Name: Transforming energy analytics with Spotfire® Data Science
Uploaded: 2025-05-06T16:16:09.295Z
Duration: 51 min 26 s
Description: Transforming energy analytics with Spotfire® Data Science

Transcript for "Transforming energy analytics with Spotfire® Data Science": Hello, everyone, and welcome to today's webinar, transforming energy analytics with Spotfire Data Science. We're thrilled to have you with us. I'm JP Richard Charman, and I'll be your host for this session. Now before we get started, just wanted to cover a few housekeeping items to ensure you have the best experience. The webinar will last up to forty five minutes with a q and a that will be held after that time. If you have any questions during the presentation, please use the q and a panel located on the right side of your screen. We'll address as many questions as possible, during the q and a segment at the end. Additionally, we've made a few assets linked to today's webinar, available in the docs section of our webinar platform. So that's located on the right hand side of your screen next to the q and a tab. So please do feel free to access these. After today's session, a recording of today's webinar will be made available on demand, and we'll email you the link shortly after the event as well. Now with that, let's dive in. I'm excited to introduce our presenters, Alessandro Chimera, our industry solution lead here at Spotfire, Dan Rope, our senior principal product manager at Spotfire, and Glenn Hoskins, our senior principal solution architect at Spotfire. Now today's agenda for the session, we'll be diving into why visual data science is important for industries, which Alessandro will be covering. Dan will be showcasing the powerful capabilities of Spotfire data science, and we've got an exclusive demo of Spotfire Data Science for energy, which will be presented by Glenn Hoskins. Before we get to the q and a segment, we'll also be discussing some of the key successes our customers have had in oil and gas, and we'll then dive right into our q and a segment of this presentation. Now with that being said, I'd like to hand it over to Alessandro. Okay. So thank you for the introduction. So I'm excited to kick off today's webinar session on how we can transform energy analytics with Spotfire data science, specifically in the exploration and production part of the oil and gas industry. In an industry where every decision can, impact millions of dollars, the question is whether your teams are kept with the tools they need to move fast and make confident decisions. Because too often, they're put in data silos waiting for on other teams or stuck using tools that don't talk to each other. And today, we explore how visual data science with Spotfire changes that, putting powerful AI enhanced analytics directly in the hand of the engineers so they can focus on what they can do best, solving problems and delivering results. So let's start with a quick look to reflect on some of the pressures and challenges that the oil and gas industry is facing today and how our technology can help. First, the oil and gas sector is built around long and complex asset life cycles, from initial exploration to production and decommissioning. All the decisions we make today can affect outcomes years later. That puts a lot of pressure on making the right decisions quickly and sometimes with limited data. So the first need is clear. It is necessary to reduce risk and decision making and build confidence in those decisions. Second, we all know how capital intensive this industry is. Every well, every facility is a huge investment. Operations are complex and costly and safety is always the top priority. And here the need is to improve efficiency, maximize effectiveness and ensure safe operations. And then third, we operate in the global environment where supply chains are critical and commodity prices change fast. One disruption or market shift can impact the whole operation. And that's why we need to maximize throughput, optimize recovery, and stay agile again supported by data. And thinking about data across all these challenges, there's a common threat. We are working with complex, fragmented and technical data across subsurface, drilling, production and behind. And that's precisely where Spotfire data science plays a critical role because when we give our teams the ability to see the data clearly, interact with it, and apply their expertise, they can move just from reacting to drive decisions. And that's what Spotfire enables, bringing data, AI, and human insights together. So engineers and teams, they can mark make faster, smarter decisions every day. You might ask, yourself, but what is Spotfire data science? So let me give you an overview on the categories of tools that are on the market today, and it's most likely that you're already using them. So upstream oil and gas operators typically rely on two categories of software, specialist tools and then statistical tools. In regards to the first categories of the statistical tools, most engineers in the oil and gas industry aren't statistic experts. Today, we might call those experts data scientists who specialize in selecting the right algorithm and tweaking them for specific purposes. But it's challenges for engineers to leverage that expertise directly. The common frustration that I often hear is, hey. Send me your data and then I send you back some answers and we can review them then at the end of the week. But then the line for the start off office is always long with someone else problems ahead of yours. And today, integrating advanced models into upstream operation isn't really straightforward. You need to code in Python to build application, then eventually write JavaScript to implement those models into an online platform. And when it comes to specialist tools, the second category of software, you have to be a specialist to operate them. There's always that one person who knows every feature of the tool while the rest of us just uses the standard report and processes. But most problems in upstreams