AI Use Cases in Oil and Gas

Studies have shown that using AI for reservoir modelling can increase accuracy by up to 30% and reduce modelling time by 50%.

Generative AI can revolutionise reservoir characterisation and modelling in the oil and gas industry. By analysing seismic data, well logs, and production history, AI algorithms can create detailed 3D models of subsurface reservoirs. LLMs enable geoscientists and engineers to better understand reservoir behaviour, predict fluid flow, and optimise well placement for maximum productivity.

Research indicates that AI-powered predictive maintenance can reduce maintenance costs by up to 20% and increase equipment uptime by 25%.

With generative AI, businesses can predict equipment failures before they occur. This way, oil and gas companies can avoid costly downtime and maintenance delays. LLMs can detect patterns indicative of impending malfunctions by analysing sensor data from pumps, compressors, and other critical assets. So, maintenance teams can proactively address issues, schedule repairs, and optimise equipment performance.

Companies can optimise wellbore trajectories using generative AI to maximise hydrocarbon recovery while minimising drilling costs by 15%.

AI algorithms can recommend the most efficient well paths by analysing geological data, drilling parameters, and reservoir characteristics. GenAI technologies can enhance drilling efficiency by up to 40%  and reduce environmental impact by avoiding unnecessary deviations.

AI-powered real-time drilling monitoring can reportedly reduce non-productive time by up to 30% and enhance drilling safety by 20%.

Generative AI can provide real-time monitoring and analysis of drilling operations, improving safety and efficiency on the rig. LLMs can detect anomalies, predict drilling hazards, and recommend corrective actions by processing data from downhole sensors, drilling parameters, and historical drilling records. As a result, drilling supervisors make informed decisions quickly, mitigating risks and optimising drilling performance.

With generative AI, companies can optimise oil and gas production by up to 25% by analysing production data, reservoir performance, and operational constraints.

AI algorithms can help operators maximise output and minimise costs by forecasting production rates, identifying bottlenecks, and recommending production strategies. This leads to improved profitability and sustainable resource management.

Studies show that using AI for ecological risk assessment can reduce environmental incidents by up to 35% and improve regulatory compliance by 30%.

Using generative AI in oil and gas operations can help businesses assess and mitigate environmental risks. By analysing environmental data, regulatory requirements, and historical incidents, AI algorithms can identify potential hazards, assess environmental impacts, and recommend risk mitigation measures. LLMs help ensure compliance with regulations, minimise ecological liabilities and enhance corporate sustainability.

AI-driven supply chain optimisation has reduced inventory holding costs by up to 20% and improved supply chain responsiveness by 30%.

Generative AI can optimise the complex supply chain operations in the oil and gas industry. LLMs can optimise procurement, storage, and distribution processes by analysing demand forecasts, inventory levels, transportation logistics, and market trends. As a result, supply chain visibility improves, lead times lower, and cost efficiency increases.

By analysing geological data, seismic surveys, and economic indicators, GenAI-enabled exploration portfolio management can increase exploration success rates by up to 40% and minimise exploration costs by 25%.

AI algorithms can help companies optimise their exploration portfolio and allocate resources effectively by assessing exploration risks, estimating resource potential, and prioritising investment opportunities. This way, companies can make more weighted decisions about exploration projects, maximise returns, and reduce risks.