Oil and Gas Startups Podcast

0:00 What is going on, guys? Welcome back to another episode of the Willing guest service podcast. But we don't have a startup today. We've got something even better. We've got Kate Stevenson with

0:10 Novus Consulting. How are you? Good. How are you? Thanks for having me. Absolutely. We've gotten into each other. I think as of pretty recently, I've known some of your people over the years.

0:20 And so it's been awesome to get to know you, get to know the team. But for those that are listening who are not familiar with Novice, what is it that you guys do? Sure. So we are data management

0:28 and technology consultants for the energy sector. So we do a number of things from strategies around what tech you should purchase or implement to data strategies and architectures. We do

0:42 implementations of tech with an energy. And we also do integrations, data management, implementations, help clean up data, a lot of that. And a lot of work recently has been around MA. So we've

0:57 been doing a lot of data conversions and standups around the MA activity in the market. So it's so funny. We were just talking before we started recording. I came into the industry in the data

1:09 management space that was like, you know, I'm not an engineer, I'm not a land man, I'm not anything else, came in from the technology side, played very heavily in the data management space. And

1:18 I think in a lot of ways, a lot of things have changed, but a lot of things remain the same. And one thing that remains the same was that I still think this is single-handedly the biggest issue

1:28 that the industry faces. And there's a lot of like really cool things that I want to dive into that we can do, but without like a solid foundation of like good data, it's absolutely impossible.

1:38 And so that's why it's like super important what you guys are doing. So before we get there, what is your background? I don't know anything about your story. Oh, sure. So I started my career off

1:47 at Accenture in Kristin and loved consulting. Were you from Houston originally? No, I'm from Oklahoma City. Okay. So I went to school in Texas, or San Antonio, Texas, so went to Trinity

1:59 University, and then started working for Accenture for about five years, maybe four and a half, five years. Did you have a tech degree or? No, business. Business, okay. But they were

2:08 recruiting people with psychology degrees and so forth. They just wanted problem solvers. Yeah. And so we, and it was Anderson consulting when I first, when I got hired, and then they broke off

2:19 and became Accenture, and I went the Accenture route And just got into utilities. Yeah. And I worked on, or caught. Okay. So yeah, don't blame me for everything going on. But yeah, no,

2:33 stayed there for about four and a half years. Love consulting, but I hated the hours. So it was, it was 80 hour work weeks, every week, lots of travel. So I decided to move back home and work

2:46 for Chesapeake, and stayed there for about a year. Which did not Chesapeake I was in IT, I was a project manager, and I worked with

3:04 One of my partners now, Kevin Decker, and I worked on their custom field solution. So we built an app, a field data capture app, for Chesapeake that they're still using today. I know a lot of

3:06 that. Yeah, it's fine. And that's how I learned the industry, 'cause I came from utilities, working at Accenture, and then moved over into Chesapeake, and that's how going out to the field,

3:15 seeing the field offices, visiting with people, doing ride-alongs, and just fell in love with it. You just got oil in your blood, you just couldn't get it out. I did Well, I'm third-generation

3:23 oil and gas, so. Really? Yeah, my grandfather was a pumper. Okay. And then my dad is an oil and gas attorney. Okay. And so, and now I'm following in their footsteps. I just couldn't escape

3:32 it. I couldn't escape it. I love it, I love it. So how long did you get a Chesapeake? I was there about a year, got recruited over to Sandridge. Okay. And then just loved consulting so much,

3:43 so it's just been in and out of consulting since then. Did you do the same thing at Sandridge as you did Chesapeake? I did, just IT. Okay. Yeah. You're there for a couple years?

3:52 A little bit less than that, a little less than that. I loved it, but I just missed, I realized how much I missed consulting 'cause I really thrived at Accenture. And so I left Sandridge and

4:04 started working for Elizabeth Gerbel at EAG. And I stayed there for about four or five years too. That's a name I haven't heard in a while. I sat down with Elizabeth in 2014 when I first started up

4:15 and we showed exactly what we were working on. You know, we were competing against Quorum and P2 or IHS and inertia and all these different groups. And so, yeah, I was really appreciative that

4:28 she had us in at that time.

