Derivative-Free Optimization for Oil Field Operations
نویسندگان
چکیده
A variety of optimization problems associated with oil production involve cost functions and constraints that require calls to a subsurface flow simulator. In many situations gradient information cannot be obtained efficiently, or a global search is required. This motivates the use of derivative-free (non-invasive, blackbox) optimization methods. This chapter describes the use of several derivative-free techniques, including generalized pattern search, Hooke-Jeeves direct search, a genetic algorithm, and particle swarm optimization, for three key problems that arise in oil field management. These problems are the optimization of settings (pressure or flow rate) in existing wells, optimization of the locations of new wells, and data assimilation or history matching. The performance of the derivative-free algorithms is shown to be quite acceptable, especially when they are implemented within a distributed computing environment.
منابع مشابه
Application of derivative-free methodologies to generally constrained oil production optimisation problems
Oil production optimisation involves the determination of optimum well controls (well pressures, injection rates) to maximise an objective function such as net present value. These problems typically include physical and economic restrictions, which introduce general constraints into the optimisations. Cost function and constraint evaluations entail calls to a reservoir flow simulator. In many ...
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