Oil and Gas Startups Podcast

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0:57 Was that walkout is welcome back to another episode of the Willing Ass Service podcast. If you've been listening to the show since the beginning, then you know this next guy, John Farrell, CEO,

1:07 one of the co-founders of Well Database, good friend of the digital walkout is a good friend of mine. You were like episode three, I think. In the beginning, 2019, March 2019, Julie looked it

1:20 up. And did the first episode, I think, I don't know if we've had any on back since then. No, I haven't been back here I did, I jumped on with Jeremy. We talked about, I've had an entire kid

1:30 since the last time I've even seen you. Yeah, I grew up, my hearing became white Jesus and then cut it off and then I'm no longer there. The reason we haven't seen each other is 'cause you moved

1:38 to Montana. I did. You moved to the middle of nowhere and to paradise, you escaped the concrete jungle that is, that is Houston. I'm a little jealous, to be honest with you. I'm gonna go look

1:46 at some houses in Hamilton, Montana. You have a standing invitation to come visit. I need to. I need to. I need to, I need to for someone. But then as soon as you moved up there, COVID hit the

1:55 world shut down. Yeah, just craziness, but for one, it's great to catch up. Two, I wanna learn about what has changed. You know, since, we're talking about like four years. Yeah, it's crazy.

2:06 And the company, I know a lot of things has changed. For you guys, a lot has changed for us since then. Yeah, it's like a lifetime. You realized like we had not met before we did that first.

2:14 Really? Yeah, we had just connected on LinkedIn and you know, we chatted a little bit. And so I walked in and met you and Colin that day. It's the very first time we ever met. Oh my gosh. It

2:23 feels like I've known you forever. I know, it's enough. All the times we've hung out since then But you think about like energy tech night one, like you were there, presenting a couple of the

2:32 other events. I think we did like some of the collective stuff. Oh was that, yeah a lot. I mean, we were in everything like I couldn't. It was, but like it - The night me you and Josh Robbins

2:39 went out.

2:42 Oh, Josh, I was talking to him the other day for the first time in a long time. He's

2:47 always fun to talk to. He's a goofball. Love that guy. No, it's crazy when you think about that though, 'cause I guess it's rewind back there.

2:58 oil and gas startups podcast was, to me, like the very first avenue for a startup in this industry to get any kind of exposure, and

3:08 I think about it now, I'm like, Oh, there's all kinds of avenues now, but I actually think three quarters of them are coming through digital wild counters right now, and so it's not because the

3:16 industry changed, and now it's like you guys have just grown and expanded more, but I couldn't tell you, I remember how thrilled I was, because at the time we started Well Database, probably, I

3:26 don't know, five, six years before that, and you feel like you're on the island, you feel like you're the only person out there doing a startup, and you walk into these big companies, and

3:35 they're like, Who are you and why do I care? You walk into an investment group, and they're like, You want to go head-to-head was two of the biggest and oldest companies in the industry? Good

3:43 luck. We'll see you later. So like, yeah, for years, I felt like I had just losing my mind. This was never going to happen, but then slowly, but surely we get this ecosystem going. It was

3:54 pretty cool. So yeah, I mean, that kicked off a ton of things that we worked together on and stuff. And so it's been a blast. So for those who are not like familiar with you guys, what high

4:05 level of review, what do you guys do? Oh yeah, we do data. No, we go and we,

4:10 you know, at the end of the day, it's funny, everyone uses data data and the new oil. And that was like years ago, people used to say that stuff. But still people use it, but like it's still

4:18 too expensive and still hard to get and cumbersome to deal with. Like I talked to people who spend, you know, majority of their jobs, just cleansing data that has already purchased from a third

4:28 party. So anyway, we go out and we wrote automation around all these things, go grab all this data and pull it into a single platform. And then we provide it to our users. Like, I mean, five,

4:38 six different ways. You want to query snowflake API. You want to get into our portal. You want to do direct exports to any of the applications. And that's really been kind of key. So we get all