4:31 Man, that seems like a lifetime ago, it's crazy. But I know they're still growing. They are, they're doing great. And she was a great mentor of mine. But, you know, I had started having kids

4:42 and it got harder and harder to get to Houston. So I went independent, started working at Dev and Energy and stay there for about three years independent. And then went back into consulting at

4:55 Stonebridge. Again, I just can't get away from it. I was there for about seven years and led their advisory practice. Okay. So kind of grew, kind of what we're doing now at Novos, just that

5:06 advisory piece. So is it less hours than the Accenture? Is it more of these boutique things? It is, when you're in boutique consulting, you have a little bit more control over what you do. And

5:17 it's specialized, right? So you really kind of dig into

5:23 what you're good at. Now also, when I moved back to Oklahoma City the first time, right after I left Accenture, they didn't have an Accenture office up in Oklahoma City. So I really didn't have a

5:35 choice. I kind of had to leave because at the time, there was no remote work like there is now. So it was in the dark ages. So you spend a good amount of time

5:46 really focused on data management and specifically an oil and gas. Yes. Why? Well, what I like about consulting is solving problems and doing it for multiple accounts. So I don't, I would like

6:01 moving around a lot. I like change. I like seeing new challenges and trying to figure out how to solve those challenges. And every time I go to a new client or I learn something new, whereas when

6:12 I was kind of staying at those, at Chesapeake or Sandra, I was just kind of in one little area and I wasn't able to branch out and really learn and solve all of these problems. You might be similar

6:24 to me. After my first startup, things long story short ended up leaving my partner and I had a falling out. And so I'm at this kind of crossroads where it's like, do I stay in the industry, I was

6:38 like burned really, really hard, do I continue to work in this industry, I'm going to find something entirely new to do it And ultimately I came back in and just kind of kept trudging forward with

6:48 the exact same vision of what I was trying to do to begin. And the reason being. is that every time I would talk to like a new company, it was just like the problems were just growing and growing

6:57 and growing and things weren't being solved. And I was like, well, somebody's gonna be able to like tackle this, you know? And so, yeah, so dedicated more years of my life to being able to

7:06 solve the like the state of management issue, which like we said is still an issue. And it's only getting worse as we're adding more and more systems and capturing more and more data. And it's like

7:16 we have more data than we even know what to do with Well, and everyone wants to do all these amazing things with AI and machine learning, and they wanna build these beautiful reports, but

7:27 ultimately they fail because the data is so bad. And there's not that foundation. And another problem in the industry is the amount of MA transactions, right? So you could have one piece of data

7:40 start, you know, one company, it goes through five different transactions, ends up in the final resting place, looks completely different than it did before just because every time it moves

7:52 company, you lose a little bit of it. You lose a little bit of the validity of it. So. The way that it used to be done at least 10 years ago, and I'm not sure if it's changed or not, was a lot

8:02 of the times what we saw was whenever there was a transaction, the due diligence on not just the data, but really the asset was to pick a small portion of it because they didn't have the time and

8:15 capability to be able to do true due diligence in every single asset You take a small sample size, you run it, you're like, yeah, this looks good. Or you take a couple of sample sizes until the

8:25 numbers make sense, you're like, yeah, this is good. Not really knowing what the rest of the assets should truly look like, is that still the same? Well, it is for, you know, the thing about

8:36 MA is that everyone's at a time crunch, right? You gotta get off the TSA, you gotta get those high critical assets into your system. So yeah, if they're in a time crunch, they might just look at

8:46 a sample size and say it looks good and move in. A lot of times people just throw it in and don't even look at it, to be honest. So that's one of the things that we do is with our kind of our data

8:57 conversion is we have rules, pre-built rules, and we can run queries off the entire data set so that when it's coming in, we throw it through that data pipe, and then we send it to the source

