Artificial intelligence drives the high-quality development of the LNG industry

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Artificial intelligence drives the high-quality development of the LNG industry. March 19, 2025. Currently, the global energy landscape is undergoing profound changes. Driven by artificial intelligence (AI), liquefied natural gas (LNG) is experiencing rapid growth under new circumstances. From the intelligent upgrading of exploration and development in the upstream phase, to the smart scheduling of storage and transportation in the midstream phase, and then to the efficient optimization of end-use applications in the downstream phase, artificial intelligence technology is helping the LNG industry move toward greater efficiency, safety, and sustainability.   Improved efficiency and low-carbon transformation   Artificial intelligence drives the upgrading of the LNG industry   According to the International Energy Agency’s (IEA) ‘Gas Market Outlook 2025’, global LNG demand is expected to exceed 440 million tons by 2025. In the face of rapid growth in market demand, the application of artificial intelligence in the LNG industry currently focuses on two main areas: improving operational efficiency and facilitating a low-carbon transition.   Through end-to-end data integration and automated control, artificial intelligence technology is breaking through the efficiency limits of the traditional LNG industry. In the field of exploration and development, ExxonMobil’s artificial intelligence-based geological modeling system, introduced in 2024, improves exploration success rates by 20% by analyzing 3D seismic data and drilling logs from shale gas fields in North America, while reducing the development cost per well by 15%. The digital twin technology deployed by TotalEnergies in Mozambique’s LNG project enables real-time simulation of equipment operation, reducing unplanned downtime by 30% and increasing annual production capacity by 500,000 tons. Technological breakthroughs driven by artificial intelligence have played a crucial role in supporting the global plans for additional production capacity by 2025.   Artificial intelligence technology is becoming an important tool for the LNG industry to address climate challenges. At its Yokohama Power Plant, Japan’s JERA utilizes AI algorithms to dynamically adjust the mixture ratio of LNG and ammonia fuel. In 2024, this led to a 22% reduction in carbon emission intensity, making it the first example of a multi-fuel smart power plant in Asia. This innovation gives Japan a significant competitive advantage under the new carbon tariff system in 2025. Cheniere Energy’s smart cargo tank management system in the United States optimizes the pressure in LNG ship cargo tanks in real time, improving the efficiency of BOG recovery by 25% and reducing carbon emissions per shipping trip by 150 tons. This technology has been included in the International Maritime Organization’s (IMO) emission reduction guidelines for 2025.   Dynamic monitoring, optimized evaluation   Artificial intelligence is making its way into every aspect of the LNG value chain. With the continuous advancement of artificial intelligence technologies, all segments of the LNG industry are accelerating their transition toward digitalization and intelligence. From production and transportation to end-use, artificial intelligence technologies not only improve operational efficiency but also drive the green transformation and sustainable development of the LNG industry chain.   During the production and liquefaction phases, the deep integration of artificial intelligence and Internet of Things (IoT) technologies enables companies to monitor the status of equipment in real time and predict potential issues through big data analysis, thereby improving the stability and accuracy of operations. For example, the Geodesic platform developed by Shell in collaboration with Microsoft uses artificial intelligence algorithms to improve the precision of directional control in horizontal wells, enabling drilling rigs to reach reservoirs with high oil and gas content more accurately and thus increasing the efficiency of resource extraction. The integrated cloud platform developed jointly by ExxonMobil and Microsoft enables real-time collection and intelligent analysis of oil field data across different regions, overcoming limitations related to time and space and enhancing the transparency of resource management as well as the efficiency of production decision-making. Furthermore, the DELFI cloud computing platform developed by Chevron, Schlumberger, and Microsoft enhances data management and analysis capabilities through the sharing of a data ecosystem.   During transportation and storage, the safe management of LNG is particularly crucial. The intelligent risk assessment system developed by the risk management and quality assurance organization DNV?GL enables precise analysis of leakage risks during transportation and provides optimization solutions to enhance the safety of global LNG fleets. In addition, Alfa?Laval’s intelligent ship vapor management system improves the energy efficiency of LNG transportation by optimizing the operation of dual-fuel boilers, thereby reducing operating costs and carbon emissions. With the increase in LNG trade volume, the deep integration of artificial intelligence technology is driving the LNG transportation and storage processes to become safer, more economical, and more efficient.   