Digital Twins in Gas Distribution Networks: Optimizing Performance and Maintenance

Businesses always strive to calculate risks and make their product as profitable as possible. This applies to both digital technologies and production processes.

Currently, most gas distribution companies, in developing their growth strategies, are betting on digital technologies. This is a completely justified decision: more traditional forms of managing production processes lag behind current digital models in almost all parameters.

Initially, the technology was developed for space exploration programs. It was used to account for the loads placed on shuttles and satellites outside the Earth's atmosphere. The usefulness of this approach became evident for devices on the planet as well, leading to the adaptation of the system for individual products or entire enterprises. Thus, digital twins are virtual models of objects that exist in the real world. IoT sensors are used to transmit real parameters of the object to a server, where they are processed by specialized software, while potential situations are calculated using IT infrastructure.

The idea of studying physical objects using digital twins was proposed by NASA in the 1960s. Specialists on Earth created replicas of spacecraft in space to use these twins in research missions. The capabilities of the technology were clearly demonstrated during the Apollo 13 mission. Using the connected twins, the control centre was able to quickly adjust simulations according to the conditions of the damaged spacecraft. The team adapted troubleshooting strategies and successfully brought the astronauts home safely.

"The main task of the digital twin is to create, test, and construct NASA technology in a virtual environment. Only when engineers are convinced that the digital twin meets all requirements does the transition to actual production take place. Then, it is necessary to ensure feedback from the real structure to the digital twin using sensors, and to do so in a way that the digital twin contains all the information that could be obtained during technical inspections of the real structure," - a statement from the article. "Can the digital twin transform manufacturing," 2015, by John Vickers, a leading expert at NASA and a specialist at NASA's Advanced Manufacturing Technology National Center since 2012; co-author of the publication M. Grieves.

By the early 1970s, mainframe computers served as a partial analogue of digital twins. They were used to managing large objects such as power plants. In the 1980s, 2D CAD systems (such as AutoCAD) emerged, allowing for the creation of technical drawings. This technology enabled computer-aided design of virtually anything and quickly became part of the toolkit for millions of designers and engineers.

By the 2000s, 3D CAD systems with parametric modeling and simulation capabilities appeared. They opened up more intelligent ways to design complex structures, such as databases with interrelated objects. Fast forward to the mid-2010s. At this time, all leading 3D CAD system providers launched cloud solutions for collaboration and project management, and soon for generative design as well. Despite this, CAD tools still had to be installed on desktop computers.

Today marks the beginning of a new era of digital twins based on RT3D, which surpass traditional dashboards and 3D models (such as BIM, CAD, or GIS). This new technology collects data from various sources on any device or platform, enhancing collaboration, visualization, and decision-making.

This is a cornerstone element of modern object management systems: it transforms collected data into a digital gas system, helping to choose and calculate the optimal operating mode and load level, forecast and conduct virtual experiments with minimal risks to the object and the business, and track and predict changes in the object's state throughout its lifecycle.

Well-organized work with the digital twin creates all the conditions for in-depth analysis, control, and the fastest and most accurate decision-making.

Depending on the goals set by the company, digital twins can vary significantly. There are four fundamental parameters that determine the complexity of implementing a twin:

The complexity level of the digital twin is determined individually for the success of each object, depending on the goals, context of use, and budget.

Performance Optimization:

Predictive Maintenance and Asset Management:

Enhanced Safety and Risk Management:

By deploying a digital twin, clients will immediately notice how much more accessible the data has become. Over time, maintenance costs will decrease, and clients will be able to make more informed decisions regarding changes in workflows. Furthermore, they will be able to significantly save costs, optimize maintenance, and enhance operational efficiency. A successful initial project will positively impact subsequent stages of work, as design determines 80–90% of costs during the construction, operation, and maintenance of an object.

The use of digital twins has improved collaboration and communication during design, as well as simplified data collection and coordination during the construction phase and when operating the gas distribution system. Safety instructions, as well as control and quality assurance measures made possible by digital twins, have significantly reduced the number of errors and accidents in the gas distribution sector. Maintenance and operations using digital twins allow for the optimization of operational activities, reducing downtime and cutting costs related to maintenance and personnel.

The digital twin for gas distribution networks represents a significant leap forward in operational efficiency, risk management, and strategic planning. By providing a comprehensive virtual model of physical assets and processes in real time, digital twins offer unparalleled understanding and forecasting capabilities, enabling more informed decision-making and more efficient operations.

The integration process of digital twins for gas distribution systems is ongoing. As technology continues to evolve and further integrates with AI, IoT, and other new technologies, its potential will continue to expand. Companies in the oil and gas sector must remain flexible, leveraging these advancements to stay competitive and relevant in a rapidly changing industry landscape.

For companies striving to thrive in the digital age, investing in digital twin technology is not just an opportunity but a NECESSITY.