Using GE Vernova’s Risk-Based Inspection (RBI) Software to Enhance your Mechanical Integrity & Reliability

Using GE Vernova’s Risk-Based Inspection (RBI) Software

Leaks, corrosion, loss of pressure containment… for energy companies, lots of things can go wrong. For those operating a high volume of static equipment (piping systems, vessels, heat exchangers, etc.), that risk is even higher.
 
So, how can energy organizations mitigate risks and ensure the highest levels of safety?
 
In this webinar, GE Vernova breaks down the Risk-Based Inspection (RBI) capability in its Mechanical Integrity application. RBI is designed to assess the likelihood and consequences of asset failure, calculate risk, and help improve inspection effectiveness.



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TRANSCRIPT

Good morning, good afternoon and good evening, everyone, and welcome to today's webinar on using GE Vernova’s Risk-based Inspection application, to enhance your mechanical integrity and reliability programs for your fixed equipments. First of all, I want to again, thank you for taking time from your busy schedule and attending this webinar. Before we get started, I want to go through, quickly, some housekeeping items.  
 
This, webinar is being recorded and will be available on demand to view later. And we posting it in our YouTube channels too. At any point of time, if you have questions or comments, please don't forget to paste them or post them in our Q&A chat box. And if you are interested in further details after you watch, this webinar, we have a lot of additional content available in our resources center. So I wanted to do the honor, you know, honor of presenting it. The presenter. So first of all, I'm really excited to introduce Abed Esmaeili, who will be my co-presenter and who will be leading it in various ways.  
 
Abed is a senior RBI engineer at OQ Oman with nearly 20 years of experience in asset integrity, corrosion management and risk based inspection within refining and petrochemical sectors. He holds a master's degree in metallurgy. He's currently a superuser of GE Vernova’s RBI modules within OQ. His key area of expertise is corporate level, financial and area based risk assessment, and has played a key role in defining and optimizing turnaround scopes. It's also been instrumental in analyzing risk transition to really support that rules and also make decisions during turnaround planning. His technical strengths include, root cause failure analysis, fitness for service calculations, corrosion control, material selection, and has a deep command on standard codes and standards like API, ASME needs, and ASDL.  
 
And as I said in my LinkedIn post recently that, you know, we have been hosting a lot of webinars over the years. But, I'm really excited about this one because this is, our own user, taking through his journey and how they used our application to get value out of it. So welcome, Abed. And thank you again for participating and to introduce myself. My name is, Vipin Nair, and I'm a director of digital product management, for asset performance management solution for GE Vernova. I have been in the space for 20 years. Coming from, again, similarly from an industrial background, come from a petrochemical, mechanical integrity background. But since last year, eight years, I've been developing enterprise level products for both mechanical integrity and reliability.  
 
So it's a pleasure, to talk through this, webinar. Before Abed jumps into the use case of, you know, how he has been using the application to get a value. I'd like to take you through, our mechanical integrity suite of solution, how it sits in the overall APM ecosystem. And we'll also dive a bit into the RBI space or the RBI specific application. So this first slide, shows, current enterprise level asset performance management offering that we provide. It ranges all the way from, predictive analytics suite which is called SmartSignal. We have got health monitoring solution, which is really a, APM health. We have got a strategy optimization which is enabled through APM strategy. Then we have our APM integrity, Safety and Reliability solutions. And today's focus would be really the Integrity Solution set, which is focused on keeping your facilities contained and complying. And but we'll also talk about how the reliability solution can be leveraged to kind of extend your journey from just a mechanical integrity to a holistic reliability program. And I will be talking much more on that. So we typically focus on codes and standards. We calculate the risk.  
 