aren't really standard. Moreover, extending these specialist tools to include additional data is always a challenge. They simply lack, the flexibility needed for complex operation. But Spotfire is different. It is designed to be the indispensable data analysis tool for oil and gas engineers. By combining agile exploratory visualizations with advanced industry specific algorithms, Spotfire puts your data at your fingertips and Spotify helps to bring together data from anywhere and displayed however you need it. And here engineers can clean and analyze data, identify anomalies and create new variables or metrics on the fly. And Spotfire natively supports Python, R, and Spotfire statistical, allowing engineers to develop reusable data functions that can be easily integrated into the platform. And this unique and powerful combination of capabilities, this is what we call Viso data science. So here is an overview on how domain experts across the industry can benefit of a Viso data science platform. We have the reservoir and geoscience engineer. They can identify the best extraction place and strategies. They can plan and communicate the value of development projects and ultimately manage the life cycle of the reservoir. And then we have the completions and drilling engineer. They can create bore and perforation strategy that provides steady and complete depletion. They can analyze well plans, execute drill operation safely, maximize penetration rates, and minimize reservoir damage. And then we have the production engineer. They can analyze forecast production rates, determine remaining reserves on the production life, for wells. They can orchestrate, completions, infills, and artificial lift strategies to maintain economic productions. And then we have the process and equipment engineer. Based on data, they can create process receipts that can meet production and or eventually the customer specifications. They can characterize variations and optimize for quality yield and safety. And with predictive analysis, they can schedule equipment maintenance and repair. And last but not least, our data scientists. They are empowered with a platform to support all the teams by building data functions developing Python or Accord and delivering prototypes. So across the 5,000 plus engine and energy organization currently using Spotfire, we've seen that our Viso data science approach delivers better data science improvements to support transformative and sustainable initiatives across all stages of the oil and gas industry. Overall, we deliver a platform that is not just powerful, but built for how engineers think and solve problems. With Spotfire, your team can build and share visual data science applications, accelerating time to insights, capturing best practices, and scaling expertise across the organizations. And even if you might not have heard about Spotfire before, we are very widely deployed across the energy industry. In fact, nine of the top global energy companies rely on Spotfire every day across exploration, drilling, production, and even a transition, to more sustainable energy resources. And later in this presentation, I'll walk you through a couple of use cases of our customers showing how they're using Spotfire to solve complex challenges in their operations. So and to sum it up, Spotfire offers something very different, complementary to the statistical and specialist tools. And our technology is designed for every engineer and their day to day job to dive into any kind of technical data and to apply any kind of analytics method. And as a vendor who has been in the BI and analytics space for a long time, the types of use cases that we address are not the traditional BI use cases. Visual data science is at the intersection of visualizations and advanced analytics, a differentiated approach to what is currently available in the market. Here in the above analysis that you can see, there's a visualization to understand the optimal spacing of wells and the oil and gas sector to optimize operations and increase profitability and improve resource usage at the same time. So and with that, I hand it over to Dan Rope, who will dive deeper into specific business data science capabilities. Fantastic. Thank you so much, Alessandro. Okay. Thank you very much. So I'm gonna talk about our Spotfire data science product today and how it can relate to usage in energy as well. So first off, just give you a vision, just sort of relating back to what Alessandro was saying. The thing that's unique about Spotfire is its ability to combine analytics and visualization. We're really gonna be doubling down on that investment in Spotfire and Spotfire data science and really kinda making that a focus of what we're doing going forward. And in addition to that, we're gonna start bringing in more industry focused types of analytics, interactive features, and visualizations. These are things that we've built some things we'll be building new and some things that you might have used on our community before, but now we're gonna we're gonna productize those and and make them more robust and add new features to them as well and support them. And then finally, enterprise scale, being able to deploy Spotfire out to thousands of users and also scaling with respect to sizes of datasets as well too. So we think that these three things are sort of unique about Spotfire, and this is where we are with Spotfire and kinda where we're going. So specifically on Spotfire data science, Spotfire data science builds on on Spotfire analytics, which is what everyone knows and loves about Spotfire, the ability to analyze data, to build analytic applications, and use interactive visualizations and advanced analytics. So Spotfire data science builds on top of that, and we're focusing on on adding adding features for to help with data understanding and preparation so you can identify patterns and see relationships and see what those potential issues might be in your data. It's also the place where we'll have our modeling and prediction, capabilities, being