9:08 systems and our destination systems, and we're able to get those uploaded with at least, you know, good data that pass our rules What kind of, what kind of things are surprising to you, if any,

9:23 in the space? Of the MA space. Would you know, just in the current state of just like data management? Well, I think, like you said, I think what's surprising is when I started in the industry

9:35 15 years ago, we're having the same conversations that we had 15 years ago, right? And I think a lot of people felt that, hey, if I implement these big ERP sexy, systems, It'll solve all my

9:48 problems. Well, what we're finding is, is because they haven't focused on the field, they haven't focused on operational data, production data. And they thought that if they just fixed

9:57 accounting, everything else would fall into place. That's not the case. You fixed accounting data and it looks great. But what the heart of the business is the field. The heart of the business is

10:09 the information coming from the field. And we have to get that right and we have to work on getting that right. Why is it taking this so long to get that right? I'm trying to figure out, I want to

10:19 get to the bottom of this, because I don't know. Why is it such a challenge for us to be able to get a handle on data management? Because you've got all these different data models, I know PPD,

10:32 I've tried to push one for a while, you had decent adoption, but it wasn't industry-wide adoption. You've got some other standards pop out, technology has changed, we move from relational

10:40 databases to things like MongoDB.

10:45 Right. We have so much more tools. Barriers are so much lower than it used to be before. Why can't we get there to this promised land of 100 just clean data? Yeah, and I don't think it'll ever be

10:57 clean, right? It's never gonna be perfect, but at least we could get it to 96 or 95 and be good. But honestly, I think the MA piece is one where people just keep buying stuff and they don't give

11:12 the back office or they don't put a playbook or a plan in place to integrate that data to match the rest of company standards. The other thing is there are no company standards. So when you think of

11:24 master data, what is a well? What is a facility? If you create those rules and then every time data comes into your environment, it follows those rules, then it just over time gets better,

11:35 especially in an MA. But third, I just don't think people gave the field in operations, enough credit. I think they felt Oh, we'll just clean it up when it gets into the accounting system, or

11:50 we'll just clean it up when we get it into

11:54 the warehouse. We're not gonna make the field do any more than they need to, and they just got ignored. But then when you go out to the field and you talk to a lease operator, like they're excited

12:04 about technology and they want technology because now they have a phone and they've learned to use their phone and they have all these apps on their phone and they want that same capability and their

12:16 day-to-day. So it's getting better. Do you think there's intentionality with thinking about the end goal of what they actually wanna see and then being able to reverse engineer that into cleaning up

12:26 the data and figuring out which data you actually capture, right? 'Cause there's a lot of just, it's like unused. Or do you think it's, we struggle to get everything in and then once we get it in,

12:33 it's like, let's just see what we can do with this, I guess. Well, so we have a philosophy of, Good in the field, great when it gets in the corporate environment and the very best data when

12:45 you're doing revenue, right? And if we can get a good data set from the field, get it into the system, apply use technology to apply data standards and MDM capabilities, and some of the newer

12:60 tech, like maybe AI, machine learning, to get data really good before it goes into what needs to be very best, and that's really where we're trying to shift everybody to look. Do you think we're

13:10 breaking down departmental silos more than we used to, because I feel like that's a huge issue, from one from an organizational standpoint of actually running the company, misaligned incentives to

13:19 where drilling and completions are not on the same page, they're not on the same page with production, production is on the same page with the entire finance department, right? Has that changed or

13:30 is that changing? The same thing applies from a data standpoint of being able to have everybody understand that all of this flows together and ties together to the success of the company. It's

13:40 changing. It's getting better. When you talk about data standards, everyone knows that they need to have it, that they need to create what we call a well lifecycle. Let's track from an inception

13:52 of when a well is created, when it's a prospect, all the way from when it's divested. In that process, you have to meet with the other departments, define what those processes are, where data

14:03 originates, how it's created, how it's used within the organization. That exercise has been phenomenal in breaking those barriers. And then if you all sit down and agree on, this is our standards,