4:47 that data, we clean it and it runs through by being automated. There was a time where I was like, we automate things because it reduces our cost. And that's still true. but what we found is by

4:57 automating things, it meant that every time we found a problem with data, we created a rule for it, and that rule fixes that point, and you can run it over historical data, and then you're

5:05 running all future data, right? And so that iterative process, feedback we're getting from people today, is that we have the cleanest data in the industry, and it's not because we were magical,

5:15 it's just that, you know, if you've got 17, 000 people telling you all the problems in your data, and you're writing a rule for each one, of course you're gonna get clean data. So anyway,

5:23 that's what we do. We get the data, we make it clean, make it usable, and then our platform is like, blown up as far as analytic-wise, what you can do all baked into well-database. Yeah, so

5:33 you guys are the David to the Goliath, so you know, the inverse and the IHS, or not I guess now SP, but it's been so awesome to see you guys grow. So now, so I remember back then, and I've used

5:45 the platform extensively.

5:48 I think you all had like, it was like the data, then you all had like all the built-in kind of visualizations and kind of front-end and stuff, but it seems like now you all, piping a lot of just

5:56 the data itself into other places where people can take that, play with it, put it in other visualizations. What's the split between that? Is it like half your customers do half half the other?

6:07 Yeah, what you know what it is, is that especially majors, they'll have spot fire systems, Tableau, Power BI, whatever it is, and you go talk to them, these meetings are great. They're like,

6:17 what do you do with data? I'm like, well, we just get the data and we put it in our systems and run. And you're like, okay, so what is it? And they tell you the technology Okay, so you just

6:24 query yourself like if you wanna do that. So we pipe it in there so you can get it that way. If you want custom exports, we'll deliver it to you. If you have a local on-prem database, you wanna

6:31 manage that kind of thing. But what we found was so funny is that all of the hoops are jumping through with these other platforms. Like our platform had evolved to do all that for you. And so like,

6:43 we'll go into these demos and say, we just need the data. And so we'll, I try them to waste people time. So I'm like, okay, here's what our data is. Here's what it is here, our access method.

6:51 five minutes, you want to see the portal? And they're like, oh, sure. And by the time we get to the end of the portal, they're like, man, if we did this, then we don't need these four extra

6:60 systems that have been laying around here that we just use because there's never been anything better. And so it's one of those, I made the joke the other day, Henry Ford, that fake line that's

7:10 not real, that if I'd have given my customer sort of wind of a faster horse, people think they just want the data, but they don't realize the power of an online, you know, SaaS analytics engine

7:19 that they can do. So back to your question, it probably, you know, half our customers at Enterprise now and half are more kind of off the street, ones and twos kind of situation. But every

7:31 single Enterprise customer that we have started off saying all they want is the data. And today they have users using the platform every single day. So it's a little bit of what they didn't know

7:42 they could do that they can do now. It's easy to use. I mean, we used it extensively, but I think I think particularly during like 20. in 2019 and 2020. Yeah, when you were buying wells. Yeah,

7:53 we were using it a lot. And

7:56 yeah, it's just like really simple to find out like whatever answers you're looking for that you guys have put a lot of thought into any and every possible metric you could want to know is in there.

8:05 Yeah, and that's a good and a bad thing. We joked about this not that long ago. Like Josh and I, the founders were tech guys. And so of course, like we're always around tech, you know, adding

8:14 new features, new abilities and stuff. And the joke is there, like I think we forgot to sell it somewhere along the way. Like I go talk to people and they think we're just another data provider

8:24 and like they can just get the data. They had no, I mean, they literally have no clue that, you know, you can do decline curve analysis and economics and breakdown analytical type curve

8:34 comparisons between operators and plays and 3D visualizations of directional surveys. And like it's all there in one platform. So it's something we need to do better at marketing But what is,

8:46 what's changed the most in the last years. Oh, man. I could probably go four different directions with that, but like, Hey, however you want to answer the thing that hammers me the most, it's

8:57 like, especially being a technical co-founder, which it's something that's, it's seemingly kind of rare, right? I mean, almost three quarters of the people seem like it's becoming more rare.

