Pipeline integrity data management for Export

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good day everyone and welcome i'm kathy mayo executive director of pods association and it's really good to be with you again today uh today is unit five you know five out of six units of pods basics and today's analysis of pipeline data uh we did slightly adjust the title uh from what we had originally advertised but you know stick with us i'm sure you're going to find a lot of good information um it's i've just been really excited about julie parker's presentations um she's with teach me gis so uh joining me today is jackie smith president of pods association board of directors jackie hi welcome everyone glad to see everybody in this morning so this is our fifth training session um to increase awareness of pods to get everybody educated and aligned i want to encourage you to send feedback there's a survey that will be distributed please send your honest and critical if you'd like feedback and hopefully it's positive but we are open to anything you need to say and to make this better we want to continue to improve our sessions and continue to to get the training out throughout the year um and also we may do this in the fall so we're we're looking at planning our fall conference we are looking at planning it virtually as many of you know our fall conference is usually in um september october time frame and uh with the state of the events in the world we're still thinking virtual is going to be the way to go so uh we have a great presentation for you today and then at the end of this session we also have a vendor what we call lightning talk and will actually be presented by rosen which is myself and my team one of my teammates anyway so i'll give it back to uh julie for this training session thanks thank you jackie hi julie it's good to welcome you um most of you are familiar with julie uh again i i just welcome her i'm excited about what she brings to the table for pods association and this pods basics training and she's just so well versed in the topic has tons of experience that she brings to the table and with that i'm just going to turn it over to you juliet again welcome thank you thanks good morning everybody happy monday morning to you uh welcome so this is our next to the last session together this one is going to be covering analysis originally we were going to just focus on spatial analysis but there's just such a whole other world of analysis and and other topics that kind of need to fit into this under this tent that is analysis of pods data that we decided to broaden the topic so at some point in the future we may do a deeper dive on just spatial analysis but for today we're going to look at spatial as well as tabular analysis so um now that i've said good morning i'm going to turn my webcam off and we'll get started with the presentation okay so hopefully you can see so this podcast or this uh pods presentation is forged as intended for gis or it professionals certainly people that have a a an initial interest in pods and just simply want to learn more about it if you're new to the pipeline industry perhaps you've recently joined the industry and you've had little or no exposure to pods this is a good place to start just to get your feet wet and to kind of get a good foundation for future learning so moving on this is unit five as cathy said of six units our next one will be on pods implementation so if you happen to be in a company that is considering implementing pods you'll definitely want to make sure that that you tune into this one and also any anyone else in your organization that might be working on implementation this will be a really great great session to to end our series on but today we're going to focus on just the analysis components of pipeline data so as you can see here's our lovely pods model this is the the 4.02 version of the model that we looked at in more detail last time but i wanted to just kind of re reorient you to the model and and thinking about the model and how the tables relate to each other and and what the overall purpose is of pods so by this point in the series i i really hope that you're realizing some of the benefits of the data model for storing and managing our pipeline system data we focused a lot of our attention on the ways in which pod and pods improves data organization and quality integration and certainly efficiency today our attention will be extending on the usability of pods data so one of the things that that that we need to visit about is is yes we've been talking about the pods model we've been talking about um you know how the tables relate to each other and and and you know lots of different components of why you need pods and so on but we haven't spent any time talking about what you actually do with pods once you get it installed and you get data loaded into it and and i need to clear a misconception that might be out there that pods is is everything the container the analytical tool the reporting tool it's it's a one-stop shop and and that's not the case so i want to make sure you understand that that when you see the pods data model or when you consider pods it's it's the container for your data so you're going to need additional tools that come from third parties there's many vendors out there many specialty vendors that focus on certain aspects of pipeline data management or certain types of operations so these type of vendors are the resources that you'll seek out once you are in the process or begin the process of implementing pods in many cases you'll actually seek the the help of a third party vendor to maybe convert or do data management practice before implementing your data or integrating your data into the pods data model so just understand that that pods is is a stealthy container for your data but unless you use other tools in conjunction with it it's going to just be a container and and nothing more so you'll never realize the true value of pods without these third-party applications so i'd like to take some time to just kind of sort things um into buckets if you will for