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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 a 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.
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.