Abstract
Abstract This study explores the application of artificial intelligence (AI) for real-time optimization of drilling parameters in complex geological formations, addressing the challenges of heterogeneity and unpredictability in subsurface environments. Traditional drilling methods struggle to adapt to dynamic conditions, leading to inefficiencies and risks. AI models, including deep neural networks and reinforcement learning, were developed and integrated with real-time sensor data (pressure, torque, temperature) to dynamically adjust weight on bit, rotation speed, and drilling mud flow rate. Results demonstrate a 47% increase in rate of penetration (25 m/h vs. 17 m/h) and a 29% cost reduction ($850/m vs. $1,200/m) compared to conventional approaches, with rapid response times averaging 1.2 seconds. These improvements enhance efficiency, reduce downtime, and mitigate risks like kicks and equipment wear. Challenges, such as data quality and computational demands, are noted, alongside implications for safety and sustainability. This research highlights AI’s transformative potential in drilling, paving the way for scalable, adaptive solutions Proposed adaptive AI system is designed for seamless integration with existing reservoir management and drilling control systems, enabling real-time, AI-driven decision-making at the rig sit.