Is it Data or Data?
0:00 All right, welcome back to another episode of Energy 101. We have a little bit of a different DW crew today. It is our last recording of the year. It is. Some people have already - Last workday.
0:13 Last workday. Some of our Energy 101 bad bitches have already departed. So it is me, Sydney, Jules, special guest appearance by John, our resident rad dad And John's going to introduce our guest
0:30 for today. Yeah, today we've got Bobby Neeland with us. Bobby and I used to work together. And so I wanted to get him out on the show. Bobby
0:42 is currently working at Grace and Mill Energy. What is your title, technically, all things data? Yeah, I'm a senior data analytics engineer. Cool. And so give you just a minute to introduce us
0:58 you give us a background of kind of. Where you're from, how you got into data and the energy space? Sure. Yeah. So
1:08 I got into industry about, I guess, eight years ago, 2014. And before that, I was a teacher and a coach for about five years. Todd Math, I got a math degree, coach baseball, basketball,
1:17 football, a little bit of everything. Are you a high school teacher? I was a high school teacher, yeah. Todd, algebra, geometry, all up to pre-cal. And then. All the classes I hated and high
1:26 school. Yeah. You're welcome
1:30 But Bobby was a cool teacher, though. You were the cool teacher. Yeah, of course. But once summer I was coaching a summer baseball team and one of the dads, like, Oh, I think you'd be good at
1:40 this whole, well, like, assing. And I was like, Dude, I don't know anything about all my gas. He was like, Yeah, it doesn't matter. You know, it was like 2012, 2013 at that point. So it
1:47 was rocking and rolling. And,
1:50 you know, I put off everybody a year and then I suddenly go shadow and see if it's something I could do or would enjoy And I'm shadowing a couple people at Conoco and the rest was history. Yeah,
2:01 joined in July 2014 and yeah, showed up and I mean, it's kind of my standard line, but like I literally didn't even know what a VLOOK was and Excel, you know, you look back, I'm like, why did
2:11 they even hire me? I didn't know that, but oil, I didn't know it was about, you know - 'Cause you have potential - Yeah, so, and then they told me, you're gonna be the spot fire guy, like,
2:20 what's that? Which, I guess we can get into that - Yeah, I was like, I don't know what that is - Yeah, spot fire is like a visualization tool that's pretty heavily in the industry. So yeah,
2:29 kind of cut my teeth there And then I actually joined Reservoir Data Systems where John and I met and, you know, we're there to F stuff up - Got 'em over to the services dark side - Yeah - For a
2:40 little bit. And then was that University of Lands for a year or two, part of the UT system. And then
2:49 basically from there went on joined the crazy little energy we're in at now - Great - It's been a quite the journey - Well, welcome, we're having you here. I know it's close to the holidays, so
2:58 appreciate you coming out. We were supposed to record this a few days ago. The guys got a little too into the pickleball. We were very scheduled. Very sweaty yesterday after a pickleball game and
3:09 we were all disgusting. So appreciate the change on the fly. Yeah, Joel's is going to kick us off. Yeah, our first hard hitting question. Um, I only want to know for personal reasons. Do you
3:20 say data or data? I say data personally, but data is there. Is there a right or wrong way to say it?
3:28 I mean, yeah, I was going to say I'm going to answer your question with a question though. Is it, is it this data or these data? Oh, it's a so it's not context. So I guess it kind of goes back
3:51 to like a salatin root because data is plural. So like if you took any academic, it'll be these data. They're talking about data. They'll be these data say this or whatever, which most normal
3:51 people just say this data or something. You know, like, right? Yeah. But I guess, but the singular of data is datum I have no idea. We're having English. I know - So that's what I was also
3:60 gonna say is Bobby. Bobby also has a very strong English - My mom was an English teacher, yeah - No, really - Bobby is a double threat as far as that goes. He can help you with the algebra and the
4:13 math on something and then he can also make sure that the document that you're sending to a customer doesn't make you sound like a complete idiot - Well, I mean, but ultimately there's like rules,
4:21 and math you gotta follow rules - Yeah, yeah - And then in English, there's some patterns that you gotta, you know, those very good proofreading as I went - I've never even thought about that.
4:28 That makes sense - So you say data, do you judge people that say data - No, I mean, I may notice it, but I don't say anything - You're like, yeah - Well, except for her - Yeah, so stupid - No,
4:40 this past week in the office, we've been having a debate on like, do you say pecans or pecans or caramel caramel? So if I say pecan in the office, I'm gonna get fired. Yeah - Well, I think some
4:50 of that maybe come up 'cause John sent a couple invites, like to Grace in the middle and he felt GREY.
4:56 We did talk about that - We were just talking about that - Gray and gray - Which is gray - We were, that was the other thing - Yeah, when I sent that, well, I was at home trying to get my kids in
5:04 bed and I was just like - Yeah, just banging it out - Yeah, but I've got to do this before I forget to do it. And so I sent the invite and I wasn't even paying attention. So I spelled it wrong, I
5:15 put an S on it, it was Bubby, Bubby saved me on that end - But - It's fine - We made it - No, I wanted to ask you, really, here's my dumb question, is why do oiling gas companies need data?