for how and what is connected to the pods data to the pods data model so many types of pipeline management activities are improved and integrated when pods is integrated into a business so pods often becomes the system of record or the authoritative source for the organization's pipeline data as well as the hub for all the pipeline related data connecting to in-house applications as well as many third-party software applications so what i'd like to do is is look at four groups of applications if you will or or four usages of data within the pods data model so this is this is going to be looking at those applications dealing with data quality data accessible excuse me accessibility data analytics and reporting certainly operational integration as well so these are four main areas that a pods data model serves in an organization and we're going to step through these these various applications so you get a really good idea of of many of the different activities and and services that are performed under these various headings so let's move on so if you've been with us for previous units in this series you'll remember the concept of the the of linear referencing that's how pod's data becomes spatialized so it becomes viewable and also is able to be analyzed with geospatial tools so just to reach back a bit i'll redefine linear referencing for you so it's it's mainly when a singular linear feature for example a pipeline is used as a reference the measurement of that pipeline is used for locating additional pipeline features and all the characteristics of those features along the length of that line so it begins with the center line which is the core of linear referencing and then all the ancillary uh features of that pipeline or assets of that pipeline the valves the meters etc are placed by referencing the measured center line distances remember in linear referencing these additional features that are linearly referenced can maintain their own internal measurement systems but they are referenced to the earth's surface by using the measurement system of the of the center line you'll also recall last week's exploration of the data models conceptual and logical diagram in which we saw the related tables that are connecting data elements for all the aspects of pipeline data management so you may recall this graphic that we had last week just showing the different relationships that exist between tables in the pod's data model and also you might remember the cardinality the type of relationship that exists so we won't go back into that again but i just wanted to toss that vocabulary back out to you so it sticks so let's take the very first heading here which is data quality and let's talk about one of the first items that falls under that realm alignment sheets so what you're seeing before you now is what we call an alignment sheet and this one is is is a very good example because it's on one sheet it's giving a tremendous amount of information so alignment sheets graphically show the exact route and the location of the pipeline in this associated facilities much of the knowledge that exists about that pipeline during the design phase often accompanies the graphical information on the sheet so therefore you'll see that there's a vital set of documents for maintenance these these are vital for maintenance for recovery purposes for location of assets and things of that nature they're very valuable documents that that tend to live with the documentation of the pipeline different sets of alignment sheets are typically made during the pipelines planning construction and operational phases so there may be an initial maybe during the permitting phase you'll see one set of alignment sheets with the proposed route and different features surrounding the proposed route and other notations on the sheet and then once a pipeline is constructed you'll see a post construction alignment sheet which is really important certainly for repair purposes and inspection purposes it shows the pipeline as it exists in the ground today so alignment sheets are are generated by third-party applications that are connected to pods so everything you're going to be seeing in terms of of these these capabilities are all being fueled by the pods data model so one thing you might be wondering is this certainly this this this length of pipe that you're currently seeing right now is only one segment of a pipeline how does how does this represent an entire system that may be many miles long and the answer is contiguous sheets so typically there will be additional sheets for the subsequent sections of pipeline or the adjacent segments of pipeline to this one that you're viewing right now and there's usually some type of way of matching these together so that there is a type of continuity there so you'll see that the scale will oftentimes remain constant as the different sheets are generated there is software available that actually will will create alignment sheets for every segment of the pipeline put them all in order get them into pdf format or whatever format that you may need so on the next next part of this assignment or a data model area we're talking about the fact that gis teams are often managing pods data in many companies you'll see gis enabled software and platforms that are facilitating the spread of pipeline data across entire organizations and this is a great example here you can see a dashboard of this area of pipeline on your screen you can see information you can see a tabulation of corroded areas versus those that are not corroded you can see changes in depths uh comparison of anomalies in the data over time and so on so just quick little dashboards that are deriving information from pods and putting it in an easily digestible meaningful format for users we'll get to this in a little bit but i'll give you a little bit of a preview these these applications these gis enabled applications also spill over into the mobile environment so there are many different applications that allow teams to go out with a handheld unit of some type maybe a data collector and maybe a full-on gi gps unit to