Automatic analysis of drill string dynamics

A non-transitory computer-readable medium storing one or more instructions executable by a computer system to perform operations comprising:

• capturing, using an image sensor directed at a drilling system, an image feed of movement of a component of the drilling system with respect to a vertical reference line;
• converting the image feed into a digital representation of the movement of the component, the digital representation comprising a number of offset pixels from the component to the vertical reference line in the image feed;
• converting the digital representation into a machine learning (ML), the ML representation including a plurality of vectors each comprising the number of offset pixels from the component to the vertical reference line at a respective time;
• training a ML model using the ML representation to characterize the movement of the component as normal or abnormal;
• and
• using the trained ML model to characterize the movement of the component in real-time as normal or abnormal.

A system, comprising:

• one or more processors;
• and
• a non-transitory computer-readable storage medium coupled to the one or more processors and storing programming instructions for execution by the one or more processors, the programming instructions instructing the one or more processors to perform operations comprising:
• capturing, using an image sensor directed at a drilling system, an image feed of movement of a component of the drilling system with respect to a vertical reference line;
• converting the image feed into a digital representation of the movement of the component, the digital representation comprising a number of offset pixels from the component to the vertical reference line in the image feed;
• converting the digital representation into a machine learning (ML) representation, the ML representation including a plurality of vectors each comprising the number of offset pixels from the component to the vertical reference line at a respective time;
• training a ML model using the ML representation to characterize the movement of the component as normal or abnormal;
• and
• using the trained ML model to characterize the movement of the component in real-time as normal or abnormal.