Welcome to the third episode of Pipeline Things Arc 3! In this episode, Rhett and Chris sit down with Rick Desaulniers with Entegra. They discuss how Entegra is pushing the boundaries of seam assessment by using a unique, practical approach to finding solutions. As new technologies make it less necessary to physically go out into the fields, it is becoming even more necessary to pass explicit knowledge to the next generation of operators. Tune into this episode to learn more about how Entegra finds diamonds in the data through innovative processes!
Highlights:
- What is the difference between theoretical and practical solutions?
- How is Entegra pushing the boundaries of seam assessment?
- Why should you touch the pipe?
Connect:
Be sure to subscribe and leave a comment or rating!
Pipeline Things is presented by D2 Integrity and produced by ADV Marketing.
D2 Integrity (D2I) is providing this podcast as an educational resource, but it is neither a legal interpretation nor a statement of D2I policy. Reference to any specific product or entity does not constitute an endorsement or recommendation by D2 Integrity. The views expressed by guests are their own and their appearance on the program does not imply an endorsement of them or any entity they represent. Views and opinions expressed by D2I employees are those of the employees and do not necessarily reflect the view of the D2I or any of its officials. If you have any questions about this disclaimer, please contact Lina Adams at lina.adams@advmarketing.com.
Rhett:
All right, welcome to today’s episode of Pipeline Things. If you are not watching the YouTube, this might not make sense. You’re also probably wondering why my mouth isn’t moving, but you hear my voice.
Chris:
People still not watching the YouTube?
Rhett:
You should watch the YouTube. If you’ve ever wondered what a new definition of data model looks like, or whether or not some people like data more than anything else in life, you need to listen to today’s episode where we sit down with Rick DiZoniay from Integra and talk about Integra’s new advances at PPIM 23.
Chris:
And he talks about two girtholds and naked pipe
Rhett:
And no Rick. You can’t have a cardboard cutout. They’re only for us Listen to the episode. Thanks joining us.
Rhett:
– Welcome to our PPIM 23 Arc, where we are talking to vendors about their technology, the things that you guys are interested in, and we’ve had multiple guests, if you’ve been on us on this journey. Today is no different. We are sitting here with Rick D ‘Zoneier.
Chris:
D ‘Zoneier.
Rhett:
I’m gonna try that, Rick D ‘Zoneier.
Chris:
Didn’t you grow up south of I -10?
Rhett:
I did, I don’t that name came from South of the I -10.
Chris:
Yeah, but you should have some swag in how you say it though, bro.
Rhett:
Rick D ‘Zonnier, is it work? Did I do okay?
Rick:
Yeah, it’s fine, D ‘Zonnier.
Rhett:
And Rick is the manager of data science with Integra. And we are super excited to have him as we’re gonna talk a little bit about some of the exciting stuff that they’re doing. But before we get into that, so I talked to some of your colleagues before and I understand that a lot of guys in the office used to have like pictures models on that thing , you actually put pictures of data around your wall that’s that’s that’s what gets you –
Chris:
Youre really cool you’re like
Rick:
yes well only a couple pictures
Chris:
okay I get carried away-
Rhett:
but only the best pictures of data looking pictures
Rick:
the best -looking pictures yes
Chris:
best -looking data pictures
Rick:
I don’t want to see two girth wells and nice clean pipe – An operator wants to see it, but I don’t. (laughing)
Chris:
Two girth wells and nice clean pipe. Oh man, you can take that so many ways.
Rhett:
You did really good. The marketing guy was like, “Do we need to edit this out now?” He’s not really sure, and we got started off.
Chris
That is so great.
Rhett:
Rick, we really want to thank you for joining us. And I want you to introduce yourself. He tells us about you. Who are you? You’re manager of Data Science for Integra, but beyond that, who’s Rick?
Rick:
Rick is Rick D ‘Zonia as we all found out the last few minutes ago I’m not a city boy I’m country bumpkin okay but yeah I’ve been in the inspection business for too long I think back in the 80s I was six then when I started so I’m only 40 now
Chris:
so no child labor laws.
Rick:
Yeah well in Canada there is none so it’s good.
Rhett:
I didn’t know this. Have we talked about opening up businesses in Canada?
Chris:
We’ve talked about Mexico but maybe we need to look in Canada.
Rhett:
Yeah I know for real.
Rick:
My daughter’s looking for a job.
Chris:
How old is she?
Rick:
She’s 16. She’s a bit late in the game but you know
Chris:
she learns quick.
Rick:
Yes she’s learned from her papa.
Rhett:
Do you talk data with her? Is she interested in data?
Rick:
Well, she often comes down and asks questions, “Dad, what are you doing?” Yeah. And other than sleeping. I said, “That space mark on my forehead is not from the keyboard.” But no, I actually had a daughter that did it for a summer job.
Rhett:
Really?
Rick:
Yes. During COVID, she couldn’t find work. Yeah. and she needed a job between university years.
Chris:
Yeah. And she– – Is she gonna stay or is she running for her life?
Rhett:
That’s a question. Was she like, “What you do is terrible?” Or was she like, “I love this?”
Rick:
For some reason, she doesn’t want to do it again. I don’t know if it was the person a meter away from her telling her, “Stop looking on your phone.”
Chris:
Yeah. (laughing) I’m gonna tell your mom. (laughing)
Rhett:
No, that’s great. So I understand, so you didn’t actually start with like a, you were a geologist, you told me.
Rick:
Yeah, I was a hard rock geologist, looking for gold and silver and diamonds and walking over the tundra and walking over—
Rhett:
And now he finds diamonds in the data.
Rick:
That’s right. I still find gold and—
Chris:
Diamonds in the rough.
Rick:
I still find nuggets.
Chris:
Yeah, yeah, ooh, I bet you, do you talk to the data? When you’re scrolling through, you’re like, “Oh, I’m going to f– Oh, oh, do you talk to it?”
—
He looked around.
Rhett:
Alright, if you are listening and didn’t watch the YouTube, you got to watch the YouTube channel to catch the full gist of that joke. Otherwise, you missed it. What Rick was doing there.
Chris:
This is so good.
Rhett:
It is going to be great. So, you know, again, what we see with Integrity, you guys have, I think, three papers? Three or four papers at PPI.
Rick:
Yeah, four papers.
Rhett:
Four papers, and you guys are always presenting on things that you’re doing with MFL, which is the reason that we wanted to have you on the show. We like to talk about the things that people are talking about. We like to make it relevant to our clients. We see you guys doing work with pits. See you guys doing work with long seams. Tell us, what sorts of new things are you working on, and where does your role fit into that?