able to build models for for doing predictions. And with regards to in industry specific and energy, specifically, we'll have some additional energy specific visualization, algorithms, and connectivity. So example, connecting to LAS files, and using these visualizations for doing things like optimizing well placement, drilling operations, and assessing values of leased lands, and and many others as well too. So that's generally what we're doing with Spotfire. So first off, we'll talk about some of the new algorithms that we're adding in Spotfire. Our first batch of algorithms are primarily focused on time series, geoanalytics, and missing data. So as part of data science available on the fly out for your data functions will be these new data functions that you can immediately start using, without writing any code at all. And with Spotfire data science, you can you can configure these, and you can use these and apply these to your data. They are largely designed around energy manufacturing and also some cross industry, problems as well. So the geospatial functions that we're adding, initially here, and times when you're working with survey data, this allows you to transform that data into a court reference system, so you can have accurate well accurate positioning, in your data and supports both for coordinates and also geometric shapes as well too, geographic shapes. In addition to that, we have some distance calculations as well too. You can you can find the closest neighbors, to any given point or any given well. You can find a a a full matrix of all distances between a full set of, full set of data. And these distances also take or they're precise. They take another geodesic distance calculation. So they take into account things like the curvature of the Earth to, to give you the precision that you need for calculating those distances. And then, also, there's the ability to calculate geographic areas of polygons in this release as as well too. And these these offer options to do things like adjusting for accuracy versus precision trade offs, and there's a lot of rich features within each one of these to allow you to do exactly what you need to do and fine tune these these algorithms. Additionally, dynamic time warping. For those of you that aren't familiar, it's actually kind of it's it's a a time, technique, but it's applicable as well in-depth. This came from originally where, like, if you had two different audio recordings and one person was talking faster than the other, but they're both saying the same thing, This is the kind of algorithm that could align that and help you understand that those are actually similar. Well, the same kinds of things can happen as time as it does with depth, and so you can apply this to, to to looking at measurements throughout depth to get those aligned, say that those, even though they might be happening at different frequencies throughout depth, you can align those and and see if they're actually the same thing that's actually happening or something similar to help, access to make things like production and so forth. Now next, wanna introduce Spotfire data science add-ons. So what you'll be able to do this isn't a way that we're gonna keep up with the pace of innovation. We're gonna be adding new Spotfire visualizations and new actions that are available directly from the product that you can get, without having to have a full update or new release of the product. Visualizations are exactly that. Those are new visualizations custom tailored for a specific purpose. Actions, if you're not familiar with those, those are essentially, automated k automated things in Spotfire that we can script and provide a nice input dialogue to fill out that you can for how you wanna use that. You can build those yourself, but we're gonna be providing, actions that are prewritten and available directly for download download and and immediate use from within the product. And then you'll be able to look at both the ones that we provide as part of Spotfire data science, and, also, we have several community assets that will be available through this, through this add on browser as well too. And so, yeah, we'll be continuously adding new add ons over time, and we'll be constantly, you know, you know, providing those to you, you know, as you and we'll we'll be listening to what people are using and what they're looking to do, and we'll be adding more as we move along. And they're fully supported and covered by the warranty and support. So, initially, it will provide, eight visualizations and five actions. They're largely designed for industry specific and cross industry problems, but most of them are largely around energy use. And and, again, those new actions will be available without requiring a platform upgrade upgrade, and they will be supported like any other feature. So I just wanna go through a few of these. So some of these, if you've been using Spotfire and using our community assets, you might recognize some of these that have been available in the community. And what we've done is we've we've invested time in these to enhance them, to make them more robust, so that they can be fully supported. So the well log chart is one of those that's is, quite popular, and it allows you to use data, you know, well log data and be able to portray it in a way that's expected. So you can put the individual measurements on different tracks. You can configure it to move whatever measurement you want on whatever track. You can see along the depth alongside what formation might be there, and you can even see the individual geology that might be there as as well too. And there's there's, this will be demonstrated in just a few moments, but there's a lot of configuration options for you to to adjust this exactly how you need it, and it's makes it easy to get started on being able to visualize data in this shape, which is kind of which is sort of expected here. The well spacing gun barrel diagram, again, another one that we've we've had before, but now we've enhanced it. So this