At a Glance
- Tasks: Dive into fluid modelling and tackle real-world challenges in the oil and gas industry.
- Company: Join SLB, a leader in energy innovation with a commitment to diversity.
- Benefits: Gain hands-on experience, mentorship, and a chance to publish your work.
- Other info: Flexible recruitment process to accommodate diverse needs.
- Why this job: Make an impact by solving complex problems in asphaltene modelling.
- Qualifications: Pursuing a Masters in Chemical Engineering or related field with programming skills.
The predicted salary is between 20000 - 30000 £ per year.
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.
Scientific Software Engineer Intern (3 months) in Abingdon employer: SLB
SLB is an exceptional employer that fosters a collaborative and innovative work culture, particularly in the dynamic field of scientific software engineering. Located in Abingdon, Oxfordshire, employees benefit from a supportive environment that encourages professional growth through hands-on experience and mentorship in cutting-edge projects. With a commitment to diversity and inclusion, SLB offers unique opportunities for interns to contribute to meaningful research while developing valuable skills in thermodynamics and fluid modeling.
StudySmarter Expert Advice🤫
We think this is how you could land Scientific Software Engineer Intern (3 months) in Abingdon
✨Tip Number 1
Network like a pro! Reach out to professionals in the field of scientific software engineering, especially those who work with thermodynamics and fluid modelling. Use platforms like LinkedIn to connect and engage with them; you never know who might have a lead on an internship or can offer valuable advice.
✨Tip Number 2
Prepare for interviews by brushing up on your technical skills. Since this role involves programming in Python and understanding complex concepts like Equation of State tuning, practice coding challenges and review relevant literature. Show us that you’re not just a candidate, but a passionate problem-solver!
✨Tip Number 3
Don’t underestimate the power of a good follow-up! After an interview, send a thank-you email expressing your appreciation for the opportunity. This not only shows your enthusiasm but also keeps you fresh in their minds as they make their decision.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows us that you’re genuinely interested in joining our team. So, don’t wait – get your application in and let’s tackle those asphaltene challenges together!
We think you need these skills to ace Scientific Software Engineer Intern (3 months) in Abingdon
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Scientific Software Engineer Intern role. Highlight your experience in thermodynamics, fluid modelling, and any relevant programming skills, especially in Python. We want to see how your background fits with our needs!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about asphaltene modeling and how your studies align with our project goals. Keep it engaging and personal – we love to see your enthusiasm!
Showcase Your Projects:If you've worked on any relevant projects or research, make sure to mention them! Whether it's a university project or personal work, we want to know how you've applied your knowledge in real-world scenarios. This helps us see your practical skills in action.
Apply Through Our Website:Don't forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it gives you a chance to explore more about what we do at StudySmarter!
How to prepare for a job interview at SLB
✨Know Your Thermodynamics
Brush up on your thermodynamics and fluid modelling concepts before the interview. Be ready to discuss how these principles apply to asphaltene modeling and Equation of State tuning, as this will show your understanding of the role's technical requirements.
✨Show Off Your Programming Skills
Since programming in Python is a key part of this internship, be prepared to talk about your experience with it. Bring examples of projects you've worked on or challenges you've solved using Python, especially those related to data analysis or scientific documentation.
✨Prepare for Technical Questions
Expect technical questions that dive into the specifics of EoS models like Peng-Robinson and Soave-Redlich-Kwong. Familiarise yourself with their applications and limitations, and think about how you would approach tuning methodologies for real-world datasets.
✨Demonstrate Your Research Skills
Since literature review is part of the role, be ready to discuss recent advancements in asphaltene modeling and PVT laboratory data interpretation. Showing that you can engage with current research will highlight your initiative and passion for the field.