Rick:
Yeah, well, I think it’s important when you do these papers, it doesn’t come over to commercial. Obviously the management and the upper people want exposure. Of course we understand that. But to me it’s always about teaching something. Someone walks away going, “I didn’t know that.” And that’s the important thing that I think that I try to strive when I do papers is I try to teach people something. Something that maybe they didn’t know. And being in data science, because I’m not doing data analysis on a daily basis anymore, but I’m constantly combing over data at my own speed. So there’s no operator saying, “Where is that report?” I’m looking at data because I’m looking to see what’s in there. And when you’ve seen a lot of stuff, you know, no one’s an expert. People just have different experiences, and I
think I just have a lot of experiences doing this and doing it at the time when we didn’t have computers and so you physically had to go and touch a T, kiss a valve. You know, you had to do these things because these things were not…
Chris:
Did that come across clear? Like was that clear? Touch a T and kiss a valve? Is that right? Okay, let’s make sure we said that properly. We want to enunciate.
Rhett:
I’m not sure if I know what that means.
Rick:
I didn’t mean it in the other way. No, okay, just making sure. But But yeah, no, it’s important to have that 3D image, and the 3D image is being out in the field and being in the office. And we don’t have that luxury anymore because now everything’s so digital that everything is done from a distance on a computer. And so it’s important to have experienced personnel that can instill into others who wanna absorb it, these things. And I think that’s a key discipline that you try to teach people.
Rhett:
So I didn’t intend to go here, but now I am, because we’re going to open up, because you said something that’s really close to my heart, which is you mentioned actually putting your eyes on the pipe. So I want to make an assumption, you’ve seen a lot of defects in the ditch? Yeah, I actually had the luxury of being stationed in the Middle East in the 90s for months at a time. The family didn’t like it too much, but I was there two or three months at a time and we do a run once a month and so between those runs it was groundhog day everything was the exact same and so what I did as I just said well I’m taking the pickup and all the pipes were sitting on the sand none were buried and so I took the truck rode along right away use the odometer okay I got to go 300 meters and then I go 3 meters because there’s something on my computer I don’t know what it is. I go 300 meters down, I have a T -Mike, a wall thickness unit, and I’m just measuring, if I can’t find it, I measure it to see what’s going on. And I think that’s invaluable stuff to have is having that ability to go and touch the curvature of the pipe, put your hand through a dent. I don’t see that dent. Oh, but I see it. Yeah, so that’s what I mean is actually, not just using your eyes on a screen, Using your eyes and your other senses when you’re looking at pipe because there’s some things that are going on You you cannot see on a screen.
Chris:
I heard I heard once someone say this It was in when I was at a at a serving as an ILI service providers as well And they were like MFL tools never lie If there’s if it sees something there’s something. So you have to be able to find it, right? Yeah.
Rick:
Yeah, I think the truth of that is When you eliminate any electronic glitches, and they’re very easy detectable. Yeah, you can see when they occur Yeah, so they’re not there-
Chris:
not data artifacts,
Rick:
but true indications may just be hard to prove It’s there. Yeah, and hard spots is one of those things where you can’t see it
Rhett:
Know what’s what I love about what you’re saying because it’s so close to my heart But this is before I went to Rosen so that nobody confuses it. I was talking to a data analyst from a vendor and they were showing me, and they’d never actually seen a dent. Never laid their hands on a dent. They’d never actually seen a crack. And I realized like you’re calling all these features but you don’t actually realize what it is. They see pictures. But you’ve never put your hands on a 36 inch pipe with a three or 4 % dent and seen what that looks like or a ripple or a wrinkle or a buckle. And so it seems to me like you had the luxury of getting hands -on validation, right? Like, hey, this is what I’m seeing in the data. What’s on the pipe? How do you translate that to, ’cause you’re the manager of data science. You said you don’t actually do a lot. How do you translate that to the analysts underneath you now?
Rick:
Well, luckily over the years, I’ve managed to keep a lot of documentation of this, documentation and photographs. When photographs were photographs and—
Rhett:
So you break out a picture book?
Rick:
Yeah.
Chris:
So
it sounds like process control right? Knowledge transfer and process control.
Rick:
Yeah but what I find as you get older when you’re in your 20s and early 30s what I find is you’re trying to attain some knowledge. When you get into your late 30s and your 40s you have some knowledge and you’re trying to make a special effort for yourself and then when you get into your early 50s and 50s now you’re looking forward saying there’s a lot left scale on here then it is going backwards and now you realize I got to give this to someone because when I was younger someone gave it to me and you got to instill that onto others and I think that is something that is a stage where you’re at is you got to instill that stuff to others.
Rhett:
Absolutely.
Chris:
That’s a good point.
Rhett:
So and I’m curious and I’ve had the benefit in full disclosure of working with you. I’ve seen you go through data with us and it really is great because you look at data from a different perspective.
As Integra is kind of pushing the boundaries of what you guys are doing with MFL, so things like pits and pits, things like selective scene wild corrosion, you said hard spots, maybe even material verification. Help us understand and the audience understand at Integra, is it a thing where you’re so deep in the data, you’re like guys I think we can see this and I think we can do this? Or do y ‘all sit around a conference room table and say, you know what, let’s make the day to do that. Say, how does that—
Chris:
Yeah, talked about the process and how you get your solutions.
Rick:
The process is that I will go through a more practical, you know, engineers are very good in theory theoretical. And I think that’s what makes Integra different is when we built our product, we didn’t build it based solely on engineering and engineering and software going here, analyst, work with this. When that tool was built, we had the luxury of having experienced the analyst going,
“Okay, you don’t want to do that because that’s going to affect us doing this.” So we didn’t get things shoved down our throat, it was a cooperation between the two groups to build a product. And the people that are building the product, they are building that product with 30 years of experience, even though Integra is seven years old, we have people that have been building electronically and mechanically these tools for 30 years. So they know how to build the tools. And then you have people that over 30 years have been analyzing data. And so there’s that constant discussion about, well, theoretically this should work. And then that’s where we step in and go, uh -uh, that’s not gonna work or, yeah, that’s perfect. And we’ve had past lives where we’ve done this for other companies And we saw things where we go, “You know what? We can’t change it here because it’s too big of a mountain to climb.” But, you know, if I ever do this again, I’m gonna do this. Right, and that’s one of the things that we’ve had be able to do when we started CPIG, the same thing. We realized where our weakness was. We designed something. We were the first ones to come out with a combo tool, MFL Cal. Yeah, and then now the paradigm that we’re trying to shift now is yes We realize axiom ethyl tools are not for every single type of seam plot that exist But they can probably nail 80 % of the issues that clients have with seam plots. So it things usually work from a proactive stage where I will you know or someone or a group will go Hey, you know, can we can we do this Or I think we can do this because I’ve seen this and we we comb over dig results constantly. And part of it is a bit selfish because yeah We need to give the client back a report on how the tool did but the other is I need better pictures because I’m seeing something else that I picture never took and do you have another picture that just goes to the right a bit more Because I want to see what’s going on there and the discussions that us three had with the client, you know, those kind of discussions are very important and you sitting there and you know I don’t have to see the data sets but I can see you you know looking at the screen and then looking at our screen and going you know this is this is crazy I mean I can I can read both yep and and that’s my life is that’s what I do is I look at data and try to find similarities between data sets And and
Chris:
So come up with these performing analysis.
