Revolutionising Barrier Management on the Jotun FPSO: Inside the New Barrier Status Panel 

Industrial operations depend on the relationships between assets, equipment, operating envelopes, processes and history. But below the surface most of this operational context remains inside people’s heads.

The data is there. The dashboards update. But the delay comes from everything in between: cleaning, aligning, and making data usable. That’s the real bottleneck. Until that’s solved, real-time data won’t lead to real-time decisions. We see this gap a lot at Eigen.

Power BI is one of the most widely used business intelligence (BI) tools in the world, and for good reason. It’s powerful, affordable, and deeply embedded in the Microsoft ecosystem. For many teams, it’s the obvious first step into data visualization and reporting.

When industrial digitalisation projects stall, it’s rarely because of the technology. The more common reason is that the right people weren’t in the right roles from the start. We’ve seen this pattern many times: capable teams, good intentions, solid tools but no clear ownership or alignment between the operations and management layers.

As an independent FPSO (Floating Production Storage and Offloading) operator, being told by your client that you need to set up your own process monitoring infrastructure can be a daunting prospect.

This article outlines a novel approach for extracting instrument tags from piping and instrumentation diagrams (P&IDs), a crucial task for accelerating the process of digitalisation in the process industry, particularly older, mature fields that lack digital-first engineering documentation.

There was once an old onshore field in a far, far away land. Outdated and weary, it struggled with the burden of constant manual work. The dream of being part of the fully connected, automated, digitised offshore light-oil fields of the world seemed way out of reach. But one day, what was once just a dream, became a reality.

Liberate your engineers from the manual and mundane. Free up their potential to deliver outsize performance.

There’s been a huge explosion in dashboards over the past decade. And yet, managers continue to complain that they don’t have the data to make good decisions.¹

Distributed Component Object Model (DCOM) is a Microsoft remote communication protocol at the heart of many real time data links.

Context and clarity through dynamic monitoring and visualisation

There is definitely something intuitive about building knowledge graphs, after all, they mirror the way we as humans acquire knowledge and retain it through connections and context.

Addressing challenges of IT partners about knowledge graph technology

The trend in many mature basins, just like the UK and Norwegian continental shelves, has been one of steady divestment by the supermajors and a new generation of smaller oil and gas operators.

In short, electricity prices vary hugely compared to petrol or diesel; you can pay anywhere from 5p/kWh to 70p/kWh so it costs me anywhere from 3p/mile to 35p/mile depending on where I charge.

Would I recommend an electric car? Yes, definitely. They are nicer to drive, much cheaper to run and need less maintenance. Range isn’t the issue, maybe the real question is “are you prepared to change your habits?”.

When you say you own an electric car everyone’s first question is about that magic word “Range” – “How far can it go on a charge?”. Having owned an electric car for over six years now I can tell you that range is much less important than the ability to recharge at your destination. Let me explain why.

At Eigen, we build and deploy live data models as part of a digital twin for oil and gas facilities. One of the challenges in doing this is how to combine the physical and the abstract into a working data model. And a great example of this is the bow tie – a diagram showing all the protective measures against hazardous events and their potential consequences.

As The automobile and industrial designer Freeman Thomas said “We were promised a simpler life, and technology has only complicated our lives.” In complex domains, like oil and gas, technology may well have simplified some things but it has certainly complicated others, particularly where data is concerned.

Death and taxes may historically have been the only two certainties in life, but we might consider a third – increasing volumes of data. With ubiquitous sensing, unlimited cloud storage and insatiable demands from every walk of life, data is growing exponentially.

Much of the focus of digital transformation is given to big data, clever systems and game-changing innovations, like robotics, AI and IoT. But what is often missing is the focus on people, who have been irrevocably transformed by digital.

We formed Eigen almost fifteen years ago, although the original founding team had all been working in oil and gas for the previous decade. As individuals, then later as Eigen, we worked with bp to deploy its breakthrough Field of the Future programme in Azerbaijan, and have since been part of many oil and gas operators’ digital transformation journeys.

