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- Type of Document: Ph.D. Dissertation
- Language: Farsi
- Document No: 45312 (06)
- University: Sharif University of Technology
- Department: Chemical and Petroleum Engineering
- Advisor(s): Pishvaie, Mahmoud Reza; Khorasheh, Farhad; Salahshour, Karim; Kharrat, Riyaz
- Abstract:
- Reservoir lifetime optimization is an important issue. The policies that are used for maximum production using optimization methods become challenging in the presence of uncertainty. Robust Optimization (RO) method is a technique to deal with uncertainty. Because of insufficient data, uncertainty is an inherent characteristic of reservoir model. This leads to inexact solutions of an optimization problem. Also, design based on uncertainty in economic parameters, especially uncertain oil price is a challenging issue. Taking the uncertainty explicitly is a solution when there is no measurement available. To take into account uncertainty several possible values (realizations) for the uncertainty are considered. RO of water-flooding process using a large number of realizations leads to extensive computational run time. Besides, RO is inherently multi-objective that has not been taken in to account in the previous studies. In this regards, in the first part of this study, RO based on ranked realizations has been considered in the absence of measurements. Also, in order to compare the results three other dynamic RO based on random sampling have been investigated. The results of ROs have been compared with 100 nominal optimizations and basic situations. In the second part, two multi-objective robust optimization problems have been investigated using the robust optimization concept based on sampled realizations. In both problems the desired objective functions were performance (expected value) and variance of the base objective function, with Net Present Value (NPV) as the base objective. The proposed optimization model was applied to two sets of 100 realizations for investigation and validation of the results. The third part of this work presents a robust multi-objective optimization methodology by incorporating three dedicated objective functions. Dynamic optimization of water-flooding often lacks robustness due to uncertainties. Also, variation of economical parameters forces such high computational optimization works to regenerate their optimum water injection policies. Finally we focuse on reducing the sensitivity to the uncertainty in the model and objective function parameters when no measurement information is assumed to be available. In all three cases the goal is to determine optimized and robust water injection policies under geological uncertainty (permeability) for all injection wells. The comparative test studies clearly demonstrate superiority of the proposed methodologies to give optimal robust solutions
- Keywords:
- Multiobjective Optimization ; Water Flooding ; Uncertainty ; Net Present Value ; Realization ; Robust Optimization ; Single Objective Optimization
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