Automatic interpretation of drilling dynamics data

A computing system for interpreting drilling dynamics data, the computing system comprising:

• one or more processors;
• and
• a memory system comprising one or more non-transitory computer-readable media storing instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations, the operations comprising:
• receiving multi-dimensional drilling dynamics data collected by a sensor positioned in a drilling tool;
• determining a feature zone from a feature map based on the multi-dimensional drilling dynamics data, wherein;
• the feature map comprises a two-dimensional representation of a plurality of points that encodes multi-dimensional drilling dynamics data;
• the feature zone includes a subset of the plurality of points within a polygon region in the two-dimensional representation of the feature map;
• the feature zone corresponds to a predetermined dynamic state associated with the received multi-dimensional drilling dynamics data;
• and
• the feature zone is determined using a supervised neural network trained to associate a plurality of feature zones with a plurality of dynamic states;
• and
• selecting at least one drilling parameter for a drill string based on the dynamic state corresponding to the determined feature zone.

A non-transitory computer-readable medium storing instructions that, when executed by one or more processors of a computing system, cause the computing system to perform a method for interpreting drilling dynamics data, the method comprising:

• receiving multi-dimensional drilling dynamics data collected by a sensor positioned in a drilling tool;
• determining a feature zone from a feature map based on of the multi-dimensional drilling dynamics data, wherein:
• the feature map comprises a two-dimensional representation of a plurality of points that encodes multi-dimensional drilling dynamics data;
• the feature zone includes a subset of the plurality of points within a polygon region in the two-dimensional representation of the feature map;
• the feature zone corresponds to a predetermined dynamic state associated with the received multi-dimensional drilling dynamics data;
• and
• the feature zone is determined using a supervised neural network trained to associate a plurality of feature zones with a plurality of dynamic states;
• and
• selecting at least one drilling parameter for a drill string based on the dynamic state corresponding to the determined feature zone.