Job title:
Data Science Intern (3-6 Months) - Physics Informed Machine Learning Model for Numerical Simulation
About Us:
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. Ensuring progress for people and the planet, on the journey to net zero and beyond. For a balanced planet.
Location:
Abingdon, UK
Job Summary:
Numerical simulation remains the only reliable method to predict the future state of a system - be it weather, fluid flow, landing on Mars or physics powered realistic gaming. A reservoir simulator is used to model fluid flow in porous media for various applications including carbon capture and storage and geothermal energy systems. The drawback of these methods is that they are computationally extremely slow and hence not practical for many realistic workflows. In this project, you will work on developing a machine learning model that is trained on the physics rather than data alone. These models are estimated to be orders of magnitude faster than the conventional simulators. Once the model is trained, it will replace the simulator in workflows with heavy computational loads.
Numerical simulation remains the only reliable method to predict the future state of a system - be it weather, fluid flow, landing on Mars or physics powered realistic gaming. A reservoir simulator is used to model fluid flow in porous media for various applications including carbon capture and storage and geothermal energy systems. The drawback of these methods is that they are computationally extremely slow and hence not practical for many realistic workflows. In this project, you will work on developing a machine learning model that is trained on the physics rather than data alone. These models are estimated to be orders of magnitude faster than the conventional simulators. Once the model is trained, it will replace the simulator in workflows with heavy computational loads.
Essential Responsibilities and Duties:
As part of the numerical simulation team you will work on developing a physics informed machine learning model using autoencoder architecture and graph methods. This model will be trained on data generated by a numerical simulator. You will also integrating this model in full workflows and show that the workflows can be run orders of magnitude faster. You will exclusively work with Python and Tensorflow and get opportunities to deploy ML pipelines on Azure cloud.
As part of the numerical simulation team you will work on developing a physics informed machine learning model using autoencoder architecture and graph methods. This model will be trained on data generated by a numerical simulator. You will also integrating this model in full workflows and show that the workflows can be run orders of magnitude faster. You will exclusively work with Python and Tensorflow and get opportunities to deploy ML pipelines on Azure cloud.
Qualifications:
Penultimate or final year student, studying towards Bachelors or Masters in Computer Science, Maths, Data Science or related field.
Penultimate or final year student, studying towards Bachelors or Masters in Computer Science, Maths, Data Science or related field.
Competencies:
Required skills:
Required skills:
- Machine learning
- Convolutional Neural Networks
- Numerical methods
- Graph methods
- Willingness to learn
- Good communication skills
Experience in one of the following would be advantageous
- Knowledge of mass conservations equations and material balance
- Simulation basics (numerical methods, computer science, etc)
BlueFlex:
We are open to flexible, hybrid working with a combination of on-site & home working days.
SLB is an equal employment opportunity employer. Qualified applicants are considered without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, or other characteristics protected by law