collect or update data and so this screenshot is just an example of that and we'll talk a little bit more about that later of course there's always going to be maps associated in with gis and so here you can see just an overview of a basic map of a route of pipeline and also some analysis so you can see the data tables here that are tied to this particular pipeline that's being analyzed at the moment and you can see the results of an operation there in gis so gis is a very strong part because the spatial component of pods is is very strong these pipelines as we talked about in our very first session don't exist in a vacuum they exist in an environment and they have locations and they have all types of features surrounding them that have to be viewed and worked with in a spatial fashion so most large companies have an asset management software and pods also connects to this type or these types of software so this software of course is used for managing physical assets such as equipment and buildings plants machinery vehicles and whatnot two of the most used applications are ibm's maximo you may have heard of that one or sap e am software so these are applications that most major companies use to track their physical assets so physical pipeline systems are certainly assets of the operator and also all the associated facilities are physical assets of the companies that own them pods can be connected with asset management applications providing updated information on the pipeline assets without unnecessary duplication of data asset management applications will also manage data regarding maintenance work orders inventory reporting and lots more so here in the gold boxes you can see several different examples of the types of of information that you might find in an asset management system connected to pods so one term that you may hear is maop and so we're going to dive into this just a little bit because there are a number of analytics and reporting functions that are supported by pods the energy transportation network of the united states consists of over two and a half million miles of pipelines that's enough to circle the earth about a hundred times that's crazy that's just in the u.s alone these pipelines are operated by approximately three thousand companies large and small the pipeline and hazardous materials safety administration is the federal agency charged with safe and secure movement of hazardous materials by all modes of transportation the agency also oversees the nation's pipeline infrastructure so if you're interested in learning more about the fimsa as it's called organization or agency it's the pipeline hazardous materials safety administration phmsa you can find them on the web you may have recently heard fenzo is revising the federal pipeline safety regulations to improve the safety of onshore gas transmission lines this final rule addresses congressional mandates national transportation safety board recommendations and response to public input a recent femza regulation requires operators to provide confirmation of their maop values operators using paws will be able to use various data analysis techniques to carry out their compliance activities so maop in this instance is is what we [Music] we call the maximum allowable operating pressure that a pipeline is sanctioned to operate at and that's going to vary depending on what the pipeline is made of as well as the thickness of the pipeline the diameter of the pipeline and so on so for for all segments of pipeline there's there's going to be a pressure maximum allowable operating pressure set for that particular pipeline segment so this is important uh it's a primary consideration in terms of public safety and certainly for the operators having the correct pressure for your pipeline segments means that that pipeline can transport its cargo so to speak with great efficiency so it's important that the operating pressure is is correct for the pipelines and monitored this information is stored in the pods data model if you get a chance and you still have access to to uh the mob the model that we looked at last week see if you can find it so another aspect of data analysis is in the reporting of risk and integrity of the pipeline so pipeline safety regulations include requirements for operators of hazardous liquids and other natural and other gas transmission as well as distribution of pipelines to develop and implement comprehensive integrity management programs an integrity management program is a set of safety management operations maintenance evaluation and assessment processes that are implemented in an integrated and rigorous manner to ensure that operators provide enhanced protection for high consequence areas so we'll talk about hcas next but these tables that you see before you are part of the pods model so you can see there are areas to store information in pods that are accessible by other applications that you can use to run analyses on your risk and integrity so you can also see there is a separate in this particular version of pods there are risk tables there's also a place to store repairs that have been used or done on a particular pipeline segment so there are there are areas within the model that are storing this type of information that will need to be referenced in order to do analysis of the pipeline maintain to maintain safety and integrity so an integrity management system is a set of safety management operations and maintenance evaluation and assessment processes that are implemented in an integrated and a rigorous manner to ensure that operators are providing enhanced protection for high consequence areas or hca for short a high consequence area is a legally defined area and it's especially an area that is especially vulnerable to damage in the event of a pipeline leak or a failure operators use a graded method of class locations to identify areas of population density where increased levels of safety precautions may be needed so as you can see in the graphic here some of the areas that would be considered high consequence areas are populated places areas where there are