This week’s Pipeliners Podcast episode features Sam Acheson of PODS continuing the conversation about how the Pipeline Open Data Standard supports pipeline integrity and risk management.
In this episode, you will learn about the importance of using technology such as GIS to support integrity management, how to collect and organize location-centric data in a system, the difference between qualitative and quantitative risk assessment, and more. Plus, hear about exciting, futuristic technology that is in the works to support inline inspection.
PODS for Integrity & Risk Management: Show Notes, Links, and Insider Terms
- Sam Acheson is on the PODS Board of Directors and a principal, Integrity Management Systems, for ROSEN Group. Connect with Sam on LinkedIn.
- PODS (Pipeline Open Data Standard) supports the growing and changing needs of the pipeline industry through ongoing development, maintenance and advancement of the Data Model and Standards. PODS also serves as a member association to maintain the PODS Data Model.
- The Pipeline Open Standard is a database schema (architecture) for pipelines. It functions by creating populated database information relevant to the life-cycle of a pipeline.
- PODS 7 is the latest version of the Pipeline Open Data Standard, released in May 2019. [Members-only access]
- The PODS Association is a not-for-profit industry standards association that develops and maintains the PODS Data Model — the pipeline data storage and interchange standard for the oil & gas industry.
- PODS Lite is a FREE subset of PODS 7.0 and provides an opportunity to preview and evaluate the PODS 7.0 Pipeline Data Model capabilities and value.
- GIS (Geographic Information System) is a system designed to capture, store, manipulate, analyze, manage, and present spatial or geographic data.
- The Mega Rule is a set of new pipeline safety standards issued by PHMSA in October 2019 that brings 500,000 miles of pipeline under federal jurisdiction to ensure the safe transport of gas product.
- The Gas Gathering Rule (Safety of Gas Transmission and Gathering Pipelines) was initiated in 2016 when PHMSA issued a notice seeking comments on changes to the pipeline safety regulations for gas transmission and gathering pipelines. The proposed rule has advanced through various stages to expected issuance in 2019.
- Integrity Management (Pipeline Integrity Management) is a systematic approach to operate and manage pipelines in a safe manner that complies with PHMSA regulations.
- CFR 192 and 195 provide regulatory guidance on the pipeline transport of natural gas and hazardous liquids, respectively.
- HCA (High-Consequence Areas) are defined by PHMSA as a potential impact zone that contains 20 or more structures intended for human occupancy or an identified site. PHMSA identifies how pipeline operators must identify, prioritize, assess, evaluate, repair, and validate the integrity of gas transmission pipelines that could, in the event of a leak or failure, affect HCAs.
- MCA (Moderate-Consequence Areas or Medium-Consequence Area) are designated areas for gas transmission pipelines.
- ILI (Inline Inspection) is a method to assess the integrity and condition of a pipe by determining the existence of cracks, deformities, or other structural issues that could cause a leak.
- VTC (Verifiable Traceable Complete) or TVC (Traceable Verifiable Complete) is the ability to describe and follow the life of a requirement in both a forward and backward direction (i.e., from its origins, through its development and specification, to its subsequent deployment and use, and through periods of ongoing refinement and iteration in any of these phases).
- Asset Management includes the fundamental principle of risk-based management.
- POF (or PoF) is the probability of failure.
- COF (or CoF) is the consequence of failure.
PODS for Integrity & Risk Management: Full Episode Transcript
Russel Treat: Welcome to the Pipeliners Podcast, episode 105, sponsored by EnerSys Corporation, providers of POEMS, the Pipeline Operations Excellence Management System, SCADA, compliance, and operations software for the pipeline control center. Find out more about POEMS at enersyscorp.com.
Announcer: The Pipeliners Podcast, where professionals, Bubba geeks, and industry insiders share their knowledge and experience about technology, projects, and pipeline operations. Now your host, Russel Treat.
