Chuck Yates Needs A Job

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0:50 I'll bring in and book a bit of a baseball analogy here. We have been, I say we, my son is 17, is a junior, is a pitcher. But back when he got into Kidpitch in Little League, right before he

1:04 got into Kidpitch, I decided I was going to turn over the pitching instruction to an expert. And I just got randomly referred to a guy who was with the National Pitching Association And that is Tom

1:21 House, who was a real innovator and is viewed in a lot of ways,

1:28 is somewhat eccentric as well. He had his pitchers when he was a pitching coach with the Texas Rangers throwing footballs in the 90s. And there's a lot, there's a lot around that. No one has

1:37 better data and information. Tom 74, he's been doing this for a long time But he also is a great example of someone who is able to adapt. and changed based upon new information in the learning that

1:52 comes from it. He does it very objectively. And so, he works with the best in the world. He works with Tom Brady, for example. Those guys are seeking not five to 10, but maybe a half percent to

2:05 1 improvement, which for an elite athlete. But that's all based upon better information and better data in terms of training and decision making. And it's proven to be very successful In my

2:18 experience, I'm glad I went that path with something pretty important, which is arm health and being a very, very efficient pitcher. And we've seen the results of that. Now that's been a nine

2:34 year process for us, but are we doing the same things that we were doing nine years ago? No, the program is adapted because the data has changed. And I know it's much to your chagrin,

2:49 being a Dallas Cowboy fan. The Cowboys back in the 70s made these wild draft picks. I don't know where two tall Jones, the number one overall pick, who's ever some walls from Grambling State, et

3:01 cetera. And that was all driven by the computer. You always heard about the computer. The whole basis. I had forgotten about that. But remember, it was all about the computer. The whole basis

3:12 of the computer was this. They hired a guy from India to run the system that knew nothing about football. And what he did is he graded each player coming into the draft, and then at year three,

3:27 year five, and year seven in their career. And he went back and said, Okay, our initial assessment, how did it compare? And just being objective, looking at data, what he found out was every

3:42 scouting system by NFL every team was set up by geography And that was driven by cost. So a scout worked Florida, 'cause they could get in their car and drive around and go see Florida State and

3:54 Florida Play and all that. And what he found out is the guy in Florida wasn't necessarily good at every position. He was better at running backs, where the guy in the Northeast was better at

4:10 offensive linemen. So the Cowboys took film data of all the players they were looking at sent it to every scout. And the computer actually weighed what the guy in Florida who's good at running backs

4:26 said about all the running backs. More. What more than the guy in California who turns out to be better at quarterbacks. And

4:37 that's why they were so much better at drafting back in the day was just they kind of turned scouting on its head.

4:48 And I think it's the undoing project.

4:52 Talks about Darryl Moray, he used to be the GM of the rockets. One, they got it in a little bit of political hot water for his comments a couple of years ago. But anyway, they talk about,

5:07 and Darryl was probably the pioneer in the NBA in terms of having these types of models Right, and being very, very data, very robust from a data, and kind of trying to take as much bias out of

5:24 scouting and player selection and development. And the first chapter is about him and the whole evolution. It's titled Man Boops.

5:35 And it talks about the fact that the rockets passed on

5:42 Mark Gasol because someone had seen a picture of him on the beach in Spain. And so made judgments about his skills, capabilities, and just kind of talent as a player. And they go in and describe,

5:59 you know, what restrictions they put on scouts going forward in terms of interviewing players, whether it had to do with geography, or other kinds of biases and preconceived notions And it really

6:16 changed the way that they went through their player evaluation and ultimately selection. I guess everything in this somewhat modern era, everybody points back to Moneyball, right? The Oakland A

6:29 story, but it's gotten much more sophisticated than that. And that, I'd love to sit down and have a conversation with them about, you know, all the metrics and statistics and things that they

6:41 look at that, you know, bus conventional wisdom about productivity and potential. First step, Jeremy Lin, at one of the quickest first steps

6:54 ever, right? At least in his draft peers, but people didn't perceive that for whatever reason. Yeah. The interesting thing about Moneyball was not that he was using statistics to come up with

7:09 better players He was using statistics to create the most runs he could given his limited budget, right? And he found that in the marketplace, someone that could walk and get on base was under

7:25 priced vis-a-vis someone that just had a lot of RBIs. And generally, they had a lot of RBIs. One, they were a good player, but two, they were also given the position to generate it. And so if

7:37 you look at the machine that Jeff Luna built, again, forget about trash cans, right,

7:43 the most

7:46 surprising thing to me. when I got my first tour and got to spend some time with the various front office groups. They had scouting, they had RD, they had international, was twofold. One,

8:02 just how small the headcount overall was. And two, how young the front office is. And I look at, you know, I've seen various presentations on analytics headcount per front office staff and the

8:17 growth over time with some of the large market teams tend to solve the problem or try to solve the problem by throwing a lot of money and increasing headcount. Houston has remained relatively lean

8:30 and leveraged in its analytics capability. I don't know how that's changed since Click took over as GM. But I

8:41 mean, you had someone who was part of Luna's front office in St. Louis. that actually broke into the Astro's data system and went to prison for that. So there's something different about that

8:57 model. And they didn't answer all the questions that you were just chomping at the bit to ask. And for example, I was,

9:08 it's a name that has now been associated with a bit of Infamy Brandon Taubman, who

9:16 was his assistant GM, who got fired after his unfortunate outburst in the clubhouse after the LCS in 2019.

9:25 But I noted that in the RD room, you had one guy who looked, he was the gray-haired sage, he may have been all 35 years old, but he had a PhD come to find out. And this was among the data

9:39 scientists function that translated what scouting needed to have.

9:49 And they were developing the tools, the analytics tools. But I asked Tom and I said, so and so has the degree and PhD in behavioral psychology. I care to expand on that a little bit more. He said,

10:04 so there is a, there's a lot of intangible stuff in terms of how that all comes together. It'll be interesting to see the evolution of that under new leadership We've had one full season post all

10:18 that and hopefully they'll play this year. Bye!

10:30 Now we think I know she

10:40 Everybody welcome to Chuck Yates needs a job the podcast cool guests today Mark Meyer Mark thanks for coming in Pleasure to be here. Thanks for asking. Oh, absolutely. This is a so I love what

10:51 you're wearing because we're set up as you can tell this is different camera angle than we normally do the podcast we're playing with things at digital wildcatters and We were setting this up. This

11:02 is the BDE Studio and I was tired of the way we did podcast it digital wildcatters where we had the awkward you sit next to each other And you kind of look over at each other and you look at the

11:13 camera So I was like I want Joe Rogan style. I want to be able to sit there Look at my guest talk about all this and we set all this up yesterday And I realized if we wear black we will look like

11:24 floating heads

11:26 I Was originally gonna wear my favorite piece of TPH swag which is a more month Vest it's in black as a nod to mannered and Bobby's last day at TPH so Yeah, I get the email yesterday about that too.

11:39 Yeah, maybe if you would have worn a white shirt under it, then that would have worked. But yeah, I had visions of us just these floating heads. Well, the risk of this particular

11:51 fleece is that we get detoured into baseball conversation, which I'm always willing to go pretty deep on. Well, Hayes and I did that. So we'll definitely touch on baseball Now, I think the first

12:05 time, it seems like you and I had known each other a long time, just kind of financed to finance. But I think the first time we really hung out, was it six or seven years ago, but in Nashville.

12:19 Because at Tudor Prickering Hall, you used to do a deal where you'd pull together, some folks in the industry, hang out, do country music, but at the same time have kind of a closed door,

12:30 Chatham House Rules discussion on energy, which was really cool.

