The Pipeliners Podcast is excited to kick off a series of episodes with Giancarlo Milano of Atmos International. In the first episode of the series on leak detection, Russel Treat and Giancarlo discuss the real-time transient model for leak detection.
In this episode, you will learn about the technical details of leak detection, the latest technology that supports RTTM, and the resources that help pipeline operators implement, maintain, and diagnose using the real-time transient model.
In the next episode of the series, Russel and Giancarlo will discuss the statistical volume balance model as a leak detection approach and how it differs from RTTM.
RTTM Model for Leak Detection: Show Notes, Links, and Insider Terms
- Giancarlo Milano is the Senior Simulation Support Engineer at Atmos International. Connect with Giancarlo on LinkedIn.
- As part of this series with Giancarlo, enter to win our book giveaway contest for the “Introduction to Pipeline Leak Detection” by Atmos founders Michael Twomey and Jun Zhang.
- Read this blog from Atmos on The Challenges for Effective Leak Detection on Large Diameter Pipelines.
- Leak detection systems include external and internal methods.
- External methods are based on observing external factors within the pipeline to see if any product is released outside the line.
- Internal methods are based on measuring parameters of the hydraulics of the pipeline such as flow rate, pressure, density, or temperature. The information is placed in a computational algorithm to determines whether there is a leak.
- The Real-Time Transient Model (RTTM) simulates the behavior of a pipeline using computational algorithms. The model, which is driven by the field instrumentation, monitors discrepancy between the measured and calculated values potential caused by a leak. RTTM uses flow, pressure, temperature, and density among many other variables.
- Tuning or optimization is the process of analyzing the results from the model and the results from the instrumentation to verify the accuracy of the data from the field.
- Robustness captures the ability of the leak detection model to perform under a range of scenarios that happen in real-world operating conditions.
- API 1130 is a recommended practice published by the American Petroleum Institute and incorporated by reference into the U.S. pipeline regulations in 49 CFR 195.134 and 49 CFR 195.444 for how pipeline operators should design, operate, and maintain their computational pipeline monitoring (CPM) systems.
- API 1175 is a recommended practice published by the American Petroleum Institute addressing how pipeline operators should maintain their leak detection program. The goal of the standard is to have the best leak detection system possible by always looking for continuous improvements to the individual LDS components achieving operational buy-in with the culture, strategies, KPIs, and testing.
RTTM Model for Leak Detection: Full Episode Transcript
Russel Treat: Welcome to “Pipeliners Podcast, Episode 24.”
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. We appreciate you taking the time, and to show that appreciation, we are giving away a customized YETI tumbler to one listener each episode. This week, our winner is Jessica Webb with Energy Transfer. Congratulation, Jessica. Your YETI is on its way.
If you’d like to win this cool customized Pipeliners Podcast’s YETI tumbler, simply visit pipelinepodcastnetwork.com/win and enter yourself in the drawing. Giancarlo, thanks for being on the Pipeliners Podcast. We appreciate you coming back.
Giancarlo Milano: Thank you very much for having us, Russel. My pleasure to be here.
Russel: Thanks. I wanted to say thank you to you and to Atmos International for agreeing to do this series. This is the first time we’ve done something like this. This will be the first of a four-part series on leak detection.
We’re going to try and drive into some of the technical details in more depth than we might otherwise do. Atmos has also agreed to do a book giveaway. We’ll be telling you more about that at the end of the episode. Again, thanks to you, Giancarlo, and thanks to Atmos International for supporting the podcast.
Giancarlo: You’re very welcome. Also, thank you for the listeners out there to hear what we have to say about leak detection.
Russel: With that, let’s dive right in. Can you tell me in layman’s terms, what is a Real Time Transient Model?
Giancarlo: When we are talking about a Real Time Transient Model to do leak detection, this is pretty much when we configure a system to simulate what is happening inside the pipeline based on assumptions that are driven from instrumentation data.
When we’re configuring a Real Time Transient Model, this could be done either offline, which is driven from values that we manually enter. This is typically configured during situations where a user wants to know what would happen during a certain pipeline or operating conditions, but they’re completely offline. There’s no link between the model itself and the real world as far as a specific operation, other than the user entering the parameters that he wants to know about.
Then the next step, which will be when we use an RTTM system for leak detection. This is when we take that hydraulic simulation and we convert that simulation from an offline system to an online system. What that means exactly is that the model itself that has been designed and configured for a specific pipeline segment will actually be driven from the values that originates from the field. Instrumentation values such as flow rate, pressures and temperatures, maybe density and viscosity for liquid lines or gas composition for gas pipeline.