14:16 these are our standards, then all of a sudden everyone starts moving in that direction. But it's getting that agreement and that alignment that's often tough and difficult. But we're seeing more

14:27 and more projects or more and more people request those projects from us and us. talking to them about those projects, 'cause they know that it's critical in order for them to be able to go do the

14:37 things that are really neat around reporting an AI. So by just something you said, it's like gonna go. So what is a well? What is a well? So I always define a well as from prospect. So when it's

14:51 defined as a prospect and that's when you need to start capturing data. And then we use well status across each phase of the life cycle So, you know, when it's kind of being proposed to when it's

15:05 starting to stake location, to drilling, to completing, to producing and then to divesting. So it's really important to understand at a high level what those phases of the well are and then define

15:19 all the processes that feed it. So is it a well bore or is it its own? Oh, I sort of say. Yes, it is a, well, we do well bore is really how we've been defining it, yeah. Yeah, I've just seen

15:33 some like mess of accounting towards a single well bore, multiple zones,

15:38 different working interest partners in different zones, it can get so hairy. It can, and especially because systems are different, and sorry, I'm having to go back to a long time ago when I did

15:50 that, but yeah, it just depends on how you want to define it within your organization. Yeah, what, are there any, if you want to say, are there any like particular softwares that you guys are

16:03 really, really fond of, that you guys are partnered up with? We partner with a lot of softwares, we try to stay agnostic and try to pick what's best, but we have really great partnerships with

16:15 Peloton, with Quorum, really spend a lot of time working with them, we're engaging with pack energy. So really looking at their products and what they're doing there, they're doing some really

16:26 great things at pack. And then of course, we started discussions with W Energy again. So we're really trying to focus in on the major players in the market. There's vendors really realize that hey,

16:41 we need partners to go out and do this because if they really wanna grow their licensing, they don't have the arms and the legs to go implement everything themselves. So partnering with us has been

16:52 great for them and great for us. Talk to me about, you guys see so many things, you're in so many different companies, you're in the weeds, you're in the trenches.

17:08 What is everybody talking about right now? Like what are some of the trends that you're seeing? Obviously AI, we can dive into that. Let's talk everything outside of AI first and we can talk, we

17:11 can spend an hour talking about AI. Yeah, actually we're seeing a pretty interesting trend around the field. So more and more people want to manage the work and go to, I mean, we've been talking

17:23 about operate by exception for how many years now, right? And no one's been able to actually get there. But Chesapeake had a very interesting tool called Well Tender and it's something we've

17:37 actually hired a lot of people that worked on that tool. Because we're based out of Oklahoma City, so everyone worked at Chesapeake at some point But we've really focused in on the field and

17:51 operations. And one of the things that our clients are asking us for is how can we initiate work automatically to go automatically send tickets and work orders out to lease operators to where they're

18:06 not going to a single route every day anymore. They're just working what needs to be worked for that day based SCADA from in coming that's data the on,

18:17 or maybe there's a preventative maintenance ticket that needs to be done. Maybe there's a spill that's occurring. So how do we reorganize the work automatically through technology instead of it

18:31 being done by a human? So how do we dispatch that work? And that's been something that's been extremely interesting to work on given our experience at Novus and what we've done there. Is that a

18:43 homegrown solution for chess speakers? Is this resonant school? It was homegrown, I mean, it was homegrown. So they own it, they've built it. I think there's a lot of vendors out there trying

18:53 to do it because that's where we see a lot of the demand. So there's a lot of tools that are starting to move that way. Seven Lakes, or not Seven Lakes, sorry, W Energy now. Scout with Pack

19:08 Energy. We're seeing, you know, Maximo is another tool that will do something like that. applications out there where they're trying to build that and solve that problem. So anything else outside

19:18 of the field stuff? A lot of, again, MA work,

19:23 a lot of trending with, how do I, okay, so we're going to, we're going to all of a sudden consume this company. So how are we going to get the data and how are we going to get the data in right?

