Drill bit repair type prediction using machine learning
US11676000B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Aug 31, 2018 |
| Grant date | Jun 13, 2023 |
| Priority date | — |
| Expiry date | Mar 20, 2040 |
Classification
- Technology area (CPC E)Fixed Constructions
- CPC primaryE21B2200/22
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The subject disclosure provides for a mechanism implemented with neural networks through machine learning to predict wear and relative performance metrics for performing repairs on drill bits in a next repair cycle, which can improve decision making by drill bit repair model engines, drill bit design, and help reduce the cost of drill bit repairs. The machine learning mechanism includes obtaining drill bit data from different data sources and integrating the drill bit data from each of the data sources into an integrated dataset. The integrated dataset is pre-processed to filter out outliers. The filtered dataset is applied to a neural network to build a machine learning based model and extract features that indicate significant parameters affecting wear. A repair type prediction is determined with the applied machine learning based model and is provided as a signal for facilitating a drill bit operation on a cutter of the drill bit.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.