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Optimum implementation of non-conventional wells allows us to increaseconsiderably hydrocarbon recovery. By considering the high drilling cost andthe potential improvement in well productivity, well placement decision is animportant issue in field development. Considering complex reservoir geology andhigh reservoir heterogeneities, stochastic optimization methods are the mostsuitable approaches for optimum well placement. This paper proposes anoptimization methodology to determine optimal well location and trajectorybased upon the Covariance Matrix Adaptation - Evolution Strategy (CMA-ES) whichis a variant of Evolution Strategies recognized as one of the most powerfulderivative-free optimizers for continuous optimization. To improve theoptimization procedure, two new techniques are investigated: (1). Adaptivepenalization with rejection is developed to handle well placement constraints.(2). A meta-model, based on locally weighted regression, is incorporated intoCMA-ES using an approximate ranking procedure. Therefore, we can reduce thenumber of reservoir simulations, which are computationally expensive. Severalexamples are presented. Our new approach is compared with a Genetic Algorithmincorporating the Genocop III technique. It is shown that our approachoutperforms the genetic algorithm: it leads in general to both a higher NPV anda significant reduction of the number of reservoir simulations.

Using Evolution Strategy with Meta-models for Well Placement Optimization