5:31 Why is that important to, or energy companies in general - Yeah, I mean, I guess you answer it, what does any company need data? I mean, I think it's just to help you make more informed
5:43 decisions, and make decisions more quickly with confidence, right? I mean,
5:49 otherwise it's just, you know, gut feelings and there's plenty of really talented people in the industry that have good gut feelings, Sometimes those gut feelings are wrong and sometimes the data
5:57 is wrong too. But I think it's important to be as informed as possible when you're making million dollar decisions and help sometimes billion dollars in right. When you're doing acquisition,
6:07 divester type stuff, people spend billions of dollars. I mean, look at when Grayson Mill, the thing Equinor or Sad Oil Time bought the asset that we're operating after 4 billion and they sold it
6:17 to us for 900 million. So someone's - Geez. Someone's data was not cool. Yeah. Yeah
6:26 I mean, you're also being able to make better decisions and optimize the business. So the other question I had, which I think a lot of people will find interesting because I don't feel like a lot
6:37 of people that are outside, even within the industry, don't really know a lot about it. But your experience at university lands, you just kind of give an high level overview of what they are,
6:48 what that is, what they do, how that ties into everything because it's - I feel like yeah, unless you know. you have like, you have no idea. And it's completely like shocking to you. The
6:59 university system in Texas is the largest land holder, right? Or mineral holder - Yeah - I think - Well, that and then even the endowment is like, now one of the largest, if not the largest in
7:09 the country. But yeah, so, University lands as part of the UT system. And I mean, this goes back even before the oil and gas boom and you know, a spindle tap and all that stuff. But this was
7:17 like 1800s, but they're like land grants and whatever. But ultimately, once oil boom, they discovered oil in the Permian. Yeah. So I mean, long story short, there's 21 million acres of land
7:29 that UT owns mainly in West Texas. And about a million acres of that is oil and gas producing. And it's all pretty solid parts of the Permian. So basically, all the royalties from that go into
7:41 what's called the Permian University Fund. And so University lands is in charge of getting the money into there. And then there's another group called the UTMCO that manages the endowment that that
7:50 goes into But I think that's my man, it's like 24. 3 billion that the permanent university fund is worth. And I think in the last year was a record year. I think their fiscal calendar was
8:01 September to September and I think did well over a billion dollars this year to go into it - So what do they use that money for - I mean, it gets split out between all the school. So I've set back
8:11 two thirds of that goes to actually to the UT system. And one third goes to AM. I think there was some - Yeah - I think they worked out. I think AM one and a half, but I guess, be happy with the
8:20 third that you got - Right, take what you can get So yeah, it's AM, UT - But I mean, it's all the UT systems and the UT schools. I mean,
8:29 UT Arlington, UTSA, MD Anderson. So it's like oil and gas, cures, cancer -
8:36 Oh, dang. I don't think about it that way. That's great - We educate children and we cure cancer - But I think, yeah, I mean, I think it's a lot of scholarships and then stuff goes into
8:43 infrastructure for the schools. And that's the main part of it. Like the, so the oil and gas royalties go into that, but there's also like, they have like surface rights also. you know, grazing,
8:54 solar, wind, cattle, right away, all this kind of stuff. So all the money from that type of stuff goes like right into what's called the auxiliary fund. And then that gets just broken out and
9:02 distributed every year. So like the endowment goes up in there and it's invested and saved. And then they choose distributions each year to send out the schools. So they're just like rolling in the
9:11 dough over there. That's why AM can pay Jimbo Fisher and have the best recruiting class and be terrible. So they don't care if they want to fire him and lose 90 million dollars. And they don't need
9:22 it. Yeah Or like, I know you're like, oh, I like that. None of us are AM fans. So we can't comment directly on that. Whatever the opposite of an AM fan is, that's you. It's most of us. Yeah.
9:30 Hopefully. All Texas fans. To be fair, I don't really care about AM. I just am not a Jimbo fan for obvious reasons. Yeah. Someone had a hard breakup with Jim. Or did you want him? He left us
9:45 like the little Christmas grin she is. Yeah. He is. He put his Christmas tree. Or is he a city? He put his Christmas tree on the street and just jetted out. Later on. He did. Well, we're back.
9:59 So y'all are back in a much better way. Yeah.
10:05 Maybe he did y'all. He did every time I meet him. And I'm like, so how's that working out? Yeah. You couldn't have him back. I'm like, enjoy. But no, that's a continuum. No, you're good.
10:15 But me I mean, ultimately, so really, they've been operating for a while. But it was about probably like a six or seven plus years ago. Now, some of the things, some of the people on the board,
10:26 UT, were like, wait, we've got this asset and we're not optimizing it. They didn't have an oil and gas group of engineers and geologists and such to manage them and make sure the operators that
10:37 were on it. We're actually doing the right thing, you know, it's not destroying value and everything. So they spun up a group of, again, of geologists and engineers, become a subsurface team.
10:48 And I ended up joining them to help them, like, wearing a regular data because through our lease agreements, you have access to any data that's created on the land. So drill-in-the-appletions,
10:57 like, in detailed stuff that most people don't have access to. So we were able to take that and generate insights and go back to operators and be like, look, you're doing really well with this,
11:05 or hey, these people in, you know, adjacent acreage are doing better, and here's what we think you could do to optimize that. And there were different leverage that could be pulled as far as,
11:13 you know, royalty. Like, hey, we think this will help you that much, that will give you a discount on the royalty and so on So give me, give us an example of, like, the type of data that you
11:24 would, like, pull from, like, land. Like, is it just like, just like one basic example. I mean, you're saying, like, you and your lands are just in general oil and gas. Either one. 'Cause
11:36 ultimately the data ended up, was the same, like, the operators would send it to us. But, I mean, there's a whole spectrum. I mean, on a day-to-day basis, we're focusing on, like, the oil
11:43 and gas production, how much oil or gas and water, whatever did we produce in a day. There's drilling, I mean, how deeper are we, how fast, you know, cycle times, completion, how much fluid,
11:55 everything's pumped down. But we were solving that problem or trying to solve that problem at RDS too. I mean, there's so much, I mean, of horsepower, there's so many things you could optimize
12:03 there. I mean, on the land side, there's all like, it's a geospatial thing. Like you've got two dimensional, but also three dimensional problems. Cause like it's like, as certain depths,
12:12 certain mineral owners own only down to this depth and so on, accounting data and ultimately, that's kind of, you know, maybe we'll get to, but like what my job is just integrating all that data.