This information would be used to hydraulically calculate the behavior of the pipeline, either during steady state, or during transient conditions as its name refer to of RTTM, Real Time Transient Models. The model will be running in real time. It will be measuring the values from the field. It will be calculating what the flows and the pressures at different locations of the pipeline would be.
Once we get to this step, the leak detection part comes in from the point of view that these values are being measured. They’re used to calculate what’s happening inside the pipeline. The results of that simulation would actually be used with some specific measurements from the pipeline to determine whether there’s a leak in the line or not.
When there’s a difference between the simulated value and the measurement values, that’s when the system decides whether there’s a leak in the pipeline or not. For RTTM systems to determine this, there’s actually two methods.
There’s the method where, let’s say a flow rate is compared at a specific location. If there’s a difference of a certain threshold then a leak alarm is declared. There’s also another approach where it’s more of an inventory calculation, where the model is calculating what the inventory of the pipeline is. In the presence of a leak, the inventory will drastically decrease. If the inventory of the pipeline, when it’s compared to the inventory calculated by the model differentiates by a certain amount and it goes above that specific threshold is, then a leak alarm will be declared.
Russel: I’m going to try to unpack this a little bit. Tell me if you think I have it right.
Giancarlo: Sure thing.
Russel: I try to think about technology like this, Giancarlo, in terms of the history. Hydraulic models have been around a long time. We were doing hydraulic models in the ’60s and ’70s on mainframe computers in Fortran.
Giancarlo: They have definitely been around for 40 years now.
Russel: What has happened is the technologies improved. Somebody had the brilliant idea that, “Hey, I can run this in real time and all the time and over and over again. Oh, by the way, I can compare it to what I’m actually reading from the live telemetry, from the instrumentation. That’s going to give me an indication of is something not working the way it should.”
Did I summarize that in an accurate way?
Giancarlo: Yes, that is very accurate.
Russel: Moving on and diving in a little deeper, I know that API 1130 has an Appendix C. It gives four criteria for which to evaluate various approaches to leak detection, particularly Computer Pipeline Monitoring type approaches to leak detection. One of those things is sensitivity. How sensitive can a Real Time Transient Model be?
Giancarlo: RTTM systems can be configured to detect leaks pretty much as small as one percent under the right conditions of course. The problem with an online system is that it takes quite a lot to make sure that the results from the model are as accurate as possible.
When you think about it, when we’re looking at the sensitivity of the system, we’re looking at what’s the smallest leak that we could actually detect without generating many false alarms?
In order to make sure that we’re able to detect that small of a leak, we need to make sure that the calculations from our simulation model are as accurate as they can possibly be when comparing that value to the real pipeline operation. There’s a lot of parameters that need to be taken into consideration in order to simulate a pipeline accurately and get a good result.
That includes information from the pipeline physical properties, external diameters, wall thickness, topology of the line, and also has a lot to do with the product that we’re moving. Whether it’s a single product or it’s changing. For example, let’s say crude oil, which has a standard set of parameters, but it could vary from well to well or from different tanks or originators.
It could change slightly in its parameters. All of that needs to be taken into consideration. In order to make sure an RTTM can be as sensitive as possible, we have to make sure that all of this information that we’re using to configure the model as close to reality as possible. That will really get us to the one percent under the right conditions.
Russel: You’re making an excellent point when you talk about the right conditions. I’ll just use one very specific example that I think most people will understand. The math of the model assumes that the numbers are correct. Anybody who works around instrumentation knows that instrumentation doesn’t give you an exact number, it gives you an approximate number depending on the inherent accuracy of the instrument that I purchased and the level of maintenance and calibration I’m performing on the instrument. Sensitivity in an RTTM is very much related to the quality of the data that’s been fed into the RTTM. I think that’s the point you’re making.
The other thing you talked about was false alarms, which Appendix C to 1130 talks about as reliability. That is the ability to detect a leak without generating false alarms. How does that relate to a Real Time Transient Model?
Giancarlo: You’re correct. When we talk about reliability, it’s how reliable under the right conditions is the system to make sure that the leak is detected? When it comes to RTTM, all leak detection systems are prone to false alarms to some extent.
When it comes to RTTM, it’s more to do with during changes in operational conditions, such as pump starts or stops, or maybe change in the set point for flow or pressure control in the pipeline, something that’s going to cause a surge either on an injection or a delivery point.
