Rig State Estimation Using Computer Vision and Sensor Fusion

The patent replaces manual human observation and mechanical sensing systems with automated computer vision technology. The system uses video cameras and machine learning algorithms to automatically detect and track rig state, eliminating the need for manual video analysis and reducing reliance on traditional sensor arrays. This substitution directly improves productivity by enabling continuous automated monitoring while managing system complexity through software-based solutions.

The system creates visual copies of the rig environment through video cameras and processes these optical copies to estimate rig state. Instead of directly measuring physical parameters with sensors, the system captures visual information and uses computer vision algorithms to infer operational state. This approach improves measurement precision for complex rig behaviors while avoiding the complexity of installing and integrating numerous specialized sensors.

The computer vision system performs multiple functions simultaneously: it monitors safety conditions, tracks equipment position, observes human activities, and determines overall rig state from a single integrated system. This multi-functional approach replaces the need for multiple specialized sensors and their complex aggregation, as the vision system naturally captures holistic information about rig operations that individual sensors cannot provide.

The patent merges multiple data sources including video feeds from various cameras, sensor data, and operational logs into a unified rig state estimation system. By combining these diverse information streams through machine learning algorithms, the system recovers the big-picture rig state information that was lost when relying on individual sensor readings in isolation, effectively reversing the information loss problem.