9:07 Yeah, like they just want to outsource the tech part and they know the business side and that's okay. It can work to get off the ground, but like you always input this massive amount of technical

9:17 debt that you have to deal with at some point in time and people just don't realize. And then the technical side, like they don't respect the business side. Like they're like, we just make a great

9:26 product, you build it and they'll come. That's not true.

9:29 So anyway, the biggest thing that has changed is I've started to respect all aspects of what it takes to run a business. We've hired people and I've had to get out of the I do all things for

9:39 everyone and start delegating to different people, different segments. It's hard isn't it? It's really hard, especially because like Like, can you see it? And you're like, I wouldn't do it that

9:48 way. got to keep my mouth shut because I, this is their job. I have to let them run with this. I think, I think, 'cause I just 100 struggle with the same thing I think all of us here have at

9:59 some point is it's, like you said, you guys have been doing this six years before we even met. And so in the early days, it's a young company, you're scrappy, you're having to wear a lot of hats,

10:09 you're having to do a lot of things. It is hard to break out of that mode once you kind of get into a growth cycle of being like, I need to hire, delegate and push responsibilities down as much as

10:21 possible, because it does take time to articulate processes and things like that. But it's also, if you find the right people, I think it's, you know, the wrong people, it's very, very costly,

10:36 and you feel like you have to micromanage and all that kind of stuff. The right people come in and say, you're doing it wrong. Right. Yeah. And then there's another million, a million better

10:42 ways to do that. But I noticed that you guys expanded with like Aaron I think we made a couple of higher. Yeah, yeah, we've. this year alone, doubled our team size-wise. But yeah, it's just,

10:53 and I'm like, so hands-on was so, so much and so detail-oriented. And you're right, like, I would be in there, like, I think it should really be just like this. And sometimes I need to step

11:06 back and be like, okay, leave it alone and let them do it. And other times, I'll say something and they'll let me know what I don't know, which is great. That's when you know you got the right

11:14 people. And I think we, yeah, we've got the crazy team We were at ERTEC and it was really cool 'cause we just rented a big loft and we all stayed in one big loft together,

11:26 which sounded weird at first to everybody and they never really thought about a bunch of people staying in a loft, men, women, whatever, but it was like the best experience. Like we - Little team

11:36 bonding. Oh, yeah, 'cause I mean, we were - 'Cause you're a distributed team. Yes, yeah, we're all over the place, yeah. And so, I mean, we've got so, in Canada, Oklahoma City, so

11:45 Calgary, Oklahoma City. Houston and I'm in Montana, but we have the oil

11:52 and gas Mecca. But now, you know, we're going to be bringing on people in in Denver and

11:59 I don't know if it's Midland. Anyway, but yeah, it's distributed, but we get together and we just have a crazy awesome team, like it's so much fun. And so yeah, that's the biggest thing that

12:09 changed from a personal standpoint, you know, it's like letting that go. That is such a hard thing to do, especially when your boots draft and like, whenever you, you get a lot of capital, like

12:21 up front in the beginning, and you start just bringing in people, you never do those jobs. And so you don't really have an idea. But you guys know this because you guys ran without capital for a

12:29 long time. And you kind of know how you like to do everything. And then no one's going to do it just like you. But you got to let them do it their way and probably is better than your way anyway.

12:39 Yeah, I mean, and if not, then you're not the right person. Right. That's very true. Yeah, so managing people versus doing the job is the biggest change personally. Company-wise though, yeah,

12:50 just expanding, talking to more enterprises. We went from low barrier to entry, get individuals off the street, credit card pay, and I don't know how many, I feel like we're one of the only

12:58 companies that still do that today, but that was where we lived and breathed, so marketing was more of like a broad strokes, trying to get more people, more eyes on the site, whereas enterprise

13:09 is a lot of outreach a lot of sales cycle, planning, process, contracts, negotiations,

13:17 and it's all a good thing, and I always want to keep that balance, kind of 5050 is trying to make sure we don't get too heavy in one side or the other. So that's kind of changed a lot of what our