19:36 The other thing that we talked about earlier, again, is the data cleansing The look people have on their faces when you say you need to clean data is just amazing. They do not want to do it. No

19:49 one wants to do it. No one wants, that's something that is boring and they'll come up with the standards, but the thought of having to go in and cleanse data in the source systems and then get it

19:59 into the warehouse is something they just don't want to do. So we've had a lot of projects of where we're helping people cleanse their data to get prepped for more fun projects. I can't tell you how

20:10 much manual cleansing of data I had to do. just, oh, it's so boring, years of my life, just looking at rows and rows and database manually. We didn't have AI back then. No, and we're coming up

20:24 with, you know, because we see a lot of those projects, we're trying to make it easier for our data engineers. So we're working on tools and we have IP inside our house to help with that, so that

20:32 we can work faster and more efficient as well for our clients. Yeah, all right, we'll start with AI

20:39 stuff. What are you hearing? What are you seeing? What are people talking about? I think, I just don't think people know what to do with it yet. I've seen a lot of ideas. Yep. You know, and

20:51 we've talked about some ideas too. I think there are ways that we can use AI to solve those problems like data cleansing that people don't want to do. I think we can use AI to help people with

21:07 internal portals and be able to,

21:11 ask questions and get answers back on internal data. But

21:17 the problem is, is that it's only going to get partway there if we don't figure out what their data architectures are going to look like. And things are changing so fast with technology. Everyone's

21:32 kind of getting into SaaS environment single platforms versus having everything on prem and these big huge warehouses So even data architectures are changing. The way we bring data in is changing.

21:45 People are having to rebuild all of the interfaces because they're moving onto the SaaS platforms of their vendors. So there's a lot of things changing on data architectures and then there's this AI

21:57 thing that looks really cool and beautiful and fantastic and everyone wants it. But the question is, how do I marry all of that together? Yeah,

22:06 we've had a lot of conversations with a lot of different technology. teams at a lot of these EMPs or field service companies, particularly over the last six months. AI is top of mind for everybody.

22:16 One ounce of everybody. There are certain companies where I go in and I'm like, Hey, have you guys been playing with Chad JBT? And they're like, What is that? And I'm like, What? What? How do

22:24 you, and I'm talking young people. Really? Young engineers, like fresh out of college that are letting some of these EMPs that are like not paying attention to what is happening in the AI space.

22:35 And then you have some of these older leaders that are just resistant to change, resistance to new innovation, skeptical. They're not playing with it. If you're not, just talking to you guys, if

22:45 you guys are not playing with AI at least a little bit, you are going to get so left behind. Agreed. You're gonna have somebody who understands this and knows how to work with this. It's not

22:56 rocket science. It's much easier to work with than you would possibly imagine. You don't have to be an engineer, but you will get left behind so quickly if you do not learn how to use these tools

23:06 And it's, there really are a. efficient. I mean, just looking at, you know, how AI is going to change the way we work, how AI is going to take those low level things that we don't want to do

23:21 anymore. I think it's going to completely change. This is a point in time in our industry where we can look back and say, okay, this is when the landscape of data is going to change. Because of

23:32 all these new innovations that are coming into the market. Because I can tell you that the technology space, everyone is laser focused in on it and they're developing really quickly tools and an AI

23:45 machine learning and all these things that are going to be going into their applications and it's going to be amazing. What I'm seeing that's really encouraging is that talking to some of these teams

23:55 you really get to understand what's coming and understand what the opportunity is. This company is saying, hey, we have like 25 terabytes of this that is sitting here.

24:05 And it's so overwhelming that we don't know what to do with it, but we know that there's massive insights that we can unlock. We want to deploy AI on this one particularly used case to kind of

24:13 figure out what do we do with this? And so it's really, really cool to see where we're going. The industry will get leaner.