allows you to look down the barrel, down the the well, itself horizontally, and you can see the exact distances between the wells. You can see the formation tops in this view. There are interactive tools. You can actually judge distances that might you might be plotting to put a new a new well. And and and and, again, it's highly configurable, and, this will also be in the demonstration in just a few moments as well too. The wellbore diagram, it's the schematic, of a wellbore. So it tells you, and you can overlay that on positions where, things like fluid might be. You can then and see the metrics of those fluids such as density, temperature, and concentration. And it allows you to diagnose, issues or problems that might be happening, throughout the the wellbore. The three d surface and line chart, this gives you a a nice three d view on what's happening underneath where the well where the wells actually are. You can pan and zoom, to see detail. You can see the formation tops. You can see the exact where the wells are and the locations, the perforations. And and, essentially, they can work well with the gut with the gun barrel diagram we showed earlier, and then you can see kind of a slice vertical slice throughout the throughout the ground, and it gives you that that representation so you can drill into that detail and work with it more directly. Now in addition, we're providing some time series capabilities. And, again, a lot of the time series capabilities apply directly to depth measurements as well too. And this really, the focus is allowing you to prepare this time series data so you can use it for analysis. So we're introducing actions and and also, data functions to allow you to, for example, normalize time series so it can be on the same scale, so it makes it more relevant for comparison. Imputation. So a lot of times you have gaps in your in your time series. This allows a variety of techniques to fill in those gaps. Imputation or last value or other other techniques as well too. It allows you to resample, in other words, adjust the frequency. So if you need a a finer granularity of of time measurements or a coarser granularity, you can use the resampling to to do that adjustment. And, additionally, a smoothing feature as well too. So you can see the patterns and, through the noise, essentially, through what the time series is. And there's also gonna be some ways from that based off those moves to determine what might be an outlying factor by comparing it to what the the expected smooth value is. And finally, we have actions available for missing data. So missing data is a problem that everybody tends to have. This is it it allow would allow you to, examine your your dataset. It'll tell you not only the patterns of missing data that might be happening, like what columns have missing data, what rows, and how much it is, but also some indications on which columns you might remove first in order to optimize the amount available data that you would have. So the action will cut kinda covers both of those things as well too, and it gives you a nice visual presentation on on what on what those missing data patterns actually are. K? And with that, I think I'm gonna hand it over to, to Glenn to provide a demo. Alright. Thank you, Dan. So I'm gonna share, an analysis here, a DXP analysis, that will show some of these new visualization models that Dan was talking about, and how they fit into, energy context and how they can be used in different phases of the kind of the exploration, drilling planning, logging, completions life cycle, just some various facets of where they could be used. So to start with, this is more of a, you know, reservoir planning, maybe a geologist type tool. We have a series of wells, scattered across an area. We can make use of the three d visualization here to generate, formation tops based off of survey data from those various wells. So here I've got a, map of the Wilson Basin in, North Dakota with several thousand wells, with formation data associated with it. So there's a data function sitting behind this and as I select and mark these wells here, the data function will interrogate the formation tops and generate surfaces based off of that data. So you can see it's going through the function here. It should render shortly. And you can see this is this is now our formation tops across this zone of the area, and we can rotate and zoom in and zoom out. And, we also probably wanna filter out some of these some of these formations as well. So let's get rid of this one and that one that one. And so you can see, exactly the topography of the geology that's lying inside of the area that you selected. So this would be useful during planning exploration planning phases, in conjunction with, looking at maybe historical production data as well to, plan new wells in the existing area. So you can see the service quite well covered, but there may be opportunities where we could drill down literally and make some more, provide some more production from the from the same formations. So in conjunction with that, we wanna look at how existing wells interact with, other other wells that are in the in the zone that are being produced from. So this is similar to the animations that both, Alessandro and Dan have shown in their slides. This is the actual analysis, though. So, again, this is our three d view of the wells. So we have our well sticks now in addition to our formation tops. I can zoom in and zoom out as well. I'm gonna filter this down so that we're just focused on the horizontal section of these wells because that's the most interesting bits. So if I filter this down, it's gonna filter. And now we can see our our wells a bit better. The yellow segments on here represent perforation intervals, so we can see the trajectory as well as the completion zones inside of that. We could also overlay, formation colors on these as well if we were going through multiple formation layers and producing in multiple formation layers. You can, add additional information like that. So these