12:36 It was a free form discussion. We had private equity leadership. We had hedge funds - And me, but anyway - We had, yeah, and me. Fixed income, we had Dan participated from his perspective,

12:50 both of a leader of cell-side research and then running TPHS at management at that time. And just mixed it up. The one that I recall was in 2016, most notably, which was right in front of the

13:07 election. And so that event was always scheduled to coincide with really a destination or an event, which our guests could bring significant others and attend the country music, the CMA Awards in

13:23 Nashville. One of my long-time friends, I never had a brother, but he is like the brother I never had as a 35-year music executive. actually from Texas, but has lived in Nashville for a number of

13:38 years and ran the CMA for a while and really a career in promotion. Done a lot with him both

13:48 just as a friend, but also helping him with a foundation, a charity that he runs in Nashville as well. So a bit of unique access, I guess, and we created an event around that, which the

14:02 discussions of the round tables that you alluded to always had a lot of depth and a lot of, I think, a lot of substance to them - Well, what was cool is I would be pigeonholed in my EMP world,

14:16 private startups and the like, hearing a refiner talk about the issues they're dealing with, talking about, hearing a fixed income person talking about LIBOR rates and the like, and you just

14:28 realized how much all

14:32 of these pieces fit together I don't think I ever It is all connected and I go back to when I left Simmons in the mid-2000s to start my biceide career. I started out with Jerry Castellini at

14:47 Castellarck in Chicago, so I didn't have enough sense not to get back into a commuting situation, so I was flying to Chicago every week. It was a great intro into portfolio management for me

15:03 At that time, really the convergence between the midstream and the transformation that the midstream was undergoing relative to what was happening in the upstream, moving much more toward GNP,

15:15 asset centric and away from more of the traditional pipeline and terminally asset.

15:22 That really sparked an interest in understanding how the convergence and - What affects one affects the other? Yeah, so when you're with Jerry, is it long only, public traded securities, what was

15:37 kind of the - We had some, we had both, we had a main fund that was long short with a lot of discretion in terms of exposure, either direction. And then we had some dedicated long only accounts,

15:52 really just building into mostly endowment world in terms of our LPs that wanted exposure And Jerry's one of the most knowledgeable investors and certainly energy investors, and I think in the world.

16:09 So it has been a long short? So the long short stuff was more trying to provide an absolute return to folks. And then the long only was, hey, you need your energy exposure. Here we are. And how

16:22 were you? I've always lived in an absolute world, both during my time at Castle Arc, which was shorter, and then the funds that Rob Raymond and I. co-founded at RCH shortly after I left Castle

16:37 Art - So that's actually pretty interesting 'cause I think the one thing perspective has done for me, being unemployed now, going on whatever, 18 months is, I mean, I was two weeks out of Caine

16:52 when I looked up and said, Man, shit, it's just all beta. I mean, you know, beta dominates this, running around chasing alpha probably led to really stupid mistakes It was hard to do. I think

17:03 John Farber's comment about it was, well, of course, you've never achieved alpha. So, whoa, whoa, whoa, whoa. But so were you trying to be, trying to be beta neutral at the end of each day?

17:15 Or just trying to absolute return - Absolute return. And we had, I'll speak to really the framework of how we looked at risk at RCH. We never levered any of our funds. we took risk in the form of

17:32 concentration. And when I'm talking concentration, I'm saying core positions could be double digit percentages. And our philosophy was that we were underwriting the assets and the companies and the

17:50 stocks from a perspective of deeper industry knowledge, technical knowledge of the asset class. And if we were going to take risk, it was going to be risk that we were able to assess and frame. So

18:06 we never levered any of our funds on either

18:11 the more ENP centric ones that I ran and certainly not on the midstream side - So what was the investor mindset back then? Why were they investing with you? Were they, 'cause it's my sense on at

18:25 least on the private equity side, call it 2005 to.

18:34 2007, 2008, somewhere in there we started becoming an allocation. It was, you know, back in the late 90s, Ken Hirsch would go in and raise money for NGP and it was energy's great and he's

18:45 competing in the private capital bucket with buyout venture, all the other stuff. You know, fast forward mid-2000s to 2010, we become an allocation and then it's YS versus the capital, the

18:57 capital, the capital, the capital, the capital, the capital, the capital, the capital, the capital, the capital, the capital, the capital, the capital, the capital, the capital, the

19:09 capital, the capital, the capital, the capital, the capital, the capital, the capital, the capital, the capital, the capital, the capital, the capital, the capital, the capital, the

19:21 capital, the capital, the capital, the capital, the capital, the capital, had direct relationships and access through the work that Rob had. been doing with RCH prior to that and really

19:34 establishing one of the largest and I think most successful MLP portfolios coming out of the Crow Family Office. That whole joint effort RCH started in the summer of 2004. And so we actually first

19:49 started talking. I'd been, when I was at Simmons, Crow was an account or a client and I'd established a relationship with Rob as an analyst when he was at Crow. And fast forward to my time at

20:05 Castle Art, I was meeting with Rob marketing our funds in Harlan, corn vase, so you may know, HBK Capital, one of the original rainwater guys.

20:17 We were talking a lot about the convergence between the midstream and the upstream And most of my experience and expertise was heavily EMP or upstream oriented. And

20:33 it really sparked an idea that there was an extension of what RCH was doing that we could create some strategies together that

20:48 around the long short model

20:51 and the long only model that would take advantage of, I think at that time we said to do a good job of investing in the midstream, you've got to have a much better understanding, much deeper

21:04 understanding what's going on in the upstream and vice versa. And that was really the coming together moment. So we launched that in March of 2007 and raised capital fairly quickly, pretty small

21:18 size,

21:21 drafting on the relationships that Crow and RCH had already built The thing that changed it for us, and I think the whole institutional mindset. changed after obviously 2008, 2009.

21:36 We had one of our best years in 2008, I think because of that integrated due diligence that we - Yeah, I mean, that's the Shell revolution. I mean, people are actually out there, drilling wells,

21:50 creating supply, we're way more supply than we'd seen - Because of our window into the midstream and what was going on with demand, just based on crude and product flows that we were seeing, we

22:05 were able to see that at 120 going to 147 WTI that we've got a demand problem. And so we were able to shorten up the EP centric book, the long short book, the main fund that I ran, we were able to

22:19 shorten that up pretty substantially in front of the carnage. And so we ended up, I think we were down on a growth basis a little over 3 in 2008, which compared very favorably with. the indexes.

22:33 Not that we were benchmarking, but others were looking at that and we go to some cap intro conferences and institutions are starting to take interest and they want more beta and exposure in their

22:45 portfolios and that's what I learned from some of these allocators on the institutional side is that they wanted more beta and the volatility was fine.

22:55 You really start to get into it where you have a fun philosophy and framework and a set of fun rules that allow you the discretion to take as much direction either long or short. We were mostly long.

23:09 That's, I think, long investors are wired one way and generators on the alpha side, or on the short side, alpha generators on the short side are a different way of being an animal. But that

23:23 started to attract a lot of institutional attention and we grew pretty quickly after that moment of outperformance. even though we didn't make an absolute return in

23:34 2008, we didn't suffer the drawdowns and we're able to participate in a lot of the, the interesting thing is coming out of 2008 into 2009. There was a lot of dislocated high yield as you know in

23:46 some companies that had very good asset coverage. And so those trading opportunities were not very abundant, but there was some pretty outsized opportunities

24:00 We were looking to connect with institutions that wanted deeper knowledge about the asset class. It was on the cusp coming out of

24:13 gas shales into oil shales. If you remember Hanesville, Aubrey had that - Well, I remember, I mean, this is wild, but I remember back in 2005 with Dave Lenormen drove on an oil well that made 1,

24:26 500 barrels a day and that was the kiss of death you were just like, oh my. God, this is going to be horrible because in three days, it's going to be a hundred barrels a day and then nothing. And

24:36 it really watching the transformation of horizontal oil, well, bad to horizontal oil, well, good was, was pretty interesting. I remember going to one of the IPAO just conferences in New York and

24:51 Aubrey was one of the keynotes and he basically talked about, you know, the collapse on the gas side was a function of us being too good at what we do. And he was exactly right. And I was a firm

25:06 believer that, you know, oils harder, the molecules are bigger. They're much more complex. We're talking about tight rock. We certainly can't do this on the oil side. Well, we did, right.

