• TEPUK is currently one of the largest operators on the UK continental shelf (UKCS) in terms of production and reserves. The company employs around 1000 people, split between its onshore sites and its offshore facilities. As part of the Total group the workforce includes both local and expatriate staff, drawn from more than 35 countries.
  • Within TEPUK, and part of the Total group's international research and development (R&D) network, the Geoscience Research Centre (GRC) conducts applied research for oil and gas exploration and production. It also sponsors and steers partnership research with academic institutions, and technology development with external specialist providers.
  • A significant number of numerical reservoir simulations are typically needed for tasks such as history matching, uncertainty quantification and production optimization. In fluid mechanics or air pollutions problems, data driven models have been used as an efficient numerical tool to reduce the computational cost.

Objectives of the mission

  • The main objective of your mission is to develop innovative deep learning based proxy models for production optimization. The trained neural network is expected to predict the production performance under different production strategies at a much shorter timescale, which is not possible using conventional modelling techniques.
  • Your second objective is to utilize data analytics and machine learning in the field of reservoir management.
  • For e.g., investigation of reservoir performance, develop methodologies to assess recovery potential, including systematic identification of under-performance in brownfield areas and prediction of recoveries for greenfield areas etc.
  • The planned work scope will cover QC and understanding of sub-surface static & dynamic data, review of wider/global dataset and utilisation of advanced statistical and/or machine learning techniques to evaluate reservoir performance.

Context and environment

  • The selected candidate will receive a VIE allowance according to the Business France rates and the subsidiary will provide an accommodation.
  • Please check the following website to make sure that your application complies with the criteria to carry out a VIE assignment:

Candidate profile

Desired qualifications:

  • MSc or equivalent in Petroleum/Reservoir Engineering with computational experience

Technical skills:

  • Petroleum Engineering: some experience in reservoir performance evaluation and modelling desirable. Experience in reservoir simulation techniques, integrated reservoir modelling and decline curve analysis. Experience in carrying out simulation work on Eclipse and lntersect platform.
  • Solid understanding of fundamentals of reservoir physics from a multi-discipline perspective. Strong background in classic reservoir and production engineering and experience with solving complex problems in an innovative and cost-effective manner.
  • Good understanding of transverse geological and geophysical concepts.
  • Machine Learning: knowledge of machine learning techniques is advantageous.

Computing skills:

  • Experience in Python is desirable; preferably also with Anaconda.
  • Experience with Linux and/or C++ would be an advantage.


  • English: fluent
  • French: level B2