Rick:
Yeah
Chris:
I’m not just running through some algorithm or some routine, but yeah performing an analysis
Rick:
Yeah, because there is a team that’s doing the day -to -day stuff because we have to push out reports. Bthat’s not my job to push out reports, my job is to is to put out fires if they pop up is to talk about any special things that we can develop What do we need to change in the tool if we were to be able to do that?
Chris:
So it sounds like to me like so you’re telling me you don’t just deliver report and walk away?
Rick:
Well It’s definitely not educated that way to the to the data analyst because I Think it’s very important. There’s a whole process inside vendors Yeah, you got to have the sales to get the jobs. You got to have the engineering to build the tools Yeah, you got to have the option operation crews to run the tools first one And then you’ve got to have the DA to create the report. But really, to me, for the operator, the wheels hit the road when they get that document saying, you got this in your pipeline. And now that’s when the time starts ticking for them. And so that’s when it’s very important. And that’s when the operator needs most assistance. And I think that is key, is getting involved with operators. And sometimes you’ve got to, You know, they’re the client. You got to take a step back and you only can be invited when they ask you to come invited.
—
You can give a few good responses on occasion.
Chris:
And that’s pretty dynamic, huh?
Rick:
Yeah, yeah.
Chris:
Some customers are more needy than others. Others are maybe more hands -off.
Rick:
Yeah, and I think needy sounds
to me—
Rhett:
Chris is needy, I use it for him a lot.
Rick:
Yeah, and I think what people need is they don’t, they don’t have the experience to, And maybe they’re just they just realize oh we got some lines that are now they have to be inspected What’s MEOP mean? What does that have to do? You know you get these questions, you do I think —
Rhett:
Rick came alive man And that is actually because I want to pick back up on on your role in the analyst role and some of those processes in that Feedback loop, it’s really important, but we’re gonna hear from BJ and Ben our sponsors at PPIM before we come right back. Thank you.
PJ Lowe:
PJ Lowe here, Clarion Technical Conferences, and if it’s February, it must be PPIM.
We’re here, that’s right, for the 35th time in Houston, Texas, starting in 1989, and here we are in 2023 with another record -breaking turnout for the world’s largest pipeline technology – related event. We’ve got almost 3 ,500 people coming for four or five days, almost 200 companies exhibiting on the trade show floor, another sellout. It’s not too early to begin thinking about 2024. We sell out around June or July, so clarion.org is where you go to sign up. We hope you can join us for the next conference if you’re not here this year. Another big part of the conference is our training and education program, which takes place on the Monday and Tuesday of the week. And this year we have eight really, really great courses, world -class instructors, and almost 300 people showing up for these specialized courses on different aspects of pipeline integrity technology. I’m here with Ben Stroman, my colleague at Clarion. Hey Ben, what’s up?
Ben
Hey, doing good, always happy to be here because it’s, as we say, PPIM season, it’s February. So I would like to remind everybody that we also have a very specialized technical conference that’s associated with PPIM. And this year I’m proud to say that we have the largest conference program that we’ve ever had, it has 94 technical papers that were chosen from a large selection of submissions that were very high quality so we’re very pleased to be putting on these 94 papers during the week. I’d also like to mention that we do have online training if you aren’t able to make it to PPIM each year. We present online training throughout the year. You can check more of that out at clarion.org and we hope to see you in one way or another online or in person and if you’re not here at the 35th version of PPIM I hope you’re here at the 36th.
PJ
definitely.
>>
Rhett:
All right, welcome back. Thanks. Second string, Sarah, keeping us on track with time. We appreciate it. I’m sorry, I had to fit in the joke there since Miss Producer can’t be with us. So we’re here with with Rick, D’Zione, I said it quicker that time. We’ve been talking about his background and what Integra’s doing different. And so I want to dive in now. You guys have a paper on long scene assessments. And that’s long scene assessments with MFLA, right?
Rick:
Yes.
Rhett:
Yeah, so Rick, I would like you to help us understand ’cause that’s an area that a lot of people are challenged by, right? Traditionally, we’ve said MFLA can’t or shouldn’t do up with long seams. So how is it that Integra is pushing the boundaries on that?
Rick:
Yeah, and I think with the tool we’ve coined the tool UHR, ultra -high resolution, and you know and I know that terminology is used by others,but we turned that for a reason. But you know when it comes to detecting the long seam, that’s the most important. You got to to detect the seam. Pretty well everyone detects flash welded and double surged. They have excess relief, you know the extra thickness on the pipe. It’s an E or W one really that is a trickier one to determine. And luckily E or W is usually on smaller diameter pipe. It doesn’t venture above 24 too much but on the smaller diameter side. So you have to have ability to detect and have a high confidence of detecting that scene. That’s the first thing and that’s what we recognize right away is we have a high confidence of detecting the scene with the UHR tool and when we detect that scene now we’re starting seeing anomalies and if you’ve got pinhole anomalies or you have anomalies that are rather small or contained within the scene then it leads you down another path. Because when you have corrosion, corrosion is unlike if it’s not selective seam, corrosion will be in the seam but also will penetrate into the body of the pipe. Where selective seam kind of stuff stays within the seam, it uses that balance. So you we can see that difference between that and traditionally people have always said don’t use AMFL for reporting anomalies on the seam and you got to remember that the tool, the UHR tool is seeing the seam, ERW, flash weld or whatever, you’re seeing it with all the sensor types. So not just the primary MFL, you’re seeing it with the caliper sensors, you’re seeing it with the IDOD sensors. So you got that redundancy of having that ability and that’s very important because you could have flaws that may not show up very well in the data or you have a seam type, so seam identification. Low frequency, high frequency for example. Different signatures. Well, those signatures will look different in one of the three channels and that’s how you’re telling the difference. Oh look-
Chris:
For discrimination, so that leads us to not just detection but identification.
Rick:
Yes, so that’s POD, POI, which is important when it comes to the scene. So, and that’s not being done right now by anyone, even even transverse MFL tools, they’re not labeling the scene type, they’re going with Mr. Operator, Mr. Operator, you’re calling it this, okay, I’m going to run with that. Because the hardest thing is to tell an operator that’s not, you know, this, that is this. Yeah, and we have to explain to them and into detail and you go into data and say these marks here are here because of this thing that’s why it’s not this so you’re getting that distinction with the tool to see that now experience does help you know sure where you’ve seen a lot of different seam types you’ve seen a lot of different seam flaws
Chris:
fit you back to your expertise near experiences yes experiences
Rick:
Yeah experience always helps out when it comes to looking at flaws that may you have seen in the past.
