Bearing failure of roller cone bits may result in a time-consuming fishing job, and lead to significant increase in drilling costs. The bearing failure generally comes from over wear of frictional pairs (surfaces between the journal and bearing of the cone). A bearing wear model has been developed to predict the wear status through multi-variable nonlinear regression analysis based on field data. The wear model considers four variables including weight on bit (WOB), revolution per minute (RPM), diameter of bit and hours drilled as a function of IADC bit bearing wear. Some abnormal bit run field reported bearing failures were removed in order to acquire the best regression of the field data. A bearing failure probability model is then introduced to predict the survival probability of the bit, the parameter of which is obtained through statistics of more than 500 bit runs.

The wear status, including instantaneous and cumulative wear, for different roller cone bits and different wells drilled in Western Canada is simulated respectively with the wear model.

A good correlation coefficient was obtained for different IADC bit types including both milled tooth and insert roller cone bits. The cumulative wear values from the model match close those from the field.

The wear model and the failure probability model can help drilling engineers evaluate bearing wear status during real time drilling operations through simulation, and make a decision on when to pull out the bit in time to avoid bearing failures and the possibly lost cones. Better bearing wear predictability will result in better drilling results and effect the total drilling cost.

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