As that surge travels through the line, that transient moves through the system, that’s going to cause upsets and difference in the measurements, which could give some false alarms. I would rank the reliability of an RTTM system at something medium to high.
It could be reliable, but we need to make sure, or the users that are using this type of system need to make sure that these models are tuned and optimized in order to make sure that they have a low false alarm ratio, and that they’re able to maintain or continue to do leak detection during these conditions.
There are some RTTM systems that tend to decrease their leak size [increase the size of the detectable leak] that they’re looking for, or maybe desensitize the leak detection system all together during transient condition. That’s definitely something that you want to try to avoid. When you’re having operational transient conditions, that’s when your pipeline is more subject to having a leak due to a drastic change in pressure.
A user needs to make sure that this type of system is working properly not only during steady states, but also during transient conditions, while they’re still being able to detect small leak size and not generating false alarms.
Russel: If I were going to tell the listeners what’s the one key takeaway in this conversation, and I think this relates to any leak detection approach, is that there is a trade-off between sensitivity, detecting a smaller leak and detecting it faster, versus reliability, meaning, a low level of false alarms.
Giancarlo: Yes. It definitely really is. I’ve visited many customers during my career where customers wanted to be able to detect the smallest possible leak size. They’re looking for the itsy bitty, tiny, small leak on their pipeline, but they’re not realizing that the smaller this number is, the more false alarms they’re going to be subject to during transient conditions. It’s something that applies to really any technology. It is something that the user needs to take into account.
The lower or smaller the leak size that you’re looking for, the more prompt you’re going to be to false alarms, and the more maintenance and accuracy, as far as the optimization of the system, is that you need to give it in order to make sure that these alarms are not generated, because the system is able to cope with every single transient operation that occurs in the system.
It’s highly important that the user has a broad view of what’s the most important aspect of a leak detection system. Yes, you want to be able to detect small leaks, but at the same time, you don’t want to create false alarms where the operator loses confidence on the leak detection system, and just ignores those signals.
Russel: That’s a really, really key point, this idea of losing confidence, because anybody that thinks about this will understand that if I get an alarm or a notification and it’s meaningless, it doesn’t take very long for me to start ignoring it.
Russel: What you want is a clear signal there’s something wrong, and when you get that clear signal for it, to be right. That’s not easy to achieve as we’re talking about here.
Let’s move on. You mentioned gas and liquid, you mentioned crude oil versus products. All of these different pipelines have different behaviors, if you will. Different technologies might make better sense for leak detection on different kinds of processes. Where do you think RTTM is best applied?
Giancarlo: You are correct. There’s not one leak detection technology that applies to all the pipelines in the world. That’s just unfortunately not the way it is. Every pipeline behaves differently, and it’s going to benefit from different technology.
In my opinion, I do believe that RTTM systems tend to be more useful on pipelines that are, let’s say, gas pipeline networks, maybe a gas distribution or gathering systems form the gas perspective, because the hydraulic model has a potential to very accurately model the inventory of the products.
That is something that other technologies have a little bit of difficulty with, keeping track of a good, accurate number of what the inventory of the line is. In my opinion, RTTM could work very well for gas systems provided that they’re tuned and optimized to the best of their abilities.
Russel: Would it be fair to say, Giancarlo, that the complexity of the pipeline network itself tends to make it work better with an RTTM? When you say gathering and distribution, I’m thinking about a complex network versus a straight run of pipe.
Giancarlo: I wouldn’t say distribution or a gathering network itself that it’ll work best in those conditions, but more towards a gas system because of the compressibility of the gas in the pipeline. The ability that the RTTM will have to keep track of that inventory change as transient operating conditions start occurring at either a single injection or multiple injections or delivery points.
An RTTM system will be able to track how much volume is kept inside the pipeline. For that particular reason, it’ll better equipped to react to leaks and maintain a more reliable result for this particular type of application.
Russel: It’s interesting that you mentioned that because it’s bringing to mind a project we did a number of years ago, where we put in a gas pipeline SCADA system. It was a line coming off of a LNG gasification plant.
One of the things they were very concerned about in operating that pipeline was pressure ways — or pressure effects — and temperature effects, because the LNG’s very cold, and when they gasify it, it’s very high pressure. They wanted to try and mitigate that pressure and temperature before it hit the end of the gas line.
They used the RTTM to forecast that pressure wave down the line. It was an interesting application, but fairly complex to get that in and make it work reliably.
