The concept of twins has long mystified and intrigued societies across the world for thousands of years, often representing two sides of the same entity – the Ying and the Yang, the ‘Great Twin’ stars of Pollux and Castor in the Gemini constellation, the biblical duo of Romulus and Remus – each reflecting the other through differing characteristics.  

Within this paradigm and the need to create a better understanding of their operations, energy companies have engaged in developing the concept of ‘digital twins’ – a digital model that reflects the physical. By integrating a network of sensors and monitoring equipment throughout a system, there can be a real-time oversight of operations and wider capabilities to analyse system efficiencies over time. Ultimately, increasing the data and knowledge with which a company runs their operations will lead to lower emissions, lower costs and a more effective business model.

The Energy Council looks at this emerging ‘twin’ technology, the Kognitwin being developed by Kongsberg Digital, who are seeking to provide digital advancement for a sector keen to shift its reputation as climate offenders and magnify their reputation as great innovators. 

Unlocking Labour Performance

The labour force within energy companies are often highly educated, technically capable and productive workers – and yet, without the information they require, they are left scraping together answers from a range of colleagues, none of whom will answer instantly. Inevitably, the time taken to gather data from different sources, collating that data and utilizing the findings to inform a decision has clear and direct impacts on performance and productivity.

‘Time on tools’ is a crucial aspect of energy production capabilities, especially in the world of oil and gas which relies on daily production targets. By removing the barriers to data access and expanding competences to include real-time asset performance – workers can give added focus to their core responsibilities while their colleagues assess and maximize their data.

By integrating digital technologies into mechanical equipment, will not just generate efficiencies in the production systems but also permit employees to spend time concentrating on their core responsibilities – creating new efficiencies for the workforce.

Unification & Visualisation

One of the great challenges of the energy transition is to integrate modern technologies into ageing, pre-internet constructions. In the past 20-odd years, our capacity to extract and monitor data has risen exponentially – providing a far deeper understanding of the processes under our control. Central to these advances is the networking and utilization of information between different instruments that are connected to the internet, known as the ‘Internet of Things’ (IoT).

Utilising an IoT network will only produce workable results if the worker knows how to best use and present it that data in a practical way, a time consuming task often exposed to human errors. To most data can be useless or intimidating when presented in its raw, unfiltered state. It is in this realm where data contextualization systems can cut through the clutter, collate the right information and present it in a functional state.

Kongsberg’s KogniTwin refers to this as “unification” software, which unifies data from all systems and process data from an asset into a single useable program. By contextualizing the systems in this way, the company believes productivity is increased as workflows are simplified and users are provided with the necessary support of access to information in real-time.

These processes rely heavily on advanced artificial intelligence and machine learning algorithms that actively process and interpret data with little human inputs, rather learning from previous uses and incidences. Once complete, the data is presented in 3D representations and 2D models that allow the user to visualize and interrogate the system in real-time.

Optimisation through Simulation

Central to the twins capabilities are the manner in which they translate data and visuals to use beyond surface analysis. Kongsberg Digital describes these dynamic simulation processes as, “the combination of physics-based models and data science approaches and cloud scalability lends operators to streamline and scale testing of hypothetical scenarios”.

Such simulations permit energy companies to begin to predict outcomes to drive production optimisation anticipate maintenance and enhance performance – all with the intended outcome of improving decision making at far faster rates than the industry standard.

The energy sector is all about creating efficiencies wherever they can, as more efficient systems simply allow for more time spent on production and less on maintenance, which directly affect the bottom-line. As the saying goes, ‘a penny saved is a penny earned’.