Data Science Intern ( Driving Carbon Neutrality through Advanced Geochemical Modeling and Machine Learning) - Summer Intern

About SLB

We are a global technology company, driving energy innovation for a balanced planet.​ Together, we create amazing technology that unlocks access to energy for the benefit of all.​ At SLB, we recognize that our innovation, creativity, and success stem from our differences. We actively recruit people with a diverse range of backgrounds and cultivate a culture of inclusion that unlocks the benefits of our diversity. We want to ensure that everyone feels a sense of belonging here and we encourage, enable, and empower our people to foster inclusivity, build trust, and demonstrate respect for all across the organization.

Description & Scope

The urgent quest for carbon neutrality requires innovative strategies for Carbon Capture, Usage, and Storage (CCSU). Among these, Geological Carbon Storage (GCS) in depleted reservoirs and deep saline aquifers has garnered attention for its simplicity and reliability. However, its effective implementation requires addressing several challenges, particularly the efficient modeling of complex geochemical processes such as ionic trapping, mineral trapping, and mineral alteration/dissolution during CO2 injection. While traditional geochemical modeling is well-established, it is computationally intensive. By leveraging Machine Learning (ML), we can develop surrogate models that significantly reduce computational costs. Although the application of ML in geochemistry is still emerging, it holds great promise for overcoming current limitations, especially when integrated with reservoir flow simulators. Project Goals:

• Develop a Machine Learning (ML) Proxy Model: This project aims to create an ML-based proxy model to simulate the geochemical processes involved in typical CO2 underground storage applications.
• Assess Viability in GCS Uncertainty Analysis: We will demonstrate the model’s effectiveness in GCS uncertainty assessment, focusing on performance analysis during training and prediction phases.

Deliverables

• Train Machine-Learning models for typical geochemistry phenomena that occur during CO2 storage
• Conduct simulation studies to evaluate trained models in terms of accuracy and performance Deliverables
• Deliver an ML proxy model for the relevant geochemistry of typical CO2 underground storage applications
• Show surrogate model viability for GCS uncertainty assessment in a synthetic example   

Required Skills & Qualifications

• Studying for a Masters Degree - (Penultimate or Final year)  in Mathematics; Engineering (Civil, Mechanical, Chemical, Petroleum, or Computer); Computer Sciences  or a related discipline
• Machine Learning knowledge
• Python & Tensorflow
• Reservoir/Fluid Flow Simulation

We are open to flexible, hybrid working with a combination of on-site & home working days.