Optimizing Drilling Operations in Real-Time: The Role of Digital Twins in Reducing Risks and Enhancing Performance

This paper presents an innovative approach to optimize drilling operations in real-time, offering enhanced risk management, improved efficiency and cost reduction. The focal point is an automated rate of penetration (ROP) optimization system powered by digital twins and artificial intelligence (AI) technologies, which effectively enhances drilling performance while mitigating potential issues. At the core of this system are digital twins continuously calibrated using real-time data, a risk detection module leveraging model-based reasoning in AI and a fast auto-optimization module. It incorporates predictive analytics and multiple what-if scenarios to propose forward-looking advice on potential risks and operational parameters. To enhance performance while reducing risks, the system evaluates imminent risks and anticipated hole-cleaning requirements for ROP optimization. The effectiveness of the advisory system has been rigorously tested and verified, with a specific focus on risk detection and auto-optimization modules. As shown in the case studies, the system automatically detects potential risks including poor hole cleaning, poor cuttings transport, and a leakage or washout in real-time operations. Moreover, it optimizes real-time drilling parameters by evaluating multiple scenarios on combined objectives of reducing potential drilling risks, ensuring hole cleanness, and improving drilling efficiency, ensuring the optimized results are risk-free in pursuing higher ROP. It results in substantial time savings, with up to 4.75 hours saved during a single drilling section. The results clearly demonstrate the value created through reduced drilling time, empowering the rig team to operate independently and minimizing reliance on back-office support. Overall, this real-time optimization system offers a transformative approach to drilling operations, yielding significant time and cost reductions. Its supervisory capabilities streamline the implementation of advanced digital twin models and parameter optimization, thereby improving operational performance and driving further integrated drilling automation.