There’s a lot of hype about digital twins in oil and gas. Much is well-founded, since the integration of the underlying technologies that make digital twins possible have potential to accelerate much needed digital transformation in the sector. But you can’t believe everything you read.

The concept of digital twin has a futuristic quality at a time when robotics and artificial intelligence also are coming of age. But whilst there’s no disputing the transformative impact of digital twins, in industries as wide-ranging as construction, marine, agriculture and oil and gas, what makes up a digital twin is technology of now.

One of the problems of asset-heavy industries, including Oil & Gas and Petrochemical operations, is decision-making for maximum impact, under resource constraint conditions. These industries typically manage an array of highly interconnected assets and relationships which bear on the impact of the decision.

When it comes to digital transformation and learning how to discover insights hidden in data, one question that regularly arises is whether we should copy all of our data into a data warehouse or data lake, or alternatively connect directly to the source systems.

In this blog series we’re looking at how you turn a bowtie diagram into an online Digital Twin showing live barrier health information. The previous part looked at the level of detail required to make a working data model in practice. In this instalment we’re looking at the challenge of assessing the health of protective functions not directly related to the physical SECEs (Safety and Environmentally Critical Equipment).

In this blog series we’re looking at how you turn a bowtie diagram into an online Digital Twin showing live barrier health information. In this instalment we’re considering the level of detail required to make a working data model in practice.

How do you turn a bowtie diagram into an online Digital Twin showing live barrier health information?

Imagine the situation: Hydraulic fluid pressure is dropping fast; supply tank levels are also dropping. You are pretty sure there is a big leak in the system, but you have no idea where it is. Pretty soon the operation will be compromised: The pressure will be below the minimum required and you may be forced to shut down the field. It is a scary picture.

There is a lot of buzz nowadays about “Digital Twins”, and every Digitalisation Director out there is thinking they need to have a Digital Twin of their assets. It makes sense: If you have a digital representation of your assets; you can simulate, test scenarios, plan operations and visualise data within the context afforded by the twin, without having to visit or “be present” in the real asset, especially if these assets are remote.

Ah, the old ones still the best ????. But such questions can sometimes be worth considering. For example, when is Agile not Agile?

Two months ago I switched my phone from an Apple iPhone 7 to an Android based POCO X3 NFC. At the same time one of my daughters upgraded their phone to an iPhone XR so we’ve been comparing experiences. So what’s it been like??

In an earlier blog, I wrote about what Agile is. And that’s all well and good, but what has Agile ever done for us?

I’ve been using Apple products since 2007. Every smart phone I’ve had has been an iPhone but yesterday I ordered a new Android phone. I’m so fed up with shelling out around £1000 every couple of years but I’ve been trapped because all my music is in iTunes, all my family have iPhones and we run Macs*. Man, I hate being locked in! I wouldn’t mind if it was still value for money, but it’s not – I feel like I’m being taken for a ride now.

In November (this month as I write this) we have not long closed out possible two of the best custom software projects we have done. Sure there were bugs in the testing and there will be more found by the users I’m sure, but what made them so good? We hit the schedule to within a week on both of them and there were no scope changes over the entire project, so we hit the budget as well. This is particularly impressive for one of them because it was a completely new piece of software, written from scratch; taken from powerpoint to in production in 5 months.

This picture makes me laugh and if it makes you laugh too then you’ve probably been involved in purchasing or project delivery at some point.

Production Well Testing is an essential part of managing an Oil & Gas field if the field has more than one well and production measurement resources are limited, as is generally the case.

You have probably heard software developers talk about being Agile, and maybe wondered what they mean. Perhaps they do star jumps and touch their toes before breakfast, or write code while limboing under desks? Well, it’s nothing like that… read on to find out more!

I’m really excited about the long term partnership we have recently signed with Wintershall Dea for a number of reasons not least because it’s a great team and we have a lot of fun working together!

Back in July I asked myself the question “Why are we still in business after 11 years?”. A small specialist company in a big competitive industry. We’ve been through 3 downturns now and we continue to develop longstanding relationships with some of the most innovative operators in the business. Clearly we are doing something right but what is it?