We manage your inspection programs. But we really also want to see how you can use the work history data. The actual data, which is event that has happened, used reliability analytics to calculate some of the key variables, understand the bad actors, use a root cause analysis to identify the actual root cause and eliminate it. So the theme of today's session will be really covered, within our using our integrated solution and the reliability solution within our portfolio. Now, if I double click on the mechanical integrity suite of solution, as I mentioned, that itself is again an enterprise level mechanical integrity solution which is inclusive of, risk-based inspection application, which really helps you with calculating the likelihood and consequence and calculating the risk for various asset types, which is really fixed, focused on static occupants. We do have a holistic inspection database management system, where we help you document the condition and track any inspection recommendation or anomalies to closure.  
 
We recently launched our Integrity mobile. I would say it's been a couple of years, but that's a new area of investment and focus where we are helping inspectors to log in their inspections, manage tasks on a mobile device with latest technologies like capturing and annotating images, creating recommendations both in an online and offline mode. So that is really expanding your inspection management as an application. We do help manage large scale corrosion and thickness measurement programs in your organization using our thickness monitoring module, which helps you calculate various, corrosion analysis summary as per the code. But the great thing about it is that it is also feeding back to both RBI and your inspection management processes. Again recently we launched, our 2D visualization tool, which is to help you give some contextual information of your asset in various part of the ... modules, which is also in thickness monitoring and RBI. And we hope to expand it. And whenever we find a use case to give you more contextual information to take decisions.  
 
And last but not the least, we have a complete automated, program for managing your time based inspection specific to your regions. It's called compliance management. So that's the detail of the entire, product suite of mechanical integrity. Now we'll spend some time on, again, double clicking on our RBI solution as a whole. Right. As I mentioned, one of our most used and most, you know, value added capabilities are risk based inspection, which we'll be talking through. But I wanted to give a quick synopsis on the various flavors of it, which is very important to understand. The first one, which we call as RBI or semi quantitative offering. And on the visual spectrum, if I kind of come from a for a qualitative to a completely quantitative analysis. RBI 580 sits somewhere in the middle, which really helps you to provide a qualitative or a semi quantitative approach. This is based on a legacy industry model and the probability of failure is inspired I would say by the API 581 second edition. The consequence of failure is based on the Risk management program offsite consequence analysis document by the EPA. So it's still being used by a lot of our chemical, petrochemical users. And then comes our RBI 581 solution. Even though it says quantitative, I would still say that it's not a fully quantitative calculation. Right.  
 
There is still some elements which is, semi quantitative there. And this is based on API And currently we are working to update it to the newly released fourth edition too. And then finally we have got something called Flexi-RBI which is really serving the customers in any if you're placing anywhere between the entire spectrum, we help you manage your program. So if you have your own damage mechanism, you can model it. If you have your own consequence, we can help you model it. If you want to have a different way of rolling up the risk, we can help you do that too. It's called Flexi-RBI and it sits on top of our RBI 580 solution. So with that short introduction of the entire asset performance management portfolio, mechanical integrity solution, and then also deep diving into RBI, now I would like to hand over the presentation to Abed. And, Abed, again want to thank you and welcome to this webinar. Abed, sorry to interject. I think I'm not able to hear you. Maybe you want to you might be muted. Here to to thank you. Given, my mic was off. Sorry.  
 
Hello and welcome, everyone. Thank you for joining us today for from whenever, wherever you are. And thank you so much for tuning in today. My name is Abed and I work at the OQ Company in Oman as the part of reliability section. And RBI team. I'm so excited to be a part of today's session for sharing our knowledge and experience. Using the GE APM and the Real Cases study. And I, like to express my frank appreciation for GE Vernova’s teams. Sir Vipin, Darlene, Rahul and others as a highly professional and motivated group. Let us, get to start. First of all, I would say a little bit about OQ company and GE APM story of OQ. OQ manage the American energy investment within the, energy sector, such as oil and gas exploration and production, oil refining, petrochemical production, trading, operate, alternative energy, power generation, and so on.  
 