Chris:
I wanna ask a little bit of a side question. I mean, so that’s system specific, right? I mean, that’s your experience with your UHR system, right? I mean, that’s very much intrinsic to the value prop that you offer of the combination of your tool and your analysis process, right? That can’t be universally said as it’s, oh, well Rick said you can use MFL A for long -term assessments right? This is very much your special sauce, your tool, your analysis process enables operators this way.
Rick:
All I can talk about is is what we can do and you know and I you know whatever someone else is selling to the operator that’s selling you know.
Chris:
He said there’s a process but sales, operations, evaluation.
Rick:
I mean, I have no problems.
Rhett:
Well, I think what he’s trying to do, Rick, and it’s important from this standpoint, operators don’t often see a lot of times, they just see MFL, right? And so if somebody says MFL can do X, I think what you’re bringing out is that MFL in your application with your experience and with your analysis can do X, all that stuff you mentioned before where you had Analysts feeding into the development of the tools plays a role and whether or not MFL can do that like you can’t just take Hey, you know what? I mean in tech said we could do it. So let’s run Technology Y and we expect them to do it integral. It’s just a caution, right? It’s a point of caution. Yeah.
Rick:
Yeah. No Yeah, like I say, I think Integra with the UHR tools we developed them like I said , we developed them from an engineering and a DA perspective to develop them.
Chris:
And by DA you mean data analysis, not direct assessment.
Rick:
By data analysis, we directed that tool to a certain place to be because we knew from historical findings that this design works better when you do this. And it’s interesting to me because what you did is you started with the end user of the data but not the end user being the client, the end user being data analyst and said, “Hey, what would help you do your job better?” And And that issue, yeah, and you know very well working for some of the former companies that that came from engineering down. They designed something it came here. It is for you to use and then you have to use it
Chris:
So I want to I want to maybe switch directions just a little bit here So if you’re able to detect and identify long seams then talk to us a little bit about y ‘all’s ability to help operators with populations in the process of material verification.
Rick:
Oh yeah, we currently do several jobs with clients in that particular principle. And it’s very important when you’re doing material identification, it’s not just pipe grade. It is diameter, which is pretty simple if it’s a single diameter line, but it’s diameter, it is wall thickness, it is seam type, and it is pipe -grade, all those four parameters make a difference.
Rhett:
As these problems come to you, I’m curious. So does somebody approach you and they say, hey, we want to identify long -seam and you’re like, whoa, I’ve been doing this for 20 years, that can’t be done? Or is it kind of collaborative, right? So I’m curious, like the subject of long -seams with MFLA, which most people would say can’t be done. How did that conversation or how did that process evolve within Integra? Was that a thing we were like, look, from a data -analyst perspective, I think we can do or were clients pushing you? Was it market driven? Was it data driven?
Rick:
I think, well, it was data driven because if I left the engineers design something, they would have built a nice tool, but it wouldn’t be—
Chris:
Well, who knows if it would have worked? – You know, the– – Who’s the art engineers?
Rhett:
It would have worked.
Chris:
He’s not an engineer, he’s a geologist, right? I mean, we totally understand that.
Rick:
Yeah, so, but it’s a collaborative effort. It’s not one over strong in the other like we’ll have some ideas and of course they’ll say we can’t we can’t make a square tool right yeah you know and so you know whatever whatever the case may be so they will step in when there’s something that’s obviously—
Chris:
Let’s talk about that right so for long scene you said detection is first and then you can identify it right I think for me when we think about these whole populations right if I was a rank order them you would say yeah diameter is probably the easiest right yeah Then you’d probably go along and say wall thickness is probably the next one. There’s some analysis required, some algorithms, but that’s probably the next one using MFL and Caliper. Next, I’d probably say it’d be long seam as feasible, right? Because you’re saying you can detect it, therefore you can then identify it. And then if you’ve got, you know the diameter, you know the wall thickness, and you know the pipe type, then you can begin to understand what are the possible grades, right? Because that’s how pipe was made so when you say great talk just a little about how you’re getting there I feel like I kind of road mapped it but I want to hear it from you
Rick:
Yeah well to get to grade you have to go through the other three yeah it’s very important you have to go through the other three because that’s gonna make a distinction but there’s a lot of other things going on in the background that that you’re doing on the fly is you’re doing grade determination look at the manufacturer because We have found, by looking at different manufacturers, certain things are appearing on the data that we’re seeing are telltale signs of this letter.
Chris:
Your tool has a response to certain materials and you’re capturing that and saying, “Now I know this signature…”
Rick:
And we may not understand why all the time that response is happening, but you know, with this American Steel Pipeline Company, they’re always producing this, we see this in the data.
Chris:
So it’s the response of fingerprint knowledge.
Rick:
Yeah, so that’s what it is. It’s basically, we just align a bunch of fingerprints with the URH tool, looking to align all the different things that we’re seeing, and then they kind of fit into categories. And then we look over those in the categories and say, it could even–
Rhett:
So the way this works is it’s like Rick’s picture book, and it’s like a mug shot. Somebody puts it on the screen, and Rick breaks out the picture book and they’re like, I think it’s operator, 607 is like a mug shop right there.
Rick:
Well, keep in mind, I know we’re joking about Rick and Rick and Rick, and really it’s really the team and Rick.
Chris:
Well, you’re the manager, right? So the ideas you’re representing the team, so that’s the clarification there.
Rhett:
So we got a few, just a little bit in our last few moments. Anything you share? What’s coming down the pipe for Integra, man? What Are you excited about the future?
—
Rhett
The presidential election doesn’t count.
Rick:
Okay. How about the Canadian election? No?
Rhett:
Okay. I don’t know. Is that Justin Trudeau?
Rick:
Yeah, yeah.
Rhett:
Oh, okay. Yeah, let’s not go there. We’ll affect—
Chris:
Back to technical stuff. Back to technical stuff. Land that plane.
Rick:
Yes. So I think there’s a lot of exciting things and I think one of the things is now clients are recognizing we have that ability to do those things and now there’s more sharing. Before it was you know looking on the clipboard that they’re hiding from you but now clients are realizing wait a minute they do have these abilities and we it’s got to be more of a team effort it always should be a team effort. – If they have an engineering firm working on the side they should be able to work with the different parties to get to the same solution.
Chris:
Data integration is fundamental.
Rhett:
I know it’s a collaboration, he’s singing the love language of collaboration man, with an operator you don’t, an operator should never work with an ILI vendor like they’re trying to prove them wrong, I always used to tell people the reason data analysts are so guarded is because half the time you only call them if they’re wrong, you gotta call you data –
Chris:
When am I gonna get my report and why did you call this? It was wrong.
Rick:
Or you called this, you realize where that is. You better change that call.
Chris:
Are you sure? Well not change the call. Make sure you’re right. Yeah yeah that’s so good.
Rhett:
Well I tell you what Rick we’ve really appreciated having you one. Thank you so much for for joining us and talking a little bit about what Integra’s doing.