drinking water sources if there are any sensitive ecological areas and certainly areas where there are very dense populations all of these require increased safety and risk management activities femso's office of pipeline safety monitors operator compliance with excuse me through field operations and field inspections of pipeline facilities and construction projects inspections of operator management systems and procedures and their work processes as well as incident investigations all fall under fimsa's purview operators must follow safety rules to maintain and supply documentation cooperate with inspections and accident investigations so compliance is also a very large component with many tables as you can see in this version of pods newer models will look different from this but again we're we're dealing with our our model we've been looking at through class which has been 4.02 so if you want to see newer models of pods different models of paws you would need to see where these areas are treated within the model for the pods version that you're working with needless to say though compliance activities and garner a significant amount of table space and it's it's they're important because there's a lot of permitting and lots of regulations that are around all of this information so it needs to be accessible and it needs to be easily analyzed so we talked a little bit about as built early on we talked a little bit about the alignment sheet and i mentioned that there's pre-construction ones and so and post-construction ones as well and what what you're looking at right now highlighted here in the highlighted area is the as built information so these as builds then are are done after the the pipeline is in ground it's been constructed and it this alignment sheet post construction then contains all the measurement data and locational references along that pipeline so you can see in the highlighted area here are all the measurements and stationing points along this particular section so again or additionally on the operational integration front we mentioned briefly at the beginning of this unit about mobile devices and how they are oftentimes connected through gis in this case i'm showing you an example of a mobile application and just kind of stepping you through the field data collection process so as you can see in step one we've got a gentleman that has is in position in field he needs to capture on his mobile device information about a pipeline segment that is going to be used as part in construction so he is taking a photograph of this of the markings on the pipeline and i won't go into all the detail about what those markings are but he's capturing this information he's adding more specific information about the diameter of the pipe the grade of the pipe and so on and then he's uploading it into a mobile application so you can see in step four he's transmitted that data he saved the data that he has gathered on site this data would then be used to update the pods database with so this is how you see the influence of geographic information systems and you also see the the pods data model working in tandem with each other to give a complete picture of this pipeline and all the associated segments so on the analysis side we'll leave the the groups of of uh analytical capabilities and practices that are that are done with pods data and turn a little bit now towards the analysis of pods data so by now i hope you're getting a good picture of the number the various different facets of analytical capabilities that exist when you have your data in pods and you're using third-party applications that fit the needs of your organization as well as your regulatory requirements so your analysis of pods data then is is made easier because you've got your data in a standard database and it's easily accessible we've already talked about the fact that your data is is governed there is data quality issues excuse me data quality enhancements that exist in the pods data model as well so that you're able to take advantage of of reporting and other types of of uh statistical analysis that you need to do and in association with your your reporting functions so in this case you're you're looking at just a couple of different analytical products that are used with pods data that's been gathered maybe some of it has been gathered before a pipe went into the ground maybe in some cases we're looking at historical information and so on and so forth but the most important thing for you to get right here is that asking questions of your data and getting these answers back in the form of a map or a chart or a dashboard or something of that nature is really powerful and and it wouldn't be near as powerful without the data that's in pods so here are some examples of some typical analysis questions that you might pose to your pods database typically pods database questions so to speak or queries will be asked programmatically sometimes through software or through user interaction with some sort of third-party tool output from data analysis might be a paper report might be a spreadsheet might be a digital dashboard there are lots of different ways to view the output from data analysis in this screenshot what you can see is is a shot provided by a third party vendor and this is this is one way that they are are asking their users to be able to query pod's data and here's some examples of some questions that they might typically pose but what they're what you've got here is is a query on inspection data and you're actually able to use drop downs and mathematical operators to filter and refine the results of your query to the pods database so this application connecting to the posit database then gives you the tools to customize a query or a question to that database and get the results that you need software certainly is helpful for performing analysis on pods data tables you're not going to be do you're not going to be doing a whole lot of work without some sort of additional software here's some other examples of some vendor analysis tools these tools are from ong and from pivot and they connect