Both rooted in Sultanate of Oman and, operating across 17 countries, OQ asset covered the energy value chain from exploration and production to marketing and distribution of the end user product to serve more than 2000 customers in eight countries. I like to briefly walk through what you through the journey of GE Vernova’s APM within OQ and how it contribute to our operational excellence. Since 2015 and to 2016, the journey began with data gathering and implementation phase of GE APM version 3.0. In 2019, we advance to version 4.3 and most recently we have updated to revision 5.1, which, continues to support our goals and predictive maintenance, asset reliability and cost optimization. So before diving to topic, let's say we have got a lot of cover, today. And I will try to go over everything as far as time allows. I think if, there is one takeaway today, it's this. RBI tells us where we might fail, but, reliability tells us how long we can avoid it. I am strongly believe only when we, use this two approach together and connected. Can we turn insight to uptime and convert intelligence into continuous performance. Okay. Let's, Let me start by briefly outlining the key goal of this presentation. Our first objective is to explain how we can reliably shift a turnaround by using structure risk transition study. Secondly, we will explore a real case study that show how this method can lead us to cost saving around 67 million USD.  
 
Not just only by delaying activity, but also doing so in a smart and controlled way. Because, we know our way is not only about, less inspection, but also about smarter inspection. And finally, we know we aim to show how we can extend the inspection interval with confidence while still meeting all safety and compliance requirements. With, those objectives in mind, let me, move ahead and start looking through the methodology methodology in more details. There are some assumptions here. This study was implemented only in on equipment and piping covered by our RBI GE APM. We use the Weibull distribution and RBI factors to estimate future risk, assuming very, variable and probability of failure and consent in consequence point of view, it only includes item with deadline before Q4 metrics for the electrical instrument and so on. Likewise, they could be included. Okay. Mr. Vipin, I believe that the next couple of the slide might, might, touch on the area where you may have insight or concern. Please feel free to jump in at any point.  
 
Your your input would be highly valuable. And, for our discussion, let us make this a two way conversation. Okay. Absolutely. Yeah, yeah. Okay. In this slide, we talk about the strategic rationale behind turnaround rescheduling. The first and the most compelling reason is cost saving. We know I'm pretty sure that, you know, how the shifting TA can help companies improve scheduling flexibility and reduce downtime, leading to more efficient operation and increase productivity as well. Secondly, we have a market condition due to economic fluctuation, customer demand or, geopolitical reasons. Third is strategic uh, alignment as company refines their long term goals. Of course there are some other contribution factor as well, such as the shortage of material protection, demand, governmental regulation and dependency to, on other plants. All of this can influence the our, decision about the shifting TA. Okay. So any question, from your side, Vipin?  
 
Yeah, yeah. So I just I think, you know, all those are great and, what we see, you know, RBI, requirements changes from region to region, right? You know, in the United States, it is acceptable to kind of carry out RBI and extend it because the government regulation allows allows you. But in a different region, it is it is still you know, you have a max cap, which is put by the, the, the local government regulation. So, more curious, here, Abed, in your part of the world, what what is your current turnaround frequency? Right. And how much are you trying to kind of extend it? With the help of risk based inspection as an approach? Yeah. Right. Yeah. In OQ we have, like, in our area, we have seven main plants. Each typically assigned a turnaround interval, usually for, depending on the plant type. For example, refinery and methanol plants are usually on five year cycle, while petrochemical units such as assume cracking unit, aromatic polypropylene and LPG unit generally follows by four year cycle. And regarding extension of this interval, it's depend on the their respective risk level and may vary accordingly. Any TA differal can only be approved through the a committee which include the representative from RBI reliability technical service services, asset integrity PSM as well and other relevant disciplines. So, as long as the plant continue to operate under safe and reliable condition, a deferment, from outside, will start from 1 to 3 years may be considered typically 1 to 3 years, in the one or maximum three time shifting, you know, this is what, we followed the system. Does it make sense? Absolutely. I think the message here is, you know, you can, but you have to have a very solid approach to convince the government regulations that you're still operating within the acceptable risk targets, which I think we will hear more in your upcoming slides. So thank you, Abed. That does answer my question. Yeah.  
 