kind of surfaces are a bit obstructive, so there's few options in here. We can add some transparency in here so that we can see the way things are looking inside. These formations are quite flat, but typically, they tend to be more folded, and being able to add transparency allows you to view in a bit more detail. So this is a this is, a new visualization as part of Spotfire data science. This is combined with surface well locations here. So this if we rotate this up like this, this is basically the view that we see in the well chart, in in the map chart. This is a, surface projection of these wells on the surface of the earth. Now we also would be interested in looking at what does it look like in a vertical section. So if I rotate it in this way and look at it like this, this is the gun barrel view that Dan mentioned before. I've also heard this called a wine rack, so I guess you can think about this in a couple of different ways. So if we create slices through this in three dimensions, now I can do that by adding these slices here. You'll see there's now a gray plane has now sliced through these wells in these formations, and a black line has appeared across the wells. That has generated now a well spacing diagram. We would go through a data function that would look at the the formation tops, as well as the three d, trajectories of those wells and generate some interpolated data to calculate what the true vertical depth is and the section distances across this gray plane. And we can do that at different light, different different points. So, you know, this is three different slices, and you can see the gun barrel is changing ever so slightly at each point. I think it is almost like an MRI where you're doing slices through a through almost like a brain or or through the three d, zone that we're producing from. So the gun barrel itself, this is looks just like a scatter plot, but the additional intelligence in this is tied into the showing the distances between the wells, which are calculated inside of the visualization. So I can show perpendicular distances between these wells, and this shows you the two nearest wells to each well. So you can use this to look at, say, how production might be impacted by proximity of wells. You can see we have a cluster of wells here. So we may, for example, find in our production that these wells are, somewhat reduced compared to the ones down below, which are more spaced further apart. We may wanna plan shutting in one of these wells in order to optimize production from the other two. Likewise, we might look at this and say, we have a big zone here where we might want to add another tier of horizontal wells in this space because there may be additional production, opportunities in here that we're missing. So this diagram allows, engineers to plan what a new drilling program might look like in this area. So that's perpendicular distances. We can also do horizontal and vertical, which shows you kind of in that dimension the distance between the wells. And, again, there's some ad hoc tools in here that we can use for measuring distances into and at any arbitrary point, these are called measuring sticks. At the moment, these wells are being grouped by their individual formation, but we can then, ungroup them and they will do distances across formations as well. So once we've identified where we're going to plan maybe another tier of wells in here, the next thing we're gonna do is design a drilling program in response to that. So once we have our drilling program in mind, we'll, contract a rig company. That rig company will go on the site, and they will start, drilling drilling drilling into the formations and drilling into the reservoirs. So we're gonna be interested in capturing that data and looking at it. This is a, a view from what's called the IoT drilling accelerator. So, typically, this view would be attached to a real time data feed from the WixML servers that rigs tend to feed. And in this case, we have, a well log mod, which is the, vertical line chart view that is done, index on a on a y axis that's done by time. So, typically, drilling programs, WITSML data will be produced either a time based or depth based. In this instance, it's showing as time based. We can see a summary of what the, rig is doing in it at this point in time, some gauges that just show you what the last values are for things like rate of penetration, weight on bit, rotary RPM, upload, key metrics that are part of the drilling cross process. Now the raw data that came in was analyzed by by this drilling accelerator, and it does a rig state classification. So based on the metrics that are actually being produced at any given point in time, it can classify it into any one of these different stages or different operations that the rig would be doing. So typically, rotating means that we're making whole at that time. The the the bits rotating and we're we're increasing the bit, the depth of the hole at that at that point. So if I select the rotating in this bar chart here, it highlights on the well log the zones where we were actually rotating. So we can review the individual metrics at that point. So block height, that's an interesting one, fairly obvious. As the traveling block goes up and down, as you're rotating, it's gonna be going downwards as the pipe moves down into the hole and the bit advances. Tripping out is another interesting stage. So if we look at this, you can see there's various points where the the bit, where where the traveling block is going up and down as pipe is being added and removed, inside of the inside of the trajectory. So at the end of this, particular run, you can see there's a trip out here. The upload is dropping as the pipe stands are being pulled off of the string. The block height going back and forth as as you would expect in a fairly short period of time. So this entire log is a single run of the of of of a drilling, process. So once we've completed drilling our hole, we're gonna wanna do some, open hole logging. And what we can do with that