12:21 Like, because while I seem, you know, in their own little silo or vacuum, it all ends up coming to play. And that's probably what's been the coolest thing about being at Grayson Mills. Like I'm
12:30 starting to see that full picture where I was like, Conoco, I was in a reservoir engineering group and, um, you know, university lands was a little more broad scope, but it was still focused on
12:39 more on subsurface and everything. But now I'm seeing how it plays across the whole business and how like, you know, the economics and the - the accounting side plays a big role in basically
12:48 everything and how upstream feeds into the midstream side. It's pretty fascinating - So you're taking all the data and saying, we're good at this, we're not good at this, this is too fast, this
12:57 is too slow, we need you more of this, et cetera, et cetera. You're also looking for patterns to - Yeah, yeah, I mean there's, so I mean, I think with data in general, there's kind of like
13:09 these maybe three types of basic analytics and there's like descriptive analytics, which is like what happened in the past - Yeah - The last day or two years, whatever. And I mean, honestly,
13:18 that's a still huge part of it, there's still a ton of like, you know, what about low hanging fruit, like in that space, but then there's predictive analytics, which is trying to predict what's
13:27 going to happen in the future. So, you know, say in the oil and gas space, you'd think, you know, definitely we've been doing it for years of like forecasting Wells production. Like here's how
13:35 much it's made and then we're gonna fit curves through it, you know, to predict how much we're gonna make. And then there's also prescriptive analytics, which is like, hey, I think you should do
13:44 this the data and like the patterns like you're talking about. So you start getting some of that maybe even more on the real time side where we're drilling or completing and you see patterns and it's
13:53 like, hey, we should stop track 'cause we think we're gonna screen out. I don't know if anyone's gotten that Holy Grail yet, but or,
14:01 you know, you need to speed up the drill bit, you know, back and down. And I'm gonna get way out of my depth. You know, if I get too far talking about drilling, but
14:10 you can use the data to help, you know, prescribe, like what should you be doing also So, but then it's just a matter of like kind of pulling all together creating business metrics. You know,
14:19 one that's, you know, we're using it that's pretty common as like say, LOE per barrel. Like basically how much does it cost us to get a barrel of oil out of the ground - Mm-hmm. Just for all the
14:29 one-on-ones there, what does LOE mean - Yeah, sorry, LOE is the least operating expense - Okay - You know, I can get down to the well level and then that can come all the way up to like different,
14:38 you know, buckets, you know, whether it's, you know, different areas and assets and so on.
14:45 probably want that number to be as low as possible - Yeah, of course. That's the game plan for most people, but not always the case - Right - Well, that's the, I think that's really the more,
14:58 one of the interesting things about it, generally speaking, right, is like, people think of like the traditional perception of oil and gas as it's, you know, this big archaic, old, you know,
15:10 industry, so to speak And it's like, well, it's incredibly complex, like on that,
15:18 the call that, I don't remember - They don't whiskey - Yeah, the data on whiskey deal shameless plug for Jeff Hughes and the rest of those guys for hosting that. But
15:29 they were talking about, you know, how do we basically back out a unique identifier, right, for like our wells, right? And so like thinking about it, even at that, right? Like, okay, what
15:39 do we, how do we uniquely identify Everything associated with a well and it's like well you know, on the accounting side, you've got X, Y, and Z. And then like, okay, we've got the API, the
15:51 American Petroleum Institute standards for assigning wells these unique IDs, but even those get tricky and people don't abide by all of those things. And then it's like, one of y'all mentioned,
16:03 you know, recomplates, right? Like that gets a new API number, even though it's the same well bore. So then how do you - That's a sort of well, but - Or some of the wells aren't handled the same
16:11 way - Right, and so how do you handle that on the accounting side when it's the same well, but it's a different API number and like all of these different things - And then it also had into land,
16:18 like so like, you know, the land side of it, because then anytime, like say, in this case, you saw a re-fract, like they've tracked it before and maybe a couple years later, maybe it wasn't
16:26 optimal, they came in refract it. So that's like a capital expenditure. And then I think if you come up with a CapEx above a certain level or capital project, you have to go back out to the land
16:35 or the working interest owners and say, are you willing to pay your portion of this? And if not, they non-consent. So like they could be getting paid from that well currently, but they don't want
16:42 to spend the money on that portion but then a whole new like. deck of,
16:49 you know, that they get paid on, is started and then they don't get paid until that pays out to all the other working owners. So it's like, you start to see these chain reactions of like, I was
16:59 working on refracts at Conoco. We were helping identify wells that should be refract and identifying from subsurface. But I was in my own little, but one had no idea the implications downstream,
17:07 right, the accounting side or the land side. It's a giant logistical machine, right? Like people don't, I mean, even just something as simple as like the minerals, right? Like people don't
17:16 realize that, you know, whoever's grandparents originally own those minerals are great, great, great grandparents back in the 1700s or whatever has now been distributed amongst every single one of
17:28 their relatives that has ever been born. If they didn't sell it, or they might have sold 50 to some other family. And now it has been passed down through all of these kids. And so now you've got,
17:39 you know, 128th, 160th, you got all those fractional interest when I got into, I guess like energy just. I don't think I realized that mineral rights and subsurface rights, all that was a thing
17:50 that I was just like, Oh, in my mind, I'm like people on land that they draw oil on. Well, that's a big thing. A lot of people don't realize and I didn't realize it until I think I learned it in
18:02 my master's program, but is that the US is the only country in the world where individuals can own mineral rights in every other country, the government owns it, which is why all of them - You all
18:13 surface with them, whatever happens below that. Yeah Which is why every country has their own international oil company. Because the oil company owns all the mineral rights, so they get all of the
18:23 money for it, which is why they're all typically pretty corrupt. But most of the civilians hate them because they don't get any benefits from it. Well, now I'm mad at my ancestors for not securing
18:34 it. Yeah. And then it gets even more complicated because it's also a state-by-state thing. So in some states, you can break up the vertical So you can own the lease. from 5, 000 feet to from
18:47 zero to 5, 000 feet. And then you could sell the remaining rights from 5, 000 to basement to Bobby. And so it's like, okay, well, if we're producing above 5, 000 feet, then it goes to you.
18:59 But if we're producing below, then it goes to him. What if we're producing from both at the same time - Right - Well, that happens - So, you know what the horizontal is - Right, horizontal - And
19:07 you can get in like, I mean, how high did the fracks propagate? Like, so are your fracks going up? Are they draining a portion of mine? And then should we be distributing that? Like, five to
19:14 four - That's a very like messy - It is, oh, it's extremely messy - And so, is it the, I feel like this is the dumb question, is it the drilling company that pays whoever owns the money - The
19:25 operator - The operator - Yeah - So we're gonna, we wanna come drill, you have the mineral rights, we're gonna pay you this much to do that - Yeah, I mean, again, I got a high level - Right -
19:35 And again, anytime you get lawyers and women involved, you can really blur the lines really quick, but yeah - I mean, there's the, what, the working interest and then the. I forgot the other
19:47 one - Royalty interest - Royalty interest - Well, even though that is overriding - Right, the overrides - Yeah - All the land stuff gets really interested in the production stuff, gets interesting.
