Many LNG projects continue to be managed with unsuitable tools. The real challenge is no longer launching projects, but executing them effectively.
As LNG projects boom, AI and industrial data are helping companies reduce delays, optimize their assets and accelerate their return on investment. In this sector where a liquefaction plant can cost up to $40 billion, every day saved on production counts. However, many projects continue to be managed with unsuitable tools. The real challenge is no longer launching projects, but executing them effectively.
Structuring data to manage complexity
Engineering and construction companies and operators must work with a lot of data from engineering studies, sensors and even maintenance and operations reports. Much of this data is often poorly managed, unconnected and too difficult to access to quickly extract useful operational information.
Companies today must deal with increasing costs, changing regulatory constraints and increased pressure on turnaround times. In this context, data control becomes a strategic lever.
Using a single data platform that gives everyone an integrated view across all assets in real time appears to be the only solution to solving this common organizational problem.
The Thai giant SCG Chemicals provides proof of this. Thanks to prescriptive AI deployed on an industrial data platform, the company increased the reliability of its installations from 98% to 100% and multiplied its return on investment ninefold in just six months. Teams can now anticipate failures and optimize their operations in real time from a single environment.
A new form of collaboration to stay competitive
After several years of experimentation, artificial intelligence is now entering a new phase: that of large-scale deployment. The most advanced companies do not only stand out for their technological investments, but also for their ability to break free from closed ecosystems.
This radical collaboration involves building open, agnostic platforms that bring ecosystems together instead of locking customers into a single format. It’s about integrating operational data with business data (ERP, engineering systems, process data, weather) to create AI models that reflect the complexity of the real world.
Customers are looking for flexibility to use different equipment, platforms and systems, while ensuring their data remains protected, controlled and AI-enabled.
This means, for example, that engineering data produced during construction is not lost during delivery of works. This is the foundation of the “Digital Twin” which then allows teams to use AI to make better decisions, faster.
Concretely, a field engineer can query the company’s technical repositories in natural language and instantly obtain the information necessary for the operation or maintenance of equipment. This capability facilitates knowledge transfer and improves the efficiency of asset management teams.
As the sector approaches a shortage of qualified labor, business-oriented AI allows operators to maximize their productivity without depending on expertise that is increasingly difficult to recruit and retain.
A new opportunity
According to forecasts of theOUCH (International Energy Agency), global LNG trade is expected to increase by 7% by 2028. The various projects are progressing as expected. Competitive advantage is no longer played solely on the ability to build quickly. It is now based on the ability to transform project data into operational performance from the first day of operation. Companies that master this digital continuity will be best placed to win future contracts and accelerate the profitability of their assets.