25:15 And it just speaks to how great this industry is with, you know, innovation and applying technology and techniques that, you know, make otherwise an economic and impossible. geology to exploit,

25:32 exploitable. Now we can go into a discussion around, you know, IRRs and - Right, right - The well-watching game and - Do we still use paper ledgers for the back office - Right, right - Yeah,

25:46 yeah - You're right - So in our experience, in my recollection, the institutions were looking for, they were looking to up their beta exposure. We were oriented much more fundamentally Our mission

26:00 was always to generate outsized absolute returns. Yes, we

26:06 did a lot of active hedging in our books. We were quite active in some of the equity options where we had big positions, et cetera. But one of the things that changed all that fairly rapidly is

26:18 that you started jamming a bunch of market neutral pods into the equation. And that became, I think, a recipe for a lot of liquidity - liquidity trying to force its way into a still a fairly small

26:32 pipe. And you'd have periods where the trading behavior of stocks diverged a bit from what you believe the fundamentals pointed to, and that month to month can create some

26:50 eye-popping results both directions if you've got long exposure Yeah, my sense from that is maybe kind of through the mid-90s to the early 90s, it just felt like energy investing was your inflation

27:12 hedge. Right. You know, just kind of, well, we've got to have exposure to it. And if you're disciplined enough that when prices pop and you sell some, you always made some money I think you saw

27:22 a little bit with 3D seismic of, holy shit, there's some alpha here. I mean, we can go image something and all this. And I do think that when the Shell Revolution happened and you saw it with gas

27:35 and then with oil, the investors actually were saying they wanted kind of more beta, but they were really going, no, there's an alpha story here that kind of turned into a bubble, that money just

27:47 kept pouring into Chesapeake, et cetera, and the like - I'll speak to that and you'll remember this history -

27:57 Natural gas closed 1231, 2000 at 10 spot 00 for MMBTO. And I remember I was covering all of the names at the time, XTO, Louis Dreyfus, there's a blast from the past, all the, the big, bigger

28:16 gas-levered names that had really come on the scene early as it became an investable story and unconventional on the gas side. There were a number, including Chesapeake, First week of January of

28:29 2001, they announced these big, juicy colors on 12 to 18 months of production, as I recall.

28:38 I'm talking five by nine's type of stuff, right? And stocks got absolutely hammered. Yeah. Because the belief was this thing is going to continue to run to the upside And I remember, as we were

28:55 putting together our story,

28:58 this is the interesting part of the growth of Simmons on the research side that Dan and Matt had the, I think the vision to expand beyond the Wolfland Services. If you know anything about Simmons

29:10 and Company's legacy, it started in '74. Strictly, the Wolfland Services banking took on securities in '90 or

29:17 '91, the late-great Mike Fraser built and ran that business for Simmons And then

29:25 diversified into other things including EMP last, somewhat ironically, in the late 90s with me, but they had missed the downturn in '97, '98, that inflection point. This time we had at least a

29:43 franchise that was directly following the EMPs. And so big credit to my number two, really my partner, Ryan Zorn, you may know, has been in Colorado for

30:02 a long time now, he's from Colorado, but we took a basin level view and said, because the oil field services analysts were asking the question. And I think the spark for all of that was Bob

30:11 Allison had said in Platts in an interview, it doesn't matter if Gasko's back to two, we're still gonna keep blowing and going in the bojour And I think they had, I think in a dark way, it's 60

30:22 some odd. vertical rigs running at that time. And I'll put it a little bit in elegantly. We looked around and decided to call bullshit on that. And so

30:35 we started building what I believe were the first type well models by basin using what then was known as Dwight's PI data and running out decline curves and then putting

30:47 calculations on various costs and price sensitivities based on cost structure based on our view of what the decline profile and the recovery looked like. And so what's rational to do at certain price

31:02 levels and cost structures and make a rough cut at what rig count at that time. I think the peak for guest directed drilling was 1, 068 rigs, six in my mind. That was in June or July of 2001. And

31:17 leading up to all that, we were marketing that If you start seeing a problem. in the early injection period in storage with injections that had typically been in the mid 80s per week, BCF per week.

31:31 If you start seeing triple digit numbers and you've got a problem from a demand side, that's how we were going to define it. And then on the other side, we were looking at the potential for laying

31:42 down rigs because the projects at lower price levels became an economic, you know, elevated cost structure because it was a fairly inflationary period. And that's indeed what happened. We pulled

31:51 the plug on the cycle, which was just a great group call. It's one of the best calls that I've been associated with and got our clients out of the way. I mean, we didn't perfectly time it, but we

32:07 saw what was coming from the standpoint of both demand and then ultimately what a rational player investing in tight gas wells would do at certain

32:19 cost and price frameworks Yeah, no, get. 'Cause it was, I mean, I remember back in, I don't know, some point in the mid to late 90s, Louis Dreyfus, that was their thing. We have this great

32:36 trading organization we're associated with, we're gonna hedge out, we're gonna drill a well, we're gonna know it, it produces, we're gonna hedge that out, we're gonna walk in or return. And it

32:46 traded it like one time, Zebit Dop. Because of it, the market just hated it So it was clearly a lot of the investable dollars were there because they wanted exposure to

32:58 the commodity hedge inflation, all that.

33:02 And then we transitioned into this alpha of, hey, we can go use new technology, drill horizontal wells, we can make all this money and do great things. And what I think kind of happened in that

33:15 is you went from all these institutions establishing allocations, 10 and 12. percent

33:25 and throwing money at it. And I think at the end of the day, it just became kind of the classic bubble and you're at the mercy of the beta there. Now, that's me as a private equity guy trying to

33:38 defend the fact that my track record is 12 times your money in an 8 IRR, which by the way is one of the better ones out there But yeah, nobody wants to talk about track record. Yeah. Well, and

33:54 you don't see, and maybe it's because I'm not in the flow or in the market right now, but nobody on the public side is out raising a dedicated energy fund. The way we think about it. I don't know

34:10 how many of the market-natural pods are left. I know there was a fair bit of contraction around that. But and with, you know, a lot of institutional pressure to not invest. And you and I have

34:26 talked about it way back when, back at the

34:30 onset of COVID and separation and all that, that I think we were talking about the notion of SPACs and you said, look, nobody is going to touch anything hydrocarbon related in SPAC world. And that

34:47 was year and a half ago - Yeah, I did join the board of black mountain - Right, of course, of course, see that, but no, it's definitely - One of Dan's adages that I'll interject here and we

35:01 still joke about it is, it's never different. If you say it is and especially in Brent, then you're fired as a research analyst. So I think that your point about, and the anecdote about the

35:17 stocks that I think took the various stutes top of putting some surety around outsized cash flows, meaning the color is that some thoughtful operators put on at the beginning of 2001 after seeing 10

35:30 gas with a bit of a nod to demand, but nobody talked about it. I mean, everybody talked about their supply analysis till the end of time. We couldn't grow production, et cetera, et cetera. But

35:43 I think those that needed some surety of cash flows, certainly for maintenance capital, whether we were explicit about it or not, that was just a good business decision. But the investment