19:55 But I feel like it's generally straightforward once you know the lingo. But it's essentially, who's got working interest, who's got royalties, which ultimately boils down to, who's paying in and
20:07 how much are you getting paid out - Right - Ultimately, it's how that kind of breaks down. And like a lot of that, I believe, originally some from these big international
20:17 wells, historically, where like Shell, Conoco, whoever would go into whatever foreign oil field. Doing work for these big international
20:30 oil and gas companies. And it's, you know, you're talking about hundreds of millions, if not probably billions of dollars to spend. And they're sitting there like, hey, that's a really big risk,
20:40 right? us to go spend a billion dollars on this offshore project in Guyana that. We literally know nothing about the geology, the reservoir, anything. And so they start fractionalizing it by
20:51 saying, okay, we'll take 50 and conoco, you take the other 50. And that way if it's a dud, it's not that painful to us as a company versus just going online. It's kind of the same thing
21:02 ultimately mixed with all the messy family royalty, past downs and selling and all of that stuff. You know, like North Coast, even worse. I say that thing. I have no experience in North Dakota I
21:14 don't even know, but that's the other crazy part is that everything also has state, every state has their own regulations that are all different. Even like on the, that's actually an interesting
21:24 question, like on the data side, which state has the best data and which state has the worst data? Um, yeah. So I mean, again, all the regulatory bodies, I mean, I've really only dealt with
21:36 two. So far, I mean, I've dealt with the railroad commission in Texas and then NDIC of North Dakota and I mean I just I think they're pretty similar except the NDIC is better because they allocate
21:48 production per well. Whereas the -
21:52 It's an interesting topic. I think you should dive into it right now. Explain it back up and explain it. Yeah, so in Texas - Okay, so first it comes down to how is a well classified. I think it
22:01 gets classified based off its initial well test, I think. But it's either classified as an oil well or a gas well. Right. But I mean, that's not to say - We got that. Yeah. But that's not to
22:10 say a gas well can't produce oil. It's just the - Right Okay. Like with the majority of it would be gas, natural gas in that case, but if it's a gas well. So if it - or at least rather, I'm
22:22 sorry, I need to - We're all - We're all still kidding. So, as oil leaves are gas leaves. So if it's a gas lease, they have to operate or just have to report the data per well. If it's an oil
22:35 lease, they can combing all it all and just report it at a lease level. What does that mean for the - So you don't know what each individual well is doing.
22:44 Correct, so that's where some of these third party data vendors like Trish and her IHS, they got by SMV Global and then Invarius and well database, they have allocation algorithms or methods. So
22:58 one thing that does get recorded per well and it's on a semi-regular basis is individual well tests. So that takes them to well tests or if they knew it was only one well at least for a while, now
23:10 they brought on three wells, they can kind of use those to kind of back out to allocate the data to a specific well, but when you're using it, you have to always take it with a grain of salt
23:19 because that's not what that well made. Whereas in North Dakota, when I get it from any of the sources of other direct from the NDIC or Invarius or whatever, that is what that well made in that -
23:29 My boyfriend used to work for one of those companies you just mentioned and he told me that they just still used Excel and that it was a hot mess - It doesn't surprise me - Well, yeah, I mean,
23:39 that's the thing, right - Good, good - At least can have no wells, it can have. lots of wells. And so like the fact that in time, I don't know, I'm sure that was made a long time ago, that
23:51 decision was made without any, uh, understand air context of what the future would look like as far as all that stuff goes. But yeah, it's like, Hey, if I'm an operator and I've got this lease
24:02 and it's for oil wells, and I drill five really crappy ones, but I have one really good one. Right. No one's going to know because generally speaking, the more wells I put the less I have, the
24:14 less room I have to kind of hide that, so to speak. But yeah, it's like, so now if I'm Bobby's competitor and I'm using this public data, I don't really have a great understanding of, I mean,
24:25 generally speaking, I kind of do, but it's very hard for the industry to do that, right? Like even within the same company, right? You've got North Dakota data, which probably has different
24:36 structure, different requirements and all that versus the Texas data that you got all this commingling Is every.
24:43 operator required to send data to someone - Yeah, I mean, they're required - Which makes sense, I mean, it's but - So you've got all the permitting process and stuff, which is all state dependent.
24:58 And then you've got, yeah, the requirements that - You have to report for those - Like production especially, I mean, but even then say on the completion side now that we got frack focus, which
25:09 is like a environmental thing, right? Like ultimately - That stemmed from the joke of a movie that
25:16 I will adamantly call it that until I die, even though I believe it won some prestigious awards called Gasland that I don't wanna give any more time to on this show because it's a joke, but
25:30 it came from, yeah, a lot of
25:34 poorly framed and
25:37 what I fear mongering from that movie where people thought that when we were fracking we were. contaminating people's water supplies and so that they could light it on fire and we were pumping all
25:48 these - Oh my God, I feel like I saw like - Crazy chemicals - Yeah - And then you come to find out that 99 of a frack is water. And it's like, okay. So 90, and then like, even then I've seen
26:01 people are like, oh yeah, they're using acetic acid. Acetic acid is vinegar. Yeah. And it's like, it's just this fear mongering. But like people don't know, so they don't know And so there was
26:13 all this pushback and stuff. And so they wanted the oil and gas companies or the service companies basically to report what chemicals they were using. The flip side of that was, well, these are
26:24 our, every service company has patents around the chemicals and their compositions and all that stuff. So that's like their IP - Yeah - And so like we can't report our IP because then that just
26:36 gives it away to all of our competitors. And so back and forth anyway, they've got, they now report most of that data. level. So who does that go to here in Texas? Like what was to practice?
26:47 It's a national organization. Oh, okay. So that's one more at a higher level that has to get reported. Gotcha. There, but yeah, because even initially, right? Like, I believe when that first
26:59 started, it was optional to report it and like, probably, yeah, and now it's required, right? And so, I'm not against regulations by any means. Those things like that, I feel like we need to
27:09 do more of where it's like, hey, we have this data. It's valuable. It's useful. It needs to be reported, right? Yeah.
