Model-Based Multiobjective Optimization Methods for Efficient Management of Subsurface Flow
- Jianlin Fu (Chevron) | Xian-Huan Wen (Chevron)
- Document ID
- Society of Petroleum Engineers
- SPE Journal
- Publication Date
- December 2017
- Document Type
- Journal Paper
- 1,984 - 1,998
- 2017.Society of Petroleum Engineers
- Reservoir management optimization, Population-based optimizer, Gradient-based optimizer, Adjoint, Multiscale hybrid strategy
- 1 in the last 30 days
- 209 since 2007
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Multiobjective optimization (MOO), which accounts for several distinct, possibly conflicting, objectives, is expected to be capable of providing improved reservoir-management (RM) solutions for efficient oilfield development because of the overall optimization of subsurface flow. Considering the complexity and diversity of MOO problems in model-based RM, we develop three MOO methods--MOAdjoint, MOGA, and MOPSO--in this work to address various oilfield-development problems. MOAdjoint combines a weighted-sum technique with a gradient-based method for solving large-scale continuous problems that have thousands of variables. An adjoint method is used to efficiently compute the derivatives of objective functions with respect to decision variables, and a sequential quadratic-programming method is used for optimization search. MOGA is a population-based method, which combines a Pareto-ranking technique with genetic algorithm (GA) to address small-scale (discrete) problems. MOPSO is another population-based method, which combines a Pareto technique with particle-swarm optimization (PSO) for a wide spectrum of optimization problems. Their advantages and disadvantages are highlighted. To take advantage of the strengths and overcome the drawbacks of these methods, a multiscale hybrid strategy is further formulated for solving complex, large-scale optimization problems by combining these methods at various scales. An example is used to compare these methods. Results show that all three methods can yield improved solutions. MOPSO seems particularly suitable for medium-scale RM problems, mainly because of its relatively fast convergence speed and efficient recovery of the Pareto front. With a proper initial guess and a set of effective weight coefficients, MOAdjoint can most efficiently solve large-scale continuous problems, particularly if model uncertainty is considered. The multiscale hybrid strategy is able to offer the best result.
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