35:55 community at that time said, Don't cap my upside in the commodity - Right - Okay. Go buy the commodity. These are

36:06 businesses that have, and I think one of

36:10 the reasons XTO was my best call ever, covered it when I went to Goldman And this was still very much at the cusp of. going from exploration driven conventional models to the

36:26 unconventional

36:30 engineering driven the manufacturing I've heard. Manufacturing I don't really like that term when we're talking about geology, but that's another kind of old guy. Old finance guy thing. Yeah,

36:40 manufacturing. Yeah, it's not manufacturing when mother nature's involved. Yeah, it's actually hard as shit. Yeah, it's hard as shit. Although this was great back when I was at Stevens, the

36:51 old guy John Jacoby that kind of ran all of Stevens family money, really smart guy. It's seen everything in investing. Any time you brought a manufacturing business in to invest in, he'd always

37:02 say, Man, I hate this 'cause every time we try to make shit it breaks. Another term we used to bristle out was crack the code. Yeah, yeah, exactly. It's not that simple. Exactly. But

37:15 you know, what happened post the transition from gas post the financial crisis. into oil became really a very different animal from

37:29 an allocation and investing standpoint. I think chasing alpha and I never ran a market neutral strategy. I just really just given the fundamental dynamics of

37:49 the MP sector, particularly when you're talking about small and mid caps, which is where you generate most of your alpha And we were trying to do it through deep underwriting and concentration in

37:52 our portfolio.

37:56 That is not

37:60 sometimes that is

38:03 in some cases masquerading as alpha when it's really beta. And there were so many distortions coming into the day to day trading as a result of jam of a lot of funds doing the exact same thing.

38:21 pods there were in total at one time, but I know one firm had 15 energy pods from what I could tell were doing the exact same thing in the sector. And it just, it made for a lot of difficult to

38:35 maneuver around trading behavior. And you'd get these periods of disconnect from the fundamentals. And then your, you know, investors were looking around and they see elevated volatility and it's,

38:48 it reaches a point where, you know, institutions have got, they've got other rules that they have to follow. What I would say is that the notion that we want more exposure, we want more beta, we

39:04 don't mind the vol until that is truly tested in reality.

39:11 I think is one that you have to take with a grain of salt because when it does start I mean, you would see, I'm not saying. not talking specifically to our numbers, but you would see moves on a

39:25 long, short basis, you know, upper to mid single digits, sometimes higher than that, similar to the downside, right? That's a month to month thing. I don't think anything in this asset class,

39:39 particularly on the EP side, is

39:45 fits well with an investment horizon under kind of 18 to 36 months,

39:50 because you've really got to generate the data, particularly from an asset level standpoint of understanding what the decline profile looks like, what does that asset look like from a

40:02 reserve potential? That's important You're jerking things around on a 24-hour IP announcement. It makes continuity tough.

40:19 It makes a lot of sense and I may take us off the freeway as we're kind of going through the narrative real quick.

40:31 But one of the things I think is the biggest problem just investing in our industry is the transparency of the data. I mean, you get one reserve report at the end of the year. For most of our

40:44 career, it used the December 31st price deck - Whatever it was - And well, it wasn't even a price deck. It was the price that day - The price that day - You just ran it out - Yeah - That's the way

40:57 that worked - Natural gas prices double that day - And the old SEC standard of reasonable certainty - Yeah - Right? And you have this,

41:08 you've always had this probabilistic versus deterministic

41:13 debate raging among the real denizens SPWE community.

41:23 And

41:26 we said, we launched on a collection of shale stocks back in mid 2000s, and it was a compendium report, names like Gasko and Southwestern was in there. It was right at the dawn of the Fayetteville,

41:42 I think Quicksilver was part of it, don't recall evergreen, some of the other names But

41:49 we were very explicit that you're not buying, we called it the story stock report, you're not buying these for their approvers or value. So we had these waterfall charts that showed the stack of

42:01 evaluation and clearly, if you're paying for high visibility of proof reserve value, then no one would ever buy these stocks because they're ridiculously expensive. It's all about underwriting the

42:16 upside. And that's when you start getting into the acreage in the NAV conversation. And you better know what you're doing in terms of how to risk that, both from a, certainly a financial

42:28 standpoint, but I think good financial underwriting in that asset class starts with a good technical understanding of what it is. Right? Yeah. Now, I remember back in the days where I compared

42:43 dollar per acre to dollar prior balls that they were valuing internet companies. Dot com. Yeah, dot com stuff. It was crazy that the whole thing I never understood - because private equity, we

42:57 would sign our confidentiality agreement. We would see our logs. We would see all our production history. We'd be able to look at the AFEs. We'd be able to look at actual costs. We were kind of

43:08 able to put it together. I never understood how you could make that technical assessment from the public date. I mean, and

43:19 I actually have a theory that no one's ever gonna try and all, but if I ran a public EMP company, I think I might publish all my production data, individual curves, cost, and just put it all out

43:35 there, 'cause I think at least some of the disconnect, I mean, we clearly have the green problem, we clearly have the red problem, but some of the valuation metric issues is just the uncertainty

43:45 of, I don't even know what that tail's worth - I sat on the board of a TSX Venture company that was backed by NCAP, and it was their first, I believe, first real non-North American international

44:00 foray. It was a

44:04 reverse merge into an existing shell very seemingly look and feel like SPAC called GFI, and it played by Canadian rules. And if you know anything about Canadian reserve reporting rules, they are a

44:20 lot more granular, deeper, and transparent. Or at least they were at the time. I forget just like, and I don't even remember the numerical designation. We actually had a reserves committee that

44:31 I chaired at GFI. We had a lot of speculative on the cum type of assets. So the question of, how do I get my arms in mind around an NAV for the Gulf of Thailand asset that you've got or this near

44:46 shore Sumatra gas asset? We had to do a lot

44:52 of disclosure around - and it was compelled by the reporting rules, as I recall at that time. And I've always believed that that is a significant blind spot. And you pointed it out. It's really

45:06 hard on the public side without anything beyond production. You don't have pressure data, you don't have logs, a lot of stuff that a technical, technically proficient or an analyst with a degree

45:23 of technical expertise has based upon the publicly available information. And I'm jumping around here a little bit. One of the opportunities that we were given when I ran research at TPH back in

45:37 2015, did a lot of petroleum engineers who were coming out of internships and now looking at full time opportunities or so they thought that we're having their offers rescinded at the 11th hour. And

45:50 so we bolstered our EP effort by bringing on some of that technical talent as junior analysts that were working with the analysts and associates on our EP team and really added a lot of technical

46:07 judgment, if you will, to I remember one of the engineers after he first started working.

46:14 in my office, he said, Can I ask you a question? He said, Yeah.