Russel: Thanks for listening to the Pipeliners Podcast. I appreciate you taking the time. To show that appreciation, we give away a customized YETI tumbler to one listener each episode. This week, our winner is Ash Titzer with CountryMark Refining and Logistics. Congratulations, Ash, your YETI is on its way. To learn how you can win this signature price pack, stick around until the end of the episode.
On this week’s episode of the Pipeliners Podcast, we continue our conversation about PODS with Sam Acheson and dive a little deeper into the use of PODS for integrity management and for risk management, and how that applies to Gas Gathering. With that, let us welcome Sam.
Sam, welcome to the Pipeliners Podcast.
Sam Acheson: Hey. Thanks, Russel.
Russel: Right now, as we record this, I don’t know when it’s actually going to go out on the schedule. It may be some weeks after the conference, but we’re still here at the PODS conference. Sam has come by, and we’re going to talk about PODS and integrity management and risk management. Sam, let’s start. Why don’t you tell our listeners about who you are and your background, how you got involved with PODS?
Sam: Yeah, that sounds great. I graduated from college back in 1994. At that time, I found this thing called GIS. I found that I had a pretty good knack for programming in it. I started my career off as a GIS software developer.
About 2010 timeframe, found my way into the pipeline industry due to the nature of how the pipeline industry is using and leveraging these GIS technologies. You can’t be in the pipeline industry, in technology, without being a part PODS.
Russel: You can. I’m in the pipeline business. I’m in technology. I’m only now becoming a part of PODS, so I guess it is possible.
Sam: You are opening my eyes, sir.
Russel: I deal in control systems. It’s a whole different world. Anyways, I’m just teasing. You’re right. PODS has a lot to offer. I’m really interested in this conversation about integrity management.
When we were talking in the last episode we did with Jackie and Christopher — we were talking about what PODS is and what it does — they made the comment, many times, that PODS is location-based. At its core, it’s a location-based database.
If I’m thinking about integrity management program, location becomes pretty self-evident. Would that be right?
Sam: I totally agree with that comment.
Russel: How would you unpack that for me? What is important about location as it relates to integrity management and risk?
Sam: First of all, you have the pipe location of physically where the pipe is in the ground. In a GIS, we call that the center line. Then, we get to describe how that pipe is made up and what equipment is on it, where the valves are, where the casings are. This starts to go into the makeup of what that pipeline looks like.
Then you take into account these other environmental factors. This is where the GIS really starts to come into play. You take into account these other factors of environmentally-sensitive areas. You can bring in soils information. You could bring in districts and structures and where houses and buildings are. You bring in other right-of-ways.
There is even a very strong component, as we think about this, to a “call before you dig” program and publishing of one call data as it relates to maybe third-party line strike. All of that really starts to play into that location component.
Russel: That’s when location starts becoming material as to risk, right? The thing to me that’s most compelling in what you said, a lot of people that do pipelining and do integrity management, they’re pretty clear about things like high-consequence areas and soon to be clear about things like medium-consequence areas.
As we’re sitting here today, it’s the 30th of September. The new rules coming out publish tomorrow. It’s going to have medium-consequence areas in it. All of that is fairly straightforward.
When you start talking about third-party dig records and integrating that into a risk analysis or an integrity management program, to me that’s fairly compelling. I know some of the major incidents that we’ve had in the past at least had some aspect of a third-party line strike to them.
Sam: I totally agree, and you probably couldn’t get a more emphatic agreement from a self-described geo nerd.
Russel: A geo nerd [laughs], it’s a new kind of Bubba geek.
Sam: It is.
Russel: We should get t-shirts.
Russel: When you’re looking at PODS, how does PODS play a role in integrity management? PODS is just a data standard, but how does PODS play a role in all this?
Sam: My mind, for some reason, when I start to characterize questions like that, I think in threes. First and foremost, that location standard, that location core that you mentioned. We know where stuff is.
Then when you place those, for example, the defining characteristics of the pipe, the wall thickness and the diameter, and you couple that with the location, you really start to unfold a bigger picture on the pipe itself.
