Downstream use cases
Customer retail
Asset management use cases Look, listen, feel (LLF) automation Inspection data management system (IDMS) Smart meters and sensors Smart defence (Cyber OT security) AV/VR, APC-based training for O&M E -permits Unified asset performance and mechanical integrity management Digital twin for APM Digital enablement of paper/manual processes HSE & sustainability use cases Dynamic risk assessments (DRA) Fuel compliance management (FCM) GHG accounting and reporting Renewable energy certi cate (REC) accounting Renewable energy production Carbon capture utilisation and storage (CCUS)
Supply chain optimisation Customer 360 – Omnichannel CS and support Customer loyalty Cash ow analytics City gas distribution – customer portal
AI for production optimisation Pricing optimisation dashboard
Gas processing optimisation Plant performance monitoring Integrated supply chain optimisation Real time optimiser (RTO) Yield and throughput optimisation Grade market and sequence optimisation
Upstream use cases
AI for subsurface data analysis Cloud and OSDU for subsurface data Digitisation of historical data ESG compliance for upstream Mobility-enabled field operations Well - developed applications Augmented w ell development Advanced r ig scheduling Autononomous well operations
Cracker health monitoring Energy demand forecasting Automated fuel re-ordering Digital product passport Digital terminals AI/ML-based yield and quality
Electronic proof of delivery (ePOD) End-to-end lube inventory visibility Loyalty program me s for customers Hyper-localisation of customer data Remote fuel retail monitoring Commodity trading and risk management automation
Midstream use cases
Pipeline health analysis Intelligent pipeline management Pipeline corrosion detection Turnaround optimisation with drones Mobility - based field services
Figure 3 An illustrative compendium of AI-driven use cases
agents manage specific components of the whole model, exercise autonomous decision- making, and interact with other agents to arrive at a global optimal solution – holds significant potential to transform the oil and gas industry. AI-powered supply chain optimisation synchronises procurement, production, and dispatch decisions across the refinery network. Using predictive analytics and reinforcement learning, AI continuously recalibrates schedules, tank allocations, and logistics plans based on live market signals, feedstock variability, and demand fluctuations. This enables smarter crude selection, reduced demurrage costs, and tighter inventory control, thereby minimising working capital and enhancing responsiveness to market volatility. In production operations, AI-driven process optimisation eliminates manual trial-and-error by using digital twin simulations to test and implement optimal parameters in real time. It dynamically fine-tunes blending ratios, energy loads, and unit constraints to maximise product quality and minimise resource waste. Predictive maintenance models simultaneously forecast equipment degradation patterns from vibration, pressure, and flow data, enabling early intervention and avoiding costly shutdowns. By creating a connected, data-driven operations layer across the refinery’s value chain – from crude input to product dispatch
– AI enables real-time decision-making, higher margins, and improved sustainability, marking the transition toward the fully autonomous ‘digital refinery’ of the future. In this regard, multiple readily implementable use cases (see Figure 3 ) can be developed across streams using AI, enabling integration that drives significant gains such as controlling costs, maximising yields, optimising plant operations to handle various feeds, manufacturing high-yield catalysts, and streamlining supply chains. Against this backdrop, there is a pressing need to accelerate the adoption of advanced AI to deliver real, quantifiable monetary benefits across every operating asset – where even marginal improvements in yields, conversion rates, or reductions in energy and feed consumption can substantially enhance margins and justify investments in AI/ML. Uncovering a live AI-driven industry example of RTO These transformative capabilities are not purely theoretical; they are real-world implementations across the industry. They demonstrate the tangible value AI-driven optimisation delivers. Figure 4 shows a live industry example illustrating the impact of AI-enabled real-time optimisation. The real-time optimisation (RTO) system integrates plant data, including feedstock rates, composition, and operating parameters,
Refining India
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