24:25 That's not just this industry. That's every single industry. You're going to have people that are supercharged engineers that are able to do a thousand times where an engineer used to be able to do

24:33 in a fraction of the time. Exactly. You know, it's going to be phenomenal. But I think we're going to need the large companies or the super majors to lead the way because the mid-market, the

24:46 smalls in the mids, they're just not going to invest in it. They're going to wait until the diamondbacks or the chevrons or the Devon energies are going out and doing it first. And then that's

24:58 where the innovation is going to to happen and then it'll trickle down into the other areas where people may be more resistant. So we need to lean on kind of the larges and the super majors to really

25:08 invest and develop it because it'll change how we do business. So in my experience, maybe it's different for you, I would say that the independence that I've talked to are leagues ahead of the

25:18 super majors. Really? Yes. In

25:22 terms of actionable things that are actually happening, like actual projects where the road meets the road versus staffing up a massive team of people that are doing sandbox projects and then we're

25:32 actually making it into production of the business units. Yeah. That's what I'm seeing. I can be totally wrong, but that's literally just limited to my experience and who I've kind of seen.

25:40 'Cause they're more agile, right? So they're probably able to pick things up quickly. The question is, is how much are they willing to invest in it while they're arms and legs or busy off drilling

25:52 wells and producing wells? So that's something that we've always struggled with. But then again, we're also on the consulting side So those small independents, they don't necessarily always want

26:02 to hire. consultants, they kind of want to wait until the innovation happens up here and then they'll just grab it when it gets developed. Yeah, the cool thing about that is that there's a huge

26:14 opportunity for you guys with them once they come around to realize and that they're oil and gas companies and they should just pump oil and gas and stop staffing up massive IT departments. Great.

26:23 Right. Yeah. And that's, you know, you ask about trends, that is something that's a trend we're seeing, you know, after COVID, even a little bit before COVID, IT departments are shrinking.

26:35 Yeah. They're shrinking. And they're becoming more infrastructure focused. Um, we're seeing, well, the smaller guys, the, the mid market, we're seeing, Hey, we don't, we're just going to

26:46 either outsource all of IT. And then the business is responsible for their own applications and their own data. And so there's pros and cons of that without having a centralized group that licensing

26:59 data, everything goes into, but there it is a trend that we're saying is less IT in the

27:08 business side of the house. Yeah. One of the things about AI that a lot of people don't realize is that it's quite difficult to apply an LOM to just a kind of generic data set when it requires

27:24 contextualization for the industry. There's this entire context layer that is typically missing which we're working on that And another thing that kind of ties to that is, and the way you can kind

27:35 of capture this is just domain expertise internally. You

27:40 think about like we have all these old guys getting out that have years and years of this tribal knowledge. Like if we don't capture this, we're going to forget how to drill vertical wells. Exactly.

27:49 Right. I was at an office of a pretty large company recently. I asked him, I was like, if you guys have a question, like if you have an engineering problem and you're looking to like solve this,

27:58 you don't have an answer for it. Where do you go? We walked on the hall and hopefully asked somebody that has the answer. Literally, I mean, 5, 000 employees. We literally have no single

28:06 reposter to worry for like company-based knowledge. Wow. And that is pretty much across the entire industry. The super majors do have their own things. I know SLB has something, Halliburton has

28:17 something, but I would say for a large percentage of the independence, there's absolutely nothing. We are missing out on one of the biggest opportunities to take what our people know and being able

28:28 to capture that for the people that come behind them and then use that to contextualize the data that we have

28:37 for AI. Exactly, exactly. I agree. It is something that is a huge miss in our industry, that knowledge share, right? And, you know, but the question is, is how do we, how do we get that

28:51 data out of their heads, out of their systems and into a right format to where we can surf that up? right? We'll see. We'll get there. We'll see. We'll make it happen. TBD. We've got some

29:07 pretty cool initiatives. Yeah. Behind the scenes with Clive Pro. But what else is anything else outside of AI that's exciting to you? All of it's exciting. We've just been about 80 of our