46:20 As a kid from down in your area, he's used to calling by a vowel. He had one of those last names that was lacking in vowels. Anyway, so, and

46:31 I won't mention who it is. I think he's often, he might be off in private equity world. But he said,

46:40 Do we normally do decline curve analysis in equivalent units? He'd never seen a BOE or an

46:50 MCFE decline curve. And that's typically the way it was done - Well, that was always my favorite thing. Private equity, we'd get a business plan and you'd have the deck come over and there'd be

47:07 BOEs if all price was at 100 and you'd dig in, you'd figure out if it was all gas or vice versa whatever the commodity déjour was. Yeah, everybody started reporting in MCFEs when gas was cool or

47:19 hot. And

47:22 then we'd start changing the six to one ratio to 20 to one or whatever with price. But now it just seems

47:30 like there's something to do with more information that would potentially help

47:36 just give investors some certainty and as we all know, uncertainty's bad in investing Uncertainty is bad and I think the most interesting and complex problem out there for anyone who is problem

47:52 solving analytically oriented is interested in an applied analytics career and certainly in the physical sciences, figuring out the subsurface and unconventional and how to predict things like

48:09 optimal well spacing completion design. It's a very. I used to say, when I ran the technology group at Apache, it's the world's most complex or most difficult three-dimensional nonlinear math

48:23 problem - Yeah - And one of the things that, and you're looking at it through lenses that are kind of blurry - Well, you're looking at it

48:32 through a static lens, number one - Correct - You have a model defined as the

48:40 petroleum system, the geology and the reservoir physics that you didn't build, right - Yeah - And you can't see it - Yeah - Except for maybe, you know, a small OD size of a full core, or logs

48:56 which have, you know, radius of investigation, that's, you know, not very, very big. So what happens between there and a mile away, geologically from a, you know, what kind of variability do

49:11 I have? there's uncertainty. So how do we model that - I mean, we used to sit there and look at geologic maps that were 3D seismic driven and you recognize that the data that 3D seismic space on is

49:23 like 50 and 75 feet - Right - I mean, it's just precision - The precision is, the precision that we

49:31 normally think about and say manufacturing, right? It's not even close. It just inherently is not And that's the, you know,

49:44 we saw a number of third party providers. I think the subsurface is horrendously difficult to predict and model. But I think where the industry is making great strides is figuring out how to

50:01 integrate the numerical and the physical data. And companies have troves of 3D seismic, Core data, fluid data, pressure data, all of that. And we used to joke, you know, the kind of the easy

50:16 first commercialization and applied analytics in the oil field was high frequency drilling data and drilling analytics. Well, that was all pointed at drilling faster and drilling a better hole. But

50:28 there's a lot of derivative data that you can get out of, you know, like mechanical properties that come off the drill bit in the bottom hole assembly that you're recording at very high frequency

50:37 Well, can I extend that into the model description that I'm using to figure out my completion design, for example, on how the reservoir is going to behave under certain well spacing and profit

50:48 loading and depletion scenarios. All of that is got a ton of data blind spots. And the more you can effectively integrate into the model, the better your predictive model becomes You can start to

51:03 rely on, you know, I can drill this section on the screen as opposed to having to actually do it brute force trial and error.

51:13 you know, in a 24-well pad, you can burn up a lot of capital and then find out you're wrong. Right. Find out you suboptimized, you over-capitalize, which is typically the problem. And so. Well,

51:25 you know, this was the wildest thing. It's hard. It's. So we did a lot of lease and drills at Caine, where we'd literally drill the first horizontal well in a county. We were early stage assets,

51:35 and we kind of felt like our competitive advantage, all these engineers running around They can see the latest and greatest in completion technology, so we can take something that works up in the

51:45 Bakken and apply it to certain analogous rock in the Permian or whatever. That was kind of our shtick. And a lot of the mindset on that is, let's go ahead and make sure we hit it with as big a

51:58 hammer as we need to to find out what the reservoir will do, and then you optimize later. Right. You can make it cheaper. We can reduce fracksizes, whatever

52:09 Let's don't under-frack something and go, Gosh, we'd only hit it. bigger, maybe we'd have a play. So we figure out that mindset. It's the total exact opposite figuring out spacing. If you think

52:21 ideal spacing is eight wells, you should drill four - Right - Because you're so penalized by the excessive capital. So it took, believe it or not, that sounds simple. It took us a while to figure

52:35 that out. The thing that hit us with exponential type problems on it is stack and scoop. If the ideal spacing was eight, and you'd use 10 wells or 12 wells, you would think, okay, well, I just

52:50 spent, two extra wells, four extra wells, it's capital. What we found was if you put one too many straws in the reservoir, instead of getting the whole milkshake out, but just costing too much,

53:02 you'd get half the milkshake out. I mean, and it was absolutely crazy sitting there trying to figure out spacing. And so what's wild is, I think in 2019,

53:19 the drilling results we had, we're calling it 35 IRR, just DNC and actual results. We probably needed 45 to 50 to justify for what we paid for the acreage, but it was getting that spacing right,

53:36 that's just crazy. And it's so hard to do by trial and error because of the real dollars. I mean, that's an offshore well. You know, drilling a pad. What's even more? What's all

53:52 the bells and whistles and facilities and everything else? If you're drilling a 20-well pad, that's probably close to 200 million. Yeah, right. Yeah, there are some exploratory wells, but the

54:03 great thing about

54:05 what I call the geologic driven model as to opposed the engineering driven model,

54:10 is, you know, Once you do the logs and maybe some cores and DSTs, if you're going that far, you kind of know what you have from

54:22 a fundamental commercial standpoint. And so you stop spending money. Yeah, right. There's a lot of unconventional that,

54:31 I used to say the worst kind of dry hole you can drill is one that you have to complete before you know it's dry. And most of the costs in unconventional is embedded in completion Well, nobody

54:41 listening to this podcast probably even remembers the phrase set pipe decision. Yeah, I've got some set pipe stories, but those are old, old, old driller stories. But

54:53 yeah, look, I think

54:56 the opportunity, the level of intellectual challenge of solving subsurface modeling and predicting through that modeling more robust and integrated data sets and training data sets to get higher

55:15 confidence and things like well spacing and completion design on the screen before you actually put a bit in the ground is front of mine and top priority. And I think the EMP industry, as I kind of

55:29 ramped into analytics middle of the last part of decade at TPH and then carried it over to my time at Apache, I think the EMP industry gets criticized a lot for being a bit laggard on

55:45 getting ahead of digital - Just say laggard - Digital disruption - Yep - Well, no one faces the vexing problem and it's the most difficult problem to solve of exactly what we've been talking about.

55:58 The model eight to 10, 000 feet underground, you can't see it, you didn't build it It's got horrendous variability, right? It's got a lot of blind spots. And so there are a lot of important

56:13 things that you need to measure. And a lot of what we've done in the past over the long history of the industry is that a lot of the data is inferred. Surface pressure and flow rates. You're not

56:28 directly measuring a lot of things. Now, the industry before the big event a year and a half ago was starting to really get some traction on things like fiber optic, dynamic data collection and

56:44 completions, completion operations. But I can tell you from direct experience, that stuff's expensive. You add that to a program, you're adding significant capital cost. Now, the costs were

56:56 coming down pretty rapidly with innovation. And I don't know where that all stands now, but today and who's doing what, but that informs a lot in your model. Right. Yeah. And you know, because

57:10 when you look. back at sort of the history of, let's just call it big data - Right - Is there was always this promise that it was gonna deliver and it never seemed to deliver. And I'd love to get

57:23 your opinion, is it, we just didn't have enough computing power. Was it beta, oil prices, so drove our industry that you really just couldn't focus on it? Is it a mindset problem? Why has big

57:39 data not delivered and will it be different going forward -

57:46 I think it was a fundamental strategy problem, just for the industry. And we started to see this unfold in 2015, 16, 17. I remember

57:58 one of the Hotter and Hell conferences that I hosted as head of securities. I think it was 2017

58:07 every other sidebar presentation. you name it, there were the feature investor relations slides, kind of the one-upsmanship on what we're doing in big data and analytics and digital. Everybody's

58:25 kind of jumping - I call it the cool stuff model, which is

58:31 let's go get the cool stuff and good stuff will happen.

58:35 We had an ethos in our small but very high leverage data. Analytics organization is that we're going to be about ruthless prioritization, meaning what is relevant to making a commercial impact on

58:51 the business to an EP company. It's not about - there was one operator. I don't remember who it was. And they've all done - had varying degrees of success and failure with their analytics efforts.