You’re welcome. Okay. Thank you. No. Let's see here at and look at the next slide. Yeah. One of the most important factor, for, to highlight here is the age of the plant from my side, because for new plant, the decision to shifting TA is often more straightforward. So, however, for older plant, the situation is very different. This facility have, Yeah. Like, aged asset with, higher possibility of the failure. That's why a much more careful and data driven analysis is essential for us before making any postponement decision. To do this properly, several key condition must be meets, a quantitative or semi quantitative risk matrix must be in place. Or at least find a way to convert the qualitative assessment into the, semi quantitative or quantitative one using adjustment factor. Second one, the there need to be it needs to be, clearly defined risk targets, ideally expressed in financial term or in terms of the, probability of the failure as targets. So the risk target isn't just a technical metric. It's, strategic goal that's balanced financial, operational and safety priority.  
 
Also possible to define a cumulative risk target independently, even by correlating it with, your plan, market cap. We, must understand the real risk curve for each component. We have to, define this one using reliability model like Weibull analysis with, specific parameters. It's also critical to calculate the priorities current risk and projected future risk. This risk transition should apply for all components and assets. And finally, for any asset that's met exceeded the risk threshold we need to define. Absolutely. We need to define the mitigation action in advance. So while TA postponement is typically possible, it's not a one size fits all decision. This is, very important. So let's, explore a few challenges, which, we have noticed with other common methods. First of all, many of them assume the, linear, risk growth, so, which isn't realistic. Mostly risk usually grow exponentially. It rarely, cases. Maybe we're faced with, linear growth, but, majority of this should be followed by exponential growth. Second one by they often focus on the individual risk rather than looking at the cumulative risk across the whole asset.  
 
There is, some delay on the ART, the parameters almost ratio, which I mentioned earlier, recently and recently we, you know, they, they, they eliminated from the API 581 fourth edition, which, only applied on to thinning, the ART is only applied for thinning. And that's other damage mechanism. This is one of the other limitation. Fourth, they treat each metric cell as a fixed financial value without using adjustment factor to precise quantitative risk value. This is what we have. We faced with, other approaches, once we trying to, shifting the turn around TAs. Yeah. So I think, Mr. Vipin, you may have... Yeah, yeah. Thank you. Yeah. I have one one question, I think. Thanks for highlighting some of the challenges that we do have. Is, curious. And one thing is definitely, you know, we can cover more asset types of just by being peer pressure with some asset exchanges. But on the flip side, you talked about thinning of the, by, you know, the PART table. What's your thought on, damage mechanisms or damage modes like cracking?  
 
I believe you cannot, categorize that in the same bucket as the. That's a non age related. Right. And is that part of your approach too, Abed? Like, say a cracking damage mode? Yeah. Let me break it down in, yeah, yeah, it's a fantastic question. I like it because you're right. In fact, for non age related damage mechanism, the answer is absolutely no. So this model is best suited for age related and not for those which are driven by random or environmental assets cracking. As you correctly mentioned. But you know, some of this cracking mechanism or, mechanisms or in, age related category, we have three main category mechanical energy called cracking, hydrogen induced cracking and stress cracking. So for metallurgical and mechanical cracking like creep, a strain H cracking, tempering the filament, fatigue, mechanical fatigue, thermal fatigue, sigma phase in brittle months. Yeah we can do it because this is, age related. This is age related to all of this. But for all in the balance, no, it's impossible, as you mentioned, for any kind of circuit, this risk cracking for hydrogen stress cracking or polarizers cracking is not, possible because it's not, age related.  
 