13:28 product looks like. Which challenges do you have on like actually like selling this into the enterprise? Because to me, it's always seemed like a no-brainer, right? Because you're giving higher

13:40 quality data for a fraction of the cost compared to whoever. else is out there, right? And so it just seems like that would just kind of like sell itself, but like obviously there are cultural

13:52 barriers, I think

13:55 sometimes in this industry. So I'm kind of curious, is any of that changed over time? Or am I, I don't know. I sometimes think it's gotten worse in some ways. And so like not to be too obvious

14:05 about it, but like I'm, you know, John and I'm a IT guy at, you know, Chevron or something like that Like what is my motivation to go rip out my previous data provider and put well database in?

14:18 Yeah. You know, I, if I don't have a bonus king off of saving this capital, if I don't have some data piece missing, then I'm doing nothing but creating work for myself to save the company a

14:31 couple hundred grand, right? And like, you could say, well, that's, you know, that shouldn't be how it is, but it's just, I mean, people - That's the reality of it. Yeah, they need a

14:40 reason.

14:42 Yeah, a reason to push out. So I mean, and so that drives so much of our inbound traffic is like someone got a goal to reduce data spend by 50 and so they reach out to us and that conversation is

14:53 great because we've got the medicine for your pain there. Outbound's a little bit different though. We gotta like just kind of be there and our strategy is really around selling to individuals so

15:06 that we can start like alleviating John at Chevron's pain, the thing that he's spending 20 hours screwing with data, we can make that take it down to nothing. And so he now wants us, make that

15:16 internal champion where they start breaking out. But yeah, it's really those motivations. The motivations, it's a feature, not a book. Yeah, that's nice. It's those motivations that would

15:26 drive the processes and you guys see this too, the technology, the reason why people adopt technology, you really wish that it was like the best technology wins, but it's not, it's the technology

15:37 that makes the most sense for a company in a particular position particular goal. So yeah, it's completely evasive to answer to your question, but like it is just the timing matters just as much as

15:51 your product fit. So. Is there anything else you feel like you've,

15:56 you've really just see the learning that the easy way or the hard way of the last four years? No.

16:03 Hiring, don't hire, don't post stuff on just open that you're hiring people. You just get inundated with the - With unqualified people? Yeah, well, recruiters Unqualified people, you waste your

16:13 time. Hiring sucks, and I'm not a big fan of hiring. But no, the,

16:21 yeah, the biggest thing is, I guess, respecting that business side of things is the technical person. I did, I think I probably said it in the very first podcast, and I've known this, so I

16:31 didn't learn it in the last four years, but still, like, you have the best product. It doesn't mean people are just gonna buy it, you know? You've got to make it make sense to them So, and you

16:41 know. At the end of the day, our trajectory has been kind of slower and steady, but we never took capital. We still never taken capital. And so I think that's expected. I was going to ask you

16:52 about that because I know that once upon a time you were talking about it and it was like substantial numbers. Right. We've had term sheets and

16:59 things like that come across and stuff we really, really considered.

17:04 At the end of the day, what we do that for like the entrepreneurs who are listening here are thinking about like, because that is kind of the road less taken, right? Right. Especially with, you

17:16 know, you and I were talking about it and like we're closing this round of capital and it's finally for the first time, like given us the resources to be able to allocate to actually making this

17:27 vision for a collide and really the whole future of the company like come to life and we've been talking about it for years, laying the foundation for it, but we just weren't capitalized well enough

17:34 to pull it off with given, you you know, GA and expenses for the events and everything else. Right. Yeah, I'm kind of curious like what y'all's thought process was and you know like I mean, I

17:45 think we we had the kind of I Think like any any entrepreneur would look at it and be like yeah It's some capital would help me accelerate a lot of my processes I mean to bring on more sales marketing

17:55 team build out more technical team and all this There's all the everyone knows the pros of it But we rarely think about the cons of what that looks like and a term sheet that sure enough real good job