27:18 Cool. Now that's, uh, yeah, there's a lot like people don't realize just how much interconnected create like crazy data, even again, going back to the land side. My roommate, when I first
27:29 moved down here was a land man, and he just started and he was got back from work. And he was like, yeah, I went from, you know, whatever, 18 something to 1930. And I was like, that's a
27:42 really random like. timeframe. I was like, why did you? Is that like, is that where the, the agreements started or whatever? He's like, no, they go back another couple hundred years. He was
27:50 like, that's when they started using a typewriter. It's like everything before that is literally handwritten that these land guys are going through manually by hand to find that's who's family owned
28:00 it and who sold it and who's kids and all of that stuff. And it's like, that's just how long have you been working in the industry? Eight years. Can you like describe what it was like eight years
28:11 ago to now, like in regarding tech technology? Sure. Like in the past week, AI has completely Oh my God. I know. No, no, definitely there.
28:24 I think this is the big thing. It's kind of like, I think the cloud has been like, it was really advanced. So it was still there, but it's really just taken off now and just how much compute
28:33 capacity and just how fast we can shoot through data. You what? even their adoption, right? Like, a lot of people were very hesitant to go. into the cloud to begin with. Yeah, I mean, like
28:42 Conica, when I started like, we were getting our new data warehouse, which was with a vendor, it was a technology called TerraData, and it was a big deal, but that was all deployed on our own
28:51 servers, on-premises. And
28:54 now, I mean, like say, you know, Blockup is using Snowflake or BigQuery, which is like Google's data warehouse, and those are all done in the cloud. And it's like, you can go sign up for right
29:03 now, you know, if you wanted to, and within five, 10 minutes, you could have literally like, basically a supercomputer data warehouse at your fingertips, you'd scale it up to as much computers
29:13 you would ever need. And then scale it down, that's, yeah. And scale it down, turn it off. Like, so I think it's just that ability, not just with data warehousing, but just using just the
29:22 services. I mean, how fast you can get up and running and just have compute to do things that they need to do.
29:29 Yeah, 'cause I mean, like, I mean, one thing too, it used to be kind of a has and have nots kind of thing, whereas like, you know, Conica and BP, or even though you look on a server side,
29:36 Fumber Day had these huge data rooms.
29:40 But then obviously little guys didn't, but now I mean a little guy could have access to that if they wanted to just by logging into AWS or Azure - Yep - And that people used to have to wait weeks or
29:50 months in a queue to run a reservoir simulation on these high powered machines - Wow - You know, they just had to wait for it. It wasn't available and now it's like, well now you can spin up a
30:01 reservoir simulator on Azure and run it with a more compete than you ever had available before - Run it with an infinite amount of continuous - Yeah, I mean obviously you can pay for it - Yeah, for
30:10 that instance, but you don't have to buy the - It's gonna say the alternative is so much heavier lift, right? You've got to physically go out, get your IT department to buy the servers which costs
30:21 money, then they have to set up the servers or they've got to be delivered. COVID made that even more fun. And then they've got to stand them up, set them up, do all the permit. Like you're
30:30 talking about what weeks to months, at least once - Yeah, once - And especially once approved - Yeah, I mean like the whole procurement process itself could be just a nightmare. So yeah, I think
30:38 it's just the velocity at which you can do things now. I mean, again, just having an available, then also how fast it can truth their data. I think what was a big data problem eight years ago is
30:48 not, you know, blip now. I mean, so much data gets generated. Well, I mean, where they always say, let's like 98, 9 of
30:56 the data has been generated in the last like 10 years. Like in the world. Oh, like of all time. Yeah. It's just, it's just infinite growth in me. You've got all the IoT devices. I mean, your
31:03 phones are generating data every second. Yeah And it's just data's growing exponentially. So what was the problem a long time ago? It was not a problem anymore because we just have more compute at
31:13 our fingertips. Yeah. And even I know, so Jules runs all of our social media and her and Julie all the time talk about our like user data and what, you know, it's again, it's wild to me that
31:24 when I came on board, like I didn't know that when someone buys a ticket to empower or whatever, we know exactly like, yeah, you can treat how they the day where and there when got they
31:35 clicked from and. Yeah, I mean, if it's nuts - If we're doing our job right, we should know that - It's not always the case for most some companies, but it's a - It's so useful - It really is,
31:49 but I mean, I get why it's so important 'cause you want that data for past, present, future and to make business decisions. And we, you know, right, 'cause otherwise you're just kind of
31:59 floating guessing. You are busy, right, and it's nice to know - Yeah, I mean, it's much the same you were asking earlier why it's important, or ever, but like, I mean, it's how do you
32:07 allocate your money then, right? I mean, like, I don't know anything. It's like, do I allocate it to Instagram, or it's a Twitter, or TikTok, you know, 'cause I know it's actually driving -
32:16 Which ad on those platforms - Yeah, really - Yeah, what kind of content is generating leads and so on, so - Yeah, mm-hmm - Yeah, I mean, and I guess, you know, that is where the whole, like,
32:27 has been 10, 15 years, but the whole data is the new oil thing, right? 'Cause like, there's so much of it, and there's so much to be, you know, but you have to really refine it and pull it out
32:35 the insights that you need - I feel like if someone puts some sort of like data report in front of me, I would pass out. I would just look at it - Honestly, I think that's the - We're all into the
32:44 back of my head - But I think that's like my job. You know, is to like put it out to the most business users in like in a consumable format. They don't need to know how the sausage is made, right?
32:55 But like when they get it, you know, you give them something that they can understand and can make a better decision - They want it to look appetizing and taste good when they consume it - No,
33:04 that's, I mean, that's ultimately like, I feel like probably in the last five, 10 years, that's become the bigger problem is it's not, we need the, it went from we need more data to, we have
33:15 too much data that we have so much data that we as human beings can't even start putting it all together because there is generally so much data - Yeah - And so now that's where I think like you were
33:27 talking about right with a lot of the AI stuff, like that is gonna be one of the biggest kind of Crutches, assists. short term at least for every industry is just the ability for a computer to you
33:41 load a bunch of data into a database or point it to a certain spot with relatively minimal effort and then it to start telling you, Hey, these things are related and if you do more of this, that
33:55 means this thing goes up or this thing goes down. I mean, even basic stuff, like when we were, when Bobby and I worked together, right? Like I looked at all these different variables that went
34:05 into, you know, from the energy industry, like rig count and permits and all of this stuff. And ultimately it was all driven by oil price. Right. And it's like, at the end of the day, if oil
34:17 prices are down, guess what? You're probably not going to be making as much money. But if they're up, probably going to be making a little bit more money. It sounds like a, the French, like a
34:25 science fair project where you like are making these like correlations between variables and if this, then this and, That's a lot of what it really is - Yeah, well it's funny if this and this,
34:38 'cause like, there's some of those memes, but it's like, what you think is AI is really just a nifty statement - It's like a mess-to-dip statement - Yeah,
34:46 it's one of my favorite - But I mean, I think your point too, kinda gets into, I talk about the basic types of analytics, like predictive or descriptive predictive and prescriptive, but there's
34:57 kind of another, expanded one, there's also like, they talk about cognitive analytics, where it's like the algorithms or whatever are generating questions that you even know you had about the data
35:08 - Right, it's nuts - Yeah, kinda scary -
35:13 AI, it's, I know - Yeah, I mean, John, I talk about it - I know - It's been two or three weeks in chat, GPT thing, but it's like, and that's like, one of the first iterations - I know, it's
35:23 only been out a few weeks - And you walk into the office and it's up on everyone's computer screen and runs just talking to the robot - It is a shortcut on my homepage, I'm like, oh, okay.
