Artificial Intelligence (AI) in drilling has rapidly evolved from predictive analytics to autonomous operations, with systems now independently controlling drilling parameters, optimizing well placement in real-time, and predicting equipment failures before they occur.  Current implementations are achieving dramatic results, including a 60% increase in the rate of penetration and a 50% reduction in drilling time, with projections suggesting that 15% of all wells globally will be autonomously controlled within the next three to five years. On the seismic side, AI is reducing interpretation time dramatically, identifying faults and reservoir features that took weeks to map, and helping geoscientists identify prospects they might have missed. National oil companies and major service providers are deploying large-scale AI platforms, processing billions of daily data points, with some systems using foundation models with hundreds of billions of parameters specifically trained on subsurface and drilling operations. The trajectory is moving from reactive to predictive and now prescriptive operations, with the next frontier being fully autonomous drilling that integrates subsurface intelligence, real-time data fusion, and agentic AI capable of making complex decisions across the entire drilling lifecycle. Increased use of AI and robotics on drilling rigs has improved the efficiency and safety of drilling operations through reduced red zone activity, predictive maintenance,  and reduced power consumption.

 

The state-of-the-art for field development and EOR leverages Intelligent Field systems with thousands of sensors feeding real-time data into AI-driven Process Digital Twins that continuously optimize operations across the entire asset lifecycle, delivering production increases and dramatic improvements in response times. Machine learning models now update reservoir models in real-time based on observed data while neural networks optimize EOR parameters—from waterflooding to polymer injection timing, CO2 cycles —enabling large-scale scenario evaluation at computational speeds impossible with traditional simulation alone. Smart wells with remote real-time control from central operations centers, combined with physics-informed neural networks running 1,000 times faster than conventional simulators, are bringing us to the threshold of truly autonomous oilfield operations where AI systems predict and optimize production while engineers guide strategic decisions rather than manual computations. This session will focus on what it will take to make the above scenario a reality by 2030, including a discussion of emerging technologies and machine learning techniques that can facilitate this scenario as well as key challenges that will need to be overcome.