Application of a particle swarm optimization algorithm for determining optimum well location and type

By J. Onwunalu, Louis J. Durlofsky. 2010

399 citations recorded by the source corpus.

Subfields: Mechanical Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/3ba4bbdce6a5057dca22544b641ccd9de5a51ca8

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In Mechanical Engineering

In Ocean Engineering

Citations in the corpus

Joint optimization of well placement and control for nonconventional well types (2014, methodology): “…stochastic approximation (SPSA) (Bangerth et al., 2006), covariance matrix adaptation (Bouzarkouna et al., 2012), particle swarm optimization (PSO) (Onwunalu and Durlofsky, 2010, 2011; Ding et al., 2014) and differential evolution (DE) (Nwankwor et al., 2013) have also been applied successfully to…”
Well placement optimization subject to realistic field development constraints (2016, methodology): “objective function containing multiple optima [16].”
On the topography of the cost functional in linear and nonlinear inverse problems (2012, background): “, 2010b) as well as in other fields such as reservoir optimization (Onwunalu and Durlofsky, 2010; Fernández Martínez et al., 2010c).”
Joint optimization of oil well placement and controls (2012, methodology): “Examples include genetic algorithms [21, 24, 39], stochastic perturbation methods [5], and particle swarm optimization [9, 14, 29].”
A derivative-free methodology with local and global search for the constrained joint optimization of well locations and controls (2014, methodology): “3.3 Global derivative-free method: Particle Swarm Optimization PSO algorithms are a family of global stochastic search procedures introduced by Eberhart and Kennedy [15].”
Well placement optimization with the covariance matrix adaptation evolution strategy and meta-models (2011, methodology): “An illustration can be found in [47] where the NPV of a single vertical well placement is sampled to construct the objective function surface.”