As you have the ability then — in that second part or my second part of my answer there — with the standard nature of PODS in that PODS has described a way or defined a way that you describe your pipe characteristics.
It’s defined a way that you describe your valves and whatnot, but the other thing that PODS does a little more indirectly is it gives you that foundation on how to extend it, and use those PODS core best practices for extending.
For example, if you want to track other information on your pipe like those third-party line strikes, you have a template inside of PODS that you can extend that model and do so, if you’re not going to use what’s already provided inside of the PODS model.
Then the last part of that is, and maybe an extension on what I just said, is a place to then put the results of that analysis after you’ve performed it. If you think about…
Russel: Is PODS someplace I would store the results of my tool runs?
Sam: Of course.
Russel: What I know about tool runs, again, [laughs] what I know about tool runs I learned by doing this podcast with a guy named Mark Lamontagne when we did a whole series. He’s as PhD engineer in inline inspection of pipelines, the guy is brilliant. What I learned there, is that the nature of that data is unique.
Normally what the operator is working with is some level of analyzed data that comes from the vendor after they run the tool. They’re not really working with the raw data, because that’s more unique to the vendor, right?
Sam: Yeah. First and foremost, I think that you’ll hear those tools vendors will push back a little bit to the term raw data. They call it signal data. That signal data, absolutely, and if you think about the volume of that data on the line, and how much of that data really gives you good value towards managing that asset, you want to have something that’s post processed, and want something to give you that three-dimensional profile of that, what I call a linear asset, that pipeline.
Russel: Do the vendors give you the post-processed data in a PODS format? Is that even possible?
Russel: You work for Rosen, I could say, “Rosen, I want you to run a tool for me, and when you’re done, I want you to give me the feature report, and the analysis, and all that. I want that in a post-processed form, and I want that so I can load it straight into my PODS database.”
Russel: Why would anybody do anything different than that?
Sam: There’s a couple of reasons. I think first and foremost, maybe just naivete. They might not know that they can ask for that information and not get it. Secondly, I think that when you think about the volumes of that data, even the processed ILI data, you’re still talking about tens or hundreds of thousands of records per line.
Then, when you think about recurring inspections on the pipeline, and you think about companies that are managing multiple pipelines, you have millions of records of just ILI data that are coming into that. The managing and the controlling of that data can seem a bit daunting as it relates to those.
Russel: Yeah, I know, that makes sense to me. That’s what I do know about ILI data sets, they’re enormous. They’re not just big, they’re enormous.
Sam: You know it’s funny, and not to segue, I apologize, but we’re working with a big data group right now, and they actually call us medium data.
Sam: When you talk about Facebook and tracking their hits that they get from different people around the globe every second, and then you just talk about our millions of records of data, it’s really put in perspective.
Russel: Oh my gosh.
Russel: That’s…okay, now I’m a little befuddled, because I’ve seen ILI data sets, and I think of those as pretty big data sets, but I guess when you start thinking about something like…Wow. Big data’s relative, I guess is the point.
Sam: It’s relative. The other thing I would like to say, though, as it relates to PODS and what the PODS organization has done in developing this PODS 7 model, is really created an architecture that will scale with those data volumes much better, and this is way above my paygrade in the PODS organization, but people that are…
Christopher Moravec, who was on the previous conversation, is much more technically and intimately involved in those decisions than I am. However, has taken the knowledge and experience from previous PODS model and seen where those volumes of data have become problematic and really corrected that in this new model moving forward.
Russel: Okay, cool. Kind of getting back to what we’re here to talk about, I could do a whole 30-minute — I could do a lot more than 30 minutes just on data, and data sizes, and all of that. To me that’s a really fascinating conversation. I don’t know that it’s useful.
Sam: For a podcast for us.
Russel: [laughs] We agreed to talk about, but that happens sometimes. Anyway, so kind of coming back. We’re talking about you’ve got PODS, it’s location-centric, we’re getting all this information about the pipe and about the things around the pipe, and then we can use that to build a risk construct around all that.