In terms of end-use applications, artificial intelligence is accelerating the adoption of LNG in the transportation sector, while also improving the operational efficiency and cost-effectiveness of LNG-powered vehicles. Compared to conventional diesel, LNG fuel can reduce costs by up to 50%, significantly cut particulate and nitrogen oxide emissions by over 90%, and have a carbon emission intensity 25% to 30% lower than that of diesel. Its engine features closed-loop cooling technology, enabling stable operation in temperatures as low as minus 40 degrees Celsius. Norway’s **energy company Equinor’s smart fleet management system monitors a ship’s energy consumption in real time and utilizes speed optimization algorithms, enabling LNG carriers to achieve an 18% improvement in efficiency while maintaining the same fuel consumption. This results in a significant reduction in fuel use and carbon emissions, with approximately 8,000 tons of carbon dioxide equivalents being avoided each year. Through technological innovation and ecological partnerships, ExxonMobil promotes efficient operation in areas such as commercial vehicles, heavy trucks, ships, rail transport, and urban public transportation systems. The LNG truck digital maintenance platform it has established in China integrates 2 million pieces of vehicle operation data, reducing maintenance response time to within 4 hours.   International cooperation and integration of industry and education – The intelligent LNG industry moves forward amid challenges. Although artificial intelligence technologies greatly contribute to the sustainable development of the entire LNG industry chain, various challenges still require global cooperation to address. By deepening international cooperation, promoting standardization, and enhancing security measures, the LNG industry will gain broader opportunities for growth amid the wave of artificial intelligence.   Firstly, the most significant challenge is economic volatility and increasing investment risks. Data from the Carbon Tracker 2024 report shows that 40% of the planned LNG projects may face economic viability risks. For example, the expansion project of Cheniere Energy’s Corpus Christi LNG plant in the United States resulted in financial losses due to errors in the artificial intelligence systems used in the supply chain. The artificial intelligence system predicted too optimistically that the key equipment would be delivered on time, without taking into account potential logistics delays, which forced the project timeline to be extended by 6 months, resulting in direct economic losses of up to $430 million. In this regard, flexible capacity allocation is a good option. ExxonMobil is carrying out a \"dual-fuel\" conversion at the Freeport LNG plant, using an artificial intelligence control system to dynamically switch 20% of its production capacity to blue hydrogen production. This flexible design enabled the project to maintain a 14% return rate amid gas price fluctuations in 2024, which is 6 percentage points higher than that of traditional projects.   Secondly, the challenges posed by technical standards and barriers to data interoperability cannot be ignored either. The digital divide in global LNG infrastructure severely hinders the adoption of new technologies. It is imperative to coordinate different technical standards and break down barriers. In response, Chevron took the lead in establishing a cross-border data alliance, which utilized encrypted distributed ledger technology to enhance the integration of blockchain technologies.   Finally, the challenges of cybersecurity and the talent gap are also very significant. Data from the International Energy Agency shows that global cyberattacks on LNG facilities increased by 67% in 2024; the LNG terminal on Bonny Island in Nigeria suffered losses of $18 million in a single day due to a ransomware attack. Meanwhile, McKinsey research shows a 47% shortage of interdisciplinary AI talent in this industry, and the density of senior algorithm engineers in emerging manufacturing countries such as Indonesia is only 1/927 that of the United States. To address this challenge, establishing quantum security measures and strengthening the integration of industry and education are the solutions. The quantum-resistant encryption protocol developed by Shell in collaboration with Palo Alto Networks provides nanosecond-level threat detection for its global LNG fleet. In 2024, this technology successfully intercepted an Advanced Persistent Threat (APT) attack targeting reception stations in the Middle East, protecting goods worth $930 million. The \"Energy Artificial Intelligence Laboratory\" established by BP in collaboration with MIT saw its graduates in 2024 use real-time data prediction models to identify and fix potential faults in the condensers of Australia’s Curtis LNG project in advance. The talents cultivated by these institutions are high-end professionals possessing both process engineering and algorithm design capabilities. They have effectively helped enterprises increase the accuracy of fault prediction for their process equipment by 35%, significantly reducing production and operational risks.