Sustainability and Resilience in LNG Supply Chain

Natural gas is an essential fuel in the transition towards a sustainable energy future as it is considered a cleaner source of fuel when compared to other hydrocarbon sources. To enable natural gas delivery from the producer to consumers, natural gas is liquified to enhance transportation efficiency and reliability. As an energy commodity is increasing, whilst being subjected to risks, uncertainties, and disturbances. An analysis of experiences from the global LNG supply chain highlights many of these risks. As such, there is an incumbent need to develop resilient LNG supply chains. In this study, the risks associated with the LNG supply chain are categorized into four dimensions: Political and regulatory, safety and security, environmental effects, and reliability of new technologies. A SWOT method is then implemented to identify strengths, weaknesses, opportunities, and threats in the LNG supply chain, where the LNG supply chain of Qatar is considered as a case study. Relevant strategies are then recommended using a SWOT matrix to maximize strengths and opportunities while avoiding or minimizing weaknesses and threats within the LNG supply chain. Major parameters to be considered to develop a resilient LNG management model are listed based on the level of priority from LNG producer and receiver perspectives. Thus, as part of creating a robust LNG supply chain, decision-makers and stakeholders are urged to use the learnings from the SWOT analysis and experiences from LNG supply chain management. Moreover, this study contributes toward advancing decision support systems within the LNG supply chain to enhance sustainability and resilience. Five scenarios are investigated, which are: vessel types (Conventional, Q-Flex, Q-Max, and mixed fleet), delivery operation modes (single discharge and multi-discharge), and different bunker fuels (HFO, LNG, and dual-fuel). The Mixed Integer Programming model is used to schedule, assign and deliver a fixed number of cargoes for the LNG supply chain within one month considering total transportation costs and emissions. The developed model, which is implemented using the Binary Particle Swarm Optimization algorithm using is subjected to economic and environmental objectives within an overarching strategic aim for sustainability and resilience. The results demonstrate that using LNG as a bunker fuel supports the reduction in the total emissions within LNG transportation leading to enhancing the resilience of LNG delivery operations against growing environmental constraints. Outputs of the study indicate that the multi-discharge LNG-fueled operation mode can achieve a cost reduction of 23.4% and a total emission reduction of 19.7% relative to a single discharge operation mode, where boil-off gas has a minor impact compared to the emissions released from fuel consumption.