18:05 of laying out What expectations are gonna look like? So they're gonna give you this, you know, several million dollars But in return you're gonna have these board seats board meetings. You're

18:15 gonna have oversight You're gonna have to meet weekly with this group and talk

18:24 to this group on this time and You give yourself a boss boss is you do and you know, I feel like you're your second You're if you do a second round you end up in a better position that way because it

18:32 helps you the first round can help establish an identity in the second round. They basically are coming in like it's already established, you can be a part of what we're doing. That also means your

18:42 first round had to go successful and raise your revenue numbers. But

18:47 if you do go out to raise capital, it's a full-time job. It's not something you can do on the side. You can't run a business and grow a business, bootstrapped with two, three people, and raise

18:57 capital. I mean, Colin started this last round that we're just now kind of wrapping up, technically started in December of last year. Press pause for a few months in the middle of the year because

19:08 there was no traction. And then fired it back up, and now we're closing it and we're almost to October. That was a full-time thing. That is where he spent most of his time over the last year.

19:19 Yeah. And so, you have to make a choice. I mean, especially, so people who do it at the IDFA's, before our products, I mean, there's jokes in the kind of Silicon Valley side. The worst thing

19:31 he started to do is make profit because as long as you're not making making any profit or as long as I'm making revenue. It's all theoretical and you can be worth whatever you want. Of course, you

19:40 have to sell it, but still, I've seen people sell some crazy stuff. So there's a buyer out there for it, but does it full-time job? And if you've got a company that's making revenue and you're

19:52 just a normal person like me who has a family and bills, I can't just be broke. And so you have to make that call. Are you going to raise capital? Are you going to grow your business? And then,

20:04 you know, on the off chance you got like we had where we had people inbound and we said sure we'll talk and then we met, you know, it didn't constitute a full time job. It wasn't bad, but then

20:16 when you get the term sheet, you realize just how much you're giving up. And I'm not even talking about the equity percent is that that is what it is, it's value. That's math, it's easy. It's

20:25 the control, it's your vision. And when you talk about well database, you know, we're a freemium software, freemium and oil and gas just they don't go together. Like people don't think we should

20:36 give anything away ever. we have bigger plans for monetizing our user base. This industry doesn't wrap their head around that idea. And so if we had taken any of that investment any of those times,

20:48 we would not be the company we are today. It's just they wouldn't have allowed it. They would have focused on raising our prices. They would have pushed us to hammer just on enterprise where the

20:57 big contract, big ticket items are, and they wouldn't have been wrong. We would have made more money, but I think our ceiling would have been lower. And so allowing us to spend our time to build

21:07 out this user base that we have, that quickly, we have over 17, 000 people in our platform now, which in this industry is pretty good. And so next year you'll be seeing a number of new things

21:19 come out that help us monetize those users. And it's going to, again, change our ceiling. And so it's my advice to

21:28 anyone who cares of what it's worth is if you want to raise, just do that, you know, get your thesis, everything pulled together in the beginning and go raise right off the bat. Um, and then

21:40 you're going to need to do your seed rounds or angel or seed and first and ABCDE, whatever you're going to do, you're going to have to keep raising and it's not going to stop, but do it from the

21:48 beginning and have that, that's part of your, of your business and, and always keep in mind why you're doing what you're doing. If you're doing it to flip it, to go sell it one day, then share a

21:57 raised capital. You know, dilute your equity. It doesn't matter. Keep in revenue growth. Then sell it. Um, if you're actually trying to build a sustainable business that's making a difference

22:06 in doing something good, then I'd reconsider getting investment, at least at that early round. Um, honestly, until you have some good footing about what you're going to be and you can dedicate

22:16 that time and energy and not lose the growth on your product. I think you're spot on. I don't percent agree with that across, across the board. There's a lot of things to take into consideration.