Giancarlo: Yes. RTTM type of systems, they’re not just used for leak detection. They could also be used for look-aheads and what-if scenarios. In gas systems, it could be used for determining the survival time of the pipeline based on the packing and unpacking operation. It’s definitely something that RTTMs could benefit an operator with.
Russel: There’s other value to hydraulic modeling in Real Time beyond just leak detection.
Russel: That’s probably something maybe a lot of people wouldn’t realize unless they’re involved in that kind of operation. That’s a good segue. What does it take to get an RTTM or Real Time Transient Model installed?
Giancarlo: The first thing that you’ll need is to make sure that you have all the key information to build the model. You have to make sure that you take this pipeline from the physical to the virtual environment.
What you want to do is simulate every single aspect of the area where you want to do leak detection. You have to make sure that you have as close as reality as possible of every input that you’re putting into the model. That would be the pipeline details, sections between each block valve. What’s the external diameter? What’s the wall thickness? What’s the typology? Is it going through a very steady plane or is it going through a drastic elevation change?
You’ve got to make sure that you have very detailed information of where the pipeline is moving across. You also need to make sure that you have accurate information on the product itself. What are the characteristics of the products you’re configuring? Whether it’s liquids or whether it’s gas.
When it comes to liquids, you’ve got to make sure that you know what the viscosity of the product is, what the density of the product is, specific gravity, thermal conductivity. There’s quite a bit of parameters that you’ll need to know for the product that you’re trying to model. If any of these parameters changes by a little bit, that’s going to affect your results.
Then, when you’re comparing your hydraulic simulation results with real values, then you are going to have a discrepancy which could throw off your leak detection system. You need to make sure that all these values that you’re fitting into the model when you’re virtualizing the pipeline are as realistic as possible.
Something else that is also very important is the location of the instrumentation. When you’re taking the pressure meter at a particular location and you’re telling the model that the pressure at this particular location has this value, which is the value that is being read from the field, you have to make sure that that pressure meter is at the exact same location in your model.
If it’s not, then that’s also going to throw off your results. Additional information that could be beneficial for the models are pumps, block valves, or control valves curve. That information is typically used for the purpose of knowing how much pressure drop or pressure boost you have or are expected to have across a station at a particular location. The model will be able to calculate for this.
Once you have your model built, the next process is to optimize it. Obviously, you or your operator is going to do its best job to make sure the numbers that they’re inputting are the best possible numbers. There’s always going to be an error.
When that error comes in, we start the process of what’s called a tuning or optimization of the model where we are analyzing the results of the model with the results from the instrumentation.
We’re making sure that the model — when it runs through various operational transient conditions — is giving us the exact same results as the numbers I would see from the field instruments. Once we tuned the model, then we’re able to start using that model for leak detection.
Russel: One of the things that people may not understand, but again it’s implied in what you’re telling us here is that it’s not only getting it in, but it’s maintaining it. So, it means anytime I change out a pump and the pump curves different, anytime I make a modification to the physical configuration of the pipeline, anytime I start moving a product with a different set of properties around viscosity, density and so forth, anytime any of those changes occur, I need to be making that modification in the model so the model continues to operate well.
Giancarlo: That is correct. Even parameters such as ambient temperature where a section of the pipeline is exposed, not buried, exposed. The ambient temperature could have an effect of how the pipeline behave. If that’s not taken into consideration, then your simulation is going to be off. That could throw your system off and give you some false alarms.
That’s one of the aspects of RTTM. In order to make it a successful system, which it can be, you need to make sure that you have someone in-house that knows how the RTTM technology works, that knows your pipelines in and out, as well as the day-to-day operation and how it changes.
It requires that someone is on top of that system to make sure that it’s always accurate and it’s been tuned and optimized for every new operation that occurs.
Russel: Let’s continue this conversation about the four criteria out of API 1130. One of the things we hadn’t talked about yet is robustness. My understanding of robustness, simply stated is, it’s the ability of the leak detection to perform under a range of scenarios that might happen in the real world. How do RTTM stack up as it relates to robustness?
Giancarlo: Change in conditions, pipeline operations. As we were mentioning earlier, when you have an RTTM system and it’s going through various operating conditions, we have to make sure that we tune the model for the various conditions that the pipeline is experiencing.
The robustness of an RTTM, it could be high, provided that there’s someone that’s sitting on top of it to make sure that that particular model is tuned, optimized the various conditions that is seen on its day-to-day operation.