Then I’ve heard you guys talk several times about bringing the data together through asset management. What is asset management? How does that play into this?
Sam: I’m going to be just a little bit different there, I’m just going to call it bringing it together through data management. Asset management in my mind really starts to think about the accounting side of the business, and we just want to bring data that we want to use for our analyses.
First and foremost, I want to qualify this and say I am not a risk engineer. The people that are maybe listening to this that are, I’ve got a couple of years of experience in working with smart people like that, but I am not one.
Russel: Just a side note to anybody who actually is a risk engineer and is listening to this conversation, we’ll be linking up Sam’s contact information, and feel free to comment on his LinkedIn page or whatever, so that we can continue the conversation even outside the podcast.
Sam: Sounds good.
Russel: I wish you could see Sam’s face right now, got a big old grin like, “Yeah, that’ll be fun.”
Sam: That’ll be fun, yeah.
Russel: Sorry, go ahead.
Sam: That’s all right. What I do know about risk is first and foremost, as these algorithms go from a simple question and answer form on a fully qualitative risk analysis and start getting into these quantified analyses, you can start to take more finite parts of the line and you can actually start to take absolute data from what’s surrounding the line.
For example, if you have the results of your inline inspection run, and aligned to your pipeline and loaded into your PODS data model, you may see an excess of a certain type of corrosion, or maybe you see external, some dents or something, against the pipeline along the top side of the pipe.
As you start to bring that together with things like maybe right of ways, you might see that maybe you have AC-induced corrosion because you’ve got transmission lines that are close to your pipeline, or because you’re close to maybe an area that’s going through some development right now, and these third-party line strikes, you have back hoes hitting your line.
You can really start to cater the response and how you react to the threats against the pipeline based on being more quantified about the data you’re pulling into that risk algorithm.
Russel: I’m processing what you’re saying. Said another way, if I understand I have a particular feature, I’ve got a corrosion. I need to estimate the rate of growth of that corrosion, well then the fact that there’s transmission lines in the area could impact how I prioritize that mitigation.
Russel: Likewise, if that is also in a transmission right-of-way that’s adjacent to a neighborhood, that could also prioritize how quickly versus in a transmission right-of-way that’s running through farmland, and there’s nothing close within miles.
Russel: That could also likewise impact how I prioritize the mitigation for that finding, if you will.
Sam: Absolutely, and for anybody who plays in this risk world knows that your two factors that you’re driving to that total risk score is the probability of your failure, and the consequence of that failure. Those threats against the pipeline, and then what happens if it does fail. In those examples, you can also use risk to prioritize the order of those responses.
Russel: Right, exactly. Risk is one element, there’s other elements, too, about operational constraints and other things. I think the thing that’s interesting to me about this is that by being able to get the data together in one place, being able to have it all in a standard way, I could become more quantifiable versus qualitative.
We probably ought to talk about what is qualitative versus quantitative versus probabilistic. Probably ought to unpack that for people. What’s the difference between those three things?
Sam: Again, I feel like I’m just dipping my big toe into this pool, and being interviewed on it here. A qualified risk assessment is literally a question and answer form that you ask specific questions about the pipeline assets, the pipeline integrity engineer can respond to those questions, and then you’re able to relatively prioritize…
Russel: I would say it’s engineering judgment. Qualitative is engineering judgement. I’m looking at the data that’s available to me, and I’m using my experience, and my education, my training, and my understanding of the standards and company police to make a judgement. It’s qualitative, but it’s not quantitative.
Sam: Exactly. Then as you start to get into the quantitative, many shades of gray in that quantitative risk assessment, where you can still partially ask questions and as you say, use that engineering judgement as it plays into that algorithm, but then start to do — for lack of a better term — quantify some of those inputs and the variables.
Russel: You should never take the qualitative out, right?
Russel: You should always be looking at that as one aspect of the decision-making, but the quantitative supports it.
