(117c) Data-Driven Decision-Making for Flexible Natural Gas Allocation Under Uncertainties

The global growth in population imposes greater demand on energy resources and synthesised products. In addition to applications for power generation, natural gas is a fundamental feedstock to different industries for producing chemicals and consumer products. Hence, a producer must optimise natural gas monetisation to different processes in alignment with the market needs. Optimal allocation does not only support maximising profitability, but targets such as risk management and environmental mitigation could be satisfied. For the state of Qatar, the natural gas industry is the country's most active and bullish sector. Industries such as liquified natural gas (LNG), gas-to-liquids (GTL), methanol, ammonia, urea, and ethylene dominate the sector for cost-effective natural gas monetisation. However, the increased uncertainties in final markets induced by shocks and environmental regulations have impacted the sellers significantly in the last few years. In this context, this study investigates the economic profitability of flexible natural gas allocations to different utilisation routes based on market data. First, process data were obtained from simulating the processes using Aspen HYSYS. Second, simulation results, forecasted annual price and demand data, and operational production bounds were used as inputs to an agent-based model for deciding on optimal yearly allocation. Assessment of a Qatar-based case study revealed that power and LNG are fellfield, while an additional profit was obtained from methanol production. Moreover, considering environmental constraints, urea production was identified to offset environmental emissions. The results conclude that flexible natural gas allocation to utilisation routes allows the decision-maker to respond proactively to market changes. In alignment with the “real options theory”, the approach gives the producer the right, but not the obligation, to switch production capacities to seize market opportunities when the market is bullish and minimise risks when the market is low.