Did you ever learn to play “Sweet child of mine” on the guitar? Like almost every other teenager who had a guitar I learned the opening riff to “Sweet child of mine” and I remember playing it in a guitar shop when I was testing out an amp, only to have one of the staff point to a sign on the wall that said anyone playing “sweet child of mine” would be thrown out the shop and told never to return!

The world was shocked to learn about the massive explosion in the port of Beirut: This has been a terrible tragedy with an astronomical cost in terms of lives lost and damage to property; one could even argue, to the whole country, as even the strategic grain reserves were hit.

Now we are potentially getting right in amongst the weeds and many a data model gets stuck in the weeds because this issue is not understood.

Once upon a time, in a land far far away, hard disks and memory were small and really expensive. In 1969 the computer that landed man on the moon only had 4k of RAM and 72k of ‘disk’ (ROM) storage.

Having the best datamodel in the world won’t help you grow your digital capabilities if the only way to access it is through proprietary drivers.

Google search is not a validated datamodel. Nor is it comprehensive, but it’s good enough for most of us.

“A well structured class library allows you to build applications that are portable across different assets. They overcome the problems of different naming conventions across assets or sites.”

Trying to model humans is probably the easiest way I know to break an inflexible model. Why? Because humans are recursive – that means they keep repeating the same thing and also they keep changing the rules! Humans start off as a child and become parents, they form relationships with each other, they marry each other and they can now change gender. If you designed your database schema in the 1500’s you would have had to rebuild it several times since!

The problem with hierarchies is they are subjective and only relevant to a certain viewpoint or problem. For example, what is the hierarchy of equipment on an industrial facility? Should it be based on area or on equipment size, or on position in the process?

Every Digital Twin needs a data model somewhere inside it. The Data Model is the digital map of where everything is; it’s the central reference for how everything is connected; it’s the search engine for your information.

Not all process real time visualisation and collaboration tools are created equal. Always taking advantage of the latest digital technologies, Eigen Ingenuity exhibits five key features that make working remotely, working from home and working from anywhere, much easier.

If you are being tasked with creating reports for management on a daily or weekly basis, you are probably extracting real time data into Excel, adding manual figures, performing calculations (efficiency, availability, etc.) and then plotting the results.

Ok, so this one isn’t entirely a myth, there are AI based tools that do make you more efficient.
Like the time one of my kids asked me what kind of plant this was when we were out on a walk; a quick snap using the Picture. This app and a few seconds later we knew it was a “Common milkwort”! Brilliant! (Though whether we’ll ever remember that for next time is a different matter).

We have all been there… it’s another downturn! There is pressure from management to demonstrate that our mature oil field is profitable.

We need to take action to maximise free cash flow to ensure the profitability of our operation and that means maximising production, minimising water and sand, while making sure well integrity is preserved. On top of that we also have to deal with foaming, scaling or emulsions, while optimising the use of expensive chemicals.

Oh how I wish this one were actually true!

Asking data scientists to estimate the cost of a project is like asking a bricklayer to estimate the cost based on his bricks per hour when in reality you’ve got a plot of jungle, a sketch on a napkin and all the bricks are hidden.

Around 15 years ago Autonomy claimed this but it turned out to be a myth. In reality this is a really really hard problem for AI to solve. Here’s why, and then I’ll talk about some of the ways you can at least speed it up.

“It’s so cool, I can actually access live data and monitor my systems from home, on my Mac!”

Getting new digital functionality working is complicated, especially when it involves integrating to existing systems from different vendors. The idea of copying all this data into a single ‘data lake’ is very attractive; everything will be in one place, your architecture drawings looks really clean, you don’t need to worry about how to connect to legacy systems or overloading them, the performance should be blisteringly fast. A modern one-stop-shop for all future development. I know this because this was my dream when we started Eigen 12 years ago! We were trying to build the one unifying data model that would enable all our functionality dreams. But here’s what we’ve found over the last 12 years.

We’ve all been there right, the project seemed simple to start with but once we got into it, it started getting complicated and all sorts of thorns started to crop up that took way more time to solve than anyone on the project governance board would believe.