22:24 There's pros or cons. There's, you know, there is, uh, trade-offs and ultimately you have to kind of decide on, you know, what kind of business do you want to build and how do you want that

22:34 experience to go control us, and it's very, very important to call in a name. And so we've been very cognizant of that as even as we're taking on capital We've never been taking a dollar of

22:44 institutional capital. Yeah, it's often individuals I think that's important to the note And again, you know if you want to build a business that you you run do a five-year and then an exit then

22:54 sure raise capital You know blow out your revenue run in the red never make up penny But that revenue growth will afford you to sell and makes decent money and that's it's fine. It's fine process

23:05 That's what you want to take but that's to me, you know, there's two books that I always refer to back to on the start-up side It's it's zero to one is actually my favorite because that's kind of

23:17 where my head always is like I don't want to just do as already there. I want to make something new that never existed before I want to yeah, I want to change things Investors hate that there's a

23:25 ton of doubt in that because you know, it's a Unknown The other one is the what's it the startup? Oh my god, I'm telling you

23:39 Anyway, the one where you define your problem and you get feed-in buy-in from people before you even start your company. Lean startup? Lean startup. That's out. Yeah. So you get all your

23:47 feedback and basically ensure, remove all doubt before you even get off the ground with the product. And that's just getting people what they're asking for. Again, not a bad idea. But if you're

23:58 doing that, if you're doing the lean startup situation, then you can get capital pretty easily because you've proven it. You can give people what they're asking for and then turn around and sell it,

24:06 exit as you need to If you're doing a zero-to-one situation, you're building something that never existed before. You need freedom. People don't want to give you money and then you shouldn't want

24:14 to take it either because once you take the money, then they're going to direct where you go and if you want to build something new, you're not going to be able to. How has the vision changed for

24:23 you guys over the years? You know, it's actually pretty drastic. In the beginning, we really were focused on building online tools, things that we do but the data was never really. what we

24:38 wanted to do. We wanted to build online tools where people would do mapping and analytics and all these pieces. But early on, we recognized and even to do that, we need the data to kind of play

24:49 with it. So we kind of just dabbled. We grabbed Louisiana data or Texas data. Yeah.

24:54 And so what it evolved to now is it's a foundation. The data is a foundation of everything we do. And the platform sits on top of it.

25:03 And so it's evolved from being a tool to now like our vision is more of an ecosystem, a platform, like a single unified data platform. Our goal in the end is with you want data in this industry,

25:17 in the energy industry, then you come to well database because if you're looking for logs, you can get that. If you're looking for maps, you can get that. If you're looking for production data,

25:25 you can get that. If you wanna get third party data and it piped into the platform, you'll be able to get that. And so we've gone from being a tool to a platform is really what our goal, what our

25:35 vision has changed to be. Is anything changed about the North Star about where you guys are headed? It hasn't actually. The only thing I say will change are, and I've probably said this before,

25:44 our goal, we never ever built an exit plan in mind. Our goal was always to build the best product we could serve our customers the best we can and our exit will figure itself out. So that's still

25:57 the same way actually. I think that's honestly the best strategy. Yeah, same with here. We never planned on building this into a company in the first place, right? There's just a podcast and

26:08 then turned into kind of everything else, but. Yeah, no, and it's hard though. It's another area, like if you get especially institutional money, that's not okay. They want an exit plan, a

26:18 full blown exit plan for them and yeah, both, so. By not doing that, it's allowed us to be a little more agile and kind of shift that vision. Although, I mean, the vision just got bigger,

26:29 really, is all that happened. But no, we still have that same kind of mindset that we, I mean, and I say it to these. to the staff now kind of ad nauseam. I was like, you know, you worry

26:41 about marketing. Are you hitting them too much? Are you emailing too much? Are you contacting too much? Are you posting too much or too little? All these things. And I'm just like, if we're

26:48 providing something value for the customer, then it doesn't matter. Like it's when we stop providing anything good for them, then our decisions get bad. And that seems basic, but still, and the

27:00 same thing on our sales process. Like if we're truly trying to help them, then making them sign a contract here to ensure that they go through our onboarding process to ensure they get the best

27:11 possible experience with well database. As long as our intentions are good, it'll be all right. If you're trying to lock them into a contract 'cause you don't, you want to be kind of sneaky and