directly to pods data where they perform analytical tasks on pods tables so in in the image that you see on the left the query is looking for coding defects are there coding defects here and you can see the interface is quite sophisticated you can see an image of the pipeline corridor you can also see a photograph of the area and you can see the physical markings of of the right of way for that pipeline and then you can see an overview map of where this is in space and then you've got all these widgets along the side here that allow you to manipulate the data in in such a way that you get the information that you're looking for either through a query or through um maybe even just changing the colors of the symbols and symbolizing by certain values that exist in the database on the other side the right side of the screen you can see a route analysis and this is a really interesting spatial application because before a pipeline is in the ground there has to be a route approved and decided upon and planned and surveyed and everything else before anything happens but bef when there's uh the first inkling that there's going to be a pipeline required in an area root analysis is a very key part of the planning's process and so tools such as the one you see on the right help you to plan the appropriate route for a particular pipeline it takes into consideration all the different things that that you must be aware of maybe population densities maybe availability of land may be certain restrictions in the area that exist all these things can be weighted using the little slider bars that you see here to help you determine for a particular area where you would find the best route for a particular pipeline so this is a type of application that's that's very valuable in the planning of a potential new pipeline so this gives you some really powerful examples of queries to databases that are connected to pods applications that connect to pods and allow you to to interact with pods data in a very user friendly way one that might be more job specific for you and help you make the most of your investment in pods so we'll leave the tabular side of analysis here and move over to looking at the spatial analysis side so getting answers to spatial questions is possible by spatializing the pods data again we're going back to linear referencing which is making that pipeline system data accessible for mapping and spatial queries such as the examples you see above sometimes the simple act of simply colorizing data or symbolizing data can be considered an analytical technique so in this particular analysis that you see you can see the questions being posed are finding the closest valves upstream and downstream to this location highlighted in yellow so you can see those valves that meet that criteria and then if you want to drill down for example and learn more about these types of valves you can get information by posing another query and seeing that this is a valve that's in the gis system and this is an ill excuse me an ili valve so this is a very simple diagram here just to give you the idea that we're dealing with spatial data at this point and it also is getting fuel from the pods database so these features are appearing on the map because they've been linear referenced and you can use a gis software such as esri gis and you can use these types of tools to conduct various types of analysis including spatial analysis and this is a very simple simple case here but you can get very very complex here's another example so in this case this is actually a flood scenario where imagery has been analyzed to determine areas or some of the work sites that were impacted by colorado or excuse me by flooding in colorado several years ago and so you can see the imagery analysis has been performed with gis and it has determined the areas where flood damage has occurred to these facilities the symbology or the symbolization and coloring of these rings that you see indicate those features that are under water that have been flooded versus those that have not been impacted by foot by flooding so it's not just when we're talking about spatial analysis sometimes that also involves using imagery as a backdrop for your analysis and then conducting various different analytical tools that work on imagery imagery to determine certain conditions so in the gis world you can work with vector data lines points polygons as well as imagery data or sometimes people refer to these as raster data sources so in this case you can see areas pad sites and other equipment sites that are completely underwater and those that are completely dry so there's other types of spatial questions that pod users are asking of their data any any gis system like the one you see here this is a screenshot from arcgis you can see that um the pipeline in the graphic has been symbolized by color based upon what type of coding exists so you can see coding coding type 1 2 and 3 there you can see how those colors change along the line and get a very easy idea just by looking at the change in color how coding types are changing along the length of that line but you can also ask some other questions of this data so using analytical techniques and gis you can find out where's the nearest control station to a particular valve or how long is the longest segment of this class two coating that you see so it's very important to be able to to analyze pipeline data spatially as well as tabularly and i hope you're getting an idea that there are many many tools out there that can be used to analyze pipeline data coming from pods one last slide before we move on to the presentation so in this case we're looking at two gis layers you can see a a colorized pipeline it's symbolized by maop remember maximum allowable operating pressure symbolized as linear events so you can see the line changing symbology but then also the circles are symbolized by their pressure rating values so as you think about this it requires a little bit of of spatial thinking on your part to think spatially how would you what kinds of questions would you pose to the database that