Job Title:

AI Engineer Intern (3 or 6 months) - Starting Summer 2026

Project Title: ​​​Application of a cell-based machine learning model as a non-linear solver pre-conditioner​

About SLB:

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 has 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.

Our purpose: Together, we create amazing technology that unlocks access to energy for the benefit of all. You can find out more about us on https://www.slb.com/who-we-are

Location:

Abingdon, Oxfordshire

Description & Scope:

​​​​In reservoir simulation, a system of non-linear equations is discretized and solved implicitly using the Newton-Raphson method. To enhance solver performance and convergence, various strategies are employed, including inexact Newton methods, preconditioning techniques, multiscale solvers, and others.

This internship explores the integration of a Neural Network (NN) single-cell method as a preconditioner for the non-linear system. The approach involves collecting simulation runtime data for each grid cell and feeding it into a pre-trained neural network to generate improved initial guesses prior to invoking the non-linear solver.

This technique is inspired by the methodology presented in “Learning to Solve Parameterized Single-Cell Problems Offline to Expedite Reservoir Simulation” by Abdul-Akeem Olawoyin and Rami Younis (SPE-212175-MS, 2023). ​

Responsibilities

​The intern will work closely with the Intersect innovation team to implement and test the  pre-conditioner inside Intersect code. By the end of the internship the following technical deliverables are expected:

• Coupling pre-trained neural-network models with an existent commercial simulator to create a pre-conditioner for the non-linear solvers
• Testing different neural-network architectures to identify how to best solve this problem
• Propose an automatic method to export simulation data in a structured format, to be used as training data for the pre-conditioner 
• This an innovation work, with an opportunity to publish it in a research journal.​

Qualifications:

• ​​Studying a Masters in ​Software development, Artificial Intelligence, Neural Networks, Machine Learning, Numerical methods​  or a related discipline
• Python and C++ coding languages, neural-network model and training, numerical methods for solving ODEs and PDEs.​

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.

The recruiting process and the position can be adapted to fit most disabilities, please do not hesitate to mention this when applying.