Digital Twins

Eigen's approach to digital twin relies on our long track record building shared models for oil and gas operations

A digital twin is a model of a real world asset; a representation of often incredibly complex oil and gas systems subject to the laws of physics.

At Eigen, we build digital twins for oil and gas operators to help improve safety, optimise production and drive operational efficiencies.

Challenge

No out-of-the-box digital twin solution exists for complex oil and gas operational systems

Designing a digital twin of a complete operational oil and gas system that generates billions of data points a day would take years – and it would likely be wrong.

FAQs

All you need to know about digital twins

Solutions

At Eigen we build simple, straightforward solutions – even for complex digital twins.

Oil and gas experience

  • We have more than fifteen years, designing and building data models models of oil and gas operations; these form the bedrock of the digital twin.
  • We operate within the ecosystem of specialist oil and gas technology provides and act as the glue, integrating data from multiple sources to build fully-functioning digital twins.
  • Our engineering background means we offer practical, real-world solutions based on deep knowhow and experience.

Smart technology choices

  • At Eigen we build digital twins for our clients in Ingenuity, based on the leading graph database opensource technology, Neo4j.
  • In line with Eigen’s open approach, Ingenuity is fully cloud native, meaning it can be deployed on your premises or in the cloud and is backed by the latest continuous integration/continuous deployment processes.
  • Ingenuity provides advanced visualisation of data from multiple sources, performs powerful online calculations so users can create virtual data series on-the-fly and build their own reports and dashboards.

How we work

  • Today’s digital twin technology brings advantages of flexibility in design. No need to define schema containers in advance and tackle the whole system.
  • We work in a modular way side-by-side withour clients, using an iterative, progressive method, to get a design right.
  • Rather than tackle the whole system, we take a specific use-case and develop technologies to solve it; we then move to the next use-case. In this way, we help clients payback their investments in digital twin technology fast.
Miles Daffin

Miles is an Applied Mathematics and Theoretical Physics graduate, with 5 years experience in developing advanced visualisation solutions for Eigen's most demanding Oil & Gas customers. His abstract thinking training serves him well in transforming equipment and tag lists into the Neo4j graph model at the heart of Eigen Ingenuity's digital twin.

“I love to deliver working solutions to our customers. I particularly enjoyed developing the python script that automatically verifies all blowdown events. The python plug-in provides so much power to automate workflows!”

Impact

Digital twins that increase control and minimise interventions

Eigen’s digital twin technology helps clients manage their operations more safely, efficiently and reliably – and helps avoid making costly mistakes.

eigen blog

Digital twins: a myth buster

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.
1 week
of a senior engineer’s time saved by automating the verification of blowdown events​
0 %
Improvement in efficiency through quick access to information​
0
Infinite possibilities thanks to graph database technology​

6.6% improvement in Efficiency through quick access to information​

Case Studies - Making HSE Information Visible with Eigen
Challenge

Finding the necessary information to do a task was time consuming because the information is spread across multiple systems, such as a master Equipment Database, Document Management System, data Historian, Maintenance Management System etc.​

Solution

By creating a data model that links (or contextualises) the information for an asset in Neo4j and combing it with a powerful search tool such as Elastic, users are able to use free text or more specific searches to find the information they need.​

Impact

Using the combination of Search and the Data Model, users are able to find and work with information much faster. On average it is estimated as saving 30mins per user per day, or 1/16th of the working hours in the day – 6.6%.​

6.6%

Improvement in efficiency through quick access to information​

2.5hrs

Saved per week

Automating verification of blowdown events

Challenge

Manual analysis of the depressurisation of >fifty different sections of piping and equipment in a facility after plant shutdown to a senior engineer a week of effort. It involved going into multiple systems to find the right data and manually importing it into spreadsheets.

Solution

Using our Ingenuity platform, we extended the existing data model of the facility with new objects for the blowdown segments and associated equipment and connected this to the systems holding the source data, adding dashboards for visualisation.

Impact

Now blowdown analysis runs automatically after any plant shutdowns, saving a week of a senior engineer’s time and providing improved management information.

1

Number of weeks of a senior engineer’s time by automating the verification of blowdown events

Infinite possibilities thanks to graph database technology​

Challenge

Coming up with a data model (schema) that is fit for every future use case is impossible. The rigid structures of relational databases make it difficult to change the schema later on so it becomes hard to move quickly to digitise operations. Lengthy business analysis phases are then followed by lengthy contextualisation processes were the data in ingested into a data lake before the user functionality can be delivered.​

Solution

Graph databases such as Neo4j work completely differently and allow information to be linked together just like a person would draw things on a white board. New information and relationships can be continuously added without affecting what has gone before.​

Impact

The data model at the heart of Eigen Ingenuity allows infinite possibilities for linking and modelling data and information. Structures can be imported from spreadsheets and the model can be evolved over time allowing you to take a step-by-step approach to building your Digital Twin.​

More digital solutions from Eigen

Optimising workflows and activities to improve productivity and reduce costs
Integrating data, dashboards and tools to improve quality decisions
Moving manual tasks online to drive collaboration, knowledge-sharing and pace
Monitoring and managing safety barriers and risk status to enable early intervention

At Eigen, we focus on designing, building and deploying innovative digital solutions that protect and create as much value as possible for our clients.

Eigen products for Digital Twin

Ingenuity 6.0 is the latest generation of online platform for doing work in industrial operations.

Imagine if you could combine a Digital Twin of your information with search functionality like Google, powerful visualisation tools like PI Vision and Power BI and the ease of doing analysis in Excel.

That’s Eigen Ingenuity.

Book a demo

Like to find out more? Book a time below to talk to one of our engineers.

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FAQs

At Eigen, we have been developing Digital Twins to support the solution of the most complex digitalisation problems in Oil & Gas, since 2015. Our digital twin technology is based on open source graph database technology.

Our Digital Twin technology enables:

  • Safety Barrier Management
  • Overall Risk Management
  • Templated Dashboards
  • Automated Process Safety Verifications
  • Application scalability

A digital twin is a virtual representation of a system or asset that calculates system states and makes system information available, through integrated models and data, with the purpose of providing decision support over its life cycle. (Source: DNVGL-RP-A204)

A Digital Twin may be designed during the design phase of an asset to validate constructability, performance, functionality or capacities before the physical asset comes into existence. However, more and more, Digital Twins are being used during the operational phase of an asset to provide users with information, interaction/training, visualisation, search functions and as the basis for business logic calculations and recommended actions.


A Digital Twin should be designed with a clear purpose to provide value. In this respect, it should include the models, logic and data integration necessary to provide the information and interaction environments where it can deliver such value. Each Digital Twin includes a virtual representation of an asset and a connection to the real asset: The connection to the physical asset should provide the Digital Twin with the physical information required to perform its virtual representation functions.

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