is, we can we can create, a well log again using the same well log mod. In this case, what I've done is I've created just a basic gamma ray mod gamma ray log. What I'm gonna do is configure this in real time so we can see how to add additional curves onto this. So gamma ray is good, but we're gonna want some extra stuff. So I'm gonna choose the, x axis here and I'm gonna select some additional columns. So I'm gonna add, a caliper. I'm going to add a bulk density and neutron. I'm going to add some resistivities. And for some reason, there's no spontaneous potential in this data, which came out of the last file. Otherwise, I would add that as well, but we don't have that at the moment. So what it's done by adding those curves, it's now added all of this data onto one track, which isn't typically how we're gonna wanna look at this data. What we'll do now is separate these curves out into different tracks so they're a bit more, what we're used to seeing. So we do that through this interface, which is called the tracks and curves configuration. I'm gonna add another continuous track and another one. And what I'll do is I'm gonna drag over my neutron and my bulk density, and then I'm gonna put my resistivities over here. I'm not gonna rearrange the order of these if I want to change how they appear in the stack. I can adjust the spacing between the tracks to make them sit together. I can make this a, logarithmic scale for resistivity. That's fairly common. And now we have all of our curves now separated across track. So that's a good start. There's some other stuff that we're gonna wanna do here. So if I take my gamma ray, I'm gonna wanna set a scale range of zero to one fifty. My neutron, I'm gonna wanna reverse it and go from 0.15 to 0.45. And my bulk density, I'm gonna set that to that. So now we've calibrated our scales. We might see some more interesting geology in here. So what I'm gonna do now is I'm gonna add a fill between these two curves. So curve, that'll color fill, fill the area to the to another curve. I pick the curve as the row b. I'm gonna leave the colors as positive green, negative red. And now we have to see some interesting zones in here. So in addition to this, we can add in now, geology information or formation information. These are categorical data on top of this continuous one. I'm gonna show you what that looks like here in the best best tradition of Julie Child. This is the one I prepared earlier. So here we have our formations, named as they are. This is in the in in the The Netherlands. So there's a lot of, like, salt and anhydride and and carbonate layers in here. So So you can see these big kind of big fat red layers here. They're actually salt when you look at them inside of the geology, for this particular well. And in between these two salt layers, we've got a little bit of limestone with some, hydrocarbons shown here, and there's actually gas in here. They flow tested as well, and there was gas shown, and there was some completion intervals where they were doing flow testing. So that's what these black bars on this this side over here is. The geology now as well has fill, patterns associated with it, so you can see what a clay stone looks like versus a dolomite. And there's a number of different things that you can do in terms of enhancing the view of these, of these of these charts. So in addition to this, we can also look at the three d again as part of drilling, jumping back to that very quickly. We're interested in looking what the trajectory of the well is as it's being drilled. So, typically, we'll have a drilling plan that will be, designed upfront. That's what the red red line is here. We can also look at this in real time as the survey progresses and zoom in and see how accurate we are with our plan here. The blue line in this instance is the actual as we're drilling. And you can see there's a slight deviation here from the red line, which is the plan. So this would be something that the the drilling engineer would want to address, as they're doing their drilling program. So once the, hole is completed, the wells open hole is logged. The next thing you'd wanna do is start completions. So as part of the completions process, again, there's a running casing into the hole and cementing. There's a number of use cases in there for using things like the well log. These are showing, wireline casing column locators. For example, the, running for coil tubing as well. This is another use of the well log mod, and pumping, fluids down into the well. So as part of that process, the fracturing stimulation phase of completions, we have another mod in here which is the wellbore mod. So this is a treatment plot, which is, showing frac stages for a well, as it's being stimulated. So the use case of the wellbore in this instance is it giving you a two dimensional view of a three-dimensional well. So on the y axis, we've got the true vertical depth. The x axis in this instance is actually the delta between the two, the measured depth, and the true vertical depth. So in the vertical section of the well, it kinda sits over here on the left hand side. As it starts to incline, it will start to move over to the right a little bit. And then when we go through our heel, when we go through our 90 degree degree turn, we see that on this two dimensional view as well. And then in the horizontal section, we're staying at the same true vertical depth where our measured depth keeps increasing. So the interesting part about this one is we can use it to look at the geometry of the well. So we have our trajectory here, which is just based off of TBD and MD. I'm gonna zoom in down below here, which is the most interesting part we're interested in. Now we can add in the, key key, features of the of the completions process. So for example, we would want to to perforate, casing. So we'd wanna know where guns are located at any given point in time. We can add a layer to show a gun. We can show where those perforations ultimately end up once they're done by adding that. We can add plugs, which