would help you understand more about this population excuse me this pipeline and the features around it are attached to it so this is an open question for you maybe to think about if you don't have the opportunity to work with gis or you don't haven't really thought about spatial questions don't be afraid of this you ask spatial questions all the time but thinking about them with pipeline data might be something new for you so at this point maybe sometime after you've downloaded the presentation come back to this slide and just think for a few minutes about the various types of spatial questions that you might ask about this data what's near what's nearest what's far how long something is or or what's the spacing between certain types of valves and so on and so forth so there's a lot of questions that you can sit and list but the exercise here is to exercise your mind spatially to begin thinking about spatial concepts and spatial relationships so at this point i'm going to end this presentation and i think i've left ample time for our presenters today so jackie smith who was was introduced earlier is going to take over and she is going to provide a lightning talk jackie yeah hi can you all hear me yeah okay great and you see my my screen sure do great thanks julie appreciate it all right so if we have any questions and answers for the pods uh discussion we can definitely do that at the end um so i'm jackie smith again i'm the president of pods and that is a volunteer position so right now i'm in my uh put on my different hat i put on my day job hat i also i work for rosen resin group um i'll tell you a little bit about them but our lightning talk today is going to be about using ili data to improve your pods data this presentation uh was submitted by todd nowicki who was not able to join us today but he helped offer the presentation and then i have my team member marcilio torres who is a senior application specialist been with rosen for um over five years now and he will present uh more of a demo style after i'm done with my my section hey jack you might want to swap screens oh okay great and i will do that um so you see my my with my notes i do okay i thought we did this last time okay let me try to figure this out here uh swap screens okay all right can you see anything at all there is no powerpoint in front of us right now okay now yep you're good you're good all right cool okay okay back to our regular scheduled program all right introduce rosen so we are privately owned since 1981 uh the german company started out of germany lincoln germany right now we have offices in 20 countries we have over 3 500 employees and we've expanded in the u.s we have about 400 employees here in houston um and in columbus ohio and then we also have an r d campus in lingan an engineering hub in newcastle those are some of the highlights of the company i wanted to talk about um so roseanne is known in the integrity engineering space for its inline inspection tools or pigs as you may have heard them referred to an ili tool so they're mostly known in this inspection or diagnostic solution area here this first red circle there are other areas to rows in here on the screen we have process control systems and enhanced materials however the the group that i set in is integrity solutions here the second red circle and this is where we have our integrity management services and our systems such as software solutions and gis consulting to name a few things today we're here to talk about one specific area pipeline data specifically the value that we can add from ili data and how we can improve the data within your operators pods databases so what are some pipeline data sources pipeline data and information can come from a variety of sources most would say that as built or weld maps are the most traceable verifiable or complete this tvc topic was explained well last week by petroit those records have to be made before the pipeline is buried however once the pipeline is buried very few options remain that do not require digging up the pipeline excavation which is expensive and introduces the risk of external damage more external damage and health and safety issues so so it is another way that we can gather data after the pipeline has been varied so this is where the ili technology or the pig would come in we can use ila technology which ili stands for inline inspection as a form of non-destructive testing approved by fimsa so we heard about fimsa and julie's slides there are regulatory authority in the united states um and a non-destructive method is to run an inspection tool inside the pipeline you can also once you download this presentation or or watch this webinar you can click on this uh video to get a lot of information about running a pig and a pipeline gives you a little a show and tell there basically the tool is placed in a launcher a launcher launcher and receiver you'll recognize from pods terminology those are stored in the pods data model um you run that through the pipeline it generates a ton of data readings stored on the actual tool itself and that tool is extracted at a receiver and data from the sensors is checked and then what's resulting is a pipe tally which is a is a consolidated information provided by the in-house team erosion that evaluates the raw ili data feeds so the data is captured and this is can be put into your pipe attribute information in your your pods model you'll recognize pods five pipe segment table perhaps here in blue tracks things like wall wall thickness and outside diameter um your fitting location along the line and your attribute information can also be recouped from this ili data that comes from that pig you get your girth well locations your valves flanges casings sleeves tees taps again more things you may recognize from your pods training or your pods implementation at your company um the centerline xyz coordinates of all these things can be gathered if you run a toll that has an imu unit and the immune unit you may recognize from it you know it's in cameras and drones and things to capture location so what do you do with all this this