  Optimal well placement and well injection-production are crucial for the
reservoir development to maximize the financial profits during the project
lifetime. Meta-heuristic algorithms have showed good performance in solving
complex, nonlinear and non-continuous optimization problems. However, a large
number of numerical simulation runs are involved during the optimization
process. In this work, a novel and efficient data-driven evolutionary
algorithm, called generalized data-driven differential evolutionary algorithm
(GDDE), is proposed to reduce the number of simulation runs on well-placement
and control optimization problems. Probabilistic neural network (PNN) is
adopted as the classifier to select informative and promising candidates, and
the most uncertain candidate based on Euclidean distance is prescreened and
evaluated with a numerical simulator. Subsequently, local surrogate model is
built by radial basis function (RBF) and the optimum of the surrogate, found by
optimizer, is evaluated by the numerical simulator to accelerate the
convergence. It is worth noting that the shape factors of RBF model and PNN are
optimized via solving hyper-parameter sub-expensive optimization problem. The
results show the optimization algorithm proposed in this study is very
promising for a well-placement optimization problem of two-dimensional
reservoir and joint optimization of Egg model.

Neural and Evolutionary Computing