Real-Time Drilling Rig Control With AI Anomaly Detection

The patent replaces the mechanical system of human operators with an automated AI/ML-based monitoring system. The system uses machine learning models trained on historical drilling data to automatically detect anomalies, well kicks, and other critical events, eliminating the need for multiple human operators while maintaining or improving detection accuracy.

The monitoring system performs self-service by automatically analyzing drilling data, detecting anomalies, and generating alerts without human intervention. The AI models continuously learn from new data and improve their detection capabilities autonomously, reducing dependency on human expertise while maintaining high detection accuracy.

The patent replaces the need for extensive human training by substituting human operators with pre-trained AI/ML models. These models are trained on large datasets of historical drilling operations, including various abnormal conditions, enabling them to immediately detect problems without requiring the long training periods that human workers need to acquire equivalent expertise.

The AI models perform preliminary learning by being trained on comprehensive datasets before deployment. This preliminary training action captures years of expert knowledge and experience in the model parameters, allowing the system to immediately possess problem detection capabilities that would otherwise require extensive human training time to develop.

The patent transitions from using simplified physics formulas with empirical coefficients to AI/ML models that learn complex relationships directly from data. This parameter change involves moving from predetermined mathematical models to adaptive models that can capture non-linear and complex interactions in drilling processes, improving accuracy while maintaining real-time calculation speed through optimized algorithms.

The patent replaces traditional empirical modeling approaches with data-driven AI/ML models. These models substitute simplified physics formulas with learned representations from training data, achieving higher accuracy by capturing complex patterns that empirical models miss, while maintaining computational efficiency through optimized inference processes.

The patent segments the AI/ML system into modular components: offline training phase and online inference phase. The computationally intensive training is performed offline using historical data, while the deployed model performs lightweight real-time inference with minimal computational resources. This segmentation allows high automation level during operation while reducing real-time computational resource requirements.

The system performs preliminary computational work during the offline training phase, where complex model learning and pattern recognition occur. Once trained, the model requires minimal computational resources for real-time predictions. This preliminary action transfers the heavy computational burden from the operational phase to the preparation phase, enabling high automation with low real-time resource consumption.