27:19 make sure they don't leave the nets. That's shitty and you shouldn't do that. So that helps us kind of guide all everything we do, kind of keeping that base who's important, who we're serving here

27:33 I love it. Let's um This is totally random, but since you're one of the most like technical guys that I know,

27:41 how does all the new AI stuff play into the oiling? Yes, please. I'm the worst person to ask that question to because I'm just been in your log cabin out there. No, it's not that like I'm a

27:51 purist. I'm like, here's data, here's analytics, here's machine learning, here's AI. Yeah. And people, people will throw AI into standard analytics and that bugs shit out of me. But since

28:07 that's the landscape, I'm gonna put aside my own kind of weird bias and whatnot. The biggest problems that people are throwing these technologies in areas that they're kind of bastardizing the

28:17 reasons and purposes for them just because they need the buzzwords, they need the. Oh, that's always the case, right? The same thing with the blockchain. It was like, you don't need a

28:25 blockchain for certain things, it was just like they throw that in there, they were able to go and raise more money. Yeah, and what sucks is that there are very real applications for these things,

28:35 and someone like myself just gets so annoyed with the fact that blockchain made a fantastic vehicle for gas trading in the pipeline world where people are constantly changing volumes and moving and

28:49 they're having to, these contracts are flipping really quick and I don't know that world really great. This actually all came up, Trev and Trev on and I were talking about it one day and it was

28:59 really remarkable hearing how much goes on and how an automated ledger would be just brilliant for that and blockchain provides that and never saw anything implemented in that regard. So it's like

29:10 this great technology but it's not utilized right. The same thing goes with like machine learning and AI. People want to talk about it using machine learning and type curve analysis. I'm like well

29:18 that's I mean really in type curves and decline curve analysis.

29:23 You could use machine learning in it, but honestly, in algorithmic, along with statistics and probabilities, that gets you the real answer. If you're throwing machine learning and you're just

29:31 doing it to get the buzzword. But then there's other areas where we have a little internal tool we're working on for offset picking. It seems hilarious because it seems simple, but people are

29:46 notoriously picking bad offset wells and running analytics based upon them and then creating type curbs and forecast based off of poor foundational data. And so using machine learning, what we could

29:59 do is we can take an area of data. We have all the data in place. We can start to define the drivers of production and then classify them as which offsets are more alike to each other. And that

30:10 works in the small area, but also for entire basins, finding in now the less basins across things That's where machine learning has real value, real fun. Yeah, there's a million variables when

30:21 you're actually diving deep.

30:31 into figuring out those comps. It's almost like, I don't know, half an art, half a science currently, but I feel like the machine learning could definitely help. Yeah, at least it gets concrete

30:34 to it. That's what it is, 'cause early on, and I feel like we don't get it quite as much, but I swear like the silver bullets I heard, it's lateral length, it's profit loading, it's pressures,

30:45 and I mean, there's all these different - And those could all be true. Well, yeah, for any particular play, for any particular time, and I mean, there's all kinds of reasons why that is or

30:54 isn't true, but like there's no one silver bullet. Everything is related to where you're at, and I mean, to be perfectly honest, and value real have that applications again real, transparency

31:00 like do to in trying, we're our platform system our, in though is technology about super But. important see can't, that's you that underground what what's, you get with like tech nerds, isn't it?

31:01 Like we hate the idea of handing people data, asking them to trust it when they have no clue how it came to be.