Chris;
Everybody knows how to reach you so if y ‘all have got any questions. You’re looking for Rick D’Zione. I said it quicker every time I said it now. And thank you for joining us at PPIM 23 and to our audience out there. If you want to find Rick or Integra or any of the papers they published at PPIM, you can be on the lookout. They’ve got four of them there. Highly recommend you take a look at them. Thanks and we’ll see you in two weeks.
Rick:
I have a question about, do I get a cut out of myself later? Was that, is that coming, that can be shipped to me?
Rhett:
Second string, Sarah, we can talk about a cutout for Integra.
Rick
I don’t want him. With his signature.
Rhett:
I thought I ended the show?
All right, welcome to today’s episode of Pipeline Things. If you are not watching the YouTube, this might not make sense. You’re also probably wondering why my mouth isn’t moving, but you hear my voice.
Chris:
People still not watching the YouTube?
Rhett:
You should watch the YouTube. If you’ve ever wondered what a new definition of data model looks like, or whether or not some people like data more than anything else in life, you need to listen to today’s episode where we sit down with Rick DiZoniay from Integra and talk about Integra’s new advances at PPIM 23.
Chris:
And he talks about two girtholds and naked pipe
Rhett:
And no Rick. You can’t have a cardboard cutout. They’re only for us Listen to the episode. Thanks joining us.
Rhett:
– Welcome to our PPIM 23 Arc, where we are talking to vendors about their technology, the things that you guys are interested in, and we’ve had multiple guests, if you’ve been on us on this journey. Today is no different. We are sitting here with Rick D ‘Zoneier.
Chris:
D ‘Zoneier.
Rhett:
I’m gonna try that, Rick D ‘Zoneier.
Chris:
Didn’t you grow up south of I -10?
Rhett:
I did, I don’t that name came from South of the I -10.
Chris:
Yeah, but you should have some swag in how you say it though, bro.
Rhett:
Rick D ‘Zonnier, is it work? Did I do okay?
Rick:
Yeah, it’s fine, D ‘Zonnier.
Rhett:
And Rick is the manager of data science with Integra. And we are super excited to have him as we’re gonna talk a little bit about some of the exciting stuff that they’re doing. But before we get into that, so I talked to some of your colleagues before and I understand that a lot of guys in the office used to have like pictures models on that thing , you actually put pictures of data around your wall that’s that’s that’s what gets you –
Chris:
Youre really cool you’re like
Rick:
yes well only a couple pictures
Chris:
okay I get carried away-
Rhett:
but only the best pictures of data looking pictures
Rick:
the best -looking pictures yes
Chris:
best -looking data pictures
Rick:
I don’t want to see two girth wells and nice clean pipe – An operator wants to see it, but I don’t. (laughing)
Chris:
Two girth wells and nice clean pipe. Oh man, you can take that so many ways.
Rhett:
You did really good. The marketing guy was like, “Do we need to edit this out now?” He’s not really sure, and we got started off.
Chris
That is so great.
Rhett:
Rick, we really want to thank you for joining us. And I want you to introduce yourself. He tells us about you. Who are you? You’re manager of Data Science for Integra, but beyond that, who’s Rick?
Rick:
Rick is Rick D ‘Zonia as we all found out the last few minutes ago I’m not a city boy I’m country bumpkin okay but yeah I’ve been in the inspection business for too long I think back in the 80s I was six then when I started so I’m only 40 now
Chris:
so no child labor laws.
Rick:
Yeah well in Canada there is none so it’s good.
Rhett:
I didn’t know this. Have we talked about opening up businesses in Canada?
Chris:
We’ve talked about Mexico but maybe we need to look in Canada.
Rhett:
Yeah I know for real.
Rick:
My daughter’s looking for a job.
Chris:
How old is she?
Rick:
She’s 16. She’s a bit late in the game but you know
Chris:
she learns quick.
Rick:
Yes she’s learned from her papa.
Rhett:
Do you talk data with her? Is she interested in data?
Rick:
Well, she often comes down and asks questions, “Dad, what are you doing?” Yeah. And other than sleeping. I said, “That space mark on my forehead is not from the keyboard.” But no, I actually had a daughter that did it for a summer job.
Rhett:
Really?
Rick:
Yes. During COVID, she couldn’t find work. Yeah. and she needed a job between university years.
Chris:
Yeah. And she– – Is she gonna stay or is she running for her life?
Rhett:
That’s a question. Was she like, “What you do is terrible?” Or was she like, “I love this?”
Rick:
For some reason, she doesn’t want to do it again. I don’t know if it was the person a meter away from her telling her, “Stop looking on your phone.”
Chris:
Yeah. (laughing) I’m gonna tell your mom. (laughing)
Rhett:
No, that’s great. So I understand, so you didn’t actually start with like a, you were a geologist, you told me.
Rick:
Yeah, I was a hard rock geologist, looking for gold and silver and diamonds and walking over the tundra and walking over—
Rhett:
And now he finds diamonds in the data.
Rick:
That’s right. I still find gold and—
Chris:
Diamonds in the rough.
Rick:
I still find nuggets.
Chris:
Yeah, yeah, ooh, I bet you, do you talk to the data? When you’re scrolling through, you’re like, “Oh, I’m going to f– Oh, oh, do you talk to it?”
—
He looked around.
Rhett:
Alright, if you are listening and didn’t watch the YouTube, you got to watch the YouTube channel to catch the full gist of that joke. Otherwise, you missed it. What Rick was doing there.
Chris:
This is so good.
Rhett:
It is going to be great. So, you know, again, what we see with Integrity, you guys have, I think, three papers? Three or four papers at PPI.
Rick:
Yeah, four papers.
Rhett:
Four papers, and you guys are always presenting on things that you’re doing with MFL, which is the reason that we wanted to have you on the show. We like to talk about the things that people are talking about. We like to make it relevant to our clients. We see you guys doing work with pits. See you guys doing work with long seams. Tell us, what sorts of new things are you working on, and where does your role fit into that?
Rick:
Yeah, well, I think it’s important when you do these papers, it doesn’t come over to commercial. Obviously the management and the upper people want exposure. Of course we understand that. But to me it’s always about teaching something. Someone walks away going, “I didn’t know that.” And that’s the important thing that I think that I try to strive when I do papers is I try to teach people something. Something that maybe they didn’t know. And being in data science, because I’m not doing data analysis on a daily basis anymore, but I’m constantly combing over data at my own speed. So there’s no operator saying, “Where is that report?” I’m looking at data because I’m looking to see what’s in there. And when you’ve seen a lot of stuff, you know, no one’s an expert. People just have different experiences, and I
think I just have a lot of experiences doing this and doing it at the time when we didn’t have computers and so you physically had to go and touch a T, kiss a valve. You know, you had to do these things because these things were not…
Chris:
Did that come across clear? Like was that clear? Touch a T and kiss a valve? Is that right? Okay, let’s make sure we said that properly. We want to enunciate.
