Data Scientist Intern (3 months) - Starting Summer 2025

Abingdon - United Kingdom

Job title:

Data Scientist Intern (3 months)

Project: Physics-Informed Machine Learning for Numerical Simulation

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.

Global in outlook, local in practice – and with a united, shared passion for discovering solutions, we hire talented, driven people and support them to succeed, personally and professionally.

Location:

Abingdon, Oxfordshire

Description & Scope

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Classical thermodynamics is a fundamental discipline to model different engineering applications. The behavior of fluid and solid systems is described using Equation of States (EoS), used to model the relationship between pressure, volume, temperature and composition. For pure components, different EoS are reported in literature, using data coming from laboratory experiments, making the tuning problem irrelevant for most of the practical problems. Although, when we start model the behavior of component mixtures, especially hydrocarbons, the higher number of pure components and its isomers make it difficult or even impossible to model each component separately.

To tackle the problem of modeling mixtures with a large number of elements, techniques such as lumping and tuning are applied to reduce the real components to pseudo-component mixtures, trying to obtain similar thermodynamic behavior using a fraction of computational power. In this context, we can write the tuning as a data-assimilation problem and solve it by applying different methods.

During this internship, we are looking at the application of Ensemble Smoother methods to solving the tuning problem, incorporating the uncertainty on laboratory PVT experiments into EoS adjustments.

Deliverables

The intern will work closely with the Intersect innovation team to implement and test the Ensemble Smoother code in Python. By the end of the internship the following technical deliverables are expected:

• Implementation of Python plugin to communicate the Ensemble Smoother with the Fluid Modeler results

• Testing different Equation-of-States (EOS) applications

• Quantification of the uncertainty on laboratory PVT experiments

• Showing the effectiveness of the new method with real laboratory data

Note that this is an innovation work, with opportunity to publish it in a research journal.

Required Skills & Qualifications

• Masters Degree - (Penultimate or Final year) in Petroleum engineering, Chemistry Mathematics, Science Computing or a related discipline

• Analysis and Statistics

• Python code development

• Non-linear optimization

• Thermodynamics

• Equation of state

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.