Impregnated diamond (ID) drilling is a successful and widely used technique for drilling abrasive and hard rock formations. This is in part due to their self-sharpening mechanism, which consists of a constant renewal of diamonds acting in the cutting face as matrix wear takes place. This mechanism, however, can be altered by the blunting of the bit. Therefore, our objective in this paper is to evaluate the feasibility of artificial intelligence (AI) based techniques to monitor tool condition of ID bits, sharp or blunt. Accordingly, topologically- invariant tests are performed with sharp and blunt bits while recording acoustic emissions (AE) and measuring- while-drilling variables. These are then utilised as inputs variables to the tool condition monitoring system. Acceptable pattern recognition rates were obtained for multiple input variables combinations with different pattern recognition algorithms, particularly, that composed by AE root mean square (AErms) and torque-on-bit (tob).


Impregnated diamond (ID) bits are part of the introduction of diamond-based rock cutting tools that has substantially changed the rock cutting industry. They are mainly used for drilling hard and abrasive rock formations. Performance in ID drilling operations is based on the experience of the operator who controls the operational parameters of the drill rig in order to achieve optimal drilling conditions. This increases the susceptibility to errors during the process.

An important part of the success of the ID bits is due to the so-called self-sharpening mechanism. Considerable amounts of research have been devoted to better understand this mechanism of ID bits (Borri-Brunetto et al. 2003, Bullen 1984, Miller & Ball 1991, Tian & Tian 1994, Mostofi 2014). It is widely accepted that the wear process of ID bits is, ideally, composed of three sequential stages. Initially, active diamonds are worn because of their contact with the rock (polishing process). Then, blunted diamonds are stripped off the matrix bonding. Finally, the matrix is worn until fresh diamonds are exposed again (or self-sharpening mechanism).

It is also known that any interruption in the replacement of blunt diamonds can alter the balance between wear rate of diamonds and matrix, resulting in an unstable response or bluntness of the bit (Franca et al. 2015). This bluntness can eventually lead to a less than optimal performance of the bit which is reflected in lower rates of penetration. this, combined with the difficult and time consuming direct assessment of the bit wear condition in deep drilling, highlight the importance of an on-line tool condition, sharp or blunt, monitoring system.

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