We couldn't able to, implement this strategy. Yeah, you're totally right. And, but at the end, two of these three categories are not age related. And, yeah, but, still, we have some cracking, some damage mechanism, which will end up with the cracking, which we can use it, in this methodology. Does that address it? Yeah. Thank you know, it does it does it does it it does. Thank you, thank you. Yeah. Thank you so much. Yeah, yeah. Welcome. All right. That was insightful. Thank you again for sharing your perspective. I will move to the next slide here. This is, our workflow. The flow diagram shows the overhauled process of where we use for risk transition. We start by selecting the asset and crack and checking the RBI analysis in semi quantitative. If not we adjust the matrix value.  
 
If it is we define the variable parameters beta and eta for each component and each individual asset, for each interval and each risk category. All of this important for us. Then we set the flexibility risk target, calculate the probability of failure and assess whether the asset qualified for shifting or not. This is a kind of decision tree, decision making theory. So the next slide, we define the inspection or maintenance interval based on the both risk and reliability. We start by setting a risk target. This can be graphical qualitative and quantitative. Then I'd like to highlight this point that, whenever we talk about the, risk and reliability, we are not just dealing with the fixed number. We are working with the range of data. This includes the input and output, the probability of the failure even within a single category, as well as zoom circuit threshold. All of this is included in a range. So consequence of a failure has range, data affects or has range relative risk ranking where no absolute risk value are generated as an output of RBI. So an important importantly, it's it's up to the asset owner to decide where they want to position themselves, whether at the lower or or at the upper of this threshold. In my book, this is where the real distinction between, a typical organization and the world class, one major company, a close attention to this nuance. They don't just sit there, they strategically customize them. And that level of care in decision making, has translated and translated into a million of savings. Simply by choosing the right threshold at the right time for the right object, which name, let's say target.  
 
By flexi target, we can, expand the entire world for low risk asset and squeeze it for high risk. This slide, will show you the threshold definition. I'm pretty sure the majority of you guys know well about this risk capacity, risk appetite, risk tolerance, risk target is the best. Is that the is the ideal level of the risk that company aims to stay at or below. It acts as the guiding, point for the decision making, for entire of the company. The target is defined as the maximum value as per, API 581 clearly mentioned, that target is defined as the maximum value acceptable for continued operation without requiring mitigation and action. As you can see, the key takeaway here is the maximum target, act as the initial boundary for risk limitation. So risk target is a range of data. So the maximum of this will be initial point of the boundary for risk limitation. Finally we have risking it is our strict boundary set to make sure the company doesn't go beyond of this.  
 
Understanding and clearly defining this layers, will help the company into a strong risk culture and decision making process. Moving to next yeah here. This graph shows the probability of failure based on the API the PoF growth. And better understanding of how flexi target could identify it. It's up to you how you deal with the targets. Here you can see three red line this representative to the risk target for different level of risk. The top line is Then the middle line at 0.1 is the target for medium risk. This is what we, decided to do. And the bottom line at, It makes sense. The main point here is that, that although choosing a single risk target is acceptable, it's my it might be more conservative.  
 
With, that in mind, let's go to the next slide, which, shows more information about, the range of this, flexi target based on the risk category. Here you can see the, the following the the previous slide. On this slide we are looking at how risk is categorized. You can selected the certain amount of each one. Sorry I want to highlight this part. As I mentioned before in the risk and reliability concept, the almost working with the range of the continuous value more than anything in district value, it's up to you to select a precise and accurate amount of PoF because RBI qualitative, only give you a range of the the probability of failure as a category like PoF three. So it's not accurate among between lowest and highest amount. For example, if you selected the maximum amount of PoF as API defined in part one. And so the PoF level will be increased through shifting. If you select the maximum theshold, It will be jump to upper level after even one day, one hour it will be jump. So, you have to before before doing the risk study, you have to identify that precise amount of the PoF But even though but it doesn't mean the necessary necessarily the risk level will be increased through shifting. The probability may be increased, but, the risk level may be, kept as it is.  
 
