Using Genetic Algorithm to Optimize Well Placement in Gas Condensate Reservoirs
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Well placement within a reservoir is a significant and difficult step in the reservoir development process. Determining the optimal well location is a complex problem involving many factors including geological considerations, reservoir and fluid properties, economic costs, and technical ability. The most thorough approach to this problem is that of an exhaustive search, in which a simulation is run for every conceivable well position in the reservoir. Although thorough and accurate, this approach is typically not used in real world applications due to the time constraints from the excessive number of simulations. This paper suggests the use of a genetic algorithm applied to the horizontal well placement problem in a gas reservoir to reduce the required number of simulations. The objectives are to identify the optimized well placement and then to determine the benefit of optimization. Performance of the genetic algorithm was analyzed through different case scenarios. The genetic algorithm approach is used to evaluate the effect of well placement in heterogeneous and anisotropic reservoirs on reservoir recovery. The wells are constrained by surface gas rate and bottom-hole pressure for each case. We apply this technique to find the optimal horizontal well location in a gas condensate reservoir in Qatar's North field. The research provides evidence that well placement optimization is an important criterion during the reservoir development phase for horizontal wells in gas reservoirs, but it is less significant to vertical wells in a homogeneous reservoir. It is also shown that genetic algorithms are an extremely efficient and robust tool to find the optimal location. © 2010, European Association of Geoscientists and Engineers.
author list (cited authors)
Morales, A., Gibbs, T., Nasrabadi, H., & Zhu, D.