Artificial Intelligence (AI) is no longer a buzzword—it is driving real transformation in subsurface and production operations. From history matching and waterflood management to fast-track field development planning, AI-enabled workflows are accelerating complex engineering tasks, uncovering deeper insights from existing data, and improving the speed and quality of decisions. Yet, despite this promise, adoption remains challenging due to data quality issues, model trust, integration with physics, and cultural resistance.
This lecture presents real-world case studies where AI and hybrid AI–physics approaches have delivered tangible value. Examples include ML-assisted history matching in complex reservoirs, hybrid waterflood optimization using data-driven models enriched with physics, and accelerated FDP studies supported by uncertainty workflows and high-performance cloud simulation. These demonstrate how combining AI with domain expertise and scalable engineering frameworks moves solutions beyond pilots into production environments.
The session also explores how Generative AI and agent-based systems will extend today’s fast-track workflows into autonomous, adaptive field management. GenAI can generate insights and recommendations, while agents can integrate those insights with domain tools, creating continuous, closed-loop optimization. They represent a natural evolution from today’s fast-track workflows toward more adaptive, semi-autonomous field management.
By connecting technology, fostering agile innovation, and enabling people through training, transparency, and collaboration, the industry can overcome barriers and move from pilots to enterprise-scale transformation.
The central message is: AI is not here to replace engineers—it is here to augment expertise, accelerate decision-making, and enable smarter, more resilient operations.
Biography: Shripad Biniwale is the Global Innovation Manager at SLB and a Principal Petroleum Engineer with over 20 years of experience spanning reservoir engineering, production optimization, field development planning, and data science. He holds a Bachelor’s in Petroleum Engineering, a Master’s in Reservoir Engineering, and an Executive MBA. Shripad leads SLB’s AI and digital innovation efforts for subsurface workflows, driving the development and deployment of hybrid AI–physics models, intelligent agents, and fast-track field decision systems. He has authored 37 technical papers and is a two-time recipient of the SLB CEO Award. An SPE Regional Service Awardee, two-time Student Paper Contest winner, he actively contributes as a speaker, panelist, and mentor, advancing AI-enabled transformation in the energy industry.