Drill Bit Damage Assessment Using Image Analysis and Deep Learning as an Alternative to Traditional IADC Dull Grading
Notice bibliographique
Résumé
Abstract 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.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».