This paper presents an autonomous drilling advisory system powered by digital twins and AI solutions. Such an advisory system aims to automate real-time monitoring and parameter optimization, reduce subject-matter experts, and meet the demands for safer and more efficient drilling toward autonomous operation.

The methodology proposed in this research involves the creation of a comprehensive Digital Twin model that accurately replicates the drilling process by integrating hydraulic, thermal dynamic, and mechanical models. To ensure high model accuracy, an auto-calibration approach is developed, driven by real-time data, to fine-tune the Digital Twin models. Additionally, AI-based model reasoning techniques are employed to detect potential hazards and risks ahead of the bit proactively. This is achieved by comparing the ideal behavior of the digital twin replica with the actual behavior observed from downhole and the rig. As a result, real-time diagnostics are generated to supervise ongoing operations, accompanied by suggestions to mitigate identified risks. Furthermore, the system leverages the capabilities of the Digital Twin and optimization methods to create multiple combinations of operational parameters. These parameters are optimized by ranking the predicted performance derived from the Digital Twin. The optimized operational parameters are automatically generated as forward advice to drillers, enabling them to make informed decisions and enhance drilling performance.

Testing results on multiple wells from different operators are presented, showcasing the system's capabilities in real-time monitoring and drilling parameter optimization. The system demonstrates its effectiveness in providing diagnostic messages with early anomaly detection during drilling and casing running. These diagnostic warnings include losses, leakage, poor hole cleaning, and stuck pipe, enabling proactive intervention to mitigate risks. Furthermore, the system optimizes operational parameters during drilling and tripping in real-time without requiring human intervention. This optimization covers parameters such as flow rate, rotary speed (RPM), and rate of penetration (ROP) during drilling, and tripping speed during tripping in and pulling out of the hole. The time savings achieved through the use of optimized parameters are quantified for both cases, demonstrating a substantial improvement in operational efficiency while maintaining safety margins. The scalability and adaptability of the system are also highlighted, emphasizing its ability to accommodate diverse drilling scenarios and integrate with existing solutions in various deployment conditions.

The proposed methodology demonstrates the development of a robust and efficient system that enhances decision-making and improves drilling performance. In addition, the results highlight the potential benefits of combining AI and Digital Twin technologies in the drilling industry, paving the way for future innovations and advancements in the field.

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