Rhett:
I’m not sure if I know what that means.
Rick:
I didn’t mean it in the other way. No, okay, just making sure. But But yeah, no, it’s important to have that 3D image, and the 3D image is being out in the field and being in the office. And we don’t have that luxury anymore because now everything’s so digital that everything is done from a distance on a computer. And so it’s important to have experienced personnel that can instill into others who wanna absorb it, these things. And I think that’s a key discipline that you try to teach people.
Rhett:
So I didn’t intend to go here, but now I am, because we’re going to open up, because you said something that’s really close to my heart, which is you mentioned actually putting your eyes on the pipe. So I want to make an assumption, you’ve seen a lot of defects in the ditch? Yeah, I actually had the luxury of being stationed in the Middle East in the 90s for months at a time. The family didn’t like it too much, but I was there two or three months at a time and we do a run once a month and so between those runs it was groundhog day everything was the exact same and so what I did as I just said well I’m taking the pickup and all the pipes were sitting on the sand none were buried and so I took the truck rode along right away use the odometer okay I got to go 300 meters and then I go 3 meters because there’s something on my computer I don’t know what it is. I go 300 meters down, I have a T -Mike, a wall thickness unit, and I’m just measuring, if I can’t find it, I measure it to see what’s going on. And I think that’s invaluable stuff to have is having that ability to go and touch the curvature of the pipe, put your hand through a dent. I don’t see that dent. Oh, but I see it. Yeah, so that’s what I mean is actually, not just using your eyes on a screen, Using your eyes and your other senses when you’re looking at pipe because there’s some things that are going on You you cannot see on a screen.
Chris:
I heard I heard once someone say this It was in when I was at a at a serving as an ILI service providers as well And they were like MFL tools never lie If there’s if it sees something there’s something. So you have to be able to find it, right? Yeah.
Rick:
Yeah, I think the truth of that is When you eliminate any electronic glitches, and they’re very easy detectable. Yeah, you can see when they occur Yeah, so they’re not there-
Chris:
not data artifacts,
Rick:
but true indications may just be hard to prove It’s there. Yeah, and hard spots is one of those things where you can’t see it
Rhett:
Know what’s what I love about what you’re saying because it’s so close to my heart But this is before I went to Rosen so that nobody confuses it. I was talking to a data analyst from a vendor and they were showing me, and they’d never actually seen a dent. Never laid their hands on a dent. They’d never actually seen a crack. And I realized like you’re calling all these features but you don’t actually realize what it is. They see pictures. But you’ve never put your hands on a 36 inch pipe with a three or 4 % dent and seen what that looks like or a ripple or a wrinkle or a buckle. And so it seems to me like you had the luxury of getting hands -on validation, right? Like, hey, this is what I’m seeing in the data. What’s on the pipe? How do you translate that to, ’cause you’re the manager of data science. You said you don’t actually do a lot. How do you translate that to the analysts underneath you now?
Rick:
Well, luckily over the years, I’ve managed to keep a lot of documentation of this, documentation and photographs. When photographs were photographs and—
Rhett:
So you break out a picture book?
Rick:
Yeah.
Chris:
So
it sounds like process control right? Knowledge transfer and process control.
Rick:
Yeah but what I find as you get older when you’re in your 20s and early 30s what I find is you’re trying to attain some knowledge. When you get into your late 30s and your 40s you have some knowledge and you’re trying to make a special effort for yourself and then when you get into your early 50s and 50s now you’re looking forward saying there’s a lot left scale on here then it is going backwards and now you realize I got to give this to someone because when I was younger someone gave it to me and you got to instill that onto others and I think that is something that is a stage where you’re at is you got to instill that stuff to others.
Rhett:
Absolutely.
Chris:
That’s a good point.
Rhett:
So and I’m curious and I’ve had the benefit in full disclosure of working with you. I’ve seen you go through data with us and it really is great because you look at data from a different perspective.
As Integra is kind of pushing the boundaries of what you guys are doing with MFL, so things like pits and pits, things like selective scene wild corrosion, you said hard spots, maybe even material verification. Help us understand and the audience understand at Integra, is it a thing where you’re so deep in the data, you’re like guys I think we can see this and I think we can do this? Or do y ‘all sit around a conference room table and say, you know what, let’s make the day to do that. Say, how does that—
Chris:
Yeah, talked about the process and how you get your solutions.
Rick:
The process is that I will go through a more practical, you know, engineers are very good in theory theoretical. And I think that’s what makes Integra different is when we built our product, we didn’t build it based solely on engineering and engineering and software going here, analyst, work with this. When that tool was built, we had the luxury of having experienced the analyst going,
“Okay, you don’t want to do that because that’s going to affect us doing this.” So we didn’t get things shoved down our throat, it was a cooperation between the two groups to build a product. And the people that are building the product, they are building that product with 30 years of experience, even though Integra is seven years old, we have people that have been building electronically and mechanically these tools for 30 years. So they know how to build the tools. And then you have people that over 30 years have been analyzing data. And so there’s that constant discussion about, well, theoretically this should work. And then that’s where we step in and go, uh -uh, that’s not gonna work or, yeah, that’s perfect. And we’ve had past lives where we’ve done this for other companies And we saw things where we go, “You know what? We can’t change it here because it’s too big of a mountain to climb.” But, you know, if I ever do this again, I’m gonna do this. Right, and that’s one of the things that we’ve had be able to do when we started CPIG, the same thing. We realized where our weakness was. We designed something. We were the first ones to come out with a combo tool, MFL Cal. Yeah, and then now the paradigm that we’re trying to shift now is yes We realize axiom ethyl tools are not for every single type of seam plot that exist But they can probably nail 80 % of the issues that clients have with seam plots. So it things usually work from a proactive stage where I will you know or someone or a group will go Hey, you know, can we can we do this Or I think we can do this because I’ve seen this and we we comb over dig results constantly. And part of it is a bit selfish because yeah We need to give the client back a report on how the tool did but the other is I need better pictures because I’m seeing something else that I picture never took and do you have another picture that just goes to the right a bit more Because I want to see what’s going on there and the discussions that us three had with the client, you know, those kind of discussions are very important and you sitting there and you know I don’t have to see the data sets but I can see you you know looking at the screen and then looking at our screen and going you know this is this is crazy I mean I can I can read both yep and and that’s my life is that’s what I do is I look at data and try to find similarities between data sets And and
Chris:
So come up with these performing analysis.
Rick:
Yeah
Chris:
I’m not just running through some algorithm or some routine, but yeah performing an analysis
Rick:
Yeah, because there is a team that’s doing the day -to -day stuff because we have to push out reports. Bthat’s not my job to push out reports, my job is to is to put out fires if they pop up is to talk about any special things that we can develop What do we need to change in the tool if we were to be able to do that?
Chris:
So it sounds like to me like so you’re telling me you don’t just deliver report and walk away?