If there’s a small change in the operation, something that the RTTM system has not previously seen or hasn’t been tuned for, the RTTM system is going to be subject to provide false alarms during those particular situations.
Again, it’s something that applies to every system. When we see a new operation, we want to make sure that the system is working under those particular conditions. We classify robustness as being medium high when it comes to RTTM systems, as there’s going to be someone involved to make sure that’s taken care of.
Russel: I think again, the thing that I would encourage the listeners to take away in this conversation is it’s not just about the technology. It’s about the effort in systems organization around the technology to make it perform. The technology by itself, it’s not a fire-and-forget kind of thing.
Giancarlo: Two things just to follow up your comments. One of them, just talking a little bit back to robustness, another aspect would be the ability of your leak detection system to continue to do leak detection during external changing factors such as, let’s say communication failure or maybe an instrument not working properly.
When that happens, will your leak detection system continue to do leak detection? RTTM systems, they could — provided that the right validation of data has taken place and the information that you’re feeding the system — it’s still able to work for the algorithm within a certain degree of accuracy.
Something that can be done is alert the operator that there’s a communication failure, there’s an instrument failure. You’re still doing leak detection, but some of your instruments or communication at your station might be faulty so be on the lookout for false alarms.
If the operator does receive that alarm, they’ll know that it could be due to some other conditions other than a real leak. It’s all about keeping the operators informed about what’s the health of the data that is coming into the leak detection system.
Russel: It’s also about additional configurations I might be able to make. If I lose a pressure transmitter, is there another pressure transmitter there that I can use as a replacement? Or can I default to a reasonable value? I’m going to lose performance, but I’m going to continue operations.
If I lose a flow meter, can I put two segments together, do a longer section of pipe? Do things like that in order to continue to have leak detection coverage, even though I may be sacrificing some performance in terms of sensitivity or accuracy.
Giancarlo: Correct. It’s making sure that you’re taking into account these particular situations to make sure that you continue to have leak detection, which leads me to the second point to follow up on your previous comment, which is API 1175.
Having a leak detection system program within a company to make sure that you’re doing everything possible to have the best leak detection system for a particular pipeline or for all your pipelines.
You and I were both at API, 1175. We both saw how much emphasis there is these days on leak detection and how much of an effect 1175 has put in the industry to make sure the operators are doing everything possible to have the best leak detection program and system in place.
When it comes to these four aspects of the API — reliability, robustness, accuracy, and sensitivity — 1175 will make sure that you’re on top of those.
Russel: Programmatically, beyond just what you’re doing. It’s a whole program of leak detection, which includes physical inspection, flyovers, cameras, other things. It’s all about understanding your risk and understanding the approach given the risk.
You mentioned accuracy. We haven’t talked about that, so we probably have to cover that as well. Accuracy is the ability to identify a leak and accurately say of what size and at what location. How do RTTM line up with relationship to that?
Giancarlo: An RTTM system is able to detect three aspects of a leak detection. When you do have a leak alarm, it will be able to detect your leak flow rate, because you’ll be able to know how much product is coming in and out of your pipeline system.
It will also be able to determine when the leak approximately started in a provided start-up time estimate. By doing that, combined with the amount of flow rate that you have, you will be able to determine a total amount of volume loss from the moment that the leak is estimated to have started.
Lastly, it will also be able to determine a leak location by using a pressure method to identify where a leak is inside the pipelines. How accurate is obviously going to depend on how well these systems are tuned and optimized, as well as the instrumentation itself, how accurate is the readings from flow at the inlet and outlet and the pressure meters themselves for the purpose of the leak detection.
Accurate is something that it is met by this technology, but it obviously needs to be taken into consideration for the instrumentation itself, how accurate are these instruments to be able to provide the best possible result.
Russel: We could do a whole episode, actually, probably do a whole series on just measurement accuracy, measurement uncertainty and how that relates to leak detection, but we won’t go there this time. [laughs]
Giancarlo: It’s a broad topic to cover.
Russel: Yeah, no doubt, no doubt.
Giancarlo: When we’re looking at these types of leak detections, CPM type of systems, flow and pressure are the key elements that are (required), or needed in order to configure these systems, RTTMs tend to need a little bit more. Flow and pressure is just not enough to tell you the whole picture from the simulation perspective.
Often enough, you need other meters, such as temperature, density, viscosity. If you’re looking at a gas system, you might need gas composition meters in order to know the product itself, the details, the characteristics of the product that you are actually moving.
