IADC dull bit grading is the current industry standard to assess the condition of a drill bit when it comes out of the hole. It is intended to capture the impact of drilling issues (e.g. drilling abrasive hard rock, drilling dysfunctions) on the bit and to improve future bit selection. However, the grading process is manual and subjective, making the bit grading outcome an inconsistent and unreliable metric. Recent advances in image processing and deep learning allow for bit grading to become more consistent and automated. Such a process is described in this paper. The dataset used in this project consisted of multiple images (taken from different perspectives in a random manner) of used drill bits from 13 bit runs across multiple wells. As a preliminary step in developing the approach, only PDC bits were considered in this project. The first task was to identify all the cutters on a drill bit image using Convolutional Neural Networks (CNN). The CNN approach was chosen since it has shown remarkable success in solving the problem of object detection and classification in other fields. Next, the amount of damage to each cutter was quantified using image processing techniques. Finally, from information gathered in the previous steps, a holistic damage assessment of the drill bit was made. The trained CNN was able to detect the cutters in an image to a high degree of accuracy. The accuracy of cutter detection was further improved through the use of heuristics that predict potential locations of cutters based on blade location and shape. The identification of unique cutters from a group of images of the same bit proved more challenging. Since the images could not be appropriately stitched together, each image was graded independently, and a holistic assessment of the bit was made by aggregation of the individual assessments. Additionally, not all of the cutters identified could be positively identified as damaged or not. For example, if the perspective that was available was at a right angle to the cutter's face, it is inherently not possible to quantify the damage. The computer-generated assessment of the bit was validated with collaborative assessments made by multiple human operators. This paper presents a novel approach to bit damage classification that removes the subjective bias that comes with human evaluations. The application of deep learning techniques to cutter identification, damage detection and quantification is unique and has the potential to significantly improve bit design, selection, and thus, drilling efficiency.

In this paper, we define event expression over sentences of natural language and semantic relations between events. Based on this definition, we formally consider text understanding process having events as basic unit.

In this paper, we propose an algorithm of searching for both positive and negative itemsets of interest which should be given at the first stage for positive and negative association rules mining. Traditional association rule mining algorithms extract positive association rules based on frequent itemsets, for which the frequent itemsets, i.e. only positive itemsets of interest are searched. Further, there are useful itemsets among the frequent itemsets pruned from the traditional algorithms to reduce the search space, for mining of negative association rules. Therefore, the traditional algorithms have not come true to find negative itemsets needed in mining of negative association rules. Our new algorithm to search for both positive and negative itemsets of interest prepares preconditions for mining of all positive and negative association rules.

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