Dr M Haghighat Sefat
No more applications being accepted
Competition Funded PhD Project (Students Worldwide)
About the Project
Robust, oil production optimisation aims to propose the development and management strategy for a given oil field so as to maximise its value while honouring multiple constraints and uncertainty. Many robust production optimisation studies (including 1 and 2) were performed in the context of exploration, appraisal and early development (i.e. green field studies). A large number of E&P companies are also interested in similar decision making practices in ‘brown’ fields, i.e. in the mature fields with declining production. These fields provide about two-thirds of the world’s daily oil production.
While there are similarities between the green and brown field production optimisation problems (e.g. the large number of control variables with computationally expensive and conditional objective functions) the following differences exist:
•Both the static (geological) and dynamic (e.g. well productivity) uncertainties are smaller and become comparable to a range of previously neglected uncertainties such as equipment failure, sweep efficiency, system response to production control, etc.
•There is access to large but often imperfect volumes of field data
•There is a more complex network of imposed constraints on various components of the system (e.g. fluid processing/disposal/injection limits, workover availability, and artificial lift performance)
•The performance of the whole system at this stage should be accurately captured using integrated models coupling the performance of the reservoir, wells, and surface facilities ultimately feeding into complex economics models.
This project starts with investigating the field integrated modelling approach and its challenges. It will then study risks and uncertainties in the context of brown field optimisation to identify the most influential uncertainties and quantify them by making the best use of the available field data. A fast and efficient framework is then developed for robust, mature field production optimisation under uncertainty.
Funding Notes
Scholarships will cover tuition fees and provide an annual stipend of approximately £14,700 for the 36 month duration of the project.
To be eligible, applicants should have a first-class honours degree in a relevant subject or a 2.1 honours degree plus Masters (or equivalent). Scholarships will be awarded by competitive merit, taking into account the academic ability of the applicant.
References
[1] Haghighat Sefat, M., Elsheikh, A. H., Muradov, K. M. & Davies, D. R. 2016. Reservoir uncertainty tolerant, proactive control of intelligent wells. Computational Geosciences, 20, 655–676.
[2] Haghighat Sefat, M., Muradov, K. M. & Davies, D. R. 2016. “Optimal Field Development and Control Yields Accelerated, More Reliable, Production: A North Sea Case Study”, SPE Intelligent Energy International (SPE-181110-MS), Aberdeen, Scotland.