Michel Kattar
Use of Machine Learning to Optimize Drilling Equipment Performance and Life Cycle Focus: Predictive Maintenance.
Rel. Marilena Cardu. Politecnico di Torino, Master of science program in Petroleum And Mining Engineering, 2022
|
Preview |
PDF (Tesi_di_laurea)
- Thesis
Licence: Creative Commons Attribution Non-commercial No Derivatives. Download (5MB) | Preview |
Abstract
In the oil and gas industry, any unplanned breakdown that stops the operations in progress can cost the companies millions of dollars. Being able to predict machinery failures and perform a scheduled maintenance ahead of time, won’t only increase the life of the equipment but will also reduce the sudden breakdowns saving the industry a lot of money. To add on that, even the maintenance costs will be reduced since unnecessary ones will not take place anymore. This study talks about all the steps done from real data collection, data cleaning and processing as well as the model generation for the failure prediction of some components of the drilling rig.
These equipment are the blower and the lube oil of the top drive system, as well as the filter, the heat exchanger and the hydraulic pumps of the hydraulic power unit
Relators
Academic year
Publication type
Number of Pages
Course of studies
Classe di laurea
Aziende collaboratrici
URI
![]() |
Modify record (reserved for operators) |