are another key part of the fracturing process to be able to isolate, zone perforation zones. And then we can actually show the level of fluid in the well. So, typically, again, this would be done as part of a real time process. They would know the volume of data that's being pumped into the well through some calculations and the use of things like the capacity factor of the casing. They can calculate how deep the fluid is at any given point in time. And as the fluid approaches the bend and then approaches the plug and the perforations, that's the point where the stimulation activity actually starts. So this is just showing a fill of column fluid. We can also show what, fluid with particular values would look like at any given depth. So for example, it might be density, proper concentration, fluid density, other metrics such as that. Yeah. I think that's that's all my demos. So Alessandro is now going to show us, some more, Spotfire success use cases. Thank you. Thank you very much, Glenn, for the for the demo. So let me share my screen again. Slowly, it's coming. Okay. Perfect. So as promised, let me walk you through customer success stories where Spotfire made the difference. So Wintershall is one of the largest producer of natural gas in the Dutch sec sector of the North Sea. And, however, the challenge, they faced was that they had a relatively small lean data engineering team. And the team was overwhelmed with a request from the exploration department, constantly being asked to extract and combine data from multiple disconnected silos. And each of the requests could take an employee up to eight hours to complete, often involving up to five different application just to pull the data together. And as a midsize operating company, they were looking for a way to empower their exploration geologists with a self-service tool to access and analyze various datasets and variables, enabling them to identify locations where drilling and exploration well could successfully advance to development and ultimately then in production. And hereby using Spotfire, they created an internal data analytics platform that they called Crossfire, which allows team to see what data is available internally, out of the data engineering team, to any missing or nice to have data, start visually interacting with data in a single no code platform. And, that freed up the data engineering team to set up processes to identify and ingest data from the open community data set to reduce the level of uncertainty associated with the findings. And here with Spotfire, they have achieved a well portfolio optimization and reduced cost and time by over 85%. So the next example is Enertell, and their challenge was that investing in oil and gas has evolved into a high risk game, often with a higher hurdle, for returns. Mistakes, during the drilling process can easily escalate into costing multiple millions of dollars. So determining where to drill and how to complete wells for Maxima ROI increasingly requires a sophisticated data drive edge. And here the solution, that we have provided, the team developed a unique advanced production and economic forecasting software that they called Quantum by leveraging the power of Spotfire visual analytics to discover value opportunities and increase efficiency. And the software enabled, the company to connect to a variety of data sources and dive deeper to make better investment decision. And the results, now they can, calculate well spacing from public oil and gas data points across a basin of 10 or thousands of wells and deliver data driven intelligence, throughout software solutions and suggest better strategic advisory. They also discovered a new approach to downspacing, preventing million dollar drilling on or investment mistakes. So and before closing, a few final takeaways. So as you've seen, visual data science is something new and exciting in the industry. Spotfire combines best in class visual data exploration with advanced oil and gas analytics to speed up problem solving and increase cycles of learning for engineers. And Spotfire helps you to bring together heterogeneous technical data to find insights, apply industry standard advanced analytics, or develop and drop in our or your own techniques. And finally, Spotfire is designed to scale to larger communities of technical users, allowing them to share access to data, share best practices, and share also their findings. So if you recognize, a few of the technical challenges that we described, and you would like to unleash the expertise and creativity of your engineers, we would, we would love to talk to you, about your specific solution, situation, and share experiences from other customers in the oil and gas industry. And with that, I hand it over to JP. Looks like I was on mute, but I just wanted to thank, our speakers, Alessandro, Dan, and Glenn for that insightful presentation, and thank you to everyone who joined us today. Now before we get on to our q and a segment, a few things that we want to share. So with regards to our webinars, we have two great webinar series are being added to on a regular basis. So whether you're looking to find out more about Spotfire or looking to learn about the latest in terms of what's new, don't hesitate to register to the full series, and we'll add the links to the chat very shortly as well for these series. In terms of on demand access, a recording of today's webinar will be available soon, so keep an eye in your inbox for the link. Now if you're interested in learning more, feel free to connect with us, to to visit our website at sportfire.com or contact us directly. There are lots of ways to interact with us, whether it is via our socials, through our community. Additionally, our blog site has lots of great content where we share the latest on visual data science. Dive into Spotfire data science in more detail. And last but not least, if there are any enhancements that you would like to see or are have any ideas that you'd like to share with us, don't hesitate to visit our ideas portal. Now