Russel: You want to make sure that, well, what my engineering judgment is telling me lines up with the facts that I’m able to determine. To the extent I get more facts, then the better job I can do that. Then what’s probabilistic?
Sam: Well now…
Russel: We probably need to get a risk engineer in here to answer this question…
Sam: We do.
Russel: …because we both…We’ll give it a try anyways, we’ll give it a whirl.
Sam: In my mind when you start thinking probabilistic is when your risk algorithm is advanced enough, the data that you have going into that algorithm is truthed enough and quantified enough that you can actually start making forward decisions about that pipeline. Not managing the pipeline reactively, but actually start managing it proactively.
Russel: Can you give me an example? Something that you could anchor to, maybe something that you’ve done in the past?
Sam: Boy. No.
Sam: Fair answer.
Russel: As you start getting more technical, as you start getting more geeky, if you will, in these conversations, I find that it’s often helpful to try to come up with something that…
Sam: For an example, and again, anecdotal, and in people that I work with, and in pushing as much of the credit for any of these examples to those people as possible, but if you are looking into measuring the effects of population growth as it relates to the right-of-way that you’re pushing your pipelines through.
You may get advanced knowledge that an area has been zoned and everybody loves to use the example that they put a Walmart up on my pipeline, right?
Sam: At that point, you can preemptively go in and maybe start planning years down the road for a reroute…
Sam: …so you are not driving into some of the challenges that are coming around that Walmart. The other thing that I can think of is trying to get ahead of some of the corrosion factors that are going into play.
Russel: What I’m trying to do is forecast a future state fairly long range.
Russel: So that I can prioritize investment in a multi-year capital campaign, or multi-year O&M (operations and maintenance) campaign for addressing my overall integrity management program.
Sam: Exactly right.
Russel: I’m looking at well, where is population growing? What’s going on with the weather and soil conditions, and how’s that affecting my pipe? I’m here now, but where am I going to be next year, the year after that, the year…Starting to look at a three-year program.
Russel: Try to get in front of it.
Sam: One of the things that I have been able to appreciate in the contributions I’ve given to some of these projects, is and again, trying to bring this back to maybe our conversation and PODS, in general, is when you have that unified consolidated location to store that information around the pipeline itself, then you can start to draw on that from that common unified location.
Russel: Yeah, we were actually, after the conversation with Christopher Moravec, we actually were talking about big data, and data analytics, and one of the other podcasts I had recently done, because we’re geeks and that’s what we talk about when we’re not talking about something else.
One of the things that I think a lot of people don’t understand about this data is that to do AI, artificial intelligence, to do predictive analytics, first requires an accurate, well understood, consistent data set. It’s only with that that I could do any other analysis.
Russel: What I guess what we’re saying here is that PODS can create a foundation that will support that analysis in a consistent way.
Sam: I am typically quite the pessimist when people talk about, “Oh, another new data model, all of our problems are solved.”
Sam: Like I said, I’ve been doing this for 26 years, and in this industry for almost a decade, and I have seen operators struggle with the exact same challenges year over year, vendor over vendor, PODS release over PODS release, and it is a daunting challenge to take on.
Where I get a little bit excited about what PODS has done with this PODS 7 model, as opposed to and talk about getting nerd, about half your listeners are going to turn off right now, but earlier versions of PODS were really focused on this event-driven core.
As you added things to your pipeline database, this event core got bigger. At a certain point, we started breaking this event core. What happened, well, you started selectively pulling data out. Maybe I’m not going to be worried about my corrosion anomalies that are less than 20 percent, because that’ll pull 60 percent of my data set out, and now I can manage that data that’s left in my PODS model.
Russel: You just said a mouthful right there, because that makes perfect sense, and yet all that data has value.
Sam: All that data has value.
Russel: Particularly when you’re trying to do something probabilistic and forward-looking.
Sam: Absolutely. If you think about the simple concept of corrosion growth rate mapping. You want to align that data to your line, which you do inside of PODS. You want to get as much of that data into that data model as possible, and then wait for Years Two and Year Three of those inspection intervals to happen again.