data once you have that standard data in a format that you can use and align it you align it to the ili pipe tally to the pods of the gis center line we were able to use common systems to start to assess the data the system that rosen has developed to do this is called nema you can see the bullet points here um it can be on on-premise or hosted in the cloud it integrates as your gis it enables your entire organization to to work with data however it is more of a an integrity engineer's playground um it's python based and it is based on a continuous improvement plan do check act so you're able to do iterations to really improve your analysis and your and your your data work um so i guess what i'd like to do is just turn it over to marcelia now and he can show you a use case of how we've developed um this this this nema platform to help us check our ili data against our pods data and fill those gaps so let me um and my slideshow here and hopefully y'all can there you go thanks okay you can you all see my screen right now the dashboard with the map perfect yes i can see it thanks cool so again good morning i'm marcella torres working with jackie and rosin again i'm senior application specialist been with rosen for over like five years and what i do have in front of you is what the nema software is and just to make it very clear the objective here was to basically use the inline inspection data to compare to an existing pods database so we can try to use the inline inspection data to improve the quality of the pods database so as we said or jackie explained on the previous slides this software itself it is configurable and we can set things to be step driven meaning that our goal is to get our data better and the application itself will guide you through the steps to complete or to achieve this goal so here what i have in front of you right now is a very basic map because what we're doing right now is let's say that we did have our pods database built let's say 10 years ago for example using s build or whatever technology we had back in that day but now we ran an inline inspection meaning that we did have a tool that ran through all this pipeline with a unit inside it actually got the coordinates right so what we're trying to do with this thing right now we did plot what was identified in the inline inspection versus what we did have on pods so with this map we would at least have an idea like is the pipeline still in the same place or is it rerouted or something like that and if we believe that this is acceptable we could do something about it right but this is again these are just tools that would help or like support the gis analyst in making decisions because usually if you have a big pods database and you want to fix it or improve it you ask yourself where do i start right so here for example it's just very basic you will see that the the the darker line is the route where the actual pig went through and the orange line is actually where the pods database had in the past so you see that we have like small discrepancies in here but if you think this was inspected 15 years after it was built we could say that is pretty acceptable right and also what we do have in the same part right here is we imagine that if we roll this pipeline as a single thing it was going from start to end we plot what was identified in the pods database and the assets along this line so we can have some comparison of what we're looking for and what we're trying to help with these things right here right so you can see that if we have like a dynamic that we can zoom in things and get closer looks to see how the data does behave on that particular point and we could see that for example on this portion of the pipe we do have the joint numbers we do have some wells that were here we have evolved and we can see already that we did have a highlight the wall thickness that was identified in here but we didn't have anything on the actual pos database right and so once we do that we're okay with the route we can see some comparisons what can we do next right so once we start the actual analysis we could run a procedure or account or a query that would just break down what was identified what was detected on the ili versus what we had on the gis right and here with the use of python we do have the counts so we can start to find some discrepancies here what we're trying to do is we're trying to use the ili as the master or where we're trying to go to but we can see that we do have for example 46 markers that were identified and the ili versus only 14 that was built when pods database was built so that could be newer installations newer updates and stuff like that and here again we do have what we call the linear comparison where we put this on charts and we start comparing them to see which ones match and which ones did not match and here for example we can see that there's this valve right here that matched with this one right here and of course we are using some tolerances like a hundred feet or something just to make sure that we actually have like an emerging of error because we don't expect them to be a hundred percent match but this is try starting to tell us that there is some data on the inline inspection that happened or was updated over this past 10 years that was still not in the pods database so this is just a start right and moving forward this we're doing with the pipeline fixtures these tabs valves and etc but we can also use this to compare the actual pipeline properties such as the wall thicknesses and the diameter right so here we could see where it did match and where it did not match but right now we can see that there's a not a lot of matches because the pos database probably was not built with the correct information so you see that there's a lot of variation in the wall thickness that was detected on the inline inspection that was not reflected on the pots database right so again with this three first steps is just comparison it's just showing you what you can see but what can you do about it right and here what we start to do is we run again a procedure in python that