Robust Production Optimization for Integrated Field Development and Control
Robust optimization aims to propose the development and management strategy for a given oil field so as to maximize its value while honoring multiple constraints and uncertainties. This results in a complex optimization problem with large number of control variables, and computationally expensive objective function. Many robust optimization studies were performed in the context of production optimisation 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. mature fields with declining production) or in other aspects of field development such as well placement and completion design.
This project aims to construct robust optimization framework and utilize machine learning for uncertainty quantification to find the optimal field development and control strategy in integrated models of reservoirs and production systems. It will then study risks and uncertainties in the context of green and brown field optimization to identify the most influential uncertainties and efficiently quantify them by making the best use of the available field data. A fast and efficient framework is then developed for robust, optimal field development and control.
Dr. Morteza Haghighat Sefat
Dr. Khafiz Muradov