We are a global technology company, driving energy innovation for a balanced planet.
At SLB we create amazing technology that unlocks access to energy for the benefit of all. That is our purpose. As innovators, that’s been our mission for 100 years. We are facing the world’s greatest balancing act- how to simultaneously reduce emissions and meet the world’s growing energy demands. We’re working on that answer. Every day, a step closer.
Our collective future depends on decarbonizing the fossil fuel industry, while innovating a new energy landscape. It’s what drives us. E
nsuring progress for people and the planet, on the journey to net zero and beyond. For a balanced planet.
Together, we create amazing technology that unlocks access to energy for the benefit of all.
You can find out more about us on https://www.slb.com/who-we-are
Job title: Internship – Mechanical Engineer (5-6 months)
Location: Clamart, France
Scope of the internship:
The electronics embedded in Schlumberger equipment are vital for the success of our operations. They provide support such as control, navigation and front-end data analysis from sensors.
Due to the extremely challenging operating conditions in downhole tools (high pressure, high temperature, severe shock and vibration), electronics can be subjected to complex failure modes leading to operational downtime.
Physics-based model to predict the time-to-failure and provide conditions-based maintenance exist.
To be able to capture the accuracy of the physics, those models are high fidelity and therefore, strongly time-consuming.
They also do not capture uncertainties coming from manufacturing variabilities and they lack operational conditions data.
On the other hand, data-driven models based on machine learning methods are increasingly being applied with available data collected directly from the sensors.
Those methods, although fast to be used, tend to be not capable of capturing complex relationships between the input parameters and desired output, difficult to describe without using physics.
The mission aims at developing a hybrid approach that is a combination of physics based and data-driven models, resulting in more accurate remaining useful lifetime estimation by finely tuning prediction models that have better capability to manage uncertainty.
The hybrid approach should fuse physics and data for failure prediction. The models should consider vibration and shock conditions.
The mechanics of the electronics system will be characterized by using surrogate models calibrated with finite element simulations.
1. Perform literature review and identify different concepts for parametric reduced order
2. Build Finite-Element models of printed wiring assemblies (PWA) in vibration and shock environment
3. Develop surrogate models of PWA by exploring different concepts identified
4. Demonstrate a methodology to develop reduced order models of electrical boards
5. Propose a framework to fuse experimental data and FEA based models
6. Write reports and communicate efficiently the findings to the team
- Good level of English
- Studying towards a Masters in Mechanical Engineering or related field.
- Modeling and simulation in structural mechanics (ABAQUS or ANSYS)
- System modeling (Modelica, Simulink or equivalent)
- Specialty in waves propagation / Shock & Vibration theory
- Competencies in statistics and probability
- Knowledge in AI/machine learning