35:33 That's a big deal - That's, yeah - Yeah, well the audit dodge one was ridiculous too. It was like a million people in the first, not even the first week - Yeah - Yeah - You'll get lucky if you log
35:42 in and it lets you in and then that's overloaded right now - Well I saw something on Twitter that it's like millions of dollars a day for them - It was like three million a day to run it - Yeah -
35:53 Like the servers and stuff that are flying behind it and it's like it's people to maintain it. But like, but I saw today what Microsoft invested a billion dollars in it - I was gonna say someone -
36:01 Yeah, it's, yeah - Even Canva, Julie was showing me today, like Canva now has their own AI right - Text to image generator, Dolly - Like what? It's crazy - It's like I said, I think I've told
36:14 you all this, but I genuinely haven't felt like, I haven't used a technology where I was like, man, this is a real game changer like that since I was on like AIM and like Napster - Yeah - Where
36:27 you're like, holy shit, this changes everything - Mm-hmm remember the the napster was like hey I need Photoshop because I want to make a stupid picture. Oh, I can go on a Napster and download it
36:38 for free. And then that all went away, right? But now it's kind of the same thing. We don't even know how this is going to affect all of our lives. And so - It may get ahead and everyone's even
36:48 just thinking about the app itself right now. But really, there's this kind of APIs behind it where people are going to build products on top of it - Yeah - Tell people what APIs are - Oh, yeah,
36:57 okay. So yeah, APIs - How do you use it - Which it's funny too, because I know he mentioned the API American Petroleum Institute is handy. So it can get really fuzzy if you're a data computer
37:05 person working in web gas, because there's APIs that it's the application programming interface. Or like the most common - when people say API, usually they mean what's called a REST for API. And
37:17 I don't remember what that acronym is for REST, necessarily. But
37:23 it's something that's way for different applications to talk to each other. Or websites to talk to each other. So
37:30 again, Facebook, Instagram, they have APIs and you can. those APIs and get information out of it from behind, or you can connect two applications together so that you can transfer data back and
37:39 forth between them. So again, it's being like chat GPT. You can write APIs against it and have your own chat bot set or utilizing chat GPT in the back end. But you're providing the interface to
37:50 get it in and then serve it out to people in a way that they can use it more effectively. I've got a
37:57 Slack chat GPT Zapier
38:01 walk guide bookmark for next week that I'm going to start. I feel like these are the things that like bring John. Absolutely does. Yeah. Any time I feel like I can save someone. Very rad dead. A
38:15 few minutes on what they're doing. I feel like, so I'm an engineer, right? Like I've been on the sales and kind of data side most of my career, but as an engineer at my core, I like fixing
38:26 things. I like making things. I'm always looking at like, yeah How can we integrate? How can we make this more efficient? mouth probably 50 times a day. We're going to make a dream game. How
38:36 can we integrate that into Hubs? Drink every time.
38:40 That's a new game of the office. That's actually a fun one because ultimately that's I mean, that's like kind of what Bobby's talking about, right? It's like data. You know, we have our Google
38:48 analytics data. We've got our Instagram data. We've got our Facebook data. We've our LinkedIn data. We've got our TikTok data. We've got our Hubs bot data and you've got and we've got email.
38:51 Like you've got data from all of these sources. And like to your point, like if that's all set up correctly, then we should know that, you know, Bobby saw an Instagram post, liked this LinkedIn
39:05 post, uh, clicked
39:09 the link and the email that Julie sent, which led him to this page. And that was when he bought his empower ticket. Yeah. Right. And like that's good for us to know because we can start refining
39:19 that process saying where our customers like being served content, where they don't what type of content they like being served, so on and so forth. And so like ultimately it makes everyone's
39:29 experience better, right? Like we're more efficient at what we do. our customers are happier because we're not bombarding them with a bunch of crap where they don't want to get it. And yeah, at
39:40 the end of the day, I just like to fix that - How do I change my data so that my Instagram ads aren't dresses that are 900? Because I see them and I'm like, I need that for whatever. It's so cute
39:50 and I click on it and it's 900. I'm like, I can't afford that - I'm a big proponent of rental runway - Thank you, John -
39:60 I know - So it's, yeah, I was gonna say stop shopping at Revolve or quit following along - Yeah, they can't, they can't make my hair down -
40:09 Revolve was 15 a month - But it comes in two days. It's so fast and it's free returns. Like they have 900 but in two days. Like if I need something last minute, I'm spending more money 'cause I
40:21 know Revolve's gonna get it to me in two days - I saw like a Twitter Instagram, I saw some poster meme today and it was like 299 Plus 25 shipping, hell no. 325 plus free shipping, absolutely -
40:34 Absolutely - It's like a whole lot of it - It's consumer psychology - Yeah - It's consumer psychology - I love it - Jules, do you have anything to add before Jules drives it home with our - I've got
40:45 one question - All right - What are you most excited about in the data space - That's a good one - For the energy industry.
