Predicting Mechanical Failure Using Non-Operational Data
Lead Research Organisation:
University of Oxford
Department Name: Engineering Science
Abstract
Summary:
The Project is directed toward predicting mechanical failure using non-operational data.
Background:
In the process of Well Construction (planning, drilling, and completion of oil and gas wells), the mechanical tools that constitute the drilling assembly are subjected to extremely harsh conditions, experiencing high pressures and temperatures and high levels of shock and vibration. Mechanical failure of a tool during the drilling process can lead to costly and time-consuming recovery and replacement. Understanding how these tools wear and fatigue during the drilling process is critical to efficiently constructing cost-effective wells.
In an ideal situation, the condition of key components of the tools would be monitored in real time, such that failure could be predicted, and maintenance performed before failure occurs. However, even if such measurements could be made, data communication rates between the subsurface and the surface are not currently sufficient to allow this type of information to be communicated in real time.
A more realistic goal would be to monitor the condition of the tools both before they are put into the well, and when they are pulled out of the well. With the combination of non-destructive testing, and an appropriate (data-driven) model of how the tool will fatigue under certain drilling conditions, the probability of the tool failing during the next run could be calculated. This would allow an informed decision to be made as to whether that tool can be re-used, or whether corrective maintenance is required.
Project Description:
The aim of this project is to develop a system that demonstrates the ability to make such a prediction. This would combine a number of aspects, that may include:
- The development of a simple model of a downhole drilling tool to investigate mechanical failures. For example, this may involve using techniques such as Finite Element Analysis to model failure modes.
- The development of a laboratory scale experimental setup to recreate failures
- The development of a non-destructive testing technique to assess the current condition of a tool
- The use of the generated data (simulated and/or measured) to determine the probability of failure of a component within a following time period.
The project will therefore allow the development of expertise in:
- Advanced modelling techniques
- Laboratory Scale Experiments
- State of the art non-destructive testing
- Data Analytics and Data Science.
This project falls within the EPSRC Engineering research area.
This project is an EPSRC Industrial CASE Studentship with Schlumberger Gould Research Centre.
The Project is directed toward predicting mechanical failure using non-operational data.
Background:
In the process of Well Construction (planning, drilling, and completion of oil and gas wells), the mechanical tools that constitute the drilling assembly are subjected to extremely harsh conditions, experiencing high pressures and temperatures and high levels of shock and vibration. Mechanical failure of a tool during the drilling process can lead to costly and time-consuming recovery and replacement. Understanding how these tools wear and fatigue during the drilling process is critical to efficiently constructing cost-effective wells.
In an ideal situation, the condition of key components of the tools would be monitored in real time, such that failure could be predicted, and maintenance performed before failure occurs. However, even if such measurements could be made, data communication rates between the subsurface and the surface are not currently sufficient to allow this type of information to be communicated in real time.
A more realistic goal would be to monitor the condition of the tools both before they are put into the well, and when they are pulled out of the well. With the combination of non-destructive testing, and an appropriate (data-driven) model of how the tool will fatigue under certain drilling conditions, the probability of the tool failing during the next run could be calculated. This would allow an informed decision to be made as to whether that tool can be re-used, or whether corrective maintenance is required.
Project Description:
The aim of this project is to develop a system that demonstrates the ability to make such a prediction. This would combine a number of aspects, that may include:
- The development of a simple model of a downhole drilling tool to investigate mechanical failures. For example, this may involve using techniques such as Finite Element Analysis to model failure modes.
- The development of a laboratory scale experimental setup to recreate failures
- The development of a non-destructive testing technique to assess the current condition of a tool
- The use of the generated data (simulated and/or measured) to determine the probability of failure of a component within a following time period.
The project will therefore allow the development of expertise in:
- Advanced modelling techniques
- Laboratory Scale Experiments
- State of the art non-destructive testing
- Data Analytics and Data Science.
This project falls within the EPSRC Engineering research area.
This project is an EPSRC Industrial CASE Studentship with Schlumberger Gould Research Centre.
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/T517653/1 | 01/10/2019 | 30/09/2025 | |||
| 2279576 | Studentship | EP/T517653/1 | 01/10/2019 | 30/09/2023 | Felipe Igea |