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We are Schlumberger, the leading provider of technology and services to the energy industry. Operating in over 120 countries, our people provide leading digital solutions and deploy ground-breaking technologies to unlock cleaner, safer access to energy for every community—including those we live and work in. We’re looking for innovators to join our diverse community of colleagues and develop new solutions and push the limits of what’s possible. If you share our passion for discovery and want to find out what you could really do, then here is the place to do it.
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.
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. 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, Mathematics or related field.
Competencies
Required skills:
- Machine learning
- Convolutional Neural Networks
- Passionate interest in programming
- 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)
Schlumberger 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.