Research in drilling automation, at the University of Nottingham, has the ultimate objective of achieving computerised drill control through the the application of an intelligent based system.

From a laboratory drill rig, various parameters are measured and stored to a database for a variety of rock types and drilling conditions. The database is used by the Inteldrill system to generate a series of Self Learning Prediction Matrices [S.L.M.P.] which allow the determination of the best drilling performance likely to be achieved. The control can be based on either maximum penetration rate or minimum cost drilling.

Minimum cost drilling is based on a cost equation which is configured with constants for the various cost centres. The computer passes drilling conditions from the drilling S.L.P.M. through the cost equation to generate a cost matrix. This matrix is then searched to determine the most economical drilling parameters. The system has a self-learning capability such that the more information in the database, the greater the accuracy of the S.L.P.M.'s.

A prototype system has been developed and is currently undergoing trials, using data generated by laboratory rig, together with simulated data.

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