Rick:
Well It’s definitely not educated that way to the to the data analyst because I Think it’s very important. There’s a whole process inside vendors Yeah, you got to have the sales to get the jobs. You got to have the engineering to build the tools Yeah, you got to have the option operation crews to run the tools first one And then you’ve got to have the DA to create the report. But really, to me, for the operator, the wheels hit the road when they get that document saying, you got this in your pipeline. And now that’s when the time starts ticking for them. And so that’s when it’s very important. And that’s when the operator needs most assistance. And I think that is key, is getting involved with operators. And sometimes you’ve got to, You know, they’re the client. You got to take a step back and you only can be invited when they ask you to come invited.
—
You can give a few good responses on occasion.
Chris:
And that’s pretty dynamic, huh?
Rick:
Yeah, yeah.
Chris:
Some customers are more needy than others. Others are maybe more hands -off.
Rick:
Yeah, and I think needy sounds
to me—
Rhett:
Chris is needy, I use it for him a lot.
Rick:
Yeah, and I think what people need is they don’t, they don’t have the experience to, And maybe they’re just they just realize oh we got some lines that are now they have to be inspected What’s MEOP mean? What does that have to do? You know you get these questions, you do I think —
Rhett:
Rick came alive man And that is actually because I want to pick back up on on your role in the analyst role and some of those processes in that Feedback loop, it’s really important, but we’re gonna hear from BJ and Ben our sponsors at PPIM before we come right back. Thank you.
PJ Lowe:
PJ Lowe here, Clarion Technical Conferences, and if it’s February, it must be PPIM.
We’re here, that’s right, for the 35th time in Houston, Texas, starting in 1989, and here we are in 2023 with another record -breaking turnout for the world’s largest pipeline technology – related event. We’ve got almost 3 ,500 people coming for four or five days, almost 200 companies exhibiting on the trade show floor, another sellout. It’s not too early to begin thinking about 2024. We sell out around June or July, so clarion.org is where you go to sign up. We hope you can join us for the next conference if you’re not here this year. Another big part of the conference is our training and education program, which takes place on the Monday and Tuesday of the week. And this year we have eight really, really great courses, world -class instructors, and almost 300 people showing up for these specialized courses on different aspects of pipeline integrity technology. I’m here with Ben Stroman, my colleague at Clarion. Hey Ben, what’s up?
Ben
Hey, doing good, always happy to be here because it’s, as we say, PPIM season, it’s February. So I would like to remind everybody that we also have a very specialized technical conference that’s associated with PPIM. And this year I’m proud to say that we have the largest conference program that we’ve ever had, it has 94 technical papers that were chosen from a large selection of submissions that were very high quality so we’re very pleased to be putting on these 94 papers during the week. I’d also like to mention that we do have online training if you aren’t able to make it to PPIM each year. We present online training throughout the year. You can check more of that out at clarion.org and we hope to see you in one way or another online or in person and if you’re not here at the 35th version of PPIM I hope you’re here at the 36th.
PJ
definitely.
>>
Rhett:
All right, welcome back. Thanks. Second string, Sarah, keeping us on track with time. We appreciate it. I’m sorry, I had to fit in the joke there since Miss Producer can’t be with us. So we’re here with with Rick, D’Zione, I said it quicker that time. We’ve been talking about his background and what Integra’s doing different. And so I want to dive in now. You guys have a paper on long scene assessments. And that’s long scene assessments with MFLA, right?
Rick:
Yes.
Rhett:
Yeah, so Rick, I would like you to help us understand ’cause that’s an area that a lot of people are challenged by, right? Traditionally, we’ve said MFLA can’t or shouldn’t do up with long seams. So how is it that Integra is pushing the boundaries on that?
Rick:
Yeah, and I think with the tool we’ve coined the tool UHR, ultra -high resolution, and you know and I know that terminology is used by others,but we turned that for a reason. But you know when it comes to detecting the long seam, that’s the most important. You got to to detect the seam. Pretty well everyone detects flash welded and double surged. They have excess relief, you know the extra thickness on the pipe. It’s an E or W one really that is a trickier one to determine. And luckily E or W is usually on smaller diameter pipe. It doesn’t venture above 24 too much but on the smaller diameter side. So you have to have ability to detect and have a high confidence of detecting that scene. That’s the first thing and that’s what we recognize right away is we have a high confidence of detecting the scene with the UHR tool and when we detect that scene now we’re starting seeing anomalies and if you’ve got pinhole anomalies or you have anomalies that are rather small or contained within the scene then it leads you down another path. Because when you have corrosion, corrosion is unlike if it’s not selective seam, corrosion will be in the seam but also will penetrate into the body of the pipe. Where selective seam kind of stuff stays within the seam, it uses that balance. So you we can see that difference between that and traditionally people have always said don’t use AMFL for reporting anomalies on the seam and you got to remember that the tool, the UHR tool is seeing the seam, ERW, flash weld or whatever, you’re seeing it with all the sensor types. So not just the primary MFL, you’re seeing it with the caliper sensors, you’re seeing it with the IDOD sensors. So you got that redundancy of having that ability and that’s very important because you could have flaws that may not show up very well in the data or you have a seam type, so seam identification. Low frequency, high frequency for example. Different signatures. Well, those signatures will look different in one of the three channels and that’s how you’re telling the difference. Oh look-
Chris:
For discrimination, so that leads us to not just detection but identification.
Rick:
Yes, so that’s POD, POI, which is important when it comes to the scene. So, and that’s not being done right now by anyone, even even transverse MFL tools, they’re not labeling the scene type, they’re going with Mr. Operator, Mr. Operator, you’re calling it this, okay, I’m going to run with that. Because the hardest thing is to tell an operator that’s not, you know, this, that is this. Yeah, and we have to explain to them and into detail and you go into data and say these marks here are here because of this thing that’s why it’s not this so you’re getting that distinction with the tool to see that now experience does help you know sure where you’ve seen a lot of different seam types you’ve seen a lot of different seam flaws
Chris:
fit you back to your expertise near experiences yes experiences
Rick:
Yeah experience always helps out when it comes to looking at flaws that may you have seen in the past.
Chris:
I wanna ask a little bit of a side question. I mean, so that’s system specific, right? I mean, that’s your experience with your UHR system, right? I mean, that’s very much intrinsic to the value prop that you offer of the combination of your tool and your analysis process, right? That can’t be universally said as it’s, oh, well Rick said you can use MFL A for long -term assessments right? This is very much your special sauce, your tool, your analysis process enables operators this way.
Rick:
All I can talk about is is what we can do and you know and I you know whatever someone else is selling to the operator that’s selling you know.
Chris:
He said there’s a process but sales, operations, evaluation.
Rick:
I mean, I have no problems.