what is doing is getting those pipeline fixtures that were in that identified on the ili and we're bringing that into the pod structure again the software is not telling you what to do but that's at least a start right so if there's for example here we do have the route the start and end where does that fall all in the pod structure and this is what we had in parts before and this is what we based on the inline inspection what we're suggesting that should be added on the pods database from the inline inspection right so this is where we actually start to after we find the discrepancies we start to take action to correct the database and here the same type of analysis we do that with the pi properties meaning that if we found those wall thicknesses in the ili that were not in the pods database we now bring those measures into the pod schema so we can update the database back and actually have more accurate data and how we're doing that right now we use this as an export so we bring the pos database into nema we process these analysis and we export back the results so the person which is actually responsible for the data in pods can assess if they want to put this back in parts or not so again just to make this very okay the objective again is we have a pods database that we know it's not up to date or has some work done on it we do have an inline inspection data set which has data that's more recent so what we're doing here we're just trying to use the inline inspection data to update our pods database to make the pods database more accurate and we are using the rosin nema software as a tool to get us started to at least automate some of that heavy lifting work so the people that actually is doing the analysis only to care about the actual analysis and not putting everything together and this is what i had to present for now and if you have any questions or i guess i switching back to jackie or kathy and but this is what i had to present from the rose inside barcelo thank you so much um we are a little bit over we do have a couple questions and uh i'd like to uh for those who can still stick around i'd like to uh ask one of our presenters one or two of these questions is the imagery utilized for gis analysis updated on a daily basis to ascertain any damage caused by a natural disaster such as a cyclone or flooding so really the question is is is um how is it updated how often is it updated if you could address that yeah i mean i can adjust that i used to work for an operator for williams and the the aerial photography um it just depends on how they've uh who they uh contracted with um most of the time that data is not updated um that often um and sometimes it just depends on the need um a lot of times yeah yeah go ahead julie so i used to work for a digital globe which is a satellite uh imaging company and um i will i will say that that it is in cases of widespread disasters like hurricanes and typhoons and things of that nature there are operators that do produce data you know maybe not every day but certainly aerial flights can take place many times throughout the day and they do take place many times during the day the federal government if it's a u.s storm or calamity that takes place you'll see that that most government agencies will have access to that daily updated data there is a small there's a satellite operator called planet and they also have pretty short turnaround on their satellite data but most satellites have to be pre-positioned and so it's not usually a 24-hour turnaround but it could be shorter timelines like maybe once or twice a week or something like that um ariel is probably your drone is is probably going to be the the quickest update source thank you there's also somebody who would like to have an example of a search of data based on pipeline kp values how could we get him an answer offline yeah i would give him an answer offline kathy thank you uh and then one last uh question from uh ronaldo with excel mobile how often would you run an ili with a pig so that you would get more info to make sure that what is collected is indeed more accurate um yeah you can uh i'm not sure exactly how often the the ili tools are run um not in that area but uh they can run them you know as often as they need to um they're not you know that they're unfortunately i don't think they're very inexpensive i think running an ili tool is pretty expensive so if it's uh depending on the type of tool actually but um there are some that you can run uh annually but they are cost effective to run annually it does depend on like where does the pipeline is running through and also about regulations or what there's like could be either three or five every five years and i think i can also there's another question from ronaldo at first where it says bon gia and i can also say bon gia process but that's kind of how it is it really depends on what product does the pipeline transport what but if you want to run every year if you can pay for it it's entirely up to you but ideally it would be either like three or three years or five years or seven years depending on how critical the pipeline is and what the product it does transport thank you marcelio so with that we're going to conclude unit 5. um i'd like to thank julie parker i'd like to thank those who provided the lightning talk both uh marcilio and jackie we appreciate it um i just think that the presentations to date have really underscored the importance of a data standard to pipeline operations maintenance and overall data management pods has been developed by seasoned pipeline operators with really decades of experience and pods data standards and data models are approved through consensus of our membership so what you get as a user is standards and models that are really well thought out and those by those who are committed to continuous improvement which is what we see with pod seven and you'll be hearing more about that as we move forward so thank you for attending next week is uh unit six our final presentation in pods basics and uh we look forward to seeing you then uh thank you and have a great week you