59:07 But there were some models that were out there trying to Silicon Valley and building giant

59:16 GNA capabilities, GNA heavy capabilities in house. And one of the first things that I

59:24 said or developed as a philosophy as I started learning about this and I'll give some credit to an executive you probably know that got me really interested in it is that

59:37 you don't wanna be, you don't wanna fool yourself into thinking that you've become a software development company because

59:46 you're starting to leverage data and analytics. There's a lot of cool applications out there. A lot of them don't have relevance or are clunky or really costly to maintain or become obsolete

1:00:01 especially if they're homegrown. You gotta have the objectivity to be able to look at the outside world and the outside market to see what's available and what's relevant to your business There's a

1:00:10 great story out there.

1:00:13 that John Gibson used to tell. He's at, what's the name of the company he runs these days - I don't know, he was a tech advisor for Manard at TPH for a long time. I love John.

1:00:27 And he tells a story when he was running landmark graphics. And I forget what, there's some generic mapping software that he had a need for And he had a whole team of developers captive to landmark.

1:00:44 And he goes and visits one of the big Silicon Valley players and finds out that he can license the same capability for, you know, mere pennies on the dollar versus what he's paying to develop it in

1:00:56 house. And that was a very illuminating thing is that we've got to leverage markets and capabilities and tools. that are not in our realm of expertise. And one of the things that's great about oil

1:01:12 and gas companies that I also think is another edge to the sword is that they're pretty good at problem solving, very good at analytics and math and very good at building stuff. And so, probably to

1:01:25 this day, there's a lot of what is known as

1:01:29 citizen data science going on, where you've taken it on as a side light and you want people to be fluent, data fluent and data conversion And you want them to be

1:01:41 kind of facile with the tools, but in an applied in user way, not in a development way, in an oil and gas company, that's my philosophy. Now, we did have those expert resources, but it is a

1:01:53 thing in and of itself that requires a really professional grade approach. And you run the risk of not managing it that way toward some view of standards. and standardization really fosters

1:02:11 innovation. conventionalism, I think, thinks about it the opposite way. But if you think about, you know, across the portfolio where you have thousands of the same type of artificial lift

1:02:23 application rod pump. Okay, can we solve with just good basic business intelligence level? I'm not talking about sophisticated stuff. What is this data data telling us about the behavior

1:02:36 performance of these artificial lift installations, you know, fluid pound? Okay, can I create a very efficiently prioritized

1:02:48 and highly visualizable to make up a term, series of pumpcards that my maintenance folks can look at and optimize the work program related to artificial lift?

1:03:00 That's a very basic thing. It's not very sexy, but it's scalable. is very low hanging fruit and importantly, it's measurable. And so when senior management teams, like I was a part of in board

1:03:15 started asking about what value are we capturing and generating? I couldn't answer the question of, okay, we're doing all this great, really sophisticated stuff down whole, because you don't know

1:03:27 until you've had some runtime producing a well, but in terms of uptime, cost reduction, asset performance efficiency, safety metric improvement, all of those things are immediately measurable and

1:03:44 scalable in certain situations, a lot of low hanging fruit. So I always believe you gotta generate some early wins. When we started on the digitalization of the research library at TPH was our

1:03:58 first effort,

1:04:01 we had a pretty quick failure because we tried to take on too much. We tried to boil the ocean. instead of addressing some things that would lend themselves to immediate measures and observations

1:04:17 that would then build momentum around enthusiasm for this stuff, because particularly when you're in a down part of the cycle or the business is contracting and you're spending money on developing

1:04:29 new capabilities, people look around and there's quite a bit of cynicism around that, where it's actually upgrading tasks on the most basic stuff. You're bringing in automation on things that have

1:04:45 been unnecessarily manual for too long, give you an example of that. Hedge funds used to send, or probably still do, they would send a worksheet to fill out where they'd have your EP analysts open

1:04:58 each one of their company models and run these sensitivities and populate this table for all these metrics at these. seven price decks that I want. And they would send those out to probably half a

1:05:12 dozen brokers. Well, we believe that getting paid for that, which is horrendously time consuming, if you do it manually, literally opening each company model, changing price deck, and then

1:05:25 transcribing each one of those metrics into the worksheet. Well, first thing we did was automate that process And I recall when we first tested it. And again, this is Ned's first reader stuff.

1:05:41 This is very remedial. You know, put a lookup page in each one of your models. You're not going to be able to automate everything, but you can frame the problem where it literally is a push of

1:05:54 button and I can turn around that request in five minutes. And that makes my salesperson on the desk look really good. And my chance of getting paid for that completely rote exercise. is pretty

1:06:07 high, right? So I kind of went off on a tangent there, but - No, it's - The data is the thing, right? Yeah, and I wonder what the potential for it is going forward because every time you and I

1:06:24 grab a beer, you give me an example. Isn't it mostly wine? Yeah, it's mostly wine. But when we chat about it, there's always a data-driven thing that we talk about that results and savings. And

1:06:41 I'm on the board, or the advisory board of Montrose Lane, and they're investing in energy, technology, stuff. And I just see such a huge potential there for running really good, efficient

1:06:55 businesses. And it just doesn't seem to happen. And all these things that I'll just kind of scatter plot throw at you. We have to build everything in-house. We can't use generic off that. That

1:07:07 makes no sense to me.

1:07:10 Everything in my mind should be data driven. I mean, one of the things, I talked to one of the companies that does AI on Lyft and they say about 85 of the time, company, the data proves out that

1:07:25 the companies run and the pumps too fast. That when they actually get all the data they present it and have performance to back it up. And it's 'cause a pumper out there in the field who's really

1:07:39 smart, I'm not denigrating the pumper, but they're making decisions like that. Sure. Just based on kind of gut feel and this is how we always, always, always did it. And it just, it's shocking

1:07:52 to me that we haven't been able to do better to this point. And I think we can I had a venture tech firm during my time on the beach several months that I've - We should have done this at the beach -

1:08:09 That

1:08:11 I'll call on kind of informal advisory on some insights and

1:08:18 a window into traditional energy mainly oil and gas. And the

1:08:30 venture partner starts asking questions around exactly what you're talking about. Surely the way it works is the enterprise system generates

1:08:42 a problem and a solution that work gets digitally transmitted and is ultimately efficiently executed in the field, right?

1:08:54 Because everybody's come that far and the oil field has not. And for a lot of reasons, again, I think

1:09:02 while in gas, the upstream faces.

1:09:05 unique challenges that no other industry faces. I talked about the subsurface, but look at just the aerial spread of the assets. Plus you're out exposed to climate and weather and all of the

1:09:17 difficulties - In all the garden spots in the world, right - Right, in the communications issues that you sometimes find,

1:09:27 it's just, it's a different animal But having said all that with fewer people, another round of, or two of cuts that we've experienced here in the last couple of years,

1:09:44 you're going to have to do, this is cliche to say, you're going to have to do more with less in terms of eyeballs and arms and legs. And that's where the data, filling in those blind spots and

1:09:57 maybe making you a bit more both corrective and predictive.

1:10:02 very routine, mundane, boring stuff, whether it's doing more predictive or relationship analysis on things as simple as geotab data that you collect on your fleet vehicles to make people more aware

1:10:21 of certain behaviors and driving that lead to a higher correlation or higher risk of incidents I mean, how many company vehicles are scattered throughout the oil field that have that same you can get

1:10:38 that, they call it a dongle from your insurance company that records all of your driving data. There's a tremendous amount of already built-in data quality and conditioning into that data set that

1:10:51 you can immediately use to start identifying patterns and relationships to make you better at execution. And in this case, in this example, it is, you know, the all import, we want people to go

1:11:03 home with the wellbeing they left the house because as you guys digital wildcatters in particular has been very out front talking about is this is a lot of folks out there taking a lot of risk. It's

1:11:20 a dangerous environment always has been. Safety should actually matter. So the data can help you. I think, you know, that was that was because you and I have talked about that that literally a

1:11:34 punchline to study in that data was don't put the truck in reverse. Yeah.