31:24 And, you know, some of this is easy and some is more complicated, like allocating production data in Texas, you know, the other companies just give it to you and say, Here's the number. We give

31:33 you that and the process that we took together and all the variables we used and anything you want to know to recreate it yourself you have from us. And so that's where our kind of hesitation on some

31:42 of the AI and machine learning we can use because you do end up a little black boxing and we want to avoid some of that But I do love a chat GPT whenever it helps me write talks and things like that

31:54 and marketing materials and I just I feel like there's so many options with like how much data you guys have like there's so many opportunities because I think about like so I've got this whoop here

32:03 right fitness tracker for those who are not familiar with it right traditional fitness tracker is just like tractor shit and just like told you it's what your metrics are right with the whoop and just

32:13 as many users they have on there they have like these uh you know crazy out for once it's the most accurate it's out there but too they have all these algorithms where they're starting to make all

32:21 these correlations and starting If you have two drinks, this is a journaling aspect to it. You capture certain features on the night before every morning it prompts you. And it's like, oh, if you

32:31 drink past a certain time, your recovery rate is gonna be dropped off by 40. Oh, if you have a late meal, oh, if you strain with this high, it starts to give you suggestions and now you're

32:41 starting to use it to make decisions from one that are clear to you, but also even more important, the decisions that are not clear that it's inferring from the data 'cause it's capturing

32:50 information 247, the more you wear it, the more And then it's also pointed into how do you stack up? Yeah, so they're starting to be able to tell, this thing tells me when I'm sick before I even

32:60 know that I'm sick. It'll say, your skin temperature's elevated, your blood oxygen's too low. Yeah, yeah, yeah, I'm like, I don't feel bad yet, but then four or five hours later, I'm like,

33:08 yeah, I don't feel very good. Yeah, that was it. But using that as an analogy, you guys have arguably more data than anybody being able to apply that same technology there to where it's like, oh,

33:19 there's things that we weren't even looking for that are now kind of being spit out as suggestions. Yeah, no, that's actually perfect. I did a talk on that once about the almost every machine

33:31 learning and AI project that has been publicized in the oil and gas world. I mean, a, most of them are failures that I'd like to talk about, and I feel like that's just people trying to nay-sick.

33:40 But that's okay. But when you read the details, like their data sets are too small. They're basically, you're getting a thousand wells that you drilled, that you have all the data for, and

33:51 saying it didn't give you any insight when you're in a machine learning model on it. It's like, well, I mean, you fed it all the data. What did you expect? Like, and I get it maybe there was

33:59 something un. But we have smart people here. Like, they definitely know what's going on on the wells that they drill, but they don't know what's going on in their neighbors, or bigger picture,

34:10 vintage, or the depositional formations. And anyway, so that's where ours does come in place. Why I kind of lean toward that offset analysis piece, 'cause it's like, how do we better What the

34:20 offsets look like and then how do we better from there determine the drivers of production? And again that you know, that's that's real use of machine learning that can help You know make move

34:31 things forward and answer questions Not just you know random things that can be done with an excel spreadsheet

34:38 That's always the million dollar question. It is just like why why do I need this versus you just plug it? So I watched and I don't even say who or anything But I watched a whole talk about using

34:49 this ML model to calculate you know well intervention schedules and things like that And the whole time i'm watching i'm really interested like learning what they're doing and I get to the end And I

34:58 realize that like a simple ratio would have done what they were trying to accomplish if they were you know measuring to these variables Side-by-side and of course you probably want to use like second

35:07 derivative people forget the calculus exists by the way That's the other thing machine learning is an answer for everything when you know There's a physics world that answers a lot of things for us

35:15 too. Anyway It was a ratio that Like this whole machine learning model thing that they spent six months on, like it was a complete waste of time if someone just stepped back and was like, yeah, no,

35:25 we just need to do the second derivative rate of change to define when the ratios change. And that gives us our well intervention schedule. It's even predictable that way. And so we have to be

35:33 careful whenever we're implementing these ML projects, AI projects, when we're doing it just because we want to do it. And you're 100 spot on. Well, dude, I'm starving when we go from lunch.

35:44 Yeah, let's go. Let's do it. Guys, if you liked the episode, take two seconds See what's written in review. You can go check out what database that I'm, I think it's a world databasecom, right?

35:52 Oh, yeah, what database? Yeah, so check them out. John's on LinkedIn. They're gonna have a profile on D2B Insights. If you're a subscriber there, you can check it out there, learn more

35:59 information. Thanks for coming on again. Man, it's been great. Let's make sure we don't make it four years again. Absolutely.