So let's, so, explain how this value affect the on inspection interval. Next slide. Here you can see effect of the flexi target this inspection interval inspection interval. And explore how flexible target can influence inspection interval. What we notice here is that, not all assets behave the same. Some, show a sharp increase in risk, while others raise, more gradually. So let's simply we can say the higher the risk, the shorter the interval and the higher the threshold, the wider the interval. We all probably know. You know, target means know valuable risk assessment. This is a formula which, you can see reliability formula. And how can you utilize it for eta calculation if we had eta. So it would be easy. But otherwise we have to estimate or calculate it through some sort of method. So the the the reaching for the current eta and beta, it's the main, main aims for, for the, TA shifting for using the Weibull.  
 
So without selecting the, correct and precise, eta and beta, your study, maybe, have not enough, value for the system. This example here. In the bottom of this, slide, you can see the example we have that, vessel with the 20 year design life and the beta of two and its target reliability of 90%. So, when we plug it in, the formula we get it is approximately 61.7 here. This means the system is expected to, last longer than, its design life before reaching a 90% reliability threshold. As you are perhaps aware, there are some define the time between API 581 and some others. In the best practice, you can find this. And I think in GE, library, they have some, example for that time based on the Auriga and some other database. So, this slide, also shows our case study was, a steam cracking unit.  
 
With, the TA due date at 02/04/2024, which we requested to shifted to 02/04/2025 for one year more. In this assessment, we, reviewed a total of, linked to 715 different asset. This task were scheduled to prioritize based on the risk and operational needs. If we look at the timeline, you will see the planning a span, from 2022, to 2026, with the major turnaround schedule for 2024. This timeline help us to visualize how tasks are distributed over the years. On the right side of the bar chart, give us a broader, broader view. So. And in this slide, you can see the, the metal method, our methodology basis and what we have for our basic risk calculation based on the GE Vernova's APM models. So we follow this data as for using for inspection effectiveness. For consequence category for probability of failure calculation, and risk matrix and inspection priority as well. In this slide you can see the how we use the matrix transition. These are the methodology basis and we can see three main area in the left side of the matrix. You can see the POF driven area, COF driving area and the top of that, we have a Mixed Driven area where both probability and consequence play a significant role.  
 
So we can define the separate, risk targets for, consequence of, consequence of failure driven area and for probability of failure driven. So, this kind of the break breaking breakdown of the this matrix will help us decide where to focus our attention. Whether we need to reduce the chance of failure, minimize the impact, or address both. So in the right side, you can see the matrix one need the laptop absolute financial risk by dollar per year. And the matrix to middle and bottom normalized risk scoring by and right side. The adjust monetary this matrix after applying adjustment factor. It's a part of a standardized risk spending scale. And this is the line you can find it, the, matrix two middle on bottom. Will illustrate how targets have been performed, based on our project. Next slide. Yeah. This is a classical, graph which we know everywhere you can see it. Formulas for beta and reliability. This graph shows how these shape parameters or beta affected our probability of failure over time as beta increase the curve shift indicating different failure patterns. For example, if the beta is less than one we all know we see early life failure for beta equal one, we assume that, the failure rate is constant and forget of more than one is reflected zero failure. So majority of our, cases or in this, category for beta more than one.  
 
So, for example, you can see, just selected, you can select the beta, for example, for pressure vessel, based on the damage mechanism for general corrosion and cleaning, you can select a 2 or 3 again for assess corrosion cracking and pressure vessel, 3 or 4 for fatigue. You can select 3 to 5. And for creep the worst case you can select 4 to 6. And once you put it this number in the Weibull parameter you can see, the the how it will be affect on the, final probability of failure. So the equation, on this slide can help calculate the cumulative cumulative distribution function, which is we call it, POF as POF. So from the right side you can see two books from, where. And here, they are excellent reference from, in my opinion. If you'd like to explore this topic further, you can refer to this. Thank you. They will give you the the solid foundation in the viability. Next slide. Thanks. Move to the next slide. Yeah.  
 