Rhett:
Well, I think what he’s trying to do, Rick, and it’s important from this standpoint, operators don’t often see a lot of times, they just see MFL, right? And so if somebody says MFL can do X, I think what you’re bringing out is that MFL in your application with your experience and with your analysis can do X, all that stuff you mentioned before where you had Analysts feeding into the development of the tools plays a role and whether or not MFL can do that like you can’t just take Hey, you know what? I mean in tech said we could do it. So let’s run Technology Y and we expect them to do it integral. It’s just a caution, right? It’s a point of caution. Yeah.
Rick:
Yeah. No Yeah, like I say, I think Integra with the UHR tools we developed them like I said , we developed them from an engineering and a DA perspective to develop them.
Chris:
And by DA you mean data analysis, not direct assessment.
Rick:
By data analysis, we directed that tool to a certain place to be because we knew from historical findings that this design works better when you do this. And it’s interesting to me because what you did is you started with the end user of the data but not the end user being the client, the end user being data analyst and said, “Hey, what would help you do your job better?” And And that issue, yeah, and you know very well working for some of the former companies that that came from engineering down. They designed something it came here. It is for you to use and then you have to use it
Chris:
So I want to I want to maybe switch directions just a little bit here So if you’re able to detect and identify long seams then talk to us a little bit about y ‘all’s ability to help operators with populations in the process of material verification.
Rick:
Oh yeah, we currently do several jobs with clients in that particular principle. And it’s very important when you’re doing material identification, it’s not just pipe grade. It is diameter, which is pretty simple if it’s a single diameter line, but it’s diameter, it is wall thickness, it is seam type, and it is pipe -grade, all those four parameters make a difference.
Rhett:
As these problems come to you, I’m curious. So does somebody approach you and they say, hey, we want to identify long -seam and you’re like, whoa, I’ve been doing this for 20 years, that can’t be done? Or is it kind of collaborative, right? So I’m curious, like the subject of long -seams with MFLA, which most people would say can’t be done. How did that conversation or how did that process evolve within Integra? Was that a thing we were like, look, from a data -analyst perspective, I think we can do or were clients pushing you? Was it market driven? Was it data driven?
Rick:
I think, well, it was data driven because if I left the engineers design something, they would have built a nice tool, but it wouldn’t be—
Chris:
Well, who knows if it would have worked? – You know, the– – Who’s the art engineers?
Rhett:
It would have worked.
Chris:
He’s not an engineer, he’s a geologist, right? I mean, we totally understand that.
Rick:
Yeah, so, but it’s a collaborative effort. It’s not one over strong in the other like we’ll have some ideas and of course they’ll say we can’t we can’t make a square tool right yeah you know and so you know whatever whatever the case may be so they will step in when there’s something that’s obviously—
Chris:
Let’s talk about that right so for long scene you said detection is first and then you can identify it right I think for me when we think about these whole populations right if I was a rank order them you would say yeah diameter is probably the easiest right yeah Then you’d probably go along and say wall thickness is probably the next one. There’s some analysis required, some algorithms, but that’s probably the next one using MFL and Caliper. Next, I’d probably say it’d be long seam as feasible, right? Because you’re saying you can detect it, therefore you can then identify it. And then if you’ve got, you know the diameter, you know the wall thickness, and you know the pipe type, then you can begin to understand what are the possible grades, right? Because that’s how pipe was made so when you say great talk just a little about how you’re getting there I feel like I kind of road mapped it but I want to hear it from you
Rick:
Yeah well to get to grade you have to go through the other three yeah it’s very important you have to go through the other three because that’s gonna make a distinction but there’s a lot of other things going on in the background that that you’re doing on the fly is you’re doing grade determination look at the manufacturer because We have found, by looking at different manufacturers, certain things are appearing on the data that we’re seeing are telltale signs of this letter.
Chris:
Your tool has a response to certain materials and you’re capturing that and saying, “Now I know this signature…”
Rick:
And we may not understand why all the time that response is happening, but you know, with this American Steel Pipeline Company, they’re always producing this, we see this in the data.
Chris:
So it’s the response of fingerprint knowledge.
Rick:
Yeah, so that’s what it is. It’s basically, we just align a bunch of fingerprints with the URH tool, looking to align all the different things that we’re seeing, and then they kind of fit into categories. And then we look over those in the categories and say, it could even–
Rhett:
So the way this works is it’s like Rick’s picture book, and it’s like a mug shot. Somebody puts it on the screen, and Rick breaks out the picture book and they’re like, I think it’s operator, 607 is like a mug shop right there.
Rick:
Well, keep in mind, I know we’re joking about Rick and Rick and Rick, and really it’s really the team and Rick.
Chris:
Well, you’re the manager, right? So the ideas you’re representing the team, so that’s the clarification there.
Rhett:
So we got a few, just a little bit in our last few moments. Anything you share? What’s coming down the pipe for Integra, man? What Are you excited about the future?
—
Rhett
The presidential election doesn’t count.
Rick:
Okay. How about the Canadian election? No?
Rhett:
Okay. I don’t know. Is that Justin Trudeau?
Rick:
Yeah, yeah.
Rhett:
Oh, okay. Yeah, let’s not go there. We’ll affect—
Chris:
Back to technical stuff. Back to technical stuff. Land that plane.
Rick:
Yes. So I think there’s a lot of exciting things and I think one of the things is now clients are recognizing we have that ability to do those things and now there’s more sharing. Before it was you know looking on the clipboard that they’re hiding from you but now clients are realizing wait a minute they do have these abilities and we it’s got to be more of a team effort it always should be a team effort. – If they have an engineering firm working on the side they should be able to work with the different parties to get to the same solution.
Chris:
Data integration is fundamental.
Rhett:
I know it’s a collaboration, he’s singing the love language of collaboration man, with an operator you don’t, an operator should never work with an ILI vendor like they’re trying to prove them wrong, I always used to tell people the reason data analysts are so guarded is because half the time you only call them if they’re wrong, you gotta call you data –
Chris:
When am I gonna get my report and why did you call this? It was wrong.
Rick:
Or you called this, you realize where that is. You better change that call.
Chris:
Are you sure? Well not change the call. Make sure you’re right. Yeah yeah that’s so good.
Rhett:
Well I tell you what Rick we’ve really appreciated having you one. Thank you so much for for joining us and talking a little bit about what Integra’s doing.
Chris;
Everybody knows how to reach you so if y ‘all have got any questions. You’re looking for Rick D’Zione. I said it quicker every time I said it now. And thank you for joining us at PPIM 23 and to our audience out there. If you want to find Rick or Integra or any of the papers they published at PPIM, you can be on the lookout. They’ve got four of them there. Highly recommend you take a look at them. Thanks and we’ll see you in two weeks.
Rick:
I have a question about, do I get a cut out of myself later? Was that, is that coming, that can be shipped to me?
Rhett:
Second string, Sarah, we can talk about a cutout for Integra.
Rick
I don’t want him. With his signature.
Rhett:
I thought I ended the show?
