Scientific Software Engineer Intern (3 months)
Project Title: Asphaltene Fluid Modeling and Equation of State Tuning
Location
Abingdon, Oxfordshire, United Kingdom
Description & Scope
Classical thermodynamics forms the foundation for modeling a wide range of engineering applications, particularly in the oil and gas industry. The behavior of fluid and solid systems is often described using Equation of State (EoS), which capture the relationship between pressure, volume, temperature and composition. For pure components, well‑established EoS models exist, calibrated using laboratory data, thereby reducing the need for extensive tuning in practical applications.
However, modeling the behavior of mixtures, particularly hydrocarbon mixtures, presents a significant challenge. The presence of numerous components, including isomers, makes it impractical to model each component individually. This complexity is further amplified in systems involving asphaltenes – highly complex and poorly characterized fractions of crude oil.
To address these challenges, techniques such as lumping and tuning are employed to simplify real‐component systems into pseudo‑component mixtures. These approaches aim to retain the key thermodynamic behaviours of the system while significantly reducing computational costs. In this context, we can write the tuning as a data‑assimilation problem and solve it by applying different methods.
This internship focuses on tackling the tuning problem for asphaltene modeling using various EoS formulations, including Peng‑Robinson, Soave‑Redlich‑Kwong and Cubic Plus Association (CPA). The goal is to develop a robust workflow that converts asphaltene PVT laboratory data into EoS inputs applicable to real‑world oil industry scenarios.
Responsibilities
* Literature review: conduct an in-depth review of the state‑of‑the‑art in asphaltene modeling, PVT laboratory data interpretation, and Equation of State tuning techniques
* Data tuning: implement and apply tuning methodologies to real asphaltene PVT laboratory datasets
* Workflow development: design and document a systematic workflow for converting asphaltene PVT laboratory data into EoS inputs
* Validation and comparison: evaluate and compare the performance of different EoS models, identifying strengths and limitations for each
* Manuscript preparation: prepare a detailed manuscript summarizing the workflow, methodology, and key findings. The manuscript will serve as a basis for potential submission to a research journal
Qualifications
* Studying a Masters in Chemical Engineering, Fluid Modeling, Applied Physics or a related discipline
* Thermodynamics and fluid modelling
* Data analysis
* Programming (Python)
* Scientific documentation
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.
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