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Nowadays, the success of drilling operations has become more sensitive to cost control. As one of the most effective cost reductions is the elimination of extra trips, real-time bit wear prediction has been a challenge for appropriate decision-making in drilling operations to reach the highest drilling performance and avoid serious bit issues. This present study shows the combination of mechanical specific energy (MSE), principal component analysis (PCA) and wavelet analysis in real-time bit wear prediction with mud logger data for the Kymera bit. The aforementioned mathematical approaches were combined with the traditional MSE method to monitor the drilling efficiency, especially to eliminate the loss of cone. Novel analytical approaches like PCA and wavelet analysis were introduced to predict the real-time bit wear grade for PDC part of the Kymera bit. Two field cases with several applications were selected for this study to illustrate the advantages of the aforementioned methods and demonstrate their efficiency in bit wear prediction. The first case study shows the bit wear prediction at pulling depth as Grade 5, while the actual dull grade of PDC part is Grade 5-5 without losing the cone. The second case study shows the bit wear prediction at the pulling depth is Grade 1, while the actual dull grade of PDC part is Grade 1-1 without losing the cone. The novelty of this bit wear prediction model is the ability to predict the Kymera bit wear in real time using mathematical approaches with practical applications and valid results in the mentioned study area.

Real-time bit wear prediction using mud logger data with mathematical approaches