Rapid Bit Design Improvement Through Automatic Extraction of Drill Bit Geometric Information from Videos Captured on a Mobile Device

Abstract Detailed drill bit geometry information allows for the development of 3D bit rock interaction (BRI) models to study dynamic drilling performance. Measurement devices that were built to extract such information from drilled bits are large and not easily deployable at scale at a rig site. The goal of this work is to demonstrate the ability to extract such information from unstructured videos captured on mobile devices. Multiple steps are necessary to obtain the 3D geometrical information of a drill bit from captured videos. First, the open-source package PYCOLMAP can be used to construct a 3D point cloud of the bit using hundreds of frames. This allows for the computation of the image depth and relative coordinates of the objects on the drill bit such as cutters and blades. After obtaining a detailed geometric representation of the drill bit, the noise in the mesh can be reduced to achieve a more visually aesthetic 3D representation through the application of several filtering techniques. Following this, a segmentation model, based on a deep learning architecture, can be used to extract all the cutters from the point cloud, which then allows for the calculation of the coordinate and the orientation of the cutters in 3D space. In this paper, we introduce advanced computer vision techniques for the geometric reconstruction of a dulled bit using videos captured on a mobile device. The results from cutter geometry analysis are then compared with the actual ground truth to evaluate the accuracy of the analysis. The 3D model and the derived geometric features are used in BRI models to understand drilling dynamics. This helps to conduct bit forensics, i.e. root cause investigation into the cause of the observed bit wear and damage, to better understand the progression of these with time. This can subsequently be used to both improve drill bit design and to better control the surface and downhole operational parameters to mitigate any unwanted bit-induced drilling dysfunction, such as different types of damaging drill string vibrations.

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Publication Details

Journal
SPE Annual Technical Conference and Exhibition
Published
2024-09-20
DOI
https://doi.org/10.2118/220828-ms
Citations
3
Primary Topic
Drilling and Well Engineering
Type
article
Field-Weighted Citation Impact
2.06
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article

Rapid Bit Design Improvement Through Automatic Extraction of Drill Bit Geometric Information from Videos Captured on a Mobile Device

Laurent Gerbaud, Eric van Oort, Alisher Safarov, Pradeepkumar Ashok et al.
3 citations
SPE Annual Technical Conference and Exhibition
Drilling and Well Engineering
2.06
article

Rapid Bit Design Improvement Through Automatic Extraction of Drill Bit Geometric Information from Videos Captured on a Mobile Device

Laurent Gerbaud, Eric van Oort, Alisher Safarov, Pradeepkumar Ashok, V. Nagarajan, Ye Zhang, J.R. Cortez, O. Stab
article en
3 citations

Abstract

Abstract Detailed drill bit geometry information allows for the development of 3D bit rock interaction (BRI) models to study dynamic drilling performance. Measurement devices that were built to extract such information from drilled bits are large and not easily deployable at scale at a rig site. The goal of this work is to demonstrate the ability to extract such information from unstructured videos captured on mobile devices. Multiple steps are necessary to obtain the 3D geometrical information of a drill bit from captured videos. First, the open-source package PYCOLMAP can be used to construct a 3D point cloud of the bit using hundreds of frames. This allows for the computation of the image depth and relative coordinates of the objects on the drill bit such as cutters and blades. After obtaining a detailed geometric representation of the drill bit, the noise in the mesh can be reduced to achieve a more visually aesthetic 3D representation through the application of several filtering techniques. Following this, a segmentation model, based on a deep learning architecture, can be used to extract all the cutters from the point cloud, which then allows for the calculation of the coordinate and the orientation of the cutters in 3D space. In this paper, we introduce advanced computer vision techniques for the geometric reconstruction of a dulled bit using videos captured on a mobile device. The results from cutter geometry analysis are then compared with the actual ground truth to evaluate the accuracy of the analysis. The 3D model and the derived geometric features are used in BRI models to understand drilling dynamics. This helps to conduct bit forensics, i.e. root cause investigation into the cause of the observed bit wear and damage, to better understand the progression of these with time. This can subsequently be used to both improve drill bit design and to better control the surface and downhole operational parameters to mitigate any unwanted bit-induced drilling dysfunction, such as different types of damaging drill string vibrations.

SPE Annual Technical Conference and Exhibition
Université Paris Sciences et Lettres (FR), Centre de Géosciences (FR), The University of Texas at Austin (US)
Sustainable cities and communities
Openalex Percentile: Top 12%
Drilling and Well Engineering
2.06
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