Thank you for joining us for this podcast on adopting digital twins in the oil and gas industry. This is Ralph Rio and I'm an industry analyst with ARC Advisor Group since 2000. With me is Jan Johanneson, who is with Tata Consultancy Services or TCS. Jan, would you like to give a short introduction of yourself? Yes, good morning or good afternoon for everybody around wherever you are. My name is John Erica Johnson. I am the Industry advisor for Upstream Oil and Gas with the Tata Consultancy Services. I've been with them for about almost six years now, Excellent. And Jan has a long history prior to that in the oil and gas industry. He's a well versed expert in the dynamics of that industry. So let's go to the first question for Jan. The oil and gas industry is often disrupted by events and you know the current COVID-19 pandemic is certainly one of them. That has caused a lot of chaos both from. There was an oversupply and certainly. The COVID-19 pandemic caused a reduction in demand, those two things that are just generating all kinds of chaos in the industry. Jan, if you could just give us your thoughts for managing this chaos Well, I think that the chaos that the COVID-19 has created kind of shadowed or created an over focus on just the COVID-19 problem when we actually had a fundamental supply demand issue already in the industry prior to this. They, the event of the US becoming a major or the world leading pursuit producer, producer of crude again have created an enormous oversupply versus the demand. The COVID-19 situation just amplified that particularly on the demand side is the demand side that went down to almost 0. So I think that is where we see the effect. So what do we do for this? I mean how do we manage this chaos? Well, I think that with the tools that were or the solutions that was being worked on prior to these. Pandemic is the same tools that we need to focus on now, but maybe a little sharper, but it allows us to put solution in place that will last after this. And then it gets going by and benefits the industry in the long term. I think TCS has been quick in this and focused something we call our business 4.0 model that is providing services and support in very much in agile fashion, which is location independent and allows us to work with remotely. We've been able to put 85 more than 85% of our workforce on working from home during this pandemic. And still being able to service our customer. Agility for the industry is important. TCS has been leading the way here by declaring that we will be an agile company with 500% by the end of 2020. So I think that this is things that we are going to have to look at going forward and just make sure that we do not react, overreact to the pandemic to put tools in place that will last us going forward excellent. So the digital twin which is. A virtual representation of a physical asset. Seems to map well to the TCS Business 4.0 model. In that it gives real time visibility to what's going on in an asset. How would this technology apply to managing the chaos in the oil and gas industry? I think that the well there's AI think digital twin is just one tool. That is not necessarily new to in general, it's been around in other parts of other industries for quite some time. But I think that the digital twin in combination with other tools like automation and digitization is where the power comes in. Digital twins allows the automation to become become much more efficient and I think that's where the power will come. Wow, when you talk about the automation becoming. Much more efficient. I've heard of Digital Twin improving the operation of a equipment and preventing unplanned downtime. Are those the kinds of aspects of efficiency that you're referring to partially, I think that's the one thing that in the TCS and operating model, it's also the what we call the machine first model, which we're saying that the if the decision is or who can do this task, let's try to do it with the machine first. So can we automate many more tasks now digital twin providing a real time model where two things we can do with it. One is obviously we can simulate the automation efforts prior to actually implementing them. And secondly, once it's up and running, the digital twin can process and provide the data into the this particular task or process that we have automated and we can monitor that on a real time basis. So I think. Digital Twin plays two roles in this. I think the automation will be go further than just helping us seeing what the for example if a particular piece of equipment in terms of maintenance can be become predictive. But it's also in how does this piece of equipment play in to the overall asset if you have a complete picture of your asset, wow that's a very comprehensive and thorough explanation of digital twin. I like that. Analytics are also a key aspect of digital twin. Of course the analytics need good data to operate in. Well good data yields good results. Can you talk a little bit about the issues that occur with the data? Yes, I think data is near and dear to my heart. It's been that for as you point out earlier in the my career in the oil and gas has been around have been around data and. The I think that we are in as we need to discover now how poorly we have managed our data and we are now realising that it would be run analytics and to use these two automation, digital twins, machine first as we said machine learning etcetera. Data has to be good, data has to be clean, clear, verified. So that you can trust the data. I take an example. If you want to make the drilling process, you want to make sure you've got good data because you don't really want something wrong to happen while you're drilling a well somewhere. And you maybe have some incidents that you need to manage that usually can be managed. In the fashion if you have good data, the wrong data, you get a wrong decision. So I think that the data is critical and the industry but you can see that a lot of people are now waking up to it. We see creation of chief data officers etcetera that data is becoming important. Wow excellent. What would you recommend for managing the data. You know you've given a very good explanation why data quality is important and. why it needs to be managed, but do you have any recommendations for managing the data for Real Time Digital Twins? Yes, I think yes, they do and I but I think I'd like to add a little bit from the previous question. We're also looking at a lot of more data types trying to enter into, we want to do proper analytics and automation. I think the integration between IT and OT, the back office data with the operational data is becoming vital. You also have worked a lot of Indian industry calls dark data. We saw the written reports and all that knowledge that was put on paper but never really entered into the digital world that has to come into place. So to do that we need a much stronger data governance than we have at present. And we one of the things that seem to help a lot, I think the soybeans Oxley Act showed it to us is that if you add financial value to it to data like you do on acid and equipment etcetera. Suddenly people pay attention to it, and I don't know if that's the solution, but it certainly would help focus management's attention to the value of the data. We used to say hello, one of the true measurement was what's the cost to regenerate that data if you lose it, lost it. So I think that the strong data governance, add financial value. And make sure you have a If you're going to use the digital twin for a real time step, make sure you have a solid. Inspection program. To update your data for your asset and it has to be comprehensive because anything that changes. On an asset lean validation model, unless the model is updated, are you the digit, sorry, I should say the digital twin model, not just the model. So I think that's critical that you have that in place. OK, excellent. I might be, I might be maybe I should add also that the other part of the integration is the surface and subsurface data that needs to be integrated. I think that we have a lot of data that is below surface and we have a lot of data on top of surface. That data needs to be mostly looked at as a in a continuum if the digital twin is going to work properly. Excellent. Jan, I have to thank you for these very interesting insights. And folks, thank you for listening.