had power, pricing might be a little rough and in our direction. And I do wish the the service companies and oil and gas companies sit down and partner better. Am I just jaded, cynical, or am I right about that? Yeah, I'd look. At the end of the day, we are a service company.

00:49:46:03 - 00:50:02:18
Unknown
That's what we do and that's what we love. I've been doing this for a long time, and and I'm super passionate about it. Like all the tools that we've built, you know, it's been in the making for a really long time. And, you know, I mean, look, it's it's a tool that we build to help our customers get better.

00:50:02:18 - 00:50:24:03
Unknown
And so, you know, in order to continue to get better, we need we need to be able to collaborate and partner with the MPs to to truly know if or when the knobs that we turn our, affect is and working, the right ways. So I think, you know, those types of partnerships are, a key.

00:50:24:05 - 00:50:44:04
Unknown
All right. Especially, you know, here today and in the future. No, I mean, I think you're absolutely right. Like size most itself has a lot of its success, to contribute to our our relationship with Hess, you know, they help kind of steer us in the right direction. As far as where does the product need to be? What does it need to be, need to be able to do in order to have value?

00:50:44:06 - 00:51:22:20
Unknown
And so those type of relationships are extremely important for the continuing to push the envelope on, on, things like that we're trying to do here with both. Right. So we're not going to always have all the answers. And so having their knowledge base to will certainly expedite that learning curve. So maybe to maybe to close it out, I'm going to turn the table a little bit from the last podcast, the last podcast I told you I was really worried about the loss of tribal knowledge and how do we create tribal knowledge.

00:51:22:22 - 00:51:46:17
Unknown
If I is doing a lot of the menial work that we all started our careers doing, I'm going to say a different hypothesis this time. And you take it any direction you want to go with it. I'm going to say the loss of tribal knowledge is really good because we're going to get young, smart people with AI looking at data with fresh eyes.

00:51:46:19 - 00:52:06:12
Unknown
And an appropriate answer to that is, you're an idiot. Yeah, but, what do you think? I mean, I think there's definitely going to be a transition of of roles. And I think they alluded to alluded to it earlier. Yeah. We've we've been you know, the tribal knowledge that we have, we've been, we've been taking and building into our ProPilot.

00:52:06:12 - 00:52:24:03
Unknown
So we've been doing that for years. We know collaborating with all the teams that know the engines and the transmissions and the engineers and you name it, I, I don't think that tribal knowledge is ever going to leave. I think it's going to continue to grow. It's just going to be in a different aspect of of what we do.

00:52:24:05 - 00:52:43:15
Unknown
You know, in order to run AI, you have to lie and you have to be able to create the prompts and, and, and run the systems. And so we're going to continue to, to build and develop that. And then that's going to become tribal knowledge as we continue to innovate and add more technology to the stack. So yeah, I see the tribal knowledge as something that's still going to be there.

00:52:43:17 - 00:53:17:13
Unknown
The only thing that you'll remove is maybe the bias of the human, like the human is always going to have some bias, whether it's, a bad experience or a good experience is always going to weigh heavily on how they decide things. But I would echo a lot of what what Larry said, as far as you know, when I think of it from the seismic perspective, more so we're going to steer AI in a direction of how do I how do I free up more time so that my talented people can continue to be excellent and so or take that busywork away, allow them to spend more time on the things that are

00:53:17:13 - 00:53:29:18
Unknown
going to make us better. I love it, I always say nothing can be more misleading and personal experience, and doing in it. Guys, I think this was great.