1:11:41 Again, it's crawl walk run. It's finding

1:11:47 finding the hierarchy of commercial impact if you were able to scale this, which is where standardization comes in. I don't want a bunch of proliferation of data sets and technology tools. I want

1:12:03 simple and standard, I think, all the way back to why one of the reasons Southwest Airlines was such a low-cost provider, they used the same aircraft configuration and they simplified the cockpit

1:12:13 and the instrumentation, right?

1:12:17 That's a long time ago, but that principle, I think, is very relevant to how we think about, and I'll go back to the cool stuff, strategy inclination that I think was driving a lot of the first

1:12:33 response to the MP Industries got to get hip with big data. We start talking about it in every investor presentation or conference. Well, what are we actually doing that fits and aligns with the

1:12:48 business and helps us do what we're trying to do, which is improve margins and free cash flow and get more capital efficient, right? Improve safety and environmental performance. those things

1:13:04 aren't way up the sub. In a lot of cases, I split it into below ground, which is what I talked about earlier, and then all this stuff above ground, both operationally and safety, health, and

1:13:13 environmental ESG, whatever you wanna call it. There's all kinds of data that you already have from your normal a there's and, cadence operational

1:13:24 ton

1:13:26 of underutilized data out there. 'Cause the thing I hear is we use less than one person of the data we've collected. I don't know if that's right. It's somewhat ironic that the oil field is the

1:13:36 original big data, if you think about 3D seismic. Right? And some of the largest dedicated

1:13:46 computing centers that have been built specific to an industry are the ones that some of the majors have built for themselves. Yeah, it was the government, the military, and oil and gas. Right,

1:13:58 yeah. So.

1:14:01 I think that

1:14:05 there's a

1:14:08 justifiable reticence in, I think that's what you alluded to in your Montrose Lane example is that there's a lot of really great potential here.

1:14:18 The sales cycle from an outside provider, a technology provider to an oil and gas company is much longer than certainly a venture tech person I think people get married sooner than you're actually

1:14:34 able to sell technology to a major. In fact, one of the questions and one of these advisory that makes it sounds like it was more of a big deal than it was, but I got the question, Well, what are

1:14:48 we looking at in terms of sales cycle with oil and gas companies for this technology? Is it weeks? Is it months? I said no, it's more like quarters and years There is a, there is a.

1:15:01 just, I think,

1:15:04 cultural reticence. There's some of that. I think

1:15:10 the problems are unique and uniquely difficult. The applications are tougher, but I also think there's a view that they've got to be so complex and sophisticated in many cases that you end up being

1:15:24 scared off by the price tag, or it's expensive. And clearly in the last year and a half, two years, we're in an environment where every discretionary expenditure has come under extreme scrutiny

1:15:38 and has been cut. Right? I think the flip of that is this business now for the next layer improvement, yeah, you took a lot of costs out of the system, for example, and you've probably gotten

1:15:50 better at hygrating, I think you were in a debate on Twitter the other day asking why, you know, production's not down more. And I think it's on the capital and the expense side, it's hygrating,

1:16:05 right? You have the golden screwdriver on the maintenance side, you have hygrating on picking and choosing kind of your tier one or your core locations that you drill. And there was a good answer

1:16:18 in there around kind of duck blow down too. But

1:16:24 from this point forward, I think a lot of it's going to be hoovering up the pennies, nickels and dimes and doing that thousands and thousands of times over. And it's a really boring thing to talk

1:16:37 about on the surface. But if I can

1:16:44 improve uptime because I've hydrated my maintenance intelligence

1:16:51 through simple analysis of daily, skated data that gives me a good way to prioritize well work.

1:17:01 scale that up over hundreds, if not thousands of wells, that makes a meaningful difference - If I had one thing I could tell CEOs out there, just my two cents worth sitting on the sidelines is,

1:17:14 dude, you're internally generating cash flows all you got - Right - And that's the foreseeable future - Right - So if you want to spend extra dollars doing this, that or that, guess what? That's

1:17:26 what you're eating at. That's the pantry, you know, because I don't see capital coming back in a meaningful way. We talked about this on the BDE show this week is, you know, if you're 3 of the

1:17:38 SP 500, every money manager to some degree is judged on how they perform vis-a-vis the SP 500. Well, guess what? You can't hurt them or help them, so they're gonna ignore you - Right, you're not

1:17:52 in all of it - Yeah, and you're not gonna stick your neck out. No CIO's gonna stick their neck out.

1:18:00 a big risk and

1:18:03 something that doesn't move the needle. You'd have to make kind of radical changes. And we've got to remember these investment teams, these PMs and CIOs, they're worried about job security too -

1:18:15 Yeah - Right - So if we look at the industry, let's do this. We've got tons of technology, tons of data I think we can use to improve that we just haven't We've been a lager,

1:18:31 two things. How do we get the industry to improve that way? And then number two, what's the magnitude of that? If we have a dollar a cash flow today, and let's just say it's 75 oiled to pick

1:18:46 something and this is spitballing so we're gonna make it any assumption you want with good best practices, study and data, can that dollar turn into

1:19:01 120, 150, 2, any idea -

1:19:06 I'm just recollection in early pilots on again the more mundane above ground, immediately addressable optimization opportunities. 20 to 30 is highly achievable - Got it - In terms of operating

1:19:23 margins And there's probably not full objectivity in terms of how your portfolio is performing. And I can go off into this philosophical tension as well, but I would say

1:19:41 one of the things that I learned is a PM and all the great tools and technology that we have in a public portfolio. I can see individually and then on an integrated basis how my portfolio is

1:19:55 performing.

1:19:57 Contribution, you know. asset level contribution returns, all of those portfolio management overlays that we are now able to see that there are others who are way more proficient today. And I'm

1:20:10 sure the technology has improved since I last ran money.

1:20:15 I fundamentally believe that even though it's got a different kind of duration and liquidity profile, I think an asset portfolio, a physical asset portfolio like EP, like producing properties, you

1:20:30 approach it with the same dynamic or active portfolio management perspective, right? And approach. And that is inherently,

1:20:41 it requires much more data, feeding a, my grand vision was having a dashboard, I love dashboards, a dashboard for my producing portfolio If that makes sense. No, that makes a lot of sense. And

1:21:03 then I can make all these predictive things that you can do, sub surfaces, as the technology and the learning and the train data sets get more and more accurate in terms of what the NPV potential is

1:21:16 of this particular undeveloped lease. And, you know, there's something across the lease line that I know that my competitor doesn't know, you know, is there a trade opportunity, because I can

1:21:29 confidently predict what the economic profile of that undeveloped asset looks like. And I should always be thinking about that and evaluating my portfolio in that regard and looking to transact

1:21:42 around that if I've got something that is dragging on the portfolio performance way easier said than done that

1:21:53 is a coordinated

1:21:57 data grounded way to manage, I think, any asset portfolio. Again, the

1:22:05 timelines, liquidity,

1:22:08 the ease with which trades are made, transactions are

1:22:14 conceptualized and completed, that's all different. But you can apply some of the same portfolio management philosophies that an active portfolio manager would to a stock portfolio, if that makes

1:22:26 sense No, that makes a lot of sense. And I do think it's interesting you bring up portfolio management because too often, I think we've been tied to growth for the sake of growth and you can't get

1:22:36 smaller. I would actually reward a management team that went through and said, This 25 of our assets, we just can't operate this as well as other people. If we could split the baby with somebody

1:22:51 else operating it, neither get paid for that or just let them operate. I think would make a lot of sense. It's the 8020 rule. If you're spending 80 of your time, energy and effort on 20, that's

1:23:02 the

1:23:06 problem. Just get rid of the 20 - You don't care how big you are. If you've got

1:23:15 10, 15, 000 legacy wells in the

1:23:19 Permian, for example You don't have the

1:23:26 capacity to do kind of full optimized management of all of those assets. And I think there are countless examples out there that you could go uncover with a little legwork.