This is a fantastic slide. A closer look at the graph which shows the conversion between the three modal, Exponential, Weibull and Weibayes. Each one predicted how the probability of failure increases. That's the time goes on. At the bottom side you can see the formula used for calculation. The character is correct. A characteristic life which we call it eta. It's, depending on the whether failure probable or not. This whole estimate, the failure probability more accurately when, data is limited. So, that is what fire way by which, we, in the APA 581 dimension as Weibayes. It's it start with an assumed variable prior. We call it prior, analysis and adjusted based on the limited data. Few or no or no failure. Then plug into the same pure formula as Weibull. In classical Weibull, we know, you need a lot of failure data to estimate the right and correct certain itself.  
 
So but usually we don't have in the industry, we don't have enough data for the, estimating the, like designed life calculation. So if we have a Weibayes, especially for, we use it for a single rate of failure or a smooth history, we use the gamma function, as you can see, right side in the formula it's seen. But but, let's say it's more conservative. The green one. And then the red one. Sorry. Yep. This, Yeah. So highlighted how bad calculation are, handled during the turnaround activity. We use this database. This table shows, a snapshot of or as a database where you, where each row represent the two, individual asset and each column reflected specific factors. So, we don't have enough time to deep diving in, one by one, but, only I listed out, like two, for beta and eta and design life. We assume 90% reliability at the design life. This is what we, assume. And, the, timing service is important. The time which we added, for example, here one year as a shifting duration, prior to a value POF flexi target. We define the flexible POF target as previously I had mentioned. And the financial risk target. We define the financial risk target for those equipment which in by COF, in 3.3 million USD.  
 
So those equipment which we exceed this target, it's not acceptable for shifting. And the final decision, will be taken based on this to criteria POF crieteria and COF criteria. This is, the result of what we had in the system. In total, we identified 115 critical, of worse risk, along with the 22 high risk cases. Additionally, 11 items were flagged, and we as needing a specific mitigation action. Looking at how this risk can be managed, Out of them, 100, without any mitigation and nine cases required mitigation before they can be deferred and two cases cannot be postponed based on our analysis. And, and must be addressed directly. For and and we need to get the approval from the top management about this, or at least define some mitigation action in advance if possible. Okay. Let's move to next slide. And this is in this table we listed out, these 11 critical components.  
 
Each affected by different degradation mechanism. All of this equipment fall under the medium high risk category, which already seen, signals that need for attention. More of this component, components are expected to shift into the high risk zone. And two of them are even projected to enter to the failure area means, it's very dangerous, mainly due to the mechanical fatigue and sulfate stress cracking. And, in the next couple of, slides, you can see the reason why we, this gives us a clear picture of the risk transition over a one year interval. In the worst case scenario, I only selected each one.  
 
Our asset, we we have to define for each one this kind of the the graph. I only selected the worst cases for medium high, for example, here and one year interval. This is, what we can see here. It's goes to the failure area after one year transition from year 4 to 5. And, second one, Is, the risk transition for medium high risk and two years interval, worst case scenario. Again, That's one year. This graph shows how the, the combined probability of least risk increase over the years. So, the background color, as you can see here, maybe not, clear enough, but the background color help us visualize the shift from the lower risk in green and to high risk red. As we can see, the risk moved quickly into the high and very high zoom. If no action is taken. The left side, you can see the risk or probability of failure as, main driver of the, risk in these cases. And third one is risk transition for medium high risk and four year interval.  
 