29:19 business right now. It's just MA work. Really just trying to get people to keep it. It's keeping you busy considering it's MA season. It is, I mean what, 190 billion last year, of which 144

29:31 billion last quarter. So it's big. There's a lot going on. And really with cleaning up data, trying to get it into the systems, working in the field, we are stepping into midstream quite a bit,

29:51 which has been a lot of fun. So we implemented a Maximo for one of a gas big gas plant out in Wyoming. And then what we did is we standardized it and put it in all of their gas plans. So now all of

30:05 their gas plans are operating the same way, same processes, procedures, getting all the data in place, and then we moved into pipeline. And that's been really fun. And so we're trying to get and

30:16 move into that direction as well. But it all goes back to trying to clean up operational data, regardless of what sector we're in. It's operational data that needs the most help Is midstream better

30:28 or worse off than upstream?

30:31 I would actually say where we were and what we were doing was a little bit worse. And I've seen it in some of the companies, but it just depends. And I think a lot of it was because

30:44 those gas plants just were bought and sold so many times. They didn't have that focus of someone to say, what systems are you running and what should Jupy running and then so when they got bought by

30:58 our client and the leader there was just had this vision of I want to consolidate and put everything into one system, same processes. And I want to be able to have a shared services across all of

31:15 our plants. So he created in his department a shared services group and now he's able to save money because he's moving like an I and E tech from this gas plant over to that gas plant to go work what

31:27 needs to be done versus trying to versus contracting it out because they're all on the same system, same processes and they're all connected. Yeah. So it was pretty fun to watch because one of my

31:38 favorite projects. Do you all hear much chatter internally when you're working with clients around Bitcoin mining? No. No, I've been, it's interesting to me. It comes from the engineering teams

31:48 to the way, yeah, yeah. It's interesting to me. We are keeping a pulse on it, but it hasn't stepped into our world just yet It's IT's. kind of rarely involved in that. So a lot of times it's a

31:59 simple gas purchasing agreement. So it's really engineering teams and the funds gets involved and that's pretty much it. But it's very interesting. And it's getting a lot of buzz. It's getting a

32:08 lot of buzz. A lot of us. And so we're keeping a pulse on it and we're trying to see how we can help in that area as it gets even bigger. It's one of the best tools or it's the best tool in my

32:19 opinion to be one of monetized, stranded or wasted energy with gas, with its renewables Exactly. It's a substation that's running at 20 efficiency. It's amazing. It is. Obviously, we pitch firm

32:31 power event in a few weeks. So obviously we're heavily invested.

32:37 No, it's exciting. And honestly, I've learned a lot from watching you guys and learning what you guys are doing around the space. You're going to spread the gospel. It's it's a lot of fun to

32:46 watch. It's been really cool to be kind of in the. The ground floor of the Bitcoin money space kind of accidentally whenever we did our first in power, it was supposed to be back here barbecue. It

32:58 wasn't supposed to be like this big event and then it ballooned into what it became, we had 1200 people, had all these energy companies, we had all these Bitcoin miners. And it was just a while.

33:09 So you kind of stepped into something and now we're like accidentally kind of at like the center of like this little ecosystem And it's growing and I'm sure Bitcoin is absolutely ripping right now and

33:21 you've got the Bitcoin ETFs, you've got the halving coming up, so price appreciation from that, you've got more regulations and oil and gas, so you can't flare anymore, you can, but you can get

33:30 penalized for it, solution for it, all of these crazy things More demand response programs here in Arcan and PJMs with either grids as well, and so load balancing demand response kind of benefits

33:44 there, that's wild. It's very complex too. I mean, it's just like one thing, it's a domino, right? I mean, it's just so many things have to go into it. The deeper you get into it, just like

33:56 one of the guests, do you get into it the more you realize you don't know anything? Right. And it's much harder to understand than you could possibly imagine. Yeah.

34:03 Kate, this has been awesome. Yes, thanks for having me on, it was fun. Absolutely, we'll do it again sometime.