1:23:44 But around that whole issue, and it's not just a function of what's happened lately, there's just real inertia in the culture, right? Because you do believe. that you are best suited to own and

1:23:60 operate that asset. Now, there are some other externalities that I think come into play. We're gonna see more of it. If I bring someone in and hand over operatorship, for example, are they

1:24:17 posing

1:24:20 some risk to

1:24:22 all the things we've been talking about here lately in the ESG realm - Right, so there's a whole other liability conversation and assessment that has to happen now because of the crosswinds of all

1:24:34 that in the industry - So kind of action plan coming out of this podcast, if

1:24:44 we were to go buy an oil and gas company, I mean, you look at portfolio management, figure out the assets, you can actually do well versus not do well and figure out something to do with them.

1:24:57 I like how you break it down above the ground, below the ground. We're digging through data, looking for early winds. I really like that because I think, at least what I've seen being involved

1:25:09 with Montrose Lane and some of their portfolio companies discussing things, the adoption takes so long and the results that are expected are immediate. If they're not there, it shuts down the whole

1:25:25 system So we're looking above ground, we're going to figure out kind of the immediate things. And then it just gets us into the below ground, much bigger issue there in terms of figuring out how to

1:25:37 manage the reservoir. Well, you really hit the nail on the head when you said, I don't see how someone on the outside, a cell-side analyst, for example, can

1:25:51 do diligence or underwrite the assets because you're missing so much data. On the flip. companies have access to great data that has far more leverage than is being exercised.

1:26:06 The sub-sirp figuring out the subsurface optimization, the well-spacing completion design to put it more succinctly, is a long horizon exercise. You're going to get uncomfortable questions at the

1:26:19 outset because you're spending, it's not a lot, you can do a lot with a lot less than people think, but you need to bring in some capability and expertise in the data science and engineering

1:26:35 capability, for example. You don't need a lot of it, but you need the right mix.

1:26:43 The above ground is where the crawl comes in and you're starting to generate easy winds again. I didn't really like. don't really like having to include explicitly digital or technology and the

1:27:00 conversation about strategy. I think it should just be inherent. But the whole thing starts with the foundation of data. And where a lot of this stuff breaks down is the slog, which is very manual

1:27:15 that you have to go through. It's the least glamorous part of all this, is that you have got to condition the data, right? That's a manual exercise. However, I will say on some of the things

1:27:28 that we had going on in my organization, meeting with

1:27:33 the experts and the asset teams and stepping back and doing some of this, that this became cultural, it didn't become, I have to ask you about this, it was just natural. One of the real kind of

1:27:50 enthusiasms that came out of all that, turning into a data lead. culture and organization was, this has given us the reason and time to step back and really look critically at our data and

1:28:02 condition it, which again, is not, that's not the sexy glamorous part of all this. But you can't really jump over that to the cool stuff and expect to get really great results. And cynicism, I

1:28:16 think, has set in over a period of time because we wanted to go around that and get to the really cool stuff as an industry. And the problems are just really, really hard. But there are some

1:28:29 things that are very straightforward from a business intelligence standpoint. I don't need to be up here on the most sophisticated part of the continuum. I can be down here at basic business

1:28:38 intelligence and making a real commercial impact on the business, improving, you know, uptime, improving safety performance, I

1:28:49 don't know where Fugitive Methane. emissions surveillance is coming in, but I got to believe that there's a lot of really low-cost effective programs that are being set up that are leveraging

1:29:02 baseline data that can be captured through some fairly low costs, but very reliable technology. All those things are going to be important. The ESG conversation, we could, I guess, do another

1:29:14 whole other podcast on that

1:29:18 I want to know what

1:29:20 I impact and influence. I want to know how to measure it. When I go out and measure it again based on something that I've done to address it, can I prove through the data and through the measures

1:29:33 that I've actually improved it, or performed to that objective? Yeah. No, I think when we look at the ESG-type metrics, I think we need to do two things, exactly what you just said, measure,

1:29:48 improvement, show all that data. The other thing we have. to do is we have to develop, I think qualitative narratives around that because raw data of we went from point four to point two is great.

1:30:03 We've shown a trend, but is point two really, really bad or is point two? Oh my gosh, that's great. Right. So I, and I think that kind of goes back to one of the problems we've had in our

1:30:14 industry is we're just not very good at telling a story because we don't have to mark it, right? We, we generate a barrel of oil, we just sell it and, and no offense to my esteemed guests today,

1:30:27 but we're run by a bunch of engineers that don't tell stories. And so being able to kind of craft true, believable, responsible narratives around what the data actually means is important because

1:30:42 if we don't, we let somebody else define it, you know. And as I had started having conversations after kind of the the big upset and spring of 2020, we started talking pretty regularly then. I

1:30:57 still believe that the industry is going to accelerate in this regard, and then there's tremendous opportunity to play a role. And my quest to get back in the arena is oriented around that, because

1:31:15 I think this is gonna be the most, the next decade plus is going to be the most interesting one of my career for so many reasons. A lot of

1:31:27 it is the competition for equity capital,

1:31:31 ESG crosswinds,

1:31:35 just pure investment performance, which is now

1:31:40 what people point to and say the industry is finally being tasked with generating actual sustainable free cash flow and good returns. both on and of, right? And so there's a lot more discipline.

1:31:57 The business has gotten more complex. It's just, the problem solving is much more interesting. And I think if you have a data advantage, you're gonna be better at it. Let's do it again sometime -

1:32:12 Absolutely - If not just as a dry run with another Zoom, wine session or maybe - Yeah, there we go. Next time we'll - The Richmond, I know where the Richmond house is There we go. Next time we'll

1:32:23 wine - I will put a plug in for one of my guys that I executive produced, big distinction, several years ago in my one record Nashville recording career, has got a new release out. It's an artist

1:32:39 named Rick Huckabee. He's playing up in the

1:32:41 woodlands. He released his new album called Long Ride Home on Monday the 3rd and he's playing out at the Big Barn, Dosey Doe. in the woodlands, which is a great music coffee house venue.

1:32:56 He's opening for Marshall Tucker next Wednesday night, and then he's playing an acoustic show at the Dosey Dose Whiskey Bar on Research Forest on Thursday night - So if we get our act together next

1:33:09 Wednesday night, literally we can drop this Wednesday morning. So it'll be tonight. As you're listening to this tonight, Wednesday - He's playing two shows He's opening for Marshall Tucker Band at

1:33:22 the Big Barn, which is right there on I-45, on South Bend I-45, Access Road, Frontage Road. My dad calls it Access Road between Woodlands Parkway and sawdust, but then farther north on Research

1:33:35 Forest, which is one of the East West corridors through the Woodlands. There's another Dosey Dose venue, which is a barbecue joint whiskey bar. He's playing an acoustic set, a full kind of

1:33:47 headlining acoustic set there on Thursday night after, for Marshall Tucker. But Rick is a 25 year Nashville guy, Louisiana boy, his dad

1:33:57 said basketball coach at Marshall University. He's kind of rocker at his core, one of one of my favorite Nashville friends that I have. Great songwriter, great musician. January 12th and 13th

1:34:15 12th and 13th up.