40:57 In the next five years -
41:03 Or you don't even have to be one thing, but what are areas where you see significant kind of game changing impacts in the energy data technologies - Yeah. I don't know why it's popping in my head,
41:16 but I'm saying maybe if they're able to deploy 5G in the field, because you're gonna be able to push more data out. I mean, get more data back in, the first consume, but also make it more
41:28 actionable on the edge for the people that are - automate some more things. Yeah. And then you can reduce, you know, manpower out there where it's more dangerous. But yeah, I see that being a
41:40 big game changer just where like, we can already do a lot out there, but even just to see where that takes us. Yeah. I think 5G, because I mean, I think the cloud is going to be there and it'll
41:50 be pretty impressive where it goes. But I think it's just going to be a one to end kind of thing rather than zero. I think you can maybe go more zero to one type out in the field with 5G What's the
41:60 most frustrating thing in the data technology space from your perspective experience?
42:10 Because I asked you made me think of that, because when you said 5G and more data, I was like, just what the oil field needs, just more crappy data. Yeah, I mean, I think it's just, it's the
42:20 job, right? But like data quality is always just going to always be a thing. Again, yeah, we try to handle that as best we can. But just again, the more you get, and it's even more of a burden
42:30 to make us a good thing. me from job security, but like when nobody will adopt a standard for any type of data that in the oil field that at least the service companies.
42:42 Direct message to every service company that's listening, please figure out even if it's just your own internal company data standard. Please standardize your data. Yeah, I think it's not perfect,
42:53 but I think you saw when drilling brought in with some more, like it's allowed a lot more innovation that side. That's why it's a lot further ahead, I think, and some other sides of the
43:03 operation side of the business. This is not Bobby plugging with some L. No, it's not. For completion. Yeah, but I mean, just like, but the fact that there was a standard people were able to
43:13 build that much. I mean, I guess someone would be excited about like if OSD you could actually can actually do it. I think that'd be interesting. That's open subsurface data universe. I think
43:23 Shell and its big consortium, but like they're trying to work on like a data standard, which which would allow APIs between a
43:28 bunch of oil fields. products, generators. So trying to make it happen. I think people try before. I hope they can do it. Yeah. But not optimistic again, just because industry is resistant
43:38 sometimes to change. But I think that's one of those things where then, like, so I think the banking industry did it, and then that's where you're able to get like things like stripe and plaid out
43:45 of it that allow you to, you know, communicate with banks and do transactions a lot more easily. So I think if they can get there with a standard like that, then it'll, it'll really make the use
43:55 of data a lot more seamless and better. So my last piece of rant from that conversation, which is quit making consortiums where you have to pay a ton of money to participate in them because that
44:09 defeats the entire purpose. All of our listeners. These are just things that infuriate the help me so much about our industry. It's like, hey, we've got this data problem. Let's create a
44:19 consortium so we can come up with a data standard. Okay, great. It's 250, 000 to join the consortium. If you just want to join, it's a million dollars. If you I'm not making these numbers up.
44:29 but it's like, if you're a small service company, you're an operator, like, you're not gonna pay that because it's unnecessary, right? And then you look at like the tech space and you've got
44:40 like Bluetooth and Wi-Fi, like all of these USB, right? Like USB from A to C, where right now, that all came from joint collaboration between the tech companies trying to figure out, hey, we
44:52 need a way to transfer this. Let's come up with that. They didn't say, hey, everybody that wants to participate, it's gonna be 100 million if you want to be in it - You can't afford it - Like it
45:02 just defeats the entire point. Anyway, I'm done with rant over to the new tools - This is a new content idea, Rance with John - Yeah, Rance with Rad Dad - Oh, that's really cool - Rance with Rad
45:12 Dad - There you go - I like it - We look forward to our weekly set. Rance with Rad Dad - It's 20, just 20, 23 - He goes going, you're like, wait, hold that thought - Yeah - Let's get in the
45:22 studio - We're in the phone on - Yeah - I love the angels Oh, another one. amount of acronyms that the oil and gas industry has. I'm just like, we need a glossary - It's every individual - What
45:32 are you saying -
45:35 I'm sorry, so I was just gonna plug one of you times on the plug slumber's Tlimmer today. They do have an oil and gas glossary - Really? For all the acronyms - I think that a lot of the - Most of
45:46 the acronyms that you would probably hear regularly between, I mean, some companies are gonna have their own and that's just gonna be able to do it - Yeah, right, right - But yeah, so it's pretty
45:53 good. 'Cause yeah, when I walked in the door, you know, again, teacher didn't know anything I was like, sitting on a meeting line - Yeah, I'm like, yeah. Do I ask them questions or do I
46:01 pretend like I know what they're talking about - Deer and the, what are they talking - POH Yeah - about - Yeah -
46:07 'Cause we also have some weird acronyms, like PO who just is pulling out a poll. And that's on pretty much every drilling report that's ever been created - I'm sharing my face here - I do have
46:19 glossary on my to-do list of things for our little energy 101 - 'Cause the horse is a glossary at basic And I'm like, what are you saying?
46:28 What we're talking about is CAC and LTV, and I'm just like, okay - Yeah - Well, even, even - I'm sitting over in the corner
46:38 searching on Google, I'm good, what does it stand for - What Colin, Adamantly wants us to not use acronyms because, yeah, muddy excludes people - Us, yeah - Yeah, we are team - We feel like
46:50 squaring - We all came from outside of oil and gas, so we're learning in a constant state of learning - Yeah - I feel like it's not industry-dependent, though, 'cause when I started - No, yeah,
47:00 I'm sure - Like, all the business classes I took, when I first started, I was like, what the hell are they talking about? And it's just a fancy way of saying, you're making money or you're not -
47:09 Yeah, we just wanna make your life harder - Right, well, that's the thing. It's like, it makes the people that know, feel like they're in the know, and the people don't - Yeah, not to steal a
47:18 total, you know, Gen Z thing, but it's like gatekeeping, right? And it's what I thought about, when you're going to consortium, though, too, it's like, keep those people out Yeah.
47:27 But again, I don't think people are using acronyms to do that at all times. I think it is smart to try to use that. Yeah. Yeah. All right. Here we go. Grab the fire. Number one. What's the
47:40 number one misconception about the energy industry? So
47:45 I, oh, okay. I'm going to keep it on the data theme. I think it's the fact that people think that we don't use data or technology as well as other industries. That's a good one. I think You know,
47:56 because again, I think everyone looks at Silicon Valley and everything out there so far ahead and remember, but I think John I've talked about this a lot. But it's like, you know, you've got
48:04 these predictive and prescriptive type things with, say, with Spotify and well, if Spotify. predicts or says like, Hey, I think you might like the song. You don't like it. You just skip it,
48:13 right? Yeah. What's the consequence? But like if you're using AI or machine learning and you mix prediction in the field and like, I mean, like you can, I mean, there's major consequence.