If you can see here, on the date time due due date, the risk is equal to it's close to, jumping to the high rate. So, and here you can see, medium high risk and shifted into high risk over a time for those asset which have four years interval. But in this cases. Yeah. The next slide. Cumulative cost, you can see here, this this slide, give us a clear picture of the cumulative number of asset and cost analysis, which is grouped by different list level low, medium and medium high. This breakdowns will help us to understand how risk level influence costs over time. Yet I will show you this result in the next slide. What we see here is the financial estimation of, how inspection effectiveness can impact this, transition, over time. Yeah. And, for example, if inspection are highly effective in level II, level eight, having the financial risk, in 2025 will drop significantly to about 8.9 million USD dollar. As, effectiveness decrease, the risk will be increased accordingly. What we can see in the system shows us the relation between it's depend on you how you can select the effectiveness and, how you gonna deal with the inspection effectiveness and its selection. The next slide, the results show the financial financial risk analysis here.  
 
Our findings show that, The main driving of for financial risk is, the shift from the medium high to high risk. The other scenario, the, the amount of the financial risk is not, too much. So we can only focus on the probability of failure for them. But in case of the COF driven, we can use it, this financial scenario as a reference, this transition lead, leads to a largest increase in cost. So altogether, the total financial impacts across all across all 115 assets, was approximately 67 million USD with, that mind led, this amount is only regarding to inspection and the, the, financial risk of the, the asset is not, protection lost or, whatever. So let's move to the last slide to discuss how we can address this risk effectively. Is here, this is, we will conclude, with this slide. However, there are two cases, I only want to emphasize that, that these two cases that cannot be transferred, this will need approval from top management, but they can still be managed through normal operation based on all cases by some, definition, some alternative inspection method.  
 
At the end, let's say postponing the TA by one year percent. By one year, like, a clear financial a they have a clear financial, advantages we know, clearly it's, obviously we you can see the benefit of this, from the financial point of view by, eliminating one, eliminating the one month of operational downtime, the organization can retain resulting in considerable cost saving based on the data protection value, as per the nature of the plans. So, one more. The point is, from today's session, the, I want to say about the accuracy of this methodology, this approach is not only in yearly needs, but also daily, even, hourly. You can calculate the risk based on the, the formula. You can use the, put the, the, six months later, whatever you want. And using the, the and seeing the what is the result?  
 
What will we end up with? The, future probability of failure, based on the, history and what we have, and our knowledge about our asset, our damage mechanism and precise date, data. So, with that, I will, conclude my presentation. Thank you all for your time and interesting and participation. I will be happy to answer any questions you may have. And, thank you again for your time. So, Mr. Vipin, from my side, I don't have anything major. Thank you. Yeah. Absolutely. I think there's so much of engagement Abed, there's a lot of questions I try to answer a few of them. We'll answer a lot by emails. But there's one question which I just want to kind of take a minute, which is from Mandy. And, you know, the question is, how do we calculate reliable eta and beta for new plant without inspection history and failure history? So I think I my hypothesis that you might want to start with the Weibayes graph is, is, with the slide that you showed where you showed for the, having a Weibayes approach, to start with. And then when you get the inspection history or the inspection effectiveness, then you kind of update your, eta value.  
 
So, is that how you will be typically handling any, you know, assets, which is, does not have much of inspection history. And failure history in it. This is from Mandy, is the question clear, Abed? But yeah it yeah it's clear Vipin, but you know in the lack of the good, information and data, we can use the, we can use the benchmark, we can use other data, library data resources as, as a benchmark and, you know. Absolutely. They have. Yeah, they have many, many, references to address at this point. Yeah. Absolutely. Yeah, I think I, I wish we could continue more, but I think this has been a fascinating webinar, Abed.  
 
It shows your commitment and your passion for the subject. We would like to thank you again for your time. We would follow up with all the questions later that we have, and we will coordinate with Abed for any of the answers. But, again, if you have any, detailed questions, you can go back to our resources center, which includes a demo of the brochure and also any of the case studies from OQ. So thank you again, for a wonderful session. And thank you again, Abed, for your time, your passion and all the content. Thank you. You're welcome. Welcome. Thank you. Thank you.