Then you can start to look at that thing and if you see two data points that show 10 percent growth, eh, maybe that’s showing that you have some corrosion. If you show three data points that show 10 percent growth inspection over inspection, you might actually have more of a problem than you think you have.
Russel: Right. There’s certainly value in knowing what’s happening over time. There’s also value in saying, “Well, here’s where I think I’m going to be when I run the next tool.” Then run the next tool and see where you really are.
Sam: That’s exactly right.
Russel: That kind of analysis is really valuable as well. Let’s talk about the analysis itself. The thing that I’m doing in an integrity management program, I’m trying to continually calculate the useful life of my pipe.
Sam: I would even argue to extend the useful life of that pipe through intelligent inspection activities, whether that’s inline inspection or direct assessment where you can remove areas of the pipe that’s remaining life has expired, and then extend the life of the rest of the pipe that you’re managing. But, I apologize.
Russel: No, that’s exactly where I was going, you’re kind of reading my mail there. The thing that’s really fascinating to me is about how much information we can collect on a stick of pipe.
We know now, given the rules and how we manage pipe, we know everything about it, and we inspect it when it comes out of the mill, we inspect it again before it goes in the ground, and if we ever do a dig, we dig that hole segment up and we inspect the whole thing again. There’s three detailed inspections of the pipe…
Sam: Then look at what’s happening with the inline inspection technology that’s happening as well.
Russel: Oh, yeah.
Sam: Every time you run that thing, you’re getting that 3D profile of that…
Russel: You’re getting the larger signal concentration, and you’re putting multiple signals on a tool. Where before, you ran one tool and you got one signal. Now you run one tool, you get three or more off a single tool. Lots and lots, lots and lots more data coming.
Sam: New signal technology that’s being developed. It’s mind-boggling. You’re absolutely right.
Russel: Somebody’s going to figure out how we can walk down the center line of a pipe wearing a set of 3D goggles and see everything that’s going on in the pipe. Then somebody else is going to figure out how to put cameras and we’ll actually be watching the pig run. All that’s coming, right?
Sam: Oh, yeah. I love the augmented reality guys at all the trade shows, because the people that walk up to the booth, put the goggles on, and then look funny, but that’s a different conversation, too.
Russel: You don’t want to know what I think about augmented reality right now. It’s a technology looking for a market. It’ll find one, we’re just not there yet. Anyway, so how does PODS support the actual analysis.
Sam: Great question. When you configure, and if we’re talking more specifically here about risk, however we can extrapolate this to different types of analyses that can happen. If we think about risk, you have these quantified risk models that are looking for data, you have captured either through the PODS model what’s happening on the pipeline or the pipeline itself.
You can actually then because of that location core attach that PODS model to its spatial surroundings, so you can grab the soil types that are there, you can actually get an assessment and slope and be checking for geotechnical threats against the pipeline.
You then start to bring in this more complete picture of it, you execute these risk analyses, and then the results themselves if you think about what I’m going to call the linear reference nature of a PODS model as it relates to that GIS, you can actually save those results back in that same linear referenced fashion.
You can get down to a very specific join of pipe that you have given a dollar amount risk score to, based on that POF and COF that you’ve calculated.
Russel: Let’s kind of wrap this up. I want to talk about what’s the state of the industry around integrity management and risk management, and how is PODS trying to react to that. I’m asking specifically about the regulatory environment, and what’s going on around risk management programs for integrity as it relates to the regulatory environment.
Sam: That’s a great question, and this is the part I could stick my neck out a bit far. I personally, and this is my personal opinion, I feel that the industry is a very prescriptive and very mandated reactive approach right now.
I don’t think that we are using data models in the vein of what PODS can organize and what it can structure. I don’t think that we are leveraging risk analysis for everything that it can do, because we are so focused on meeting the needs of the regulations.
