Knowledge graphs from Eigen

Tame your data monster with Eigen’s knowledge graphs

Why knowledge graphs?

Google’s mission is to organise the world’s information and make it universally accessible and useful. It fulfils its mission through its knowledge graph, the underlying technology that enables it to surface relevant search results and adverts in microseconds.

In oil and gas, our domain and data are fortunately more narrowly constrained than Google’s but both are complex and dynamic, creating challenges to operate safely, efficiently and for maximum value.

Knowledge graphs, like Google’s, can be built for oil and gas operators as a way of bringing everything an operator knows into a powerful brain – without necessarily moving a thing.

At Eigen, we build knowledge graphs for operators struggling to integrate complex, and often messy, oil and gas systems and data to make better decisions and increasingly to take action on those decisions.

Talk to us about how knowledge graphs can help you tame your data monster.

What is a knowledge graph?

A knowledge graph is simply a network of objects and connectors. The objects are ‘things’ and the connectors connect the things and define context for their relationship.

For instance: barrier element_ABC prevents major accident or safety valve_123 protects separator_XYZ

Knowledge graphs begin with simple building blocks.

But these inevitably become complex very quickly to mirror the complexity of an operation – and require computing to scale.

300,000

Number of inter-related objects in a typical Eigen knowledge graph, with potential for several million.

Is it just a very big database?

In a way it is, but whereas a database needs pre-defining, a knowledge graph evolves and can learn. It only points to objects and (ideally source) data, so no big migration or redundancy challenges. A knowledge graph has the potential to adapt and change in line with business needs.

For instance, with one client we began by building a knowledge graph of just the objects and connectors related to its blowdown events – but now that is defined and operational, the knowledge graph adapts to take on new objects and solve new use cases.

Read more on using knowledge graphs for multi-criteria decision-making and prioritisation problems

Benefits of knowledge graph technology

You can trial the Eigen Analytics Platform to experience the power of user-configurable analytics – or we can work with you to solve a pain point starting with a Discovery Phase.

1. Knowledge graphs bring context and meaning to data: systems like Ingenuity and analytical tools like EAP can perform higher order insight analysis.
2. Knowledge graphs can scale and adapt: a knowledge graph can be built for a specific use case and then expanded to solve specific challenges, building new connections and codifying new knowledge.
3. Computers and humans can read knowledge graphs: for humans, knowledge graphs operate much like neural networks in the human brain as we learn; for computers, relatively simple technologies like Microsoft Power Automate can integrate directly with events surfaced from the knowledge graph as well as more sophisticated machine learning and artificial intelligence solutions.
4. Knowledge graphs break down siloes: complex oil and gas operations rarely restrict themselves to a specific function, knowledge graphs provide a more integrated picture and enable much faster cross-functional event analysis than humans.
5. Knowledge graphs present a more sustainable alternative to data lakes: as far as possible data remains at source, no migration is required, reducing the risk of duplication.

Read our blog on knowledge graphs as a bridge

Find out more about how knowledge graphs can help you become a safer, more efficient operator

Use cases

A knowledge graph of safety barriers can handle the complexity of multiple data inputs (last inspection date, maintenance records, sensor data etc) to validate whether the barrier is functioning as expected.

A knowledge graph brings transparency to otherwise complex and sometimes hidden activities, enabling engineers to drill down to investigate underlying factors.

A knowledge graph can make connections that are often missed by humans, surfacing new opportunities for optimisation and growth.

Read a case study: How we built a knoweldge graph for Lundin

Building a knowledge graph for your organisation

One of the key advantages of knowledge graph technologies is you can just start building, use case by use case – along agile principles. No need to define future scope or boundaries, just focus on the problem and the data that, once integrated, can solve it.

We use open source Neo4j technology on which to build our knowledge graphs for operators. It offers flexibility with some major head starts to start delivering value quickly, as well as run experiments fast, without investing much time or budget.

Talk to us about our approach to building your first knowledge graph

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Over the past fifteen years, we have worked with many of the major oil and gas operators. We are trusted as engineering experts who deploy digital technologies to create value.

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