From Simulation to Strategy: Integrating Generative AI and Digital Twins in the Downstream Oil And Gas Sector

Abstract

There is growing pressure on the downstream oil and gas and City Gas Distribution (CGD) sectors to increase efficiency, safety, and sustainability—issues that traditional tools struggle to address. By enabling simulated operations, anticipating failures, and promoting prudent decision-making across the asset lifecycle, generative artificial intelligence (GenAI) offers powerful solutions when paired with digital twin technology.

This knowledge is based on qualitative research that examined over 60 academic and commercial sources. Using GANs, LLMs, and transformers, it demonstrates early GenAI and Digital Twin applications in refining, logistics, CGD, and customer service operations.  While GenAI can model refining process deviations to optimise crude blends and energy systems, digital twins help monitor catalyst life and unit performance, reducing unplanned shutdowns. Logistics can cut emissions and fuel waste by using AI-driven demand forecasting and route optimisation.

Through the use of AI in CGD, real-time pipeline stress monitoring, leak prediction, and emergency simulation are made possible, increasing network safety and efficiency. GenAI also aids in the creation of synthetic data for model validation, addressing privacy and data availability concerns.

AI-powered chatbots and behavioural modelling enhance customer service and payment efficiency. Predictive analytics support smart meter maintenance and load balancing. GenAI helps simulate pricing strategies and demand across regulatory contexts, which informs strategic marketing and supply decisions.

Together, these applications show how GenAI and Digital Twins are advancing downstream and CGD operations toward a more resilient, intelligent, and secure future.