It does require a bit more, and the more you add the more effects from the instrumentation side you’re going to have. That’s going to affect your accuracy, that’s going to give you a level of error, which could affect your leak rates, your volume total lost or your leak detection itself. The precision of these instruments are quite important.
Russel: Real Time Transient Models have been around for a while. We as guys who do SCADA systems and control centers, we are involved in leak detection, working and supporting our customers as they’re making decisions in that domain.
It certainly seems to me that these Real Time Transient Models are kind of losing their appeal. Would you say that’s accurate? If yes, and then explain to me your answer, if you might.
Giancarlo: I would say that there’s a certain degree of accuracy on that statement. RTTM systems are one of the first leak detection system that was implemented. As we mentioned earlier, it’s been around for over 40 years, the technology. I think that in the past, there was not that many options for the users. This type of system was attempted to be installed and implemented for any pipeline.
In today’s day and age, I think we, as far as the users or the operators, have realized that there’s just not one technology that can cover all the pipelines in the world. Operators have opted to look at other approaches to determine whether there’s a leak on their lines or not. That has driven away some of the, let’s say, customers from RTTM to the other technologies that are available.
Another aspect that also needs to take into consideration is that most pipeline or hydraulic engineers are, at some point, involved in hydraulic simulation or fluid dynamics. The next step after fluid dynamics or mechanics is to simulate that and hydraulically calculate what’s happening inside the pipeline.
We as engineers are somewhat driven to this type of technology or solution from the simulation perspective, which, RTTM is just right there. It’s something that we can use in order to understand the systems, in order to do leak detection.
Russel: I think that’s an excellent point, Giancarlo, that engineers are trained to do hydraulic simulation.
Part of what you have to do to be a hydraulics engineer is you’ve got to learn the math behind that. If I go through a process of learning that math, and then I use tools to apply that math to design, it makes sense, because I understand that math and I understand those tools to extend that technology and use it for leak detection.
That’s kind of a logical path, right?
Giancarlo: Right, and many of us are comfortable when we come out of school with this idea of simulation, hydraulics or understanding. When we come to the work fields and we start doing leak detection, hey, there’s a model. We already know what that is.
The customers might say, this simulation is great, but it does require quite a lot of time, which drives through a financial needs. It could become very expensive, but it’s very time-consuming.
I think that for that reason, many customers are not just losing their appeal, I mean, I don’t think it’s about losing their appeal. It’s more about looking for other options that might give them the best leak detection possible for their particular operation.
Russel: Well, this conversation’s actually, it’s opening up for me a different way of thinking about this. Certainly, we have, or I have some experience working with some of the larger operators that use Real Time Transient Models as their primary mechanism for leak detection.
Those organizations have a huge investment, not just in the technology in itself, but in all the people, resources, policies and procedures to implement, maintain, troubleshoot, diagnose, etc., their leak detection capability. If I’m a new operator and I haven’t built that up and I’m evaluating what model do I want to put in place, what approach do I want to put in place for leak detection.
That’s a very different calculus decision if, versus, I’m a large operator that’s been doing Real Time Transient Model for 15 or 20 years and have a whole organizational capability around that. I’m evaluating making a change to move to something different.
Russel: This is probably a great place to wrap up this episode. Giancarlo, thanks. Next week, Giancarlo and I are going to be talking about statistical volume balance as a leak detection approach and what it is and how it’s different than Real Time Transient, and its strengths and limitations. Join us next week for that. Again, thank you, Giancarlo.
Giancarlo: Thank you, Russel, and thank you to the listeners out there.
Russel: I hope you enjoyed this week’s episode of the Pipeliners Podcast. I certainly enjoyed the conversation with Giancarlo, and I’m looking forward to continuing it as we talk about other kinds of leak detection.
In addition to having the opportunity to win a Pipeliners Podcast YETI tumbler, Atmos International is giving away a free copy of the book, “Introduction to Leak Detection” to the first five listeners that register to receive the book. This is, get there first and you get a copy of the book.
You need to go to the PipelinersPodcast.com website, find the episode on Real Time Transient Models for leak detection, go to the show notes, and click through to enter yourself to win the book.
Russel: Thanks for listening to the Pipeliners Podcast. If you have ideas, questions, or topics you’d be interested in, please let us know on the Contact Us page at PipelinersPodcast.com or reach out to me directly on LinkedIn. My name’s Russel Treat. Thanks again for listening. We’ll talk to you next week.
Transcription by CastingWords