with that, I'd like to move on to our q and a segment of this webinar. And we've received a few questions, so just, kick it kick it off. I believe this question would be for yourself, Alessandro. But in terms of limitations of traditional specialist tools and statistics tools compared to Spotfire for engineering data analysis, can you go into what those limitations are? Yeah. Sure. So thank you for the question. So, let's say that traditional specialist tools in the energy sector such as those for geophysics or well born analysis are powerful. But then often, they are complex and require specialized knowledge to use effectively. And they tend to have arbitrary methods, and there are they have also inflexible workflows, making it challenging to adapt them to new questions or to combine data from different kind of sources. Well, consequently, only, few experts in the organization as traditionally might be able to utilize those tools fully while others rely on them for assistance or they're just using the predefined workflows. Statistics tools on the other side, requires a high level of statistical expertise, often with a degree in statistics or extensive training. And this can make them inaccessible to many engineers, leading them to reliance on spreadsheets or basic data analysis. And here, Spotfire all counts these kind of limitations by providing an intuitive visual interface that allows engineers to explore and analyze data without requiring deep expertise and their specialist software or advanced statistics knowledge. It's, let's say, Spotfire is more flexible and extensible and enable user to adapt the platform to their specific needs and integrate, various analytical methods as needed as you have seen also during the presentation. Perfect. Thank you very much for that, Alessandro. Initial question that came through, and I believe this would be more for, Glenn and Dan. Is there a possibility to update the three d scatterplot in Spotfire Analytics to be able to zoom in and out and move the image with the click of a mouse, drag it like you've shown in your demo, for Spotfire data science, Glenn? Yeah. I'll I'll take that one. So I think for Spotfire analysts, yeah, we'll definitely take into consideration some of those features and and where they've been requested. But, generally, you know, for this particular visualization, you know, this is gonna be the fastest way for us to provide those capabilities will be via Spotfire data science, and they're tailored towards, towards using it, for for subsurface data. I don't know if you wanna add anything to that, Glenn. Yeah. I'd I'd just add that, for additional features like that for product, product capabilities, the ideas portal is the best place to go because you can log those features in there, and they'll then be considered for additional. Yeah. Excellent point. Perfect. Onto our next question. How does Spotfire support agile decision making in engineering workflows? I believe this would be more for, once again, for yourself, Dan Rope and Glenn. Yeah. So I'll take a first stab at it. So, yeah, I think, you know, almost by definition, Spotfire is extraordinarily agile. You know, the what we're showing, today is things that we've built inside of Spotfire. But everything that you've seen, everything from the algorithm to a visualization to an interaction that that governs those and and and and drives those, you can build all of those yourself. Right? So you can you can customize Spotfire to exactly what you need it to be, and that gives you the ability to to really rapidly, tailor, Spotfire to what to what you actually need. Glenn, you wanna add on to that? Yeah. Also the multiple data sources as well. You can build a a visualization view that would combine things that you typically wouldn't see in perhaps specialist engineering tools that you might have in the industry. So, for example, being able to combine three d views along with map map views, along with production history, along with well logs, all on one single pane of glass, that's not something you would find typically maybe in a Petrella or an industry solution. Whereas Spotfire does give you that capability and the agility to be able to build exactly what you want. Fantastic. Thank you very much, Dan Glenn. Seems that we've got one last question here. How does Spotfire empower users to share analytical best practices and collaborate on data science? Would you like to take this one down? Yeah. Sure. So yeah. So it's quite similar to the to the previous question, I think. So, you know, there there's, you know, just the ability to extend Spotfire, and embed your knowledge into an analytic workflow and then provide it for somebody else to use is a great way to share best practices. And the flexibility and the application development features of Spotfire really, really help you do that. And that's that's largely what we've seen a lot of uses of Spotfire for. You know, you can create very much, you know, guided applications for, people that might be using what you're developing, and that that really allows you to, you know, expand those use cases. So Perfect. Thank you very much. We don't have any additional questions, but in case you did have, some questions that come up after this session, please don't hesitate to get in touch with us directly, and we'll be sure to answer those questions. Once again, I'd like to thank you all for joining us for today's session, and we hope to see you at one of our future webinars. Our next webinar is on Thursday, so Thursday, the eighth, and they will be diving into what's new with Spotfire, and we'll be really going into what you can expect in our upcoming 14.5 release. Additionally, we have another webinar next Tuesday on the thirteenth focused on Spotfire data science once again, but this time focusing on manufacturing, on high-tech manufacturing. And with that, thank you once again for joining us, and we'll close off the webinar. And just wanted to wish you all a fantastic rest of your day. Thank you again. Thank you very much. Thank you. Thank you.