How to increase production efficiency for brownfield projects

23 August 2018 by Roar Nilsen
The benefits from using digital twins for brownfields are likely greater than you realize.

Most people prepare before taking a trip. They compare hotels, read up on restaurants, and plan their sightseeing. Now, imagine that you could take a virtual vacation before you left: try out hotels, sample the menu, tour the sights, and then customize your journey to make sure it goes smoothly from start to finish. Sound good?

That is basically how a digital twin for a brownfield works. It integrates as-built design and construction data with real-time operation and model prediction data. All this can be accessed through a context-dependent user-interface that enriches existing 3D models and drawings with information. To engineers it becomes a virtual and highly accurate test bench for decision support in operations, modifications, maintenance, or repairs before changes are implemented. This is possible because the accessible data from the platform or ship allows for extremely accurate digital twins, with all parameters represented.

How does a digital twin work?

Engineers gather the data for a digital twin from multiple sources, including 3D drawings of the physical unit from the customer and contractor, operational data from the field, and specialized systems. Once the twin is established, they can introduce more detailed data to simulate anticipated modifications, or variations of the basic design, in a parallel system.

You can go back and forth in time, re-run previous operations, or test out future planned operations in a safe environment where you have access to all relevant data when you need it. This allows you to create many different scenarios and test the outcomes. With the right applications, you will even gain full overview of everything that contributes to the scenarios you are testing.

A digital twin enables better decision-making

At the heart of a digital twin that is used for a brownfield, is a digital platform. The digital platform functions as the infrastructure of the digital twin, enabling data sharing and analysis, hosting all relevant data and enabling integration with the client’s master data which is hosted in a different digital platform.

A digital twin provides a lot of information that will help you make better-informed decisions relating to design configurations as well as equipment choices.

Have you ever tried locating a particular part or section based on the drawings of a platform or ship? It can be very difficult, simply because the physical layout often differs from the drawings. With a digital twin, on the other hand, it’s easy: Thanks to data sharing and analysis, it functions like Google Maps giving you a “street view” of the system.

Put another way, a digital twin uncovers perspectives that are hidden in real life. It enables you to get inside a system and inspect configurations and conditions that you are unable to view, much less access, on the physical unit.

I’m sure you’ll agree, it is a great advantage to know the operability of your design modifications before you start implementing them. A digital twin makes this possible: You can use it to simulate the effect of wear and tear by seeing what will happen at various points in time using various designs. A part of this is what I consider to be one of its most important benefits: the ability to identify the exact operational behavior and how different design will affect the production efficiency or the likelihood of failure throughout the lifetime of the physical unit.

That’s one big sandbox

If you are in the business, you might have experienced the challenges of modifying production units that are in full operation. It’s a highly complex job – and might be costly, especially if it’s not done right. A digital twin will function as a sandbox, a place where you can play around with the options with no physical consequences whatsoever.

A digital twin improves safety for your crew too: Running what-if scenarios to test what might happen under extreme conditions, or if something goes wrong, will keep your crew safe and enable you to find the safest solution when you need to solve dangerous situations.

Predict maintenance requirements for an entire facility

Today, collecting data that will allow you to explore the options on a particular platform, is usually a manual task, and you need many different experts and applications to do so. With a digital platform, you can link a digital twin for an entire facility to, for instance, a maintenance system to acquire the historical information you need to predict maintenance requirements.

Should your company use digital twins for brownfields?

My answer is short, but sweet: Yes. If you work with brownfields, you should definitely use digital twins.

Digital twins provide such an abundance of benefits and great results, time-, safety- and, not least, moneywise. You simply cannot argue that using them is a cost issue.

A digital twin even makes it easier to collaborate on multi-discipline tasks. Collaboration across disciplines is a challenge, but a common system motivates sharing. A digital twin with an intuitive interface will help users start at a basic level and advance as they gain more experience.

If digital twins are so great, why don’t everybody use them?

To be completely honest, I find it really hard to understand why some companies still choose not to use digital twins. The results they provide are so great, arguing it’s a cost issue just doesn’t make sense to me.

What is your view on digital twins?

What are your thoughts on the use of digital twins for brownfields? Do you agree with me? Do you see other advantages – or perhaps disadvantages – than those I have described here? Let me know in the comments below!

Machine learning
About the writer
Roar Nilsen
Roar Nilsen is product manager for digital twins used in the energy sector at Kongsberg Digital. He has been with KONGSBERG since 1990, giving him deep insight into the possibilities offered by the latest technology as well as the requirements of companies within the energy sector.