I am not making a qualified statement to the value of the regulations, but I think that we could take this further. We could actually be managing the pipelines more proactively if we embrace the value of the data, the value of the analysis, and how we value the results.
Russel: I actually have a little bit different take on that, and like you, this is way outside of my core expertise, so I’m out on a thin limb. I think one of the challenges is that we are so bound-up, I mean integrity management’s a big deal, and we’re so bound up in the policies and practices that we have and just getting the work done, that it’s very difficult to modify that work process not to mention, extremely risky.
Probably the most risky thing you ever do in a pipeline operations is anything that you change in your integrity management programs. That’s a big deal. I think that the resistance is more around those things than the regulatory oversight.
Sam: That’s good insight, and good input.
Russel: I could be completely wrong, and you could be completely right. Who knows, right? We could both be right. I think there’s truth in both perspectives. I do think the idea of getting the data normalized and getting the ability to apply some of this algorithmic approaches to the data, we do have an opportunity for quantum improvements in the safety of the pipeline systems in the U.S.
Sam: I absolutely agree.
Russel: I think what the regulators are trying to do, and I know some regulators, I think by and large what they’re trying to do is help the industry get better. That doesn’t always feel that way.
I did a recent episode with Dan Scarberry. He talked about this from a control room perspective. Very enlightening, his take on the regulators and what happened when he changed his mindset about how to interact with the regulators.
All that being said, I do think that we are being gently nudged, maybe not so gently, towards things that are more quantitative and more probabilistic. That’s in the best interests of industry.
I would say also that because of the risk associated with changing one of these programs, it requires the regulator involvement to actually move the needle. I don’t know if we could move the needle without their nudging.
Sam: We are seeing, from a regulatory perspective, a push in more of a probabilistic direction. I am fortunate enough to work with some really, really smart risk engineers. One of the things that I have learned from them is if anybody can appreciate the risk in change, it’s somebody who is calculating risk for a job, right?
Russel: Yeah, exactly.
Sam: One of the things that I have learned is there is a way to minimize the risk as it relates to the integrity management program by providing a risk algorithm that gives you the results that you’re looking for while in turn extending that model to think in that more probabilistic fashion.
Russel: That’s true. That’s coming. We’re going to see a lot of development in that domain in the near term. There’s a lot of interesting work going on in that domain, but there’s a big difference between having a technology that works and having a technology in practice. We’ve got a ways to go to get it in practice, for sure.
Sam: I agree. If I can just say one more thing, I would say that the one thing that I do value and that I would implore on anybody that has influence inside of their organization, the importance of capturing and maintaining this data in a structured and consistent — you hear TVC all the time (in a traceable, verifiable, and complete fashion) — is only going to enable you to extend those abilities downstream.
Russel: That’s certainly where we’re going, without a doubt.
Sam: It is. It’s a hard pill to swallow when you’re looking at budgetary dollars, but the importance of it is huge.
Russel: Sam, thanks for coming on. I appreciate it. If somebody wanted to get in touch with you and ask questions about PODS and how to apply it for an integrity management program, how would they reach out to you?
Sam: I would say the best way to reach out to me is through my LinkedIn profile. I could be found there.
Russel: We’ll link that up in the show notes. Thanks. Appreciate it.
Sam: Thank you, sir.
Russel: I hope you enjoyed this week’s episode of the Pipeliners Podcast and our conversation with Sam. Just a reminder before you go. You should register to win our customized Pipeliners Podcast YETI tumbler. Simply visit pipelinerspodcast.com/win to enter yourself in the drawing.
If you would like to support this podcast, the best way to do that is to leave us a review. You can do that on iTunes, Apple Podcast, Google Play, whatever smart device podcast app you happen to use. You can find instructions at pipelinerspodcast.com.
Russel: If you have ideas, questions, or topics you’d be interested in, please let me know on the Contact Us page at pipelinerspodcast.com or reach out to me directly on LinkedIn. Thanks for listening. I’ll talk to you next week.
Transcription by CastingWords