Discover how applied machine learning and physics-informed models are actively solving complex challenges in drilling, completions, and reservoir management. This session highlights diverse, field-proven applications—including predictive maintenance for stimulation engines, real-time kick detection, and completion design analysis across Permian horizontals. Emphasizing a shift from black-box methods to transparent, expert-logic frameworks, presentations will explore drilling parameter optimization and reinforcement learning for well placement in underground hydrogen storage. Join us to see how integrating complex data with domain physics delivers actionable decision support, reducing operational risk and maximizing efficiency across the well lifecycle.