48:24 millions of dollars, environmental consequences, life, safety. Yeah. So I mean, it's just the stakes are a lot higher if you're wrong. So I think on that side of it, and honestly, I mean,
48:35 oil and gas was solving big data problems before big data became a thing. I mean, we got seismic and I mean, reservoir simulation, like skita, right? Skita has been around for a long time, but
48:45 mostly in industrial or oil and gas applications, almost only, right? And then like, and it's funny, everyone thinks that they're behind, right? Like, he's talking to people and we're like,
48:53 Oh, we're so far behind. But then you go talk to someone in healthcare and it's like, Oh, we're so far behind. But again, heavily regulated industries too, where they just can't move as fast as.
49:00 Healthcare is probably the same way. Yeah. So that's literally people's lives. Yeah, it's definitely worse. I think there's all kinds of data privacy stuff that goes with that too. But like,
49:10 even when I was at like, I've still, right? Like talking to manufacturing and some mining and all these other things. Yeah. They're all, it's all the. Everyone's in the same boat, right? Yeah
49:19 wireless cellular, like everyone's trying to roll out 5G and. 5G requires all these new additional hardware installations and equipment and it's like, it's the same thing, right? It's like, hey,
49:30 we're trying to upgrade, but we've got all this legacy infrastructure software, hardware, et cetera, that we can't, we've got to figure out what to do with it before we can upgrade. We're not
49:40 starting from zero - Yeah, number two, why should we care about the energy industry? 'Cause we came from outside of it. Don't know anything about it. Well, we're waiting - Yeah, so I mean, go
49:51 - I don't like, I don't like, we don't warn out that we did - 'Cause I mean, I think whether you or anyone else knows it or not, like they're consuming energy. I mean, I think I heard y'all
50:01 talking to some of the other ones, but it's like, you know, the electricity doesn't come from the wall, right - Right, yeah - Like, you guys generate it. And I don't, I think people way
50:07 underestimate the scope of like,
50:11 how much the energy industry, you know, any type of energy, like impacts their life. I mean,
50:17 like, going into this freeze here pretty soon - Yeah - And February 2021. It was amazing when things were shut down, as far as people's houses were up. We went from a first roll to, I don't want
50:29 to over-trivialize it, it felt like third world real quick. There was one restaurant open and you got lines out the door and people were trying to get what they can to survive - No water, no power
50:38 - Power is down - It was like the apocalypse. It was really what it felt like - And people just don't realize that it's how much it impacts it. I mean, even driving my car, I can sit in a car with
50:46 air conditioning or heat now and you're comfortable. You don't realize how comfortable it was People don't realize how much it impacts them until it impacts them - Yeah, that's our goal here. Our
50:56 underlying theme, 'cause every person says the same thing, so we're planting their brains.
51:03 All right, last one, this is our favorite one. What's your most embarrassing story of your career?
51:08 Your most embarrassing - Yeah, since personally or profoundly or both - Yeah, I mean, I think second year in the social media that John's room, I'll probably go like, so I was at Reservoir Data
51:18 System that I was working on an alerting feature.
51:24 Yeah, and I was testing it and it was like me and a couple, and I set this thing off and my cell phone and CCLR VP of engineering and a couple others, I tried to turn it off but they were just
51:36 getting hammered. Do, do, do it with text messages and it was like 30 minutes to an hour. Like I've just - Like an amber alert level like just like - It was literally a text message, but there's
51:46 like - Oh, constant - Like they were just - The feed of new text messages. It was literally alerting every second - I know that's because my phone was one. I was getting this. I was like, I think
51:55 I call your text. I don't remember if we were even - I mean, I couldn't call our text. I mean, 'cause you can do it - Oh, wait, Paul, you can't use your phone, yeah - I couldn't do anything.
52:03 And so, I think I came up - We just had to wait for them, like 'cause I think it set off a bunch and like they were just all in a queue, I guess. So it was like, you know, I shot off a service.
52:10 Like they were just - You
52:13 saw the way - Oh,
52:19 yeah -
52:22 I was like, Bobby, what is going on - He's like, Nothing - I'm just a scientist. I mean, I like that -
52:25 That's so neat - I mean, that could have been way worse, really - Oh yeah, for sure, but - Sending out someone's personal, private something to someone a million times or a picture you don't want
52:35 someone to see a million times - Yeah, I'm waiting for the day that I accidentally keep my computer mirrored to - Oh, why is this green - I do it all the time - You did that one day - Luckily it
52:47 hasn't been weird yet - We were in the conference room - Yeah - Y'all were looking and it was mirroring in the big room - But you guys wanted these days - I'm super cognizant of that with the audio
52:58 because we use my phone to play music most of the time - Yeah, I do have to do that - I'll go to the bathroom
53:05 and like - Totally looking on blast, 'cause you'll be in there and you'll be like, I don't know whatever you're listening to. And I'm like, watch it - I'm death scrolling through Instagram and
53:12 it's just like, real's playing and I realize there's not sound coming from my phone. I'm like, oh shit - Julie's got that - I'm playing the office still - Okay, Julie's done that before. And
53:20 we're just like, what is that - Oh my gosh - Oh my gosh - She forgot to - I love it. Oh Bobby, thank you so much for joining us - I'm not gonna stop you - Yeah, to everyone listening, like I said,
53:30 last recorded episode of the year. Make sure to subscribe if you guys have any ideas of topics you wanna learn, people we should talk to, let us know - Check out the newsletter as well - Yes,
53:42 newsletters. Julie's newsletter, Energy 101 comes out so you can subscribe on her LinkedIn And I think from all of us at Digital All Cadders, happy holidays, happy new year. And come hang out
53:54 with us on February 9th, Energy Tech Night, yes, Julie - And the heights, yeah - And then Empower will be a month after that - Yeah - I also wanna make sure that everyone knows that this is the
54:05 day that it's supposed to swing like whatever 60, 70 degrees. And so we'll see how this non-event storm that's coming through that they've told us - Mm-hmm, yeah - We shouldn't have any issues with,
54:17 we'll actually work ready in a week. Oops - Stay tuned - Bye everybody.
